diff --git a/.gitignore b/.gitignore
index 28f2aca854b..53c1fb056bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -61,7 +61,9 @@ Makefile.config
 data/*
 models/*
 *.caffemodel
+*.caffemodel.h5
 *.solverstate
+*.solverstate.h5
 *.binaryproto
 *leveldb
 *lmdb
diff --git a/.travis.yml b/.travis.yml
index 955aa8c3ba2..4dc7ed72d6c 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,28 +2,36 @@
 # one using CMake, and one using make.
 env:
   matrix:
-    - WITH_CUDA=false WITH_CMAKE=false
-    - WITH_CUDA=false WITH_CMAKE=true
-    - WITH_CUDA=true WITH_CMAKE=false
-    - WITH_CUDA=true WITH_CMAKE=true
+    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=true
+    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=true PYTHON_VERSION=3
+    - WITH_CUDA=true WITH_CMAKE=false WITH_IO=true
+    - WITH_CUDA=true WITH_CMAKE=true WITH_IO=true
+    - WITH_CUDA=false WITH_CMAKE=false WITH_IO=false
+    - WITH_CUDA=false WITH_CMAKE=true WITH_IO=false PYTHON_VERSION=3
 
 language: cpp
 
 # Cache Ubuntu apt packages.
-cache: apt
+cache:
+  apt: true
+  directories:
+  - /home/travis/miniconda
+  - /home/travis/miniconda2
+  - /home/travis/miniconda3
 
 compiler: gcc
 
 before_install:
   - export NUM_THREADS=4
   - export SCRIPTS=./scripts/travis
+  - export CONDA_DIR="/home/travis/miniconda$PYTHON_VERSION"
 
 install:
   - sudo -E $SCRIPTS/travis_install.sh
 
 before_script:
-  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64
-  - export PATH=/home/travis/miniconda/bin:$PATH
+  - export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib:/usr/local/cuda/lib64:$CONDA_DIR/lib
+  - export PATH=$CONDA_DIR/bin:$PATH
   - if ! $WITH_CMAKE; then $SCRIPTS/travis_setup_makefile_config.sh; fi
 
 script: $SCRIPTS/travis_build_and_test.sh
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 74fa70c9d20..c5d99cef9dd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,11 +1,24 @@
 cmake_minimum_required(VERSION 2.8.7)
+if(POLICY CMP0046)
+  cmake_policy(SET CMP0046 NEW)
+endif()
+if(POLICY CMP0054)
+  cmake_policy(SET CMP0054 NEW)
+endif()
 
 # ---[ Caffe project
 project(Caffe C CXX)
 
+# ---[ Caffe version
+set(CAFFE_TARGET_VERSION "1.0.0-rc3")
+set(CAFFE_TARGET_SOVERSION "1.0.0-rc3")
+add_definitions(-DCAFFE_VERSION=${CAFFE_TARGET_VERSION})
+
 # ---[ Using cmake scripts and modules
 list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake/Modules)
 
+include(ExternalProject)
+
 include(cmake/Utils.cmake)
 include(cmake/Targets.cmake)
 include(cmake/Misc.cmake)
@@ -13,14 +26,18 @@ include(cmake/Summary.cmake)
 include(cmake/ConfigGen.cmake)
 
 # ---[ Options
-caffe_option(CPU_ONLY  "Build Caffe wihtout CUDA support" OFF) # TODO: rename to USE_CUDA
-caffe_option(USE_CUDNN "Build Caffe with cuDNN libary support" ON IF NOT CPU_ONLY)
+caffe_option(CPU_ONLY  "Build Caffe without CUDA support" OFF) # TODO: rename to USE_CUDA
+caffe_option(USE_CUDNN "Build Caffe with cuDNN library support" ON IF NOT CPU_ONLY)
 caffe_option(BUILD_SHARED_LIBS "Build shared libraries" ON)
 caffe_option(BUILD_python "Build Python wrapper" ON)
-set(python_version "2" CACHE STRING "Specify which python version to use")
+set(python_version "2" CACHE STRING "Specify which Python version to use")
 caffe_option(BUILD_matlab "Build Matlab wrapper" OFF IF UNIX OR APPLE)
 caffe_option(BUILD_docs   "Build documentation" ON IF UNIX OR APPLE)
-caffe_option(BUILD_python_layer "Build the caffe python layer" ON)
+caffe_option(BUILD_python_layer "Build the Caffe Python layer" ON)
+caffe_option(USE_OPENCV "Build with OpenCV support" ON)
+caffe_option(USE_LEVELDB "Build with levelDB" ON)
+caffe_option(USE_LMDB "Build with lmdb" ON)
+caffe_option(ALLOW_LMDB_NOLOCK "Allow MDB_NOLOCK when reading LMDB files (only if necessary)" OFF)
 
 # ---[ Dependencies
 include(cmake/Dependencies.cmake)
@@ -30,6 +47,8 @@ if(UNIX OR APPLE)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC -Wall")
 endif()
 
+caffe_set_caffe_link()
+
 if(USE_libstdcpp)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -stdlib=libstdc++")
   message("-- Warning: forcing libstdc++ (controlled by USE_libstdcpp option in cmake)")
@@ -60,6 +79,12 @@ add_subdirectory(docs)
 # ---[ Linter target
 add_custom_target(lint COMMAND ${CMAKE_COMMAND} -P ${PROJECT_SOURCE_DIR}/cmake/lint.cmake)
 
+# ---[ pytest target
+if(BUILD_python)
+  add_custom_target(pytest COMMAND python${python_version} -m unittest discover -s caffe/test WORKING_DIRECTORY ${PROJECT_SOURCE_DIR}/python )
+  add_dependencies(pytest pycaffe)
+endif()
+
 # ---[ Configuration summary
 caffe_print_configuration_summary()
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000000..8cd5e56ca49
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,30 @@
+# Contributing
+
+## Issues
+
+Specific Caffe design and development issues, bugs, and feature requests are maintained by GitHub Issues.
+
+_Please do not post usage, installation, or modeling questions, or other requests for help to Issues._
+Use the [caffe-users list](https://groups.google.com/forum/#!forum/caffe-users) instead. This helps developers maintain a clear, uncluttered, and efficient view of the state of Caffe.
+
+When reporting a bug, it's most helpful to provide the following information, where applicable:
+
+* What steps reproduce the bug?
+* Can you reproduce the bug using the latest [master](https://github.com/BVLC/caffe/tree/master), compiled with the `DEBUG` make option?
+* What hardware and operating system/distribution are you running?
+* If the bug is a crash, provide the backtrace (usually printed by Caffe; always obtainable with `gdb`).
+
+Try to give your issue a title that is succinct and specific. The devs will rename issues as needed to keep track of them.
+
+## Pull Requests
+
+Caffe welcomes all contributions.
+
+See the [contributing guide](http://caffe.berkeleyvision.org/development.html) for details.
+
+Briefly: read commit by commit, a PR should tell a clean, compelling story of _one_ improvement to Caffe. In particular:
+
+* A PR should do one clear thing that obviously improves Caffe, and nothing more. Making many smaller PRs is better than making one large PR; review effort is superlinear in the amount of code involved.
+* Similarly, each commit should be a small, atomic change representing one step in development. PRs should be made of many commits where appropriate.
+* Please do rewrite PR history to be clean rather than chronological. Within-PR bugfixes, style cleanups, reversions, etc. should be squashed and should not appear in merged PR history.
+* Anything nonobvious from the code should be explained in comments, commit messages, or the PR description, as appropriate.
diff --git a/INSTALL.md b/INSTALL.md
index 42fcf027ec2..05c714dbda8 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -3,5 +3,5 @@
 See http://caffe.berkeleyvision.org/installation.html for the latest
 installation instructions.
 
-Check the issue tracker in case you need help:
-https://github.com/BVLC/caffe/issues
+Check the users group in case you need help:
+https://groups.google.com/forum/#!forum/caffe-users
diff --git a/LICENSE b/LICENSE
index efcc5c5b6b0..d69d16f5bc7 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,11 +1,11 @@
 COPYRIGHT
 
 All contributions by the University of California:
-Copyright (c) 2014, The Regents of the University of California (Regents)
+Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
 All rights reserved.
 
 All other contributions:
-Copyright (c) 2014, the respective contributors
+Copyright (c) 2014, 2015, the respective contributors
 All rights reserved.
 
 Caffe uses a shared copyright model: each contributor holds copyright over
diff --git a/Makefile b/Makefile
index 706959ad0ca..2f81aca84e7 100644
--- a/Makefile
+++ b/Makefile
@@ -1,11 +1,19 @@
 PROJECT := caffe
 
 CONFIG_FILE := Makefile.config
+# Explicitly check for the config file, otherwise make -k will proceed anyway.
+ifeq ($(wildcard $(CONFIG_FILE)),)
+$(error $(CONFIG_FILE) not found. See $(CONFIG_FILE).example.)
+endif
 include $(CONFIG_FILE)
 
 BUILD_DIR_LINK := $(BUILD_DIR)
-RELEASE_BUILD_DIR ?= .$(BUILD_DIR)_release
-DEBUG_BUILD_DIR ?= .$(BUILD_DIR)_debug
+ifeq ($(RELEASE_BUILD_DIR),)
+	RELEASE_BUILD_DIR := .$(BUILD_DIR)_release
+endif
+ifeq ($(DEBUG_BUILD_DIR),)
+	DEBUG_BUILD_DIR := .$(BUILD_DIR)_debug
+endif
 
 DEBUG ?= 0
 ifeq ($(DEBUG), 1)
@@ -21,9 +29,17 @@ SRC_DIRS := $(shell find * -type d -exec bash -c "find {} -maxdepth 1 \
 	\( -name '*.cpp' -o -name '*.proto' \) | grep -q ." \; -print)
 
 # The target shared library name
+LIBRARY_NAME := $(PROJECT)
 LIB_BUILD_DIR := $(BUILD_DIR)/lib
-STATIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).a
-DYNAMIC_NAME := $(LIB_BUILD_DIR)/lib$(PROJECT).so
+STATIC_NAME := $(LIB_BUILD_DIR)/lib$(LIBRARY_NAME).a
+DYNAMIC_VERSION_MAJOR 		:= 1
+DYNAMIC_VERSION_MINOR 		:= 0
+DYNAMIC_VERSION_REVISION 	:= 0-rc3
+DYNAMIC_NAME_SHORT := lib$(LIBRARY_NAME).so
+#DYNAMIC_SONAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR)
+DYNAMIC_VERSIONED_NAME_SHORT := $(DYNAMIC_NAME_SHORT).$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
+DYNAMIC_NAME := $(LIB_BUILD_DIR)/$(DYNAMIC_VERSIONED_NAME_SHORT)
+COMMON_FLAGS += -DCAFFE_VERSION=$(DYNAMIC_VERSION_MAJOR).$(DYNAMIC_VERSION_MINOR).$(DYNAMIC_VERSION_REVISION)
 
 ##############################
 # Get all source files
@@ -57,7 +73,7 @@ NONGEN_CXX_SRCS := $(shell find \
 	src/$(PROJECT) \
 	include/$(PROJECT) \
 	python/$(PROJECT) \
-	matlab/$(PROJECT) \
+	matlab/+$(PROJECT)/private \
 	examples \
 	tools \
 	-name "*.cpp" -or -name "*.hpp" -or -name "*.cu" -or -name "*.cuh")
@@ -70,13 +86,13 @@ NONEMPTY_LINT_REPORT := $(BUILD_DIR)/$(LINT_EXT)
 # PY$(PROJECT)_SRC is the python wrapper for $(PROJECT)
 PY$(PROJECT)_SRC := python/$(PROJECT)/_$(PROJECT).cpp
 PY$(PROJECT)_SO := python/$(PROJECT)/_$(PROJECT).so
-PY$(PROJECT)_HXX := include/$(PROJECT)/python_layer.hpp
-# MAT$(PROJECT)_SRC is the matlab wrapper for $(PROJECT)
-MAT$(PROJECT)_SRC := matlab/$(PROJECT)/mat$(PROJECT).cpp
+PY$(PROJECT)_HXX := include/$(PROJECT)/layers/python_layer.hpp
+# MAT$(PROJECT)_SRC is the mex entrance point of matlab package for $(PROJECT)
+MAT$(PROJECT)_SRC := matlab/+$(PROJECT)/private/$(PROJECT)_.cpp
 ifneq ($(MATLAB_DIR),)
 	MAT_SO_EXT := $(shell $(MATLAB_DIR)/bin/mexext)
 endif
-MAT$(PROJECT)_SO := matlab/$(PROJECT)/$(PROJECT).$(MAT_SO_EXT)
+MAT$(PROJECT)_SO := matlab/+$(PROJECT)/private/$(PROJECT)_.$(MAT_SO_EXT)
 
 ##############################
 # Derive generated files
@@ -110,7 +126,7 @@ GTEST_OBJ := $(addprefix $(BUILD_DIR)/, ${GTEST_SRC:.cpp=.o})
 EXAMPLE_OBJS := $(addprefix $(BUILD_DIR)/, ${EXAMPLE_SRCS:.cpp=.o})
 # Output files for automatic dependency generation
 DEPS := ${CXX_OBJS:.o=.d} ${CU_OBJS:.o=.d} ${TEST_CXX_OBJS:.o=.d} \
-	${TEST_CU_OBJS:.o=.d}
+	${TEST_CU_OBJS:.o=.d} $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}
 # tool, example, and test bins
 TOOL_BINS := ${TOOL_OBJS:.o=.bin}
 EXAMPLE_BINS := ${EXAMPLE_OBJS:.o=.bin}
@@ -161,16 +177,36 @@ ifneq ($(CPU_ONLY), 1)
 	LIBRARY_DIRS += $(CUDA_LIB_DIR)
 	LIBRARIES := cudart cublas curand
 endif
-LIBRARIES += glog gflags protobuf leveldb snappy \
-	lmdb boost_system hdf5_hl hdf5 m \
-	opencv_core opencv_highgui opencv_imgproc
-PYTHON_LIBRARIES := boost_python python2.7
+
+LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_hl hdf5
+
+# handle IO dependencies
+USE_LEVELDB ?= 1
+USE_LMDB ?= 1
+USE_OPENCV ?= 1
+
+ifeq ($(USE_LEVELDB), 1)
+	LIBRARIES += leveldb snappy
+endif
+ifeq ($(USE_LMDB), 1)
+	LIBRARIES += lmdb
+endif
+ifeq ($(USE_OPENCV), 1)
+	LIBRARIES += opencv_core opencv_highgui opencv_imgproc 
+
+	ifeq ($(OPENCV_VERSION), 3)
+		LIBRARIES += opencv_imgcodecs
+	endif
+		
+endif
+PYTHON_LIBRARIES ?= boost_python python2.7
 WARNINGS := -Wall -Wno-sign-compare
 
 ##############################
 # Set build directories
 ##############################
 
+DISTRIBUTE_DIR ?= distribute
 DISTRIBUTE_SUBDIRS := $(DISTRIBUTE_DIR)/bin $(DISTRIBUTE_DIR)/lib
 DIST_ALIASES := dist
 ifneq ($(strip $(DISTRIBUTE_DIR)),distribute)
@@ -212,6 +248,8 @@ ifeq ($(UNAME), Linux)
 	LINUX := 1
 else ifeq ($(UNAME), Darwin)
 	OSX := 1
+	OSX_MAJOR_VERSION := $(shell sw_vers -productVersion | cut -f 1 -d .)
+	OSX_MINOR_VERSION := $(shell sw_vers -productVersion | cut -f 2 -d .)
 endif
 
 # Linux
@@ -219,12 +257,13 @@ ifeq ($(LINUX), 1)
 	CXX ?= /usr/bin/g++
 	GCCVERSION := $(shell $(CXX) -dumpversion | cut -f1,2 -d.)
 	# older versions of gcc are too dumb to build boost with -Wuninitalized
-	ifeq ($(shell echo $(GCCVERSION) \< 4.6 | bc), 1)
+	ifeq ($(shell echo | awk '{exit $(GCCVERSION) < 4.6;}'), 1)
 		WARNINGS += -Wno-uninitialized
 	endif
 	# boost::thread is reasonably called boost_thread (compare OS X)
 	# We will also explicitly add stdc++ to the link target.
 	LIBRARIES += boost_thread stdc++
+	VERSIONFLAGS += -Wl,-soname,$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../lib
 endif
 
 # OS X:
@@ -234,20 +273,28 @@ ifeq ($(OSX), 1)
 	CXX := /usr/bin/clang++
 	ifneq ($(CPU_ONLY), 1)
 		CUDA_VERSION := $(shell $(CUDA_DIR)/bin/nvcc -V | grep -o 'release \d' | grep -o '\d')
-		ifeq ($(shell echo $(CUDA_VERSION) \< 7.0 | bc), 1)
+		ifeq ($(shell echo | awk '{exit $(CUDA_VERSION) < 7.0;}'), 1)
 			CXXFLAGS += -stdlib=libstdc++
 			LINKFLAGS += -stdlib=libstdc++
 		endif
 		# clang throws this warning for cuda headers
 		WARNINGS += -Wno-unneeded-internal-declaration
+		# 10.11 strips DYLD_* env vars so link CUDA (rpath is available on 10.5+)
+		OSX_10_OR_LATER   := $(shell [ $(OSX_MAJOR_VERSION) -ge 10 ] && echo true)
+		OSX_10_5_OR_LATER := $(shell [ $(OSX_MINOR_VERSION) -ge 5 ] && echo true)
+		ifeq ($(OSX_10_OR_LATER),true)
+			ifeq ($(OSX_10_5_OR_LATER),true)
+				LDFLAGS += -Wl,-rpath,$(CUDA_LIB_DIR)
+			endif
+		endif
 	endif
 	# gtest needs to use its own tuple to not conflict with clang
 	COMMON_FLAGS += -DGTEST_USE_OWN_TR1_TUPLE=1
 	# boost::thread is called boost_thread-mt to mark multithreading on OS X
 	LIBRARIES += boost_thread-mt
 	# we need to explicitly ask for the rpath to be obeyed
-	DYNAMIC_FLAGS := -install_name @rpath/libcaffe.so
 	ORIGIN := @loader_path
+	VERSIONFLAGS += -Wl,-install_name,@rpath/$(DYNAMIC_VERSIONED_NAME_SHORT) -Wl,-rpath,$(ORIGIN)/../../build/lib
 else
 	ORIGIN := \$$ORIGIN
 endif
@@ -281,6 +328,20 @@ ifeq ($(USE_CUDNN), 1)
 	COMMON_FLAGS += -DUSE_CUDNN
 endif
 
+# configure IO libraries
+ifeq ($(USE_OPENCV), 1)
+	COMMON_FLAGS += -DUSE_OPENCV
+endif
+ifeq ($(USE_LEVELDB), 1)
+	COMMON_FLAGS += -DUSE_LEVELDB
+endif
+ifeq ($(USE_LMDB), 1)
+	COMMON_FLAGS += -DUSE_LMDB
+ifeq ($(ALLOW_LMDB_NOLOCK), 1)
+	COMMON_FLAGS += -DALLOW_LMDB_NOLOCK
+endif
+endif
+
 # CPU-only configuration
 ifeq ($(CPU_ONLY), 1)
 	OBJS := $(PROTO_OBJS) $(CXX_OBJS)
@@ -320,8 +381,9 @@ else
 		# OS X packages atlas as the vecLib framework
 		LIBRARIES += cblas
 		# 10.10 has accelerate while 10.9 has veclib
-		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep -o 'version: 6')
-		ifneq (,$(findstring version: 6,$(XCODE_CLT_VER)))
+		XCODE_CLT_VER := $(shell pkgutil --pkg-info=com.apple.pkg.CLTools_Executables | grep 'version' | sed 's/[^0-9]*\([0-9]\).*/\1/')
+		XCODE_CLT_GEQ_6 := $(shell [ $(XCODE_CLT_VER) -gt 5 ] && echo 1)
+		ifeq ($(XCODE_CLT_GEQ_6), 1)
 			BLAS_INCLUDE ?= /System/Library/Frameworks/Accelerate.framework/Versions/Current/Frameworks/vecLib.framework/Headers/
 			LDFLAGS += -framework Accelerate
 		else
@@ -377,11 +439,13 @@ endif
 ##############################
 # Define build targets
 ##############################
-.PHONY: all test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
+.PHONY: all lib test clean docs linecount lint lintclean tools examples $(DIST_ALIASES) \
 	py mat py$(PROJECT) mat$(PROJECT) proto runtest \
 	superclean supercleanlist supercleanfiles warn everything
 
-all: $(STATIC_NAME) $(DYNAMIC_NAME) tools examples
+all: lib tools examples
+
+lib: $(STATIC_NAME) $(DYNAMIC_NAME)
 
 everything: $(EVERYTHING_TARGETS)
 
@@ -433,7 +497,7 @@ py: $(PY$(PROJECT)_SO) $(PROTO_GEN_PY)
 $(PY$(PROJECT)_SO): $(PY$(PROJECT)_SRC) $(PY$(PROJECT)_HXX) | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) -shared -o $@ $(PY$(PROJECT)_SRC) \
-		-o $@ $(LINKFLAGS) -l$(PROJECT) $(PYTHON_LDFLAGS) \
+		-o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(PYTHON_LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../build/lib
 
 mat$(PROJECT): mat
@@ -451,6 +515,9 @@ $(MAT$(PROJECT)_SO): $(MAT$(PROJECT)_SRC) $(STATIC_NAME)
 			CXX="$(CXX)" \
 			CXXFLAGS="\$$CXXFLAGS $(MATLAB_CXXFLAGS)" \
 			CXXLIBS="\$$CXXLIBS $(STATIC_LINK_COMMAND) $(LDFLAGS)" -output $@
+	@ if [ -f "$(PROJECT)_.d" ]; then \
+		mv -f $(PROJECT)_.d $(BUILD_DIR)/${MAT$(PROJECT)_SO:.$(MAT_SO_EXT)=.d}; \
+	fi
 
 runtest: $(TEST_ALL_BIN)
 	$(TOOL_BUILD_DIR)/caffe
@@ -459,6 +526,9 @@ runtest: $(TEST_ALL_BIN)
 pytest: py
 	cd python; python -m unittest discover -s caffe/test
 
+mattest: mat
+	cd matlab; $(MATLAB_DIR)/bin/matlab -nodisplay -r 'caffe.run_tests(), exit()'
+
 warn: $(EMPTY_WARN_REPORT)
 
 $(EMPTY_WARN_REPORT): $(ALL_WARNS) | $(BUILD_DIR)
@@ -491,7 +561,8 @@ $(ALL_BUILD_DIRS): | $(BUILD_DIR_LINK)
 
 $(DYNAMIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo LD -o $@
-	$(Q)$(CXX) -shared -o $@ $(OBJS) $(LINKFLAGS) $(LDFLAGS) $(DYNAMIC_FLAGS)
+	$(Q)$(CXX) -shared -o $@ $(OBJS) $(VERSIONFLAGS) $(LINKFLAGS) $(LDFLAGS)
+	@ cd $(BUILD_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 
 $(STATIC_NAME): $(OBJS) | $(LIB_BUILD_DIR)
 	@ echo AR -o $@
@@ -522,33 +593,33 @@ $(TEST_ALL_BIN): $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
 		| $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo CXX/LD -o $@ $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $(TEST_OBJS) $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CU_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CU_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 $(TEST_CXX_BINS): $(TEST_BIN_DIR)/%.testbin: $(TEST_CXX_BUILD_DIR)/%.o \
 	$(GTEST_OBJ) | $(DYNAMIC_NAME) $(TEST_BIN_DIR)
 	@ echo LD $<
 	$(Q)$(CXX) $(TEST_MAIN_SRC) $< $(GTEST_OBJ) \
-		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(PROJECT) -Wl,-rpath,$(ORIGIN)/../lib
+		-o $@ $(LINKFLAGS) $(LDFLAGS) -l$(LIBRARY_NAME) -Wl,-rpath,$(ORIGIN)/../lib
 
 # Target for extension-less symlinks to tool binaries with extension '*.bin'.
 $(TOOL_BUILD_DIR)/%: $(TOOL_BUILD_DIR)/%.bin | $(TOOL_BUILD_DIR)
 	@ $(RM) $@
-	@ ln -s $(abspath $<) $@
+	@ ln -s $(notdir $<) $@
 
 $(TOOL_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
-	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../lib
 
 $(EXAMPLE_BINS): %.bin : %.o | $(DYNAMIC_NAME)
 	@ echo CXX/LD -o $@
-	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(PROJECT) $(LDFLAGS) \
+	$(Q)$(CXX) $< -o $@ $(LINKFLAGS) -l$(LIBRARY_NAME) $(LDFLAGS) \
 		-Wl,-rpath,$(ORIGIN)/../../lib
 
 proto: $(PROTO_GEN_CC) $(PROTO_GEN_HEADER)
@@ -600,6 +671,8 @@ superclean: clean supercleanfiles
 $(DIST_ALIASES): $(DISTRIBUTE_DIR)
 
 $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
+	# add proto
+	cp -r src/caffe/proto $(DISTRIBUTE_DIR)/
 	# add include
 	cp -r include $(DISTRIBUTE_DIR)/
 	mkdir -p $(DISTRIBUTE_DIR)/include/caffe/proto
@@ -609,7 +682,8 @@ $(DISTRIBUTE_DIR): all py | $(DISTRIBUTE_SUBDIRS)
 	cp $(EXAMPLE_BINS) $(DISTRIBUTE_DIR)/bin
 	# add libraries
 	cp $(STATIC_NAME) $(DISTRIBUTE_DIR)/lib
-	cp $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+	install -m 644 $(DYNAMIC_NAME) $(DISTRIBUTE_DIR)/lib
+	cd $(DISTRIBUTE_DIR)/lib; rm -f $(DYNAMIC_NAME_SHORT);   ln -s $(DYNAMIC_VERSIONED_NAME_SHORT) $(DYNAMIC_NAME_SHORT)
 	# add python - it's not the standard way, indeed...
 	cp -r python $(DISTRIBUTE_DIR)/python
 
diff --git a/Makefile.config.example b/Makefile.config.example
index 7a8aafd7c9f..8fd49c9c1a7 100644
--- a/Makefile.config.example
+++ b/Makefile.config.example
@@ -7,6 +7,19 @@
 # CPU-only switch (uncomment to build without GPU support).
 # CPU_ONLY := 1
 
+# uncomment to disable IO dependencies and corresponding data layers
+# USE_OPENCV := 0
+# USE_LEVELDB := 0
+# USE_LMDB := 0
+
+# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
+#	You should not set this flag if you will be reading LMDBs with any
+#	possibility of simultaneous read and write
+# ALLOW_LMDB_NOLOCK := 1
+
+# Uncomment if you're using OpenCV 3
+# OPENCV_VERSION := 3
+
 # To customize your choice of compiler, uncomment and set the following.
 # N.B. the default for Linux is g++ and the default for OSX is clang++
 # CUSTOM_CXX := g++
@@ -37,6 +50,10 @@ BLAS := atlas
 # BLAS_INCLUDE := /path/to/your/blas
 # BLAS_LIB := /path/to/your/blas
 
+# Homebrew puts openblas in a directory that is not on the standard search path
+# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
+# BLAS_LIB := $(shell brew --prefix openblas)/lib
+
 # This is required only if you will compile the matlab interface.
 # MATLAB directory should contain the mex binary in /bin.
 # MATLAB_DIR := /usr/local
@@ -53,10 +70,19 @@ PYTHON_INCLUDE := /usr/include/python2.7 \
 		# $(ANACONDA_HOME)/include/python2.7 \
 		# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include \
 
+# Uncomment to use Python 3 (default is Python 2)
+# PYTHON_LIBRARIES := boost_python3 python3.5m
+# PYTHON_INCLUDE := /usr/include/python3.5m \
+#                 /usr/lib/python3.5/dist-packages/numpy/core/include
+
 # We need to be able to find libpythonX.X.so or .dylib.
 PYTHON_LIB := /usr/lib
 # PYTHON_LIB := $(ANACONDA_HOME)/lib
 
+# Homebrew installs numpy in a non standard path (keg only)
+# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
+# PYTHON_LIB += $(shell brew --prefix numpy)/lib
+
 # Uncomment to support layers written in Python (will link against Python libs)
 # WITH_PYTHON_LAYER := 1
 
@@ -64,6 +90,10 @@ PYTHON_LIB := /usr/lib
 INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
 LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
 
+# If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
+# INCLUDE_DIRS += $(shell brew --prefix)/include
+# LIBRARY_DIRS += $(shell brew --prefix)/lib
+
 # Uncomment to use `pkg-config` to specify OpenCV library paths.
 # (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
 # USE_PKG_CONFIG := 1
diff --git a/README.md b/README.md
index ebec286d550..44b9e62c157 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,8 @@
 # Caffe
 
+[![Build Status](https://travis-ci.org/BVLC/caffe.svg?branch=master)](https://travis-ci.org/BVLC/caffe)
+[![License](https://img.shields.io/badge/license-BSD-blue.svg)](LICENSE)
+
 Caffe is a deep learning framework made with expression, speed, and modularity in mind.
 It is developed by the Berkeley Vision and Learning Center ([BVLC](http://bvlc.eecs.berkeley.edu)) and community contributors.
 
diff --git a/cmake/ConfigGen.cmake b/cmake/ConfigGen.cmake
index c82047dcc5f..056371110b5 100644
--- a/cmake/ConfigGen.cmake
+++ b/cmake/ConfigGen.cmake
@@ -11,6 +11,17 @@ function(caffe_get_current_includes includes_variable)
   list(FIND current_includes ${PROJECT_BINARY_DIR} __index)
   list(REMOVE_AT current_includes ${__index})
 
+  # removing numpy includes (since not required for client libs)
+  set(__toremove "")
+  foreach(__i ${current_includes})
+    if(${__i} MATCHES "python")
+      list(APPEND __toremove ${__i})
+    endif()
+  endforeach()
+  if(__toremove)
+    list(REMOVE_ITEM current_includes ${__toremove})
+  endif()
+
   caffe_list_unique(current_includes)
   set(${includes_variable} ${current_includes} PARENT_SCOPE)
 endfunction()
@@ -45,6 +56,21 @@ function(caffe_generate_export_configs)
     list(APPEND Caffe_DEFINITIONS -DCPU_ONLY)
   endif()
 
+  if(USE_OPENCV)
+    list(APPEND Caffe_DEFINITIONS -DUSE_OPENCV)
+  endif()
+
+  if(USE_LMDB)
+    list(APPEND Caffe_DEFINITIONS -DUSE_LMDB)
+    if (ALLOW_LMDB_NOLOCK)
+        list(APPEND Caffe_DEFINITIONS -DALLOW_LMDB_NOLOCK)
+    endif()
+  endif()
+
+  if(USE_LEVELDB)
+    list(APPEND Caffe_DEFINITIONS -DUSE_LEVELDB)
+  endif()
+
   if(NOT HAVE_CUDNN)
     set(HAVE_CUDNN FALSE)
   else()
@@ -77,7 +103,7 @@ function(caffe_generate_export_configs)
 
   configure_file("cmake/Templates/CaffeConfig.cmake.in" "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" @ONLY)
 
-  # Install the CaffeConfig.cmake and export set to use wuth install-tree
+  # Install the CaffeConfig.cmake and export set to use with install-tree
   install(FILES "${PROJECT_BINARY_DIR}/cmake/CaffeConfig.cmake" DESTINATION ${install_cmake_suffix})
   install(EXPORT CaffeTargets DESTINATION ${install_cmake_suffix})
 
diff --git a/cmake/Cuda.cmake b/cmake/Cuda.cmake
index ff58d31c166..286a42802b4 100644
--- a/cmake/Cuda.cmake
+++ b/cmake/Cuda.cmake
@@ -132,7 +132,7 @@ function(caffe_select_nvcc_arch_flags out_variable)
 endfunction()
 
 ################################################################################################
-# Short command for cuda comnpilation
+# Short command for cuda compilation
 # Usage:
 #   caffe_cuda_compile(<objlist_variable> <cuda_files>)
 macro(caffe_cuda_compile objlist_variable)
@@ -183,12 +183,41 @@ function(detect_cuDNN)
     set(HAVE_CUDNN  TRUE PARENT_SCOPE)
     set(CUDNN_FOUND TRUE PARENT_SCOPE)
 
+    file(READ ${CUDNN_INCLUDE}/cudnn.h CUDNN_VERSION_FILE_CONTENTS)
+
+    # cuDNN v3 and beyond
+    string(REGEX MATCH "define CUDNN_MAJOR * +([0-9]+)"
+           CUDNN_VERSION_MAJOR "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_MAJOR * +([0-9]+)" "\\1"
+           CUDNN_VERSION_MAJOR "${CUDNN_VERSION_MAJOR}")
+    string(REGEX MATCH "define CUDNN_MINOR * +([0-9]+)"
+           CUDNN_VERSION_MINOR "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_MINOR * +([0-9]+)" "\\1"
+           CUDNN_VERSION_MINOR "${CUDNN_VERSION_MINOR}")
+    string(REGEX MATCH "define CUDNN_PATCHLEVEL * +([0-9]+)"
+           CUDNN_VERSION_PATCH "${CUDNN_VERSION_FILE_CONTENTS}")
+    string(REGEX REPLACE "define CUDNN_PATCHLEVEL * +([0-9]+)" "\\1"
+           CUDNN_VERSION_PATCH "${CUDNN_VERSION_PATCH}")
+
+    if(NOT CUDNN_VERSION_MAJOR)
+      set(CUDNN_VERSION "???")
+    else()
+      set(CUDNN_VERSION "${CUDNN_VERSION_MAJOR}.${CUDNN_VERSION_MINOR}.${CUDNN_VERSION_PATCH}")
+    endif()
+
+    message(STATUS "Found cuDNN: ver. ${CUDNN_VERSION} found (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
+    string(COMPARE LESS "${CUDNN_VERSION_MAJOR}" 3 cuDNNVersionIncompatible)
+    if(cuDNNVersionIncompatible)
+      message(FATAL_ERROR "cuDNN version >3 is required.")
+    endif()
+
+    set(CUDNN_VERSION "${CUDNN_VERSION}" PARENT_SCOPE)
     mark_as_advanced(CUDNN_INCLUDE CUDNN_LIBRARY CUDNN_ROOT)
-    message(STATUS "Found cuDNN (include: ${CUDNN_INCLUDE}, library: ${CUDNN_LIBRARY})")
+
   endif()
 endfunction()
 
-
 ################################################################################################
 ###  Non macro section
 ################################################################################################
diff --git a/cmake/Dependencies.cmake b/cmake/Dependencies.cmake
index f328e8246ab..c7b6a17aa69 100644
--- a/cmake/Dependencies.cmake
+++ b/cmake/Dependencies.cmake
@@ -2,7 +2,7 @@
 set(Caffe_LINKER_LIBS "")
 
 # ---[ Boost
-find_package(Boost 1.46 REQUIRED COMPONENTS system thread)
+find_package(Boost 1.46 REQUIRED COMPONENTS system thread filesystem)
 include_directories(SYSTEM ${Boost_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${Boost_LIBRARIES})
 
@@ -11,12 +11,12 @@ find_package(Threads REQUIRED)
 list(APPEND Caffe_LINKER_LIBS ${CMAKE_THREAD_LIBS_INIT})
 
 # ---[ Google-glog
-find_package(Glog REQUIRED)
+include("cmake/External/glog.cmake")
 include_directories(SYSTEM ${GLOG_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${GLOG_LIBRARIES})
 
 # ---[ Google-gflags
-find_package(GFlags REQUIRED)
+include("cmake/External/gflags.cmake")
 include_directories(SYSTEM ${GFLAGS_INCLUDE_DIRS})
 list(APPEND Caffe_LINKER_LIBS ${GFLAGS_LIBRARIES})
 
@@ -29,27 +29,38 @@ include_directories(SYSTEM ${HDF5_INCLUDE_DIRS} ${HDF5_HL_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${HDF5_LIBRARIES})
 
 # ---[ LMDB
-find_package(LMDB REQUIRED)
-include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+if(USE_LMDB)
+  find_package(LMDB REQUIRED)
+  include_directories(SYSTEM ${LMDB_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${LMDB_LIBRARIES})
+  add_definitions(-DUSE_LMDB)
+  if(ALLOW_LMDB_NOLOCK)
+    add_definitions(-DALLOW_LMDB_NOLOCK)
+  endif()
+endif()
 
 # ---[ LevelDB
-find_package(LevelDB REQUIRED)
-include_directories(SYSTEM ${LevelDB_INCLUDE})
-list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+if(USE_LEVELDB)
+  find_package(LevelDB REQUIRED)
+  include_directories(SYSTEM ${LevelDB_INCLUDE})
+  list(APPEND Caffe_LINKER_LIBS ${LevelDB_LIBRARIES})
+  add_definitions(-DUSE_LEVELDB)
+endif()
 
 # ---[ Snappy
-find_package(Snappy REQUIRED)
-include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
-list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+if(USE_LEVELDB)
+  find_package(Snappy REQUIRED)
+  include_directories(SYSTEM ${Snappy_INCLUDE_DIR})
+  list(APPEND Caffe_LINKER_LIBS ${Snappy_LIBRARIES})
+endif()
 
 # ---[ CUDA
 include(cmake/Cuda.cmake)
 if(NOT HAVE_CUDA)
   if(CPU_ONLY)
-    message("-- CUDA is disabled. Building without it...")
+    message(STATUS "-- CUDA is disabled. Building without it...")
   else()
-    message("-- CUDA is not detected by cmake. Building without it...")
+    message(WARNING "-- CUDA is not detected by cmake. Building without it...")
   endif()
 
   # TODO: remove this not cross platform define in future. Use caffe_config.h instead.
@@ -57,13 +68,16 @@ if(NOT HAVE_CUDA)
 endif()
 
 # ---[ OpenCV
-find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
-if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
-  find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+if(USE_OPENCV)
+  find_package(OpenCV QUIET COMPONENTS core highgui imgproc imgcodecs)
+  if(NOT OpenCV_FOUND) # if not OpenCV 3.x, then imgcodecs are not found
+    find_package(OpenCV REQUIRED COMPONENTS core highgui imgproc)
+  endif()
+  include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
+  list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
+  message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
+  add_definitions(-DUSE_OPENCV)
 endif()
-include_directories(SYSTEM ${OpenCV_INCLUDE_DIRS})
-list(APPEND Caffe_LINKER_LIBS ${OpenCV_LIBS})
-message(STATUS "OpenCV found (${OpenCV_CONFIG_PATH})")
 
 # ---[ BLAS
 if(NOT APPLE)
@@ -100,20 +114,21 @@ if(BUILD_python)
     # Find the matching boost python implementation
     set(version ${PYTHONLIBS_VERSION_STRING})
     
-    STRING( REPLACE "." "" boost_py_version ${version} )
+    STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
     find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
     set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
     
     while(NOT "${version}" STREQUAL "" AND NOT Boost_PYTHON_FOUND)
       STRING( REGEX REPLACE "([0-9.]+).[0-9]+" "\\1" version ${version} )
+      
+      STRING( REGEX REPLACE "[^0-9]" "" boost_py_version ${version} )
+      find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
+      set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
+      
       STRING( REGEX MATCHALL "([0-9.]+).[0-9]+" has_more_version ${version} )
       if("${has_more_version}" STREQUAL "")
         break()
       endif()
-      
-      STRING( REPLACE "." "" boost_py_version ${version} )
-      find_package(Boost 1.46 COMPONENTS "python-py${boost_py_version}")
-      set(Boost_PYTHON_FOUND ${Boost_PYTHON-PY${boost_py_version}_FOUND})
     endwhile()
     if(NOT Boost_PYTHON_FOUND)
       find_package(Boost 1.46 COMPONENTS python)
diff --git a/cmake/External/gflags.cmake b/cmake/External/gflags.cmake
new file mode 100644
index 00000000000..e3dba04f33f
--- /dev/null
+++ b/cmake/External/gflags.cmake
@@ -0,0 +1,56 @@
+if (NOT __GFLAGS_INCLUDED) # guard against multiple includes
+  set(__GFLAGS_INCLUDED TRUE)
+
+  # use the system-wide gflags if present
+  find_package(GFlags)
+  if (GFLAGS_FOUND)
+    set(GFLAGS_EXTERNAL FALSE)
+  else()
+    # gflags will use pthreads if it's available in the system, so we must link with it
+    find_package(Threads)
+
+    # build directory
+    set(gflags_PREFIX ${CMAKE_BINARY_DIR}/external/gflags-prefix)
+    # install directory
+    set(gflags_INSTALL ${CMAKE_BINARY_DIR}/external/gflags-install)
+
+    # we build gflags statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+        set(GFLAGS_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GFLAGS_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+    set(GFLAGS_C_FLAGS ${CMAKE_C_FLAGS} ${GFLAGS_EXTRA_COMPILER_FLAGS})
+
+    ExternalProject_Add(gflags
+      PREFIX ${gflags_PREFIX}
+      GIT_REPOSITORY "https://github.com/gflags/gflags.git"
+      GIT_TAG "v2.1.2"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CMAKE_ARGS -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}
+                 -DCMAKE_INSTALL_PREFIX=${gflags_INSTALL}
+                 -DBUILD_SHARED_LIBS=OFF
+                 -DBUILD_STATIC_LIBS=ON
+                 -DBUILD_PACKAGING=OFF
+                 -DBUILD_TESTING=OFF
+                 -DBUILD_NC_TESTS=OFF
+                 -BUILD_CONFIG_TESTS=OFF
+                 -DINSTALL_HEADERS=ON
+                 -DCMAKE_C_FLAGS=${GFLAGS_C_FLAGS}
+                 -DCMAKE_CXX_FLAGS=${GFLAGS_CXX_FLAGS}
+      LOG_DOWNLOAD 1
+      LOG_INSTALL 1
+      )
+
+    set(GFLAGS_FOUND TRUE)
+    set(GFLAGS_INCLUDE_DIRS ${gflags_INSTALL}/include)
+    set(GFLAGS_LIBRARIES ${gflags_INSTALL}/lib/libgflags.a ${CMAKE_THREAD_LIBS_INIT})
+    set(GFLAGS_LIBRARY_DIRS ${gflags_INSTALL}/lib)
+    set(GFLAGS_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies gflags)
+  endif()
+
+endif()
diff --git a/cmake/External/glog.cmake b/cmake/External/glog.cmake
new file mode 100644
index 00000000000..a44672f2753
--- /dev/null
+++ b/cmake/External/glog.cmake
@@ -0,0 +1,56 @@
+# glog depends on gflags
+include("cmake/External/gflags.cmake")
+
+if (NOT __GLOG_INCLUDED)
+  set(__GLOG_INCLUDED TRUE)
+
+  # try the system-wide glog first
+  find_package(Glog)
+  if (GLOG_FOUND)
+      set(GLOG_EXTERNAL FALSE)
+  else()
+    # fetch and build glog from github
+
+    # build directory
+    set(glog_PREFIX ${CMAKE_BINARY_DIR}/external/glog-prefix)
+    # install directory
+    set(glog_INSTALL ${CMAKE_BINARY_DIR}/external/glog-install)
+
+    # we build glog statically, but want to link it into the caffe shared library
+    # this requires position-independent code
+    if (UNIX)
+      set(GLOG_EXTRA_COMPILER_FLAGS "-fPIC")
+    endif()
+
+    set(GLOG_CXX_FLAGS ${CMAKE_CXX_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+    set(GLOG_C_FLAGS ${CMAKE_C_FLAGS} ${GLOG_EXTRA_COMPILER_FLAGS})
+
+    # depend on gflags if we're also building it
+    if (GFLAGS_EXTERNAL)
+      set(GLOG_DEPENDS gflags)
+    endif()
+
+    ExternalProject_Add(glog
+      DEPENDS ${GLOG_DEPENDS}
+      PREFIX ${glog_PREFIX}
+      GIT_REPOSITORY "https://github.com/google/glog"
+      GIT_TAG "v0.3.4"
+      UPDATE_COMMAND ""
+      INSTALL_DIR ${gflags_INSTALL}
+      CONFIGURE_COMMAND env "CFLAGS=${GLOG_C_FLAGS}" "CXXFLAGS=${GLOG_CXX_FLAGS}" ${glog_PREFIX}/src/glog/configure --prefix=${glog_INSTALL} --enable-shared=no --enable-static=yes --with-gflags=${GFLAGS_LIBRARY_DIRS}/..
+      LOG_DOWNLOAD 1
+      LOG_CONFIGURE 1
+      LOG_INSTALL 1
+      )
+
+    set(GLOG_FOUND TRUE)
+    set(GLOG_INCLUDE_DIRS ${glog_INSTALL}/include)
+    set(GLOG_LIBRARIES ${GFLAGS_LIBRARIES} ${glog_INSTALL}/lib/libglog.a)
+    set(GLOG_LIBRARY_DIRS ${glog_INSTALL}/lib)
+    set(GLOG_EXTERNAL TRUE)
+
+    list(APPEND external_project_dependencies glog)
+  endif()
+
+endif()
+
diff --git a/cmake/Misc.cmake b/cmake/Misc.cmake
index 39569eaf996..9dd2609b36a 100644
--- a/cmake/Misc.cmake
+++ b/cmake/Misc.cmake
@@ -1,4 +1,4 @@
-# ---[ Configurations types
+# ---[ Configuration types
 set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Possible configurations" FORCE)
 mark_as_advanced(CMAKE_CONFIGURATION_TYPES)
 
@@ -46,7 +46,7 @@ endif()
 # ---[ Set debug postfix
 set(Caffe_DEBUG_POSTFIX "-d")
 
-set(CAffe_POSTFIX "")
+set(Caffe_POSTFIX "")
 if(CMAKE_BUILD_TYPE MATCHES "Debug")
-  set(CAffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
+  set(Caffe_POSTFIX ${Caffe_DEBUG_POSTFIX})
 endif()
diff --git a/cmake/Modules/FindGFlags.cmake b/cmake/Modules/FindGFlags.cmake
index 146e8455a50..29b60f05037 100644
--- a/cmake/Modules/FindGFlags.cmake
+++ b/cmake/Modules/FindGFlags.cmake
@@ -38,7 +38,7 @@ else()
     find_library(GFLAGS_LIBRARY gflags)
 endif()
 
-find_package_handle_standard_args(GFLAGS DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
+find_package_handle_standard_args(GFlags DEFAULT_MSG GFLAGS_INCLUDE_DIR GFLAGS_LIBRARY)
 
 
 if(GFLAGS_FOUND)
diff --git a/cmake/Modules/FindGlog.cmake b/cmake/Modules/FindGlog.cmake
index 56c76434897..99abbe478a0 100644
--- a/cmake/Modules/FindGlog.cmake
+++ b/cmake/Modules/FindGlog.cmake
@@ -37,7 +37,7 @@ else()
         PATH_SUFFIXES lib lib64)
 endif()
 
-find_package_handle_standard_args(GLOG DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
+find_package_handle_standard_args(Glog DEFAULT_MSG GLOG_INCLUDE_DIR GLOG_LIBRARY)
 
 if(GLOG_FOUND)
   set(GLOG_INCLUDE_DIRS ${GLOG_INCLUDE_DIR})
diff --git a/cmake/Modules/FindMKL.cmake b/cmake/Modules/FindMKL.cmake
index d2012db579a..5ab93b2d6b6 100644
--- a/cmake/Modules/FindMKL.cmake
+++ b/cmake/Modules/FindMKL.cmake
@@ -20,7 +20,7 @@ caffe_option(MKL_MULTI_THREADED  "Use multi-threading"   ON IF NOT MKL_USE_SINGL
 
 # ---[ Root folders
 set(INTEL_ROOT "/opt/intel" CACHE PATH "Folder contains intel libs")
-find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKL_ROOT} ${INTEL_ROOT}/mkl
+find_path(MKL_ROOT include/mkl.h PATHS $ENV{MKLROOT} ${INTEL_ROOT}/mkl
                                    DOC "Folder contains MKL")
 
 # ---[ Find include dir
diff --git a/cmake/Modules/FindOpenBLAS.cmake b/cmake/Modules/FindOpenBLAS.cmake
index b8434927a4d..a6512ae7e4e 100644
--- a/cmake/Modules/FindOpenBLAS.cmake
+++ b/cmake/Modules/FindOpenBLAS.cmake
@@ -2,8 +2,10 @@
 
 SET(Open_BLAS_INCLUDE_SEARCH_PATHS
   /usr/include
+  /usr/include/openblas
   /usr/include/openblas-base
   /usr/local/include
+  /usr/local/include/openblas
   /usr/local/include/openblas-base
   /opt/OpenBLAS/include
   $ENV{OpenBLAS_HOME}
diff --git a/cmake/ProtoBuf.cmake b/cmake/ProtoBuf.cmake
index 8946d66c57b..73f647f5fae 100644
--- a/cmake/ProtoBuf.cmake
+++ b/cmake/ProtoBuf.cmake
@@ -1,12 +1,12 @@
 # Finds Google Protocol Buffers library and compilers and extends
-# the standart cmake script with version and python generation support
+# the standard cmake script with version and python generation support
 
 find_package( Protobuf REQUIRED )
 include_directories(SYSTEM ${PROTOBUF_INCLUDE_DIR})
 list(APPEND Caffe_LINKER_LIBS ${PROTOBUF_LIBRARIES})
 
 # As of Ubuntu 14.04 protoc is no longer a part of libprotobuf-dev package
-# and should be installed  separately as in: sudo apt-get install protobuf-compiler
+# and should be installed separately as in: sudo apt-get install protobuf-compiler
 if(EXISTS ${PROTOBUF_PROTOC_EXECUTABLE})
   message(STATUS "Found PROTOBUF Compiler: ${PROTOBUF_PROTOC_EXECUTABLE}")
 else()
@@ -23,7 +23,7 @@ endif()
 
 # place where to generate protobuf sources
 set(proto_gen_folder "${PROJECT_BINARY_DIR}/include/caffe/proto")
-include_directories(SYSTEM "${PROJECT_BINARY_DIR}/include")
+include_directories("${PROJECT_BINARY_DIR}/include")
 
 set(PROTOBUF_GENERATE_CPP_APPEND_PATH TRUE)
 
diff --git a/cmake/Summary.cmake b/cmake/Summary.cmake
index 32931942846..ba025cf81e0 100644
--- a/cmake/Summary.cmake
+++ b/cmake/Summary.cmake
@@ -87,7 +87,7 @@ endfunction()
 
 
 ################################################################################################
-# Prints accumulatd caffe configuration summary
+# Prints accumulated caffe configuration summary
 # Usage:
 #   caffe_print_configuration_summary()
 
@@ -101,7 +101,7 @@ function(caffe_print_configuration_summary)
   caffe_status("")
   caffe_status("******************* Caffe Configuration Summary *******************")
   caffe_status("General:")
-  caffe_status("  Version           :   ${Caffe_VERSION}")
+  caffe_status("  Version           :   ${CAFFE_TARGET_VERSION}")
   caffe_status("  Git               :   ${Caffe_GIT_VERSION}")
   caffe_status("  System            :   ${CMAKE_SYSTEM_NAME}")
   caffe_status("  C++ compiler      :   ${CMAKE_CXX_COMPILER}")
@@ -114,17 +114,27 @@ function(caffe_print_configuration_summary)
   caffe_status("  BUILD_matlab      :   ${BUILD_matlab}")
   caffe_status("  BUILD_docs        :   ${BUILD_docs}")
   caffe_status("  CPU_ONLY          :   ${CPU_ONLY}")
+  caffe_status("  USE_OPENCV        :   ${USE_OPENCV}")
+  caffe_status("  USE_LEVELDB       :   ${USE_LEVELDB}")
+  caffe_status("  USE_LMDB          :   ${USE_LMDB}")
+  caffe_status("  ALLOW_LMDB_NOLOCK :   ${ALLOW_LMDB_NOLOCK}")
   caffe_status("")
   caffe_status("Dependencies:")
   caffe_status("  BLAS              : " APPLE THEN "Yes (vecLib)" ELSE "Yes (${BLAS})")
   caffe_status("  Boost             :   Yes (ver. ${Boost_MAJOR_VERSION}.${Boost_MINOR_VERSION})")
   caffe_status("  glog              :   Yes")
-  caffe_status("  gflags            :   Yes")  
+  caffe_status("  gflags            :   Yes")
   caffe_status("  protobuf          : " PROTOBUF_FOUND THEN "Yes (ver. ${PROTOBUF_VERSION})" ELSE "No" )
-  caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
-  caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
-  caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
-  caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  if(USE_LMDB)
+    caffe_status("  lmdb              : " LMDB_FOUND THEN "Yes (ver. ${LMDB_VERSION})" ELSE "No")
+  endif()
+  if(USE_LEVELDB)
+    caffe_status("  LevelDB           : " LEVELDB_FOUND THEN  "Yes (ver. ${LEVELDB_VERSION})" ELSE "No")
+    caffe_status("  Snappy            : " SNAPPY_FOUND THEN "Yes (ver. ${Snappy_VERSION})" ELSE "No" )
+  endif()
+  if(USE_OPENCV)
+    caffe_status("  OpenCV            :   Yes (ver. ${OpenCV_VERSION})")
+  endif()
   caffe_status("  CUDA              : " HAVE_CUDA THEN "Yes (ver. ${CUDA_VERSION})" ELSE "No" )
   caffe_status("")
   if(HAVE_CUDA)
@@ -132,7 +142,7 @@ function(caffe_print_configuration_summary)
     caffe_status("  Target GPU(s)     :   ${CUDA_ARCH_NAME}" )
     caffe_status("  GPU arch(s)       :   ${NVCC_FLAGS_EXTRA_readable}")
     if(USE_CUDNN)
-      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes" ELSE "Not found")
+      caffe_status("  cuDNN             : " HAVE_CUDNN THEN "Yes (ver. ${CUDNN_VERSION})" ELSE "Not found")
     else()
       caffe_status("  cuDNN             :   Disabled")
     endif()
@@ -165,4 +175,3 @@ function(caffe_print_configuration_summary)
   caffe_status("  Install path      :   ${CMAKE_INSTALL_PREFIX}")
   caffe_status("")
 endfunction()
-
diff --git a/cmake/Targets.cmake b/cmake/Targets.cmake
index e3ad872313b..a796d00548f 100644
--- a/cmake/Targets.cmake
+++ b/cmake/Targets.cmake
@@ -1,16 +1,17 @@
 ################################################################################################
 # Defines global Caffe_LINK flag, This flag is required to prevent linker from excluding
 # some objects which are not addressed directly but are registered via static constructors
-if(BUILD_SHARED_LIBS)
-  set(Caffe_LINK caffe)
-else()
-  if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
-    set(Caffe_LINK -Wl,-force_load caffe)
-  elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+macro(caffe_set_caffe_link)
+  if(BUILD_SHARED_LIBS)
+    set(Caffe_LINK caffe)
+  else()
+    if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
+      set(Caffe_LINK -Wl,-force_load caffe)
+    elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
+      set(Caffe_LINK -Wl,--whole-archive caffe -Wl,--no-whole-archive)
+    endif()
   endif()
-endif()
-
+endmacro()
 ################################################################################################
 # Convenient command to setup source group for IDEs that support this feature (VS, XCode)
 # Usage:
@@ -31,7 +32,7 @@ endfunction()
 ################################################################################################
 # Collecting sources from globbing and appending to output list variable
 # Usage:
-#   caffe_source_group(<output_variable> GLOB[_RECURSE] <globbing_expression>)
+#   caffe_collect_sources(<output_variable> GLOB[_RECURSE] <globbing_expression>)
 function(caffe_collect_sources variable)
   cmake_parse_arguments(CAFFE_COLLECT_SOURCES "" "" "GLOB;GLOB_RECURSE" ${ARGN})
   if(CAFFE_COLLECT_SOURCES_GLOB)
@@ -110,6 +111,10 @@ function(caffe_default_properties target)
     ARCHIVE_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/lib"
     RUNTIME_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/bin")
+  # make sure we build all external depepdencies first
+  if (DEFINED external_project_dependencies)
+    add_dependencies(${target} ${external_project_dependencies})
+  endif()
 endfunction()
 
 ################################################################################################
@@ -140,12 +145,12 @@ function(caffe_configure_testdatafile file)
   set(result "")
   foreach(line ${__lines})
     set(result "${result}${PROJECT_SOURCE_DIR}/${line}\n")
-  endforeach()  
+  endforeach()
   file(WRITE ${file}.gen.cmake ${result})
 endfunction()
 
 ################################################################################################
-# Filter outs all files that are not inlcuded in selected list
+# Filter out all files that are not included in selected list
 # Usage:
 #   caffe_leave_only_selected_tests(<filelist_variable> <selected_list>)
 function(caffe_leave_only_selected_tests file_list)
diff --git a/cmake/Templates/CaffeConfig.cmake.in b/cmake/Templates/CaffeConfig.cmake.in
index a4b03d961e0..73f57ac2d74 100644
--- a/cmake/Templates/CaffeConfig.cmake.in
+++ b/cmake/Templates/CaffeConfig.cmake.in
@@ -4,7 +4,7 @@
 #   Caffe and this config file depends on opencv,
 #   so put `find_package(OpenCV)` before searching Caffe
 #   via `find_package(Caffe)`. All other lib/includes
-#   dependencies are hard coded int the file
+#   dependencies are hard coded in the file
 #
 # After successful configuration the following variables
 # will be defined:
@@ -15,24 +15,26 @@
 #
 #   Caffe_HAVE_CUDA    - signals about CUDA support
 #   Caffe_HAVE_CUDNN   - signals about cuDNN support
- 
 
-# OpenCV dependency
 
-if(NOT OpenCV_FOUND)
-  set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
-  if(Caffe_OpenCV_CONFIG_PATH)
-    get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+# OpenCV dependency (optional)
 
-    if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
-      message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
-      include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
-    endif()
+if(@USE_OPENCV@)
+  if(NOT OpenCV_FOUND)
+    set(Caffe_OpenCV_CONFIG_PATH "@OpenCV_CONFIG_PATH@")
+    if(Caffe_OpenCV_CONFIG_PATH)
+      get_filename_component(Caffe_OpenCV_CONFIG_PATH ${Caffe_OpenCV_CONFIG_PATH} ABSOLUTE)
+
+      if(EXISTS ${Caffe_OpenCV_CONFIG_PATH} AND NOT TARGET opencv_core)
+        message(STATUS "Caffe: using OpenCV config from ${Caffe_OpenCV_CONFIG_PATH}")
+        include(${Caffe_OpenCV_CONFIG_PATH}/OpenCVModules.cmake)
+      endif()
 
-  else()
-    find_package(OpenCV REQUIRED)
+    else()
+      find_package(OpenCV REQUIRED)
+    endif()
+    unset(Caffe_OpenCV_CONFIG_PATH)
   endif()
-  unset(Caffe_OpenCV_CONFIG_PATH)
 endif()
 
 # Compute paths
@@ -40,7 +42,7 @@ get_filename_component(Caffe_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
 set(Caffe_INCLUDE_DIRS "@Caffe_INCLUDE_DIRS@")
 
 @Caffe_INSTALL_INCLUDE_DIR_APPEND_COMMAND@
- 
+
 # Our library dependencies
 if(NOT TARGET caffe AND NOT caffe_BINARY_DIR)
   include("${Caffe_CMAKE_DIR}/CaffeTargets.cmake")
diff --git a/cmake/Templates/CaffeConfigVersion.cmake.in b/cmake/Templates/CaffeConfigVersion.cmake.in
index cbfa514f1a6..19f85309a5f 100644
--- a/cmake/Templates/CaffeConfigVersion.cmake.in
+++ b/cmake/Templates/CaffeConfigVersion.cmake.in
@@ -1,5 +1,5 @@
 set(PACKAGE_VERSION "@Caffe_VERSION@")
- 
+
 # Check whether the requested PACKAGE_FIND_VERSION is compatible
 if("${PACKAGE_VERSION}" VERSION_LESS "${PACKAGE_FIND_VERSION}")
   set(PACKAGE_VERSION_COMPATIBLE FALSE)
diff --git a/cmake/Templates/caffe_config.h.in b/cmake/Templates/caffe_config.h.in
index 6039e8f6b21..8a31b43cabf 100644
--- a/cmake/Templates/caffe_config.h.in
+++ b/cmake/Templates/caffe_config.h.in
@@ -30,3 +30,9 @@
 
 /* Matlab */
 #cmakedefine HAVE_MATLAB
+
+/* IO libraries */
+#cmakedefine USE_OPENCV
+#cmakedefine USE_LEVELDB
+#cmakedefine USE_LMDB
+#cmakedefine ALLOW_LMDB_NOLOCK
diff --git a/cmake/Utils.cmake b/cmake/Utils.cmake
index a56c7c300c0..653de5fdf89 100644
--- a/cmake/Utils.cmake
+++ b/cmake/Utils.cmake
@@ -1,7 +1,7 @@
 ################################################################################################
 # Command alias for debugging messages
 # Usage:
-#   dmgs(<message>)
+#   dmsg(<message>)
 function(dmsg)
   message(STATUS ${ARGN})
 endfunction()
@@ -19,9 +19,9 @@ macro(caffe_list_unique)
 endmacro()
 
 ################################################################################################
-# Clears variables from lsit
+# Clears variables from list
 # Usage:
-#   caffe_list_unique(<variables_list>)
+#   caffe_clear_vars(<variables_list>)
 macro(caffe_clear_vars)
   foreach(_var ${ARGN})
     unset(${_var})
@@ -118,7 +118,7 @@ macro(caffe_parse_header FILENAME FILE_VAR)
         if(__add_cache)
           set(${name} ${${name}} CACHE INTERNAL "${name} parsed from ${FILENAME}" FORCE)
         elseif(__parnet_scope)
-          set(${name} "${${name}}" PARENT_SCOPE)          
+          set(${name} "${${name}}" PARENT_SCOPE)
         endif()
       else()
         unset(${name} CACHE)
@@ -303,7 +303,7 @@ function(caffe_get_current_cflags cflags_var)
 endfunction()
 
 ################################################################################################
-# Helper function to parse current linker libs into link directoris, libflags and osx frameworks
+# Helper function to parse current linker libs into link directories, libflags and osx frameworks
 # Usage:
 #   caffe_parse_linker_libs(<Caffe_LINKER_LIBS_var> <directories_var> <libflags_var> <frameworks_var>)
 function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_var frameworks_var)
@@ -346,10 +346,11 @@ function(caffe_parse_linker_libs Caffe_LINKER_LIBS_variable folders_var flags_va
     elseif(lib MATCHES "^-l.*")
       list(APPEND libflags ${lib})
     elseif(IS_ABSOLUTE ${lib})
-      get_filename_component(name_we ${lib} NAME_WE)
       get_filename_component(folder  ${lib} PATH)
+      get_filename_component(filename ${lib} NAME)
+      string(REGEX REPLACE "\\.[^.]*$" "" filename_without_shortest_ext ${filename})
 
-      string(REGEX MATCH "^lib(.*)" __match ${name_we})
+      string(REGEX MATCH "^lib(.*)" __match ${filename_without_shortest_ext})
       list(APPEND libflags -l${CMAKE_MATCH_1})
       list(APPEND folders    ${folder})
     else()
diff --git a/cmake/lint.cmake b/cmake/lint.cmake
index 585babb3587..70a006572bb 100644
--- a/cmake/lint.cmake
+++ b/cmake/lint.cmake
@@ -5,7 +5,7 @@ set(SRC_FILE_EXTENSIONS h hpp hu c cpp cu cc)
 set(EXCLUDE_FILE_EXTENSTIONS pb.h pb.cc)
 set(LINT_DIRS include src/caffe examples tools python matlab)
 
-cmake_policy(SET CMP0009 NEW)  # supress cmake warning
+cmake_policy(SET CMP0009 NEW)  # suppress cmake warning
 
 # find all files of interest
 foreach(ext ${SRC_FILE_EXTENSIONS})
@@ -26,7 +26,7 @@ list(REMOVE_ITEM LINT_SOURCES ${EXCLUDED_FILES})
 
 execute_process(
     COMMAND ${LINT_COMMAND} ${LINT_SOURCES}
-    ERROR_VARIABLE LINT_OUTPUT 
+    ERROR_VARIABLE LINT_OUTPUT
     ERROR_STRIP_TRAILING_WHITESPACE
 )
 
diff --git a/data/ilsvrc12/get_ilsvrc_aux.sh b/data/ilsvrc12/get_ilsvrc_aux.sh
index b9b85d21e2d..90935f25099 100755
--- a/data/ilsvrc12/get_ilsvrc_aux.sh
+++ b/data/ilsvrc12/get_ilsvrc_aux.sh
@@ -12,7 +12,7 @@ cd $DIR
 
 echo "Downloading..."
 
-wget http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
+wget -c http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
 
 echo "Unzipping..."
 
diff --git a/data/mnist/get_mnist.sh b/data/mnist/get_mnist.sh
index 8eb6aeedf9f..6d875219489 100755
--- a/data/mnist/get_mnist.sh
+++ b/data/mnist/get_mnist.sh
@@ -6,19 +6,10 @@ cd $DIR
 
 echo "Downloading..."
 
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
-wget --no-check-certificate http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
-
-echo "Unzipping..."
-
-gunzip train-images-idx3-ubyte.gz
-gunzip train-labels-idx1-ubyte.gz
-gunzip t10k-images-idx3-ubyte.gz
-gunzip t10k-labels-idx1-ubyte.gz
-
-# Creation is split out because leveldb sometimes causes segfault
-# and needs to be re-created.
-
-echo "Done."
+for fname in train-images-idx3-ubyte train-labels-idx1-ubyte t10k-images-idx3-ubyte t10k-labels-idx1-ubyte
+do
+    if [ ! -e $fname ]; then
+        wget --no-check-certificate http://yann.lecun.com/exdb/mnist/${fname}.gz
+        gunzip ${fname}.gz
+    fi
+done
diff --git a/docker/Makefile b/docker/Makefile
new file mode 100644
index 00000000000..725208c6b2b
--- /dev/null
+++ b/docker/Makefile
@@ -0,0 +1,50 @@
+# A makefile to build the docker images for caffe.
+# Two caffe images will be built:
+#   caffe:cpu --> A CPU-only build of caffe.
+#   caffe:gpu --> A GPU-enabled build using the latest CUDA and CUDNN versions.
+
+DOCKER ?= docker
+
+all: docker_files standalone
+
+.PHONY: standalone devel
+
+standalone: cpu_standalone gpu_standalone
+
+
+cpu_standalone: standalone/cpu/Dockerfile
+	$(DOCKER) build -t caffe:cpu standalone/cpu
+
+gpu_standalone: standalone/gpu/Dockerfile
+	$(DOCKER) build -t caffe:gpu standalone/gpu
+
+docker_files: standalone_files
+
+standalone_files: standalone/cpu/Dockerfile standalone/gpu/Dockerfile
+
+FROM_GPU = "nvidia/cuda:cudnn"
+FROM_CPU = "ubuntu:14.04"
+GPU_CMAKE_ARGS = -DUSE_CUDNN=1
+CPU_CMAKE_ARGS = -DCPU_ONLY=1
+
+# A make macro to select the CPU or GPU base image.
+define from_image
+$(if $(strip $(findstring gpu,$@)),$(FROM_GPU),$(FROM_CPU))
+endef
+
+# A make macro to select the CPU or GPU build args.
+define build_args
+$(if $(strip $(findstring gpu,$@)),$(GPU_CMAKE_ARGS),$(CPU_CMAKE_ARGS))
+endef
+
+# A make macro to construct the CPU or GPU Dockerfile from the template
+define create_docker_file
+	@echo creating $@
+	@echo "FROM "$(from_image) > $@
+	@cat $^ | sed 's/$${CMAKE_ARGS}/$(build_args)/' >> $@
+endef
+
+
+standalone/%/Dockerfile: templates/Dockerfile.template
+	$(create_docker_file)
+
diff --git a/docker/README.md b/docker/README.md
new file mode 100644
index 00000000000..fdab641bdca
--- /dev/null
+++ b/docker/README.md
@@ -0,0 +1,52 @@
+# Caffe standalone Dockerfiles.
+
+The `standalone` subfolder contains docker files for generating both CPU and GPU executable images for Caffe. The images can be built using make, or by running:
+
+```
+docker build -t caffe:cpu standalone/cpu
+```
+for example. (Here `gpu` can be substituted for `cpu`, but to keep the readme simple, only the `cpu` case will be discussed in detail).
+
+Note that the GPU standalone requires a CUDA 7.5 capable driver to be installed on the system and [nvidia-docker] for running the Docker containers. Here it is generally sufficient to use `nvidia-docker` instead of `docker` in any of the commands mentioned.
+
+# Running Caffe using the docker image
+
+In order to test the Caffe image, run:
+```
+docker run -ti caffe:cpu caffe --version
+```
+which should show a message like:
+```
+libdc1394 error: Failed to initialize libdc1394
+caffe version 1.0.0-rc3
+```
+
+One can also build and run the Caffe tests in the image using:
+```
+docker run -ti caffe:cpu bash -c "cd /opt/caffe/build; make runtest"
+```
+
+In order to get the most out of the caffe image, some more advanced `docker run` options could be used. For example, running:
+```
+docker run -ti --volume=$(pwd):/workspace caffe:cpu caffe train --solver=example_solver.prototxt
+```
+will train a network defined in the `example_solver.prototxt` file in the current directory (`$(pwd)` is maped to the container volume `/workspace` using the `--volume=` Docker flag).
+
+Note that docker runs all commands as root by default, and thus any output files (e.g. snapshots) generated will be owned by the root user. In order to ensure that the current user is used instead, the following command can be used:
+```
+docker run -ti --volume=$(pwd):/workspace -u $(id -u):$(id -g) caffe:cpu caffe train --solver=example_solver.prototxt
+```
+where the `-u` Docker command line option runs the commands in the container as the specified user, and the shell command `id` is used to determine the user and group ID of the current user. Note that the Caffe docker images have `/workspace` defined as the default working directory. This can be overridden using the `--workdir=` Docker command line option.
+
+# Other use-cases
+
+Although running the `caffe` command in the docker containers as described above serves many purposes, the container can also be used for more interactive use cases. For example, specifying `bash` as the command instead of `caffe` yields a shell that can be used for interactive tasks. (Since the caffe build requirements are included in the container, this can also be used to build and run local versions of caffe).
+
+Another use case is to run python scripts that depend on `caffe`'s Python modules. Using the `python` command instead of `bash` or `caffe` will allow this, and an interactive interpreter can be started by running:
+```
+docker run -ti caffe:cpu python
+```
+(`ipython` is also available in the container).
+
+Since the `caffe/python` folder is also added to the path, the utility executable scripts defined there can also be used as executables. This includes `draw_net.py`, `classify.py`, and `detect.py`
+
diff --git a/docker/standalone/cpu/Dockerfile b/docker/standalone/cpu/Dockerfile
new file mode 100644
index 00000000000..4fef25aa6a1
--- /dev/null
+++ b/docker/standalone/cpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM ubuntu:14.04
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake -DCPU_ONLY=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/standalone/gpu/Dockerfile b/docker/standalone/gpu/Dockerfile
new file mode 100644
index 00000000000..1ddc6560d16
--- /dev/null
+++ b/docker/standalone/gpu/Dockerfile
@@ -0,0 +1,43 @@
+FROM nvidia/cuda:cudnn
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake -DUSE_CUDNN=1 .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docker/templates/Dockerfile.template b/docker/templates/Dockerfile.template
new file mode 100644
index 00000000000..8834f057968
--- /dev/null
+++ b/docker/templates/Dockerfile.template
@@ -0,0 +1,42 @@
+MAINTAINER caffe-maint@googlegroups.com
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+        build-essential \
+        cmake \
+        git \
+        wget \
+        libatlas-base-dev \
+        libboost-all-dev \
+        libgflags-dev \
+        libgoogle-glog-dev \
+        libhdf5-serial-dev \
+        libleveldb-dev \
+        liblmdb-dev \
+        libopencv-dev \
+        libprotobuf-dev \
+        libsnappy-dev \
+        protobuf-compiler \
+        python-dev \
+        python-numpy \
+        python-pip \
+        python-scipy && \
+    rm -rf /var/lib/apt/lists/*
+
+ENV CAFFE_ROOT=/opt/caffe
+WORKDIR $CAFFE_ROOT
+
+# FIXME: clone a specific git tag and use ARG instead of ENV once DockerHub supports this.
+ENV CLONE_TAG=master
+
+RUN git clone -b ${CLONE_TAG} --depth 1 https://github.com/BVLC/caffe.git . && \
+    for req in $(cat python/requirements.txt) pydot; do pip install $req; done && \
+    mkdir build && cd build && \
+    cmake ${CMAKE_ARGS} .. && \
+    make -j"$(nproc)"
+
+ENV PYCAFFE_ROOT $CAFFE_ROOT/python
+ENV PYTHONPATH $PYCAFFE_ROOT:$PYTHONPATH
+ENV PATH $CAFFE_ROOT/build/tools:$PYCAFFE_ROOT:$PATH
+RUN echo "$CAFFE_ROOT/build/lib" >> /etc/ld.so.conf.d/caffe.conf && ldconfig
+
+WORKDIR /workspace
diff --git a/docs/development.md b/docs/development.md
index ccb6a29701d..107c2c3b281 100644
--- a/docs/development.md
+++ b/docs/development.md
@@ -62,28 +62,25 @@ The following is a poetic presentation of the protocol in code form.
 #### [Shelhamer's](https://github.com/shelhamer) “life of a branch in four acts”
 
 Make the `feature` branch off of the latest `bvlc/master`
-```
-git checkout master
-git pull upstream master
-git checkout -b feature
-# do your work, make commits
-```
+
+    git checkout master
+    git pull upstream master
+    git checkout -b feature
+    # do your work, make commits
 
 Prepare to merge by rebasing your branch on the latest `bvlc/master`
-```
-# make sure master is fresh
-git checkout master
-git pull upstream master
-# rebase your branch on the tip of master
-git checkout feature
-git rebase master
-```
+
+    # make sure master is fresh
+    git checkout master
+    git pull upstream master
+    # rebase your branch on the tip of master
+    git checkout feature
+    git rebase master
 
 Push your branch to pull request it into `BVLC/caffe:master`
-```
-git push origin feature
-# ...make pull request to master...
-```
+
+    git push origin feature
+    # ...make pull request to master...
 
 Now make a pull request! You can do this from the command line (`git pull-request -b master`) if you install [hub](https://github.com/github/hub). Hub has many other magical uses.
 
diff --git a/docs/install_apt.md b/docs/install_apt.md
index 75f8bec0e95..2976e3cd07c 100644
--- a/docs/install_apt.md
+++ b/docs/install_apt.md
@@ -6,7 +6,8 @@ title: Installation: Ubuntu
 
 **General dependencies**
 
-    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev
+    sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
+    sudo apt-get install --no-install-recommends libboost-all-dev
 
 **CUDA**: Install via the NVIDIA package instead of `apt-get` to be certain of the library and driver versions.
 Install the library and latest driver separately; the driver bundled with the library is usually out-of-date.
@@ -20,7 +21,7 @@ This can be skipped for CPU-only installation.
 
 Everything is packaged in 14.04.
 
-    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler
+    sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
 
 **Remaining dependencies, 12.04**
 
@@ -40,8 +41,8 @@ These dependencies need manual installation in 12.04.
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone https://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
+    git clone https://github.com/LMDB/lmdb
+    cd lmdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
diff --git a/docs/install_osx.md b/docs/install_osx.md
index c4ebd45f553..6405d8ad046 100644
--- a/docs/install_osx.md
+++ b/docs/install_osx.md
@@ -10,15 +10,15 @@ In the following, we assume that you're using Anaconda Python and Homebrew.
 
 **CUDA**: Install via the NVIDIA package that includes both CUDA and the bundled driver. **CUDA 7 is strongly suggested.** Older CUDA require `libstdc++` while clang++ is the default compiler and `libc++` the default standard library on OS X 10.9+. This disagreement makes it necessary to change the compilation settings for each of the dependencies. This is prone to error.
 
-**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
+**Library Path**: We find that everything compiles successfully if `$LD_LIBRARY_PATH` is not set at all, and `$DYLD_FALLBACK_LIBRARY_PATH` is set to provide CUDA, Python, and other relevant libraries (e.g. `/usr/local/cuda/lib:$HOME/anaconda/lib:/usr/local/lib:/usr/lib`).
 In other `ENV` settings, things may not work as expected.
 
 **General dependencies**
 
-    brew install --fresh -vd snappy leveldb gflags glog szip lmdb
+    brew install -vd snappy leveldb gflags glog szip lmdb
     # need the homebrew science source for OpenCV and hdf5
     brew tap homebrew/science
-    hdf5 opencv
+    brew install hdf5 opencv
 
 If using Anaconda Python, a modification to the OpenCV formula might be needed
 Do `brew edit opencv` and change the lines that look like the two lines below to exactly the two lines below.
@@ -31,8 +31,8 @@ If using Anaconda Python, HDF5 is bundled and the `hdf5` formula can be skipped.
 **Remaining dependencies, with / without Python**
 
     # with Python pycaffe needs dependencies built from source
-    brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
+    brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
     # without Python the usual installation suffices
     brew install protobuf boost
 
@@ -78,9 +78,9 @@ To edit the formulae in turn, run
 
 After this, run
 
-    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source --fresh -vd $x; done
-    brew uninstall protobuf; brew install --build-from-source --with-python --fresh -vd protobuf
-    brew install --build-from-source --fresh -vd boost boost-python
+    for x in snappy leveldb gflags glog szip lmdb homebrew/science/opencv; do brew uninstall $x; brew install --build-from-source -vd $x; done
+    brew uninstall protobuf; brew install --build-from-source --with-python -vd protobuf
+    brew install --build-from-source -vd boost boost-python
 
 If this is not done exactly right then linking errors will trouble you.
 
diff --git a/docs/install_yum.md b/docs/install_yum.md
index 478e7d952cc..2104912e482 100644
--- a/docs/install_yum.md
+++ b/docs/install_yum.md
@@ -28,8 +28,8 @@ title: Installation: RHEL / Fedora / CentOS
     export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
     make && make install
     # lmdb
-    git clone git://gitorious.org/mdb/mdb.git
-    cd mdb/libraries/liblmdb
+    git clone https://github.com/LMDB/lmdb
+    cd lmdb/libraries/liblmdb
     make && make install
 
 Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.
diff --git a/docs/installation.md b/docs/installation.md
index 144e6a34f67..893164584d9 100644
--- a/docs/installation.md
+++ b/docs/installation.md
@@ -17,23 +17,27 @@ When updating Caffe, it's best to `make clean` before re-compiling.
 
 ## Prerequisites
 
-Caffe has several dependencies.
+Caffe has several dependencies:
 
 * [CUDA](https://developer.nvidia.com/cuda-zone) is required for GPU mode.
     * library version 7.0 and the latest driver version are recommended, but 6.* is fine too
     * 5.5, and 5.0 are compatible but considered legacy
 * [BLAS](http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) via ATLAS, MKL, or OpenBLAS.
 * [Boost](http://www.boost.org/) >= 1.55
+* `protobuf`, `glog`, `gflags`, `hdf5`
+
+Optional dependencies:
+
 * [OpenCV](http://opencv.org/) >= 2.4 including 3.0
-* `protobuf`, `glog`, `gflags`
-* IO libraries `hdf5`, `leveldb`, `snappy`, `lmdb`
+* IO libraries: `lmdb`, `leveldb` (note: leveldb requires `snappy`)
+* cuDNN for GPU acceleration (v3)
 
 Pycaffe and Matcaffe interfaces have their own natural needs.
 
 * For Python Caffe:  `Python 2.7` or `Python 3.3+`, `numpy (>= 1.7)`, boost-provided `boost.python`
 * For MATLAB Caffe: MATLAB with the `mex` compiler.
 
-**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. For now cuDNN v1 is integrated but see [PR #1731](https://github.com/BVLC/caffe/pull/1731) for v2.
+**cuDNN Caffe**: for fastest operation Caffe is accelerated by drop-in integration of [NVIDIA cuDNN](https://developer.nvidia.com/cudnn). To speed up your Caffe models, install cuDNN then uncomment the `USE_CUDNN := 1` flag in `Makefile.config` when installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.
 
 **CPU-only Caffe**: for cold-brewed CPU-only Caffe uncomment the `CPU_ONLY := 1` flag in `Makefile.config` to configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.
 
@@ -50,7 +54,8 @@ There are several implementations of this library. The choice is yours:
 * [ATLAS](http://math-atlas.sourceforge.net/): free, open source, and so the default for Caffe.
 * [Intel MKL](http://software.intel.com/en-us/intel-mkl): commercial and optimized for Intel CPUs, with a free trial and [student](http://software.intel.com/en-us/intel-education-offerings) licenses.
     1. Install MKL.
-    2. Set `BLAS := mkl` in `Makefile.config`
+    2. Set up MKL environment (Details: [Linux](https://software.intel.com/en-us/node/528499), [OS X](https://software.intel.com/en-us/node/528659)). Example: *source /opt/intel/mkl/bin/mklvars.sh intel64*
+    3. Set `BLAS := mkl` in `Makefile.config`
 * [OpenBLAS](http://www.openblas.net/): free and open source; this optimized and parallel BLAS could require more effort to install, although it might offer a speedup.
     1. Install OpenBLAS
     2. Set `BLAS := open` in `Makefile.config`
@@ -75,7 +80,7 @@ To import the `caffe` Python module after completing the installation, add the m
 
 Install MATLAB, and make sure that its `mex` is in your `$PATH`.
 
-*Caffe's MATLAB interface works with versions 2014a/b, 2013a/b, and 2012b.*
+*Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.*
 
 #### Windows
 
@@ -83,15 +88,20 @@ There is an unofficial Windows port of Caffe at [niuzhiheng/caffe:windows](https
 
 ## Compilation
 
-Now that you have the prerequisites, edit your `Makefile.config` to change the paths for your setup The defaults should work, but uncomment the relevant lines if using Anaconda Python.
+Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
+
+### Compilation with Make
+
+Configure the build by copying and modifying the example `Makefile.config` for your setup. The defaults should work, but uncomment the relevant lines if using Anaconda Python.
 
     cp Makefile.config.example Makefile.config
-    # Adjust Makefile.config (for example, if using Anaconda Python)
+    # Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
     make all
     make test
     make runtest
 
-- For cuDNN acceleration, you should uncomment the `USE_CUDNN := 1` switch in `Makefile.config`.
+- For CPU & GPU accelerated Caffe, no changes are needed.
+- For cuDNN acceleration using NVIDIA's proprietary cuDNN software, uncomment the `USE_CUDNN := 1` switch in `Makefile.config`. cuDNN is sometimes but not always faster than Caffe's GPU acceleration.
 - For CPU-only Caffe, uncomment `CPU_ONLY := 1` in `Makefile.config`.
 
 To compile the Python and MATLAB wrappers do `make pycaffe` and `make matcaffe` respectively.
@@ -103,7 +113,7 @@ Be sure to set your MATLAB and Python paths in `Makefile.config` first!
 
 Now that you have installed Caffe, check out the [MNIST tutorial](gathered/examples/mnist.html) and the [reference ImageNet model tutorial](gathered/examples/imagenet.html).
 
-### CMake Compilation
+### Compilation with CMake
 
 In lieu of manually editing `Makefile.config` to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek, and other members of the community. It requires CMake version >= 2.8.7.
 The basic steps are as follows:
@@ -112,6 +122,7 @@ The basic steps are as follows:
     cd build
     cmake ..
     make all
+    make install
     make runtest
 
 See [PR #1667](https://github.com/BVLC/caffe/pull/1667) for options and details.
diff --git a/docs/multigpu.md b/docs/multigpu.md
new file mode 100644
index 00000000000..d91acef980d
--- /dev/null
+++ b/docs/multigpu.md
@@ -0,0 +1,26 @@
+---
+title: Multi-GPU Usage, Hardware Configuration Assumptions, and Performance
+---
+
+# Multi-GPU Usage
+
+Currently Multi-GPU is only supported via the C/C++ paths and only for training.
+
+The GPUs to be used for training can be set with the "-gpu" flag on the command line to the 'caffe' tool.  e.g. "build/tools/caffe train --solver=models/bvlc_alexnet/solver.prototxt --gpu=0,1" will train on GPUs 0 and 1.
+
+**NOTE**: each GPU runs the batchsize specified in your train_val.prototxt.  So if you go from 1 GPU to 2 GPU, your effective batchsize will double.  e.g. if your train_val.prototxt specified a batchsize of 256, if you run 2 GPUs your effective batch size is now 512.  So you need to adjust the batchsize when running multiple GPUs and/or adjust your solver params, specifically learning rate.
+
+# Hardware Configuration Assumptions
+
+The current implementation uses a tree reduction strategy.  e.g. if there are 4 GPUs in the system, 0:1, 2:3 will exchange gradients, then 0:2 (top of the tree) will exchange gradients, 0 will calculate
+updated model, 0\-\>2, and then 0\-\>1, 2\-\>3. 
+
+For best performance, P2P DMA access between devices is needed. Without P2P access, for example crossing PCIe root complex, data is copied through host and effective exchange bandwidth is greatly reduced.
+
+Current implementation has a "soft" assumption that the devices being used are homogeneous.  In practice, any devices of the same general class should work together, but performance and total size is limited by the smallest device being used.  e.g. if you combine a TitanX and a GTX980, performance will be limited by the 980.  Mixing vastly different levels of boards, e.g. Kepler and Fermi, is not supported.
+
+"nvidia-smi topo -m" will show you the connectivity matrix.  You can do P2P through PCIe bridges, but not across socket level links at this time, e.g. across CPU sockets on a multi-socket motherboard.
+
+# Scaling Performance
+
+Performance is **heavily** dependent on the PCIe topology of the system, the configuration of the neural network you are training, and the speed of each of the layers.  Systems like the DIGITS DevBox have an optimized PCIe topology (X99-E WS chipset).  In general, scaling on 2 GPUs tends to be ~1.8X on average for networks like AlexNet, CaffeNet, VGG, GoogleNet.  4 GPUs begins to have falloff in scaling.  Generally with "weak scaling" where the batchsize increases with the number of GPUs you will see 3.5x scaling or so.  With "strong scaling", the system can become communication bound, especially with layer performance optimizations like those in [cuDNNv3](http://nvidia.com/cudnn), and you will likely see closer to mid 2.x scaling in performance.  Networks that have heavy computation compared to the number of parameters tend to have the best scaling performance.
\ No newline at end of file
diff --git a/docs/performance_hardware.md b/docs/performance_hardware.md
index b35246feabd..cdd4b361dea 100644
--- a/docs/performance_hardware.md
+++ b/docs/performance_hardware.md
@@ -48,7 +48,7 @@ and then set the clock speed with
 
     sudo nvidia-smi -i 0 -ac 3004,875    # repeat with -i x for each GPU ID
 
-but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to intialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
+but note that this configuration resets across driver reloading / rebooting. Include these commands in a boot script to initialize these settings. For a simple fix, add these commands to `/etc/rc.local` (on Ubuntu).
 
 ## NVIDIA Titan
 
diff --git a/docs/tutorial/data.md b/docs/tutorial/data.md
index 40605f7cd73..3bf7d932eda 100644
--- a/docs/tutorial/data.md
+++ b/docs/tutorial/data.md
@@ -10,15 +10,15 @@ New input types are supported by developing a new data layer -- the rest of the
 
 This data layer definition
 
-    layers {
+    layer {
       name: "mnist"
-      # DATA layer loads leveldb or lmdb storage DBs for high-throughput.
-      type: DATA
+      # Data layer loads leveldb or lmdb storage DBs for high-throughput.
+      type: "Data"
       # the 1st top is the data itself: the name is only convention
       top: "data"
       # the 2nd top is the ground truth: the name is only convention
       top: "label"
-      # the DATA layer configuration
+      # the Data layer configuration
       data_param {
         # path to the DB
         source: "examples/mnist/mnist_train_lmdb"
@@ -46,9 +46,9 @@ The (data, label) pairing is a convenience for classification models.
 
 **Transformations**: data preprocessing is parametrized by transformation messages within the data layer definition.
 
-    layers {
+    layer {
       name: "data"
-      type: DATA
+      type: "Data"
       [...]
       transform_param {
         scale: 0.1
diff --git a/docs/tutorial/forward_backward.md b/docs/tutorial/forward_backward.md
index a645f002f61..528b993ba07 100644
--- a/docs/tutorial/forward_backward.md
+++ b/docs/tutorial/forward_backward.md
@@ -29,7 +29,7 @@ The backward pass begins with the loss and computes the gradient with respect to
 These computations follow immediately from defining the model: Caffe plans and carries out the forward and backward passes for you.
 
 - The `Net::Forward()` and `Net::Backward()` methods carry out the respective passes while `Layer::Forward()` and `Layer::Backward()` compute each step.
-- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
+- Every layer type has `forward_{cpu,gpu}()` and `backward_{cpu,gpu}()` methods to compute its steps according to the mode of computation. A layer may only implement CPU or GPU mode due to constraints or convenience.
 
 The [Solver](solver.html) optimizes a model by first calling forward to yield the output and loss, then calling backward to generate the gradient of the model, and then incorporating the gradient into a weight update that attempts to minimize the loss. Division of labor between the Solver, Net, and Layer keep Caffe modular and open to development.
 
diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index 17430b35c57..d7ff378239d 100644
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -11,8 +11,8 @@ The command line interface -- cmdcaffe -- is the `caffe` tool for model training
 
 **Training**: `caffe train` learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks:
 
-* All training requires a solver configuration through the `-solver solver.prototxt` argument. 
-* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot. 
+* All training requires a solver configuration through the `-solver solver.prototxt` argument.
+* Resuming requires the `-snapshot model_iter_1000.solverstate` argument to load the solver snapshot.
 * Fine-tuning requires the `-weights model.caffemodel` argument for the model initialization.
 
 For example, you can run:
@@ -31,8 +31,7 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
 
 **Testing**: `caffe test` scores models by running them in the test phase and reports the net output as its score. The net architecture must be properly defined to output an accuracy measure or loss as its output. The per-batch score is reported and then the grand average is reported last.
 
-    #
-    # score the learned LeNet model on the validation set as defined in the 
+    # score the learned LeNet model on the validation set as defined in the
     # model architeture lenet_train_test.prototxt
     caffe test -model examples/mnist/lenet_train_test.prototxt -weights examples/mnist/lenet_iter_10000.caffemodel -gpu 0 -iterations 100
 
@@ -51,11 +50,18 @@ For a full example of fine-tuning, see examples/finetuning_on_flickr_style, but
     # query the first device
     caffe device_query -gpu 0
 
+**Parallelism**: the `-gpu` flag to the `caffe` tool can take a comma separated list of IDs to run on multiple GPUs. A solver and net will be instantiated for each GPU so the batch size is effectively multiplied by the number of GPUs. To reproduce single GPU training, reduce the batch size in the network definition accordingly.
+
+    # train on GPUs 0 & 1 (doubling the batch size)
+    caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1
+    # train on all GPUs (multiplying batch size by number of devices)
+    caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all
+
 ## Python
 
 The Python interface -- pycaffe -- is the `caffe` module and its scripts in caffe/python. `import caffe` to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing.
 
-- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classsifier` and `caffe.Detector` provide convenience interfaces for common tasks.
+- `caffe.Net` is the central interface for loading, configuring, and running models. `caffe.Classifier` and `caffe.Detector` provide convenience interfaces for common tasks.
 - `caffe.SGDSolver` exposes the solving interface.
 - `caffe.io` handles input / output with preprocessing and protocol buffers.
 - `caffe.draw` visualizes network architectures.
@@ -63,14 +69,218 @@ The Python interface -- pycaffe -- is the `caffe` module and its scripts in caff
 
 Tutorial IPython notebooks are found in caffe/examples: do `ipython notebook caffe/examples` to try them. For developer reference docstrings can be found throughout the code.
 
-Compile pycaffe by `make pycaffe`. The module dir caffe/python/caffe should be installed in your PYTHONPATH for `import caffe`.
+Compile pycaffe by `make pycaffe`.
+Add the module directory to your `$PYTHONPATH` by `export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH` or the like for `import caffe`.
 
 ## MATLAB
 
-The MATLAB interface -- matcaffe -- is the `caffe` mex and its helper m-files in caffe/matlab. Load models, do forward and backward, extract output and read-only model weights, and load the binaryproto format mean as a matrix.
+The MATLAB interface -- matcaffe -- is the `caffe` package in caffe/matlab in which you can integrate Caffe in your Matlab code.
+
+In MatCaffe, you can
+
+* Creating multiple Nets in Matlab
+* Do forward and backward computation
+* Access any layer within a network, and any parameter blob in a layer
+* Get and set data or diff to any blob within a network, not restricting to input blobs or output blobs
+* Save a network's parameters to file, and load parameters from file
+* Reshape a blob and reshape a network
+* Edit network parameter and do network surgery
+* Create multiple Solvers in Matlab for training
+* Resume training from solver snapshots
+* Access train net and test nets in a solver
+* Run for a certain number of iterations and give back control to Matlab
+* Intermingle arbitrary Matlab code with gradient steps
+
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
+
+### Build MatCaffe
+
+Build MatCaffe with `make all matcaffe`. After that, you may test it using `make mattest`.
+
+Common issue: if you run into error messages like `libstdc++.so.6:version 'GLIBCXX_3.4.15' not found` during `make mattest`, then it usually means that your Matlab's runtime libraries do not match your compile-time libraries. You may need to do the following before you start Matlab:
+
+    export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
+    export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+
+Or the equivalent based on where things are installed on your system, and do `make mattest` again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your `LD_LIBRARY_PATH` environment variable. You can run `!ldd ./matlab/+caffe/private/caffe_.mexa64` (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your `LD_PRELOAD` environment variable.
+
+After successful building and testing, add this package to Matlab search PATH by starting `matlab` from caffe root folder and running the following commands in Matlab command window.
+
+    addpath ./matlab
+
+You can save your Matlab search PATH by running `savepath` so that you don't have to run the command above again every time you use MatCaffe.
+
+### Use MatCaffe
+
+MatCaffe is very similar to PyCaffe in usage.
+
+Examples below shows detailed usages and assumes you have downloaded BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) and started `matlab` from caffe root folder.
+
+    model = './models/bvlc_reference_caffenet/deploy.prototxt';
+    weights = './models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
+
+#### Set mode and device
+
+**Mode and device should always be set BEFORE you create a net or a solver.**
+
+Use CPU:
+
+    caffe.set_mode_cpu();
+
+Use GPU and specify its gpu_id:
+
+    caffe.set_mode_gpu();
+    caffe.set_device(gpu_id);
+
+#### Create a network and access its layers and blobs
+
+Create a network:
+
+    net = caffe.Net(model, weights, 'test'); % create net and load weights
+
+Or
+
+    net = caffe.Net(model, 'test'); % create net but not load weights
+    net.copy_from(weights); % load weights
+
+which creates `net` object as
+
+      Net with properties:
+
+               layer_vec: [1x23 caffe.Layer]
+                blob_vec: [1x15 caffe.Blob]
+                  inputs: {'data'}
+                 outputs: {'prob'}
+        name2layer_index: [23x1 containers.Map]
+         name2blob_index: [15x1 containers.Map]
+             layer_names: {23x1 cell}
+              blob_names: {15x1 cell}
+
+The two `containers.Map` objects are useful to find the index of a layer or a blob by its name.
+
+You have access to every blob in this network. To fill blob 'data' with all ones:
+
+    net.blobs('data').set_data(ones(net.blobs('data').shape));
+
+To multiply all values in blob 'data' by 10:
+
+    net.blobs('data').set_data(net.blobs('data').get_data() * 10);
+
+**Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are `[width, height, channels, num]`, and `width` is the fastest dimension. Also be aware that images are in BGR channels.** Also, Caffe uses single-precision float data. If your data is not single, `set_data` will automatically convert it to single.
+
+You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10:
+
+    net.params('conv1', 1).set_data(net.params('conv1', 1).get_data() * 10); % set weights
+    net.params('conv1', 2).set_data(net.params('conv1', 2).get_data() * 10); % set bias
+
+Alternatively, you can use
+
+    net.layers('conv1').params(1).set_data(net.layers('conv1').params(1).get_data() * 10);
+    net.layers('conv1').params(2).set_data(net.layers('conv1').params(2).get_data() * 10);
+
+To save the network you just modified:
+
+    net.save('my_net.caffemodel');
+
+To get a layer's type (string):
+
+    layer_type = net.layers('conv1').type;
+
+#### Forward and backward
+
+Forward pass can be done using `net.forward` or `net.forward_prefilled`. Function `net.forward` takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function `net.forward_prefilled` uses existing data in input blob(s) during forward pass, takes no input and produces no output. After creating some data for input blobs like `data = rand(net.blobs('data').shape);` you can run
+
+    res = net.forward({data});
+    prob = res{1};
+
+Or
+
+    net.blobs('data').set_data(data);
+    net.forward_prefilled();
+    prob = net.blobs('prob').get_data();
+
+Backward is similar using `net.backward` or `net.backward_prefilled` and replacing `get_data` and `set_data` with `get_diff` and `set_diff`. After creating some gradients for output blobs like `prob_diff = rand(net.blobs('prob').shape);` you can run
+
+    res = net.backward({prob_diff});
+    data_diff = res{1};
+
+Or
+
+    net.blobs('prob').set_diff(prob_diff);
+    net.backward_prefilled();
+    data_diff = net.blobs('data').get_diff();
+
+**However, the backward computation above doesn't get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set `'force_backward: true'` in your network prototxt.**
+
+After performing forward or backward pass, you can also get the data or diff in internal blobs. For example, to extract pool5 features after forward pass:
+
+    pool5_feat = net.blobs('pool5').get_data();
+
+#### Reshape
+
+Assume you want to run 1 image at a time instead of 10:
+
+    net.blobs('data').reshape([227 227 3 1]); % reshape blob 'data'
+    net.reshape();
+
+Then the whole network is reshaped, and now `net.blobs('prob').shape` should be `[1000 1]`;
+
+#### Training
+
+Assume you have created training and validation lmdbs following our [ImageNET Tutorial](http://caffe.berkeleyvision.org/gathered/examples/imagenet.html), to create a solver and train on ILSVRC 2012 classification dataset:
+
+    solver = caffe.Solver('./models/bvlc_reference_caffenet/solver.prototxt');
+
+which creates `solver` object as
+
+      Solver with properties:
+
+              net: [1x1 caffe.Net]
+        test_nets: [1x1 caffe.Net]
+
+To train:
+
+    solver.solve();
+
+Or train for only 1000 iterations (so that you can do something to its net before training more iterations)
+
+    solver.step(1000);
+
+To get iteration number:
+
+    iter = solver.iter();
+
+To get its network:
+
+    train_net = solver.net;
+    test_net = solver.test_nets(1);
+
+To resume from a snapshot "your_snapshot.solverstate":
+
+    solver.restore('your_snapshot.solverstate');
+
+#### Input and output
+
+`caffe.io` class provides basic input functions `load_image` and `read_mean`. For example, to read ILSVRC 2012 mean file (assume you have downloaded imagenet example auxiliary files by running `./data/ilsvrc12/get_ilsvrc_aux.sh`):
+
+    mean_data = caffe.io.read_mean('./data/ilsvrc12/imagenet_mean.binaryproto');
+
+To read Caffe's example image and resize to `[width, height]` and suppose we want `width = 256; height = 256;`
+
+    im_data = caffe.io.load_image('./examples/images/cat.jpg');
+    im_data = imresize(im_data, [width, height]); % resize using Matlab's imresize
+
+**Keep in mind that `width` is the fastest dimension and channels are BGR, which is different from the usual way that Matlab stores an image.** If you don't want to use `caffe.io.load_image` and prefer to load an image by yourself, you can do
+
+    im_data = imread('./examples/images/cat.jpg'); % read image
+    im_data = im_data(:, :, [3, 2, 1]); % convert from RGB to BGR
+    im_data = permute(im_data, [2, 1, 3]); % permute width and height
+    im_data = single(im_data); % convert to single precision
+
+Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image.
 
-A MATLAB demo is in caffe/matlab/caffe/matcaffe_demo.m
+We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output.
 
-Note that MATLAB matrices and memory are in column-major layout counter to Caffe's row-major layout! Double-check your work accordingly.
+#### Clear nets and solvers
 
-Compile matcaffe by `make matcaffe`.
+Call `caffe.reset_all()` to clear all solvers and stand-alone nets you have created.
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md
index 839939f5ad6..7362aac298a 100644
--- a/docs/tutorial/layers.md
+++ b/docs/tutorial/layers.md
@@ -5,9 +5,7 @@ title: Layer Catalogue
 
 To create a Caffe model you need to define the model architecture in a protocol buffer definition file (prototxt).
 
-Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto). The latest definitions are in the [dev caffe.proto](https://github.com/BVLC/caffe/blob/dev/src/caffe/proto/caffe.proto).
-
-TODO complete list of layers linking to headings
+Caffe layers and their parameters are defined in the protocol buffer definitions for the project in [caffe.proto](https://github.com/BVLC/caffe/blob/master/src/caffe/proto/caffe.proto).
 
 ### Vision Layers
 
@@ -23,7 +21,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
 
 #### Convolution
 
-* LayerType: `CONVOLUTION`
+* Layer type: `Convolution`
 * CPU implementation: `./src/caffe/layers/convolution_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/convolution_layer.cu`
 * Parameters (`ConvolutionParameter convolution_param`)
@@ -41,17 +39,17 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
     - `n * c_i * h_i * w_i`
 * Output
     - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
-      layers {
+      layer {
         name: "conv1"
-        type: CONVOLUTION
+        type: "Convolution"
         bottom: "data"
         top: "conv1"
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        # learning rate and decay multipliers for the filters
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         convolution_param {
           num_output: 96     # learn 96 filters
           kernel_size: 11    # each filter is 11x11
@@ -67,11 +65,11 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of
         }
       }
 
-The `CONVOLUTION` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
+The `Convolution` layer convolves the input image with a set of learnable filters, each producing one feature map in the output image.
 
 #### Pooling
 
-* LayerType: `POOLING`
+* Layer type: `Pooling`
 * CPU implementation: `./src/caffe/layers/pooling_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/pooling_layer.cu`
 * Parameters (`PoolingParameter pooling_param`)
@@ -85,11 +83,11 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
     - `n * c * h_i * w_i`
 * Output
     - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
-      layers {
+      layer {
         name: "pool1"
-        type: POOLING
+        type: "Pooling"
         bottom: "conv1"
         top: "pool1"
         pooling_param {
@@ -101,7 +99,7 @@ The `CONVOLUTION` layer convolves the input image with a set of learnable filter
 
 #### Local Response Normalization (LRN)
 
-* LayerType: `LRN`
+* Layer type: `LRN`
 * CPU Implementation: `./src/caffe/layers/lrn_layer.cpp`
 * CUDA GPU Implementation: `./src/caffe/layers/lrn_layer.cu`
 * Parameters (`LRNParameter lrn_param`)
@@ -115,7 +113,7 @@ The local response normalization layer performs a kind of "lateral inhibition" b
 
 #### im2col
 
-`IM2COL` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
+`Im2col` is a helper for doing the image-to-column transformation that you most likely do not need to know about. This is used in Caffe's original convolution to do matrix multiplication by laying out all patches into a matrix.
 
 ### Loss Layers
 
@@ -123,19 +121,19 @@ Loss drives learning by comparing an output to a target and assigning cost to mi
 
 #### Softmax
 
-* LayerType: `SOFTMAX_LOSS`
+* Layer type: `SoftmaxWithLoss`
 
 The softmax loss layer computes the multinomial logistic loss of the softmax of its inputs. It's conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
 
 #### Sum-of-Squares / Euclidean
 
-* LayerType: `EUCLIDEAN_LOSS`
+* Layer type: `EuclideanLoss`
 
 The Euclidean loss layer computes the sum of squares of differences of its two inputs, $$\frac 1 {2N} \sum_{i=1}^N \| x^1_i - x^2_i \|_2^2$$.
 
 #### Hinge / Margin
 
-* LayerType: `HINGE_LOSS`
+* Layer type: `HingeLoss`
 * CPU implementation: `./src/caffe/layers/hinge_loss_layer.cpp`
 * CUDA GPU implementation: none yet
 * Parameters (`HingeLossParameter hinge_loss_param`)
@@ -149,17 +147,17 @@ The Euclidean loss layer computes the sum of squares of differences of its two i
 * Samples
 
       # L1 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
       }
 
       # L2 Norm
-      layers {
+      layer {
         name: "loss"
-        type: HINGE_LOSS
+        type: "HingeLoss"
         bottom: "pred"
         bottom: "label"
         top: "loss"
@@ -172,15 +170,15 @@ The hinge loss layer computes a one-vs-all hinge or squared hinge loss.
 
 #### Sigmoid Cross-Entropy
 
-`SIGMOID_CROSS_ENTROPY_LOSS`
+`SigmoidCrossEntropyLoss`
 
 #### Infogain
 
-`INFOGAIN_LOSS`
+`InfogainLoss`
 
 #### Accuracy and Top-k
 
-`ACCURACY` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
+`Accuracy` scores the output as the accuracy of output with respect to target -- it is not actually a loss and has no backward step.
 
 ### Activation / Neuron Layers
 
@@ -193,74 +191,74 @@ In general, activation / Neuron layers are element-wise operators, taking one bo
 
 #### ReLU / Rectified-Linear and Leaky-ReLU
 
-* LayerType: `RELU`
+* Layer type: `ReLU`
 * CPU implementation: `./src/caffe/layers/relu_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/relu_layer.cu`
 * Parameters (`ReLUParameter relu_param`)
     - Optional
         - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0.
-* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`)
+* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`)
 
-      layers {
+      layer {
         name: "relu1"
-        type: RELU
+        type: "ReLU"
         bottom: "conv1"
         top: "conv1"
       }
 
-Given an input value x, The `RELU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
+Given an input value x, The `ReLU` layer computes the output as x if x > 0 and negative_slope * x if x <= 0. When the negative slope parameter is not set, it is equivalent to the standard ReLU function of taking max(x, 0). It also supports in-place computation, meaning that the bottom and the top blob could be the same to preserve memory consumption.
 
 #### Sigmoid
 
-* LayerType: `SIGMOID`
+* Layer type: `Sigmoid`
 * CPU implementation: `./src/caffe/layers/sigmoid_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/sigmoid_layer.cu`
-* Sample (as seen in `./examples/imagenet/mnist_autoencoder.prototxt`)
+* Sample (as seen in `./examples/mnist/mnist_autoencoder.prototxt`)
 
-      layers {
+      layer {
         name: "encode1neuron"
         bottom: "encode1"
         top: "encode1neuron"
-        type: SIGMOID
+        type: "Sigmoid"
       }
 
-The `SIGMOID` layer computes the output as sigmoid(x) for each input element x.
+The `Sigmoid` layer computes the output as sigmoid(x) for each input element x.
 
 #### TanH / Hyperbolic Tangent
 
-* LayerType: `TANH`
+* Layer type: `TanH`
 * CPU implementation: `./src/caffe/layers/tanh_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/tanh_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: TANH
+        type: "TanH"
       }
 
-The `TANH` layer computes the output as tanh(x) for each input element x.
+The `TanH` layer computes the output as tanh(x) for each input element x.
 
 #### Absolute Value
 
-* LayerType: `ABSVAL`
+* Layer type: `AbsVal`
 * CPU implementation: `./src/caffe/layers/absval_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/absval_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: ABSVAL
+        type: "AbsVal"
       }
 
-The `ABSVAL` layer computes the output as abs(x) for each input element x.
+The `AbsVal` layer computes the output as abs(x) for each input element x.
 
 #### Power
 
-* LayerType: `POWER`
+* Layer type: `Power`
 * CPU implementation: `./src/caffe/layers/power_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/power_layer.cu`
 * Parameters (`PowerParameter power_param`)
@@ -270,11 +268,11 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         - `shift` [default 0]
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
-        type: POWER
+        type: "Power"
         power_param {
           power: 1
           scale: 1
@@ -282,16 +280,16 @@ The `ABSVAL` layer computes the output as abs(x) for each input element x.
         }
       }
 
-The `POWER` layer computes the output as (shift + scale * x) ^ power for each input element x.
+The `Power` layer computes the output as (shift + scale * x) ^ power for each input element x.
 
 #### BNLL
 
-* LayerType: `BNLL`
+* Layer type: `BNLL`
 * CPU implementation: `./src/caffe/layers/bnll_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/bnll_layer.cu`
 * Sample
 
-      layers {
+      layer {
         name: "layer"
         bottom: "in"
         top: "out"
@@ -309,7 +307,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### Database
 
-* LayerType: `DATA`
+* Layer type: `Data`
 * Parameters
     - Required
         - `source`: the name of the directory containing the database
@@ -322,7 +320,7 @@ Common input preprocessing (mean subtraction, scaling, random cropping, and mirr
 
 #### In-Memory
 
-* LayerType: `MEMORY_DATA`
+* Layer type: `MemoryData`
 * Parameters
     - Required
         - `batch_size`, `channels`, `height`, `width`: specify the size of input chunks to read from memory
@@ -331,7 +329,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Input
 
-* LayerType: `HDF5_DATA`
+* Layer type: `HDF5Data`
 * Parameters
     - Required
         - `source`: the name of the file to read from
@@ -339,7 +337,7 @@ The memory data layer reads data directly from memory, without copying it. In or
 
 #### HDF5 Output
 
-* LayerType: `HDF5_OUTPUT`
+* Layer type: `HDF5Output`
 * Parameters
     - Required
         - `file_name`: name of file to write to
@@ -348,7 +346,7 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Images
 
-* LayerType: `IMAGE_DATA`
+* Layer type: `ImageData`
 * Parameters
     - Required
         - `source`: name of a text file, with each line giving an image filename and label
@@ -360,17 +358,17 @@ The HDF5 output layer performs the opposite function of the other layers in this
 
 #### Windows
 
-`WINDOW_DATA`
+`WindowData`
 
 #### Dummy
 
-`DUMMY_DATA` is for development and debugging. See `DummyDataParameter`.
+`DummyData` is for development and debugging. See `DummyDataParameter`.
 
 ### Common Layers
 
 #### Inner Product
 
-* LayerType: `INNER_PRODUCT`
+* Layer type: `InnerProduct`
 * CPU implementation: `./src/caffe/layers/inner_product_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/inner_product_layer.cu`
 * Parameters (`InnerProductParameter inner_product_param`)
@@ -387,13 +385,13 @@ The HDF5 output layer performs the opposite function of the other layers in this
     - `n * c_o * 1 * 1`
 * Sample
 
-      layers {
+      layer {
         name: "fc8"
-        type: INNER_PRODUCT
-        blobs_lr: 1          # learning rate multiplier for the filters
-        blobs_lr: 2          # learning rate multiplier for the biases
-        weight_decay: 1      # weight decay multiplier for the filters
-        weight_decay: 0      # weight decay multiplier for the biases
+        type: "InnerProduct"
+        # learning rate and decay multipliers for the weights
+        param { lr_mult: 1 decay_mult: 1 }
+        # learning rate and decay multipliers for the biases
+        param { lr_mult: 2 decay_mult: 0 }
         inner_product_param {
           num_output: 1000
           weight_filler {
@@ -409,79 +407,118 @@ The HDF5 output layer performs the opposite function of the other layers in this
         top: "fc8"
       }
 
-The `INNER_PRODUCT` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
+The `InnerProduct` layer (also usually referred to as the fully connected layer) treats the input as a simple vector and produces an output in the form of a single vector (with the blob's height and width set to 1).
 
 #### Splitting
 
-The `SPLIT` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
+The `Split` layer is a utility layer that splits an input blob to multiple output blobs. This is used when a blob is fed into multiple output layers.
 
 #### Flattening
 
-The `FLATTEN` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w) * 1 * 1`.
+The `Flatten` layer is a utility layer that flattens an input of shape `n * c * h * w` to a simple vector output of shape `n * (c*h*w)`
+
+#### Reshape
+
+* Layer type: `Reshape`
+* Implementation: `./src/caffe/layers/reshape_layer.cpp`
+* Parameters (`ReshapeParameter reshape_param`)
+    - Optional: (also see detailed description below)
+        - `shape`
+
+* Input
+    - a single blob with arbitrary dimensions
+* Output
+    - the same blob, with modified dimensions, as specified by `reshape_param`
+
+* Sample
+
+        layer {
+          name: "reshape"
+          type: "Reshape"
+          bottom: "input"
+          top: "output"
+          reshape_param {
+            shape {
+              dim: 0  # copy the dimension from below
+              dim: 2
+              dim: 3
+              dim: -1 # infer it from the other dimensions
+            }
+          }
+        }
+
+The `Reshape` layer can be used to change the dimensions of its input, without changing its data. Just like the `Flatten` layer, only the dimensions are changed; no data is copied in the process.
+
+Output dimensions are specified by the `ReshapeParam` proto. Positive numbers are used directly, setting the corresponding dimension of the output blob. In addition, two special values are accepted for any of the target dimension values:
+
+* **0** means "copy the respective dimension of the bottom layer". That is, if the bottom has 2 as its 1st dimension, the top will have 2 as its 1st dimension as well, given `dim: 0` as the 1st target dimension.
+* **-1** stands for "infer this from the other dimensions". This behavior is similar to that of -1 in *numpy*'s or `[]` for *MATLAB*'s reshape: this dimension is calculated to keep the overall element count the same as in the bottom layer. At most one -1 can be used in a reshape operation.
+
+As another example, specifying `reshape_param { shape { dim: 0 dim: -1 } }` makes the layer behave in exactly the same way as the `Flatten` layer.
 
 #### Concatenation
 
-* LayerType: `CONCAT`
+* Layer type: `Concat`
 * CPU implementation: `./src/caffe/layers/concat_layer.cpp`
 * CUDA GPU implementation: `./src/caffe/layers/concat_layer.cu`
 * Parameters (`ConcatParameter concat_param`)
     - Optional
-        - `concat_dim` [default 1]: 0 for concatenation along num and 1 for channels.
+        - `axis` [default 1]: 0 for concatenation along num and 1 for channels.
 * Input
     - `n_i * c_i * h * w` for each input blob i from 1 to K.
 * Output
-    - if `concat_dim = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
-    - if `concat_dim = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
+    - if `axis = 0`: `(n_1 + n_2 + ... + n_K) * c_1 * h * w`, and all input `c_i` should be the same.
+    - if `axis = 1`: `n_1 * (c_1 + c_2 + ... + c_K) * h * w`, and all input `n_i` should be the same.
 * Sample
 
-      layers {
+      layer {
         name: "concat"
         bottom: "in1"
         bottom: "in2"
         top: "out"
-        type: CONCAT
+        type: "Concat"
         concat_param {
-          concat_dim: 1
+          axis: 1
         }
       }
 
-The `CONCAT` layer is a utility layer that concatenates its multiple input blobs to one single output blob. Currently, the layer supports concatenation along num or channels only.
+The `Concat` layer is a utility layer that concatenates its multiple input blobs to one single output blob.
 
 #### Slicing
 
-The `SLICE` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
+The `Slice` layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices.
 
 * Sample
 
-      layers {
+      layer {
         name: "slicer_label"
-        type: SLICE
+        type: "Slice"
         bottom: "label"
         ## Example of label with a shape N x 3 x 1 x 1
         top: "label1"
         top: "label2"
         top: "label3"
         slice_param {
-            slice_dim: 1
-            slice_point: 1
-            slice_point: 2
+          axis: 1
+          slice_point: 1
+          slice_point: 2
         }
       }
 
-`slice_dim` indicates the target dimension and can assume only two values: 0 for num or 1 for channel; `slice_point` indicates indexes in the selected dimension (the number of indexes must be equal to the number of top blobs minus one). 
+`axis` indicates the target axis; `slice_point` indicates indexes in the selected dimension (the number of indices must be equal to the number of top blobs minus one).
 
 
 #### Elementwise Operations
 
-`ELTWISE`
+`Eltwise`
 
 #### Argmax
 
-`ARGMAX`
+`ArgMax`
 
 #### Softmax
 
-`SOFTMAX`
+`Softmax`
 
 #### Mean-Variance Normalization
 
diff --git a/docs/tutorial/loss.md b/docs/tutorial/loss.md
index aac561774bb..d2d0e77fbed 100644
--- a/docs/tutorial/loss.md
+++ b/docs/tutorial/loss.md
@@ -10,30 +10,30 @@ Hence, the goal of learning is to find a setting of the weights that *minimizes*
 The loss in Caffe is computed by the Forward pass of the network.
 Each layer takes a set of input (`bottom`) blobs and produces a set of output (`top`) blobs.
 Some of these layers' outputs may be used in the loss function.
-A typical choice of loss function for one-versus-all classification tasks is the `SOFTMAX_LOSS` function, used in a network definition as follows, for example:
+A typical choice of loss function for one-versus-all classification tasks is the `SoftmaxWithLoss` function, used in a network definition as follows, for example:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
     }
 
-In a `SOFTMAX_LOSS` function, the `top` blob is a scalar (dimensions $$1 \times 1 \times 1 \times 1$$) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
+In a `SoftmaxWithLoss` function, the `top` blob is a scalar (empty shape) which averages the loss (computed from predicted labels `pred` and actuals labels `label`) over the entire mini-batch.
 
 ### Loss weights
 
-For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SOFTMAX_LOSS` layer and reconstructs it using a `EUCLIDEAN_LOSS` layer), *loss weights* can be used to specify their relative importance.
+For nets with multiple layers producing a loss (e.g., a network that both classifies the input using a `SoftmaxWithLoss` layer and reconstructs it using a `EuclideanLoss` layer), *loss weights* can be used to specify their relative importance.
 
-By convention, Caffe layer types with the suffix `_LOSS` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
+By convention, Caffe layer types with the suffix `Loss` contribute to the loss function, but other layers are assumed to be purely used for intermediate computations.
 However, any layer can be used as a loss by adding a field `loss_weight: <float>` to a layer definition for each `top` blob produced by the layer.
-Layers with the suffix `_LOSS` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
-So, the above `SOFTMAX_LOSS` layer could be equivalently written as:
+Layers with the suffix `Loss` have an implicit `loss_weight: 1` for the first `top` blob (and `loss_weight: 0` for any additional `top`s); other layers have an implicit `loss_weight: 0` for all `top`s.
+So, the above `SoftmaxWithLoss` layer could be equivalently written as:
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "pred"
       bottom: "label"
       top: "loss"
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md
index 1f0966f88a4..d6df737439a 100644
--- a/docs/tutorial/net_layer_blob.md
+++ b/docs/tutorial/net_layer_blob.md
@@ -11,22 +11,20 @@ We will go over the details of these components in more detail.
 
 ## Blob storage and communication
 
-A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is a 4-dimensional array that stores things in the order of (Num, Channels, Height and Width), from major to minor, and stored in a C-contiguous fashion.  The main reason for putting Num (the name is due to legacy reasons, and is equivalent to the notation of "batch" as in minibatch SGD).
+A Blob is a wrapper over the actual data being processed and passed along by Caffe, and also under the hood provides synchronization capability between the CPU and the GPU. Mathematically, a blob is an N-dimensional array stored in a C-contiguous fashion.
 
-Caffe stores and communicates data in 4-dimensional arrays called blobs. Blobs provide a unified memory interface, holding data e.g. batches of images, model parameters, and derivatives for optimization.
+Caffe stores and communicates data using blobs. Blobs provide a unified memory interface holding data; e.g., batches of images, model parameters, and derivatives for optimization.
 
 Blobs conceal the computational and mental overhead of mixed CPU/GPU operation by synchronizing from the CPU host to the GPU device as needed. Memory on the host and device is allocated on demand (lazily) for efficient memory usage.
 
-The conventional blob dimensions for data are number N x channel K x height H x width W. Blob memory is row-major in layout so the last / rightmost dimension changes fastest. For example, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
+The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w.
 
-- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256.
+- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256.
 - Channel / K is the feature dimension e.g. for RGB images K = 3.
 
-Note that although we have designed blobs with its dimensions corresponding to image applications, they are named purely for notational purpose and it is totally valid for you to do non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use blobs of dimensions (Num, Channels, 1, 1) and call the InnerProductLayer (which we will cover soon).
+Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon).
 
-Caffe operations are general with respect to the channel dimension / K. Grayscale and hyperspectral imagery are fine. Caffe can likewise model and process arbitrary vectors in blobs with singleton. That is, the shape of blob holding 1000 vectors of 16 feature dimensions is 1000 x 16 x 1 x 1.
-
-Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1 x 1 x 1000 x 1024.
+Parameter blob dimensions vary according to the type and configuration of the layer. For a convolution layer with 96 filters of 11 x 11 spatial dimension and 3 inputs the blob is 96 x 3 x 11 x 11. For an inner product / fully-connected layer with 1000 output channels and 1024 input channels the parameter blob is 1000 x 1024.
 
 For custom data it may be necessary to hack your own input preparation tool or data layer. However once your data is in your job is done. The modularity of layers accomplishes the rest of the work for you.
 
@@ -95,9 +93,9 @@ A simple logistic regression classifier
 is defined by
 
     name: "LogReg"
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
       top: "data"
       top: "label"
       data_param {
@@ -105,18 +103,18 @@ is defined by
         batch_size: 64
       }
     }
-    layers {
+    layer {
       name: "ip"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "data"
       top: "ip"
       inner_product_param {
         num_output: 2
       }
     }
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip"
       bottom: "label"
       top: "loss"
@@ -135,19 +133,19 @@ Model initialization is handled by `Net::Init()`. The initialization mainly does
     I0902 22:52:17.935807 2079114000 data_layer.cpp:135] Opening leveldb input_leveldb
     I0902 22:52:17.937155 2079114000 data_layer.cpp:195] output data size: 64,1,28,28
     I0902 22:52:17.938570 2079114000 net.cpp:103] Top shape: 64 1 28 28 (50176)
-    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 1 1 1 (64)
+    I0902 22:52:17.938593 2079114000 net.cpp:103] Top shape: 64 (64)
     I0902 22:52:17.938611 2079114000 net.cpp:67] Creating Layer ip
     I0902 22:52:17.938617 2079114000 net.cpp:394] ip <- data
     I0902 22:52:17.939177 2079114000 net.cpp:356] ip -> ip
     I0902 22:52:17.939196 2079114000 net.cpp:96] Setting up ip
-    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 1 1 (128)
+    I0902 22:52:17.940289 2079114000 net.cpp:103] Top shape: 64 2 (128)
     I0902 22:52:17.941270 2079114000 net.cpp:67] Creating Layer loss
     I0902 22:52:17.941305 2079114000 net.cpp:394] loss <- ip
     I0902 22:52:17.941314 2079114000 net.cpp:394] loss <- label
     I0902 22:52:17.941323 2079114000 net.cpp:356] loss -> loss
     # set up the loss and configure the backward pass
     I0902 22:52:17.941328 2079114000 net.cpp:96] Setting up loss
-    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: 1 1 1 1 (1)
+    I0902 22:52:17.941328 2079114000 net.cpp:103] Top shape: (1)
     I0902 22:52:17.941329 2079114000 net.cpp:109]     with loss weight 1
     I0902 22:52:17.941779 2079114000 net.cpp:170] loss needs backward computation.
     I0902 22:52:17.941787 2079114000 net.cpp:170] ip needs backward computation.
diff --git a/docs/tutorial/solver.md b/docs/tutorial/solver.md
index 17f793ef778..b719f715a4b 100644
--- a/docs/tutorial/solver.md
+++ b/docs/tutorial/solver.md
@@ -6,7 +6,14 @@ title: Solver / Model Optimization
 The solver orchestrates model optimization by coordinating the network's forward inference and backward gradients to form parameter updates that attempt to improve the loss.
 The responsibilities of learning are divided between the Solver for overseeing the optimization and generating parameter updates and the Net for yielding loss and gradients.
 
-The Caffe solvers are Stochastic Gradient Descent (SGD), Adaptive Gradient (ADAGRAD), and Nesterov's Accelerated Gradient (NESTEROV).
+The Caffe solvers are:
+
+- Stochastic Gradient Descent (`type: "SGD"`),
+- AdaDelta (`type: "AdaDelta"`),
+- Adaptive Gradient (`type: "AdaGrad"`),
+- Adam (`type: "Adam"`),
+- Nesterov's Accelerated Gradient (`type: "Nesterov"`) and
+- RMSprop (`type: "RMSProp"`)
 
 The solver
 
@@ -44,7 +51,7 @@ The parameter update $$\Delta W$$ is formed by the solver from the error gradien
 
 ### SGD
 
-**Stochastic gradient descent** (`solver_type: SGD`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
+**Stochastic gradient descent** (`type: "SGD"`) updates the weights $$ W $$ by a linear combination of the negative gradient $$ \nabla L(W) $$ and the previous weight update $$ V_t $$.
 The **learning rate** $$ \alpha $$ is the weight of the negative gradient.
 The **momentum** $$ \mu $$ is the weight of the previous update.
 
@@ -104,9 +111,35 @@ If learning diverges (e.g., you start to see very large or `NaN` or `inf` loss v
     [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf).
     *Advances in Neural Information Processing Systems*, 2012.
 
+### AdaDelta
+
+The **AdaDelta** (`type: "AdaDelta"`) method (M. Zeiler [1]) is a "robust learning rate method". It is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+\begin{align}
+(v_t)_i &= \frac{\operatorname{RMS}((v_{t-1})_i)}{\operatorname{RMS}\left( \nabla L(W_t) \right)_{i}} \left( \nabla L(W_{t'}) \right)_i
+\\
+\operatorname{RMS}\left( \nabla L(W_t) \right)_{i} &= \sqrt{E[g^2] + \varepsilon}
+\\
+E[g^2]_t &= \delta{E[g^2]_{t-1} } + (1-\delta)g_{t}^2
+\end{align}
+$$
+
+and
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha
+(v_t)_i.
+$$
+
+[1] M. Zeiler
+    [ADADELTA: AN ADAPTIVE LEARNING RATE METHOD](http://arxiv.org/pdf/1212.5701.pdf).
+    *arXiv preprint*, 2012.
+
 ### AdaGrad
 
-The **adaptive gradient** (`solver_type: ADAGRAD`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
+The **adaptive gradient** (`type: "AdaGrad"`) method (Duchi et al. [1]) is a gradient-based optimization method (like SGD) that attempts to "find needles in haystacks in the form of very predictive but rarely seen features," in Duchi et al.'s words.
 Given the update information from all previous iterations $$ \left( \nabla L(W) \right)_{t'} $$ for $$ t' \in \{1, 2, ..., t\} $$,
 the update formulas proposed by [1] are as follows, specified for each component $$i$$ of the weights $$W$$:
 
@@ -124,9 +157,31 @@ Note that in practice, for weights $$ W \in \mathcal{R}^d $$, AdaGrad implementa
     [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.magicbroom.info/Papers/DuchiHaSi10.pdf).
     *The Journal of Machine Learning Research*, 2011.
 
+### Adam
+
+The **Adam** (`type: "Adam"`), proposed in Kingma et al. [1], is a gradient-based optimization method (like SGD). This includes an "adaptive moment estimation" ($$m_t, v_t$$) and can be regarded as a generalization of AdaGrad. The update formulas are
+
+$$
+(m_t)_i = \beta_1 (m_{t-1})_i + (1-\beta_1)(\nabla L(W_t))_i,\\
+(v_t)_i = \beta_2 (v_{t-1})_i + (1-\beta_2)(\nabla L(W_t))_i^2
+$$
+
+and
+
+$$
+(W_{t+1})_i =
+(W_t)_i - \alpha \frac{\sqrt{1-(\beta_2)_i^t}}{1-(\beta_1)_i^t}\frac{(m_t)_i}{\sqrt{(v_t)_i}+\varepsilon}.
+$$
+
+Kingma et al. [1] proposed to use $$\beta_1 = 0.9, \beta_2 = 0.999, \varepsilon = 10^{-8}$$ as default values. Caffe uses the values of `momemtum, momentum2, delta` for $$\beta_1, \beta_2, \varepsilon$$, respectively.
+
+[1] D. Kingma, J. Ba.
+    [Adam: A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980).
+    *International Conference for Learning Representations*, 2015.
+
 ### NAG
 
-**Nesterov's accelerated gradient** (`solver_type: NESTEROV`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
+**Nesterov's accelerated gradient** (`type: "Nesterov"`) was proposed by Nesterov [1] as an "optimal" method of convex optimization, achieving a convergence rate of $$ \mathcal{O}(1/t^2) $$ rather than the $$ \mathcal{O}(1/t) $$.
 Though the required assumptions to achieve the $$ \mathcal{O}(1/t^2) $$ convergence typically will not hold for deep networks trained with Caffe (e.g., due to non-smoothness and non-convexity), in practice NAG can be a very effective method for optimizing certain types of deep learning architectures, as demonstrated for deep MNIST autoencoders by Sutskever et al. [2].
 
 The weight update formulas look very similar to the SGD updates given above:
@@ -149,6 +204,28 @@ What distinguishes the method from SGD is the weight setting $$ W $$ on which we
     [On the Importance of Initialization and Momentum in Deep Learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf).
     *Proceedings of the 30th International Conference on Machine Learning*, 2013.
 
+### RMSprop
+
+The **RMSprop** (`type: "RMSProp"`), suggested by Tieleman in a Coursera course lecture, is a gradient-based optimization method (like SGD). The update formulas are
+
+$$
+(v_t)_i =
+\begin{cases}
+(v_{t-1})_i + \delta, &(\nabla L(W_t))_i(\nabla L(W_{t-1}))_i > 0\\
+(v_{t-1})_i \cdot (1-\delta), & \text{else}
+\end{cases}
+$$
+
+$$
+(W_{t+1})_i =(W_t)_i - \alpha (v_t)_i,
+$$
+
+If the gradient updates results in oscillations the gradient is reduced by times $$1-\delta$$. Otherwise it will be increased by $$\delta$$. The default value of $$\delta$$ (`rms_decay`) is set to $$\delta = 0.02$$.
+
+[1] T. Tieleman, and G. Hinton.
+    [RMSProp: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf).
+    *COURSERA: Neural Networks for Machine Learning.Technical report*, 2012.
+
 ## Scaffolding
 
 The solver scaffolding prepares the optimization method and initializes the model to be learned in `Solver::Presolve()`.
diff --git a/examples/00-classification.ipynb b/examples/00-classification.ipynb
new file mode 100644
index 00000000000..1950f08f638
--- /dev/null
+++ b/examples/00-classification.ipynb
@@ -0,0 +1,780 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Classification: Instant Recognition with Caffe\n",
+    "\n",
+    "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n",
+    "\n",
+    "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup\n",
+    "\n",
+    "* First, set up Python, `numpy`, and `matplotlib`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "# display plots in this notebook\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# set display defaults\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)        # large images\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\n",
+    "plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Load `caffe`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# The caffe module needs to be on the Python path;\n",
+    "#  we'll add it here explicitly.\n",
+    "import sys\n",
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CaffeNet found.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print 'CaffeNet found.'\n",
+    "else:\n",
+    "    print 'Downloading pre-trained CaffeNet model...'\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Load net and set up input preprocessing\n",
+    "\n",
+    "* Set Caffe to CPU mode and load the net from disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_mode_cpu()\n",
+    "\n",
+    "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
+    "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "\n",
+    "net = caffe.Net(model_def,      # defines the structure of the model\n",
+    "                model_weights,  # contains the trained weights\n",
+    "                caffe.TEST)     # use test mode (e.g., don't perform dropout)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n",
+    "\n",
+    "    Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n",
+    "    \n",
+    "    As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
+    "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
+    "mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\n",
+    "print 'mean-subtracted values:', zip('BGR', mu)\n",
+    "\n",
+    "# create transformer for the input called 'data'\n",
+    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
+    "\n",
+    "transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension\n",
+    "transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel\n",
+    "transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]\n",
+    "transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. CPU classification\n",
+    "\n",
+    "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set the size of the input (we can skip this if we're happy\n",
+    "#  with the default; we can also change it later, e.g., for different batch sizes)\n",
+    "net.blobs['data'].reshape(50,        # batch size\n",
+    "                          3,         # 3-channel (BGR) images\n",
+    "                          227, 227)  # image size is 227x227"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Load an image (that comes with Caffe) and perform the preprocessing we've set up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f09693a8c90>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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h6kLU2FmzlEsB8fNYTiQhRjENYmNFVkwA1AxYSvG6UBvZmxdENBNq6mK5MI8hjHGUMmj3\nSPvHc4YZREJoteCtbWfcxEQRTEp1w2qJjDjbzCLFUrZcXQq3bt7j5VeeY9keEur1HoaB+w/e4eIs\nsfBLjhY3aev7vzpes24PyCTdnMYElV9TRj1Nb4YLfLfgo899jOB0w3j77de4//5DvG3YbM948vAd\nlp0WNh/9Hd/P9736GQ6P7lFs4Pz8lKvHb9Tve5tvv/Y6b73zBh/5+A/w+S/843zh4x8BoGk97z+8\nz8XZOcbA04tzXn5FoZvP33uR1954nedfepHNxYbQdPhq8358eja9zZxenHLrzm3Oz1Rb1Pc9q9WK\nzWbHrh9ZrY4Y64n90aNHrI9f4sWPC3/70S8TPCxrJE135ybP/SPPgxWaVeHm4g7D5orLq1MAQqf3\niRTH1dWWs+0V57UgLDHjVw3BdDQhI9FjKl1/eVhYW48JHtcuFVFRi6x2dYzZZjZnGw4PD/nc5z/P\nu9/6NgCnpxckCtk6VjeeRYBchfjjcIlYR4/GHC0P1nO0TIoqDB4uB46OjnC24+KydhVly3K55OmT\nU27cDjQ+MFYBfzaZxXJBaoQuZ2zeQ3MJDhc8BUtrO4ZSKIO+dnN5RZ8KIyC54C0wHUxyVCt/WGBL\nx9YtKNvTes2E4jLZbMlkrNfO+mRJT8VhXCDUe7Nr2r3+FRjywBi1QMd7wnRoG0dab0CgtU4JEVlH\nsNZkyJd4PFGS6g+Ttmwkdng8Ke9U7kCDvaZHzTkjzpCKYKSB2hkvZQCjHZuSNAJqGr/nuMNZq7KP\n7BHCnCNaEPoh4YzmPxbjZsPAOPT1hO+wtsN6pzmj86sv+CDkMlbrfxVb55ZgF5TWVC5Xh7cT02kk\n9xbfLRliQkqYr2dMo+Z52in6zJHTOGuotDRNZHIFxi5nU4CkRGvBZC2mxMjc6tBDpEOyIoi8z0iZ\nxnfalRExVYi+l5EoNDOreFu6D3b5ciFFlaQ431LEzTo3YypNvDicN6S81fcHUCaaKNcrF2U3TtxB\njE5uLMQoWDvOJivnWkTaKg1SQXqYdHB2yhQ1UBTBlOfmQd0yKGihUHjxk2te/OSaIoqf+Bu/+A6/\n0eO3hT8wxty79p//PPCV+ue/APxBY0xjjHkF+Bjwf/x2fseHjw8fHz4+fHz4+PDx4ePDxz/sj/83\n+IM/D/wYcMsY8zbwJ4AvGmM+g9ay3wH+CICIfN0Y898DX0dnLP+6XE+Yvf6LK3jzes6Tb1s8Uuft\nVcMC1cnnVPhNtbrOUhir4jtjGIYt1hja6haSojl8SIcxmsM3tT9LnYOXrLh/BVJWh4ZU3YBpENNR\nsqOpp2tL0Mo9OHxjwKR9ZItEnBdaUVdJjGnuevg6kzbUMFuRWcBWJqp2ApxDxoYyjQut9niMVaeZ\neHtN4KydtmIGUlFkgam6qywjQ8pgExahxIx1sg+zLJkiBqkg0yEzE5WxlpQTYy44MZgis0DSB4eh\nMI6CsRHvzd7aXzSQF2PIORJlLwJMo2CCktL7vufi8jE3jg7qm+hIyepfFanJ7DWSxljwpboKHR7z\nAa1TKQrd1Dwnx4Qs1M5mUohnVm3c7GqpugsrVAePm2MLgm0ZiFgEZxxt6Bgn/EEcGOOG1gaSWJy4\n2cpcJGONwRpDlgHLgnEc6AcN53322Wd57t6Lqm0h8/jROzx5UKM5pOd4fZNbt44gKfF9hs+5wG5U\nB4oPgYIwVqEy1hFJ3Lhzm5MbR5w9vuDXXvsWAI012BJ4cP89XnjhOX7gs5/lzu1nAFge3COmgb/3\nd/4W3/j6l9nuzrm4upwuKV/40S/yE3/gx2kWx4xFeP2NbwDwtS9/ja5dc/fZe+zilh/6wc/wiY98\nEoBvfuObfPxjn+Ryc0XXrvA+8OChNq6fefZZ1us1l5cb7t6+w5NHj3nvvfcAWCwW3Dg5IRfD5vSM\niycXnF9t5s/38dGabr2GpqM9OmR1R/EGru14cnHBjZsn5GQoOEJ7gKk6P2eNhjhnMD5zcmM1jxR2\nm4HWLckl41tPNoVYBdcmtLSLDhkFGTcs2nbCS2ObgD9ccxJe4uwBXG1OCXWU2Bmn4eK77R3lHQAA\nIABJREFUHc45hiwsD2vnQe4w7LYsnK5paYzkOOWtjXgPxMjZ+VNOjo44OVKt09WVdpG6tmHYXdGE\nlrBYzPdoMQkfhAPbsWgPWB6pQaFZHbJLV3TNAZIN3dSCQaUQqe+RoWe7OaMNYZqiU8SQqnRitVhy\ncuMm+UiBq7vLc4bhgphHSi601tF1LVKlBD4NM/RRrfzMgMwcEzYmbEna3JN9IL2znuh15DNKxieh\nncC5BHq7JMarGrBusUxRVZoJiLQ0PjCn2OvFQYrTPUZaxAom6JqxSQPjOOAb1Z0iRbVYQKmjSmsN\nOWUER551o6AdDqE4R3CQyuTIVVi0MYarsmN91MzCI2fBkLDGs2g8Y+8+IG5V6KSnCR0peyxTXIsj\nRled4S3bfjdDc4v0xAwuB3wwusdkFPgKOF9t/CZhTUNMeZaKZKYukyEnVBRfx2mSK2jaq8HpAy5o\no6M/Z1t1O7J3dFrbQFQTTluBwOOoncNceoSBvo+0C0MxYdbr2daQqq7OuMmpOEFlE86rySGNCoG1\nbh/ErLIMiCOMw1Mw+j4tuo4mLEjRUorF+3bO7HWmxVuvInureIlSpSDWOQxCSurgtrgZRhtThH9Q\njZSI/KHv8r//m9/k7/8p4E/9lj/XFIwL2JqRI3iyZHBJM5Ly/olPOhbvGhrnySXuXVdS41j6HmcS\nWUYk7TlS0zhsYjB94GfiKGKVJWXA+En7pNZ6EWhcizN+Jq42LqtzxFq8D1W7NemHkgoZjahQkURT\nx0lxVLFeE/S5qB13qgYbUkqkLDRmgRRPinuRuhacShb2Ns/5bkkyprZKxyHi3XIeJSVUN5ajfpCt\nXWAkz4uGNU5Hg9QQXimUOImqzcytSpUwP2mvgni89ZoLSCHGMhdgGjlTW8110eGaLTVnmZ0mu35k\naHWE0bRrRLQQBsjF0kyOnyQ0viVjq4tSqbvA7OLIoqNAKcztXzvbmsvcgr7uM51dn6LuH0nToih1\nbCwY0X8mwfxi4cEMKiy3GSVZ1BGk08Vb9WI7nl484O6tF/nUJ78AQOdvsLl6yDtPvsUuXhEaowsu\nENwdLdCTVQ2e95j6+R7KlYYyx4YyFIKzc9E3DD03bt+gbQ547/4jrs4eqWMG2F7t+OgnPsWP/GNf\noJTCdtPz939Nx1DvvveXOT97ymq1wOYek+FHf/T3APCZz/0QBMPbbz7my1/5Fca4YXWg9+jR8R2G\nXnj9zbf4yZ/6cY6Wa/7CL+pU/7Of+RynF0/JSVh0Kx4/ecKdGmh8uFrz9MlThmHQQ1IqHB3qWHcc\nR7bbLf2QsUU1eaGOaIZcuNxcUGJisT7k6HANtcB+//4pT892xKSenMt1YLVsOFnpiG633XB5dc6t\nWzfxCw3QXiy0cL95chPnO04vNyxXCw5Wh7M9/mCxxDmDaxT5MV7uVC0LmKWDpUXoWN++S7c6oKnF\nS3v+BBkiZaHFz5gKxtd7fxhZLPaU/2EYuDzX0WbMwrC7gpSIw8jDR+9z86YWZ6vVCkNh6K9oaKFk\nak1PGzpMDko+P+hwTYPrJg2JHgJMHilmRMZCqTl8fZ+xJrBcL/HdAkNht9PCdYwDzlhKGrg82zFu\nPLnRa9YuO7plq5KEMcE41A2mHiKjARySRFlBgJF9oRGs4XChmXep4hdAtYy7NDKSGSQTvGNhJtkG\nDMZRjMXGgC8FqZrLoWwrbbuDsqBrj+aNPWfBOShphGygOILVceFBd4IRi2HEkhXlUg9mwSnHSRCy\nVaJ/nMb6VihJVD/kAqXxe1dXsTqOFgMLy3YTabtpHfKEoKG61hrylGYBSIzE3OONxfmGFPeGp6Zp\nMSyIJdK4QA719QAlRgx6iEyparCsnRMIJtQOxqncQsyclKAHzsmwU/eVqUArBVOzVTEqs9kHaBec\nDRpj4wxpzDg/udrUpFdSxiRFzTR1VB5TIeVIv9sx5JEmtFNUKCnvGWaYNDOu9LrpXjHlCFrr5wQN\n7wLO1n8aT9sGsmzrc7FzdFto0OixqOusBsC3GLHEGPHOz07IRMK5mkwB9dC9L4avoSu/6+N7FhET\ns4DNtHXDcM6pWLo4BKmK+7phZnU+WONVrS97jJbzWkkaW/BOuR+5LliusVoQ+KxC5upSALTClKJa\nJ2uxEqoVE0BqNZrJOdI03Xyik2qXD0EF15OgDcCHUG2ZBmsSkLGT4LJVC2WWBFYhkCITY0lR96V4\nSgqI9ZMbn5y0mJKaJ6hPY3J9MFf0UgNzJ9ead41W7NmSs1M9US6Iq6/RWu1GFVF7sFQkRL3ek8Ba\nUp6RDtO1IWiRCoWcBiY3r8HV61xIMZOTAh31fdICTUTACv2w4exKv7iShDWBkoaqlTNz0eOdEJwj\n+JYYIymXuYAqpWghaAJpsLV42ovioeYzGc2Vmjp/uqFpzpjaXfP8ep0NiFFHjbU1jqV+n7Mty4MD\nnLfkskMYEdFNyDcNUgznZ1f40PDqq6+yWtzk4kwjRF5775s427M+8qybliyOJuji7syBLtKScb5n\nN/ZcRn0fGxpa19BwgLMNxjVQi+ybJzdxFu7fv892e0UWx8svvAzAvXv3yC7xxhvf5qtf/lvszs+4\ndUMF5c8crwk28/D0nO//9A/y6d/xORaVI/XGd77Jl/7XX0Ky4aWPfJzl0c15AXv7zce88OIr/PRP\n/zQPHr7HX/qf/yI/+ft/PwAPHjzgyaPHfOxjn+C9+w9Ua1Zbru+88w4558qNEc7OT4mDbih3795l\nt9tx9vSM1WrJjRs3ZgHocHWJMw1nFxdYDAeL5WyICE1HRthsrhj6Lf24IOcj3rv/FgA3jk9ofMPD\nx48Zx4Hj42NyFaq3i5Z117A+6jBei5HlSjsvQ6+RFjmONGGJW63Jo3ar0tmFfrJspqewK2XeMJ59\n9kW255dsrw7Y9Ruc9Ox67fKNw4iTpJ2qYaDxgbarRbRfIouGy9NTuq7DUNhudVM4Pj7WOBYRhl3P\nwMjy4LB+9oXt1ZbFoqPpWtquU9EvsDk9RWzLsgu0naMkgdqlb28cYmIm9Vt13XrPqsI6vdV8SRMa\njNd1pfT6+d1shBBaVgfHNKvD6hZLjFt9jSVYcurJaUBKYRzjfi2yDWJ1mcwlMhZhqKL5Pke2MVIa\np2BlNLwewCZFDARZQAnEUTPeAMaYKbIjxp5UcQlNPqzX1Ne1LFdcjMOYiV3kcbYll74K9HfTuQxj\nM9aOgEaJzNsBWgyXokablBLGFpowCfgDIrq+S2kYdsPeCUeHEYsj4V3BeZnXWapFP8YBZ1pCWDBl\nEJZcr1vxUDJd6/fOQ9MgLqleLanRwOBmDVXwjlQKznhKcKopmqGjFQVkK2i5xDnGap85q+u9GqQm\nXZKolsw0GOtxXj6gWcpZO2dGPAj4mS8narIhMKYzeumhhqDHDG2neuMiQ12/pwOwAAZnFyx8B3Zf\ndFnrUQCnwXlBZIGr2aw4ZrODvp6i7npA29NqzPIIloKM14r+YvcggbxnCxrHb1lJfc8KKcFp9pq9\n9sFxoYq+qmtuKl1zBtGOVc6J1vlrbWQNz+26JcXAuB3mDkQaE9Y5UhppGovUG0u/USvlnCe42uTq\n0w1fs5sKQtJiqi6Yk6vO1bDHqWIG3YSNETIRGwzeGoqbFgXNRcupYCyavF3DK5MYHUtliLXgcxO0\nzQq+FkrWOgz/j9BewEg3u44myrrHVIF1QzENMT4Bt78xxGiC0VRXSq4ML6g/f59fZNSKNl+3XAbV\np+as7rypNjPaNo2xr4RgR57cd0nb+D44FKJZ2A1ahMQcWXYtbQjq5MzMro/ihCwWZyzWBpwYpAoS\nS+7VemsLJQd19cyk5YzzBusU1Jpymd0bpWgnzRS1nM+nIqDM+UxlLr7bpi7C3mLEE2zQTDu7m2/7\n3XZgu73kmbsvce+ZT7DZZv7el79CFu08HB83HIRDckxzd9XR1ffCkY0G6GaTIASWjW4KPnW05gCT\nPYsmIKUQJir48pC33niTYYi88MJLHBysZwH7+w/v89VvfoWUIs/cfZbu2efYXmkH8M3vvMezL73A\nP/F7fh/t6phvfet1fvWv/VUA+t059559huP1DTKGYczcf6zf96M/9mP8oX/hp/mf/of/kS996S/z\n7/zsv8371bX35a98hX/6n/yn+PrXv0a3WOG95+H76mgLTcvF5Tldp0LUq6srbt3STk7f9zx58oT1\nwYrF6oDLYcd5HTOaIuyuNrz7xlu89+03WS07juum71aHLFtHLJmmbRmGSExCX4vMJ+dX3L5xwtOn\nT0l5pB8MTavF2+rQ8v6TSw4Pj7l18zYlyXzAEBGGoSeIgWasI+UqNm8WxN0WfMPRzTu4rmO40hFG\nLJnNENmKEFZH4OxcgI3jJU4SqWSmTMYJ2NgdtKSdOlpjGunalmUzneanE7JjHDMx9vT1Zx4dnrA6\nOKDxHslCaJc0tchq1jcoYyGOO7ZXW3wTZkyBMQGzNDRdoG0Dm8srthu93rvNFXHYUXKk8R7fuHkE\n543HIfTbC+K4BR8AS+v1/RhMwVqhW2i23BjzbIoZ446UCqkI4urYfVpbrGPhG7IRcsmMMlNhMMbR\n4mZStTULchWbp9EzJjUopbIl+C1dOKrrAmAiEw/QXxv9xdSjzLqKoGgaJvTFmDbKArRqFErEfeca\nKvahZqZWTh3ooUtQ0fRum2kXBSraRHLBFKvImlbDnWGSX9j5cCkIRvaYnVjSDG0uWZE4welBaNm0\njHGDcVInFbtq699PBiyO4DzW+RnlAhB8W2UPmsGnOa7XAsun77eagyv1uZZssN6R8o7GNVi/bzxY\n51gsWnKymLLQCY1MWBR14VofsAgpb4gVjDyOPUUGfND9UJ2sk4Dd4Ko7XAOYw/xeSDHgpDYYBHBc\nVxCVIngfyDkxxn42Qxmj3dgiGeNg3O6uZaCaWqzp+FuuXxcjyP8frr3/Lx7GBJCyp00T6hhIuypS\nxpk1Y51eMIfBukDJZf6aFJRSbRfEXPB2QayLTbaJEBzRCb54clZuBej8XtlRsncZzEaDaWZeMCYT\nY6StNGmE+XukFhsTo2N2GZoACMYV3NQ2VU8D6pLTQmKqfq1zJKtjtJwzNro5CqNzldIKGDO5F6YR\nnDKOmtYzDpMGYrqBK83dthRpEX+qwcDT6MvqgmZRR5yDa+PU6edPJ5T9BzWlUaNjpovPHrmQSiZn\nYRyVdzU5PkCZXkX07zQ54H2oFl4Yy4izI0ZWOLMEcTOQNHSesa8RCcVRskNqZ8GWTB4zgiVLwZFw\nflqgFQBorIBBLbQVHqlUsqKn41Gfm7Ntfe+nlq5aZQ0FXzEcwYE1SVvlFLzzjHkNwKI74IVnX6Bx\nt/jWt17j0ZPvcHR0Mmu94ghRLI3ziAwYBqRUF5kXbLBIchijLWs/BYK6hC16ioolUlJktdLP4tvv\nvIbzgVc/+irOBh48foe333yvvkbD87efmXYVHj54zDDq6/jC7/19vPjCs7z3xhv8yl/6Xyhp5PZR\nxYmc3GVxcMAuwZhGLjcb/uBP/4sA/LM//s/xH/zpn+Wv/7W/yn/xn/2XvPXe2/z8f/cLAPzMz/wM\n3/r2a2w2G05u3uTddx5w41hHVNvdZj5wbDYbTk5O5s/To8cPWa3WWLSrdXp6Wu8T8GK4OD/n7Ok5\nOWcePXiMX2qxcNCtIVvEwihKkY+psFpPnCmIRTDOcfPkDpvdyEV1vB3dajl/ekrJl5A9m4OBg9VY\nP8OC83DcrtV1l8dZBxQDtCdrchzJfcQWz7JCTqUYXvjUCZebHRePHrEZthyu9LkeHgQ2Tx+RklMo\nowjbGoXRhAXr5SHBH9BvLyrUslYSUhhjhKIFVdsFbO3IXJ49prGOk4ObdM2ag/YYCYp3yM7hj1a4\n2JO3W+0wTC3XGJWqb1UnMsaeiwst9nMc6ZoAIVByJOYyR8QYkwglahJALMr7EUtc6O9MWEocGV0d\nq4QwB9fiWpb+gD4NbIctKQmhjqBNKUiOxNiTi1CczIeoBg/F4VKPEvkKroKBJWvXizJgbSaOW0pX\no7NQy7yCmN0s49D725FH1eCqZGRBVXQoskAS5L5u4GaWEXiv+BVlGNmKFZjC0x2GRqNeioDt58gl\ng6ffKWzZGE8xdu84F+0AlgRiEpLGvYaVFrJCm11oGcdRw+UBnOpWqcHvGtcyMo2ics4gBucNjkAK\nUOI0YlAHunPqMvfek2qRKdfW/QkNMRG+c4nk7MBknNER7IQZEoyO2YweNMXs131jDDkmYk64sgLT\n4psqP5ErxPQ6Kajonqn7DQLJIE70QGzD3OVz9TOm0hGPD3tHds6ZMU5TFAjBUGqN4bzHOkMc1QE9\nDD057urvKxRjccHq3ojMuigNQP6HtJDS52XIo34YQ+spUuqYJWEJcxWdEHyGtjJK+twT61W1ImAb\njChNu22FqXOaSYwSyUMEI7ShmaFlpTSzJTSL2pKdn4oFoSRDEqUcF5MY63y6DVph73aZxhtNszaT\n7XTEoMnn1nhisvNs2tpMskU3dRNwxu8ZLTi8n8Zh2glxU0emKOdFzAjGVY3TvhWrb7oK46Q4Cn39\nPs3oc1ap6V7Ukj+dzFylrOe5KNwXRMoc2lf4ZmKuoLNjjYHRo6Vc052llOYT/LCr4v1aEDTB4r3R\nER8OSXmWrJkgjKNgZWTRrkCaaZpG0zgkKDJCGS57jYHzFkYqRyWRY54LEF+7an6m5xvyZAEWzWeS\nYsliKBRCmACvFpMEI2C9pTFujvLxNmNsxIVIEwIxJ1atdlaeufcKjx4/5Cu/9su4JnNy44A4Rpxd\n1GtjiJLxnXYUS4ZUsxaTHfCmJfgGJx5rREcPQBy3jGmjkQ2x4ejgDk9PdRM+vnmD5595hqdPT/nm\nr72OMY6DWmjYLCy6llwiDx484Ma953jppRfq64Av/W9/hYuzU27eOiGNu7mwWa4OGWNh0/dkAv/a\nH/1jfOxjijj4t/74H+MrX/0y/8mf+c957/4T/qs/++f4l//wvwLAt7/5bb7xjW/wIz/8u/jyl7/M\nrVt3WK21GH7v3TMODw8ZxxGNDxo5e6q2+t1ux2634+nTU6y17K42TEkQcdBx9I0bNzg5Ug3M5ly7\nYxdXl5zcPOKVV17RxXPYMY4JP+lEHCyXHeujI9quY3Vym6vLfv6cvvLKK/Q7BRqWa4VNRuiWrY5v\nxCrGZOr2joVYCiE4hjxii8z6mnEcudpl+mFLc7CgXb3E1UONIt2dPYGwoHFCYx1xGPFVO5jHTDae\ntgvEQTf9FPU19H3UDdypIDoEh63j4NXBmtB2JJMoLrPrr3D1890s1xT3VKXZXkGDFT2Gbyz9+SXb\n80uMRGI/0NaDgkaiRIJzNF5HTnMmnjV6KxfVkHjjFbMwSTMKlCYQgmInxrGfD3wFZTclMoV6b9V1\n2AkEPNkGghMCllL5cqNViOOOlhQvGPM4j+D1uRoEj6NhHPbi/IODQ/Kom2kTOhBm1IxzDmcDuQxq\nkjFljrKhCN4oNgIZazRLjdyyWoCknBFTPtDFzmUHohFeFk8cPV1bx6Xek1Kk3wrWFpq2ILXllmIm\nl4Y4RByLanyp+0zrsCFUmGZAPGoeqg/nAj4vqnhctWpFptxLh6Odx3NN25LsJNvQ/cNMxZNkpk0q\np0kLW7B1eiHzJEIQ2eL9gjFucC5oIgVgTKOGrLBQCr8L5KpTjjFii6YI7MaMZDN3gZpmoTKc3Ksu\ndMqEBSwFY11FInhCWCFMRrGEQWicq+PYPdXdWJUBKWG/ynbMpGUrDIN2R8dxVL3fxLRKo+4FxWC9\nFmpu2g+N1YnNb/L4beEPPnx8+Pjw8eHjw8eHjw8fHz4+fHwPO1IlKu4/XXcMuIWCDSlIsvNUMin3\nmzhYHIG2WdLnaq9EIYjeLwg20SRhCBNYc0sRFVUyaOU6aaSM7IXZpYxkSXPHQrtiCqsc04B3jt1Y\ndRskvFtQYkZcqycrO13GTJIRL0LKdew2gffyFmTEmZYCSNkHNicBYz3Gqq4plkiYqu8Exexo3KAJ\n6ynMbhGNKxkQ6/BNw9AzO7oQR8pCCEWpu65FSiFNMDTxevIvUkcX+4p7P2+2TGj8qVu1z0bUr+cc\nr/19g+RST6SJFGUfLl00rLRzhhgHrOvmU0sbGkjCKCPOjAQb5k5WThlrl2DUOGAtmq8F5Ox0rFpF\n/Fb2LkHNxJOqrXBqUJgClEVHMVkciCIL7NSBswXJhoJ2KoNvCPVzYkXtwc4VhjhyfHSTkxONcrn/\n3rd5+OgdDk8M49iQ0xrJaXYnOQdDusJmte6ainnQ13hV0R6Zpmnwzs8CWB9sbcdnxl3CGMcLL2jq\nkifz2muvcX56xsFqQb/JuPqNJyc3uNz1XG4GPvp938dqueCNt94E4J3XX+fw8JB20VGGDe1yTbdU\nd9Y4CH1/xXp1zE/91B9mcXDIn/zZPwHAa9/5Bv/+f/in2e4iP/dzP8cf+KmfnKVzv/orf53f/bu/\nyFe/+lWGXc/zz99jU4XIoE61Ugrj2PPG62/y5IlqqyaHWhLhqF0ypKzjLKBpGppFR9M0GOOIObG5\n1DGUz5bz8wu+8503uH1ym8WyhZxmKOH52Rm7/orVwmN9YHu541aFeXoAZ+nWC4xxHB6u2NUQ5eP1\nmt3Qsx16jteNZl0O2pVYdkvSWIjjACWTizCME/1ex0Ju2LG7vKA9vs3xs4rbO7l3j+3FJW+/9nV2\n54+R3ZXSyIGIWrsXoaEJXQ0F3kdODdWNHBaBJgSamgl48+Y9bj3zHMk6EoZ2uSRNGYynjyh2B7Ho\nSD0p6gRgKAmXDK1vGHY9kq6PLAq73ZZdyrShofVuvmewk64m0DQdBVMDgOuXfdWrVLSBDWHGtKQ0\n0McBUsZIJqDwTb0XFVNgjBDRnLc4gYplygJVu72TTMmTs3ekSK5hytWsVAXsw9ArFiWp6afrunkd\n0/VE9TZFBiiFUjskeSxk1JxiXAVCTyabqTEzySqy7JcakzEmQl6SEjizIEftRLvOYe0Cawv9ZkdO\nCV87nDGr4amUGl2Dw0/OQ3E07nBez7xvSFO6hDWUlJUwTqCPBnFlXjNt1W04CSj4pewDf3EqXLBW\nk0RkZB+NpmihQoKS5+6UvvwEEigpYqxjjFvKqFOT5fKAdtkScLSmI4SGWEHFAc8uZ4oTlq1nGAak\njotTv0PcoNcvpz3KAWpOacRbTz9cgfEslif1ciuYu5DwtmBMy2RltxRwiZxHcqWv57ntVPR9QrWv\nMSViTR+YNXtGsTapalmhSovsb95z+t4VUkZtoxNZdDsOdI3Fu4AYSyrj7N4QEskJ2yJY52nb5lpi\nta0OK5Qw0rTqUgHsmEliGMxT1cNIpPOTpqFaTkV1PUaYFxWRou664uroMFImS/awIzvBW0suyuWY\nEfQIrrXVHVZf24QbsI0Kva1RnlPQYhE0sNQYQZzV+KT9VI2URpLscI2Kw71zmPmGUXKvN7qQFJPm\nGTvWMaakrfk8aZ38bEkGxQckUzRmB/bt3lLmm8tO4tBplGp11GWcpRR1Ik1J35p0HsklU7J+z/T+\nIgXvNUDZekh2spgCKRKCp2Do4xa8im9BtWUg6jBqvHrx6tofDKy7JZbENvXkkklp0kmAbxTDgNTU\nb6ZxsGqqjNWfJQVidW+IdxgRUkkUPHE0M1LAesHZjhQHTk6OODw85P6Db9XP7/usj5f0uxNMMYxF\nHWdTDJC3FkvDOGaEhNiIrURlm5WSLGZBPwYCjkkpEGgI3QGb8y137zzDyy98lPvvakH07ptvsug8\nh4eHxOS4dWfJYaejvffeuQ/W8OqrrzIMPV/72jcoUX/fs8+9wBh7xr7QLNaE4Oir3mOTEnde/Aif\n/oHfxdnVhp//+f+W115XjtQf+Tf+KCEE/r1/92f57Gc/y/MvPcMv/IJqpD7/+c/z/vvv8+abb/LF\nL36Rp09POT09rfeTQVCt4de/8Ws8evCQOye1qBFLtzyg6Touz89ZrNesZlpFoQtLQregHwud90gd\neecY6VrHxdk5l+cXPPPMXQ6PVoSmOjpzpiTLxS7RrnS8/bByrQ5WK45CIHi9n1kv5rBY6w2LsODy\n8pLYLThYLunTZLlPtKslu6srTs+vaBpPU9ehq8tzConD5ZJhOOXtb3yZOqFjcXKLO3ef4zNf+CL3\n33qd7eNHXD5WSvLV5j5x23P58BTjYbEMHLST4FgwVv1Fm74AhnWrn6fd9orNxZb1Cy/TLBfYYmgO\nqvj56SOG7QUmZrriwLvZJTdsN1xtR6xtKHlLHHbEYQr03VJypm1afLDkkjFTfIjxM0k7xoj1nusi\n33FMhOBnorW1FlP237syEIlEoa4R+lzHMrKJO0aTyJLpccS6AF6lwlAcu9iT86j6MFOZQGnElIhI\nh3caXD/pRMUM+lzKwG53CSSs0cJGx4Z1zRZI2ZGreWUYI8U4CGCKI+Y9uV0D0zV4OudIKolQdZXG\nBGXfOQdia2xJXaTKIV3TYmxC7AEi/TwmknhJkZ4iQkwbzdutY9Y4GoxvadsFcawH+0lzWpLKLazm\nxE4hzG5OZzCzRsiHDoqbbUVRImJ0zGhdJuVxfg+NVTG2NYtaqO6zRA1BdWAM+BrjJbWwG+IZTWtx\n/jaWJaW4OXILt9G9S0SLpdZqaD3qSE8pYVstyCWOszkLNF81SQFTGOIpbtBrulweU3J17RlPKTK/\nhiKaq5pzpGSLs25GHKhzWHVY1gpNcOR6k9rgavPE6DU1e2lRfSH8Zo/vXSGFzt+nvKKctWOECaqg\nNzKDtIbcY1OsLoQE7hBfs/asaTSWwxTEGpxtaZpaXfoVw7Ch71WRT7ZEmTQ0GZGBXGKFuyWG+lwm\n8XkpghdDZMBVLpHkCqB0AecEa9PslLMAVrd+hW+62Xaqs1unNlmUczTr16yhOKPOGnEKfSvTTDti\nbQ3gdabOwqdTQsQ7g+SEtYa2WTLHPbh9USEl6Otx17gd1Kc2uUZEE8pBuyCqm4ptqFLzAAAgAElE\nQVRV/OoUAQEglmIKbRNwwTL2w3zaU8RABY9azROchJVUzIArdv6daXJJRqkMLkM2EesEk6o7y66x\nNtX5uIoFmef7+jOb0JHNgiGV2bKaJeOo4csGjMgMTlVeoBZoumbrJqXXu9CgDJUYBxyQKhy1sw3D\nGLlz5x6HRy2PHr7HhFvouoY4QOM8UQqu6KYysYRKKYgHKYlhyNhg584iecDQ4H2vHT8nuGpXT/2O\ni/MrXnn54zxz5zn+/tf+7r6bc+OYhV+Sk6VbNDRhwWuvfweAxjW8+slP8eDRI95/eJ/1+mg+YfXj\nBuMdq8MjDI5dv+Fyow7Kl1/5GB/9yCe4urrib/ydX+XdB2/x4z/xz+jXXnyB//Q//jPcu3eP7//+\nT/OLv/hLvPrqq3pNjeErX/kKn/70pzk/P+f999+f8/Q2mw1d13F6eoqUkRdfeG6O12hax3LZslwu\nWXYNFMUjAHShIcfEbrfj+OYt2jbQLfSaXZ6dk9PIarXi6uqKJ0+ecHr2hKMjFf+v12va7oBiDZfb\nzM2TQzb1NYYxcX5+zu3btxXHcHbGaqWi6adPn3L37l1Yr7m6usI7R1OjddKQ6K92hKbh1p3bPHj0\nmJTP5/v7ycOHDMuOg8WSu8/ew9Qb/LXXXuO1/+tX+dgnP8NHPv5p+oN7tAvFLdzY3uXB/bdYrHew\nG4n9OX11s4ZuTdOuENG1A2NJ03WzhcvhMflxZnF8h667hamHAdcuOTC3kf6Kod9hbCD2WkQvXMPN\ne7foY+L04Y4hR2KNj7EGvNcNxFrPtTOXGm7allI07NWWgi2ZtuYQhhDQiK8JammJVf86jAOl6Aan\nm7xl6nTEGIkxI1Zw3tHh51D2ZERLniyYLHjxNJXp5qpuzGQBYxhKgUkHNPZAp/qkPDIMdrbqz7pS\nm+s6uz/siYEhRaxNWA38qXBLLayk4nOc94pcqEWd5oDqodP7Bu+auRPf9z0hOBo/YX72US9SEpvd\nSE6jCtgl7N/7YEE2pDyo06zYWVuUkhogVKuqUT1ZFVz1NTqscTjb4oKjRGbchhg9wFprKDljTYOv\n2Y6l6pZBo1tKCbPuqmnV5FVSQewSH7oaJaMNhN3uknBwgLGKKogVGjzjE2px550QJ+1cKRi0MG1b\nSxntDEDVPFSDKxbjCzn3WFuvtxOCX82QVjV/1ZifMgJGUUJ5wW4DbeWreV9z+dIOay0+wHqtBfYY\n9TqPYyInwTk/YyHiOM7v2W/0+N5xpGSrHampS+AK2FIrYYNzzfxBFUai9GqLHxNt29IYXdycbSoT\nQx0P1vtZKG2yoc87yJ5MIfs8bybFKpNIs4o+mAI+FRaUKbUozmK2gCUXqXZdo3iBepoXY7HZoPWT\nTHvz/DP1SRWo9tIyf8jUYSfFYLx2iiZshUM7XDlnsreYPMzi1+B04882q2U5LOawzJSG/enR2prw\nnq89nyogNeqULCVqkQLkZObnZSwgewFsCJacEsELPtgP2IOdsWRjkFLT6XMltdXfJ1Wsqs8v7UWO\nWYuw0OhisBvzLI4s2dA1C6zpyOX/Zu9de225rjO9Z8xLVa3LvpyzzyFFipRFUpYtyZLtuJ3ASBwg\naaeR/FL/hQD6YnQn6bbRtmNLsmzZlChKvJ7rvq1VVfMy8mHMqnUYyB2gvzAfWIAAgfvsvS5VNWvM\nMd73ecER1t1OiBFqRSXQqwEP5xX+ckSruSpDs8n6ZUyhJrY0eCgYBfjkWKlii4NQyOXI3EClczry\n+mtvsju/4Nef/BzP8ZQzWC7NycPRxIqTuRIXWGsqo33easaGWDan79QH5lqRfGDTO0oq3Np6Sp0c\n3/ndH+C15yc//lvKnLi4tGu/6wZEd3hXKZr42c9+xn5rhcQffP8P+OlPf8Ld8Y7dbkcXewuZhoZ0\n2HGxP+Pu7sDh/prvfvsHADy6eszzp8/455//E58/+RV/9N/+Ab/3g+8D8Od//ud03cCf/c//jr/6\nq//M1dUVr71m0M0f/vCHvPvuu1xcXPCjH/2Iq6uHjKON36dpou97NpsNzgmbYYB0ckMdx1vQwtXV\nFV3Xsd1bEX083DUxtGWQdaHDbVuAcLhkHidSMufSPFsA79Onxj2K3UCYK/1ux+E4sd+X9eG22+04\nHO7MCRUCNzc3ayE19D0vX77k9ddf5+lx5Pr6eiXCd7st43gk1cIQO95+402ePbXuYL6vnJ1d8Oln\nH6I5c3l+ycWuZRt+4+tcRuHDf/zP/Pyf/p5vvPddHjywzuHdYeTq669xc33PfX7Cxf51jndWnJU5\n4XrYnj/AxY4HDx+unZXNpufy8pJZMnWeEbljbmOg6eUNnYfu7IxJE9PtgU1zQ0Xg6ScfU6On73tS\n6tld2d8cj3dMx5GuH9Y1cCH2GdfvlW5TezAuXV7V2vAy0vAiabWMx+ibiLcz+ns7VwDnMbCRYptY\np8xVmNdEC8FlGHNh1gRFiQuXTypDUIu4c5ERQdyJLzcfj8jWrP5jGXG+mXDK6f2XUpjztN4X03Sk\nkAx8qbN1N5dNqwC+LLtPfKivdEEqIe6wLM4NsQv4JgdIqTCOB7p+0zanp2dQ8J0RzLM1EYIv5NY1\nTvkefEFKR9/trIguzY2OTUtKbYaVJn5funzGKLS0h1oz2sLe7WfBpg1qrsTaikD7WUVrC3AX+zvL\n5lO14Lyn1s54Xl7ZtC4utSfNwv39LX7n6fxg2Jl2LkRaQSUmtVhqkoXh6J0npRkphXkpBksi64z5\nDiMhFqa5GUKyErsjwXtDhEhdGYE2Yo+I7kGFkv0J/RAjaSqWvUoh51Ngd83FVvxifMeaxTAL2PNQ\nXzFf/abjy+tIqT1kZJ3BKrlUcB4nHarzKWQW280krQSPtRyX8RYFkWgwsCpU73GN+eTal+qkR2qx\n9vAi8XEOxfhMpVQDmrVFYyoZapuRl2pj8iXcUJxZh7WsxVdd2BciaA1UmXG+YRGW2XxJtmNTZ3N5\nZdVoAY3A7ixkU9rNYh8QVQ++3RhSTvEhC6NJPEoll8M6ukspMZdsrUxncQdoWStru7gduVhAsdNA\nXazFtZJmu3GUSnC6SgVqmS0GJRtwVEpZF5uq1SzKwSHF4z2rRqitRG0O7/AxrB2zaT5SqkPa+LOO\nCfq2M9FrBKXvHAHTO63ZytUo6ZozXox8u0S2FDUyNRWkdeKW70Y1g0TEmVZKVVcLNOrMOKJGMabC\ni5fmvvr6199kdya8/8Hf4rtEkJkytQLM7anakYu1zo3UKywXXMUcPwsHB4nmfAKKBrwYIT+Xe+Zj\nQoo9hP/ND/4nNAd++g9/x8OLh2z3j1h280Jgmo7UUnjx4gWX5xd897u/B8Df/N9/w+3tNQ8uznFe\nOBzvWVbM/dkFXeiZU+bF7S3f+vZ3Vq3X/fGeD375C16+fMrbb7/N//inf8Zf/MVfAPDkyQv+9E//\nlJ/97B+Byre//W1++MMfAvDw4UNef/11fv7zn5+QAO373u/P6fu+scUym67nvu28VZXzM+NOHcdb\nunCxfkclTTx+dMkwbMk5M00TQxt7dbFneHhFSombu2tub2/xB8/U0CfXL1+Cd+wvL5s+K69k6Lu7\nG87PzzkcDlxeXHB2tqdrXceu6xmnibu7O87Pzzke7jm0wsZvN3QXZ+Rp5u7mJTId2A6No3Q/4nPm\njYcPePniCZ/8/J/4rJ371x89JnSO1x8/5MWLl3zy/t9RvmajTd87NAxcXV3x+PEjPvnVh2hr/p7t\nHDFG4nZgv7skhJ6rh1a41q5jlI6+31DyHXM+GhMJ2L35OtOUePHyY/bdBvzEzUvrYnbBM2vh5vlL\n+uipGdLRvpftbiDNI2ka0RDoFt0Tp83liZptESJrrJQEvPNrh2Rxi9nPqnV329pTitL3y/xW6Jzi\npgPHeUJzWTfX4gO9CoMEjo3Rt2nFYqrKUa1DlxG6eupUu+jI6cicFidyReqpM1yLb2NKYU4TczLd\n1ZxHko44RqJkatFVW+RbIWm3kIIrOFk2haUBOgVawK3z9tmHYEXCnIS+78hlWvlENtbLrbhRck34\nNmmZxxHVicFfMM1tbfTLJvlIrWK4GR1xoa7fO7SiV2dSrXgxPdiyoTPAq8e6Ti0qpumNo4+U9lzz\nQWwTvT5rOkqxTmDRatpcFpmMPT9TPvDyLrHbPKSLrYurmeoy1SVKmUwyIafu3Dwf6WXDNFeLMmuO\nzVSyudsRwDqKfnmW1jtKvqGLxsnCu9XNaeO8jloKWg6mvRtbwVccqGGXiuaVM2Wfz7cuGKAdwql5\nYKPR/592pGqav7CjQSo1F4qngcaU0DoIHR2u2A6tFOU43jJsbOcdxdP5LV3YUnVmlkpowjONhRgG\nQjIoWc4Z326MWlsBoq6lxBvwEmy3Y4JwqNSV1goth4+CCwZ8E1lMx9ZJChpY0PZ2AbfPuxRdRanV\nqmrfRjs+iLVV1Rg2VXWtsJ1rI6yqRhCvxdrZgHij2ToMTjeOx3W3k9rNqlIpYhf6mi1Ds70ioJ7o\nNkgv5LnN/EOCzq0w0uDdWoAiNu50KpArWpXoTl2nWi3TLqcGtFwkUtjnrsUhRKKPxLBQ2E2APo8T\nofNt0bbFrR8Sx8k1Iq/D+bB2znJJOC0Gc9OZWtOJyK5LVpKdh+DcClwVUZx3VPHktGQ7nnRn4j0k\nR3A9t9d3XDWR8uXlA372z//AMMxIl0m5UhZmigTA4WNHHQsu2GuvwNlKG2nKKd5oyfiqgeJGPIlp\n2lEOA//mD/47AI7HA7/65a+4unzApt+RU7EMOcw63/vI4W5m6Db89nvv8rd/+9cA3N7ecvXgEqi8\nePGM/f6cs7Oz9hmN9vz8+ee8++67bDYb7g5W2Fxfv+D+cGC7PefP/u3/yj//y8/56T/+DIA//e//\nB0pWfvWrD/lf/t2/5T/9p/+47ma/9a33+Oijj5jnme122zqedtGc7fakMpOzCasPd/dcXFhHZhgG\nuq5nShOdeA6HW+aWe3f18BIR6LrAsO0pz+d14xWDI80H+s2Wx9srdvsNTz5/Rjy69bofxwOHw60J\naPPI+Zl1nfq+jX2yCZHneVq/05Qy/TCs92/X9dze2074LHrcNCIS6YeBz559jLux6+bBg0ueTgee\nPXvBxcUVF3Hg5sZQDc+fP6cfHLvdOZt+w+3tDbfPnwKw3eyJl4HP715yvj3n29/+XQ6TLe4vPn9q\neq6LCxOjDxtqWxPDdsBfPACN5NuA92m1zt+mA9vzh5wPb3D98YfkQyIf7G8eDrekmvAxcJxM5xPa\nA3i6m5EqlGpRJbbZWzAstXHpDJsSu864PAtDrpj93xhEZqhY1r5cTptOlTbmbo3jXOcGvzWEAlXX\nh1JVWzN2voNuz7HO3DddYZwnDi3GRMvcIkMWYbig3pPz1HAHA6fOmd3vi2bUOWNeAajrLFKrrb+5\nZIam1fSNMO9EEbEkikUY7VXJZaSLGxATOWdZuiA9Xe84TKPxvPypqFknDdlGqlrdKtvoYkeeJ5Ie\nGiQ0UdvrpTqC2PeDZGhRL3XtoJgG6vQcYtXOqkSqVrzv7DmmusZRCYL3lSqJUhMO4zfZLwa8dBQd\nCcE24cs9E4iIOEqdLKEj33K2b78nS9E1osyUOpPSgrCx9zfPCe8jKc9r/qxIx3ScCdGGlkkzGpfp\nRxMSqxV8QZXlqhECTnoKQi4J72Rd90tRYoyIBBy5dUzbiDnPbb1y5DziRFhygGv1VF1Uq7/5+Ap/\n8NXx1fHV8dXx1fHV8dXx1fFfeXxpHak1SHjV2DiDWzpnQkDNq3dp00emCSZApJBKImXbtfbDOeJ7\not+TOOA1LdpvqlrXy3latX9qAebcxkJaqbWY7XEZe6m1jjMZh2sE1UX5XxHJawVa5bTzrhTw0Ddq\ndqnFxPFYJ6aUTMnFdg6y7CZst6BaMOSboOVks03zRK4zXgpV3QqHXA7BgjZFrBOWGlyu1GLvRxUk\noLW03aSsvykMeGdxO9IckWD/3/fbJpA2d8Mi/K81U5nb70dzVixar6RUNQ2VTfziGvhr+UgLniCA\ndtB2l13c0HUdVSfGFQ7Zom6yMpGQ4z2yFVzs1jEjWiBkKAZoY/4igVbVdiO1muJpeS9IPo0gSm2j\niBOGw/mKEjleJx7sXuebb30LgA9+8U+IWHA25YgXTsBC8RRVPAX1jmyzUwpL3oWdb5UKtPGmX5Ls\nHbkezc1z7Phvfu9/43Cw3/vwl3/H1eUZeVbwQk0vyeMy1g5M88wQtrz19jv84z/8dAXcvvn61/DB\nSOK73Rln5+erVmA7bLm5ueHhw4fs93vu7+/o2ljo5cuXDMOGP/zDP+L6+ob/+H/+X3zvu78LwKNH\nj/j3//4/8Cd/8id8+OEHPHn6GX/8x38MwOdPPuXm5qYJzJUYwwnvkWcOxwOqlelwz6Yf6Do7v4fD\nHbe31wzbDd2wQXLla197rd1rhePxyP3xnsvLB5yfn62k6ZwzLjiqJHbbHUMfCB7ub2xdGOcjfojM\n+Z7ddo/3ntiS6YfNBuccwzCw3+85Hv0XhLylVrquY+h7uu2OQ+uQlePE4AI5ZNym4+HXf4vrX/8c\ngOcvPufR1x4z7M94+sln9L3w4KF9xvPzTB6PlDIS+57N9hHTZKOPw/0th5tnvPnWOzz77FOe5sxb\n77wHwMWjN7m+uaOIoqGjP7/EXVp3dE6JeRrpohA3+6ZBacaH8Z6b5884e7Tn8u13OPIJl71148bb\nZzx99jH30zWD30C5X9fEoT+n69p4WguVvA40fOt+hxDoWjdKYdU65RZns3StvPcr/qBqxgTA2vSh\nYbXy12qd5WUd3bSYMIDgTB6gziHV9J5Nvkn2PT7W1jlRW+v9qWtQ50zVbKiVzkZYtLtG1YCMyGhG\nmVUj5NhseuZq5zvGaBFUgOZKbGJlp0Lnw9o9kigknZnzLdFb0PT62XF4OkrJjOPIbt9TmqQhtUnI\n0pkRkbWrFoxDQJrukFCoeFI+udgR00WqjhSdjXDOgsUx6IHJdP1pzQXQiBbT/kpto1i3dHM8VSdK\nvTftmZyCkE2X6gh+0zp2HWlsF07MxNCZtlZGxjlR7+w77LrQpgUZJSEun0T6rpqWVu3aSerWSYrz\nhUgw8n1WnI8swuGsNlERjaha1NyqBReLJ0KDPRuz4Be9lhpaxXuLi7NImsUo1dz7Ys/elMaVDCDy\nRS3wbzq+tELKuCMnHYXoUshYlpv3Zt8H6L09aCmmgSjAsfFb9puIk0B1FU+kiK54gEWPswoj23gP\nWFvUzrlWaP2/xGRNwGc22cCJOVUQqRQKjoKmaWWNSPVoMGqvuIqUss7YZSGQa7YLXLQ5DGhtRGdu\nNbwVQGvWniEAlGoCOKeU5cbHRmulmk5MtfG5aG1jKUiwwsx5Z7PxRUfgHI4BcT01ZXKaoBWuQXpU\nJ0Qy3hW8yLpIpZxJao40G/mZa9C+m1dHppU015XsHqMhKko27ksNrxRuTa8VQgCxwMnSUBTTCDEI\nWjPCiMjApjlQJDiCD3iN1FSNCr7kLGaFai7CWgJoQNfWvzEdRKolkYugbZGqpZCK43CbONs85Fvv\n/g6//MW/tM83c3l1AXpnAekykxuBXOnw0ts50BlxmVJpRSdAtpu97R20ihGUAfGwiQ+Z72a+/70/\nYNbP+On7Pwbg4X7PPGe6fE/pIvM0rYVUcAWvA2+9/Rbv//O/kFLiUeMybTYbpmnibH8O3nE8jqug\n+jDNZCq/895v888/e5+rRw94/vJFu6iE1x6/wX77gP/wf/wF2+2Od9+xQvJHP/ox77zzDlUz//hP\nP+b3fu+7vHhh2pvnz5+z2exMbxYdpaY1TNYkZ5VxOqA144Py8tpGW33foyVxHDP31y/55jd+ay2y\n7u5G5nnk8eMrfIC+G9bst2efPyHEyPmDSw6HkeAd201c5QBndUPoIkWg22w5252tt7aqst1uEcxV\nFUJYERcpWb5azuYW3IeOy0uLnTneml3dF0ctW3YPHuNmG0F/8sn73N485dH5a1y88w5Pnn9GujXh\ne+cUJ5FxmqgI274nNi3MbnPO4XCHjjPvfvNbvHz5kptru6b2j/a88dY7aLWcvkOIhKZnGs7OKJoZ\nb+/ZbXYcDuM6Rt88eED/6IrjzQ1ShP2DR8xtXFgRuu0Z6WBXn6PQDct9UanVN5GyEnz8wgNkGYst\n+stS6zraWwKpX3UFn7JA4yqyzqmuHCPaOwLW13QKZXkmiODUMCvHKXF/GCmxCdh7C5z284G5CinX\n9f52DbGQ0pE02xhvCQg3FIdlqM7zgdB5M8XAqmsNzoMLpvEsp4ew9wGHrXfex5XJp2RCrKT5tsXS\nuHXjmctMFTNVpDyRptMocaowzffGxiPY+r9wqii4kPG1GGvRxbVQylMhlbH9+8wytlw2ilWNHehc\nQJxF6yxzVu+9aVzrZPeSCvPUjB8+4nxnTrpqvCdZHJQ1m7RCOss+rLrqhqZpohbTpCoZkcLYxn65\nmDvZ3JxNq9p0VyWBEpECWRMqocWrQVVH581IIOoQZOUOqpSmb1aEnlrmU6wSDjTjpWsNCtbmQgjB\nrlmnLVPVgovt3JcmSUk4Gag1M7cHbXS7VZP9rx1fWiFVSlo5RGDCwqWr4r2zL2t9KBqI0HtvX6xE\nShOl5VpRqShjK5zC2pXQegKc1VqpWnGvfCGm2ejwPjCXeV00VMB5T8CE2LXqGmngXCDnEW0RBLYb\nsr8nOCSBd71dwC6zKAWkBS4veiyDzLWFxinOC7XUJlJ2qyAx57nFqkjDB5RXAKCsF5RqJjhhXiF4\nGaIYZE4KztN2lK1DJJ6aLV1cwEJUddnRBKITkKll1SknaJsiLhpfq2UNrinvobOZvwghYPyOsjjl\nTBsVfNfEqroKK53kxqAKRN9DTUjDOOQ5G6guBqaxELzFswD4YAtyjJGUc1skTjlWph0IrRvpOSFe\nXQMNmhp90RK0E8XhUOniOe++820++vWHHA4NN/DamQVRa8TRo+WeBTehDahp+ge7Jn3QtUA/ZZw6\nnCy2hkXvU7l5oXzvvT/CucBf/c3/zuVDE3/nsqeUnlpnxvEppJ7YClBXB975re/y7NlTcjnwxmtf\nX3V+aUwtKDaRgAcPHqwPr88++zXf+973+MX7H7AddgQXuXlpguqh63nt0Rv8/U/+ntvbW37/9/+Q\nu7a7LFl4481v8OMf/Q2/9fY3GcdxZUXFaLb5GD3H433rTCxYkFOnoOsDeR5Xy7UPmKValfOzLd7R\ninpIeUJrxvnK9cvPOb94YIgPzLVWNePFoiyiE7bnO6aFdyaBbuhRbzy3zbBlu7VC0vvmHGzH0m0B\nK+yO48jFUnTe3zBsm1Pu6ozpeMPOB1yeqcfE5mvvAnCpE7dPfsHTT35JHC54+MYbTOd2fV9/8muC\nmxj6PWlypFzXEHS6nvPNDk0zhJ6zR99YTQjpODHPn3Px2tc5vzJUQ3phkNN0m5GtY3d2DqXS9cJ0\nZ0Xd+FLwu47NcIHejOTjzcpmChdnbINpfMbxjr7rW8fIut+ljITg6LrOtGXt+l0cjxZ061En9MOp\nsFXVJmR+ZUO66DVTsnxRcYRgOqrlX4UQ0CDkmiwE1502GFXVTAa5cC/KKCeGnHNmkOm7gMuBGGz9\nsNebcUGRrJQ8k3MghsUhrGb6EaXWxDiODAsnzkEqMxK06Zd05YulyTR+nTMsg2o5GXCkIBR8rKR8\nRxcHFkZezjMixUxKpXJ/n9hsm6mHbCynPFOqsNuwusOzigX91kJNE1oT09ymFClQ6kyuk2UahuZM\nW/X7gjKjUg0Q6uLaXDDM0DKZqJYru7jcnT03cqnkUvGurppEIVrOaY4ojnlKbKJdw3OeyGnCSaAU\nZ2BNWVx0xQCk6pp5Ka+5pkEqWZRcHFkdxeU1zkWcmJ7ZHhXkVPBxEb73RAeiFquGVI6zbRSC9+Ys\nt0/LgsgB0BazZEa32p6b7Zmv0jr2QqnJmFyLvrfOa5zcv3Z8aYVULvcNprWwKCLVCd73BIxuXpfR\nQLFCQqoQXESkI4gtqIuwLKnDq43elpu51CPFWaVZdbIWYl4YHp4ihVpGggSSOqS+Ak+soNq1cVpB\nXOss1ErVaCCvUIl+OnV5cs9cQDZHOlHrSLU2pnMOzQ7NxYpC/OmmydkuMqfm8quV0Krh2SXGKRGi\nt/Ba79YASo3SCodKrpnihBIXC7AzH6Kz7ojS4d1AbKI5R0/GU7WiEs3p0DpkOR2Ms+E8EjrrnLWF\nuPOO4IQSjf9RkNXRiCrBD6CFUDwZhSZW9LFj8AObrmMTHH0XGRr13fnCXEbmVFDn6YIJvQEmEnlO\nBIHYRfLxmrmFXvZhj4oQpLLrO6b70Z7MwCxqBPPUI85T47heF84Fau6bWHRsNlz72fE4E6XnW++8\nx8cffczzF09W3IB1tiLRW/GZ4WQr9kY31my4Ly8QYkDH5YL3uDDhgsPLuQkd/RJAO/Ha1Td5cPWA\nv/7rv+Tq/Iod9pqD3+FDj08tsLofuH1pn//trz1mnK558vQjzq8ek7UytjHU0mmpRdnv93Rdx0cf\nfQTA48ePOR4Kd7cj3/rt1/jo4w9XxtL3v/8DpvmWly8+4403H3NxccZf/uVftp99n6dPPubs7Awf\nPE+ffr4W5usmpNHkw2qDBHVGEw7SoXXC9Y59K06Ox5F5ToYDqMr98Y7NZtfuNWHbDxxubm2Bn4/E\nc+ssDbsth8OBu8ORzW5D329BO/xiue97Y3E5vzoGl/N/vtsjITJNR7qho9udreMtH3qCCN1ug8iE\nTD2uQXyN8+aY5iNej7g8gdp7ffj626QZyt0zpI5cP3/G9oHlMJ59fcf89ANcGnFdJIpD2ljXdwOS\nMmVIuPM9ve+Yj3Z+B7Z0vmN6fo/zW/rLS/y5dce0VEQz48snZIX+/ILdRetYHEcOH3zIXVbO3ngb\nP+zYubfsZ88+InPP2b5xyqaJPJ+wAbHl563fV+uoq3ftXAviA7GLa/G8nC5KNl8AACAASURBVHdx\nzWPVOv3L2K82Z50TM42UnEizXW+pZnteqScOgTgJuTkv7/KBSU3wfBZ3jD4xN66R1Nhs78YUcuJW\naHLRa4KLlKi2ea13pNxCuWuwgqFUtDm1puYg7ZyZauwZAlWMm2TXhaK1kNShEiiqK25B6NFaiD6i\nTpjy7XpflKR47UhFLU2hJqb2zOuCR2qPdzNIZs639J0V8MFtyUUpzoF45imt50lKplZwLqLVo9VG\nVUXNFBFjRIuniiJt/KdtPaWAr4ElPrKUyjLiSGUi1UyqM+In1NU1m9YBuQqUgyGHamQal87hxmDI\nmqAYfsg3sKhv50hcYl4QBQuCKPZGII9KmRWZQdvzQqRSHcb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P2RhGixNUQEtBnCfPplcM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6nVoYasNuXKPTAT83XmLl2g4sxfaMNRiD45o7Fyjrz942Zr4G1iVhJSqMmfOe6xnm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\nuVwu3OdlH5m9PP/E8/MLzjn+7d/+jZTrTjAOIfDy8sLnz59JqfD6+mdaH4e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caPfQS7zDQ01LG6qKyqDo+sa6G0xJdvfsKLu0wqAqIFQfAJ2qxso9ZoJj53D5hBHE4malsx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WLbkfnv6erUbMyx+5vW7kMlCo7Jd4zZUpZEaj3UxrLJPXQmPJEapFWiOnim35wB+kIogN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\n3gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f09b97eab50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')\n",
+    "transformed_image = transformer.preprocess('data', image)\n",
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Adorable! Let's classify it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predicted class is: 281\n"
+     ]
+    }
+   ],
+   "source": [
+    "# copy the image data into the memory allocated for the net\n",
+    "net.blobs['data'].data[...] = transformed_image\n",
+    "\n",
+    "### perform classification\n",
+    "output = net.forward()\n",
+    "\n",
+    "output_prob = output['prob'][0]  # the output probability vector for the first image in the batch\n",
+    "\n",
+    "print 'predicted class is:', output_prob.argmax()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The net gives us a vector of probabilities; the most probable class was the 281st one. But is that correct? Let's check the ImageNet labels..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "output label: n02123045 tabby, tabby cat\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load ImageNet labels\n",
+    "labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "if not os.path.exists(labels_file):\n",
+    "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "    \n",
+    "labels = np.loadtxt(labels_file, str, delimiter='\\t')\n",
+    "\n",
+    "print 'output label:', labels[output_prob.argmax()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* \"Tabby cat\" is correct! But let's also look at other top (but less confident predictions)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "probabilities and labels:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[(0.31243637, 'n02123045 tabby, tabby cat'),\n",
+       " (0.2379719, 'n02123159 tiger cat'),\n",
+       " (0.12387239, 'n02124075 Egyptian cat'),\n",
+       " (0.10075711, 'n02119022 red fox, Vulpes vulpes'),\n",
+       " (0.070957087, 'n02127052 lynx, catamount')]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]  # reverse sort and take five largest items\n",
+    "\n",
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* We see that less confident predictions are sensible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Switching to GPU mode\n",
+    "\n",
+    "* Let's see how long classification took, and compare it to GPU mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 loop, best of 3: 1.42 s per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* That's a while, even for a batch of 50 images. Let's switch to GPU mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10 loops, best of 3: 70.2 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "caffe.set_device(0)  # if we have multiple GPUs, pick the first one\n",
+    "caffe.set_mode_gpu()\n",
+    "net.forward()  # run once before timing to set up memory\n",
+    "%timeit net.forward()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* That should be much faster!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Examining intermediate output\n",
+    "\n",
+    "* A net is not just a black box; let's take a look at some of the parameters and intermediate activations.\n",
+    "\n",
+    "First we'll see how to read out the structure of the net in terms of activation and parameter shapes.\n",
+    "\n",
+    "* For each layer, let's look at the activation shapes, which typically have the form `(batch_size, channel_dim, height, width)`.\n",
+    "\n",
+    "    The activations are exposed as an `OrderedDict`, `net.blobs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data\t(50, 3, 227, 227)\n",
+      "conv1\t(50, 96, 55, 55)\n",
+      "pool1\t(50, 96, 27, 27)\n",
+      "norm1\t(50, 96, 27, 27)\n",
+      "conv2\t(50, 256, 27, 27)\n",
+      "pool2\t(50, 256, 13, 13)\n",
+      "norm2\t(50, 256, 13, 13)\n",
+      "conv3\t(50, 384, 13, 13)\n",
+      "conv4\t(50, 384, 13, 13)\n",
+      "conv5\t(50, 256, 13, 13)\n",
+      "pool5\t(50, 256, 6, 6)\n",
+      "fc6\t(50, 4096)\n",
+      "fc7\t(50, 4096)\n",
+      "fc8\t(50, 1000)\n",
+      "prob\t(50, 1000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for each layer, show the output shape\n",
+    "for layer_name, blob in net.blobs.iteritems():\n",
+    "    print layer_name + '\\t' + str(blob.data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Now look at the parameter shapes. The parameters are exposed as another `OrderedDict`, `net.params`. We need to index the resulting values with either `[0]` for weights or `[1]` for biases.\n",
+    "\n",
+    "    The param shapes typically have the form `(output_channels, input_channels, filter_height, filter_width)` (for the weights) and the 1-dimensional shape `(output_channels,)` (for the biases)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "conv1\t(96, 3, 11, 11) (96,)\n",
+      "conv2\t(256, 48, 5, 5) (256,)\n",
+      "conv3\t(384, 256, 3, 3) (384,)\n",
+      "conv4\t(384, 192, 3, 3) (384,)\n",
+      "conv5\t(256, 192, 3, 3) (256,)\n",
+      "fc6\t(4096, 9216) (4096,)\n",
+      "fc7\t(4096, 4096) (4096,)\n",
+      "fc8\t(1000, 4096) (1000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for layer_name, param in net.params.iteritems():\n",
+    "    print layer_name + '\\t' + str(param[0].data.shape), str(param[1].data.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def vis_square(data):\n",
+    "    \"\"\"Take an array of shape (n, height, width) or (n, height, width, 3)\n",
+    "       and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\"\"\"\n",
+    "    \n",
+    "    # normalize data for display\n",
+    "    data = (data - data.min()) / (data.max() - data.min())\n",
+    "    \n",
+    "    # force the number of filters to be square\n",
+    "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
+    "    padding = (((0, n ** 2 - data.shape[0]),\n",
+    "               (0, 1), (0, 1))                 # add some space between filters\n",
+    "               + ((0, 0),) * (data.ndim - 3))  # don't pad the last dimension (if there is one)\n",
+    "    data = np.pad(data, padding, mode='constant', constant_values=1)  # pad with ones (white)\n",
+    "    \n",
+    "    # tile the filters into an image\n",
+    "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
+    "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
+    "    \n",
+    "    plt.imshow(data); plt.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* First we'll look at the first layer filters, `conv1`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hhBAN0EuUEEIIIUQDjt6JokGafol49vvy4e971FhiHpOvYm1oSOehuxD6Pfy9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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f09692b9ed0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# the parameters are a list of [weights, biases]\n",
+    "filters = net.params['conv1'][0].data\n",
+    "vis_square(filters.transpose(0, 2, 3, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Z7nrH+wULyFbd0NdYl1dXV2PnrbVGW1lAGC4Zk0qlPEE6SUT1oNoLCAgICAgI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KfAolWVToe9N9VCm8XyZqv0slo0qQGED99oNGr0xfl8vjIBmJ2lTMH7slcUoExdwGg0arSrioNj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GFX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PwW3wODycw/2KBOpxOMWaFQ0PXI8Bguj5nGfoE102g0tC+xvuv1uvab1y82TlXkSFqGa5D+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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f09692c7350>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['conv1'].data[0, :36]\n",
+    "vis_square(feat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The fifth layer after pooling, `pool5`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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d6fwfzz33XNa6H/3oR7UeN7r2jh49msQmJiaS2LVr12rNZXNzs3jbfjUZN+Hi\nxYtZ6x555JEeZ1J/c32ufp37XMPDw1nr9u5tprxxJwoAoIAiCgCggCIKAKBAa3uiJicns9ZtbGwk\nsbW1tbrTaY1o2GYk6p2qIjrPkWiIZhPq7o1jcOUO/ay7J2pQ7Ozs9PwY0WDSaKBnvz7royGadYsG\nokYDJOl0tre3s9b1ok/KnSgAgAKKKACAAoooAIACiigAgALdpgdrdbvdrANGA+eioZK3b99OYlHj\nY9t/OTs3l7obywflvNRNLrG25FIlj4MHD2atu3nzZs9zqdug5BINa819uKXuXOqWm0uVYZu5DdRb\nW1tZx21C7nnJHbYZDYOORM369znP4YlxJwoAoIAiCgCggCIKAKCAIgoAoEBrG8vrthsbCyPRJPfZ\n2dmsbaMpu4NyXuoml1iVXB599NEkdubMmaxtv/Od79SWR93kEquSy8zMTNa6paWlJFb3g0XHjh1L\nYtHn8E9/+tMkFk3Szs0ltzE6+ntzz9/CwkJWLk2o+9qNGtCjRvrcXDoaywEA6qOIAgAooIgCACig\niAIAKJA31rQlcpvlFhcXe5xJ/4yOjiaxaLr7wyZqwhwZGUliR44cSWJRwz31+/SnP53ETpw4kcSi\nCcL3NpYz2B555JGsddEU87W1tVpzOXToUK37i0S/xnH48OGsbXMnuUcN6LtR9H2Xe66iX/KImv8f\nhDtRAAAFFFEAAAUUUQAABRRRAAAFGp9Y3ul0+jKxHACgkInlAAB1UUQBABRQRAEAFFBEAQAUaHxi\nebeb9mb98R//cRKLphu//vrrWcf4+te/nsSiBvoolz/90z9NYs8991wS++lPf5rE/u7v/i6JnT9/\nvjiXSO508typvVVyqZtcYrsxl2iyfu7+ognRV65cKcqjCYOSy9mzZ7PWvfXWWz3PpW5VchkeHs7a\ndnNzM4nt27cvid26das4l7q1/TWKvu/+5E/+JIl94QtfSGLRhPZ///d/T2J//dd/ncRWV1fvm+e9\n3IkCACigiAIAKKCIAgAooIgCACjQeGN55KWXXkpib7/9dhJ75plnmkgnS9T8undv709nbsN43U6c\nOJG17t4GYJpz7NixJPalL30pa9u///u/rzWXPXvy/n129+7dWo9LntOnTyexN998M2vbP/zDP0xi\nVa6f6FqJYtvb28XHqCL3YZ6osXxlZaXudB4q6+vrPT/G7du3K23vThQAQAFFFABAAUUUAEABRRQA\nQIFuNCXotdrEAAAXHElEQVS0pwfsdps94M/lTmbdv39/1v6iqbPRZNutra3iXJqQm8vU1FTW/h5k\n0mtpLk3Yjbk00Viem0v0XohE749oyvO9Dbq78fXJNTY2lsQ2NjZqzSVqLL9w4ULWMXIbywf5NapC\nLrEquUxOTiaxoaGhJBY1+kfHvU9dFCbjThQAQAFFFABAAUUUAEABRRQAQAGN5feYnp7O2t/S0lLP\nc2lCbi5Rs2sktwG2Si5NkEusiVwee+yxJHbvVOGrV6/2PI9c/Wosj46xs7NTnEvugzG5HrbrNpdc\nYm3PpaOxHACgPoooAIACiigAgAKKKACAAnv7nUDbRA3je/aoNaEXZmdnk9iRI0eS2NzcXBPptEKV\n6eRVRE3kJ06cyNr2ypUrteayd2/61RRdK5Fr167Vmgv8/6gOAAAKKKIAAAooogAACiiiAAAKtLax\n/PTp01nr1tfXk9j8/HzWtrmTgaMpwJGJiYkktra2lrVt242OjmatqzKxvO2GhoaSWNQA+9RTTxUf\n47XXXivetu2icxVNJI4apm/cuNGTnHazuh94eeKJJ5JY7udw3Y3lzzzzTBKLPl8juY3lU1NTWesO\nHjyYte7WrVtJLJoC33bRa37vLwZ0Op3O4uJiEovO6alTp5LYu+++W5hd+7gTBQBQQBEFAFBAEQUA\nUEARBQBQoFv31NsMjR8QAKCC9CmYjjtRAABFFFEAAAUUUQAABRRRAAAFGp9YHk0ojuROk42mIC8t\nLSWxqIE+ymXfvn1Zx40muOZONs/NJZp2G/290dT26enpJFblvDRBLrHcXEZGRrL2t7m52fNceq0t\neXQ6+bl84QtfyNrf+++/n7Xu4sWLxbk0oUouMzMzWeuiqdlVcvnKV76Stb8f/ehHWeveeOON4lwi\nhw8fzlqXO+F/N14vs7OzSex3f/d3k1g0Ff273/1ucS73404UAEABRRQAQAFFFABAAUUUAECBxieW\nd7vd5IBPPfVUsu7JJ59MYtvb20ksahRbWFhIYrlNaxMTE0kssrGxkcTqbixvwm7M5fHHH09if/EX\nf5F1jK997WtJrMr1Ejlx4kTWuitXrmStq5LL888/n7Xu1Vdf7XkudWpLHp1Ofi6HDh3K2t/a2lrx\nurafl+iBl+Xl5b7kknteonVVvjerfM4999xzSSx6sOif//mfa82lCbm5RA9Xff7zn09id+/eTWIV\nG8tNLAcAqIsiCgCggCIKAKCAIgoAoEDjE8vrFjUFV5Hb1NmEaHr66OhoEoump0fNhnX77d/+7ax1\n3/72t5NYE/n1y/79+7PW5TaWN2FsbCyJRQ9PUG5lZSVr3dDQUI8z4UFETcZnz57N2vatt94qPm70\n/ouuodzralBED5i99NJLfcjkv7kTBQBQQBEFAFBAEQUAUEARBQBQoBUTy5uwGyezRk3kUbP5zZs3\nk1g0rbVKLpG6G8t342sUmZqaylq3urra81yiCfybm5tJLGrWrDuXOrUlj05HLvczKLlED1188Ytf\nzNr2hRdeKM4liv3BH/xB1nHffffdJBb9KsGgvEZ1M7EcAKDHFFEAAAUUUQAABRRRAAAFWjuxPGoo\nixr8ogbqqHF2N7pz505WrF9eeeWVJDYzM5PEZmdnk9ilS5d6klMb5DaMN6FNE/hJRZ9p0cTyqNF1\nN76209PTSWxpaakPmeSLzn3ugxhNiK4hmuNOFABAAUUUAEABRRQAQAFFFABAgdY2ln/sYx9LYsPD\nw0ksmnz9zjvvZB0janjObXLMnQg+yD760Y8msd/6rd/K2vYv//Iv606nNQ4ePJjEoqnyD5vo/RZZ\nXFxMYk3/skIvRNfFM888k7XtlStXktjbb79dOac22LMn/bf8yMhI1rYbGxtJrO5G6+hhnmgieN2i\naz66DqLz9/rrr9eaS/Tdu7W1Vesxdit3ogAACiiiAAAKKKIAAAooogAACnT70LC5+ztEAYCHSfoz\nKh13ogAAiiiiAAAKKKIAAAooogAACjQ+sbzbDXuzEkNDQ0ksmmIbTSyPRA30ubk8/vjjSWx6ejqJ\nvf/++0ksd/pybi5VjI6OJrFo4m8TuUT6dV4ig5LLJz7xiax10ZT/6L3VlvPSljw6nWq5jI+PJ7Fo\nEvT29natueRO9Y4+hyO3b98uziXyK7/yK1nrFhYWkthbb71VnEv0905NTSWxvXvTr87l5eUkFr1u\nu/HaPXPmTNb+Hnnkkax13/3ud7NyiaaxR+c+es9E39G5v0jyIA/cuRMFAFBAEQUAUEARBQBQQBEF\nAFCg8cbyug0PDyexqMmsiqeeeiqJnTp1KolFTdpRY3m/3Llzp98pwEMrejDmsccey9r2jTfeqDWX\n3IbnJh48OX78eBJ78skns7b9wQ9+UGsuExMTSWxnZyeJRQ3tTch9ICB63ao4f/581rqo6buK6Jo8\ncuRI1rbRAwH79+9PYhcuXHjwxP4Xd6IAAAooogAACiiiAAAKKKIAAAq0trH87t27SSx3Onm/5E4V\n7peosfVhs2/fviS2srLSh0ya8V//9V9J7MMf/nASO3HiRBL7yU9+0pOcHlabm5tJbH5+vg+ZxJOg\nowbqyINMcy71wQcfJLGoUZhmRJ8PUZN29B343nvv1ZpL9DBZdD1H11CkajO8O1EAAAUUUQAABRRR\nAAAFFFEAAAW6TTQJ/p8DdrvNHvDnor+z7sm7ufqVS9RYHk0xf9jOS2SQc/n4xz+ete7111/veS6l\n2pJHpyOX+6k7l6effjpr3blz53qeSxWDksv4+HgSq/LwV24u0cNBkSoPDN2nLgpPjDtRAAAFFFEA\nAAUUUQAABRRRAAAFWjuxvO2iprpIm6asRxOT63bmzJmsdefPn6/1uNPT00lsaWmp1mMMiitXrvQ7\nBfj/Onv2bBKL3uP0T7++2/rVhH8/7kQBABRQRAEAFFBEAQAUUEQBABTQWM6uc+rUqSR24MCBJHbr\n1q0ktrOz05Ocmnb8+PEkdvDgwSS2traWxKKm/uiBgGPHjhXldj979+Z93Gxvbxftf3JyMondvn27\naF9V7d+/P4lFD6NEr1k0LXlxcTGJzc/PF2bX6QwNDSWx6BcN+tU8/KEPfSiJ7dmT/pt/eXm5iXRo\nkbGxsax10ed/pGqjujtRAAAFFFEAAAUUUQAABRRRAAAFulETY481fkAAgArCDnR3ogAACiiiAAAK\nKKIAAAooogAACjQ+sbzKdNDp6ekktrGxkcQ2NzeT2N27d2vNpYqomV8u+blE05afeeaZJLayspLE\n3nnnneJcoonJVR7MiCZER9du7nmZmprKOm70d0Si8xdNfM+9Xn7nd34na93LL7+cxBYWFv7Pf+/G\n63Z4eDhrf1tbW7XmEk3zjyaR5x43us6ia/ne16zTaf9r1IQquUS/LBCJfpWg7lzqlptL9IsVhw4d\nSmLRRP9o8n/0qw4P8rnuThQAQAFFFABAAUUUAEABRRQAQIHGG8sjuY2uS0tLPc6k0xkbG8tat729\nnRUbFFGD3/79+7O2XV5erjWXqIn1Qx/6UNa2uY3lkbqn+0dN5FXMzMwksa9+9atZ2+aua0Lue7DX\nos+lqLE+V9S4/fTTT2dte+7cueLj3rp1q3jbyOrqaq37a7vHH388a917773X40w6nRMnTmSty20s\n343m5ub6ncL/4U4UAEABRRQAQAFFFABAAUUUAECBVjSWV2nWrFvUTBpNyB7kJvJI1FRdd8N4rmja\ncjS5/mETNZY///zzfcgk9q1vfSuJRU3kbXkt636QgN2piYbxXK+88kq/U+i76NdHLl++nLVtL97T\n7kQBABRQRAEAFFBEAQAUUEQBABToNt082e12+9KtGf2d0RTuJsglJpdYbi7RQxGf+cxnklj0UMT3\nv//9WnPptbbk0ekMTi7j4+NJLHqIo4lc6iaX2G7MZXh4OGt/0edcbr1zn3XhiXEnCgCggCIKAKCA\nIgoAoIAiCgCgwK5vLM+deLwbG+iaMCi5TExMJLFosu2dO3d6nkvd5NLePDqdwcnls5/9bBLbt29f\nErt06VISe+ONN2rNZXR0NInlvncjg/Ia1W035hL9MkNkcXGx1lw6GssBAOqjiAIAKKCIAgAooIgC\nACiwt+kDRlOVd3Z2ktinP/3prP2trq4msajJkcEWNaJGNjc3k1jTD1c0KZruu7W11YdMem9kZCSJ\nRa93roMHD1ZJpzVyr4GjR48msZMnTyaxaBJ03Z+50UMhD5v9+/dnrbt161YSm52dTWLR9+xuVKVh\nvBfciQIAKKCIAgAooIgCACigiAIAKND4xPJOpzO4XbwAwCAysRwAoC6KKACAAoooAIACiigAgAKN\nTyzvdtPerCeffDJr24WFhax10TTeaNsolyZEzfy5ufzGb/xG1rp/+Zd/6XkudauSSzQJP5I7tXdQ\nzkvd6s7lq1/9ata6r3/960V55E6yn56eTmLRpO9Lly4lsSrn5DOf+UzWuu9973tZ63JzqTINO1eV\n8/K5z30ua927776bxK5du1ZrLnWrksuJEyeS2N696dd49LotLS0V5xL9GsBzzz2XxObm5pLY1atX\nk9idO3eKc8k1NTWVxPbt25fEopwf5IE7d6IAAAooogAACiiiAAAKKKIAAAo03lgeiZq+f/M3fzNr\n229+85tJbHl5uXJObfWDH/yg3yn8j9/7vd/LWvdP//RPPc7k4RM10uc2zZ88eTJr3eXLlx8opzaK\nGlgjuY3MUWM5+aKG9irN6w+bK1euJLHooYi6ffnLX05if/VXf5W17Z//+Z8nsX/9138tzuUTn/hE\nEjt69GgSi5rD/+M//qP4uPfjThQAQAFFFABAAUUUAEABRRQAQIFWNJZHE2ZzDXITeWRxcbHfKfyP\naKJzv+Q2VdMu904ir1s0zTmytrbW0zzupxeNroPg5Zdf7ncK/+Ppp5/OWnfu3LkeZxKLJpG3ydjY\nWK37Gx8fT2K/9Eu/lLVtLx7McicKAKCAIgoAoIAiCgCggCIKAKBAKxrLI3/7t3/b7xT4Bf7xH/+x\n3yk8tKo00g/CJPK6vfDCC/1OoVF3797ty3Fzp5MfPHgwia2vryexjY2Nyjnxi337299OYmfPnk1i\n3W43iUVT1qv44IMPklg0nTy6NnrRhO9OFABAAUUUAEABRRQAQAFFFABAgW7UkNXTA3a7zR7w56K/\nM2qCa4JcYnKJyaU/eUTNzTdv3uxLLrmayOX48eNJbG5urtZcPvnJT2at+/GPf5zEogb0h+01ylUl\nl5GRkSR25syZrG3feeedWnOp233qojAZd6IAAAooogAACiiiAAAKKKIAAAo0PrF8eHg4iUUNapHb\nt2/XnU5rDA0NJbF+TRUmFl2nm5ubfcik09mzJ/33T+4U89HR0SQ2NjaWxJaXl7P2FzV/RvuLRE3A\nuccoFeUWTTeenJxMYlFj+SCLmsjJNzMz0+8UGrW2tpbEcqfUR6L3fe7DcFU+Ix+EO1EAAAUUUQAA\nBRRRAAAFFFEAAAUan1je6XT6MrEcAKCQieUAAHVRRAEAFFBEAQAUUEQBABRofGL5xMREEsudWlxF\n1EBf5xTkByGXWG4uzz//fNb+3n333ax10WTuus/Lvn37statrKz0PJe9e9O3/fb2dta2ublExzh4\n8GASu3HjRtZxS/OIRJPII7m/kFAllz/6oz/KWvc3f/M3Pc8l95cjcqf078bPlkj03o32t7q6Wmsu\n0cTtEydOZB3j0qVLteYSXRunTp1KYnNzc0ks9/u97dfL/bgTBQBQQBEFAFBAEQUAUEARBQBQoPHG\n8kjUhBrJbX5lsL333ntZ66KG8X6JGsb7pYn3Ud3HyP2M2G3Gxsb6nQIttbOzk8RyG8brdujQob4c\ndzdwJwoAoIAiCgCggCIKAKCAIgoAoEDj3ZrR9NJBbRodJEePHs1ad+3atR5n0uksLS1lrYsm/kai\nBs5c0STf3//938/a9hvf+EYSG+SHJ0qnk3c6DzZB+BfJnUTehNyHJIaGhpLY3bt3a80ldxL5w6ZN\nD4XkGh4eTmJbW1vF+1tcXMxat7GxUXyM3cqdKACAAoooAIACiigAgAKKKACAAq3o6K7STBs10DVh\nfHw8iUXNr3U32nW73azj1u3mzZs9P0bdqjSM56ryUMRjjz2WxN59990q6Qysupuo2+KFF17odwoM\noCpN5JHoc+6pp55KYtF3efTwRJt+TaIqd6IAAAooogAACiiiAAAKKKIAAAq0orG87T760Y8msZmZ\nmSQ2Pz+fxHInEud69tlnk9js7GwSi6YPf+c73yk+rmnG+aamppLYo48+msQOHDiQxDSWA7tB9DkX\nPeh1586dJKaxHADgIaeIAgAooIgCACigiAIAKNB4Y/nk5GQSi6Z6504ornsya2RiYiJrXd3TySNR\nE/n09HQSu379evEx+jUVfTdaW1urdX/Oc7nosyX6fPCQRCxqCh4ZGUliVT6vI1GDcrS/9fX14v1R\nTfTe+tjHPpbEDh06lMTeeOONJHbu3Ll6EmsBd6IAAAooogAACiiiAAAKKKIAAAp0+9DIqnMWANhN\n0ieuOu5EAQAUUUQBABRQRAEAFFBEAQAUaHxieTQNuwlRA32Uy4EDB7L2t7y83PNccp08eTJr3eXL\nl3ueSxVRLnv2pHV+FMvNOdo2mmDd9vPSplyGhoaS2MGDB7P2t7CwUFse0Tl5/vnnk9i+ffuS2M2b\nN7OO+9prrxXnEol+gSCSe55yX5+dnZ2s/eUaGxtLYtGE8ei85H5+Pfroo1nrXnnllSTW9vdQlEvu\nL2VU+dWEKJfx8fEkFk3+P3z4cFYut27dKs6lTa/R/bgTBQBQQBEFAFBAEQUAUEARBQBQoPHG8rbb\n2Njodwqt9PGPfzyJ/dmf/VkSm5+fT2Jf+9rXio8bNfjdvXs3a9u9e/tzeUdN1bmNy1V8+MMfzlq3\nuLiYtS4356hJ+Vd/9Veztp2bm8ta9/LLL2etu9err76ate7s2bNF+7+f6Nrb3t6u9RjDw8NZ606d\nOpXELly4UGsuVd5ruddZbmN5m0RN/bmqNIxXEX0H5v4duddk3b74xS9mrXvppZdqP7Y7UQAABRRR\nAAAFFFEAAAUUUQAABTSW3yOaXt120STyfulXY+Hjjz+exEZGRpLYm2++2fNcmmgib7u6m6h77a23\n3urLcUsntnc6DzZVuc2iCdnRwxk//elPk1ibPvsiuQ/BtF30d1y9erUPmbSPO1EAAAUUUQAABRRR\nAAAFFFEAAAW6TTcndrvdvnRDRn9nt9vtQyaDk8vY2FgSixoQt7a2ep7L0aNHk1jUWH7x4sWe51K3\n3FyicxC5du1az3M5c+ZM1v6WlpaSWM6E7eXl5aw8mtD2a2VmZiaJNfHwQ+55GR0dTWL79u1LYnfu\n3EliKysrteaSK3eCd/R52PbrRS73fWgjTMadKACAAoooAIACiigAgAKKKACAAiaWU2xjY6PfKfyP\nKs3S1O/8+fNJLJpCHTWRHzp0KImZjlyu7RP0o4bxKNYmub/MMCgTy7k/d6IAAAooogAACiiiAAAK\nKKIAAAq0orF8z560loti29vbTaQDu1K/muv37k0/RqL36ubmZtb+5ufns2L3ipp9c6flV9k2cuzY\nsSQWTUHOfc2iZvvcpuXolwXqfihkenq61v3V/XpEjhw5ksSuX7+etW3u+ZudnX2gnH6RqampJBa9\nr3Lfa1TnThQAQAFFFABAAUUUAEABRRQAQIFu1OzYY40fEACggm4UdCcKAKCAIgoAoIAiCgCggCIK\nAKBAPyaWh81ZAAC7iTtRAAAFFFEAAAUUUQAABRRRAAAFFFEAAAUUUQAABRRRAAAFFFEAAAUUUQAA\nBRRRAAAFFFEAAAUUUQAABRRRAAAFFFEAAAUUUQAABRRRAAAFFFEAAAUUUQAABRRRAAAFFFEAAAX+\nHzreW7zFsFB/AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f09691fce50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['pool5'].data[0]\n",
+    "vis_square(feat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The first fully connected layer, `fc6` (rectified)\n",
+    "\n",
+    "    We show the output values and the histogram of the positive values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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MViiLF298EU1brscek/78Z/9lSmv16s7+2YZ9tO5i2Nebhv3ev8rvIox1o8ZaLl98PEy0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\nFLIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f096914f6d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['fc6'].data[0]\n",
+    "plt.subplot(2, 1, 1)\n",
+    "plt.plot(feat.flat)\n",
+    "plt.subplot(2, 1, 2)\n",
+    "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The final probability output, `prob`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f09587dfb50>]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAADICAYAAAC6TEOmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0969270290>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "feat = net.blobs['prob'].data[0]\n",
+    "plt.figure(figsize=(15, 3))\n",
+    "plt.plot(feat.flat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note the cluster of strong predictions; the labels are sorted semantically. The top peaks correspond to the top predicted labels, as shown above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Try your own image\n",
+    "\n",
+    "Now we'll grab an image from the web and classify it using the steps above.\n",
+    "\n",
+    "* Try setting `my_image_url` to any JPEG image URL."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# download an image\n",
+    "my_image_url = \"...\"  # paste your URL here\n",
+    "# for example:\n",
+    "# my_image_url = \"https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG\"\n",
+    "!wget -O image.jpg $my_image_url\n",
+    "\n",
+    "# transform it and copy it into the net\n",
+    "image = caffe.io.load_image('image.jpg')\n",
+    "net.blobs['data'].data[...] = transformer.preprocess('data', image)\n",
+    "\n",
+    "# perform classification\n",
+    "net.forward()\n",
+    "\n",
+    "# obtain the output probabilities\n",
+    "output_prob = net.blobs['prob'].data[0]\n",
+    "\n",
+    "# sort top five predictions from softmax output\n",
+    "top_inds = output_prob.argsort()[::-1][:5]\n",
+    "\n",
+    "plt.imshow(image)\n",
+    "\n",
+    "print 'probabilities and labels:'\n",
+    "zip(output_prob[top_inds], labels[top_inds])"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.",
+  "example_name": "Image Classification and Filter Visualization",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 1
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/01-learning-lenet.ipynb b/examples/01-learning-lenet.ipynb
new file mode 100644
index 00000000000..1c328260dfa
--- /dev/null
+++ b/examples/01-learning-lenet.ipynb
@@ -0,0 +1,1288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Solving in Python with LeNet\n",
+    "\n",
+    "In this example, we'll explore learning with Caffe in Python, using the fully-exposed `Solver` interface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Set up the Python environment: we'll use the `pylab` import for numpy and plot inline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from pylab import *\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Import `caffe`, adding it to `sys.path` if needed. Make sure you've built pycaffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* We'll be using the provided LeNet example data and networks (make sure you've downloaded the data and created the databases, as below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "Creating lmdb...\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run scripts from caffe root\n",
+    "import os\n",
+    "os.chdir(caffe_root)\n",
+    "# Download data\n",
+    "!data/mnist/get_mnist.sh\n",
+    "# Prepare data\n",
+    "!examples/mnist/create_mnist.sh\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Creating the net \n",
+    "\n",
+    "Now let's make a variant of LeNet, the classic 1989 convnet architecture.\n",
+    "\n",
+    "We'll need two external files to help out:\n",
+    "* the net `prototxt`, defining the architecture and pointing to the train/test data\n",
+    "* the solver `prototxt`, defining the learning parameters\n",
+    "\n",
+    "We start by creating the net. We'll write the net in a succinct and natural way as Python code that serializes to Caffe's protobuf model format.\n",
+    "\n",
+    "This network expects to read from pregenerated LMDBs, but reading directly from `ndarray`s is also possible using `MemoryDataLayer`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L, params as P\n",
+    "\n",
+    "def lenet(lmdb, batch_size):\n",
+    "    # our version of LeNet: a series of linear and simple nonlinear transformations\n",
+    "    n = caffe.NetSpec()\n",
+    "    \n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
+    "    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
+    "    return n.to_proto()\n",
+    "    \n",
+    "with open('mnist/lenet_auto_train.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_train_lmdb', 64)))\n",
+    "    \n",
+    "with open('mnist/lenet_auto_test.prototxt', 'w') as f:\n",
+    "    f.write(str(lenet('mnist/mnist_test_lmdb', 100)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The net has been written to disk in a more verbose but human-readable serialization format using Google's protobuf library. You can read, write, and modify this description directly. Let's take a look at the train net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "layer {\r\n",
+      "  name: \"data\"\r\n",
+      "  type: \"Data\"\r\n",
+      "  top: \"data\"\r\n",
+      "  top: \"label\"\r\n",
+      "  transform_param {\r\n",
+      "    scale: 0.00392156862745\r\n",
+      "  }\r\n",
+      "  data_param {\r\n",
+      "    source: \"mnist/mnist_train_lmdb\"\r\n",
+      "    batch_size: 64\r\n",
+      "    backend: LMDB\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv1\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"data\"\r\n",
+      "  top: \"conv1\"\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 20\r\n",
+      "    kernel_size: 5\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool1\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv1\"\r\n",
+      "  top: \"pool1\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"conv2\"\r\n",
+      "  type: \"Convolution\"\r\n",
+      "  bottom: \"pool1\"\r\n",
+      "  top: \"conv2\"\r\n",
+      "  convolution_param {\r\n",
+      "    num_output: 50\r\n",
+      "    kernel_size: 5\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"pool2\"\r\n",
+      "  type: \"Pooling\"\r\n",
+      "  bottom: \"conv2\"\r\n",
+      "  top: \"pool2\"\r\n",
+      "  pooling_param {\r\n",
+      "    pool: MAX\r\n",
+      "    kernel_size: 2\r\n",
+      "    stride: 2\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"fc1\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"pool2\"\r\n",
+      "  top: \"fc1\"\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 500\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"relu1\"\r\n",
+      "  type: \"ReLU\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"fc1\"\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"score\"\r\n",
+      "  type: \"InnerProduct\"\r\n",
+      "  bottom: \"fc1\"\r\n",
+      "  top: \"score\"\r\n",
+      "  inner_product_param {\r\n",
+      "    num_output: 10\r\n",
+      "    weight_filler {\r\n",
+      "      type: \"xavier\"\r\n",
+      "    }\r\n",
+      "  }\r\n",
+      "}\r\n",
+      "layer {\r\n",
+      "  name: \"loss\"\r\n",
+      "  type: \"SoftmaxWithLoss\"\r\n",
+      "  bottom: \"score\"\r\n",
+      "  bottom: \"label\"\r\n",
+      "  top: \"loss\"\r\n",
+      "}\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat mnist/lenet_auto_train.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's see the learning parameters, which are also written as a `prototxt` file (already provided on disk). We're using SGD with momentum, weight decay, and a specific learning rate schedule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# The train/test net protocol buffer definition\r\n",
+      "train_net: \"mnist/lenet_auto_train.prototxt\"\r\n",
+      "test_net: \"mnist/lenet_auto_test.prototxt\"\r\n",
+      "# test_iter specifies how many forward passes the test should carry out.\r\n",
+      "# In the case of MNIST, we have test batch size 100 and 100 test iterations,\r\n",
+      "# covering the full 10,000 testing images.\r\n",
+      "test_iter: 100\r\n",
+      "# Carry out testing every 500 training iterations.\r\n",
+      "test_interval: 500\r\n",
+      "# The base learning rate, momentum and the weight decay of the network.\r\n",
+      "base_lr: 0.01\r\n",
+      "momentum: 0.9\r\n",
+      "weight_decay: 0.0005\r\n",
+      "# The learning rate policy\r\n",
+      "lr_policy: \"inv\"\r\n",
+      "gamma: 0.0001\r\n",
+      "power: 0.75\r\n",
+      "# Display every 100 iterations\r\n",
+      "display: 100\r\n",
+      "# The maximum number of iterations\r\n",
+      "max_iter: 10000\r\n",
+      "# snapshot intermediate results\r\n",
+      "snapshot: 5000\r\n",
+      "snapshot_prefix: \"mnist/lenet\"\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat mnist/lenet_auto_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Loading and checking the solver\n",
+    "\n",
+    "* Let's pick a device and load the solver. We'll use SGD (with momentum), but other methods (such as Adagrad and Nesterov's accelerated gradient) are also available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.SGDSolver('mnist/lenet_auto_solver.prototxt')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* To get an idea of the architecture of our net, we can check the dimensions of the intermediate features (blobs) and parameters (these will also be useful to refer to when manipulating data later)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('data', (64, 1, 28, 28)),\n",
+       " ('label', (64,)),\n",
+       " ('conv1', (64, 20, 24, 24)),\n",
+       " ('pool1', (64, 20, 12, 12)),\n",
+       " ('conv2', (64, 50, 8, 8)),\n",
+       " ('pool2', (64, 50, 4, 4)),\n",
+       " ('fc1', (64, 500)),\n",
+       " ('score', (64, 10)),\n",
+       " ('loss', ())]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# each output is (batch size, feature dim, spatial dim)\n",
+    "[(k, v.data.shape) for k, v in solver.net.blobs.items()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('conv1', (20, 1, 5, 5)),\n",
+       " ('conv2', (50, 20, 5, 5)),\n",
+       " ('fc1', (500, 800)),\n",
+       " ('score', (10, 500))]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# just print the weight sizes (we'll omit the biases)\n",
+    "[(k, v[0].data.shape) for k, v in solver.net.params.items()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Before taking off, let's check that everything is loaded as we expect. We'll run a forward pass on the train and test nets and check that they contain our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'loss': array(2.365971088409424, dtype=float32)}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "solver.net.forward()  # train net\n",
+    "solver.test_nets[0].forward()  # test net (there can be more than one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train labels: [ 5.  0.  4.  1.  9.  2.  1.  3.]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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QPKRCoKCggObmZgYHB5PeBEroW/1z4/fC4qy2tpacnJykjWtfC7ZSqcRsNlNT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\nSUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51a65ee690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# we use a little trick to tile the first eight images\n",
+    "imshow(solver.net.blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'train labels:', solver.net.blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test labels: [ 7.  2.  1.  0.  4.  1.  4.  9.]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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cmpqaOHPmDG63W5xS9br3wqEWbKEAob29nb//+7/H6XQSCASYnp7md7/73Qt7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\nRK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51a4030a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.test_nets[0].blobs['data'].data[:8, 0].transpose(1, 0, 2).reshape(28, 8*28), cmap='gray'); axis('off')\n",
+    "print 'test labels:', solver.test_nets[0].blobs['label'].data[:8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Stepping the solver\n",
+    "\n",
+    "Both train and test nets seem to be loading data, and to have correct labels.\n",
+    "\n",
+    "* Let's take one step of (minibatch) SGD and see what happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do we have gradients propagating through our filters? Let's see the updates to the first layer, shown here as a $4 \\times 5$ grid of $5 \\times 5$ filters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.5, 24.5, 19.5, -0.5)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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C6JbfmLGXV1nXnZ8fAZeSu0ZoCmZVB12Q3QJmDGrqpYD6Di23m+ki16NAFtd2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\nLtEpzAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519c0eab90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imshow(solver.net.params['conv1'][0].diff[:, 0].reshape(4, 5, 5, 5)\n",
+    "       .transpose(0, 2, 1, 3).reshape(4*5, 5*5), cmap='gray'); axis('off')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5. Writing a custom training loop\n",
+    "\n",
+    "Something is happening. Let's run the net for a while, keeping track of a few things as it goes.\n",
+    "Note that this process will be the same as if training through the `caffe` binary. In particular:\n",
+    "* logging will continue to happen as normal\n",
+    "* snapshots will be taken at the interval specified in the solver prototxt (here, every 5000 iterations)\n",
+    "* testing will happen at the interval specified (here, every 500 iterations)\n",
+    "\n",
+    "Since we have control of the loop in Python, we're free to compute additional things as we go, as we show below. We can do many other things as well, for example:\n",
+    "* write a custom stopping criterion\n",
+    "* change the solving process by updating the net in the loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "CPU times: user 12.6 s, sys: 2.4 s, total: 15 s\n",
+      "Wall time: 14.4 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "niter = 200\n",
+    "test_interval = 25\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+    "output = zeros((niter, 8, 10))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # store the output on the first test batch\n",
+    "    # (start the forward pass at conv1 to avoid loading new data)\n",
+    "    solver.test_nets[0].forward(start='conv1')\n",
+    "    output[it] = solver.test_nets[0].blobs['score'].data[:8]\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Let's plot the train loss and test accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f5199b33610>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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tVTHRhi302f4ujBpV6KY4juMUE41VdXe4oKofBgVME5HEAnpNRIbWqmlFwmHv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\nJGkQBpAkaRA3ABGGQ9Z+SfjXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519bf16690>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Test Accuracy: {:.2f}'.format(test_acc[-1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The loss seems to have dropped quickly and coverged (except for stochasticity), while the accuracy rose correspondingly. Hooray!\n",
+    "\n",
+    "* Since we saved the results on the first test batch, we can watch how our prediction scores evolved. We'll plot time on the $x$ axis and each possible label on the $y$, with lightness indicating confidence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199aaab50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199a4ba10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51999dd210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519994c650>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51998d0e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519984f250>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51997d3ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519bee6f50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519c0b0c10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519c0d5bd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51996ff910>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519969d110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199603550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199591d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51994fb990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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FCgAAoCESKgAAgIZIqAAAABoioQIAAGiIhAoAAKAhEioAAICGujKhevjhhzvdBDTwk5/8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\nhkioAAAAGiKhAgAAaOj/AYSDQCwV4p2TAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519948af90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(output[:50, i].T, interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We started with little idea about any of these digits, and ended up with correct classifications for each. If you've been following along, you'll see the last digit is the most difficult, a slanted \"9\" that's (understandably) most confused with \"4\".\n",
+    "\n",
+    "* Note that these are the \"raw\" output scores rather than the softmax-computed probability vectors. The latter, shown below, make it easier to see the confidence of our net (but harder to see the scores for less likely digits)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51991d5790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51990357d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198fc5750>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198f2af90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198ebf310>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198e2cfd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198dc0d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51994b6550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51994cc7d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199160590>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199196050>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f51991d5f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519c085f10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519998c390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f519c0f7b50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5199b2b090>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for i in range(8):\n",
+    "    figure(figsize=(2, 2))\n",
+    "    imshow(solver.test_nets[0].blobs['data'].data[i, 0], cmap='gray')\n",
+    "    figure(figsize=(10, 2))\n",
+    "    imshow(exp(output[:50, i].T) / exp(output[:50, i].T).sum(0), interpolation='nearest', cmap='gray')\n",
+    "    xlabel('iteration')\n",
+    "    ylabel('label')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Experiment with architecture and optimization\n",
+    "\n",
+    "Now that we've defined, trained, and tested LeNet there are many possible next steps:\n",
+    "\n",
+    "- Define new architectures for comparison\n",
+    "- Tune optimization by setting `base_lr` and the like or simply training longer\n",
+    "- Switching the solver type from `SGD` to an adaptive method like `AdaDelta` or `Adam`\n",
+    "\n",
+    "Feel free to explore these directions by editing the all-in-one example that follows.\n",
+    "Look for \"`EDIT HERE`\" comments for suggested choice points.\n",
+    "\n",
+    "By default this defines a simple linear classifier as a baseline.\n",
+    "\n",
+    "In case your coffee hasn't kicked in and you'd like inspiration, try out\n",
+    "\n",
+    "1. Switch the nonlinearity from `ReLU` to `ELU` or a saturing nonlinearity like `Sigmoid`\n",
+    "2. Stack more fully connected and nonlinear layers\n",
+    "3. Search over learning rate 10x at a time (trying `0.1` and `0.001`)\n",
+    "4. Switch the solver type to `Adam` (this adaptive solver type should be less sensitive to hyperparameters, but no guarantees...)\n",
+    "5. Solve for longer by setting `niter` higher (to 500 or 1,000 for instance) to better show training differences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 0 testing...\n",
+      "Iteration 25 testing...\n",
+      "Iteration 50 testing...\n",
+      "Iteration 75 testing...\n",
+      "Iteration 100 testing...\n",
+      "Iteration 125 testing...\n",
+      "Iteration 150 testing...\n",
+      "Iteration 175 testing...\n",
+      "Iteration 200 testing...\n",
+      "Iteration 225 testing...\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f5199af9f50>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ObSIiqKpElpsBC4ETgFXAVOA8VZ0f2Se1Qs3fVXVgMdoXxwV3LzAXKzInWALC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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5198f46a10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_net_path = 'mnist/custom_auto_train.prototxt'\n",
+    "test_net_path = 'mnist/custom_auto_test.prototxt'\n",
+    "solver_config_path = 'mnist/custom_auto_solver.prototxt'\n",
+    "\n",
+    "### define net\n",
+    "def custom_net(lmdb, batch_size):\n",
+    "    # define your own net!\n",
+    "    n = caffe.NetSpec()\n",
+    "    \n",
+    "    # keep this data layer for all networks\n",
+    "    n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb,\n",
+    "                             transform_param=dict(scale=1./255), ntop=2)\n",
+    "    \n",
+    "    # EDIT HERE to try different networks\n",
+    "    # this single layer defines a simple linear classifier\n",
+    "    # (in particular this defines a multiway logistic regression)\n",
+    "    n.score =   L.InnerProduct(n.data, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # EDIT HERE this is the LeNet variant we have already tried\n",
+    "    # n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, weight_filler=dict(type='xavier'))\n",
+    "    # n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)\n",
+    "    # n.fc1 =   L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'))\n",
+    "    # EDIT HERE consider L.ELU or L.Sigmoid for the nonlinearity\n",
+    "    # n.relu1 = L.ReLU(n.fc1, in_place=True)\n",
+    "    # n.score =   L.InnerProduct(n.fc1, num_output=10, weight_filler=dict(type='xavier'))\n",
+    "    \n",
+    "    # keep this loss layer for all networks\n",
+    "    n.loss =  L.SoftmaxWithLoss(n.score, n.label)\n",
+    "    \n",
+    "    return n.to_proto()\n",
+    "\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_train_lmdb', 64)))    \n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(custom_net('mnist/mnist_test_lmdb', 100)))\n",
+    "\n",
+    "### define solver\n",
+    "from caffe.proto import caffe_pb2\n",
+    "s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "# Set a seed for reproducible experiments:\n",
+    "# this controls for randomization in training.\n",
+    "s.random_seed = 0xCAFFE\n",
+    "\n",
+    "# Specify locations of the train and (maybe) test networks.\n",
+    "s.train_net = train_net_path\n",
+    "s.test_net.append(test_net_path)\n",
+    "s.test_interval = 500  # Test after every 500 training iterations.\n",
+    "s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "s.max_iter = 10000     # no. of times to update the net (training iterations)\n",
+    " \n",
+    "# EDIT HERE to try different solvers\n",
+    "# solver types include \"SGD\", \"Adam\", and \"Nesterov\" among others.\n",
+    "s.type = \"SGD\"\n",
+    "\n",
+    "# Set the initial learning rate for SGD.\n",
+    "s.base_lr = 0.01  # EDIT HERE to try different learning rates\n",
+    "# Set momentum to accelerate learning by\n",
+    "# taking weighted average of current and previous updates.\n",
+    "s.momentum = 0.9\n",
+    "# Set weight decay to regularize and prevent overfitting\n",
+    "s.weight_decay = 5e-4\n",
+    "\n",
+    "# Set `lr_policy` to define how the learning rate changes during training.\n",
+    "# This is the same policy as our default LeNet.\n",
+    "s.lr_policy = 'inv'\n",
+    "s.gamma = 0.0001\n",
+    "s.power = 0.75\n",
+    "# EDIT HERE to try the fixed rate (and compare with adaptive solvers)\n",
+    "# `fixed` is the simplest policy that keeps the learning rate constant.\n",
+    "# s.lr_policy = 'fixed'\n",
+    "\n",
+    "# Display the current training loss and accuracy every 1000 iterations.\n",
+    "s.display = 1000\n",
+    "\n",
+    "# Snapshots are files used to store networks we've trained.\n",
+    "# We'll snapshot every 5K iterations -- twice during training.\n",
+    "s.snapshot = 5000\n",
+    "s.snapshot_prefix = 'mnist/custom_net'\n",
+    "\n",
+    "# Train on the GPU\n",
+    "s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "\n",
+    "# Write the solver to a temporary file and return its filename.\n",
+    "with open(solver_config_path, 'w') as f:\n",
+    "    f.write(str(s))\n",
+    "\n",
+    "### load the solver and create train and test nets\n",
+    "solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)\n",
+    "solver = caffe.get_solver(solver_config_path)\n",
+    "\n",
+    "### solve\n",
+    "niter = 250  # EDIT HERE increase to train for longer\n",
+    "test_interval = niter / 10\n",
+    "# losses will also be stored in the log\n",
+    "train_loss = zeros(niter)\n",
+    "test_acc = zeros(int(np.ceil(niter / test_interval)))\n",
+    "\n",
+    "# the main solver loop\n",
+    "for it in range(niter):\n",
+    "    solver.step(1)  # SGD by Caffe\n",
+    "    \n",
+    "    # store the train loss\n",
+    "    train_loss[it] = solver.net.blobs['loss'].data\n",
+    "    \n",
+    "    # run a full test every so often\n",
+    "    # (Caffe can also do this for us and write to a log, but we show here\n",
+    "    #  how to do it directly in Python, where more complicated things are easier.)\n",
+    "    if it % test_interval == 0:\n",
+    "        print 'Iteration', it, 'testing...'\n",
+    "        correct = 0\n",
+    "        for test_it in range(100):\n",
+    "            solver.test_nets[0].forward()\n",
+    "            correct += sum(solver.test_nets[0].blobs['score'].data.argmax(1)\n",
+    "                           == solver.test_nets[0].blobs['label'].data)\n",
+    "        test_acc[it // test_interval] = correct / 1e4\n",
+    "\n",
+    "_, ax1 = subplots()\n",
+    "ax2 = ax1.twinx()\n",
+    "ax1.plot(arange(niter), train_loss)\n",
+    "ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')\n",
+    "ax1.set_xlabel('iteration')\n",
+    "ax1.set_ylabel('train loss')\n",
+    "ax2.set_ylabel('test accuracy')\n",
+    "ax2.set_title('Custom Test Accuracy: {:.2f}'.format(test_acc[-1]))"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Define, train, and test the classic LeNet with the Python interface.",
+  "example_name": "Learning LeNet",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/02-fine-tuning.ipynb b/examples/02-fine-tuning.ipynb
new file mode 100644
index 00000000000..07ca8df4d74
--- /dev/null
+++ b/examples/02-fine-tuning.ipynb
@@ -0,0 +1,1175 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fine-tuning a Pretrained Network for Style Recognition\n",
+    "\n",
+    "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\n",
+    "\n",
+    "The advantage of this approach is that, since pre-trained networks are learned on a large set of images, the intermediate layers capture the \"semantics\" of the general visual appearance. Think of it as a very powerful generic visual feature that you can treat as a black box. On top of that, only a relatively small amount of data is needed for good performance on the target task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will need to prepare the data. This involves the following parts:\n",
+    "(1) Get the ImageNet ilsvrc pretrained model with the provided shell scripts.\n",
+    "(2) Download a subset of the overall Flickr style dataset for this demo.\n",
+    "(3) Compile the downloaded Flickr dataset into a database that Caffe can then consume."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "import caffe\n",
+    "\n",
+    "caffe.set_device(0)\n",
+    "caffe.set_mode_gpu()\n",
+    "\n",
+    "import numpy as np\n",
+    "from pylab import *\n",
+    "%matplotlib inline\n",
+    "import tempfile\n",
+    "\n",
+    "# Helper function for deprocessing preprocessed images, e.g., for display.\n",
+    "def deprocess_net_image(image):\n",
+    "    image = image.copy()              # don't modify destructively\n",
+    "    image = image[::-1]               # BGR -> RGB\n",
+    "    image = image.transpose(1, 2, 0)  # CHW -> HWC\n",
+    "    image += [123, 117, 104]          # (approximately) undo mean subtraction\n",
+    "\n",
+    "    # clamp values in [0, 255]\n",
+    "    image[image < 0], image[image > 255] = 0, 255\n",
+    "\n",
+    "    # round and cast from float32 to uint8\n",
+    "    image = np.round(image)\n",
+    "    image = np.require(image, dtype=np.uint8)\n",
+    "\n",
+    "    return image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Setup and dataset download\n",
+    "\n",
+    "Download data required for this exercise.\n",
+    "\n",
+    "- `get_ilsvrc_aux.sh` to download the ImageNet data mean, labels, etc.\n",
+    "- `download_model_binary.py` to download the pretrained reference model\n",
+    "- `finetune_flickr_style/assemble_data.py` downloadsd the style training and testing data\n",
+    "\n",
+    "We'll download just a small subset of the full dataset for this exercise: just 2000 of the 80K images, from 5 of the 20 style categories.  (To download the full dataset, set `full_dataset = True` in the cell below.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "--2016-02-24 00:28:36--  http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz\n",
+      "Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)... 169.229.222.251\n",
+      "Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 17858008 (17M) [application/octet-stream]\n",
+      "Saving to: ‘caffe_ilsvrc12.tar.gz’\n",
+      "\n",
+      "100%[======================================>] 17,858,008   112MB/s   in 0.2s   \n",
+      "\n",
+      "2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]\n",
+      "\n",
+      "Unzipping...\n",
+      "Done.\n",
+      "Model already exists.\n",
+      "Downloading 2000 images with 7 workers...\n",
+      "Writing train/val for 1996 successfully downloaded images.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download just a small subset of the data for this exercise.\n",
+    "# (2000 of 80K images, 5 of 20 labels.)\n",
+    "# To download the entire dataset, set `full_dataset = True`.\n",
+    "full_dataset = False\n",
+    "if full_dataset:\n",
+    "    NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1\n",
+    "else:\n",
+    "    NUM_STYLE_IMAGES = 2000\n",
+    "    NUM_STYLE_LABELS = 5\n",
+    "\n",
+    "# This downloads the ilsvrc auxiliary data (mean file, etc),\n",
+    "# and a subset of 2000 images for the style recognition task.\n",
+    "import os\n",
+    "os.chdir(caffe_root)  # run scripts from caffe root\n",
+    "!data/ilsvrc12/get_ilsvrc_aux.sh\n",
+    "!scripts/download_model_binary.py models/bvlc_reference_caffenet\n",
+    "!python examples/finetune_flickr_style/assemble_data.py \\\n",
+    "    --workers=-1  --seed=1701 \\\n",
+    "    --images=$NUM_STYLE_IMAGES  --label=$NUM_STYLE_LABELS\n",
+    "# back to examples\n",
+    "os.chdir('examples')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define `weights`, the path to the ImageNet pretrained weights we just downloaded, and make sure it exists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
+    "assert os.path.exists(weights)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load the 1000 ImageNet labels from `ilsvrc12/synset_words.txt`, and the 5 style labels from `finetune_flickr_style/style_names.txt`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded ImageNet labels:\n",
+      "n01440764 tench, Tinca tinca\n",
+      "n01443537 goldfish, Carassius auratus\n",
+      "n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\n",
+      "n01491361 tiger shark, Galeocerdo cuvieri\n",
+      "n01494475 hammerhead, hammerhead shark\n",
+      "n01496331 electric ray, crampfish, numbfish, torpedo\n",
+      "n01498041 stingray\n",
+      "n01514668 cock\n",
+      "n01514859 hen\n",
+      "n01518878 ostrich, Struthio camelus\n",
+      "...\n",
+      "\n",
+      "Loaded style labels:\n",
+      "Detailed, Pastel, Melancholy, Noir, HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load ImageNet labels to imagenet_labels\n",
+    "imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
+    "imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\\t'))\n",
+    "assert len(imagenet_labels) == 1000\n",
+    "print 'Loaded ImageNet labels:\\n', '\\n'.join(imagenet_labels[:10] + ['...'])\n",
+    "\n",
+    "# Load style labels to style_labels\n",
+    "style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'\n",
+    "style_labels = list(np.loadtxt(style_label_file, str, delimiter='\\n'))\n",
+    "if NUM_STYLE_LABELS > 0:\n",
+    "    style_labels = style_labels[:NUM_STYLE_LABELS]\n",
+    "print '\\nLoaded style labels:\\n', ', '.join(style_labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.  Defining and running the nets\n",
+    "\n",
+    "We'll start by defining `caffenet`, a function which initializes the *CaffeNet* architecture (a minor variant on *AlexNet*), taking arguments specifying the data and number of output classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "weight_param = dict(lr_mult=1, decay_mult=1)\n",
+    "bias_param   = dict(lr_mult=2, decay_mult=0)\n",
+    "learned_param = [weight_param, bias_param]\n",
+    "\n",
+    "frozen_param = [dict(lr_mult=0)] * 2\n",
+    "\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1,\n",
+    "              param=learned_param,\n",
+    "              weight_filler=dict(type='gaussian', std=0.01),\n",
+    "              bias_filler=dict(type='constant', value=0.1)):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                         num_output=nout, pad=pad, group=group,\n",
+    "                         param=param, weight_filler=weight_filler,\n",
+    "                         bias_filler=bias_filler)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "def fc_relu(bottom, nout, param=learned_param,\n",
+    "            weight_filler=dict(type='gaussian', std=0.005),\n",
+    "            bias_filler=dict(type='constant', value=0.1)):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout, param=param,\n",
+    "                        weight_filler=weight_filler,\n",
+    "                        bias_filler=bias_filler)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "def caffenet(data, label=None, train=True, num_classes=1000,\n",
+    "             classifier_name='fc8', learn_all=False):\n",
+    "    \"\"\"Returns a NetSpec specifying CaffeNet, following the original proto text\n",
+    "       specification (./models/bvlc_reference_caffenet/train_val.prototxt).\"\"\"\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data = data\n",
+    "    param = learned_param if learn_all else frozen_param\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)\n",
+    "    else:\n",
+    "        fc7input = n.relu6\n",
+    "    n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)\n",
+    "    if train:\n",
+    "        n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)\n",
+    "    else:\n",
+    "        fc8input = n.relu7\n",
+    "    # always learn fc8 (param=learned_param)\n",
+    "    fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)\n",
+    "    # give fc8 the name specified by argument `classifier_name`\n",
+    "    n.__setattr__(classifier_name, fc8)\n",
+    "    if not train:\n",
+    "        n.probs = L.Softmax(fc8)\n",
+    "    if label is not None:\n",
+    "        n.label = label\n",
+    "        n.loss = L.SoftmaxWithLoss(fc8, n.label)\n",
+    "        n.acc = L.Accuracy(fc8, n.label)\n",
+    "    # write the net to a temporary file and return its filename\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(n.to_proto()))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's create a *CaffeNet* that takes unlabeled \"dummy data\" as input, allowing us to set its input images externally and see what ImageNet classes it predicts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))\n",
+    "imagenet_net_filename = caffenet(data=dummy_data, train=False)\n",
+    "imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function `style_net` which calls `caffenet` on data from the Flickr style dataset.\n",
+    "\n",
+    "The new network will also have the *CaffeNet* architecture, with differences in the input and output:\n",
+    "\n",
+    "- the input is the Flickr style data we downloaded, provided by an `ImageData` layer\n",
+    "- the output is a distribution over 20 classes rather than the original 1000 ImageNet classes\n",
+    "- the classification layer is renamed from `fc8` to `fc8_flickr` to tell Caffe not to load the original classifier (`fc8`) weights from the ImageNet-pretrained model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def style_net(train=True, learn_all=False, subset=None):\n",
+    "    if subset is None:\n",
+    "        subset = 'train' if train else 'test'\n",
+    "    source = caffe_root + 'data/flickr_style/%s.txt' % subset\n",
+    "    transform_param = dict(mirror=train, crop_size=227,\n",
+    "        mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')\n",
+    "    style_data, style_label = L.ImageData(\n",
+    "        transform_param=transform_param, source=source,\n",
+    "        batch_size=50, new_height=256, new_width=256, ntop=2)\n",
+    "    return caffenet(data=style_data, label=style_label, train=train,\n",
+    "                    num_classes=NUM_STYLE_LABELS,\n",
+    "                    classifier_name='fc8_flickr',\n",
+    "                    learn_all=learn_all)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `style_net` function defined above to initialize `untrained_style_net`, a *CaffeNet* with input images from the style dataset and weights from the pretrained ImageNet model.\n",
+    "\n",
+    "\n",
+    "Call `forward` on `untrained_style_net` to get a batch of style training data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "untrained_style_net = caffe.Net(style_net(train=False, subset='train'),\n",
+    "                                weights, caffe.TEST)\n",
+    "untrained_style_net.forward()\n",
+    "style_data_batch = untrained_style_net.blobs['data'].data.copy()\n",
+    "style_label_batch = np.array(untrained_style_net.blobs['label'].data, dtype=np.int32)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pick one of the style net training images from the batch of 50 (we'll arbitrarily choose #8 here).  Display it, then run it through `imagenet_net`, the ImageNet-pretrained network to view its top 5 predicted classes from the 1000 ImageNet classes.\n",
+    "\n",
+    "Below we chose an image where the network's predictions happen to be reasonable, as the image is of a beach, and \"sandbar\" and \"seashore\" both happen to be ImageNet-1000 categories.  For other images, the predictions won't be this good, sometimes due to the network actually failing to recognize the object(s) present in the image, but perhaps even more often due to the fact that not all images contain an object from the (somewhat arbitrarily chosen) 1000 ImageNet categories. Modify the `batch_index` variable by changing its default setting of 8 to another value from 0-49 (since the batch size is 50) to see predictions for other images in the batch.  (To go beyond this batch of 50 images, first rerun the *above* cell to load a fresh batch of data into `style_net`.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def disp_preds(net, image, labels, k=5, name='ImageNet'):\n",
+    "    input_blob = net.blobs['data']\n",
+    "    net.blobs['data'].data[0, ...] = image\n",
+    "    probs = net.forward(start='conv1')['probs'][0]\n",
+    "    top_k = (-probs).argsort()[:k]\n",
+    "    print 'top %d predicted %s labels =' % (k, name)\n",
+    "    print '\\n'.join('\\t(%d) %5.2f%% %s' % (i+1, 100*probs[p], labels[p])\n",
+    "                    for i, p in enumerate(top_k))\n",
+    "\n",
+    "def disp_imagenet_preds(net, image):\n",
+    "    disp_preds(net, image, imagenet_labels, name='ImageNet')\n",
+    "\n",
+    "def disp_style_preds(net, image):\n",
+    "    disp_preds(net, image, style_labels, name='style')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Melancholy\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K\nXgeZAe33aKYvR2IdTeigmbP1JOo9DWnhRCfq9gN9t7T7M2Wwry9fyqgTAAxWi5KN+oL8uSkq32g2\n2qdUlFa0ZdBgXq1RPe7aviIYv6/FivJHwgyxUgkMa1OJqh/Y1xsUHuQrUfF9fOzd4+WEgcaLlyRk\nCw8DLOjE2R9G0F8Vc0ATjEsrRGvq5wRHIqxy5sr826+xaeUiuGdCAZLg/R0JlPzEBh+5SS44i3bE\n5alb04mllqPutSMTC12jwWsYoEIqyVJ16DWgPr+6bVDruq2+wC3KLyAkU8qN59VIt3Q4otfKUMlH\ne7p2ayHDUHg3AA2GbeWZam8innnD65bLb5Ol5NsK33nztYK0ne+0oN1dcb+GBEKrkyj00d/F3Sfy\nzwb8IS0fCmPzsyUINn3zoaNuLwrdAOgci9j6aEZsjZPEI1YC9y4pa/v+P8nxosIgx0pliaavbg5i\n7L1voIZj+KrhE5l7KUdBVYASAmLOKEKUeKx4vgFy8qHg+Q6rokNiG3EnAW0aWlCtuAf1HZFu5Tds\n6KFFA0ohmCnMyutL66+6iWQq3+W8Aetdma/tnxrrZRIyjKlvjDDLG95j1dTf36A5DPaOQ9LRWr0E\n491NZKuuEjKhceLaA2Ubosy1d+gEPLFs9T41NCMRcLoQtj0roZzY4l449H62Pd2vtyzp7wgRKOG4\nfFilKOwe3ErY2LoTBoVRTJ+jUOPlSrlE74/pPV5jKZ+iPIR51+/4Bug/UrOdrztetrnJ6nRjxzYn\n0VNAGAIaaFkf5KbISQRPiKiOt0ZCvvd2S0N3/BuS4lZb2fdWQgWFXADQ/t0+p/r5ENtTuwYFkdOM\nWQ1LA6jpuolEOukKupS0pOVmKgRZjrylZ3tmEoUQDZeKgqrkIdFECyej8JGjEJDQKuIzVTF2D8FQ\ndiS6xuC5BhbprZ1Ao+1f3T+x84YseA9WRkofsTFPiJmVqbu2Nba73IZd277/sLqmbG1d/Lm2z4Ku\nrnywXRhYz0oINeixjij0tfqcdV9EQHo/3+3ag0Y8cl3vK2N4nkQQ9xIIeO62ec/xYsJgrYlhCxYO\n85GETyZ0RBKyZofJJufDOOi4cjqzdm1qu1ZGES1CGr1RwHNdcScoVOPATRAziUAkrVOza/wZCcKc\neQPMON/uqZAKIV7VN1gSsZqVlJlBalcoMamtCagiKxB7NxE4kQCsQ4/pPO2EfEPnHOi6fQ6hkzQH\nxnA6tEqsFRwu7bOtazJFvq9OwxAy2JxntQ7FsPfnKYm932MJj43pdc+1dNGvbszxrmDYBYGYPbbL\nag93kU1TdbUAK6LQ+4ESLvdxpB26B7Lzks6zCqXUagg2FHkyc9IkJHg+kdYmvOrHZj586HjBTkcL\ny9kdObJJRBbLeNXPI7y65YStEqphnibGys8Xk5cUbwIpgQL91Dn4azQKILd0iah5E4QEQQGVThqp\njk3Q+jc03M0S4ys6ny2kR7wx45aVuRHvHWzs5iYB5jmYWpA3GqxOyfViMM07W5XwUfNY2MqZ7S4X\noDU8gLlqYG5Er1lB/xIOfBzo9S0fkQRsIR2aTKjU51jU9NvF28nY64RNIMQqjkQFCTcNX4xWt9Y0\nsh/vs+9Dm77QVordf8L6121B785cX7o/fQurfjNKqd2v6bbG2MwbtGKT41RLJCGhYTI/xaHFiYiR\nsHWt7GsnFQoyucnO3kJzYYB39VuhAhg2lhTL9TYIVQgii9BatBNG0A4MAJioEmkAq31xefDaHt7J\nUAVfrO4vbMteDjnbUmuvkINRSE5+iPxb+QROoeHqxOStTetZTOgHbVNCNBbsAXAUgzbH8R7MMLGy\nyxFSQzrPn2VeGcHYaJsJYrp2kWpeN2R+yOzhPpGoK1EG2QtgWML7BH1e12hZ2WW8u0DYzToxRa+x\nTEadqKNGJUyKKoWkorRF0dn2uTbq4pnw2P94V7tLmBepI4Wq4oIrtkjKzvASbPy9GF+v9oMXAgiL\n7qzMtVeG4oeOFxMGt8cF9wVn1Z2PjCbUrEIW4gCBOBdweAkDPfvwJj+EYblhxNZAY7PB0rkQRawB\nAQZdkHqbi9ouBRG0VexXW17mgL5OyA/6EoKtcNw7tGRkc+M92SYc5Gu39IYRdaRvpLSfdc6FnEYd\nkbk3E6xc/yCRrFJ/XSlH5AGjf2Yj9ogaoa6AmFKtBeIrmQkSTsoyBNREMV+PQlZabqEWCQqxZpQW\nEwPUO3cpYvux/yXYLiHotr/XG1jUsPkh8n2i0BIIfYJdO+u6YvQSSM8QSHn5674MBSsR9CHl9bSn\nuxbfTQqlKevZYsVGI0IU2zkCuJ03jFFZDh88XkwY/Lk/NvDlZ2+x3n6OHz0NfHn9GOenP8Enn7/B\nd37++/j8Rz/GH/oDP4PP5sAnXz7C5xVxGTjxEQ4E1jJMm/AL4HEAkSPBzQYcJyKcjTCdzHJDUpyx\nWKiltOgje4SsdJAttr8mweZXcxMV407UwtCmtGksRHhCcpAw1qJ3PorhcjOzKnNVXoXDyfSyB9uO\ntSLEIh7IMRlAoacc+BqRcWnlY8damE8nHo5rNluNZGxlWsoPEJWnQIYvc6CbjlS1ZXBK8Za45ZFm\nXjk32TzR2O1DFpvsaScSsAhMBIankJu7T8ENsRKvhBLVINJecKVUwxBuvIdVgmBVaTsbohjXBlaC\no0al8X7uGBAtqNCgodBJVoOimLs0ic7BH2vpDyEToZMKit6JuYr4tNxIG8qy0UxCx7zeiknJnApz\nzYXwwOeffobzPPH28RG3m4bfvf94MWHwx78NfPLqAb/zj38b9ru/gz/75/5F4Hc+xyfnd/Dqo0fE\nH/0GXn/0Cj/+wY/x9P3XePvmht//c9/C3/1/foLf/vFneP1wwF59D//4d38XX/z4d/FLv/hH8Y8+\nd+B4wMUdH10W/PBkTDMsGxjHwDDHOSfWPGGxMM0RbrA4U/MMB2wgYmG6HIUE7NpMo0BIRweWsAY3\ny86FOLKEOAl/YoUnobOTzjCHhSM8m2qZGWK2XShveAr3CcwMs4apbFXMwoYg7O8gxiyEsUFHm4GY\n2UhDxk9ezjfB6FhrYgI4RqMpnT8VZ0dVAsQ5kTA/rIuLsj4CaQ4VSusQa1hgLpoEnnsVU4I3qJFV\ny5Hm5AKwhuHAwBk5in5Glve6EGIA7oMMTnZekzkcAzmZMg8NiI1YOMYF5o7sl8KZCGNQCAXLpY0d\nlqxQIGxlyjSlPLMVEGtx7qLX58X26mGY58r7nGsi1sI6W7OvWHi63bhWC/N2w/n4BIfhnHndc07M\neSIW8MPf+QHmWljnic8//wKffvIJvvXt7+DX/t7f/1qefDFhcLwaOP/+38Uf+tlv4ydvP8XH1wPz\nYeDy3W/gkx9+gu///PcQBnz56sDP/v7v4PqN78Ke3uBPnobx5kf403/mz+C7Dx/jGL+AH/7W38ff\n/bXfwL/yz/8Z/No/+CF+4zPg6bO3+OTpAW/OwHH7DNcx8Plnn8Jvn+LbP/MHML/5s8DlFV49DPgx\ncJ7AeTOcYTjjEcMmcA4ACwNRQzzNDZOowN0Ra2JMMsXhiHXLOQS3gRWOsCxDdkw8+SoBcVr2DjiW\npaaXoGc4Mk5qzaUpTI7TTjZ4sjSdLAWFD0Ow1gMAECfDq8ncHobb7YZ5OzEuEzbsLt8/sDDGwLAk\nsDEOXM1xu03YccB8YK18SBG2jlKgljkNFpM9RhIFLGOINYKzMZ0a2xJtRGD5wjonLscB6ca1FswH\njuOKwdbLcS6c54SNQf+JENPE4QdGQrTU1hZwPxIxeZ7rqoS1i2POBbPAeTtxuz3hAsOnP/5dwAwf\nPbzG4/mEx8dHfPLpp7hcrzjnicenRzw+PuKb3/wYWIE5J87bDWsmOrleLni4PlTG5xeff4HzPBED\nePP0iMfHJzy+fYtzLpy3ZODb7Ybb+YTb7YZzTUTkWlikcGCFNmKeNDDp+4GlA3qlOaqR7BMpILIN\nfa7vb/yDf4jLOHDOu6bq7xz2T5qd9P/lYWbxf/5XfxHrt34Lf+8f/xi/9Au/D69/8ZfxxQ9+A5eP\nvptE/vYN5u0Njm99G1/85m/i+s3v4dW3v4llwNPMmcOXAC4ffRtPP/4JPn/7Br/v+7+AmDdcXr/C\n0wp8/sUNuHyMH/34x/jBb/5D/Ny3PsKPzoEf/ujH+PxHn+Pt6fgMA5/dvsQVjp/71nfg14/w9vgI\n9vHPAjZgthDjgCNRRTCHIb33KfXLmZUrij17cU76FoZlqS2TYBQhXGHsCsRMMxsZ2pIT0Tg7YEuA\nGrQRxxh00qkyDrg93XC73fDxxx8lIQI4jgNPT09Ya+HVwwWPb9/kRizg+nDB649e49PPPsPT0yO+\n9Y1v4ic/+QRffvklvve97+GLLz7HmideXa84joFzZcvueU6M4YgznazH9cBxOXB5uMDHYLJY7TeA\nYLdjCQPQPPL6zJsvv8QYDh+eCCUmPvv0c2AFnp6e8M3vfBsP1wfMpyc8rRMeOXfRYZjnxFoLxziy\nKen5NsfULwBzYs6JH/zwh3Az3M5bJU/M88Tt6cTrywVffvkGYcAxRkZI1kxGHwOxggNwJnV7miVr\nzuyGZMBtnjhXdtMqB7ZlFyqcE09zIdgSXk7DtC+yacuiYE6nZ4ZU1lrlezmuSYM59DYAIx0cF1wv\nF8CAy/VaFaduhlevHtKUPQ6c5w3/2V/9q4jdCbIdL4YMhj3g4Q/9Ev6X//5v40/+yi/i+tG38dnT\nb+L1L/4M3K64nW9g5xMiHJ/77+A7v/AHgZiAH3g4Jl5h4OnNp3jz9gt88vQGv/D9n8Htzad4G1mo\ndFxe47vf+Q7cA09fOn7XA9+6DvzyH/9l+PUV/sb/8bfx9/7O/40/+8/+Cr77/T+JWAPnm0/w9u3E\nr/+jH2M6EOf5/zL3JrG2rul91+9tv241uz/dPbetulW3XI4dYzsJ2HLkAJEIAcEEMUEIIiEkmgES\nEp4AQbKY4AEEDAGMjBCZMQiKIqMYhGKiBNlO7LKd8vWtus1p99ndar/mbRm86xxbcbksYUXlNdp7\n7aW9dvO9z/c8/+6h309EMyN5z24c8UB0EwLY+4gPgeg9MZa7QRICg0QaSY6etm0Y3ISWGp/TwUsA\nWisiGS0llTFvxpEAZd7LRWvhXrfkh7EgxgxKEWMsi1xDKIItpchCMB06AGsMKQRiBiElwQekKnoO\nn0KZmbNAa41RGu88kNFKFmWnFDzVhhQ8gkPhIRNeKwoPF3vMiXRoh18PuK/NOeX1h7vRoUC+no3l\nQTvxeoyRooxbSmtS8uVnU2WkE5SlKiE6jDHU0uCCRyvF5CNKSkIIVHWFEZJ+6mmrCh8Sg480Sh3G\ntbKoNAJKSVI6RJ8JyTYFUkhA2V0olX7T/8R8WHiaMzEGUiy7HVM86GEOVS+lgtdoY0hSEl8zNFEQ\nsiQU1xVGGbQyGKuZtS1tVb+50RijqawphdXYgv8cxpWqMuVuf8B0Qopoqd6E0SIOCt7DeKhU+bsI\nMlmVsee7Pb5nxWB49ozt9S25v+Fbnz3nq0eP2PmR4wC2rcmDp799iT4+5+jRBXRzpNGoLEn9HWG/\nIynF6YN3OX0rM7meKLa0yzP8bo9VCWESQlVoLZA5sNlfc9SfcPdkzdOPP+b87Iy3Ls7IMvHxt36H\nrDt+4Af/BG/fWzGGAd/OWV/dwDTSXTzA9yOdUqjs6Y6W0B3x2RcvMHXNZrOmlpqsBUJZnj+9xMVE\nf/WSxcNTnlzegFT0biInWG32+JC4dv4wb4PVhpAKbqGkQGtFrSxScbiYBTlEtDEHhFgXY1FKRDcS\nMkw+IIUkpnSQFicUUBtTZveYaIREGnXIEcxIEVCyHCprFS5kQgj4yZW8gwzSB4Q6aBQSKKnegJdG\nCaxSKMThzsbvdgEcItikJPpSwF6zHLW2JCDkxDA4tpPn1e0NTVVRac3QO2TO1N2c1c0Nb791n3Hq\nebVe0diKm82W2lY4PzGfzfntT7/N6WyJPb3g5vaOKo58/Uvv8ve++W20Mex2Oxe91DAAACAASURB\nVGazlpwy4zQWwBaBpeBJUoBVhpQSe7cnA7aqCTnRtQ1t02KMBV0xuohSitOTE5RSSG3wEYRSaFMd\nAMXCs8SU0FVVsIdUnKRSgTbqDRgqpCQecIIYI947pnhgf1JGxkydC7gdYiKkUK6LlFCHoj668Q3o\nmmIAEbG2wg0TWhuE/O5TwB9aDIQQPwf8BeBVzvn7D8/9J8BfAq4OL/upnPPfPHztPwL+DQot/e/l\nnP+P7/R9vQGn4K3332M5q9mNG87PH3H77Anze/cQMSGswTYGMXtIGPdoNcOPG/xqzae/9ssslkse\nfigYtaGuOupuVgI5KsV4d4dbeUTO2AxvXRwxaxtUO4NXN3ztQcujr7xfLlwluWLGTFXMK8EqNlTU\n2PUldQOirkA4wvGCtL/lsyfP0M8+453332OR9gzXE48vzlicnBNC5uXzp/ypr5+jguAfPI388A9+\nP7/8q7/JWWOZHx1RK43panY+4dYropDskuLF50+xdcV2c8cew7TdstlPVPMljRH0Y+TzTz4h1zOO\nqo4+Dmy2I4vlEqkybj+Uu2sWhChRShIFhCgY8ghkFImYwfmElqq8Zkokmcq6cRfIKRJjKu0ooIQq\nzEzwCAExCWIqnYRRBZvoEQeZNIQUSDm/0UTEDDHB5D2zRUelLMWhFHHeM2XJbpre9BOTi8zaFjeN\npGSJ2xv+0r/8J/hv/sbHfG1RI6Pn1Try7vuPWV/d0hhLZwxfffw2Q0osGktQxwQEKyf46ocfYYzG\n2gptLFLpMopIRQiZJAVaKJTSUBlETLgMQ8wg1Rt8KBww45ihPbAF4wEfEIhD2KxkjBEEVNoUufZh\nxUwZBws2o9VhneCBOQoxlk3UUiGVwhpLjpEQC77io8fnREgRqSVWVKWj0kCIxJypbIPWihTDAXtK\nGKVo6obsSuf0RyoGwP8E/FfA//x7nsvAz+Scf+YfKRxfA/4V4GvAI+BvCSE+zL93/cvhYZtztnef\n80989AhlGqRpiPstzcmC/e013ekFup4z3K6Rbc32xSvmbz1k3K1oZyfc//LXqKsKUVuW7QVJC6bN\nHT71KKmxixPSfkdKmfmspT7s6Ms5M79/ga0lTTMvCLDIfHR/zqxp0ELQdR05TThvqdtTwjCR8Gyu\nntF2Le998D6by5f4/Y7b2zsUAltbNs+/hR/2jJNk+eF7vPjN3+JPf/0D6nmH8Y6TL7+HdD03zz/l\n/OKck9PH+NwjbMMH5w/4QK2YffD9XH3+CUeP3mZ69hmcnDGfXzDisVnwy7+Q+PDHfpRn3/wmTdvw\nySfP+Yl/9ifZ3N7xzW89Z9KW66sVn1yueP7ihhgcCy3Z+0TICiUjUgis1vgM292ASB6XBY2psGog\nSE0UGuEjj09aksyMUyYkDWQ6W9iHWVMzs6UFv96P7ENBwk0UtFZz3FScdDPuhom7wWGE4GTRIY3G\nasMQAxKFy+Wg1UajtUVri38NL5gaKxTPneQnfuKC1gi+LCsygtYYvAFQzGxVaE4pCKEc1IhAS8EU\nEolIipnJx0OUYpnzFQc6kQxaE2KiMpJTW2OlQMjyWill2aIUf1e0JIXA6AIyp5QIh7EjhlykClIQ\nU8THRIjpDdMQU8EevPO8VgjGA64AkGJEaoUPnkpbXAgFq0gF3I2h0OQxlf+l874UE6VIORK9P5Be\nBRgmRuqu5na1+qMVg5zz3xZCvPsdvvSdBpB/EfhrOWcPfCaE+AT4UeDv/r5isL+ld7e8U11g5jVh\nvCYJGFc7ZkcPGTcrop/oFh0oxfLslBglFZpZTmRtcVkxRonf3mJqhd/dopf34VD9VZzo2gXbm5cM\nw552PmdmKrLUKN3gNyvu7m4wWnFqE7OjJbvbl+R2gakM+9sbbDJUdc243nNydoQ0muQF9x484PrF\nF7z1/rtUdc2w2aN1jTltaI3ld37r1zBuS57eZnu15cN3HqC1REbopMXoDrKjao7wRqK05rNXK77v\nKxXKVEhR8ennX/B9b3+NHDzn58esVzf88J/7p9lvnoMQvP2lr/Lgg6/gx8i8a/jxH/9TDDfPUF9/\nj8FvaWb3eHLb89knn/KtLy759OUdc6V5uOw4nneMIbNOGaU1C62pBHSNobKaoe8xKjOzmkYXDGu2\naLFVw27nud1NDAFQFUMSvJUSY/C4UDKs9j6wi/CF1qTZ64xGyQshEbpgJEooKqvQRmGUxihByJmY\nE1praqmo2gorE0lrHqlIdBmXCx3Xx0DCEKYRFwWjC1TWghQEH0BJYoosqopaV1SNxWgDIqG1REuB\nd46mqgjRE3xACYXziU0/ECX03qFQjH5k8h6fElpXSKkYXzMELkAGHxxZFCGdPBwtgcRUmhg93rs3\nxyb4wDQ5hBR0bUs8FAIlJSGmN4t4D1AAQkBwnrZpGN3IOEzMFnOmaaSpa/zk3ojb2rah3+2BiDWW\nfnKYAJth+qMVg+/y+HeFEP8a8MvAf5BzXgEP/5GD/5TSIfy+h3nwgA/DD6AXHWG/wSzvo0jo5QXT\n+hlaWezRKcN2RfYJKTTGOGgtdzfPC4UXJciAPLmPHwI+CdL6CjNboExLXS1JUTE/vsdicQRoJrdD\n6oq6m2OOj3nwtR9i3G+Z+lumzTXCdixPH5CGFRfvfYWgGoTzLL/8FjFl/LAnKYX0E2dKYpoapgnP\nSF03CN2iz464/Px3OH38IaK7oIobxHGHjBF5fIJJmuxX6PoRcbih7S5QUmH9xM03f5nZ8hwpI2eP\nHkLec/fiM+h+gMl58s4xjYlHb71Ne3QfkeCOK3JWbHZrpK2R7QwGRZCSr3z4Pl//+kdM+x37/Z6b\n2y1Pnz4Dn8jRE4aRYYyMAXYBXqwmYGJImdvBsRknhixxUjEFR5I7ktGkrCg34B0iOkIsCcXSGIyx\ndKZi2VRUHkKOxJzwIRKcQ4Y9ulKkULQdPhwCWI0ixsTMViitSFMELXBR4X1PazTaGIwwaC1R2mNp\nySqQtcZPA3VlwSekKqBpyBkRCw6TFYzDSFYKoyTTNGKsZbV3dLYqLIBW+Dwx9RNWWWpTEQ7AoxIS\npRRaj7zWP1RKE0JkdA6jLTkopFGYxhTwL2V2ux0CMMYSQkRrxWzesFhKnHMopRjTWChBpYhuKl0v\niRACCE0/7NFaMXiHQTFvWoZ+wBiDzNB2DcF5vHO4yTGOA/NZxzRNpBC4vb3FKPOPpRj8LPCXDx//\nZ8B/Afybf8BrvyNqoRP0+1tsu6R3GXv7kuN3voxWFhcW3N3doaZrbK0gJCqbyCkT/ETo1+ijc6bL\nZ0yra47PHiCbChUU/u6OanFKTBMxAyGSDnE+4+6GED1aTYg6kL3EeU/KmpwlQlfkBP12dbArK/y0\nJ417YizUnHcjzbxjdfkc2S6QMTO9ekGWjnR8gYged3vFxfk9js7uMa6fs98PLOcLgl+RfGRmBauX\nV+TmFKk17sUX5PMHBCIxG67u9rD6hPb8bb74xjdoTu4T+gli5jd+9Zf4YgN/8Z/7SbbrnuB36GpB\n9BOiNlhlCWGP3+7wfiJs95jZcUlKFpKj5RGL2YxpHPGTY70b+LWna/6vT9d8sRqKCEiWO5ShRmmB\n0BktDaYyGAFaS4zR5BhJUZLRRdiVEjJltBJERpLzOCGIMaGUoZYC0VaEIIghYKxB5kxnK+KhBVay\noPwiS2RtkEZDv+do3jBkydjvmS0NPkV8PzJqSaUl0zRgpWIYyjo0ETIyJ3yCTKJShqkfaOqOEDNW\naoQpY8+9hSVOHh89Rham6/h4SXATk3d01pY7c4yknImuMApKSQZZPAVaV0BCKUjesx1HEhllVNEe\nxMgwDOSc8V4wTQUf8aEAt1VVE2LEuYnK1rgYidFjlUGksgtDC0UIkXQoSlVVIcgE78kH9ZQypci2\nTYN3vjAQTYMfJ3z8x6BAzDm/ev2xEOJ/AP73w6fPgMe/56VvHZ77fY+/8r/9n7jtHVl8xp9874wf\n/6GvYm3LfhpI45b54hjZdbjNFTNVE+Yz8JJ5u2Q/DNjjR+R+IHvPTErM8T18pdgOW9qze4Tdhqnf\nE9xEymCqDi0qhFL4aWKSkdzVGKAyhojF0hBcRPqelCbu9j3t7AjTtqQQqdoW3bWEzS2m6jBKIaqa\n5uE7BNdjFyeEcSBnx0Ja+rtLQr+Dowe8Wm1prKVrZuhasDx/RJKJrCVSHzFcfsa7H/0Q1XzGr/zq\nb/DDP/aTXH7y69T3H/Pqs2+zuP+Qqmr54Gs/xAdVizIV+75nMVviQ4AckFnjh4FMwClBWO/ZhsD0\n/CnUHaenF/gYiCGjtCIq6BYt/+T3tfyZL9/jk6st33ix4oubntvtSEQRpCZkiQsedVAABgdeHWzV\nQqK0whz8BVOOxCRoTV3EMQc9hDaiAJZCEQREqVDaEoIjx8h8NkdLXdKelEBnQR9GRIaumpFF5KLS\n+KqmaVuGzQY5P2G72zImOOo6Yk5YZdiOA5WxpCnSj45Z10BMWK0xShLTBFIwjnuqumXcT4wxkIXA\nGEVwE+uDEKi11UEHJfCTx4WINhqtS0sec0BkWYRCKuGCx5oKrTXBe5SA3WZNiKFQmlJT1xbnXQH7\nUiJ4z9D3CK1pmxofPWGaigiMTMiRLDIpeKy1RUsRAuvtlspaurah3/ekULAGJSTWGILzfPE7H3Pz\n6mVRWEr5nY7iH60YCCEe5JxfHD79l4BvHD7+68D/KoT4Gcp48GXg//1O3+Nf/ws/BnlkuLqlO54x\n7CZGe0d49RmLd76OnM8J6yuk1Hz867/KV/78X0TZIpVFGowIrMeRyjS8/O3fQJ2/QueyqmvzrX8I\n7RHDsMVWNXFcIbJDSI2pLJaIX71kch1icQEuI5sF0Qu8v0UYQ7QVMmpStvR9T//5bzO79xh9eoau\nO9rjhmnYoQEnZxiZsF2HNoa7J9/kF3/pV/iRH/6T2PP30EDPxOe//vf46p/750l1R7//Fk13is2J\naBv2k8TYiBCaH/mRP02/XXH0/vdhnWd5dsF0tWI7a5ifv4WNkudPPqNta9Y+gZLsxh4pepLWCJ/R\nzMnNDBkCQg74ybG9vsPH8h7JjaArVExMU8/W73lcz/mhjy7o3Zb1zTXXz5/z5HbNRte4kw94Js9J\nFNVk8B4hy13ptW5fxMjZfIH3/uA7MAclYREd6YNZppKWrEvrHg9uzM1uizKKxlRM2x4hJUZrqspA\njviccX5iHAdIAVTR4z++uIcUkil6Xlxe0rVtGTelYEyZR+cnbPsB5ya6rqVSkka37PxIN+uotEbM\nGupxoNEGpGKhZ4XiGyei5A0IlypF3VUooVjvduyHka5tsVogtGDyCY0huYiPnrquUVKzH1YoKVge\nLTFS4p3n+OQM7x3JJJwfSaSi+DwwF04JBu9wo+d4vqCrK+5ubjk9PmG/21NXFQ+6Bt+PhGlECxhz\nQmeB845+7Lk4O+WxfIfz+w+Yz1ta3fCbv/b3/+Bz/YcpEIUQfw34CeAMuAT+Y+DPAj9IGQE+Bf6t\nnPPl4fU/RaEWA/Dv55x/4Tt8z/x3/pefLuosLUnrNYgJKzQ+SSY3UlcS7wMSSYwZWVforiM6X/IM\npgE7riAXTrx68CWS74kIwt017YNH1O0RQlucC/hpj65m5f38HvyItg2BTEoWVXWoSiEwODchlMXY\niuBGpNS4YYPAYaUAoYp4jITUhphgGPaoYU/IgbadYapjbi+fYboFdVORmgYx9Wz3a2bNCcpWhDAw\nxcTqs084e/fLiDixvr6iO7+HzBLTdnhhkEKxffo5upJMLrIdd7R2UdSKRjANI4qDas4HRu/wo0MZ\nXQpbCNRNS/C+cGOiiGf6YU/TLbBNDUIy7nq6pgHhCW7i+tU1w901bQo4qdi2D7gUp7h6Tnd8VNRw\nKeFCZHKecSxy3RgjMYTSEodQlIq5RKAZpVBSkTJYa0ghFmGRKnRbdL54E4pEsXQb0whCMOtaRCxK\nwJgiWQpmtmKcJrQ1KKWwxiByZhwn5rMZq826qBaJ/O6yalmKitYYqYu/wXvG5FFaE0I6KPgyRll8\n8lilisRbKrQ2bIce76YS3ydKmz9rakZf2nGNJMSAEKIUshDQB/FXCqFoKaqqFEElWc7mjOPIfhiY\n1w1KSAbvSDkTfAEa67rBOQchMowjpjKknJl1NUob8JHJTaWQ5YyWgtoabF3hd3uigJ/72f/y/78C\nMef8r36Hp3/uu7z+p4Gf/sO+b7EiR7JLpG6OcBW77BFTaadjBq0txImmm5Palv00UnWnKCuRURL7\nBWwukWksM32MyCTANhgMzg0EF9D1Emu7N9t+le7IYsXm5jnYOd3RMTFFokukFNDKInzExxFZKXLM\npf1Sc7JSZd6Vimm3RcZE9hPS1IRaY7s5qoLt1XOahw9YPX+GtBfUIdMniVUz+mmHEbC92yHjjou3\nv8Tm1Qtsu8BnxeZ2hR48k7UkP6J1i7Fw/WqPUBIlDDebG7TShCnjgmPyjkpKkpJv/A0mRVolDwYt\nqGqL0gqXE4RAUzdUShODYxq2iO2W7fWID57r9RpNBhQ7IcmyIaLQIjFOE7u7NbW1hT4LkTAWVWZK\nRXOvtGKaJqAUgbZu2A97fIwEH2jrhrEfUFpijcYFX+bxGBnGHqEt0XtSitw/O+PlzWWRVkvD8XLG\nfr9nco6Nc0ityYeW3Jqal5dXzNuWcRwYhv3rBcZIUYrQFEZkgqN2zujL4RGVRrtSBAIJnzxVZdm7\nkXnb4J0jhERMESVkYTtmM2IICCEJwTP05fdDCoTVxV8CaGsKgDo4jNV0XemewkFFmcn0ff8mpXk9\n7pnGCWMM87ZDqRqpBHNbM1nDbhioJIgEi1mL9yNjHJjpitt+X7qjg5x6sxtht+HR2Tku/TF1Laa0\nw+gF0WosgpASlWlQZoEjY8M1UtSEKRPCRNqNWBex7UWpgELA/IgpeCq1pQLEyQWTy1Qi4Y1GJQCD\nVGWmQyqkLXZa2Z3x8W98zuUnf5s/80/9KEcP3yfkUOS4cURIjZ9GZARV18hsmMaAriRWG1KC5cV9\npv0GF0eEn6hrQ86eGCzV8hHbJ58xu3iLfb/m+nIF0uFHj9IVkT0aRZ0bnq0ukabl5e0NCkcMpTUd\nxi1WVNxsv4U1NZW0xf1XGwiOoDVNY1mIiihqfI7Yqi2z6cGE1FQV3m3ZrG/Y70ZscFTWkIQDbbma\nHNc3N5xXhrvJY3TDrJ7T1ccM44hPiZgFG33CZSiYQ9tarKwYnWe33x8cdpGqrqmMOfDdiRgjxhi8\nc7x48QJtDYv5jO2wZz8OkBNdXTP2iX4cQUBTV7RtSz8WwLapG65vb6iahnEcOTmZk4Xk6uaW09Nj\n+mGg3+x459FDpFJ88q1PaeYzhnGk6zrqpmO922OULgEuUmIqi+gq1rsNImcm52mbiuN2htCaM21Y\n7TdYJF4blNJv3KV105Fi5vbmFqM1Vkq6ukFkwbPVLVppRMqYxnK6WKKFYL3ZkHLiZLkge0/sR5qm\n4bbfFmqxqkkhYCrLcTuDlFi7gf1+D4BViv008PTyBY/uP+SsmzGlSBKw3q05r2ccLxZ8+uo5tbZ0\nbcsUJoZ9z4OLe+zGPa+ur7j3+DsSe28e3zOj0i/+zL9DbWrkfEnWVfHPuwEXB2bH98locp7QyjD1\ne4TUUFm0c0RdIUVCSYGUlu31c1Qa8Bi680eluoeIqevifhevjSUSoRQ5R6RUPLu84W/8wi/y7v0j\n/oU//2fpXfEokg6KPG2I7pA5EF2hxbJAWkvKkX6z5frlM9K4ozk+JwiJyoKUxBv76ugmNqsVi8WC\nKShOFhXD6PAUO9roHI0UuBDJylDlIjZpFzXBWOK45dkXK770/e/TKksWmuxdUZ4hGNwOIxQWhe5m\nJFWC1Xw/kP3Efr+jkkUYlHJktdlxvV7zcF6hyYzDSN3MmELGx8g0FXdeyJ4pKaKd0esT5PE9bveO\n7RQ5PjkpLX2KxHTwPqRAP/Q4H5icJ6XEOI5UVVV4cOcIKRUWImec86VYSFnGHQXGaKZxOJh0JFVV\nsdttefzgPvtpZOxHZIYpBeZdR4oJYTTZebTRDONQZLk+0NlCo603O3RVMex3NFVdvBLJU7cNMsKY\nAvNujgiBKfgS5nLoZJTRaCFRRpHDa7twIuaMTxGrDU1VMUV/+D9W+BCYKCNBLSQagbIGoRXrzQZE\npq5qQs40xiIz3G5XGK2p64bdbosbJo7m8+La7QdsVVEpxeAmVIIxFL1CVVtmbUsms7q7oWkaRM5s\n9z2trRjcBBLun5ySg2c39vzVv/IHjwnfs2Lwd37+PyUMAyomxGKJMC1+e8fkBTM1YI4elDuOlChj\nkd7hRURISw4Rpcvz2XkwhhRGCIewCa0Rpi7pLkIf/Oqh+OMP3nlx2PrbbzbEfk3V1dh2werVK/zQ\nU3UzXMpIWQ6+G8Yyz+byzwVZLME5krICXebn6+sbTHJFFZYVs7piN0WsFbgoSg7DgbpbHJ+grEEb\nw+b2FiUVs1rzbLXmwfwYM6+5ub3i1Ys1P/ijP4CfUqGHkifHVJxoLhC8IyZPnkaSdzx58pT58REP\nLs548uQ5fnKcnB+zXq2Z+pHeeR4sagIaFxPD6CAHYpQMkyMg8aplL1rG6gjRHaGMoWpbjLWUvIXi\nyFSiOBl7N+FCEd/EEOiHAe89Qhb7dHIBoRVNXZFiwoVAXVVIBLvdjqap6ZoKrSTb/UCIEXkw5+iY\nGGNivV7z3jvvsN1tCg4QPGOO+PWO2fES5x1KKryPkCP7vqft5mit2O12dG2ND5GT2Yzb3Raryzy9\n3e4wlSnzf0okWQrqvOkYvQMpcG6CXPwjRmucL5kCVhXX5+RdSet6vcsCwIWiDYiewbvyfkoVD0LM\nDMNAEJFF0zKrG6YQSGQ0RX9QjF+ZedfRuwmjNavVmu1uy3K+AJGZNS3TMKIrjRElH6EfJza7HXVV\n4f1UfCmqGLV+/r/7b//4uRa9j8h2RtjeIUbHb/3Kb/LwSPLgw+/HpZphv6dZFq9BjoGkFDhIsUc1\nLT4ndAJhatLkUNIQRCxS1uCRwiGoSxKMTAhpYHIoW1DocRy4/OLb5CQ5efCIT58+x/hn7PqhvN/w\nBDtf8ODRY9JhzZA+OOvcVJx8hsx+mvCDwxqN0hI/7cjS8I1PX3H/bMny7JSLi5aKTHaBJDIx+eLd\nj55WanJbcXH8LnnwxOi5X9WIWByRJ92Sxbsd42ZFXdWgJC4JJJE8jMiUif0ekzOjc4zO8+R6y0dn\np/gYePDoLabdmqoS7JXg4Vv3efHiFde7CauLacYfcgJ6F7nR99H3v4SsG3zM1FqiVSYCPmfi5A6m\nqUNyj3DEEBiGkZAKs6EFNNZCTiQfWDQNsSnW89V2w/2TU1pq+n4oklqZcW7C9SPKSEzT0NkGkRMv\nb684Pznh4dkZOpWgj7NZx+QC3351TdM2iMpyvdnwzvkF+31Pu2ixQrJabxE5MvYelyKdUsy05Wa1\nYecnjBy4P7/g4qjjdj+y6ydOlh1WmCJd9wHnPV3bkm3GTwUMjSEgkOzGkZh6zDRQGUWlNEoqQozk\nLBiChyFS1zWNKerHkGKJz8uZ05MThnHkbrPiZrPm/Oik5G6MIzInZm2LD4EQE37yTPuBRdMSD+E8\ns3lH21UMuy0xCa6ur2m7GT5Ejo+PyJNnuWh58fwFX3rnXfpp+K5n8nvWGfzSz/2HaHNK6FdEVfPp\n0xcc71ecvvcWzeIeDkEKPUrXyKZBZknWsYB5gMTgSUiliXFCiUxWDUoKjIyk0ZOlKtZbqUlIYgyM\nQ19CMqYJHycEhbeVVUWYJoQfkboh5sjt1SXTbkvbNJj5EVFIpPc0VQE4P3v+nMYabjd7KiFZnHR0\n9QKpy6gws5mnL16x6BpOLi5wGUIIxXaq1CHUNKIx2LYhWl3irGIkxYiPkRwDwXvqqmIcehpjqWZF\nhiriwXrkekIYQVq0FWyurlltdhzNNSTNq6tLVncbqkZSyQrbzAnJweTYTIkJTS/nPBHnNPfeKilR\nosyqAknMiZBK8lCIxXRT/PdF0JNiQArFME2InNFGI3Jmtb4jKl24cVmUdVVdc3e3RpGp6gptFDkm\nalvjc2az3WAErPdbjo5PqLUpnV2MuGlikzwn1Zzj0yM+/uxbfPDgLW53W46bOVklru5WTLuRXGuM\nMpzPZ+ymMl4c3N8YbRiGQKMU3cwQlGImJT56pjEyBYeqK0SW3GzW1FbTGkuIgvWwpbU1QmmUApkj\nyWd88EWgVDUsZjPWfiq5BCkTXjs5DwlXvRuYnEMjqeqmZA7Ict9Kk+N6u2UaRx6enyFipqotQcB+\nt+Wm39FVLfePjrm8fMnyeIlIUBuLrS23N7d0s4679R3Hx0dUWfL06gWtrfn82RP+1t/463/8xoT/\n+2d/CmMbpBX4fkKISJoiIeyhnbO63vD43fdIUiF0QdCVEARVIspwHlG1+GFEaI1UB024qggpEaeR\nOI7E6EFpgpsI3hNCQCmDEpqMJGaPDxMkweg9Wig6WzGEgRgC1tZMfdGgx0NackiJtq6xxrIeJuZt\nQ9NUTDFgDpui6qph12/4+//wUxazY776wUOak6NDjs8hzJRyey1xfcVko7Q5GGcOacmi5PdFXxyY\nOQVAoYXAk1A5lKwCXYC74Abcbs8nn3zCk6eXfPXxMTe9RuKxKrF3mU4V49I2amJ1xIoZG9lgZwu6\ntsPoQqsVvQAIqfAh4EPk9U4Bqw1Sq4ORJ7Hb7okpUlt7kB4f2B1hCsJOIsaArSrqqmEc9mWLs9SM\nzhULcSpApK1r+n5fJNraYIxmt+vLKBYiMkbqecPddkNKkmXXIoSkaSyruw1ZFCu2VaocpJC4W21w\nk6OuNMIohLD0buT+fIbUhn0/kmKkthWTm0hKMG/b8rcPge0wsJh3dEpxu93TdS2jLxSfVYCQJCmY\nvKNtmiKb9gnvAhOR5By1LvjClMLBICVLboEy7HYbFm1HSol+cgglCqUafPbAAwAAIABJREFUEnVl\nsW2DGyeqLPCi5FdM+z1H8yW3uwJQ1pVGVyX2T6XEfuipjQUJcQp4Ij//V3/2j9+YEKZA1Si8kEgd\niAHkbEZFh9vfcHR2xN3LJ8yPz9Ay45uOJA1WtWSpiJVERkddWaYwgaogBHbbK4IL3F1fsTw5Ztis\nCINjPzpubzegFPN5w/lihg8BFyLtbE5VV9gsWN3c8rIfOD894WY/0q9ecr7oqNqG2/WEi5mjWbn4\nkszMaoMk4cOEshZjiqPOC0G9POWf+Ylznj99yXa7w1qFsDVojVK6pP1IWaStWZKiJ/kSviGkQhlT\nsAGrD9SmJHmNzAmUQMcSEuJHx269IYSeqR/5/Pk1DxcNz9oFxliOW0HOkv3oGULilWu5MyeMsyWm\n6uhmDSciFbo0Ftedz5nB54LNxIQ/eOtjTggpQUsiiXHfI4Sg7ZpyIe93pFx+n5wy22GF1gZ5UB82\ntiITUUYz7HsyxSBUa42sSpvthhFzUN9pZQoWIQptqlNmihn2E+eLU7bbHT4nZrZmtdqwGQZEhnfm\n91n1W6aQWHY1690WgcAc7vILW3N+NEfHxF0/spzPuNusiN5ztFyABD8NLGczYq6YzTo22y3Pbu6I\n+XdThLrljBQjzgVmdYPq5kyxYCZJahCw6BqkqBhGz7GdM04O5zyKgm9ZrUr3lRP73RZlDEezI+Ry\nyeQcRpQciT7siAmskqScMNpws12z73vmdUNXtQzTQNaawXtm7Zy77Ro/TXzw+DEvX736rmfye1YM\n6sWcab3GzBYEHxGNJfuRMCVErNhtrtl6xfFsj7Iz0mZNOjlnN2zQpsYoQU+iTpmYAvurO7Z3O1ZX\nG45OOo6Oj9heXTP2A7ayxfhhW+p2xtuP76EUrFcbXl0+Q4+RRw/v0aKZRk/ddkRpuH9keSUU18PI\nxULxta+8h06C3TTivcNNI01tcJNjGjJ1neFIYXVFTIGYBfsR5mfn/IOPP+fmdsVHX/vKwUpdvBZK\nlsBNpEAqi0i/a7FNB4/769iuqjJ4BLhAHCeGuzXXn3+GmLV8+9kNj+YtoqkwWnG1WvHle2ds+g2D\nKxfa1im+4U6wp+/SLZccGUUWobACATy+vLcp6r9a2SJfztA0TQnlyhyWuiRi8IfQ0dLeK6CtLTEm\nVv0ehOT+vXtkKbi+uUXXNXs3ldyimOhmc3w/oEwiUopRDIFNPxSBTwhsdztOT0+pjOBsPudus+Ko\nm7PNjjxMRCLHtuPJ8xe8+/Yj2tmMfthQtRVy3BFSZH1zy9nJkoBkXrWcLma8urtB9Bt2EY7rGX4M\nKFNjDOw2K6KUTD5wvd1jKSEnQhsu7t8jTp62NkyTY7PZII3GHbIMjxcL6qamsjU7N7Lerah2jrkt\nxeD6do3WCnlYtGKjYchwMl8ijaIfB2ZNQw6e9bana1rOlg2r/Q6rBE4KImC1YsiOeddyerQkkpnG\nge1+i4iZetaihYQQ+ej9L/HZiyfYxn7XM/k9GxP+n5//y8TdSFKZqpkRw2FBRmXw2x3by+c0bcvH\n3/yCD750n37Vc+9rH9Hailw1jJPj7vaO8a7Qdq8uX/DFk0vaSnN2dszJyTHX6xUvbrYsq4qL8xOk\n0ex3PVopQvTc3K7I0nJ0fELX1eWHyxkjwGpJEqoYfEIJ+8hSEGM5PFJprK3RB7NLOCjs6sqA0OQ0\nISIIo7F1S10ZblcbyIl2Nn+94b143N+Ek5ZEIKnkIRIbNOLNQtHgigMt9nuic7x6fsmLzZpWSbQy\nhKnHikREshsnKq24GwIOyzY1fBFnqOU5praoLA7bnCRWSUxd471H5Yw0urj/KOh0Ofup6OtTKpLw\nFMtaPAQpRbSWWKOw1jCMjugjSmp2+x1CFeqxqWvcFHB+orYWYiIIaKoKLRX77RZU6ZaIgQen59zu\nVogk0E3Fq6fPObr3gO3mhkcPHjLuekxd0W93CGuRMTJrG/bbHUkJTpqWz66uUcrQVZausvgcmUIA\nIQjDyOnZGf20Zxh6lvMllam526yxooSu9jEgUsZaxWbX09R1sT7nSKs1o4tMIVHpomzStSX6QJgC\nTVOj6jL2qZjwIeN9KB1FY1nttoyjw40Tnsx81tFoU2LPfenAeu9Ytg3jNKCMZbPdvukkGlvR78s4\npY0uEe4HZaZGsNntyWQabUucf8z8j//9f/3Hb0yYXMAoiwgFLwjjhNYaWdVIlTh6/D7D5ROWRw11\nZbllw9XTJ8yUJDYLxnEgDB5qxTT1GCk5PVuQk2A7RuY+0JiG+yeCy5s7pqeBB+enzGcdV6sV3/z0\nOeMUeXx+gpagZWmByYkYMwOxZCS4kaeXd/SDx0jBvFaMKbDajYisePvBBSenR9RGkw4yXKFASosw\nRVfvnMPHhK0PphcpyC4QCEhpDtr9giCV4pxQouwCiCmiXMC7id31KxCZ0YEWib13XBzNGfqeFCYG\n53G2InjHmARjsqxzxSdDgzh6hKws2lZkipJOH3YrBMD3e9xU7LTKF1uuVOUCl7KkCZdHQT1e71GQ\nBxdjZTVZSIb9hEue2WzGsB9p2hpHJg4R7yaMUhhTQ8hko2i0JMfIfnRgFLXWTN7hBbjkEcGxrJe8\n6ndUXcPNesNbJ6domZlEZNxsmFWGMSTaWcfcaj55vuVkseBmGLg4WeJc4Hq1wpycFwejsaScODpe\n0iDpk2E/OirjWa/WLOYzJleA3tZI2qpBypJdOHjPfhjIIqMp+QtCakJOxBAIUhC8ozY1CAjjhGnq\nkh15ULIPfiDGCbLAVsVQJEJgHAdMnUGL4jWRAmsrVvs9+jBanZ/MAcE0OpTSLJcLcoZpHHDDxOQ9\np6dHWKOprGXqRwY34Xykberveia/d1HpOZPzDlk19NseqRomdnS5RndLsvfsXGSfYXO9IibJs+tb\n3LDjq2+/RVUvkc7jXWKYdkx9j3OBi7MjVN3gvMNqyxQromh4tdugW4Odevr1ng/u3+M3n97y/HrN\n8VFL11qUVaSQEUGWCKwpY0TFh2+/xWac+Lvffs5vfONjPjqb82M/8nW2rnBsKUSm5EBIfEyoGKnb\njrq2+MkVQ4r3GG3ItiD0vt/hcsaIRDUvij6nR2Qs8VlhGIh+Tw7gtyu8d2x3IwEB2bGcn3BWa/os\nIUkGF5B2Xi7aqiJpGLzkk41j186pYyL3Ayl4pIZKK8axjAW6qjDWMF8uD8nLpUvT+rDj6ZDUHA9x\nXmXRiaSyBS8hxrLHICUqDfud4269RslIq2uWTcvdIYZdizIC9cFhlUZZCXju/IaH846cSieUZUWa\nIttdJqYBKyW91Hx0cUROin7vmRyczmrwAlNnjuY1u7sbvnr/Ids4setXbEfF+WzBg+UcR6DTFTf7\nDceLOUTB5XZNN7M8XJ4zO2m4fO64vL7luK2xtqUfI1s8UxgQQjMNIyEKKmt4cH6GlJLNfsveOXxS\nnDY1wWk+v77CCs29o2PCNKKsBUroyqKy7HY7Rim5tzxidkhYatqGfr1FomA5hzGwXq/QRiPbilev\nNqg0MV+0nJ0cMw6eq9Ud52cn7FJkuZzTdE2JU58cg5+wTUWlDTtfYu++2+N7Nib8wn/+b/9/zL1Z\nrx1Zduf323NEnHPuzEsyh6qsUlWqSlBbliw03DZgP/rbGQb8kWzYMhpCN9BuqdAaqnImk+QdzhDD\nHv2w4t70g5V+sBtZ8ZJMMi/JvCf22mv913+g213SSuVUpQqPhxPeC3318f0j2q222U2hPVjdMZ4W\n+sHRiuJuv+f26prHcSanyuV2Q7PCy7cGVKvsj0eWrLg822C8MB0Px8OqXdd8eBhp2nBxseV8e/Z8\nAHxw6Jb45rt3vHn/yO3NJZ9//nM+7Cf+7b//Z37/7Vv+5PUZ//ov/4xuGEipQS1yUwa/Wn0/BWGs\nycxKU2pCK4VVsBQlTj99hzZaisa0kE570unAcZrpXEfKCasNi2qMx5nHD3ecXWz54v0DZ33AuY5p\nTDQDCkWKhbFU/ukR3rsrQn9BRjYExmhKSdQcSfPMdndGGHpBo71nGPo1D6KK7VitWCWod2ti2bXE\nRMlJSEJWc384ACL/3XS9WH2lgtWO42lcU50SORWC9zSl6JwAjBZHQXEYRwZjGLYd45Jwqq3mKWJj\n/uFxj26a892Ad4b9tNCrjrvxATs4XnQDd6eJszBQqMQcsdrjOo+zcgj3y8iLzZaqDQHF3WEEVdDO\noWvhcT/y6c0VX98/cBhHdrsd58MGZ8Epy5gXjuOMM1ayLGrDWYfxms46UizMKZNKxXpNZwwawxRn\nFApjDTknGhprDMYo6URTIuUqwGJrdN7RW8PhtFBao/eWzhvuT0dqKvTBshkG9scTXejQtXCcIsYo\nrIEQOkrJxGVBvMKln+s7x//8P/2Pf3xjwv44MsdMTYrDdCQM5xwe78E4XFPobiBYxZwWam6UYoCF\nzgeoGqUym16xLHvIhcMpchxPnO96nHMoZwleWtoPDweWVLi5OsM5gw+Bmgy1KV68GISRqBVYjeu8\nUFPHiQxsLi657c+I88y3X37HMAQ+uQ6odslnH92gQeysmhIMQD211HVdra1dhhJatPjxNpQVq2ya\nmIe2cSTnhenhnu+//obL6wtiVnhTqNow5gI50nDcz5VN0dycX7LERQhc1lKrrP/GpfF+KexrwLoe\nay0ti4RbWw3Ko7XB+4FaRTjkg9xcp3HCeSeOvEZMNHJjtdUS/626ZjammmnRMPiA9+JrmHNlmhPL\nNNJ1HednHeO8sMxtVakqvLEcT5PoGKy4EllT2fU9hcrlVpSgeV5oJpBzZrc9Y+M13z8eUbPGB1A2\nczPccFhmCopXVxccY+bth3tuthcsJAblOU4LF51HtZ6HceFsCCwVVMsMoed+OhCsxwVLNYrbm3Ou\n0haaYi6JeaoMthJrxaDJVdKXUlmYlwUzGVLfrZkGEILDG+mAjFHUnHg4jnRrB1afUpxKxWCpVXNK\nAkbfhsD94cSw61Etc5wSpTb240Swgd2ZZ38c+e7NO/q+g1pZUqLvLHNKaOuxRgGG0HVYbTgcDjjv\nmZb5R8/kT1YMChsm10EcOSZI40K/2Yh7LJqxRc5MIC6NsTackiSZaVFk2/A+sNld8ocv3vLt3cRn\nr8+5PutpqnEYj9wfNb33dJ3js5+/ImhH33tiXvjq99/x/nHk5atLPnr9mq4fULpS50iZTuSSePdw\n4s39iWWc+bM/+YTPP/8Z+9PE4/fv+LCfRDjUe4xeI1Ga5AOqVtArb0CtLXepGd30iglqrFVUa3Et\nsWAwx5G6/54vvn3P9eUAKlCmkcuLC/aPD2S/4zguLA/37K6u+ejFObkoWnN43RhjpKjKvCSW3Hic\nImPx7MKWbw970rSnKAfOCiZg3EoOEqPu1lhlxRLhpZKkG0mcmn6WxIpjkRC5yvp1yhmCtmhViElm\n0xQXfNejjGKeZs43W9T5BWOciPNMipWhG8itMeeK8YY+eBbg0gfmceawLKRauTkfiDnjvSXpxicv\nXvAP33wFfkPeH7i8CHxyseOwjHTWEGvj9dUV284zFYMzjXF/wtOYUgWVmRfNYUlcasNxWTjlzGa7\npSiN8gaTGme7Had5oSwS/HtaJs42A8dS2fYdNVcmGrbzGDTWGkwTU9JcxUNRK4MOoocZ+o5pWWhK\nsRl6TFX0PjDFkaIa55sNAGMu9JsBtOLm8pJhG8mlYFVPrywPy8Rxmbm4uqSVwpwl+GY7dDweTozT\nyOVuw/3dA9Z7Hu7v2Qw9Z+dn/OHLL3/0TP5kxWCeRpb5hG2F4/FAKne021eEGrFWwXGkXe747t0d\n3+4bF1tprc9DYDJwfr6l5cBu2/HaaIZNhwsWrSr/8Q/3/O//+I5f3pzz159/wquzHSVnlhxx3YbN\n7Q1f3P+BN2/v2HqPf6Xp+gE6xeF45P4wsdtt+cWvf8X7/Uh6+MDbb77FWc/28opf+A0mzgTvKVhM\nlai052CRklBqRdqVJB51wUOTuLJyfOBv/vbf8SeXHa9//VsO+/e8efM9X331lqtwzfbVS7754hs+\n9VsI59TjI8F4vhwjfngks6HmyHGZCEiqUqYJdjBHjilQt7f0w5abIfHuzVdoN6BUj1KG1oRSPJ4k\noKQ1sN6KB6CSiDJtFMF7Ce2cZ5x34re3gpo8BXcUIUEZ73Cmgc4E5+idZYqR704juTbO+0AHbIeB\njOKs6ziMB05L5tx7HkpG5cyHY8Roy7DdcowT4zKiFXSdp6+ZTd+zsY6PhisYKl8dHum9R6WGPneo\nOOOsYj8d2TjPtDRudmdsho5truQCTYs126YfUNOJm4tbvn8YaSlSTg7lHTkvBNuwyjNrxd0o/gxX\n51uO00ROCe8dm6Gn5EpcElrDduNZTiOD93TGsOSEBrqu4/ryAmsapjbG48IyF1CG4EGv8m3rHLlE\nHh8m3sTE2aanc4ZxmfjycWRz3vPR9SVWa+aYMM1jtZItmVIEYzidJhmvaZyf7xj6gbu7e7bb7Y+e\nyZ+sGNzf7Tn/+AV5nLk8v2ReImed4e5YOMwKawPHOXOxC/z+/Vv+17878epsy+3ljvPO41zF68h2\nt+HV7QUxTeRl4m4/kavlv/3Nz3h1teX1yxuR0WqF9x4XDL+8PacvH/GH7x95tx/ZbCeC78hVMezO\nGHZn8qKnxIvOwEevmZfI4/0DNiXONxvqxj/Hprc1UIQVI9Ba8gnVyjIzGLHsjgtff/8ONU28vjxj\n1ytMiUynEw3Hrz67pvgt7XDkNFYOpxPVdnz3bk8/dHz28prHKaOIGKvogqD+MVVSaUxLZc5gwoAd\nOjbX59jamHLl8O5rdM2S4uR7chWLcOeEhx9jlJVrbYKIlyQviJH8hZoLdQ1WbU2s4mpN5Fye49KK\nqqSUcDawtIa2lp+9uKSkQltza2sptAbH8QQozrtAZwxXfQ9F0VQhxcJ2cMxxpNTKNnSk08Sw3bKP\nM0PvGdOJXR/YmkpuCbQoC3MSTr9pYIKji41lHlmKpFBt1hRqHzyKRj+IM/LN1vMhwjhP6Jox/YDV\n4v9Iaby83MnquxZenG0ZY8ZrwWFmDVqJiUytmavNwFIK+2leRyGhXD8ej5ScsFrTFFKMamWeI85Y\nSmn0QdPZDl1FcdhbR3CWTOPlTcemC8zzSKmFZV4oteKTpu8Has5Yo+j6jsNjxDhhiKZlYbsZKP85\nPBD//3j+8HDiM2+4PN/Qq4pV4nIz7vfUCr/59AVff/+Of3qzZ4yNV9cXfP7xDSpYuibzrA8WqzWp\naEoNKDfQ73r+/FwRgke1QsuF6izaWVIqLEukKcXu/Iq/uLxak/4aaRnlNm8SU4ZzuD4QjyP779+R\nUashZWaaJvquRzv3bEiBkmRgCRrVwtJrkhgMmlQU42nki2/e8WmofPSrX1If7rh7+5ZWE+PpxJvl\nxKssstdjyjzcPRIuDduzc+K8MNdEF/yaD5iebPZRSvQZUy6MzWL6Ldp3tNborOXF7Q1LaaSHbyGO\nAia6Dq1k/hUcppDygjIW64IAoKXQtMSFrUny5CxZgw7JAFBacAWtAWVoJQNZ5lXVJIuxc3gvBiZO\nWZRRjDFiFKjUmFPGGY2y8mOlRANxe7GhzpX9PHNzcU5qkBexa9PBMcaMMo5NsBxypqZIyolCwhjF\nxls2O8/dA6SUhQOxzLy6vuL+/oBykny9CR0lZn5+e0WLmSUlgjfkmPDOkrMmp4p3jkYhp4JRsJQM\ntdEFh6NxTGKOEnMkNrjcbdHryjovkpJsvJfDXSUJOtaMs4paM8dplvd6DZb1WlFa4WGMKK1ZxomU\nIlZLyCta3JmtE9r13fsjSim2T2vfNcmq7zumaRRz2R95frJi8Ltv3vDdm/dcbzcMXnN7s2HOhQ+n\nkV9f7JiWyPup8u++eOR86/irX77guvdULeGa2mhOS+aUCs5ObJ0j6YwNMg8brUBZSmvyAinQJmCM\nw61ZhRJFAUo3MQNZwy7MmgMYx0bJmaI1b9/fUYswFbdnG9EO1IqpSg6/sc+sQQEN1+jNqpjjI3Wu\nbLdn/Hd/fcO7f/wnmA7MceLNmzu+HyNv7xbuHva8+OszHg7w17/+lFwrD/uJbfAklBiJloJqSmjP\nrUh6dI3iEVAqWVnxHmwNqiDIKMX+5oqRhjp8L9ZYtdB8QNuBnCPOOnTn0dYwzzNDCOQomw9tzco+\nbEBbHXkzQ99hgJQztWmcNWy3mt6Ls9LpMOJ6J8GoMTN4xyllOqXZWfkMTW9EM1IbccmMMXExDExp\nYVAdkxJDEor8efuo+fTqnLePHxi6M7RuoCxVG5o13F5fcFomLjcDUTumeUbrxnYzYJykRqV5xjqR\nwtcCmcrSMn6RhOTzzUBTmeACqTVM0yxK9vxkzfvDgcFafLAob0k5CzFLK5aY2O4CGxNQpTGnRcAV\nKsNgMBp6a4lZMedCU5ouOHZ94GwTeTxNQBPnqhBoDXZDj7WKxzLz0etbPuwfCdryyfCCcZ5IKXM6\nHtltt0hyE+x2G1JKHA5HDoe9dKnmj7QY/A9//im/e3/CWUffB2znKIeJf/PbT/nd1/f8zd/+A6e5\n8OtPLkQvnjNjkpjsh3mmFkm6dZ2j946LbU/oeoatYggdKIWyhhQX9vsRZwxnOwdV0bRC60a3puc2\nZdBVGHn5GSirpCUS50jKUTgDJZNipEaPXVOJa63UnCTU0uo1Gtw8Z+ZpCt98fYeOE+Fnt3TK8oc3\n79h8aLy+vSJ0PVdhy+0u8s9B8d33D/zVn/6KxyWxrGuqnEayVpQiQRq1NWoV4LI2SeTNtZGLQq+m\nHhZQq/qxt5rbjefNvOXu8EBNCyqLUYvJlawU2SdMslgr5KlkDE0ZWs44hUR/KYUyFlRDq0bOkao1\nymgxRMkaHzytVMaSMT5IvqJrEmxSGt5ZvDaM0yRS4ZpWpSkEJ5Rnrw1h2NCoXIeA2TimnChxZnvW\nsZxOvNheyEqvbdjPM59e79Zo8owOg0S3t0JVYk5zHEdccNKFZcN2EKC2axUXCy5LDF0sheO4UAFq\nZugCoXf4omkUGgXnDEtKLGnm/GwLaLRVbJxhmSI1FvzQOCbxhDRKLga7JlOXKmY7eUkYgCIp02Hw\nvOgsrVackc0ENBF00Xj98gXj6USJhX1ZCNZhtRJlbhXLOesk0i44K2PRZsNmECelp6CWf+n5yYrB\nl3dHvnhz4OdXZ1wGS140fej44s2Jje/47OUFnenYeSu22kbYcrvdlvOLC1mTaAkYza1xWBKnXHHe\nUJWWUMqi8drw6vqCWhLzPDKOiX7Tc7bbgJbOoT638wq9EmtcCEI9LpmWC2d9x7AdcN7JQU9JgjvX\nVgwqqkgvXde8TKgUGp98+hFlHlF5ZjrMvHp5xnya2Z8SeUl8fLPhH789EothazqOcSInKDSC90xx\nXteVckvXLBZcpck/cxVJMTx1KQpltJAEm3QqF8FRzrccl1uW/SNMD9S5ULXH+IB1imXKFGNw3pJS\npGHF3RjAGJEsr+sylCLlinXgnQTFeqOFDl0k4dk5oXOnJDwJg2RRqJX8kkvGK0VpCmU1WlXONh3B\niX9EbhqrGvuxQKmoTaCmwmnO+BC5MI7HOYLR7E8TtVZ2XUfQjVMstFrEtoyC9opN8JxKgtzQrbGU\nmZQrXec4H3qm3HDFYBUUFOMyizdjrcQCnVMCBCuDGbTIoTU8HGeC9RKe6qTFrw1ZI7bCtIjpikMw\nk9wKcrwrMVeM1ZL/WGGal3X9bUhJgMkxJmrJ1LplXiI5ZgoF0wVx8qoLu7OeeZIOsVVIWYxplYLD\n8Ygzlpb/SDED6wb+q1/uuD7rhHQREyo4bMvMh0TzllIbUwbvFMooNkFi0ZrSOG/RrdAH2XE7I07A\nWmvm44mSEqdxQinP7e0NJS3cv3vHP765B+V4fXvD2fmWfhPY+l6Sc3UDVUhLYv/hDt91aBuINTJP\nEW3s6qEn8WC1SHy3xGDV1eZ6ze1bAUWymGt2WhFPld9/+Q3KNK5fv2AeE3038OFx5p/ffuDrD5mP\nLjsOU0Yhh2xpwhuLpRCUeS48GihVRgatNVo3tJacAo3cFiDEmForVsPlxhNvLvi2KQ5xxi2PFAtN\nazgVyfMzjla8tMRAzRWnxaefVvHeQ9Pk0qhFaNIteHa7LbZJt7AkuYEU0FkjNmiq4JyFlME68Yss\nikai0lhywzVYYkFtDSVmtLakWhnniAuOc2uJTXF9ueXweOI+ZXTnuBk2/P7tA5fbjrOznpoTbx9P\nBK8pKXKxGdg4CFURrWMsgiVFVThOC7pkri8cvW0o51mKfP8HFdDGiBfkLDZ7zoBWUuyC0pKCrCqq\nVXrnQFtUgxQjU5b/N+89tTZKruxnAX97b7HekskSvFIrD/sjTYlBn/eG02EmN7nsaIqHxxPGGbpt\nTy0Jo1ZeiJMV9na7gQpxNTHZbjfM88J3377h5vqK3v24UOknKwa9KRhgWma07znOC3963vFwPPK/\nfPuBw1J4ddbz0c0FV/3A1nnR+nuD1+BdT8yJogy4wGk84ZLEm2tdyaWxNJnZhtMRHwLnNy95UTx/\n//UH/v4//AFN4U9uL/irz3/Gi49eEvqOEjUlJqY4s8RI3/e8enVNNYpaiqRDp0rViookONNAP20W\naqU1EfI0pam5cn93j4l73u0jqSi2veXCG/6PL77nn756SzCev/r8Y16/qpxZzeAsS4YURRRkMWw6\nQ10ysZRV+qpXrX0lZSlE3sCSIy0nWkpioFIbrSgqDWcqt1tHK1t0uuTh+5GaI75BsxbrPZVGjDOm\nOEppdKuASRuhIZcqUWOS7htEZKUkQqzVhvVWQLSaafNMtQ6rLamJtl+XRmcUeW50XaBVBQWUklFC\nGUUtjZwVwSvmVDnfBnKuHMcJasVWxXY7MObE7aaDVri9DtxsNnx390AXLB9dDQTf8WG/x3sNRXGI\nE0tpbHqH0vB6d8XeHoixMMZMapGaNaVCsJppWuiCp/MOrTQhWDTn//iXAAAgAElEQVRwnDPTMpGS\nFFxjxVrvcBjR2tANDqOFu7FUsYK3TmOV5oWXX6utYZQm6PgsUrNB0drqh9kqofNsVlGddH+Vzrk1\nCdoKk7PzYsEeC/O4x7u1cCMaCaPh5csbTuOJPvzniVf7//z83Vd3fHxzxYtgoQpN9u3DSNOW6/Md\n57ny6iwQnMKYtnoYOjpTqTExxhnvPFZDGUeccWLRrTS1itFnTZWLvocVzOuc5Revr7h9ccZxadwf\nFlpJxNbI8yx2VMYQhh7j/eqfL7e+URajDM1q2hNaS5WvoUkUupE8hVYLtSTSnJhOMzFFpsc9Iez4\n2fWOXAtffH3H/ePM7cUFn//sBl8zn78QgcxhFL88tKIV6QxaEdMU0+Rgtyo3FUbhmqYUhWoNVRMt\nzaTFrCAq2LWjUFXh0bzoLerqjFIix+NRchJTpuQsmwNjKbrRWiWnSGqVqsBYh3dIF+LFwQeaaBWU\nGLM4vRq6ek/NWV5CF4Sz0ArKCpjYNJiUsFavAGTBGo00JA5nGxgwTosVe64Ya+icZ5lGeqXZesdx\nkRXfaU5sXSIYi66KJSdKkjh4o0AHA7rD5cqyJA41Y+xI0LJ5mnMWG32tRH1pBRtppaDQeKvXDI+C\n7zxh6KhVCrNCgmmdc+h1hqc2dMuUSTwUnXWILQwSq45mXu3hZZqTxCMJXFW0Cl0w1LqOHCvZK3SO\n2hSH0whZMY8T7qkAz5HgHG31pXNGk3Ki7xyKnv83bcJPVgx+/upC2ta0oCUwl8O4sB06/uJnLyhZ\nJLBKg7UGZRRUcDTeHvd8c3/ixeacm6sBa8XyqSaZY6syZBSHWUwsQxB6cmuy9z8Lnsut45cvL3DG\nkLUhlcoyjRitsH1PHzxjntdDWFG1iOuQlsOIEnaejAt5lSKvhppKUY3lcX/PssxshkDe7Kg4UmmU\nVNie7fgvhg6DYtsZxkkxzwWlGzFlilICalVp0ylFTE+UCJlKyc9uSXWVT7cnf6SSKTGSrUcZIwVN\nO4np1pXeaS43nlQusc6LAel0QhVxgnL9BqMLJotNfXNhpbkWqHJz1VrxPuCtkMFUE1LOFBOlKWqO\nOCuGtEsuaDIoSLXA2ll440mtyZqtPYGMkkmhlWAj3llaKhSv2fSWoDydURzGE6YPlCZS796Jw1LN\niaoNWmnuT0eMG6gVGsKHcM7QhyDEqZqw1omxjpZb2RnNkgp+jY+LpWKd8EeMlgAYqNLJADlV4iLM\nw74LaKNYYmScxRT3eBoZhgEQizujFXmpWGflsDfhAcSYZJNlDTGLx6ZeI9V100zjTAieukg+RB8s\nORUpBNqwzPE5tKaUpwAbfjBv1ZLT+GPPT1YMfvPRJd5U/vaf7/ji7gODNbzYDuyXymAnXl3tUKqQ\nMsS4sNsMLHlkP3YoG6g2EmsjlkYzoFOjFTHkBNheXHB9dSnCnZyZY8R6h9MeqzRNFeI0UrQV7n0q\nmFaJteBKw/c9MWeW04i1lrAZcE4ouis6CDI6op/ZCrJ7b+uLTefo2owpigvX8/Y4MloBkrSptFyZ\n08Lbh8KrXY+zlsMilOASI6UqMOLNR2OdJzVP4rJaZWPR2uoKraQ7qFX23a01pLWQWDqtRPQFUFLG\nlsqLoZdAF60o80hdJlpZaC2Rm6JajyoTzizUqLChx/nw3AHFCHMtUDIG0NrgnCgzlbFi/ArMacJb\ns2Isa85iFeWiWvUOpVSMMpgqDEiy8PqVblwECRJNJpFaQzuPyogEula8Aaca3x0XNr2jd46u6+i8\nfc7XfPq7xJxwVopHafL97byjNk1ME9YYVKtsOrFcq7WRShImpjPQKkZbQKGNwoU1WSotmKLW8JeM\ndYab60u6dTSYZzGOlELTxGBXV5o14gRlxafTpIazoqA9nWZygSnOYuM+VwzgnFjVB++oZc2ztFYK\nvhLT3nmOdMHRqPT9IDZqP/L8ZMXg3WFhYzVFKebcGHPhEPfsfMfPrrcEp9E1M+XMY0x0zoKzLEX0\n6L/56DVBaaaaoSlSSeimUblSWqY2hXaWs8sdZll4eLhjPIoZZgg93juJ6qqJ2OAwzTijxGUGWJaI\nVRq321IV63xeQBeUkeawgdB39dohIGhfXSKVgl8SuQn4V1rl6qzndIr4zrI/jNAqX7655+1YabcT\nL69v8U5zPFaW2hi8R62rTkHXC1pLztFTh5BLES6A1mhVnpF6rVeuw1okaJVWmhCrnrz3XMIZiw+e\nYdhw9+g5NkWjoK2AZ01bAQrnhahkJFEoMUGpBbse/IzYslm3zstKoaomprg20oqMxgVLSZlUIikV\nWhNUXistzMQpMisoTbPdBrwxeG8YT7Os5FSkVrDGoLUSb8pcMJ2nNs3t1RmlSqd2semYU2JehPyk\nQKzbckWhoVWWPGOdFQBbGfF4rI2KJlcl0vBWRc3a1PpZtB/YpkZRqiKlijZSeL0LeB+kUFeho9cG\nXRcouTDPkVzLc7QcyFZKUfGr36dsa2SNqXSm77cIx8PSirg0LylCFF2MtXq9GBTOWsxgOByP1FVw\nltJCa/X/+TCuz09WDP63v/uGs2D47NML/uLjARPEOmoTOoIWim/Tmm7oYKhrxbNYbYXVphrJ1tUL\nzjDGiFdyG7WqKDmJcjF4jK5M30z87ov3PCyZYej5s198wi8/fkWqhdAaoe9FpNPaevE3uWmt6AxB\ng2pUKm0NFm2rE7BZbxxdkVVlzuRl4e50QNOIOQlYlhPTnDnvDF++feBuTry7P/GbT294mAsvdaNm\nuNhumGohxyKAJA1lNC2tpCP1dOSVzLGtiixaa6yqa4yWUIStVmiz6giQnEVBvi3RO2pt9M5irRMv\ngVKoywlTkoTNmEJTBuU85smleJqoMTFr8M6sB9OA8qiqybWhlcMYhQ8ddV7IrRHTCoBU2YZYIzTd\nphsxCVHMWMF8mhI1aCuaZY4sS2S324oBTZX9+TKdRMeCZZkXeucYpwnrDJ3VjPPE6ZSwvadzFr2G\n1RglRCetJPUpeEdZRWVaGZoWkFShcEaxTAlvAsaotYAB69caBNOQYowUiXW3rJpYwpWmoDZyy9Qm\nQbTzaiSjAe8t1qgVtxCgFqU4pokQDMF1kupcpJCoVRmrjZXYdy3ejr0Nz+E03nuM1szLQugCyzIL\n9fxHnp9OqFQrr4cei+Z864llYesNV5c7lhihSUJvZxpnNjDFJO61vScYg+nVKgPV7Pd7/sMXH+iC\n51efvmQz9OhgmFPi/Xdv6IaA7bZEvWcfG9hKagXbWXSzTKeZkhtaG7nhW4aSqEpBFmN2te74rbVg\nhN5aa6amjKpScdO8MJdCyYm4P9A7xRwh5oUNjnFqWFU4xoWzzYb3aeSzlx2/ejFwKob9aSS4QC7i\nMaibrOFkQyH2Y0rJGqq2Rm36h5Fk1Rp4C7HM1DjRgqc1Jy+28IVXbKNgjWE3BNlbpwxVceY1+mLL\n/gTx4QMsBe0DzUimokHArFqKkHqsIS4zrVaCE429ftowJANOwmZySfjgaLVgqmZZCkVXZg2KhtWN\noetRNAZvOZ4iZzvL3Yc9BcN4ilzvLMsU2Qwe3Qsx5xQT3hpSakL5LZHzIVBLE0fghBCiloRXYIeO\nBnhjyTmhkK6o1oo2hrg6UGvVyKWJR6U1+ODW7/sTA1NGLa3lVn7SeDQk3xCFWMrXirUyRLamyDmB\nVnKhtCoxen41xq2NWqr8vWt59n4I1sifqSQRLHSenMRFurOWlBPOGnLJq/O3pImldMJ5i3WW4/FA\nCJLb8GPPT1YM/s2fvmTnZW9/fT5w2FfuDgt//9X3bIJl8IElZ24vN8SiCNbifY8PWpDmWknzAkbz\n4eHE29PMRa2olum8wuie0sEyzYyHjLOWv/rtZ3hr6KzCOsdhfxTgaNXZa2OopdKKWttIwQMK8gHV\nWKjFYKx0A0rLnvmp+aqtMi7iTvPuw4E/+2jL9esb3t8f+Or7Bw5LJE4zqWr+1W8+5b++6Km1cZwn\n3p8maHC103IzABiZV2utayFQz8xG1A9rvtJEPKS1FASVEiVOpKXHOIexioqYq7QV9BRIQxGMZ1rE\nHKNRuOgCXhseSmM63FHijPcV5wdS1ehS0E1yKy1G9upGE+PEPj4yu5VmbIWG7IKl67x4UKA5TjMN\n2Pie2sT8NcaMIVKbJEZ1fcfxKBb3tVS0riwJ9o+PXHPGtg/MywmDhPPuOv1c1BJFkphrYdcHnJEQ\nks77dbyREVBbsW5T+ofuqlVkhWqMxM2XioU1Ek5RaxM8QSmx5Vf/NyC3rSPB0zZBSYFurdIQIFAp\n8YnAmOc1X0pF+CJKEZxZ/3sLVVKpa2nUlokx0ZoiBEcfxGJOjFFkm6OAGKP8fWpjmRMuOLw3Ipm2\n5hnq+peen45n4Nxq+qlYcmHjHHub+Ju/+4YeCMFQsXxyMaCM5tc3L+iHSIuW/uqcZT+JOKharl+8\n4L+/OsehCM6xzDNKaZwPErjiO1paxPizwTJFMaOzmjwnSdU1llYTTclt0Fqj5PJ861stFTrXQo2i\nX1Ba/AD0GorinEVtCncf3uJNE9ORaaKrcD0EYml8tyz8/t0D/+VvPxVsImcG77GxMc6ZJYr9e61P\nWgC1ZiesuMX6cqwLrRUPaGgNuiqMUhglgFcpSTgCVqOl7pKzaAtqa4LoW41WFmMU0xKJMdO1zPX5\njr3VHPd7VInk6UB1PaqWHwpTibQmRdSs684lVVRWuCLjzTw3ltmRhkHYiKXSeUNUBVXVeugaJfQo\nBbswoLRmniOpZealshs6dDCEGmjA/vHAsPFk1ZjHmcUo3ny456PrK4JX5JpxxpKzHF5RE1ZiLDhj\naFHWtIWM1ZZc8zp+GZYl8hhlzTn0nRSYnFYlqhbFYRMuSV2LcF5Ht1JEN6OoLMtCa+KYVVZyljXy\nXkkn8gTwsorCsowNyshEqmS0gIqzVjCg1Um7pAxGANyyhssYLY7W2mh0p/HO4byjlERzTkDa8ke6\nTXg8LhQUu8ETc8UrxWaz49OXl7x9dySrRqrwhw8P7DrPiy5wjI1dd03fO7796j1jMXzYJ3Z94KJ3\n9KFfJblCM25LRI6r6PZLSqinub6UdWbTOC0VuZQqNF8tu95aC/WJ4queEHtBxOs6eyu1dhPIreBp\n3L64RtX3vD1EHu6/w2jH1fUObUQleGiZt28+cHW+o+s8NTUuh8bFumosrYpmohQBC586BWS1ae16\nU5UmLwkCMj7ZqTkNuURqWqglkJOs2qxCcI/Vt7HVhoBXhs3gCcEyjTNjK9jScLstm37H6bgnjnus\ngjGnFb3OVK2wvl9ZFgKgVsQKrBRxIG6lUessSsSc0NrQEhyPlRrBdZbt2RnONmpOlAIkiCUxp8Jm\nM7DZWGxwTCdh+33z4YHrckYIPQ+He9ywwYWew5w4G7Z8OB4YwpZaRb24pEoqy6oOrCJOqgllLUuT\nzzmlKBwLrSSyc10V55QppVJUou970QE0nkHKVqEUwQG0MZTVeWiOQvqyDSgyWojrfV5j6WQ00EpL\n9mQsGCNr8LquBlVj/Z6J3Lo1AQhTTNggmy3nZYSx+qljESDRGFl511V411pbA3D+5ecnKwaKwuO4\nsHWGyxfntDU1+V//yUt+13dsLcypsR0sF33HThfG0tA+YKsYefztP7/lm33EtcbLXc+ffnrLn19s\n6EJYD2tD2UCaJnqnacUIOUgprHUYZSg6ktZWX+knYkldIUMl/bRaqzFqZRzKS69WBNgajVMiWiq5\n8urlJ3w4Ft58+Q1b30hxYdgZdtsdH6dKyonffxh5PC389ue3+OBJ4xNQaLBNY1RlQV7KUquUtLU4\nSCegV8KLwrQqcIBSWKXonagMY5yoi6NoRV6t16UFVrQqbXBtDWsqQYHTGrvpGIIkRceU2ClNb3fE\nPqC0Yj8tHI8nao7r90iYcamIZ4Fh7VC8R6sGJTHnwmman1dpta24uBHfhzRPfJjnNaDF4ENH8AZn\nLa5mHh8mWi4UJTTm3XZDxqBipCqDb4rOd0zLxP1+BCT9WgGpCIo/zTO2OmJZZewozs8cOSWsFatx\nGlijuDkfsNYIGAdY58S5W8vqD5lSyTk/d21tBZ4bgt6fn21lzVsSzon1Xa3iIUmTG5218CsF1uu1\nYysSnKNFhGZXiv1TB0LjeQVpjVmVjQKwC5aTn7vIeZ5lZWm0kM7+WMeEV1dbbi42nA+Wq60FPzCP\nJ768P3J3v+eg4C9/9SmbjWe3GYDMpXLEceLhMPHi5Sf8Onf8siZQhm0f2BmxGKcVVBVzUNXAaIvk\nFMkazCh5aeVD8EIUWok0rSFpumrlDawyA7SCJjeL3AgraEjD9IGHD3fUnHg/FT57ccavPnvNV999\nSwZC55lTxVfF2TDwmVOU7x6JqfAPX77n9npg2+9ITbAJp8zz7SQfouAapWahshahHxstjiFGK4y2\nNAoOI/yHVihxJp+knWy10mpHNUbGBq3XF66R8mrXZtvzyx26Jwv3ypnuWKx0ELvdltP5ltMcOT18\ngLxQmqZog7diMFKauC+nFKm54rVbuQ8SCw/iGj2cdQxd4Hg6CkBKW92UMqc1/CZPFT9smGNm02la\nAWMN6PYsAX6cIttBMfQB5S0DIvjJTTPFhe7arizHxtY5HkujDx6MpqaGD5acJVW6Vemy+uBRXlr4\nXCopRVJs6+ZEQLpaZTxzzv7A7Vs3OC1npmWRz4+GIsncrhWqCbMUJCZNa4VuYpxSq1w6dX2/Si1A\nW1WJ8nPGCLeiNhHHpZTXjmXlqGTpZmKKgp+oJ7s786Nn8icrBscp8tHlDuchz5P45cfEf/rujvtT\n5GbXsekNF9ue3DRnl5cEE7hr70hN2qK//PgKFyy4jpojJc1iUDpHOcnWQp2kBCixBqtVoslSK5im\nsNrRqszedUXLn4g5uQlAZ7TQjLVuYvSr1DPeoVuh1MoX7x6Zpom//+7A5X/zC/7so5/zr377Genh\nAds0S4HDGLk623A3HiWkpSoyiilWdl2TYJPayEpuBtdENCt9wRPwp4TnsFqP1/YkdOW5HWxNE6z4\nDIxpJDZhRYKiOodTYk0iDkVidSYj0g8gJE2AsFLBWVCdl1sNuBw826HjXY0c9w9Y3aG1xdZENpqc\nEiVZqaRGiDs5JVIRLCh0Fm+MrOeqdAM5LczjiRACxSdybmjbMMoRvMXbLVqx5jkYUe4ZQ+cd1mi6\nYCBlTG08jpF5mcHKry+p0HlZOQu5x6B1hSK3/sODjKV+pVPr1liSoPPWOjlgpVJyoZr2rAh1xq4e\nl5WGWn9cMFU4AlOKOG1FZqyVZEmyEsKUfG7SeK6fTWXdaPBcrGlQcnnmlrRVb6O1xqz/3lpjnuXn\nnHMrKU3hcM+mtrVW2Wb8yPOTFYOvHyZA0weHt4o+T8zVcnW+4dXVBbcvzjDK4rXj8LjnfLdhXCau\nbq5IS+TNt9/jrGWjBoKDtLrMCNFDaMe1ZEwzktBTV7S3AishCSo5RiHjrL2fUisBRGl0W5nn61qv\nVjBtvT1XPr5Tcjvf3t6ynyc+73Zsdpd8ePuOXlU23cA4zuy85ZAaj9PEu/uTAD/Ar2+vxLVGSaHR\nWq8GK4JIxyWirV07AbXyXSqgSSk/6xfqEzLe5PexKDovU/xUI3WR7D+jFbWu61INGLO6MjVJSmoK\njRSbp/ayloL3ApCllGi5YlXh5cU5mxCITbwXx/1IP/RMpbDMwjikZZaqscajjRhsqFzonGFOmVMU\nWW0rYIzHWU9uDaMUy5LYnfXokuSmq5BapmTHVDJeK1on/IjTLBbuWiswclNvjKZ3jnmO7HrRH+yX\nGdMqqirmeQGl+e77R7rOsj0bCBsnKH3O5JiF60HFOyOhOCvPVCkZH1OCXLPYwynDE0HVWYOz4iYF\n0s2llNdtA+uq8EnYtt7yNLyVceLp4Mt6UoxzlHjrsSwFZcUXgfVzqqWuBDMIq8x+WWSUySqvAOcf\nqYT59sUNOc1MKTIlxRSlJfvF7SXBWYbtjqoMsw5kJ+EkJWW03jIvCfqO7W5HHWdO48L9YWRDZrPd\nYLuOJWXIUVKClRh4KiWoukLR1ltGNTCrkKdVUeC1VmVkUGZFkWHNp5I8gSa74lplHIHEpYefvbrF\ntMJpXPiH//QtcZk4O7+iUonjidD1fPnuAaUaoTX2sWBVYxxP7IathCohbaR4EAqZx1tHLlnmhidh\nVJXuITcxyqirdPnpUVpWU50DYmGOI0utKFUlYVlbiSQzrC8irMOm3EJIF4IGY+zKtitYIwy+aZ6w\nWnF1tiGXytFqagoYDNp6Wk7UnIQcliu6kyTs1Bx57amVNXjn1x293I5NNXovugRrDdM0s388kGrF\nhh5tNc5WLoYt8zJzGkdybqAMMUUuzza0lMTN2TtSzmw7xzjOnGZJeFqWTMyOvnd8eP9AtkLYGoyY\n2E5jJJayhsWCs5pGppYmlni1rgY2K9NzTaZuNBHMZdEu+LV41FaFNr+a0kCRcUFpnlK2Wdt9wYNW\nNaR5kqwjWwyg5EgfepT+gYBGA2Okk8sproC3vAtPnYNSihDCj57Jn06bcHvGNGf2h5nvH2e+OD0w\nRqHcvtjtuL1odEHmoLPNObk2alXc3z2yjBPBe2xKTDlTWmNwCqqm0kilMi+FYBSxijzWIKu0kgUw\nahVY27uiKwpD02uuYW3rfjdRlKx0jNEYq0E5qioYZbEKYqwr5z/yf/7uGw6nI04X4pj47ONLLm86\nDmXg/tv3uNp4d3/AecUfvp94eb3FeYe2fl0UNlHnrR550LDO0Kq49dT1cGqlqRWc9M2UJpoFRaOu\nNGVQtOdPt0HKzHkinSpagfEbod2u66Yn2rJaxx+ZlFbmJ0rMQY2luSYpzamQ4ogqiW030J0POAOp\nVNxS2O9PBKs4LieUka6md510QM5IulQt9F0QokzJlNIwxsGSOU4nri8uuD8dSEum6zd0AYJ11JYp\nc+QwLywFttbig2E7nK+0Y0PfWXKuOF14dzey2fYUNHEW09DDsogHY7B8fN7RsmJaCjvfGOeZOVdC\n8ATdsMYDhjlHQLQuuSZxijIKtwbVOiOKxPp0aainAlDJua7bqB+o4rU+GcEIGB28X8lschlJdya+\nm0+MwqKEgo1qq0OzFyKc0iwxys8FL9aAWq9eCnXd5vyRdgZnHQxuoMbMf9xP/O7diWlJWGN4OCRq\nbrw891xuxBbsEDNqs6WcHqElllPk5DTWWpwzOO1BF5Ylk2ax9LLGkZ9ooeuH8kQN1euOV9hgjWY0\nylooldYUSokcuJZCLAnrjOz4owRbtqZ53B+5vH3Bru9pSTEud9xNhY/OAx+/PuPqPFDnhdBtODs/\nZ//+ga0PZFU5KcvFJqCMZwiOJY7ULNVeW0HSYxbEeT2RqJUW/4xgV2lHFYJbCBou/Ifa6vPNw7oW\nNaUx5Yl4AldWALWJ2SmrzZZa8ZDnZWb7QZelV6WmDQqaZzoJ7bqUTHCWy01PqQ1vE1Y1pnlexUcy\nXihlUKtyriD6hTRPRAQJR2tMK885j+M0oprm/2LuzX5tW9Pzrt/XjW52q9vN2ft01bkq5ThuCHGE\nYzuJEgUkBBcocBEkBNzlApQrkn8gAi4Q4hKJCxqBiEAKSIQoRgRIh6PYcYJdSZXLPnXavffae3Vz\nztF9LRfvWOscOWVbihNVTalU56y11zp7rTnGN97meX7Pquto6xYdZ0osjCkx6EABNt0ap2WI6qzB\n54zWirZ2hHnm0M+SDFUkK9FHz65paLXGaMmsDH1gP3rhY+SIMoZGWdT9liRnmqbBOpkvzUHaFnTB\noBeV90KILpkYZTUYfVzcpIsN2eoH0VJKWViURb5WLTbnez+JvMrDweB9wBhD8JEpiGZBZhlp+dwi\niTfir4gxLk5MlkoXpumH1Kj08eUBhSCtVNPx6NRSaejqisY5LmrHycmaqnJQaZTXuJywuzUvXr9B\n1xWhaByy580RqlqCKpX9XBpqVEFl2b/GLLhvreTpl1MBK7p9o83SH8sBgVYoKpQuKJVAK+aY+cff\ne4Elslu3fOfDN/zckwu2j065vXzDl9864f1nZ9S1w1lL9gNvjjOH62s2XcNxHvnKW1vu5ky3OaEz\nEvs1jqKTt4t7LflA7SoKEZ3BOMs8z1I+ZhYN/WKlRm7StGjpRTUJKGH2aQpOG5TTaJ2gJCY/MudM\nKpmmlfAOg0ixixJoixwIy2BLqYe9uxYBHcWC3XSM1jH5CaZRRC9KCbxEZ2xtUHrHNE2ERbasgBI0\nVVVjlWw6rJWZSUkyHK3qGpMzwzDgnGQTGKW4uhrZ7hyrtqOqHZVZVqipME6eaZ6pKvGAFBJNXVFr\ny6qxlJKoGoNPjtmL0OzoZ+6OE4OLC7INxjlIIpKzpKSYQ6QfR2l1rMxxTBFXpbUitjJaUPNzCJRU\nFoWjRWswyAbiXqKccqLMcsCmIvoXoxXRB+KyFbi/iUH+nLGG4APjOC6rYGFVmmXrlaM4brVaKFvl\n85YkhAAsmQz2hxSIepsM8dhDu+a9x1vO1nd0tmGaEyena3ScWa1brm/uKCkz5oLqoaoc7XaDtQpX\njHAKYkYZiMiNYKKgpIoqoOVCU0laAW0tZJkA13WFWowxFiVVgawTBLahxUueFQ/RYk8uzgEZgL33\nXo0uienuBlJk3bTyBDQQssYUS9cVqs2a2I/ouuOTyzdYEzndPcHawsvLG3bbDuNqTMms24oYAlf7\nnpPTDkJBIFaaULKYamIRQ80yzMoRrFl23/cmGSMDUF1EYisE1YyqLEolpuRJ44FkNMYagZcsFYG6\nl1d8YS9dltYhF1C5YJVBWWg2lpBrjseBq7sDXVvTNQ2alhA87cowTo7rK1FX+piouxpnEDWjsXKY\noFh1a+I8kuYBlGwCphiYZ5mGr09P5caOnn4/Mhe49jOrakVQCuMsWhlqqygGUAVbMuMQMRU45Xj5\nek8xmdY2zKlgNPgwctKc0FUNGI1dnuAhydS/Hzw3tyN1ZyymaiwAACAASURBVOgqR06Jumuwi8ch\nzAEfxIeQYiSGBFHcpF3rMFYz9SNlGe6FlEjLYBIt750zRtrXUphmL+rUyVO5GleJuAjAh8RwnIGJ\nqja0XUvXiEPyXroeQpRWErkWrNWS7vQgXvv+r9/xMFBKvQP8N8BjZIz6X5ZS/gul1BnwPwLvAd8D\n/s1Syu3yNX8B+PeQSvA/KKX8te/3vV9eXZPnxHvnZ2ys5eSko787orSmNY4pB6IvjLNH3Rxx24ba\nWfbXN7htR2cMJi+R2EXYAApDUeXz6K8l5AKWIZmx4rxTkFGkUrDlc6lxzgmMqAxV+jx5OC/jLV0K\nbz/aoRcdwMUplBw43NxBkVRilEZlDSWQlGJVtyinGVThvNL8+kefcb6pqFPAKi3Bq2lmP83kUjjb\ndEsa0QT7ImYWa1g3FT5HoRw9rAUhRkFjUzToJX8il4en8P26UavF9ouImKqcmZInjj3aWCytbCQU\naJZ0JWRivggXRfWI/LvV4sk3WuLYndGkXLCqUFlNtemYRsscAtpZ7PkZx3FmGCdKKfhBiNXKGtn+\nGHHlBT+jUdjKiLxXm8WLodi2NdFP+FgYfZQEoUrmELW1WA3rSvSQVlthDvYj1jps0gx5FBFXVNxN\nE87CZrNFYOmZum7o+55xyAwhibJ0YQ8klSlFYuXmGJn3g1QGWt6DlCFEQe1bY2mcVFpDP3I8DuIJ\n0ZDC0jbkjNEy9C0FQhR3aEpL6T9HfMjENFNljdGS36k0NO3CUtBKVIYF/ByXDYS0uEXFxdloZODr\np9+zziAAf66U8itKqTXwS0qpXwD+XeAXSin/qVLqPwL+PPDnlVLfBP4t4JvAc+D/UEr9SPk+Rupf\n/eAzSlTczJ7z1YanT3Y8Oj9nOO7Zbtb4mxkRYBdM7aiqijjPGKsI80wwIhIJIcmE3FmUL7jKUIyl\naBkmykRbL7bZQCnxYbMQQ4S06MSRE+R+gluW/XKpBLZxv16MMaONVAoSLGLIcocxhiBkoQI2JWzT\nkAKMwwyVZldvePLkjHVdsT8cUG2LdRVjGJinSMjyVHhxCPRjz09/9SmXSWGnmcmPWCpcnYUgpEWE\norQihbg47PTn68koT4eEyK810k/GBa9VLKRY8H7AD0aEWfczhhLFKmwXrfx9qVCWtgGIFIwSNR0x\n02jDk7Md0zwTF9GLlNqFrBKrTcembdn3I8M0M5SlqknCYLDWivJTSRbkPQyVHDHO0rUNx+NBDuiY\nqa0l5czK1szRk2ZP0YVeQVaFumiOkycqWBnD3M8ko2mWliPmRF1rVlVNiULF+uTlIH/fXPBKyndb\nQKvCZt1w6KfFf6CYfMRE+RmVYgnVlXZTJM3yfWISVaqWLHqpMo0GXdBOC2K/wDCMhOMkQBgjLVld\nu2XQmIgxYI2S1mPhRApkdYHP3b83KS9shMIwjChtF7FXfIC7/FMdBqWUl8DL5Z+PSql/tNzk/xrw\n88sf+6+B/2s5EP514H8opQTge0qp7wJ/CPh/f+v3NtrByvHq5oDVlvVe0aiC1WIB1VpTrxvU3uFD\nokoRazXjMdKoihBE5x3mQNU6nDXSIeeM0YKgVlFYB8qAWRJocoiys7eyj04+LhwDmZprbSheACZW\nGbQVMZJFU1IipoipHMQkE/+QKCpT1RX1uqVtNR9991M+vDziY+IP/vhXWa02DMcBlTPffP9dPv7k\nU4wxnHYdQWnCZMlmYrOqeHMXOF5dsk+Gfph5drpjHhrGfESXwpurW05Pt7SVqCjnGHCuIqdESgVt\nlbypRlaElTb4IE/zlGWT4IxZ9t+JEhJ+2DOhaTdb0ALQSMu6URnxQ0hHYpbGRNoG7odfSp6tWina\nxpGyIYTIPE4oIk0lPMaq0bTVlikkrocGPw3oDL7A6L2Eh6eZbDUGzdgfKBicVxADOUaqpsPVNUpZ\njv0Nh8PIatUsMFdNvz+iUIxGUa9rNtYxz4X9PNDWLXNWXGxbMo5+HOlvLsX1ubzPOss+frvphF05\n9Mwh0041SlVLCnOirRxNU5GCJ+VMa50cCAWmKeJZDufFEZmCKBCTCqIWzXCcZvwyjJzmQEgZlTO7\n9Zq2NczTgDKOymm6rkErJZFqujBOEg+vlcJPQURPWTYMdVNTOS1tsFZ0XYNdthf/1IfBF19KqfeB\nnwR+EXhSSnm1fOoV8GT552e/5cb/BDk8/onXH/jyY5ny25pGK7qVY/KR7aZmngI313syFft+ZlfD\nza1ns+5IORFSwSmHKgmsIWmN9rLSUXVFCUmsxUURl70wOHmyKYNB5LqqKKpGNPcyvLGi6oLPS7n8\neVCJMVJ+qgJFWwyKYg1xnqi6lpILf+cf/Cbf++iSm2Ogtpofu7nl5MvPOH38Hr/4t36JogT+Wdct\n/TBRrRva1qGKILtTSTxZW06i4dcv71Avrvk3/vhP8cFn0i60i0dgUcEzTwOpkojwQial8rBFKKmA\nEu6e9IwKnTNaF6l2lnaDkojTHq8LVVmhqxptpboS7KnYfCn33/te0SAleVGyRjNLkpSrqgUAqmjq\nuEhpgZJw9aIWVIXgDFpBKIpjP9FPI7nIYbZuagbvKUkAps4K3ksZI6zDcZL2zjlQmdo6jNaMJFTR\nKJUoPnB9cyQUTcgRWzxdq/DzyOAjGUvvM00rQbHaLg7HZBiHkZQKKSqaRkJ5YpihSF5GTImieCAg\nzT4xzzNzjOQkkmEtc0ORixuF0QJ4Oc6y4pvnwBgi2mratqXOhZjEOTuMQWY2KZCjXIvWmgXPn4he\nAK6gFyBMIiNehlKEvjSMnrp2HI89lbPyd/29HgZLi/A/A/9hKeWgvjBZKqUUdU+H/P6v7/u5XW2X\nfrNhPwV0Fsnu9WGmqxKnFycQxGwSVUQXw9DPrNqGfpgBS9OIRj3HRMgyZS1R1FxGZAcLsUZ24woN\nC/Xn3okopZcQmpXWy2GzrPQWOTJ6majnz3+gBYwuEtCqws+elDIOxztPH/Ejdc1uVaOUIUyRqpp4\n8tZjPnv1hnajeX17EJJQcMw5cnk3s6k03/rkmst9z7snKz7ae8I48Xf/8Ye8te1AGdrGPRiMQsw0\nbUtRi9HK6AcXnTBP1BKCIiWkuf+5S5HhqrJLCIyAZOPcC+K8XWGrZvFkGEw2FFNQmgdBi6wc9VI1\nyPQ6yWABlZdZQluhklv27HKTaKNxKtNZTVIVVSWzhnXj2I81d4cenSLOGp6enXLbT+zzkaI0UcE8\nDeIRKYrKaVzliMFzPBwWHmAgZhn+Xt/NoMWerWLG5xFnFIckLaQxkNKM044SI7OPZCWpT/PYY7SV\nmwtZS4I4MzWCOj8cehGJaSE8s+hUYFkIp4I1eoHmFPwU8THjk2hIlNa0bU1dWQzgF42EtjKvUUrI\nSuMsNHDFcu0Whc+Sp5BjWOzNUFWGylYP18fspcoOORArR9c1v+N9/rseBkophxwE/20p5S8vH36l\nlHpaSnmplHoLuFw+/inwzhe+/O3lY//E6xd+6Vti8dSGZ08u+NLpqfwAVhOnkbpZ4Yc9ZycnhDBT\nWcvYz7SuQ9eW/TTKOkyLEywZizZFfAtGDDgqCprbWENC0mV0ljdyTgpbOUxerMjL+jGViJ8mnJML\ngSi6Al1ZEpmSlpZC3QuUROte0KgceO98y11/lB4vKMasaSP4yxs2uvD2+U7ozdrCPHE9BDatw1nH\nZtvxjbcyfYz8xnWPz5CL4Rd+9VN+9FHHH//Jb9DHIulESoOSGYoqDXf9rezineQ+5CKbBV1kC6HU\nYoa5P82QTB+jFLU1OF2YQ8L7gTl6QtVR0hpXV1Cc7BINYgJbKgAxF/EFi7UMakky5NSLU1KZhReR\nZCePKkL3Xay2jXHYEDHKYdWK28OeYTjSVR2P1h1tZajblus3b8i5cBhHNusVWluG21smH2hWK3RO\nqMoSE0zek4rMV1L0YhLKhakfUa6icmpxByqGQ8/sZ1JRhBQ52WykQrDieBTOgIBUjLWi8kOhrCP4\ngJ88caFJKaVomloi02dPKjD7mXGaUBmscTijpTJd0OjT6DFaL1F6ATVmNtv1sioMFAXHYcTY6nOZ\nuDLE2UvLaxS1q8RMlRcbfhbX7ovPPuPq9cvlwfd7qAyUlAD/FfCtUsp//oVP/a/AvwP8J8v//+Uv\nfPy/V0r9Z0h78DXg736/7/3zP/Z16q4jzx7bVAzjJOPKGHGrNbc3N5jacWYtWkd0KCQSh0NP0xim\nOBHrlrubnrPHT0S0YQyuqTnc3lLXFXbxhicvNlHtxE+A0qQUiJNALe5BJdkPYr1tK6kIjEJVNbaI\nv56U5IDI9w6zIk+WqDjZtWgdOYxH/tavfo+Prg802jAMI3/uz/4Zzp6d0PcH1PUdfuh5dtrhx4bf\neP0h4yFwuupoXM1752tWmzV/4zuvuN6PBBJznLHrNdsObKmYvUSv2awlIKVkQoRNIxF0GOm5SxQx\nUmVExZgQqeu9TkHESwW5QxOVlTZijp5pCJQUKXkFdU1xFaZoilELCAZRKmaFhH3q+xUOwIOuviDD\nVq0Mwn5Ii66joJNUbtZqjK6ojKGpKrrGcTz0sGDmqhQIk0flhLGG85NTxjBwfZxxxtBtOtbriv5m\nYO4Hdl3NPXtwHjxKZ7rK4nOA5aYZhpmmqrCVZb1pUb1mXhD1V/s9zmi0l7i9tm1wrcSn5RTFmlyE\nbRlCRmvLqnGELHmGOQSmJEE+SsvDyWGYkqwMQ1wYi8pIhZkzXVdjKo1ztcwdxkkAKUoGhW3ToJRh\nHid8zDgnswSBuETBni1KR2sNfT+gUJycnvL8+Vs4K+K7v/dLv/xPdxgAPwP828A/VEr9/eVjfwH4\nj4G/pJT691lWi8sF8C2l1F8CvoWs/f9sued6/9b/sDEScRUjxYMuEJfy0enCphOzydgfRcseEofk\nebTdcH04sm0qxjniVh0USbMJzlGXJcEmxAWdDk1doZMMz/QyqdVIbNY8xcVcZ7BOUy0AUYVgriii\nI9dKfSGCXSbqRmtUEYnzNEaUdfy9D674jTcjt1NBxYBBuHfWKXTwtJsOt16hb2+xKvLNd5/w2cs3\nbDpHVSmOUfOltUN99Smv746YtiGGnpOq5fKuxxTLNAe2509IRqjLkNmuO9RyW99j0ypnJMMgy7TZ\nKiUHgmxSpW1Yun9rNFGL487oggkZ73vGOJPajrpdUaoK6yxpcejJk1/w62Vxcyq9SKGWUl6rzwU0\nANqaz9uXpJmmcVmzabq2IsVEXRkqbZjGkXYjIqL9HGjaljlETlct6phRaSYC4zQxHvdoY5nGiRQm\nVps1ldGCSS+Km9sDTVNhEMBNCDJ41Siur/fkxZVYVRVV06GUWICHeSYrJdqAlIll8Qwo6dOtUiiV\nCX6iKDEPhWUlapzFTxMpyLjqHr1GuR/DytA6KQGW3NOnFAJuZRHDWZUgRiJRthBFoXKmaWq8T3jv\niWNaDHbqAZ8vsmfhMpRsFl/Lb//63bYJfxPQv82n/8Rv8zV/EfiLv+N/FdAWKqNIUdJoq7bBhkAM\nmVxntHU0VcXN1Z7NbsPoE7Vp+PTyBrda8fxkR4yZeS74fkTnIGvHYyCXRDSaMHusUVTLE8ZVkpEY\nk1QK917wfhKC7Nnq5POet67lQPDSC5q8KNAWiahSGmUNKidc3RFCpOA4fesJf3R1wpQSk5+5qBU6\n3NFfz5RxoN3uaE4e8SYk1NDz9qMntHXHcR759ZdXnDeaT/qe06bi6fuPSRH2R0M0hdvbmevjLSdr\nR7l+zYRi29XYDBMwF4VTanlqyzbQKkOgILxkuYisXiLWl1KyqOUAVGCywuqC1QqXJEQkDj3JB6qu\no27bB0qUsZaiy+diFoV4JJRMtu+BMSprmXXdm3IWzb0xC8gzRExtmBcFoVWRVSeBs45M6wy5JPTm\nhHEKVEre87ffOuezN9d0qxUvXie6RhQ8tqrIIRJjoChFLFq0KCFyO3iwjq5bkeYZoxJhlu0RSrIL\nu/WKHNNicRY1l58mCpqQs2RTLq5CZfTStkmbWVISrqEpECTYpHZucbwLxjwvPX0Ms0BQlViVnRZa\ntE9JoMDI76xrKzElaUmHWtViGY8xMg4DSktrEmKQNbP+fMU5jAPBe9ZduxinfvvXD0yBeNiP7HaO\npxcX9NFze7Nnt90wzSPTMFDVDXM/4XYbqk2DbWvurgcePXnOv/TH/iif/Mav8vrDT9nsTonB8/ik\ng6S4vrri7Okjhqu7xf0nSSdGI8GgJaOLIgeZjDvEZLLvj7S1rG+oNdtVK8QhW4iTJ2iFTpk8zsIT\nsGax+iLDRyDNM29vtqgTGR4pa6jSjO9n1HEiB5jiAXuYpe3ImWzgfLdhOzWsVmuuXr3gwxd3jP4V\nbb1lszY825xgE3w0TVzOM6TI7GfuRsPHFN57csHJifTQzJ6YRRE3x0ywoklvq0Z0F4oFjPE5bVkp\n2ffLk0USr40t6JhxWbIcRz8yHSTHsV2txc5bCiz2aoBFDY36AusvL9Ld++qg5Pwgb1Qo2rbF2yiE\n4CRPzMpZrMpsu4bXH13yzpef0QaFyZ5r76mtY3NW8Wjb0fcRbSJfe/8xt1OinmfUfHywmGc0u92G\nq9s98+R5cnqOUp7jNFO05mbf44zDFmiXsBNHIhlHU3f0/YH94UBjLLZyZGBeTELaGFarFYpCiIlQ\nCj4lQojUxuIqK5uqGBZsOvhhIC4Mh65rsY1hHmeGaRKpcsokMqtuJahz70kJ6qaFLOa7QqHve+Y5\nLvqPJO7KuiFqkUJPS54EqdC2LSkn/PxDmptwuZ85+hv2/cjFds3Z+Snr3Y5tily/foMpmkChsxaX\nhD6zO9sQjea7v/z3mKaJujEM/YHVdkeOiv04YrqOtt3Smx5XMsoppphIKeJsouoEvJmLiDWS0RhV\nYRW8ur5l03W4Yuiv9qw2nXgY7P1VLqvEUgoqL6pHq8lEqRSMXcwvMp/I1sCcKCOEJQZLa8s4jNjK\nCeE2JopOWFtYhYA9P+Wkbvjw9RU+SEhKVhndreDqijdXM6dPtyTv+fjlG14M8Cuf3vL7n++42DZc\n33l88rxzvqNqa7wxFLesFReFWix5GTBK+K08ocV/QZLtg1ZKjDXLQaoVzDERhgPRz7i2pW46qrpC\nIU8cWTlm8uJuuh8s3ttoRR0nLYvAZOVp2lRi9TVKLXJokRN3taM72/Dy8jWbrqbd7NgpxTB5HrUb\nDne3PL3YcbO/o1KFtVNkU3PrR2yBVdOinGEcJDrOOIvPnujFXGWMrGpJWViJ0S8Qk0zXtpQUZI1J\nIZSIVlaUqBTUwh2Yp/nzNkjL79ktBCmZ2UT8MvG3S4Va40R1OAU8Hh9FcJai4NCUlVbQKGn1coyM\nvcy2oir4WTgMxt6DS2TVmedAzolqsVQ7a+W9jomcIvGHNUTlo+s7VrWjPQ5c7QeenO4YRomD2mxX\npDHS1i1V01GGo8AgsazamuQ9+5s3tLZCdy3NdoPzkfn4itXJKVcvX0sqTS1yYTFpGIpxxFCW3q+I\ncrAoWuuoKstt7nHrBmcNJRX8OFNKEf2B/nzPXrQMzYQwltEFSkqYJfwjFTCVI4491ekpXfIcbw+8\nuOtpuo5xnLHKsN7UdNtT8tCTlOJuf0dlatq24vn5KeM041Ydqt8TkuftTpOfXfB4VaGc5c2UedR4\nirF8ernngxfXDHPmy892dJXicLzjLmQer1uUatBKcigVRVyZS08qCb8ylU7IDStGSREUucXnr43C\nx0xInrkP+HHEVQ1121LVok0wWqOtQWmDNuVBr6G1kQpkUeClh5Xn522GWQw9MlyzOKd4+viMYZwZ\njgdu727IWXF2dsZwHOmPkXVraKpTjkOgaJns79ZbGpMFKFtVTONIt9kyThPDPNHWDj9F8v28yhoh\nJVMoKXL0kXkepOxXEvaaYniAyaS44PUAVFqGeY5UitCotSLlTIiyQtSIOKtEmTuEIq1qyllaNHX/\n81ucc6LZ0IvPIcmcZxpHGleJld5Y6qoSDqJ1hGwYBhkY5pRI92TmRb9QSpZ8i9/lnvyBHQb7OZBN\nxZt+QNHzyXVPW8k0+UefP6IA9ph48qRm9fwZ8yRwCn8YmFLkOPfEGFmtGkKObHZr6tcV4+Ud7cWG\ndrVlONwIkCKkpbdTTPPIXArWijFI5JwZZxXnTSfinZSX2BSDKYkQI6WpqIxM6EvKC4xUyj0Qim02\nmrlktE+8vr7l6fNHnK5XfPrtz7jte37t9Z5vfXTLv/oT79A+esrZScX2S8+ZvGL/7W9j0ByPbyAZ\n7OaMMN3w9Okj9iGyOtkyuYbu9oZud0GcJ/7A07e43B/47PbIp1nx8U3g7bOKd09XfOnxBamt+NbH\nr/nOBx/x/rvQ6BqjFxSWEmGOyjJgnQiSwmwkDNRaQy76AdRh7mEcFJwRtZ6Pkel4x9gfqOqGdr3G\n1TUmCeqsJNk8pFJAC/G5quyihFOLD0KeoveVhOzXpSc3taaQWSuJYAcYDj2qZMIs69/Lq2usrtE5\ncnqyQZ9umObIp1fXmDnQKItVGlUCtnIcvWceZnyI7HZbamO4e/2Kfp5Jy8OhqzoKmaigRKEdGVPR\nugrnDGGaGfoBV1WQFFonxiAyz7qqiV5gsiJOWjDp1tI1HUpByPHBfai1XtjSEpJaSqIfD6hiRIKf\nRMyUl1lV3/fUTkJkrLHs9weUFSxfTCI5jilSVbVoDELAGujalkPf/4735A/sMDDO4MPEnAupFMbj\n4s3ThVf7Pe9fnPNTX37OOE+4/R270zUu9BzP1uRxptFPGHIiDBMvP/iY+v13cV2Dzz02a26vb9BW\nUoB0bRl8wrZaQCIpEKNHLzzEjMbnTG1Z8OKeZC3Ji47fhyhOR6MWG6ic/KoylCyns7E1kLHZgk4y\nVR9neu/5n37xu8zDyKw1N70n7/e8/5ULqqLYf/YCNU9UF2dwd8Mv/spL/oX3n7AykYuzM/avbnEn\njnhzxebRU9TdLd/+4CO+/u4j5lxhXcWLm1t+/vc/59XdlrdP1nTrFSHO6FnxaNXy4vSUYQ64rmby\nkxiNlKgpy/Ikrp0hW7Xo3ZMEiigFRiGoARErqQeL7BI4UilizoS55xBmTNVQtx11XWOsJZsFme7M\n4sf3AgFdBm4RaQvuh29l+Xf1haGkteKsjCmz2q6YfZRINu9JITJMd5yfPyHPgTkeUdpx1tSMypDj\nyPZ0h0USk3fbLfM04efA/nAgVY4eMF3Lpqpom4bbw5GuXVFQ7A93y4q1QHTMs1COlIJpHMQs5CrM\nEkXvp/mBQWCspXIObUV3EVNaDHVi9JJZQ1gGriL6SjGKqE0rhkmyQdq2FsNZEuFciJnUz1Dk88UH\nNHKYUIrY30uW1i9LSGxYEpp+p9cP7DBwCy/OmYTTS549GtfVVEuAxe3tHc/feUIYe+6SwCxU6Emz\n8OnW644ZD0px8/o109TLcM1UTN5zdrHl2I+EMbNabYilkGJm2PfUbUO37TApk33Gi9dR1oQhkPqJ\nbr2iGPGSZx/QbS2iIy2qMmZ5GhhToGR0Lg8JRudWYriny2v+4Nff4XrK9NPMj1ewO1kRhkSpWq4P\ne/S4Z7ub2HQNp9s1btVilfD99seJF59c4/zE0xz59gef8MmV53uXV/RppiQF2VKmzI+caLLJVGSu\nx4L1AxWGH336CJUSytXEAil4KVtLkUjwsKxXl/bH6PvyXS1hrTIjEcXhEvcFoOTPplxwJhNixI9H\n0jwR6oa6afDG4uparOR8TvYt+gta+WUyr9UXMiGLDMrutw8y4BSYjbWa2j2mHycO/cD1VeLm9g2b\nbsPFxSm+FMrguesH0jBz4hxt1RCMxlQVoy3YdUc1OIZh5KRZ4XMkFmmTdquOEDNTTHRtS20lWHUa\nB0nfMk7qRmNQLORkBJ2WksyPSikMw4A15iHjMHj/gGWXwyPK9bowB1gMW9ZaQioPnI04eaZ7FoTc\nBdhqYTUmSdcW9Fp+IFeJWUki7qcpisbjn5U34Z/1K8XE6cmO959e0PcDTWXYtAaVHcrC+49X+AJd\n1fDp9WvWXU1QhmmY6bZbtNPMQ88cPF23pnKKcVJYt6b3IyXP1KM80V6OI68PPWdn52x2OzbG4srI\nziZoO6Z+oswBpzRjKqSwRIMrQzby9CImpnGmXbWQC6VEVExYLRisqm4EChI8KE2eZ0pVoUrm+XnL\n06RJ5ZR61WFLT4gQ8kTjGsp6y/XrNzQKHq9qGlW4eXPLkyePsF2D8xN5VXEzZvoQiNry6RgocySU\nTFPgb3/3NX/6p7/Gm5sbdo3mzZQ53ezQztAlR7RBNBNLfDeA0kr4C9qJTbgUKuMwRRGjp6S4pExL\n3JyCB+aiUnJjK0TBqY3cDC5JalKceuapFy7BakWXVzhtqaoKV9kHgMr9ijGrZQ6zAGnL4sUvy0DT\nqAUVnsEqCbHZrFqaxnF+smUMnk8/ecV3/tG3uTg5YX2y40tvnXD058z9gbvDHY8eP2YOgUpbamcZ\n+szZ8yccj4M4LUOkqu5zGCNjPxKMwmA52W1QZc314cg8e5y1KNMsa8GCIjJNI+LqlEqn6zruMw1k\n0CehLKUUsk4LVblI7HvKAjEJnpyLHAJJmJMaTQpykKRwX1nJA885J6j0SuLrYgjMcxDNSZHcz6qq\nKCUSgv8d70n122iC/rm+lFLlJ7/5DXRRnHQttauoneZ81wopZvT8xFcfsaocaIvbbDje3lFCxJeM\nsoZH52dcv3rDPdy06RqSj8wpMOREjSOFSL06oW4dRUV8dhBmamc42Z2xdtC2sB+O5OpEVjPXL/Dz\nvAytPMkYplyIs7ATu65h1dZSZitFIlM5h60ESmIVaFMR80z0kvBbKo3VFn8caeqaaETZm5jRtsVh\nlr7acvfqJUUrbqaJNI7oqub2+shXv/5lxjny7Nzy3/31f8z3Lm8wxRJSJHhPVUV++u0zfu4bz7m+\nPrJ58pgpSFtjKkOcItpYfBC1WilFdALLTVaKIkS5Vkq0mwAAIABJREFUkAosfL60KDbNookXKXfM\n+QGbLuavhcK7cAdilG2Fj4lUoChpz7rVmqqu5X9V9SCKuecuKs3DQSVDNdniiMFGbhyLpdiCTrKq\ny8sgLyZJHDoeJi6vr7l+9YoUCienO1brjhfXN2yM5p2vfJn99YHRjwx9j6kqfBKepFEwDQMnZ+cE\nBdfDREmRFGf640RT1RhXoa1m7AfZJKApRrgUKkt1lBdq8X3smig+5eeJMZFJD/gz2c7K4DCn9CB4\nizFxeiqUqHGSTUgMcvMrYJjG5VDWyzYjU9lFsBTC8j0CZhnWtnWN1pr/7a/8L5RS1G+9J+EHWBmU\nZeJ6OY4000QGXt0NOAOPt2tujp7ZJTablkeVo318ztyP7K/vOAwDVwtTPqdE1TUC2ExS2m6UJnqP\nbizOBMbDLD1hq5lCpsweM9+xvXjC7BP76wP1qeP09AQ/C1NvniI+K+aQOHoPpbBSmhzlokQbYhHl\nXC4SAhtzISgFOXC+3dDsDOP1FTkaboZbvvPhNT5l+hg4HDNff7rhZ/7YzzIdb4hHz/HmEqzGBU8X\nAnfAm8sbUol88J3v8uhkQ2rO+bHHG0oxXO735LkwR0PUhbpbsd6tqbsVN0MgZ4FrlGNhu90QU8BV\nBpUE/JFSJGvBbKcQF/CyBHuwcPtTknzJotSDTVYrsTYv1fziATBLzkKWC3B5Lx6Sg3JkPNwxHi11\n21A3LXXTPFQG95iwHKNUB1+YK6R0305AVpniJfj1nhAJmWoZVJ6fbdjuVoxvPyGMkdHPeO85OT3l\ncHXLr/3ar3J+eo5pG6rUCfsizBRtCTmgq5p+HOlWHRebNX6amftMt61Q2nCcPWA53e3wzcRhHBln\nT4ywblrII/08Yp2jciIWksMzYY3FVQtLMyWMsUvmYiZ40QXEGB7CYK/fBFnJKg0LXDUtTEZnHTFn\ncXIum2/v/eJZkTmQMYtYS2t8DFTmhxR7pjMUJbhpn4RNOIdRtOb1RMkb2q6j261JJJpVh/aR1DZy\nAcaM2rYonyhaY7qWME2CB68rpsVfj4JSaQ6HA/0hs21btqsV/e0tt2miOjvj5PSM1in0/prQT3z7\no1umIqKd232PMopdW9N1Neu2pqkWfsAcqNpK/PwhMvkgJh7ruLu6JjqFnSIff/wZv/z6yEfXRzSW\nbdfwYn/k5uaGP/UvF9Znz5iOgV/7vz9ivLtiheLZl97mWcpsdjspG1PGti2v7wZaFfjD75yS7WP2\nhwMZTWHm2arm1asb9v3EgOLZo3NK1PTjzOvhirq1bDanaGXJOi4iF1BZnlzWWupKLrKQJdcPCyZb\n6eFLIZlFgVkWtr/WqCJrMF0kWiwj2QA6RqISRFtaoJwheaajZ+qP2LqhaVuaboVddv16ySzUi4lH\nlJQaskIZRcrCdUwpUhbWm4KH2DaVM04rqqYlV5lSVhImkyPzbsfl1Rs+fvmSXduxWW3QNeh1Ja4/\nt3qIYj/OHh8S0YvIKxZom5ZVU0GKZB9o6wqtFZ115GVAqKqa1shTPOck1YyRv2cMHr24OzOQYqAE\nyVS8NxHJNiCBysxzRGuRyYdpXmzpWfQFWj9UQyBELoGnLE5bpXDG4qzFWJkv/J7gJv88XwWxC5uS\n6ZYQkccXO5racNE4NpsKVxL9zR2ubWjnSD9N5NpJvzcMVMYSVMaExHx1gypZYCOLndnHmco6YR34\nSHN+weat53D7hlINfHg70qUjJycn3Lx8hWs7PvjwFW/6gVFXtK3h/OIMpwq7tSOHgE8BEzQ6ZbCK\nHD06O4x2tK3GNDVx39P7yHeuRsLNnvOvfo189f9xvtqQyWxax81YcRMn/sHf/RX+yJ/6k6zfO+Nn\nfjby8uMXXF++4c3LF3z9a7+fNx99wPvPz7mdIPuR7tEjfJy43nt+9KLhzeYxZu6BlrxAW9O6oQkz\nJUOpG1pruZlmPnt9yzdcIbgVVmlWzjKnjHU1xjrmmFApLrTfjF9KdKUSKQNFY0omLxdVyhKprhF4\nTM6ZKQhZymhNbQ162RZV1jB5T2U0VsvXhunAYR4IwVPVLc5VNF0LZGKWkjj7yFwSVdUQfVxw4cgT\nMGdUFnNUznGpJJbwEZZBmlJUlabOFfPOcbJZ8aV33ubDzz7hw48+IfYzTdexPrkgzqOE+J5u6FqL\n3m2YQyTMEasEmqOrVgC1KXHzei9P/MbiR8/UDw/hN6pkXCUuw8l7sIbaye/NpEQOkovoFyVjXJ76\ntbMPGyuQgFg/eLpuJUDUnFmvV8ScsYi8OeUkmZVJKglrjQS2OrvI6zMxRb5vb/CF1w9sZvCn/8TP\no2Ok6ypUTjS142zbibY+BlZNTSyZWok02FnHMXh0Nox+plhN3bWkaWaeZzbdikAm9BOulotq6Aec\nsWgDx16gm2jN9vSMx2894tWLS8ZpYt8nzp8+ZV0p+v6KGOTiq6wl9JO4qJSCEJZ1GeRxoRU7iyky\n0HJK42OiOzul+MDc94RZ886Xz/jrv/RtPnxxxbqx/OQ7J/ztD/bc9J7f9/YZ/8of+gZtU7GfZTft\nk2IcBubbW17cjEzDHY8fXbA7q7h++YaTzRm+abj77BNAs+vW1LXo5l+8uuVi1/LRzYFHXcN2tyOE\nGdtUoK1YuClM0yQrMjSmqilEgWKERFb3T3/NvaUml/vocVEQij9DPp6WNJ/7PjklGZTpRe+vlLSE\n9zh3wbXJ78rHRCxQMFhb0a5W0j4sfbRZvofWaknYXgaX98aoxYmZl778i9DP+9QjrSVJqyB/RxCZ\n+hwi+77nxWevuby+wVYOV9U4Y/FzpHGWzboRF6efUFGe/s5WdHWFrh0ZMWqlZdsVk2DN/TJDua9w\n7unH2miKAh+8XD9KE4Jf2ilkQ/AFQVZMUQ4VBG5jlJFkJK0XtLqROcRiTIopLi5ceY8q6wTuGsQK\n/dd+4X//bWcGP7DD4M/8yZ+jVnC+ackl47SmbhzdqsPEZdgVPco63ILWSjFTGccQg4RbImATs4RF\nRCMXWt22OGNEH1AURhX640jKssO1ribFhC6Frl3RY8kl0OTIsO+5GUaevPWEt56c4ZpOiL3jyDB5\nEgLvTNETtZR4+8NMPw48Wze8/ZW3WbeGcH3FdDhQcsVNv2efHL3W1Bkery17U1E5y844VO1gOFDv\nziRCbDywahtss+bu5o6XN3c0pbBu4FsfXfL07ISmqyWkNWXGJa16ngq//L0XtK7w2UEm4rumZldV\nXPczz09XdF3D65s9e594tGt47/GZ6OpVom5rjtcHulVL16wAAcCkJKEpqRS0tQ9pv3khMd9vrO41\nAiUXhnGGZR8PoLSAOZF7EYCQBDoqT0Wx/CY02spN2bQtdS1PYmU+D4u9H5QpJbMnozRFF4G9L05J\nreUg00o94MjKItOVDUBexE4CvrkbJm7vDrx6fUXJmfVqRQaCD0xTIOXCtu0oKokBzDnJajD3DETQ\nxkqLFSIhRmEKKKFv38NPYgiyucji2E2L+vM+YTultHAh5Tfqg+c+del+6BiisDYBmqpZ+BJyIJbl\noBXX4gLkUUJaUgr+6i/81R++AeJvfvqGde2ougo3z6x3p4Tome722NpyumqYb4444coQKstMll5z\ntYJSaE5XHG/3dK4Rs1CKGBRmzkSVKDkSpkDRltW6Ed22MdR4nLP4WPjsxQu+96pn9eiUrYo03ZaL\n3ZZtpXBlhvqMfHVFbQxhvSX1NygfICRCSQyHSTgHDWgbOX/2lEfvPOf13/k/yaPman/HXR/41Y9f\ncDPDu2eav33lWTmFCYUf/ZFnvPOo4vJyZPXmmpNnTyhk+hcvaC4e0ZTIeWvx+wMutfyBZ2v00yec\nBHi9H6kvajaD57A/0NSOi23LZzcTXzlf8fp65s0x05wpstOUpqKua25Kyy9//DEXNzXHKXC6qvnN\nq4nvfHDJ9mzLOxuDKpExWuaQePfJmq+/9xzHcpEtCK2cs7jrilB48yI9LqXQdq1sSO4l3KWIMYuy\nYNvAYSjaYHICZ4kxEXIm5YDvZ8bjHmUMbdexXm/p1muKj/iUKSpjlabqWlnXGXFMyumjyTHKgaD1\nw3S/qIJVikwUcpUW2K1xgd2qYdvWvP/sgldXN3zw0QtsLrz/7jMSio8vb7jeH1itOzarlsNhYh56\nnDPYpqGxDq1kU1VrhXVWFJrThDGOxkrUWzGa2jZQCj4ErHVUTUM/TUzzhPfiLxDkuUcnu2gvltY6\nZeqmEbqS95QsLdH9711rxdj3iw9F2uV7wZb9XQaIP7DK4Ce++fuoNPz0u89wW4fLsG4rzBIdVrUV\n4+HArumIrcMfBooTaEcIiccXFxwPB7CGPM0L91/8cyHlBzx4VTnWXc2T862wBqMnTxPHfuTybuI7\nr+74tZdHrvYjlbX84fcueP9shTaZ3dkJn7w88tmr10wl8db5KU/PVzx79zHHu555OPLq8pZffz3w\n5hhpG8fXH295drplf3fk9NEOrwy7VuMT/MOPL/n40z0v+oGTruVnf+x9xv2e3/fjP8Xlb/46qXge\nbRq61Sm5sujkKSnRmIq9NvjbW/7+tz7h+UXDdi2DrGq1BTJ10xKTws+BYRyYsmd3ck4AuHkFuuai\nGjkmzesh8Vf+0SU3R0+dI2+fbfjme2/hKsU0jHz39S13/UxdGz67EeHSj79zzo995S2quiGEuASD\nLDCVAnMIQi5ytZB7U1wewHrBqkVSltYh3VcESdiNMS9qORSpJFhaj5jBxyWt2DqMcZycnlKcw1pN\nYxyJQt00hBSpllzMjAzkpHpYoG9aE0umUlbWkYtWAe75FpqSBSxamZqkElc3e66vbmkqy3q7JoTE\ni9dXHG731At9iWzpx0m0GDljSqHrGuqmEmWqcxz7gUM/kooM/HJamA9a1opGaRF/JVlNjuMo2QmL\nDkGCViQ+rVDQRoA2JSViSAI+XbYIsKhkF0GSWsRdeaE+/T9/82/88FUGP/b+W7ROUWnDxjlyzFRa\n0+7W7F/fMA0R5Sx9msl7L+ETYUIph6k0tjOEyxFbWVxdMYdE0UswZkyiErOaafKsKifqtXZFmRTz\ncSR4T0iBu7lwmCPZFIYc+MUPX/Kdlw1ffnrC1zYXvPeNr/LOV97h1XHm2eMLKiIffve7nLczrjuB\ndsdqBWenLSebltO2wpNx2xUYwzh53n3vy2g/87PrhqunI6w6WR9WmqvasLKe001FKS2mqTj6kU7V\nHPojq6rC4ykzrHZb/vC/+DX8NBO15nh7B8oQjwPKaW7vBlpT4UPi0A/cHmZ2iwvzMAzcpJlV15KV\n48n5GdrsKT4yJLjrb/kj773Fav2c568P/P3f+Iy7vuekjby4GfmHH1+iVOYnv/E+Z7s10zTjY743\nc1JZjS55EeyUBYIhkW0pRawGpST6S1tFRDj/qhQqc7/GTGis+COSzBMqZx40DyEkbq4uQTtMZdls\ntriqoswzlEwwss0oWi+HVPn/2zuTGEuSs47/vohc31qvtu7qvWemB49t8Bh7RuAFcfEyHDCc4GaB\nxAkBEgcsc4EjQkLixgUjGR+MhBCWOVh40WCBwJ4Ze1aPu2fr7unuqnq1vKq35suMzAgOkdXTHtxj\nG4vusnh/6amy4r2q+kJR+WUs/+//v21Yqp1n9FV1JYAv5fZsvqosGY7GdHtdFEJhC0QcS52UTqvB\nPMuZZRk4y8WNdcz6KlleUBQ548kUXIE1QuX83sh0OKY5D+i0W4RYlFgaSeTdoauKwviNPl9+7Avd\ngjDAhVDZgFYaU5Qlk9mcqvRqXLeL7epyI2cdQRgRaC/Maoy3WLu9jKhLyF3lZw1hGKLkmJ4mlLMp\nvdPrRDpkPpsQ6IjBwYQVDSoIMLmh0Wsyn84QayhLRZq2UMoihWWwve1rhW2JituIy8jmBaEorAQo\npwkkZJZN2LUlxhhOTnLa7YgkCamqiM3NAVc2D8gqEAKvSiuKvBSKrUN6yx2agSWJNJfOnWEymPDS\n5Su0Oi0C0YwHE+Io5OKpdXTagnyCFQOlJUy8W9NqI6V/4wZhvcMeUJKUJQd5SbPRox1WZOMhOk0I\nVOSTlHUMBockzQaD0ZjlTsev2ytHNZ5SaUWv0aG1EXG4N2DrcMSz37nKrPKyYFlRkhWWJFBsLLf4\nyPvOUOZzpvOSvdGEi2sxn3ioQxytczArGA9nrPZaHGyPGKZzmmnKSmpRubC0tkJv1bKzN2KSV4jJ\naMRtdNggmGWY0pd0ox2iS5Txx2FFWWFtRagrHF5qTaxFB97bMVCK0nhSjrGOAL8x6MttPTvvSM3X\nWeUtNBBMZcjMDJM7ivkcpQLSZkqaNrBh6AVMxYu/BnXRjgTasymVQil/E1bGb+4dmcq2Wi2veu0n\nDd7rQCu04F2imqmfzRSGYj6hzGaESnF6bQ0JNNN5zt5gxHScUTrH1nDEzt4up1eW6a6uECYho+GY\nyTSjqglcyjlCFHNbEtbeIFVVkkYBodJoEpQKmeVzZlmG1srXVdRK3mW9FBJ8AZhXOPLiNaKEIKj3\n1ep9iTw/pgzER3/uEZqBohUl/Mp7T9JuJ9zYHtBMUuI44WA0BbEoCYg1ZLl/2nQaIaO8wmHqTJsR\naUVTB6gwoNGMscYSRgGlKHKrmU8yQgnQIbSigNVukyJwPPnSmzz9Sv+2CIdoRTPy/6ylKVmONB96\n37s4uxzTTBtkpuTm7oCzD1yk21siq4QbL75Id7mNKh1lWRJEIYE1GPA8fRRmOKF3egWVtMmGI6JY\nqJyG3BJ3GxSHGd1uzP7eHlGjSxInFJFiPhhjXUmgoQpCYhsiccTq6VVWe5pJf5NnnnuDb7w6YG9W\nQFXiVEUiIau9NqosefBEh8O9MUSaVFcknR6v39ilq4WPPLLGpbMdNgczXrk5Ym9qyAvD6/0p+7mj\nk8RcWk14z9kerZUl4igkn85JghDREcYKQeiY5TPySoiCmAAvQV9q5YU6Sj+V9X5qgjVHrDzvuGxM\n4clOStdVkwFFkeNqhyXPKfDj4+p1tidAKUrryAt/Q6A1aZISpSlJnHirtXpj2YpXERJ5S+ZN3d5k\n88pMlbi6gtI/qeVIkwDnly6lL/ixStWCpMJsljGeTshzQ6RD4iAgqwyDwwPyovIsxv093GzG+TOn\nWD+5QZ4bRrOMeWGYz3O09ia7R9JvOG/GYqqKKI2xWIqiQukjolhFXlVkR9Tnqqxl/DVlre4U1iXk\n1nrikac2+xnGN//jyeO3TKiUZeQcrpxwarlJJw1IVZfXtwbc2h2wN8vJjCegLKUhaaDpJjApFJO5\nY6OXUFUZY1NiKih0RTaZUmQhTiwnOimrS21GWcE0ASeWeek4nDvysKLRaaJ1g0YjxZQVlfVqx0X1\nllORqSxxJyXqNJiORjgVcuHhh1GTIS89d50zJ9eJ2x1oLTHr77F3MCJKI7pJSqQts4mhcMJSr+Wf\n1GZKGEZU0zlhJFzp9zkna+AqXn5ti5XlDlEIhhJmJRJpxlu7BM2UZkNxUOSU05J+f4t153joEx/m\nm1/4FiP3lvfiapzwS2darHZjVKJ5+vIurw7m4Lw564MnHR9//BJihbww/MtTb/KuSycJWy3y6Yhc\nFK1eyKl2jNLCC9f3uLw/40Jvl0fPr2LRhHFMpwmFcXREMHPD89d2mec559fb4ALOrXdppSkmCCkj\nVz9xHeaO48iqMoShpjRe89/7OzjCQFNaiAMNVelLnbUgKiDSAfM8R4ea3BSkzYhrW3usdtvMRjnF\nbMpYh8RxRBgnpGmDOIkpVa0QrfVty7mj41FE3fYjsFJ7StTHc/4m0dhA18xrX/ZeOv80XkuWEbw9\nmjGGiIhOM2Y2mzM8HJH0lpg1GtzY3mWrv0u326XV6fgNy2aDoigoCsPWTp+N9XWv+FTTi7PZzB8Z\nOpDIs22ttb7eIDjSNvT9KvL89gmLtV7QpKqTQVgLsRzbQiXBa/Y/vNKik2iiVkzPVbS0sFlaBvOC\nzAglFf0hrCWKD7zvLEJO3xnMdMLEVUyykkgiSi2M5wU6VIRpzF6WEylHkRXghINpQVEphgVsv3Kd\nRhBQWViOA7IgZJLlKAWNIGC12+XMcsyFiydY6bQoDsck3Q6Hkwo1mnJwa4unr93kxLnzJJUlyIfE\nQc7Kaspg/4BCO6yO0J0mRX9IuhwznsEsMGjmDAYTmp0WptJMRwPa7RaNdtu7TM0tB/ND2kmDINSE\ny12C0lIMDjE6wDqhCEL28oz3dDt87Ilf5R+/8m88tNpEuZLHH+jQk4rxvOJgP+f8+hK95ZI393P6\nhzO+f+OQ0eHzPHZumeVOg/+6sskbA4PTYK0wmRVYYDCaMTOFN5El4upBiXJ7/MLpJeIkYlYY4iRk\nWhqSQGikCdf3p9wc9Ckq4dGDAa3QqwYvr/fAhQQqIE5jTG25Xkm9sRV6ByYEtPLr4ShQtyXSgsBb\nqftTCUsSRaCENG5SGMP2/j4XNlapanpubmbk5Rwzm2KyjDhNSZLUK1sruT1jqKylcur27MA668Vd\n8WpGlb1D7t2XQtS29b5A62iDzwmIWOI4qGc9IWmc0uu2qZzFWGE8ydjf3WF7u8+NzU2iOKLd7rC6\nukZ3qc33Xr3CA+fO1GYpASKK0loKUzHPptjS+Bdens05aMQJ2TyrzX79TMlZbzSD9bwFb+fui5uO\ncTIwbDRSWg3h5mafl7cmXFju0Vjq0Rw7TocxV29tY6oQHUWc3OjQbcLhbkEkMDaOYQ7b45JTqeLA\n5JRaoSYFYSEEaJaX2kgrxOwPsVjG2ZTDyZxJrrhl5igJMEVBGmk+cG6dhy+cpNfpUhyOybOMk6sb\nOAqmbsD1V3dIeycYZfscGsNgOCM/mGDF0d/aItKOCx94lNf7z5O6jI7VvHFrm/apVd64vsvyqTUi\nHKYC3Va0T56gurXFfhny8tUdIhXw4Lk1RoVhHsLy2pKn1ZIynhlmoSFVGqNKklJQLc3w20/xi6dW\n+UYakg1zXD7mmWnBxOa0GytEccWlEy3e3Vvig5cCrh5W7O1N2DyY861bY9qbmVceSlKub/dpiGai\nFLk13tlZBYRWYWSOiGI/E/rjKY+uJKg05snn3+TZV7dxQYjTgrEloiHREMYdHrq4zFee2SY5yPnl\nR85zOJ5hDw+JAk2SNhCtiOLQG7+UnqNQ1XL0lTE1k87zHMJaRKaRRuS5oapKX0EughLQ4tWarS2J\nGiG2EkxZkY8PGU/HaBXQ6S6RNhtEccx8Pq/9HDy1PFDKG/EesRjrkwhXG9+Lr1LHiRdcOTKOcbZe\neKja/7KqajFYnyRwikgsa70Gvd4FNs6cZD4v6W/32draYn9nj267RVkUYK33/KgcpZkTxgHNRkgj\nXcJay2yWeWk06xmhtiqJAk0YaoqipKgMpnQ4W9W+DG/FQ1nednK+G+5bMgDFpHDcHFdc6Q8YzguC\n5gofOrNGYS3KBTxwpsfL1wdok/PzGwllXjGrcqykDOeGvdEcYy1XhxnWaQoMVyvIizHtOMYpzYMb\ny6yev8iFpS4Wxc5gyPatHYwxrLQCNtbbjLMKDGTDIYNZzvrpEyy3T5FNBmSTjFubA4IkYrDbx1ae\nmTYej9jav0mkE6ooJGg1ufXyZR67uMLWzX0yW6CaLeLxhMZaj+k449o4Yz4e0kwTkuCWN/PodWgg\nXNvc52OXzrN1WPCNr/0nNq+4ujlipaXRBLz3gw9R7M/RQYKUhmkR8p3Xd5m+eJP3XNxgvQUje5H+\nzhA7G9NNNOfWevQ6HfJIM+jvoU3JY+d7FA8EDDPD5mDC1acH9HTAXqPL1mRKWHnPIL9Wdijr3Zpm\nZUVV5TR2LacSYTk95L3rmrR1jhdujhhOM5CAJI6wrmKUG6Lc8WsfPM+XvnuNf/3uFR57+DTNbhst\nEcYZVCXYsvLTYO2XaTpQ3tHKeYIO2jPupHYXVrYkbcRe/ccrs/ifE3+e7gL/hFRa/KxCK2J8TcNs\nuMd8HBAlXqYtaqRUxusTujD0NS9HnhDUtGZPXeRIwBXniWtHbKojLzHlLK50t/UGcNTOXV53sqoc\nzlmaUUwjjllZepCHH7rIwWjITn+PV27e4sbWDloHJGFAs5EgBmzpjwfLsiSuS73z0pCkIcb4JUsY\nRaRhQFWF5GXJPDfY0OtDFKao+R6+svSdcN82EO/5H11ggQUAjhcdeYEFFjh+uJtBygILLPD/DItk\nsMACCwD3IRmIyCdF5LKIvCoin7nXf/9/CxG5JiIviMizIvJU3bYsIl8TkVdE5KsisnS/47wTIvJ3\nItIXkRfvaLtrzCLy2XpcLovIx+9P1D+Iu/Thz0XkZj0Wz4rIE3e8dxz7cFZEnhSR74nISyLyh3X7\n8RqLt9xu/u9feOm/14ALQAg8BzxyL2P4KWK/Ciy/re0vgT+prz8D/MX9jvNt8X0UeD/w4o+KGXh3\nPR5hPT6vAeqY9uHPgD/+IZ89rn04CTxaX7eAK8Ajx20s7vXM4HHgNefcNeecAf4B+NQ9juGnwdt3\nYX8d+Hx9/XngN+5tOO8M59y/Awdva75bzJ8CvuicM865a/h/wMfvRZzvhLv0Af7nWMDx7cO2c+65\n+noCfB84zTEbi3udDE4DN+74/mbd9rMAB3xdRJ4Rkd+r20445/r1dR84cX9C+4lwt5hP4cfjCMd9\nbP5ARJ4Xkc/dMb0+9n0QkQv4mc63OWZjca+Twc/yOeaHnXPvB54Afl9EPnrnm87P736m+vdjxHxc\n+/M3wEXgUWAL+Kt3+Oyx6YOItIB/Av7IOTe+873jMBb3OhncAs7e8f1ZfjADHls457bqr7vAP+On\nbX0ROQkgIhvAzv2L8MfG3WJ++9icqduOHZxzO64G8Le8NYU+tn0QkRCfCL7gnPtS3XysxuJeJ4Nn\ngEsickFEIuC3gC/f4xh+YohIQ0Ta9XUT+DjwIj72T9cf+zTwpR/+G44V7hbzl4HfFpFIRC4Cl4Cn\n7kN8PxL1jXOE38SPBRzTPoiIAJ8DXnbO/fU8RS3vAAAArUlEQVQdbx2vsbgPO6tP4HdTXwM+e793\nen/MmC/id3efA146ihtYBr4OvAJ8FVi637G+Le4vAptAgd+r+Z13ihn403pcLgOfuN/x36UPvwv8\nPfAC8Dz+BjpxzPvwEXzB43PAs/Xrk8dtLBZ05AUWWABYMBAXWGCBGotksMACCwCLZLDAAgvUWCSD\nBRZYAFgkgwUWWKDGIhkssMACwCIZLLDAAjUWyWCBBRYA4L8BEXB9iNhuVz0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75dc02e4d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 8\n",
+    "image = style_data_batch[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[style_label_batch[batch_index]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 69.89% n09421951 sandbar, sand bar\n",
+      "\t(2) 21.76% n09428293 seashore, coast, seacoast, sea-coast\n",
+      "\t(3)  3.22% n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty\n",
+      "\t(4)  1.89% n04592741 wing\n",
+      "\t(5)  1.23% n09332890 lakeside, lakeshore\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at `untrained_style_net`'s predictions, but we won't see anything interesting as its classifier hasn't been trained yet.\n",
+    "\n",
+    "In fact, since we zero-initialized the classifier (see `caffenet` definition -- no `weight_filler` is passed to the final `InnerProduct` layer), the softmax inputs should be all zero and we should therefore see a predicted probability of 1/N for each label (for N labels).  Since we set N = 5, we get a predicted probability of 20% for each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 20.00% Detailed\n",
+      "\t(2) 20.00% Pastel\n",
+      "\t(3) 20.00% Melancholy\n",
+      "\t(4) 20.00% Noir\n",
+      "\t(5) 20.00% HDR\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(untrained_style_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also verify that the activations in layer `fc7` immediately before the classification layer are the same as (or very close to) those in the ImageNet-pretrained model, since both models are using the same pretrained weights in the `conv1` through `fc7` layers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "diff = untrained_style_net.blobs['fc7'].data[0] - imagenet_net.blobs['fc7'].data[0]\n",
+    "error = (diff ** 2).sum()\n",
+    "assert error < 1e-8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delete `untrained_style_net` to save memory.  (Hang on to `imagenet_net` as we'll use it again later.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "del untrained_style_net"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Training the style classifier\n",
+    "\n",
+    "Now, we'll define a function `solver` to create our Caffe solvers, which are used to train the network (learn its weights).  In this function we'll set values for various parameters used for learning, display, and \"snapshotting\" -- see the inline comments for explanations of what they mean.  You may want to play with some of the learning parameters to see if you can improve on the results here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path=None, base_lr=0.001):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and (maybe) test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    if test_net_path is not None:\n",
+    "        s.test_net.append(test_net_path)\n",
+    "        s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "        s.test_iter.append(100) # Test on 100 batches each time we test.\n",
+    "\n",
+    "    # The number of iterations over which to average the gradient.\n",
+    "    # Effectively boosts the training batch size by the given factor, without\n",
+    "    # affecting memory utilization.\n",
+    "    s.iter_size = 1\n",
+    "    \n",
+    "    s.max_iter = 100000     # # of times to update the net (training iterations)\n",
+    "    \n",
+    "    # Solve using the stochastic gradient descent (SGD) algorithm.\n",
+    "    # Other choices include 'Adam' and 'RMSProp'.\n",
+    "    s.type = 'SGD'\n",
+    "\n",
+    "    # Set the initial learning rate for SGD.\n",
+    "    s.base_lr = base_lr\n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 20000\n",
+    "\n",
+    "    # Set other SGD hyperparameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- ten times during training.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = caffe_root + 'models/finetune_flickr_style/finetune_flickr_style'\n",
+    "    \n",
+    "    # Train on the GPU.  Using the CPU to train large networks is very slow.\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.GPU\n",
+    "    \n",
+    "    # Write the solver to a temporary file and return its filename.\n",
+    "    with tempfile.NamedTemporaryFile(delete=False) as f:\n",
+    "        f.write(str(s))\n",
+    "        return f.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll invoke the solver to train the style net's classification layer.\n",
+    "\n",
+    "For the record, if you want to train the network using only the command line tool, this is the command:\n",
+    "\n",
+    "<code>\n",
+    "build/tools/caffe train \\\n",
+    "    -solver models/finetune_flickr_style/solver.prototxt \\\n",
+    "    -weights models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \\\n",
+    "    -gpu 0\n",
+    "</code>\n",
+    "\n",
+    "However, we will train using Python in this example.\n",
+    "\n",
+    "We'll first define `run_solvers`, a function that takes a list of solvers and steps each one in a round robin manner, recording the accuracy and loss values each iteration.  At the end, the learned weights are saved to a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def run_solvers(niter, solvers, disp_interval=10):\n",
+    "    \"\"\"Run solvers for niter iterations,\n",
+    "       returning the loss and accuracy recorded each iteration.\n",
+    "       `solvers` is a list of (name, solver) tuples.\"\"\"\n",
+    "    blobs = ('loss', 'acc')\n",
+    "    loss, acc = ({name: np.zeros(niter) for name, _ in solvers}\n",
+    "                 for _ in blobs)\n",
+    "    for it in range(niter):\n",
+    "        for name, s in solvers:\n",
+    "            s.step(1)  # run a single SGD step in Caffe\n",
+    "            loss[name][it], acc[name][it] = (s.net.blobs[b].data.copy()\n",
+    "                                             for b in blobs)\n",
+    "        if it % disp_interval == 0 or it + 1 == niter:\n",
+    "            loss_disp = '; '.join('%s: loss=%.3f, acc=%2d%%' %\n",
+    "                                  (n, loss[n][it], np.round(100*acc[n][it]))\n",
+    "                                  for n, _ in solvers)\n",
+    "            print '%3d) %s' % (it, loss_disp)     \n",
+    "    # Save the learned weights from both nets.\n",
+    "    weight_dir = tempfile.mkdtemp()\n",
+    "    weights = {}\n",
+    "    for name, s in solvers:\n",
+    "        filename = 'weights.%s.caffemodel' % name\n",
+    "        weights[name] = os.path.join(weight_dir, filename)\n",
+    "        s.net.save(weights[name])\n",
+    "    return loss, acc, weights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create and run solvers to train nets for the style recognition task.  We'll create two solvers -- one (`style_solver`) will have its train net initialized to the ImageNet-pretrained weights (this is done by the call to the `copy_from` method), and the other (`scratch_style_solver`) will start from a *randomly* initialized net.\n",
+    "\n",
+    "During training, we should see that the ImageNet pretrained net is learning faster and attaining better accuracies than the scratch net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained: loss=1.609, acc=28%; scratch: loss=1.609, acc=28%\n",
+      " 10) pretrained: loss=1.293, acc=52%; scratch: loss=1.626, acc=14%\n",
+      " 20) pretrained: loss=1.110, acc=56%; scratch: loss=1.646, acc=10%\n",
+      " 30) pretrained: loss=1.084, acc=60%; scratch: loss=1.616, acc=20%\n",
+      " 40) pretrained: loss=0.898, acc=64%; scratch: loss=1.588, acc=26%\n",
+      " 50) pretrained: loss=1.024, acc=54%; scratch: loss=1.607, acc=32%\n",
+      " 60) pretrained: loss=0.925, acc=66%; scratch: loss=1.616, acc=20%\n",
+      " 70) pretrained: loss=0.861, acc=74%; scratch: loss=1.598, acc=24%\n",
+      " 80) pretrained: loss=0.967, acc=60%; scratch: loss=1.588, acc=30%\n",
+      " 90) pretrained: loss=1.274, acc=52%; scratch: loss=1.608, acc=20%\n",
+      "100) pretrained: loss=1.113, acc=62%; scratch: loss=1.588, acc=30%\n",
+      "110) pretrained: loss=0.922, acc=62%; scratch: loss=1.578, acc=36%\n",
+      "120) pretrained: loss=0.918, acc=62%; scratch: loss=1.599, acc=20%\n",
+      "130) pretrained: loss=0.959, acc=58%; scratch: loss=1.594, acc=22%\n",
+      "140) pretrained: loss=1.228, acc=50%; scratch: loss=1.608, acc=14%\n",
+      "150) pretrained: loss=0.727, acc=76%; scratch: loss=1.623, acc=16%\n",
+      "160) pretrained: loss=1.074, acc=66%; scratch: loss=1.607, acc=20%\n",
+      "170) pretrained: loss=0.887, acc=60%; scratch: loss=1.614, acc=20%\n",
+      "180) pretrained: loss=0.961, acc=62%; scratch: loss=1.614, acc=18%\n",
+      "190) pretrained: loss=0.737, acc=76%; scratch: loss=1.613, acc=18%\n",
+      "199) pretrained: loss=0.836, acc=70%; scratch: loss=1.614, acc=16%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "niter = 200  # number of iterations to train\n",
+    "\n",
+    "# Reset style_solver as before.\n",
+    "style_solver_filename = solver(style_net(train=True))\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(weights)\n",
+    "\n",
+    "# For reference, we also create a solver that isn't initialized from\n",
+    "# the pretrained ImageNet weights.\n",
+    "scratch_style_solver_filename = solver(style_net(train=True))\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained', style_solver),\n",
+    "           ('scratch', scratch_style_solver)]\n",
+    "loss, acc, weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "train_loss, scratch_train_loss = loss['pretrained'], loss['scratch']\n",
+    "train_acc, scratch_train_acc = acc['pretrained'], acc['scratch']\n",
+    "style_weights, scratch_style_weights = weights['pretrained'], weights['scratch']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the training loss and accuracy produced by the two training procedures.  Notice how quickly the ImageNet pretrained model's loss value (blue) drops, and that the randomly initialized model's loss value (green) barely (if at all) improves from training only the classifier layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1090>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ROQeFwjsdShwuuADYvx84etT1dX2eYOtWYP16632UlgI9e/LPRnFISFCzsnYE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\ncoiIiIb/AxSD6Sq0YLMCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d496e890>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_loss, scratch_train_loss]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Loss')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f75d49e1a90>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rQuOqcvDiVuL2qW1z41YqRg4clLQiB7U2kAoeIaZShjGxcyu5UQ1WKEdAmg1O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\nFwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d47cad90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot(np.vstack([train_acc, scratch_train_acc]).T)\n",
+    "xlabel('Iteration #')\n",
+    "ylabel('Accuracy')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a look at the testing accuracy after running 200 iterations of training. Note that we're classifying among 5 classes, giving chance accuracy of 20%. We expect both results to be better than chance accuracy (20%), and we further expect the result from training using the ImageNet pretraining initialization to be much better than the one from training from scratch. Let's see."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def eval_style_net(weights, test_iters=10):\n",
+    "    test_net = caffe.Net(style_net(train=False), weights, caffe.TEST)\n",
+    "    accuracy = 0\n",
+    "    for it in xrange(test_iters):\n",
+    "        accuracy += test_net.forward()['acc']\n",
+    "    accuracy /= test_iters\n",
+    "    return test_net, accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, trained from ImageNet initialization: 50.0%\n",
+      "Accuracy, trained from   random initialization: 23.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights)\n",
+    "print 'Accuracy, trained from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights)\n",
+    "print 'Accuracy, trained from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. End-to-end finetuning for style\n",
+    "\n",
+    "Finally, we'll train both nets again, starting from the weights we just learned.  The only difference this time is that we'll be learning the weights \"end-to-end\" by turning on learning in *all* layers of the network, starting from the RGB `conv1` filters directly applied to the input image.  We pass the argument `learn_all=True` to the `style_net` function defined earlier in this notebook, which tells the function to apply a positive (non-zero) `lr_mult` value for all parameters.  Under the default, `learn_all=False`, all parameters in the pretrained layers (`conv1` through `fc7`) are frozen (`lr_mult = 0`), and we learn only the classifier layer `fc8_flickr`.\n",
+    "\n",
+    "Note that both networks start at roughly the accuracy achieved at the end of the previous training session, and improve significantly with end-to-end training.  To be more scientific, we'd also want to follow the same additional training procedure *without* the end-to-end training, to ensure that our results aren't better simply because we trained for twice as long.  Feel free to try this yourself!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running solvers for 200 iterations...\n",
+      "  0) pretrained, end-to-end: loss=0.781, acc=64%; scratch, end-to-end: loss=1.585, acc=28%\n",
+      " 10) pretrained, end-to-end: loss=1.178, acc=62%; scratch, end-to-end: loss=1.638, acc=14%\n",
+      " 20) pretrained, end-to-end: loss=1.084, acc=60%; scratch, end-to-end: loss=1.637, acc= 8%\n",
+      " 30) pretrained, end-to-end: loss=0.902, acc=76%; scratch, end-to-end: loss=1.600, acc=20%\n",
+      " 40) pretrained, end-to-end: loss=0.865, acc=64%; scratch, end-to-end: loss=1.574, acc=26%\n",
+      " 50) pretrained, end-to-end: loss=0.888, acc=60%; scratch, end-to-end: loss=1.604, acc=26%\n",
+      " 60) pretrained, end-to-end: loss=0.538, acc=78%; scratch, end-to-end: loss=1.555, acc=34%\n",
+      " 70) pretrained, end-to-end: loss=0.717, acc=72%; scratch, end-to-end: loss=1.563, acc=30%\n",
+      " 80) pretrained, end-to-end: loss=0.695, acc=74%; scratch, end-to-end: loss=1.502, acc=42%\n",
+      " 90) pretrained, end-to-end: loss=0.708, acc=68%; scratch, end-to-end: loss=1.523, acc=26%\n",
+      "100) pretrained, end-to-end: loss=0.432, acc=78%; scratch, end-to-end: loss=1.500, acc=38%\n",
+      "110) pretrained, end-to-end: loss=0.611, acc=78%; scratch, end-to-end: loss=1.618, acc=18%\n",
+      "120) pretrained, end-to-end: loss=0.610, acc=76%; scratch, end-to-end: loss=1.473, acc=30%\n",
+      "130) pretrained, end-to-end: loss=0.471, acc=78%; scratch, end-to-end: loss=1.488, acc=26%\n",
+      "140) pretrained, end-to-end: loss=0.500, acc=76%; scratch, end-to-end: loss=1.514, acc=38%\n",
+      "150) pretrained, end-to-end: loss=0.476, acc=80%; scratch, end-to-end: loss=1.452, acc=46%\n",
+      "160) pretrained, end-to-end: loss=0.368, acc=82%; scratch, end-to-end: loss=1.419, acc=34%\n",
+      "170) pretrained, end-to-end: loss=0.556, acc=76%; scratch, end-to-end: loss=1.583, acc=36%\n",
+      "180) pretrained, end-to-end: loss=0.574, acc=72%; scratch, end-to-end: loss=1.556, acc=22%\n",
+      "190) pretrained, end-to-end: loss=0.360, acc=88%; scratch, end-to-end: loss=1.429, acc=44%\n",
+      "199) pretrained, end-to-end: loss=0.458, acc=78%; scratch, end-to-end: loss=1.370, acc=44%\n",
+      "Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "end_to_end_net = style_net(train=True, learn_all=True)\n",
+    "\n",
+    "# Set base_lr to 1e-3, the same as last time when learning only the classifier.\n",
+    "# You may want to play around with different values of this or other\n",
+    "# optimization parameters when fine-tuning.  For example, if learning diverges\n",
+    "# (e.g., the loss gets very large or goes to infinity/NaN), you should try\n",
+    "# decreasing base_lr (e.g., to 1e-4, then 1e-5, etc., until you find a value\n",
+    "# for which learning does not diverge).\n",
+    "base_lr = 0.001\n",
+    "\n",
+    "style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "style_solver = caffe.get_solver(style_solver_filename)\n",
+    "style_solver.net.copy_from(style_weights)\n",
+    "\n",
+    "scratch_style_solver_filename = solver(end_to_end_net, base_lr=base_lr)\n",
+    "scratch_style_solver = caffe.get_solver(scratch_style_solver_filename)\n",
+    "scratch_style_solver.net.copy_from(scratch_style_weights)\n",
+    "\n",
+    "print 'Running solvers for %d iterations...' % niter\n",
+    "solvers = [('pretrained, end-to-end', style_solver),\n",
+    "           ('scratch, end-to-end', scratch_style_solver)]\n",
+    "_, _, finetuned_weights = run_solvers(niter, solvers)\n",
+    "print 'Done.'\n",
+    "\n",
+    "style_weights_ft = finetuned_weights['pretrained, end-to-end']\n",
+    "scratch_style_weights_ft = finetuned_weights['scratch, end-to-end']\n",
+    "\n",
+    "# Delete solvers to save memory.\n",
+    "del style_solver, scratch_style_solver, solvers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now test the end-to-end finetuned models.  Since all layers have been optimized for the style recognition task at hand, we expect both nets to get better results than the ones above, which were achieved by nets with only their classifier layers trained for the style task (on top of either ImageNet pretrained or randomly initialized weights)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy, finetuned from ImageNet initialization: 53.6%\n",
+      "Accuracy, finetuned from   random initialization: 39.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_net, accuracy = eval_style_net(style_weights_ft)\n",
+    "print 'Accuracy, finetuned from ImageNet initialization: %3.1f%%' % (100*accuracy, )\n",
+    "scratch_test_net, scratch_accuracy = eval_style_net(scratch_style_weights_ft)\n",
+    "print 'Accuracy, finetuned from   random initialization: %3.1f%%' % (100*scratch_accuracy, )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll first look back at the image we started with and check our end-to-end trained model's predictions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 55.67% Melancholy\n",
+      "\t(2) 27.21% HDR\n",
+      "\t(3) 16.46% Pastel\n",
+      "\t(4)  0.63% Detailed\n",
+      "\t(5)  0.03% Noir\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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yvUJS3DfmLUsURECnW6oGgUxGBSVh7180Q52jz0UjoWnYDZxRmdmA1bqxNeau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6EmVpSTd7osozItUDOtN5ksBa2bdacbpJGyRKLhrAxEttNG0O8AWK1TShCtYaElalB19S3dVz6K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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d0683e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(deprocess_net_image(image))\n",
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whew, that looks a lot better than before!  But note that this image was from the training set, so the net got to see its label at training time.\n",
+    "\n",
+    "Finally, we'll pick an image from the test set (an image the model hasn't seen) and look at our end-to-end finetuned style model's predictions for it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual label = Pastel\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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WKs9hFZYitTN2cR87zMqTyhlYiOUvdRVeXdJREPOPBUQLuaLGrEgo3mWYO8JX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9p9Ybt4Of19W7OXfbsAgKNmoNTwI0vwBGcQKvGY5rN9HPF6L//O5aJjNCQtQWBMX/OJhfm4G1Ge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\nKJVy+/ZPkPUZu9dfpDdYJ8gc51qO7ryOOHqPk7d+SLY8oaEk6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EIa92mO1lg/t8jkyCSB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\nv+3j8qpvhwWF6TsmyabsE1+C94x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FzzNvLUUeNPY7UYZSIpOA1VfGkiSSHlslWq/o8cor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\nQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f75d05ad810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "batch_index = 1\n",
+    "image = test_net.blobs['data'].data[batch_index]\n",
+    "plt.imshow(deprocess_net_image(image))\n",
+    "print 'actual label =', style_labels[int(test_net.blobs['label'].data[batch_index])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 99.76% Pastel\n",
+      "\t(2)  0.13% HDR\n",
+      "\t(3)  0.11% Detailed\n",
+      "\t(4)  0.00% Melancholy\n",
+      "\t(5)  0.00% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the predictions of the network trained from scratch.  We see that in this case, the scratch network also predicts the correct label for the image (*Pastel*), but is much less confident in its prediction than the pretrained net."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted style labels =\n",
+      "\t(1) 49.81% Pastel\n",
+      "\t(2) 19.76% Detailed\n",
+      "\t(3) 17.06% Melancholy\n",
+      "\t(4) 11.66% HDR\n",
+      "\t(5)  1.72% Noir\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_style_preds(scratch_test_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, we can again look at the ImageNet model's predictions for the above image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top 5 predicted ImageNet labels =\n",
+      "\t(1) 34.90% n07579787 plate\n",
+      "\t(2) 21.63% n04263257 soup bowl\n",
+      "\t(3) 17.75% n07875152 potpie\n",
+      "\t(4)  5.72% n07711569 mashed potato\n",
+      "\t(5)  5.27% n07584110 consomme\n"
+     ]
+    }
+   ],
+   "source": [
+    "disp_imagenet_preds(imagenet_net, image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So we did finetuning and it is awesome. Let's take a look at what kind of results we are able to get with a longer, more complete run of the style recognition dataset. Note: the below URL might be occassionally down because it is run on a research machine.\n",
+    "\n",
+    "http://demo.vislab.berkeleyvision.org/"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Fine-tune the ImageNet-trained CaffeNet on new data.",
+  "example_name": "Fine-tuning for Style Recognition",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 3
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index f29fc7e5522..663d7360b7d 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -24,7 +24,7 @@ foreach(source_file ${examples_srcs})
   if(UNIX OR APPLE)
     # Funny command to make tutorials work
     # TODO: remove in future as soon as naming is standartaized everywhere
-    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${CAffe_POSTFIX})
+    set(__outname ${PROJECT_BINARY_DIR}/examples/${folder}/${name}${Caffe_POSTFIX})
     add_custom_command(TARGET ${name} POST_BUILD
                        COMMAND ln -sf "${__outname}" "${__outname}.bin")
   endif()
diff --git a/examples/brewing-logreg.ipynb b/examples/brewing-logreg.ipynb
new file mode 100644
index 00000000000..c053b73b39f
--- /dev/null
+++ b/examples/brewing-logreg.ipynb
@@ -0,0 +1,1164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Brewing Logistic Regression then Going Deeper\n",
+    "\n",
+    "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import os\n",
+    "os.chdir('..')\n",
+    "\n",
+    "import sys\n",
+    "sys.path.insert(0, './python')\n",
+    "import caffe\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "import h5py\n",
+    "import shutil\n",
+    "import tempfile\n",
+    "\n",
+    "import sklearn\n",
+    "import sklearn.datasets\n",
+    "import sklearn.linear_model\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZsmQhkpQhEGjir//6Ty6pBHu1mI5x9/v9yLKMy+W67OvV7Xbz29/uJBBQOHLk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\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luroFURRRlDwTE+1cdlkFb7xxknRKxGi0YTIbaGx0cPfdX/pY1lH5pP9RUhSFn/zk/5BO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BaDQabrr9dqp7euizWvEFArT399PgdGLKyOCDrVs5sH8/DodjzHaSJNHW1sbJkyfp7e4e9yJQ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S5dN9iyZT8PP/wEx47d8IYo2UgJ/+2/we///pXek++HEIk6cvw4vOMdV3pvXjtc0UrZK43z\n/iBks1mOHTvMmTMbDA7uJJMpsLh4gW9+8z66XZU/+ZMHeOyx57n77rfw4Q+/k7/6qy/Sap1ESigW\nVTKZMkIoqKrGLbfcyOjoXXz5y39Kt3s/y8s211+/h9/4jd++LE2Xy2UmJ3OcPfkCvpfBiWK6NPom\nZ4n7oguMkFCBUaBCUnaJSehDmwwxOc4jcdkDVJB0oN+uOoLDDCUMqqRxcOOAOF4BxkhSbmwS9eMU\nUOubhpsk12NW/zYDOCT9v2eBEaQc6G/LIESMlF0MYwAhUrjuBRTlPEKMoesb9HozSDnP0aNHGB62\n6HZzTE295OmQTh/g1KknmJ2dZWpq6sde5x8H9UKBjbk5UiQUKzU2xpt+zE6ujY0NHnzwCZaW1lDV\nmLjZJJCSKI5RFYVyLsfK5iaNdg8tN8L27ddy7sIpLtUFz33LxbKeI5MJsQsF6gsLlKVOmogSm8yj\nELIHSQmDJQxmKNHGIkajh4NFgEIenwoqLyBYpExAiM8KDYr9huQYaNLFIsRGJyCPhoKP6Heb5Ihp\nA1UCLOoUqLNCD4cCAQvEOJh4+LTQGSaDQBLhEbCCoIVGQIoO4yhsQcchYppmKIh7PcqKhSN0DM/A\na7XIGAbdKGKlqaEaJsGrQDxLpRKln+LMYhzHl4lIFEV84esP8NhZhzDYgRfMsjYfstqZR1Ha7M6k\nWQlbtIRKPpYESoCl5bnU7rDuCxq9OlcjKEmFbujj4FFD5bCRxws3WZIesZQM4tLou4NU+wrERSo0\n2IrspxLFzJMkeafQWabHJh5NbDp94zkLi5gOPULyCGIiukgEJh4eAZBGoYFCCwUTnwv06PQTkTqE\nLDClR5TwSKkWmzKNHYGCZEitUI8j6nKFafykfV4k6pwVqnQjk43VVapDR1FDC8Vq4lSzLAG7Dx1i\nZHQURVE4t7rKte961+Xj7bounicxjJeXxnXdIAw1ut3uG4KMPPxwoj685aefVvFTweHD8NRT/0pG\nXtdwXRfbLlMqDVGv1zhx4jSFwkFSqRDTbFIu7+ev//o+fvM3f4WPf/w3mZz8HNPTNTKZm3niie+w\nsQGmGbB161X0ehu8//1v59//+19GUZTvu/o+MT1NoZhnxX2SfKyQEIhEl3jR51WSZI/kSALRIKEP\n8yQJvj16dFDosgNJGkm3X/3PsoZLi5AuENAhokEcByQdA8MkNmhpkjLLnv47qP3/X9N/h27/cR6J\nMXWRpIC0DKwCIyhKFlhFVXcQxwG2bTE8nKZWm8W2C6RSKpOT13L11ZOk0zrnz4c888wpcrk0IyPD\nmKaJrpeZn1981cnIRz76UWZmZmi1WpRKJSYmJr4vDfgH4dSp03zqU/dRKu1mYuJqms11Hpp7gikL\nXqjX2VEosL1a5Svz84hAsmt0G7NLMzw+s0jDuBbPr+B5LYSocvLi86SlQYmYq7F4BIUyQ3Tp4tFg\nCI00g2g0GWSTNVxUXApYaJisYdCmiMIuAtYw8PEBnx4qHQRdhghQ8VnDZxWJjsIyEYMIAhRcJFuR\nbJIQ3gnq1GiQRusnQqdZ6Y+TWggy+Ogo1MggGMUlj0aOkAY+HgZVnLBFQ7bQjAARhJgyj+6HiDjG\nVhQCx2HGaXHH+DhPPfUUruOgKAo7du68olbgvV6PS5cuATAxMYFt2+y+9lqmv/hFBvJ5VlZWeHa2\ngRBTqEGdgfJuNppnsRTBkBeT11z0dIrhOOZS1GM9ytP0R9gMbPJ+gy2yzCZtXJIcqBVCGoQoYciE\nnkfxlhlAINU0M1GEisJFFFZR6DCEQxENA4GKygg+Z8liEeKi02AADwuNYQwcfFbQiBA4eGj0EMz2\np2uSdBqBh4ZHlXXy6LjEbFDDIYdHizQRNjliW8UhIPJdcoqCkBEbUZecOoAhUlixwmqsYKfHcMIs\ni76Hox5C1ZqE0SpRNA6UeceH3s/86dPU2m2aCwu0gLGDBzl0+PDlNUiUMBXX7WJZ6cvbfd9F1+Mf\nWKp7PeG//lf49V9PRmlfjzh8GD7xiSu9F68t3nBkZGhoCCm/jZSSxcWLaNoQqmrQam0yPl7uf0EG\nOHPmBW688Sgf/OD72LNnmkcfPcHOnRqbm2cZHt7G+vo0Y2M57r77Pd+Xl7GxscGnP/lJ7v/0p/Fr\nNZQ4pkHMGLCThAKkSEozs8Aa9OdmktO/6P+dRMb7tLEQZFHw+8N6HhoDhGRwKRCTx2eZgDKJ2lEm\nUTsWSWjPi3m9FsmSdUnKMSoJ6aD/uFT/eWP9PQHYwLK2YBgZQOA4s1QqDr/wC3dxzTU7cRwXwzDY\nvn0rpVKJj3/8D7h4MUexuJUgWOL552c5duwQUeSSSr36VtCqqv7Ejo9SSu699yEqlX2Xzdjy+QFu\nePNHOP7QnyPyJucWFoiBzNVXs9HRWM2WOL18mmbmCEQVbD1HFPloWoYoHCEmwqfOHCEuKQxs1L4Z\neA4TgY6DhSBRyEJgEpilxxlcJBV8uoRkUPppJTDOAM9QxkKjRwqVHDku4HKBFAE6Nuvk0RCEbODi\nAxEK54gZQhCi0UWQwmErIYu0KaNS0VN0Y5XlKDmVpZUsKFl6UUQsczhIenQZVTMMqR1CbwMosClV\nrEiS0nVavTautk7z6af571//OtlymYNXXcVTisKRO+/khv4E02uJEyee4UtfepAwzCKERNPu5T3v\neTNXX3MNZ0+e5PjZs2wuLLHZC2m7TabKI+RyZS46KwxqdTrYzMuAPSMjpEyT2soaSjxFt2OSNlSq\ngU5O5okQGOioaEh8HBqsxgGGTOIZVCTNyCdLiiYqOiEeNh5Jkm3ctyZLCm5pDFax6ZDtJ9OsY6AR\nU0DDRmMdlS4uCj0yaH1qWkYhg0aDUYoU6KBhY2GTw+McG5TpMU6aiaBDGLq0MxXi/ACN7gb5UNCT\nBlG8TE5RuYBgVdMpKAU2wx6OMoUu8vhBjONcYv/+G+l0YGFhmf/tYx9jZmYGx3GoVqsMf0/Tgqo+\nWyreAAAgAElEQVSqvPnNR/niFx9neHg/tp3BdXssLT3HnXceek36yv6lOHMmUUbuuedK78k/jyNH\n4CMfgTh+/RKmnzbecGRkeHiYgwcnOH78aZrNOlJmaTRWsSyXiYnEiVVRdBwnCftSVZXDh6/j8OHr\ngCT2vlarYRgGlUqFdrvNffd9g5Mnz2NZJppwOPnoo1x85BEG63UarRbjJAN5ZRICIkhO/QYv6RAO\ncIGkayPs3y+hcADJCuvMs4YkhSDAooqCRoMmEUUENj6bJCSiTjKWO0ZCQs6SLJPsv7pJMhXd6t+H\nREWJSEhKUhxKtBoDVX2WIDAAE0U5z9DQJn/8x7/DsWM3ceLEs5w5c5FcziCbzXLffd9maOgAtdpZ\nbDtFNluk06nz5JPH2bNHZ9eul3tHvN7Q6/VoNFzGxl6edjUwMMLe636O9773FhRFIZfLUS6X+cM/\n/HPK5cOcW/kkVjhMqxUhpYNtZ2g2m8RS6Se3jtCg0/d6EAgEFhARoRGi4DJKshovADYRFholFCR1\nPAroOAg0AgIs5ihjIvBpIgn7Vt8WaVIM4JCiQ8AwIRmSVF4NldOEfZ+SEh4KNjE6bWxabAIrRHQD\nH8M0WI86hARYIsCPJJrIEksfjzYmA2wEa3R1SYYYTVvBUcdw1RTSCLHMJlsNgVqvc9fUFDP1Ot12\nmyN79/LE177G1u3bXzZZ82qjVqvx+c8/yNDQdZhmQohdt8vf/M39/Mf/OMz7PvQhzp49y71f+SrB\n9Aq2USDWQi6uvUCn12CLnqdYSpHKCDzLwjQMEBrG4Ah7d1xFc/lptIZEUQRRnMYgREPDwiPCYgOV\njN8hR4gQKo5UcLEZpcQiG2wSohHi4vSHem1MIKDJID6FfnG1i+AFMtSABh00VDbRKRDSZR6FASRF\nMnTpsoIFlMgi0YlZxUJBRzJFjwAFnVZSfpEm+bbHhhEh7BwLTpcBs4jvR6zZZdZji06oEXEVnlon\njioYiiStBlhqHsPQ0HWHOFbRdf1lsRuvhMOHDyGE4P77H2N9PcS2Nd71rus4evTID3ze6wW///vw\nG78B6fQPf+yVwtAQFItw+jTs2/fDH/+zgDccGQF497vvYmJimi984R+4dOk0+/ffxLZt+7EsCykl\nQbDB1NThV3yuruuMjo7S6/VYX1/nL//y8zSbOQYGdjEzM8/0Qw9QiC5Rchyqqko9ivpuDS+d4oF+\nRTchJCZJacYk6XOoAVOo7MLGIMYgJmYFGOr/xBj08JF08ciRKB4F4ByJsqGSNKh2+u9Y7d83++9c\nJWmP7ZK0z7ZIlJBJEnXkPDCGYSgYRo5KpcnAgM11113Nxz72USqVCv/jf3yaRiNDoTDE+nqP48e/\nxPr6Ajfe+EuoqsGzz04TxzmkjGk0TvOf/tP/+bqXYJNyksD33ZfVtKMoRIiIXbt2vazZ8u1vv5kv\nfvFh0mmTpaVL+H4K09QQYhDPa6Eoddx4EJdlzP74bosmBnY/E8ZD0qFAh6QQlhTUYkIyWBRRqAOB\n2AQJtpgCuURMBY11BA4mGXQqQNxPNkmTIsMGJgPo2GhUcJHYrKHi9LtTICaFR44YA50RAjLAU4SE\nQRebEEObZSNcxWE3hhwiYgUbFRsXU1oEbmKwd0QLwFpi3c5QMRSKls53WoKFtTVUKanm88zNzXF4\n3z4GVZXz5869pmTkuedOo2lDl4kIgGWlEaLCqVNnuOWWY+zdu5eJiQlOn1/ha199nC7jpI0tBLgs\nd8+QMkMMV2OgUsELAuoywvPWEKuPo0UdNkQHM9IwCFGFhpQR6/TwKPf7es4xiKSjalyKDMawQQpi\nJNtQaONho9OghQ+E1BmgSx4Q5IlokUZSJWQdgyyDtGiygcCghE0bhwtIRlDIMESKNl1iIlRU8iiM\noSKQ+Ch9Q7uYOjlsJCY6gd9lKbbIjb2VpaBDp+2SKQ2xdfgavvP0V/H8RbLpcVqdDkFUR1cNPHed\n9fWTjI/nuOqqH02RFEJw+PAhDh06gOM4WJb1hhnpnZuDr34Vzp+/0nvyw3HzzYmC869k5HWMF9WO\na6+9hk9+8q+ZmfEIQ5dms8vm5hz79w8yOTn5is9dXl7m7//+Aebm1pibu0C3W+DWW/dj2zYbK+uM\nZCc5e+YZrhWCdhDgk5xgKiQlmRRJgeTF/pBNXiqoeEBMiWHSWGywiQJE2MRUqXEWjw5FBD6CgMQI\nfASJQVLsyfdvCyTtsZP9V10haYmtkFCdNgkRCUmIjNLfC4uEFklUdRPLCikUNO644yj/5b/8zuUT\n8b333k+zmWN8/EU/kRK2nefJJ49z8GCPyck9DA9PUq8nRSfXTbHt9Tb/9grQNI2bbrqG++8/zcTE\nNSiKShzHLCyc5siRlxORKIoYHBzgLW/ZTzodsLR0L4qSQspRms1LqGqLTKaM67ZZCnoMyRI2ApcV\nWkIQShONDlM0qOCzBKyjMIROB4WQkC4ChS6VjGDOKRGGbWwydMjSZpEBXFR0JBKwqNPuu88oxOjM\n0KFEi5gUbl87kZRYoUkOiY5HFo05AkAlhU0JB02CZWTRfJMqERc4TZN5LGw0TFKKJEuRHE1Q4FTQ\nY2cuxWQ5Q7fb5XS3SzYIGI9j7GaT5zc2aGazRHGMIgRR+KMZlf200O06aNr3N0VqmkWn07t8P51O\n85GP3M0jjzxPfdknjmoomo6fz7HcXmP7WJXRwUHO1+sUilmytVly6e2kM0NcTM2z1F5kEIkuU2zg\nMYtGlwolbRVNh5UwR2CotB2YiQMKrJInokKBeZZwSJGigMo5AjpMAEWyuARs0CJHhNVXTnrACgaC\nQwTEOJxFUkHBJGSUiHbf+H2FYt/3VUMlJKKOoIyCQCGkQpsuHdKsotMSu6BlEwQuup4nnU2haSuY\nZo+hgkraNMhoAV6g4oSrCLHC1q23smPHAAcP/ngW8IqikH49ywuvgP/8n+FXfzVRHV7vOHYMvvEN\n+Hf/7krvyWuDNyQZeRG6rvOhD93N9PQzTE+fQVUVbr/9MFdfvf8Vmx7r9Tp//Mefod0uETgZ5i66\nxBg8/PCTDJZznJmephwE1NZ71NUmQgpUEgLikhCQF0iowibwPIIekjIJQalhoVOmxSYWPqMEmCgs\nEzJPBo0KNhE9OsRUEGT7J6JFEvIxTuIMkCVRO14kGSFJGSYGYgzDx/f3AAskZlcWCWl5saF1DSkv\nMjKylzvv/GVMM2Rtbe1yzszJk+epVPa+7NhkMlny+WEuXjzJ3r1HME2boaFJ1tYW2LZt+BVdNV+P\nuPXWYziOy+OPP4IQaeK4y8GD27jjjjdffszKygpf/vSnkZubGIpCGvjwh+7g7IV1Tpx4nna7RjY7\nwtatEyhKwLPPdjjfMbEIyBb3oSt5euuPorGGJyJOS4FEZT8qJhpNNGrobNClp2qMDO4iuz5Po/k8\nKiVieqzjEuAxjA+s0yZLgzIaNj0WiRnAIc0yNhpZBD2Mvhq2gkWHNiHRZZq6HUGPLhViMnoKqVjU\nNYmtFBiNQtwoTw6VFAXSpkEUzVHUbVQvYh6bk9iUsUB2GAJ2b92K0e0yYFkYrssT3S6u77MWBBzb\nvv01XdMdOyZ5/PGHSL4fL8Fxamzbtu97tvncffev0G6HnD99GiWOabYrtGpneM538H0fs1rlQBCw\n0FlkYeMFms0Clm6RSemcdFx0JcZVtmBbw+wbUBgoSK657R38wxceYH7ZIJQ6Tdps0OEq6qzTRiXP\nMCouG4DKMrJvdeYjEf22c5UWPZoo9NBoMEXis1snMaAuE3MOD/AYRKXWd97doExMHUGNGIMU6/Qw\n+2VDE5MVHBxlnDi2cN1LRNEmnpfl0qUyS0sRcTyGkC0sbQFjwKbT3mQAaMochw6N8OY338zm5iZC\nCHK5HD+LWFhIvEVOnbrSe/Kj4eab4bd/+0rvxWuHNzQZgUSaP3r0eo4evf4HPm56+gR/8ief4tsP\nncWQgu3VEdROl0ZvndryMrtHTLYNDXHu1Gk6Ms0pr84WTSVN0iOy2L/1SXpDasAoRSZpM0jAkyi4\njBMy3L/6kZxDYKJg0KOAzRwqNhOkqFFng5hu/5UgISBpXupG0frbXjQ28wAHXc8gZYp0eoow9PG8\ndWAKVdWJojqJaqKhqoLx8S0MDk7Ras3ied7lY2FZBr4f8L3YsWMcXV/j0qXnsKwCntcklerwzne+\n7yddntccmqZx1113cOutN1Gv18nlchQKL/WQBEHAF++5h0kpqfaD3oIw5Mm5Of7Nv/kFVPUX+bM/\n+zTZ7FWMjm4jnc7ziU/8Ho6zjzCMCMMQtxOQLd9Jq/FZdioSKzDokOUUNQQhPQxc0tQBLxpCa5YZ\nHxzC4hQrTZcUVWwsuvgs00Rngya9fifAGiExkkMI1oiokOIU45QpYhPjskmGdUZYY5ExYsq4pIEm\ngiwqSiwwpA1qC1mq0Nu4iBq3EXKCSDFp+GsU9Bb1IGYlhmZphGp5P422S6O+SNGKsAwDhGBmfR1X\n0xjJ57n/1Cluffe7qVQqdLvd1+yqeMeOHWzd+jQzMycYGJgEYG3tIjt35tn+PcTIti2kDNmzZw97\n9uwhjmPOPv88Cyc0br7a5MZ9u3l4eprnag0y9jhbd6aYazRYbnQI9CLjYzrddpqSyJBLGQg1ROaK\nbNRBsQ9QKZoE7R5R5FL3T9KkhCoikCPE1DGp0UAhhyBGJUVMCocuMQFp2ki6iH4G8HYCFkkuOJKw\nP8kEgjUkq4QEhLhs4HAeKKGTxiJDjxU8LmBREiGeVGgpWXzho+vn+xlOoyjKfjTNQlW7FIoHaYfL\nDJkqO7ZMIuOtbLgO6eEpymmNb3z2s9iKQkdK9t10E7e/7W0/1hTbGwG/8zvwa7+W9GO8EbBjB3he\nUlr6nkzKn0m84cnIj4KnnnqaL3zhUVZW8ujxLgYyKRbXZxku2XTdTWorPZxCDlMpMNOKsPQ8gVHk\npHOOAlw2pLoe0FFoELOMRpcOBQIuAl1MRnDYFBdRJHTFIXTpExGwjgPU8Ajoihgpy/3w+AXgAPQT\naZJ36ZKoIJn+9pCkR6QHtAiCVRRljDBcQMo0L+o1cawixCJC2ICGEIL1dY8HH3yaIDjJBz5ww+Xj\nceTI1XzhC0+TTieNaGEYcuHCC7Tb83z4w7+IlFCvdxgaGuWqq/a+4aRYSDxpXqnHZXZ2Fq3ZvExE\nAHRNY1uxyKWzZ3n/r/wKqqrx2c8+iJQSz3MwzTT1+nnGxw+yseGSywkai/cSKhrnRIBJl5gidXay\nKUxCkph2WERRcrR6G3i9kJRukFYqdOIWKRaYQkEwgmCNIlnmUKlzkBiDhPIOouFjYGChUcBC4qHT\npEObLj4BLbp9u7QAA19XqMQKgYxQQo9m4yLCKKKZJZzuDJaSZjiVRw9z9FSTDcNnpLqDW/YcwDJN\njp/oMWU1WO50GJ6YoLptG7qmMd/rcfSd76TjRvzu7/6/xDGMjpa4666fY2xs7FVdS03T+NCH7ubp\np6d5+unTCAHvfOd+Dh068H29Cnv27OLrX38c1+1hWSkURSFfLHLSX2LrSNJg6YYhTVdgazFzLR/D\n2MXUaJaV5irLnVVuf8ubKeZHuDT3PCtra8xcqnN25jRBICkaU6QzaRothZy5k6Z/mrT0+6qVjcTA\np8MQOSQBy/hofW/mTSKGgGFCBCu42Lx0SbDGi6VWyT4SpXQVGMWmjSSkTQcVSafvQ7PBEA1pJClX\nehldtzCMZTzPwvclQjTJ5w0ymQJh2GO9GXG2VsPTTCa37qSyJcVQUUdbWOCm/vchjCKmv/Ut8sXi\nD3Q7fqPh7Fn40peS2zcKhEhKNd/+9r+SkZ8JRFHEffc9xsjINZx9/ttoMsTQ0ij2dmr1afSwRs47\njzOXYzGKsFSbLZUDxKGktryOGTdJEWGS/FwUiekACiF54ASgINhJHqlayNinTRqkSkiRiDYRNi4R\nMQ6oNoqQREGDhGDMk/R5bJI0o6b6e66REA2HRBm5BOhY1mHiuInva0jZItFr8ki5iqJsQVUnieMN\nQKAoKlJ22LbtGj7/+fsZHh6mVCpx8OAB5uYWmZ5+DM+zOHHiBGHY4eDBo3z962fJ5Vw+8pG7L+e+\n/CzBdV1eycs1ZZpcqtX4oz/6E+677zjNZhtdf5zt26fIZGLe9KYDLC5ewnFWUDsX2IbEV4sIrciq\nP8ciHo44jBBZ4vg8EGGqVcqmgh7M4gWrOJiYlGnTpICJRp6YEEkJQZoSMRtEJOpYETDQ+70DMREx\nEgOVgb5tno9GrBtciCSDikXaztMLPPwoQEqfUGo0nZB5JcYzLWItjxBdDNFCipAmTUJjiF67y7dO\nPIRFB+m1eGpznVt3bGN4fJy9+/bR6nbxmk3m5mrUajYjIzeiqhr1+ip/9mdf5Nd//X991T8rpmly\n441HufHGH3yCLBaLvO99b+Zv//Z+oihpH4U6b33nDcw260m8gKJwMZCkwi5+kEWjST3eoOb7qJkR\n1tbqFIsFnj/3AuvrMY6TqIphkCMw5tg/dYSev0gc2vgiwwV3ExsHgY2CxyAaWfIIWlTRkQgcurg4\nFFAJ0RFkqLGGxygRAyTE4xzJhccoApCEQI5lsv1WVYUOMU0U2uSAITzKaKYJygqKvEAcjxDHORSl\nCJSp19s0NldI2Tl0YRIHGugDLNdOccedd7Fx5iQ7RkYuHz9NVdkzPMzxhx76mSIjH/84/OZvvjF6\nRb4bt94KDz6YhPn9rOOKkREhxM8DfwisSylvfrXep9Pp4DgxAwNpqkODzJ6+iBcUMDSL+uY8B1Ia\nQxlBLhtSb0QsRz69dpNMtkBJj9geGFgEFInpCcELUjJIEoMFghDJLgwcVLpqQBA1yRBh8AINDDoM\no1LBRcNnDcIaQhgkVmljJOrHNAkJGSH58XwpbyQp01wk0WdGCAIJOEg5ixAaUnokV1EqlrUXKbtI\naWDbI7Ra8xw9ehVXXXUjy8vnmJ5+lttvvw1VVXnve9/FTTct8xd/8Wl27drG3r03oOvJaXptbYEv\nf/kf+bVfe2N9A8IwZH5+niAIGB0dfUVFp1qt0oS+lP2SDL2wscE/TJ9jY3OIavUoxaLG5uYMy8vL\nfOADb2V2VmX//sN87tOfZMQco7U0SxzrRGSwNJWpsM6a/DYdkcfXJKZaYZwuxTDEliEhw6zgsITE\nQEOlSESahOImCo6Og0qLiBeVhvOE+FjEgKBLiI6PUNJgCPKqgmnbpEwDt+0wbti0QoVn4w4dNBwM\neph4sYJwdTKZfWSLZdY6F3D8F4ASo8WjGDImaq8SEFE1u4wV80zPzLCm60TZLE1d55pbbuGBB15g\nYuLA5WNWLFZx3Q5PPPE0d911x6u1rD829u/fx9atU8zOzhLHMePj42SzWc6dO8eZZ56hMjDAzrbJ\now88SEEKpCYQZpqxVJ41p0W7HXD//V9hc3McTZvEtj16vRlUdRPXEyxvLjEwUOCF+e+gKkWymTxu\nbKDHRZTgOxhsoEQBMTYOLUxsVokQSBxCNnHpEmCzlxRN2qyTjOdPkkzQXUBSQ2EKBQ+bHmVMLHQE\nCikkIRYdDHSrzv6rDwDDnDnTJoqqKEpMHLcwTRWv56KjkspGZDMq45UJzJzN1N5bSaUseqr6fREP\nactibW6Oc+fOUSwW/8WW/VcaDz0Ejz6ahOK90fDWt8If/EFiX/8qJ3FccVzpoLxrgAdezTdJpVKo\nakwQ+ExOTbEw8jxuZ51ao4XWW4FMhnrssr7axYkUbL3KYuM0MxsxO1SNntBIyRApDLYognaUBIG7\npOkgCIjpEuLSIZA6ByybjmcRSkGamEUatBgiUgSqGCGOF5AygxB+P+xuhISQNEhIx2D/fodEFfFI\nxnlHAJ84bvdLMWq/TPNii62D617AsvLkcjrF4lbCcJmTJy+wvl5n5849rK+/PBi5VCrhOAr79x9D\nUV6SuwcGRpmbe4Rms3nZGv/1joWFBT71qb+j3VYRQkeIFm9/+w3ccMPLr+48z2PNdfnzz3+e3aOj\n7Nixg04YMr22xmrNZNeuWy8/Np2+lrk5l1arzfh4nosXT2IbHdzVSwRBjUgrgX+e7VIFzSQbe7Tk\nArORTkE2qcYBvnRBHcPEYpg6KwgCJvFYx2SYZG19wKBNRIRBooitkXiAdogJkMS08ZAESby9L1jT\nDDqBYLct0PQSZ7wGftihLibwlTE81SKMgKgOCHQ9QxSp6PpuPG+JQmES15P43WV2Z/OEocGau8y7\nD+2iVKtRq1QY+7mfo+q6nJyeZnVVMjoaon1X3no2W2ZhYfFVX98fF+l0mquuuupl23bt2nXZQ6Pt\n/ndeePQxSulhbCODjAKEcDGMDJcWThKLLFBF1y3iuEcmU8VxII6XWK2fY1l2cUOfjGkS0IWwRigh\n0gZZ99YxaJBlgDoaXZqk8Nnab1ffTch52nRoIImBrSSE9MXPQg5oIYjRaTFIjM0oyfc8i4lDlQXa\ndPB9m42NHtXqThSljGlI4iiPI+dwnW+iyRSxogMxo+UB9k7uYr3VodeL6PUCHEXBDwKMvllZGATc\n99CjPNeJueeeh4jjNldfPc4v/MKdbwib9+9FEMBHPwp/+Ievb1+Rfw67diUk5PnnYc+eK703ry6u\nZFBeA3jVg9d0Xefmm6/l/vufY3z8GnYeOMil0yfxg4uQAdFpMhokrote5HDOn6dHDpUhdJHFV2zW\n4gW6sosbSTxgEwtJhhU0DHx0NNpsUA5dKlYFL2zTjdIIWaBAxJqoY6cknieIojJC6KiqJAwXSMoz\nkuRHaJykX8SBvlCb1JHnSSTcEaRsI6UPHCaZtvERAlR1DFW9hKo6mOYgmcw2HKfDwMAher02Tzzx\nDd761l952bGJ47jPuF/eqPbimsRx/Kqty08Tnudxzz1fRtd3MDGRXMUFgcdXvvIkQ0PVyxb2Z8+e\n5Wuf/CQHCwU2DxzgudOneeLSJd7ygQ8wki1in1n9vtfOZrdw+vQsn/jE/8P8/Dx7d+d49FOf5tQT\ndRrdJSYiULUCrpQouko2chiJPQqKTsXMsOkoqJFHkMTUYRDhsIVl1lBZw0bve6J2WMMmmdVaAxZQ\nKaIzyDqz9NjERjAvcjhqilidwEjvJgjOcyY+j9FrEAmd9SDXz0cpIuI80EJRikh5lk6ngBA+Q0NF\nTHMbO3bspdn0WX7+DK2uSzFrk0pXuLS5ydDkJFEqxdP33ceYYWB0OiyfWeKJruDwTTdejk7odOps\n2/bGu3I+duwo5x+4j2dfmEETo2imgaIqOJ0G3eZ5jOxRXHcdIWKy2RS53DDr6yFra8fxehuU9Jis\nOYGgyXrPQ6GMogwQ+xl61HHYwOorm4KIEXRcAkoIdAyGgVnWiS9nTEHynYfkAiMi4gIBXSxyvOQ/\npAMWBmHi/hrnaDYvsnPnjcRRTOzDjtERhDLG4uoT0DmP1PPsG92PGRhcOHk6sSAIGrz//XczOT7E\n01/7GrsHB8mmUnzr8Sc5vtDiyF3/lnJ5GCklzz77HJnMN7nzzre9xqv0L8cf/VGSfPve917pPfnJ\nIAS87W3wT//0r2TkZwK33XYzvh/w6KOPks7abNlrs/PQIY5/8TxlJ6YrQop2ltjM0mzUuIjAxyeK\nzmMTEZLCVRVqms28V6dOBkNRGCEgJmRTOmhammzcIKV4DGRVlMhns9PAiJNI8jA8hKpmCYIZpDQI\nQ52XekIE0CRpXh0lGdGNSH6kQpIegjywBNQRYhdS5oAAIQxUNYWUSwhhIUQWMGk0zjIwkMYwMnie\nj+cFDAyUX3ZcbNtm27ZhlpYWqVS2XN5er9eoVjMvm0J5PePChQt0u/ZlIgKg6yaZzARPPnmCqakp\n4jjmG1/9KvvLZQqZDBPVKgd27mSj1eKS4zA8PNTv9Xg5HKdJpZJHCMH4+Di/+N738p1HHmPxiRl8\nmUfoaRqyRWwYZFNb6TSfZUfZYqbVY92L8GWqb+peJyYiIEOMT4cK50lj0wBsetSISKPxBIJ2YlqG\nhYqJwzh1JljnIinlegy7SizTCKHheSaYk6QGriZlNFm/+BDIgX7vkIKUMXEs+2pRA8tKMzQ0TBSt\nIgRs374Pb3OWXRNDuJ0Om50aE9dcw9iWLTxy7728Zc8e8uk0URxzYanBsxe+w5Oqx1X7D6AoKr6/\nwJEj/8truNo/HYyMjLB9316K+WW+8tg5Gis6YeRjaC127j3ARkPF9zfJZAbI50tEkU+ncw7CJraa\nwleG8KM8quKiKgZ2XCJWJE4kUKgS4rNBih5VJmmD0AnkOVRiBBEpInzaxJj9TpBhkiFtk0QNFUCV\nkLM4GKRxiDH62gp4KPhowADt9tPU6xfJGCGSENfvMVQaJShtw3fm6CktZhfOM1msUs1WmG9s0F6d\nRRHv5dgtt5AvFHjym99kc3GRp1sBh9/+a5TLiQ28lKAoBf78z79ArbbJddftY8+ePS9Tx16vWFqC\n3/1deOSRN3aJ461vhb/8S/gP/+FK78mri1f9EyWEqAKf+57NK1LKD/yw5/7Wb/3W5b9vu+02brvt\ntp9oH1RV5ed//i3ceutNtFotcrkctVqNlSceofnCHFocAYK21yESCraIKMkWkeww1K/1tqKIVtSl\ngSAgoCxVbMWkKFS6UuFC3EOqGtLtUCoMUw4DNmWPLjkCJomDUaJok0Tl2Ely6CdIpNllkv6RBslo\n7kD/7ymSH6dFEjKSBrpIWQMchDBRFAMpe4CPZTWIYw/LkoyMlDHNKvX6OQoFlXS6yKlTp0in0ziO\ng2EYbNmyhTvvvJ0//dO/5tKlJplMmV6vgaZt8MEP/uKrrlr9tOC6LrxCW6pppmg2E7Wj1Wrh1+sU\nxl/uVVHO5TgzP8+td97JPff8A/X6RQqFSYQQOE4Tz3ued7/7/2BhYYFHHnmK8+dnObvoEaT30fMW\nqEc9bG0MSZugtwrSoOb06CklWiSTNikEKjGLuGRQ6ZIYHYRYtBn7/9h78yC5z/rO//V8r3h46lEA\nACAASURBVL7v7rnvGY1G0kiWJUuyLNnY2ICBGGyDMeEMBgJZjmSXbJLdLLskW/klW0VCqNpUWNil\nEog3AQKYy5jDxiaSJV+yDuuaS3Nffd/f+/v7o8eyBUkIxLZkNu+qrur6TvfM08/T0/15Ps/7oMX5\n6SSMSztLdOLhR6WChEOQKnnWSWKKFJpooJglGuY6jYaOLHcCMq6bZH19ZaMw9eG667iuuhG86AAQ\niYyg6zYTE0+STjexrAK2bSACAYp6k2hUcNNV42wdG+PhEyfo6+4mttHbNi0Lz3OoN4ocOnSE6Qvn\n6evz8/GP/yYdLxOtpGVZPPbY4xw5chLDMClU6jxzfp2RtnGW3Tz5Wp6sHqF6bo5EIkYmMwjkKZeL\nVMtThBsn6XZcUoE2mpJg2s1RtgbxSX6aLBMWXQi5TpAodUfCk/qIomF7FYRr4hJgjRoBLGxUZGER\n90zyLNHqQTZpbUAStDYnGpENb9YMTcLY6HjUCLJ2kfhcQHJNTP0st+w+wLHJkxRrR3HcPkw7RxFI\nSd0EtQRThRpThWk6Mz5+41dezamjRzlw8CA7rrqKHVddRalU4k//9ItkMt1AK+vp+PFTzM0V0HWN\n+XmF8+cPMz5+jl/91TuveNfV//gf4dd/vXXU8XLGzTfDe98Lug4/EaP2S4UXvRjxPG8NuOkXee7z\ni5EXAsFgkGCwpVaJxWJoyTRuh0SxXqFq1kEISpJC0LVp8wKYSKxsCC1LOHgbGRPrpEFsorFhYuRT\nVhkJhmiGHdZyNVzdpVitUvU0VnAACcfJAzO0PmieDbqL0TqmqdPiijg862DSukm0uCNpYAVZ3owk\nqbjuLJK0hG13oqoentckGHTZtu16lpePsXPnGDfffBuWZXHq1FFmZ1eo1Sz+7u+O86lPfZmxsS20\ntbWRTAre8Y47+OhHf42TJ0+xuLhOe3svO3e+/mXTFQE2rMkP43neJQVUqbTK3r2tIxpN07CFwHFd\nZEkiWypxbHKepWyZnG1wy1tNfvd338unPvXXzM6eBhR8vhof+cgbiMVifOYzf4/fP0A262N5OYxh\nN1AiW8gZE/RKGp7hxxdwWbM9CpZKRNnEmlhAE0WaHhhoQJQ2NCQazDNOa31nAAmZKD1MEyWKumH5\nHqTGOiXaRS+6KFCXNXySguwVUGSPPFEQfmxbxnXB8+IIIRMINNH1ALbt0XqPTeN5KzTqp8G2kI11\nRruHWK+e4cjUEcLhDqaKTYKFPG2ZUQ4vL9O1bx+x53lm//jkOcr1DNeOjzPfbLL/xpvI5WaYmppl\n69ZLDfSuRHiex5e+dB+nT5fp6NhKMKhy9myOkttBrlRhvrCO7fYSDA/jOAXK5fPkco8yODhMV1eY\ntcpZ9m8eZ25qkbASIy6r4KxxzFhGd/pxyOI6LkFJoepAxTPAKRJW4uieRo4yGQRJ/GhIFPC4yhei\nrK9xjhxlpYFuR3guhTtFgCn6CVPA5gIKwQ1OSRaTOiHEhnNzJuJjbPMu/HWLazZ3s2/LFhzX5ccn\nTWpdY6xmCyT8fqJOFJ0Y1+9JsG1ggEMLCzQajYsS+Gg0SiSi0GhUCQYjFAoF5ubyhMMZQqEiHR2D\nCDHE6dOPMzU19TMzbC4nHn64ZaX+2c9e7pH865FMws6d8MMfwq/8yuUezYuHy6mm2Q38CTAuhPg+\ncJvXkoa8JEgkEmw/uJ9HF+4n0TVMRG8yszJDwamTIIwPiCCRJIiJRRqbKhoVNCqApvlI+EJIIk7O\n8NC0RapEKMTamK2uUnddGmzDVXrAPkmr0LBp+YpYtI5cqjxrdtT6YvLT2hFBq0gJ0Irn8zZ+XgBA\nljUyGT/5/BRCRJCkHMlkEoCDBzfT0eHjyJH7mZ29wIULeSKRAcbGeqhUBJ2dr2d5+RTj41tpNmv8\n9V9/jf/wH97PgQPXvfiT/iKhu7ubnTt7eeqpp2hrG0FRNHK5BWKx2kWL62AwyPCOHUyeOkUsEOC+\nQ+dQlR7qephAW5L/+T+/TCKhMTY2Qq1WYnx8kLvuejORSISPfey/MT9vo2lZlhen0Yth2sJpCpUa\n/vg4s405JE/gOA0agRQ0TYJOBMcOkiKBioyESasNHyNKDI0iJls2rikEeAY/YRRkfLgbiUZhZBYx\naaB6VeJymhV9moAII3sKnjBwvQqadhXl8grBoIqqtuN5RYTIIkkyQii4bgnoAsMm7NvwO8EhWc7R\nH43z6rvfSSLRhuNYTE8f4RW338qOHTv4y//xP6jU66iKwsxylbb4MMvFIn1bthAKBfH7x3jqqUe5\n9dZbLnJIrlQsLi5y5swaAwPXXixYg8E4imaRK5xBt3vw+XppfSSGsKx2HMchHB5geLgNafYU7ZkM\n+bUyZtXAh4RiefjJYTKIwI/wglSdOrbXwBNpFNqw7AgNdEx8FIVNyaug4kOICLLZRMJmQIqwHlJZ\nrK1hOzvwyAAlgsj4MMjQQYE6Tfx4gEcRgQpIyMLBH/SIJaNMzv2YV1/VyXBbG8dPnyO7bkJYomdg\ngOFEAlmSUGSZ1fwxdNMEVb0ksVySJF73uhu4994fEo9vYmUlh2la1Gpn2bfvmovzFgp1cvbs9BVb\njFgW/Lt/B3/+5y9P0uo/hje9Cb761X8rRl4UeJ73FPCqF/r31ut1jh59ghMnzqNpKnv3bmf37l3/\naEvx197/fiYmZzn8wEM0s0tgG8Rx0TBwRAXJM5AxCCOho2HjoGx4skqyScXUUYREMhKmYNjMVi0k\nXzsVI46NgSRFUCQfsjyC4zRo7XietTlaATbRWoJnc35tWjujEEK4eN6zEt8VZLkPIXzAM7S3C7Zu\nHaVUajIz8ziaVmFwsIuDB5Ps338LDzzwEEtLVYpFi0hkjFSqnYmJWXp7x9G0ELVakvX1Rfr7x5ib\nW2R2dvannCxfbrjzztvo7z/GkSMn0HWL/ftHOHDgVy6xsX/V61/P18pl/uar9+PoPSgBiVBHB71D\n3Rw+fAgIcPvtb6BWK/LUU/+AbX+ZhYUVfvTQPJHgGMgy+RULz52nM7mb5fwCTj1MrZnEQScUirJj\n/FeYnzvO6mIFD5kmHrKSRjhVPA8ghkcDjwStYrMbWVrDcyVaGbr1i0F4EgIblZK3jCcFaJoGEgJF\njlK3dYSoIAkJvDrhsJ8tW/o4e/Y8jcYKknQ1mlbDMFYRog2fkiSMS1cmScO9QNHL09HRTVc0jeNY\n+P2tjmFv79WcODHB7t27ed1b38p3/uZv0KpVSrUmultATaUZHBoCQJYVHKd1/HE5i5HFxUWOHT1K\nYW2NzoEBdu/b91Ny1PX1dYRocX9c12Vy8gTnzp1iYmKNWq2M5/WiqkEajSaGMUck0ouqguMoyHIc\nSYkyNzdNMh0h70GjWqFpNvHhx2ABTwhkKYFlr4FnE4luQ3ILaB7Y5iY8FhBqP8sNmygdKJ6PnFcl\nFq3is6rozSzCjSFJRqvLhX/DgyaMgksHbbQO3HJYKJg00aQ5/GGZG19zgDvuGKf5mn4OfevbfPFb\nD6E7KnPlJooF9toijVie7SPDSAIkSXB6aYmdr3oV6oaK5lmMj2/jfe/z86MfHWVi4gTBoM21177y\nEk6ZbVtoWpArFZ/+dMsk7PbbL/dIXjjceSf89//eKrR+Ysl+aXDls5B+DjSbTT73uf9LPh8gnR7D\nMCy+9rVjXLiwyFvecvvFyr5er/PMyZM8fvgw1eU5ehQTPeSj6plIoQClskWv5wImAWQsXJaR0YCm\n7FEnTiYQZMf4KGazyczSDPN1i7oVxdHB81rZra4LjrOIEElUtRfLegiYQpYHcJwKrc7Is2ZRRVpR\nfBKSNIPnJWh1U04DBq7bRjDYAHK0tfWxtvYo4bDG7//+Hbz3vfewuLjIl770Pb7zndMcO1YiHB4h\nEpkglRoiFEqxujpLoVAmHk8jhIzntXgEQvg2OBcvb8iyzN69e9i79x9Pa4ZWd+Qt73oXj59cIJHY\nTTAYIh6Pcfjw9wmFxmg2Sxw//hgXLkzjunG+//0fUCktIAsPrS2I40TQzAxNkWNy8XF8ah896RSz\na+cpVEtYWobVVQtEBuGbwfDaydpF/K6J55VpOXSWKWFhi/FWspGXRRYVDBGk4pVRUKjRRBZgey55\nBAoKJRc0KUC3kkSWwriqiiQPEQwUEOoCgbCGaWbp6tKZnZVx3XWwVwCHQGA3QZ8PWS+RL12gLeiy\nvlTCU0t4tQoh67mGpKYFqNVWABgZGeHdv/VbnHnmGZ4u/z2J1DYGBoYvFvblco6OjtjFo8/LgbNn\nz/K9L36RvkCA3lCI3BNPcO+TT3LX+99P1/PMvFohia3XOTV1ktOnFwiHdyFJD6JpAQzDRtfXEcJp\n8WiEh9/vsbAwT71eoVFrYjXz9HT2EI6qNPQmVXQaShRZGGhSN3hVJElFkzIMDl6Drs8hN/MsrxWx\nbYOmvowqBjd8ZjwaIkNdDFILrBFQJeR8HsddQ0gqnhuijo8GJUKEaaUfVdCpYOAjLGuE/Am6x0L8\n6Z//GeFwmHw+z2f+1/9loSio1wvUTIdUJIEa6mWhMU/+1FlSCZdNm2QGb7iBg694xT86p8PDwwwP\nD/PGN76aT3/6XmKx5wo7x7ExzRW2b78yDdEWF+FP/gSOHHl5k1Z/Er29MDzcOn561Qu+hb8y8EtV\njBw/fpJsVqW//zkNVDi8mxMnjnDddYv09vZSKpX42899DrG+zrljx4jlcsQsk9lAmHMliVJTbYVa\nyXWCjk4BE2sjW7UqJGa8OgR6WXaKpM0K4UiCnFxEpwPYhiS14zjP2rvbOM46qlrGcVrcACjhOOdp\neQmEgCmEAM9TaB3JtMLuhGgiSXE8T8V1I3heFtv2IcsKr3/9B8hkumk0yqysnGdiYpIHHjhEJDKO\n6+aIRHSSyUHK5VWmpo4iy100Ggb5/BSKohIIZEkmr9qQ9pZe0jj4yw1FUWhrSxKPp9C0Vos6n89j\nWWGmpyeYnKygqqPUanUq5RjRwCCmtcRKPkc4WMbvxbEMD0m2aU+UCPp0hFNHSJup1XTm5pbw+ZLo\nehBYpyiB58yRQkalSo0mOnF83iw6Koq0iiw0bK/OEhKWMIhLETxZI+u6FBwdRA2EhusFWPUcTNvC\nFX5sO0fDrCErK1w9tI1XvGIEx7mBwz++n8KZGdKhFDO1OnVrnoYbQDHXGI+EGevuIl/PsZk6ZysF\nauXcxfkpFJZ5xSueS2iOx+Ncd/AgyXSaL3zhAQqFIOFwnEolj2HM8+Y3v/GyEZ0dx+HB++5j54Ys\nFSAeDhPI5XjkgQf41XvuufjY4eFhQqGHyOVWmJiYJB6/iuXlBSKRTSQSy1y4MINlhQmH24Eoslyg\nUDhHQFaoNUxcO8iCuYBcXGTT6CDLVo2VmksmNkC5ISHTgQc0zToCC0Xxo6oh4pko87lp6kYOHz7C\ncoa669DwqnhenGYzSDxhsGfrKN97/CjCBY8azWYWE5ijQRqbEOvUCZCjDYMOPCeL617gne/8TwQC\nASzL4rc+8jvMnqzQHd9EVs/iWGXWV79Hpv06dKOBGqrjS0n83h99kv5/gb94JpPhttsO8K1vHQaS\nG529Aq9+9a4XPQLgF8V//a8t0uqmTZd7JC883vQm+MpX/q0YeVng/PlZYrFLmf1CCCQpyfT0DACP\nPvwwyVoNS1Xp9fmoCMFjhSbzjXZsbzNxV6PkNci78+Txk2CVTjRkPJpCwZU6keU40WQHFVtCURaJ\nxEOs5yMEAhksS8VxbIRI43nrSJKGqhax7SLgoKojOM4srpuhxRFpR9NKeF4Dx1FxHEHryEZBUQZo\nFSgCz1tFlhfp7LyKubkc3d2DaFoGVfXx5S/fj22HSKddXFfgeU0AgsEEk5OPEY368fuT2PYCMzOH\n2LKlGyEkZmefYv/+Tb80tu/lcplKpUIikfgnU4YlSWL//qt48MGz9PfvRAiB4xhMTp4jEkngugk8\nL4aug2NbKIpK0DdIXZ+kXMuSs5YxLZd4NEgqGqScMzCdToLBNPX6NI5To1qVgRCKoqP4/JStUTBP\nEvTCNAAZB5kCPjwSkRGS8UEMc52l3I/IeqMUtS4CwSC1WpFQSEHXc6hUwU5jewI8D8sNIOjGE1kU\n2U+zKWEYBqFQmL6Aj7a4RrPpElclgsjUKBCIKHSlEhQaBZJBcFHJ+IOszpxiZPM15HKLRKMVdu9+\nHQsLC7iuS1dXF6qqMjY2xm/8RojDh59gZeU8Y2PtHDx4N52dnS/pGj8fuVwO0WgQSV0qWe9KpXhk\nZgbTNC8eH/l8Pt7znjfx+c9/iUJhGcfpwLazpNMxarU4PT1LZLPnkaQwrjtPobCEX2ljIDROUAth\nWnXcSDdZ9xyRUIz9d1/Hqc88jmmG8NwmQtYQnockNXGERKWSQ5YrFIsGkdg4euMMGjZNLwjeOi4h\n/CIOElQaDQ4/M48qj1AzqkSjGXT9FHgRmtRYwAC6URlAIKNQQpFDRKLb+dY3nsDvDxEKacyfKzAQ\n70eRfQSUMLFgNxeqp9Erj9Iuw1Xtg3iNEt/4whd40z330N3d/TPneN++PYyOjjAzM4PrugwODl6x\njqznz8O3vgWTk5d7JC8O3vY22LGjxYW5jM3IFw2/VMVIJBJkYaF5yTXXdZmdepLvZX/MQFsbDz78\nMK/cvBk5GkUTgrVGg6oRQRYpkr4E67UCGgaeFyBOFlsE8CQLx7PwgoNsiQxBOsmmq3cRjUao12dp\nNl0kqYnnZQkEtiBES27qeTpC5DGMJqoawHFkfD4Nv//VFIsncV0Zz7uAZQlUdTOyXCIY3I5pygQC\nNWq1KSTJQZIkVLWComTZufNu1tez1Go1wuEwfn+IkycnKRRcUilw3SaVyhK2HebcuRN43hiuq1Au\nH6e3N053dw+VygqWdZa77jrA1VfvvEyr9cLBMAwe+OY3mTl+nKAk0QC27NvHwMgIzWaTTCZDd3f3\nxR38DTccoFAoc/z4IYSIUq8vo6oB2tt7WVmp0GzqSFKrEPFcC0/24VNNHDdJLJSkUFklHh7g9FSW\nSnMeW+zApyRR1RlMcxkhPKCG657H84JIboKMpxKRw8hyg6YisaK7yG4M07SJhSwimRg+bYilbBWb\nJVQ1TSaTQpK6WFs6QlQVlEUT4bbjeCVkYmg4CKmIX+0iGBzkhz98hC1jBt2BMJk9B5mbPw8rRS5U\nztCdGkL1h/ESCl4zx45tI4yOjqIbBl956gSOc47rrx+hq+sqPvvZv6VWkwCBz2fw5je/mrGxMXp7\ne3nrW6+cHbGmadie91MqKsu2kRTlEp5YtVoll8vx2tfeQDZbIBbrJxDYxle/+gOEyJBMbsO2ZxGi\njM/no1JRUSwN4ToYRglNg3R6mLm1ClNnJwmnxghHFdaWZ1ClKKY5iYeOP5QiGlNYW/s2mmbjOEk8\nTxDyh5GdHDiNDU6Qg+XZOHoOSThIYgtCKRMIuDiOg6b14lgNcMexnw3hE6AIGaH0I2s1Qopg6ewS\nn/zjz9HRlUAvSoRDJrajAh5CgOqoBBoVbt57A7FYGFn2M+rzcf9XvsL7fvM3/0VdrUQiwe7du1/4\nBXyB8YlPtPJnXkZCwJ8LPT2wbx987Wu/nFk1v1TFyDXX7ODJJ+/DstpR1ZZ18ZlnDiMtneJ1+28j\nGAgwGY/TWFjATqepuC6GZSNEEscTILn4ZIOwo2B4YWJqmF5VUBI2shzF1lK4wqNiuszOlikWp8lm\np/G8JWR5GMPI47rH8bwwklTD887h8zloWhu1moLn9dNoVDHNcwSDUer1tY0smT48r4rPZ9PePsbq\n6jSepxCNagSDNTRNIhqNoKojaFqIZrOErhsEg0GOHj3K6mqZTKaPSKQNWfZhmh4zM4eo1daIxfbh\neQax2Ajlso9IJIEsN7jhht3s3r3rkvmrVCqcPn2WQqFEb28nmzdvfllYQP/g/vspHj/Owd5eJEki\nVy7zuT/7XwR7dtLbuwmoMDbWxt13346maaiqyl13vZGbbsqRz+eRpByFQoCJiUl0fRHbbsPn01CV\nTiI+m0p9DsspgTeI6ZTYvG2IpeUSItzfYhRZDRxnDduW0LR9KEoTxykTjw8RDDoU1wooboOIP0bI\n18+aWUCSAjhCYFs5ZJFgMVdibq2MEN0kUm00GmtUKk0ss4nqlvERQnKbmEziEsRHAUkukQx3ITwf\ns5MruLJNPvcwaddi16YtDPePMTAwxs7aLCIe5vELC9y4u4ftw3cQ2lBRLKyvc8fb3sJd73gH1WqV\nP/uzzxMOb6O3t5Uo1mzWuPfeB/joR1NXXActkUiQHhxkbmmJged5nUysrLDtuusuFiPHjj3Nffc9\nvNH18iiXqxSLx9i9+1V0dyc5c2aeQmEOkEilttLb28eJE99BX9ep1VZIxFPEE+0YpkmtopPoTqBp\nESKROFZGxXV1FKWBLGvIcoh43EQIF11P4DgpbLuEpa8xIAXRWaRJcIOJVmvFArhb0V0BiiCkaei6\nQjQ6RLN+Asu0kbwkqhpCuAqSSBIIxDDtOfKlFdJhH7VmjUKgjOTEqDZNSnqNfKWJIqo0jRwDHSHS\nqST5wgJ79gyRiceZnJ8nm83S1tZ2mVbvhcXJk61Auc997nKP5MXFPffAZz7zb8XIFY/+/n7e+MZr\nuf/+I7huBLBZOv9D3vXKAwQDAQBGhoYoTkwgFQqE2tspnTuHpFoI1ybbLBGTZHxCxvaaKJ5FWHLI\nhMLkInFW15pYlgZuk+ncKZpeCmjDthcJBASOE8JxTIQooWlFhJCIRnsplfyoagemqSLLg9j2eRSl\nhiQ5uO4SEMTv70KWY6yszOH3q9TrZWIxmZ6ebSQSEcLhGrFYjNOnj1IsNqnV8uTzBXK5C/T0ZND1\nOktLD6Eo7ayuLpDNLhGJyITDBqVSi4+iKDKNhkw87ud73zvMnj17LrLpFxYW+Pznv4ZlJfD5Ijz6\n6ONkMo/x3ve+9aIPwZWIWq3G5FNPcWCjEPE8j4eePkcyuI1KRaO3dyuSJDh79jjf/vZ3AYlz52aJ\nRIIcOLCLnTuvYs+e7Tz5ZIU3vGEXJ04c4uzZedbWTEwTytY6kpzFaOq4VOjrGiMQbEeWpzcks1WE\nWMR146jqOI7jYNtlgkGIRjuJxwM4zjS1RpZuKUDFzLGsF6k7KWyRQfeaPDExDxh4dheaLGgWHPD1\nYppzCAr4hAOWiiYFEPZZbNYIKVHiyTGEp2CaTRTXoOaYDAxcz/qFGZ48W2JqZZVd/RGi7RmenMuS\n7h3i7OICiaCPTDJJsdFgBbj7llsAOHv2HJYVJxJ5Lto0EAgjSR2cOPEMt9zyC9kF/Yvw0EMP89RT\nZ3Fdjz17trJ//74N0uk/j9fdeSd//4UvkJ2bI0iLEp4YGeHgjTdimialUomvfe0ROjr2XuQIdXRs\n4umnv87q6qMkkwU6OkrU6wrp9E0kEp2srS3hOD6kgMBxFAxDZnVlCdezcVin6XSwvCyjKGEajQU0\nbZB0eiuVyjyKkqfREPj921HVMK7rR9OGqTQepemWiRNC4gIJ8hQIYYs0imJTa6wRDnvIcgQhVOr1\ndVTVxXbquFYajwKmGyESCCCEi2EWERSom91UdR1zPUW9cp6APERf+xgDfSFW1hYwjDW6Er2UyhcY\nHe2kt6elihG0VEjLy8tEIhEGBgaueBOzfw4f/zj83u/BP3E6+0uDN7wBPvxhmJiA0dHLPZoXFr9U\nxQjAtdfuY3x8G0tLS9i2zf3uHD3POxvdvmkTDxUKzM/MsHdsjPToKDPHzhNL9mOWPWJSGLNZwzSW\n8ESTrAtBR1Aq5TFdk0CoBz8BNNdj1Srh+Q1CoU3U66cRIoGu26iqSSQCsVgfuRz4/YNYlgk0cF0f\nkhSi2SwjhA7YCFGgXnfwPANJascwFGS5jiTFOXfuOwwPdyPLo0xPTzA/P0Eq1cfqqo5lGYBJMrkF\nSZK4cOGHmOYshuHH80K4rsrExMP4/XvRtCiu26BQWOC223bTbPqZm5tjZGQE13X58pfvJxgcex5z\nvo/FxQl++MNHuOOOK1fc3mg08AmBvJHAW6hWyZU8OpIdFAt5HMdGklSi0Q7+4i/u5YYb7iSd3o1h\nNPnyl4+wuprluuv2cvz4vRQKCps27SSXW2Nu7hClUpl0updkew/VCznCwQ56BzbjeR6RyNBGMuoc\nfv84y8s1HGcF217DcXLYdoxGI4hhVEkkJGazFudrZ7AIY3gZGpSxPT8uEWx3EVXE6I3FUQXkqlk0\nKYprW8jKOiH/IH5PpWbNYFJGExJVr0bQ8vCsGsFQgLXyKWIdSUZG9uDYBgszx1nPuUwXl0nEdULJ\nHpxygolsnSPnHqanI8PYji28/0Pvv+igWqnUUJSfPoz2+UIUi5UXdR0ffHCW9vZtG/cvcP78Bd73\nvnf8lPT0J5FIJLjnwx9mdnaWarVKNBpldnaBP/3Tz2EYNvV6Acdpp6/vOT8Nny9AX99errsuza5d\nV/H5z3+Jb33rCRKJLkxTZ35+ls7O7ZRKT7G2WsB2PPxAzZhGlwwkulhamicQCNPbm2Fy8gkUpZMt\nW/bhur2cPPk4stykWi1iWTJCdKDQTp0iHiXCFMng0efzmFcrzFqn8Rhn7943sro6z7FjT+N5WUzT\nh98fwfVWMG2Q5QJ1vYku1fC8VSL+HupGk6BvB4n0GLXadyk1Z9GXcySjCUJxmfHBq1GUAjffvO/i\npmKlUOCJqQWWvnIYWY7heQ3a2mTe9a43k0gk/omZvnLx+ONw7Bh86UuXeyQvPnw++MAH4FOfgr/8\ny8s9mhcWv3TFCEA4HL5oyHOko4NCpUIyGgXAr2m8Ys8evhePM/z617P97rvZ8dBDnHz0KWbmS6wv\nTGPqOdJKmO7YDmzX5WR1Fttns2s0zezqIk27k4bZxLaWsEih6wFMM4OqhvH7PWKxNhzHoFRawDQj\nSJJAlgWRSJhqdQ3HWQPW8PtrxGJ7qFYlmk1vQ92ygixLdHbuIxTqQpLmWV2dQ5JM9lie7gAAIABJ\nREFULCvD+PhearVzuK7E0NBNnDlznmeeeYh4fBvr6+C6MprWg6aptHwsVHT9CTyvhus2kaQyR48+\nSSYTxeer8v73vwNN0ygWLfr6LiWmdXYOcfz4Id74xtchSZeG6V0piMfjWKqKbpr4NQ3bcRBCoWHo\n+MORixkas7OTWFYbHR0tq3dV9REMXsPhw4e57rq9fPCDv8r99/+QL37xr5DlPq6++jYsK45hrOF5\ni2zffgu5XIWZmXOMjY1j22U8L0tvbw87d17Lfff9DcvLefz+OLadwLJ60PUIjlPGdSGcHKXmM2hW\nm+i2AhxAuPlWHKIXwfFsVCCqRkBzcChQlop4siBnT5G3iggRRpJ3EnRNdLHKamWNoBpE2DKammXL\nljuYnPwHVtcKOPIIkuIjX7pAvtYgrfuZnllHCJ10eoCxa15DKBTmG994kI98pA9N0+jt7cI0J2il\nyD6Hej3L0NCLyy3q79/+vPvjzM4e4/z584yPj//M58qyzPBwSwH01a9+kyefXKe7+xo0zc/Ro4eY\nmjpNV9cwyWTH856jbuQ1pfngB9/JwsIi58+fAFSSyTCuW0DTBvDHZzEVC8MpYno6qu96YrHrEELC\nMApUq0tomopt55mbe5R4PABIlMsejuNDltuxrCweVVTK+KnQRhJdKJTNOiVhEJE8fP4C2exJlpZW\nkeUVZLkHIQZxXQPP03DdpwEFvEVkScenpig1V9CUNgQVqtXTCNGDL7CLUDhLqitBKuVx002v4okf\n/RVnlpfpTiap6jqHZheIZnYzOPic0eHq6ixf+9r9vPe9b3+hlvQlw3/5L63bL7NV+vPxoQ/B2Bj8\n4R/CFXZy+q/CZfuGEUL8uhDiyMbtZ+bU/KK4/jWv4XQuR67ccjbNlst89/hxekZH6ejqYvfu3Xz4\nt3+bj/3xJ/jgx95Fx2iC3rYuuru20xCCdUzqgU0EItvZNjbGeMbPYLxJRF1FoOK5KXRdwXFSOA44\njh/L6kaShnAcgePMo+vn0PU8oZBDd3eUaNRPMhkgFGrH87oJhbYiyxkkKQm0oSgG6fR2JMlPLrfG\nwMCNtLf3EAj4KRTyZLMSy8tTOI6BzyfRbKoIkcTzJIQYQJIy+HwKjUYTVR0D2vD7O/H5ooTDeygU\nugmHN6MoW/g//+frLVXCP0Fka8n5rlxomsbem2/m6cVFSrUayUgEyy4xV8yzaXzbxdc1MzPB4OCm\nS15ny6E0xtraGplMhuHhPq699rXceeddOI5CMtlJV9cuGg2VSnmRTCqJEItUKo+TSKwQDnukUp30\n9W2ip2eA7u5dxGIJQqEhQqEklUqOSKSTTOY6fL4tdHTvx8QG0YksSyClkeVRNHkbEgor1So2NgKP\nqllGkCYSuYlU5q1Y8iZsOY0a2oTW3svOTTexe+gqYtoqmzokeke2U6tlmZ2doV4P4boBGo05XDeE\nEP3kckuYph/Py1As1pmaOkNHxyCFgszExATQkr92dUlcuHAcw2hiWQYLC+fIZBy2bXtpLd8DgTQz\nM4s/13Py+TzHjs0wMLDz4pHM4OAoQqSYnDxzyWMbjTVGR1tRAdFolN/+7Q+yZ08b11wzBhSp11UU\nJcn27a/ihle8j9Etr0XRBggE4uh6Hdd1AB+5nIGqdjIw8Fr27HkPfv849foynudHksLIcgpJGsTB\nRKJBnDiOiOBpKdalDpoM0vTStGXiaFqZaFQiGr2aaHQMy1rGMFbxvAY+Xx8Bf5R05nocbwdNO4Us\ntSPUaxByB6XSEpBEklpHh7reZHExx9NPH2bz7n1cdccdKFu30nfLLSR6xhgbu9SPp729nwsXchQK\nhV9wxS4PHnkEpqbgPe+53CN56dDe3koh/ou/uNwjeWFxOTsj3/M877NCCAU4Cvzti/FHNm/ejLjn\nHo48+CBHzp7lwtQUo21tJLJZDt97L4czGd7ynvcwNDREJBLhew89yYSwKboOricIRQcYl2IsTR2h\npHuMDGY4eXKNhgeG04Yrj4A3i/B6wdGx3Gew7Tp+fwe6DtHoOKXSIp6nUCyayHIdyDE0dC1nzjyG\nLPsJhwNoWgzLauJ5IYRYx3UNZBlM00ZRfDzxxCGqVQu/v51wuAPDWOXcuUdxnCDpdApVBdMsEgpt\npb29DdNUN0ycQti2BsyiaSkUJYNpTtHVFaezs4+VFYvp6TliMYlKJU80+pxMcnX1AldfvfmK7Yo8\ni2uvuw5/IMATDz9MeWmJoV0jLBcUJMmiVitRKq3j9zcZGPjpQDfPMy5aYs/NrRKJpBFCoCgKrutQ\nzJ1FZKdJmVlipoVplBnbtIftO6/n6NHv0tbmsLT0CJIkEwyaXLhQQlFGURRQ1TZqNZdw2KDRkHAc\nBUXtQHcsXNdGliO4rgkigCqpuKyTa3oY5gqmFEJWFCRJxbbrCJFEkhVkpc7m7fsRwsQqriObi4RG\n+lDMCMePH8O2R5GkNKGQn3weYAnPC2wUql3Ydh3TPE2x2NpSqWqUbDbP4uIiD3372zQWLlBbWuap\n+SP0Do2yf/9Orr9+/yW24S8FLKtxyXvxX4JCoYAkRS8pODOZNAMD3UxOHmVsbAcA+fwcW7cmL3Ed\n7u/v5wMfuJMHH3yUr3/9HEJsZ2Cga8Mm36ZQWCAU6iYcdggEXBqNIoZRIhTqwnVzpNMZZFkFkqjq\nALr+NK47iGNVUFhCZYEGfZwVBkk1iOlEcEUQv6KhyessrK4RzQwSjSYpFCR0HWQ5CUTw7GlcfZGw\nmiZmgqUksZQkQqwTjVroegDHiSLLs0hSAFneRCQyimGUOXbsMDt3HuDAwYMb82rx/R88gaJcevwl\nhEAIFcuy+EkUi0VyuRzhcPiySrl/Ep4H/+k/wR/8AVzhaQQvOH73d+Haa+EjH4HUz/dvcsXictrB\nz23cfTa05UXD6Ogoo6OjfPFzn+OqVIq+5zHIzy0s8Mk//EOSwSDCslg5dwJJjNC96fUXP9Rs22Z2\nxsawba6/6QCW8wgP/3AB1O0oXgEPB0my8PsSNM0IVnOespPDtutomsvu3a8ml1sin58mFpMZGtqK\nz9eP338C0zRoNPIbjqh1ZLnVQnYcnXq9giRVefrpf0DXi0hSjGo1S70+j6KUaTZ7qddPs3XrQcLh\nDLqewbYdAoEArlvD71exrCbt7W0kEh65HECW3t4EIyOtVnwkkmJpaZG77nodf/VX91Eup/D5wjSb\nBVIpi1e+8rUv5tK8IBBCcPWuXVy9axeu6yJJEnNzczz22NPkcoscONDDrbe+n29+8xiO04Mst972\n2ewimYxy0cCprS3BxMQ6iUQ7Q0PdHD8+gZQ9zog/TLo7ztpagSGfyvrpf+CU3OTmm8e4++7b+fSn\n/5JmcwHXjdDevoNazcOqLxMUAhoexbUSFT3P0NCNWNYAi4vHgBaHR5IauEhIqIRkj6pxGkVTgXaQ\nQtj2PIoiIcsGQmhEoy7pdAZJkkgkMsiBJT78kXdy+PBjnD49BzTw+VygjiRJCNGN560iSamNuYph\n2ybBYCu0w7IqSFI7X/3f/5sRv59tw8PYAwNMrazgdMe49dZbXpJi9NlwNmgpeCDL9u23/ly/IxwO\n47r1S65JksTYWD+9vWXi8Za52ytfeQ07dmz/KcJmX18f73lPHysr65w8aVGpVCkUGqyuzuM4Eo1G\nnUikg2x2kr6+TRSLCtWqjaa1iL7QMtWTpCia5uJacwSpEXYlBD5MKUhDSVMVPiJimahSwnQtDFUi\nFIwzN3ea/v592PYctp1EVSOYRgPZzaGKJFEtgc+FjOSnqjQQwU50fZpIZBzT9OHzlVCUTfh8Gs1m\nCcMo0t8/hmGIi54rqqoyNNTN6uoy6fRzXLpms0Yg4F7iIWLbNt/61gM8+eQUkhTBdRsMDiZ561vf\n+E/6+LyU+M53oFJp+W/8v4aREbjrLvjjP4ZPfvJyj+aFwZXAGfkgcN+L/UfK5TL5uTm2/IRz4Mrq\nKvmzZ3nt3Xfj8/lwl9a479DTLPvb6O7ZC0C1mmVg2M+BO27g5NISbdftY0ddsLiYRDNkdMtHrlpF\nSBqKDJ5bwjDW8Pk6CQY7aDSWSSYjdHffgCzLdHU5rK7OEQjICFHE8xJomksgkKDRmELXC5TLZwiF\nZBTFoFJZRJZ3IstduK6BZbXUF7K8QjhcR4gGg4Mhdu36Nb7xja9QKHgoikZnp8zCwgzBoI94fBP1\n+jypVCcdHdJFolqtVmJoKE1/fz+/9Vu/xjPPnCafL9PTczVbt255yXfE/1o8+8XZ399/icuk53k0\nmyYPP/woEMXzDNraVN7+9jsvPufqq3dw6NC9VKtpRkaGOX/mcait4/o8/P4eurt9dHakqXseA7s7\nedvb3sTKygq5nEsoJKjVwoRCUSrZH5DyMsiOSiQao6LPYrhL6PpO6vV1QqEU9XoBRRlAVcN43hSq\nWsYScWKBIP0DQzhSF/H4EBMTT2IYNcLhGNVqDs/rZH5+kp6eAfL5MwwMxNi7dy8+X4BarZOHHz7F\n+rpJItFDs1nBceobHKUUlpVFlnUCgRC9vX2src2SSFhUCwU6gI6NsEVFlhnr6eHxuTnm5uYYHBx8\n0detVjtJLteSkft8Bm97262kfs4tX2dnJ8PDSebmztPVNYoQAsNoUi5P8573vOlfHOx2/fW7qdXO\nk0oNMT09jW33EokkmJi4j87ODjo6hqhWZ0mnPSRJ5+ab38zExDKFQhYhmsA8gUAftjND0OsCOUzD\nmsViFZ/chc+cp09LEFD9uBRQIxEWVR1HFZTLU0QiNYLBGI2GhdGYxy8X8SntaFII16ujCEFMlene\nMoJhgKLIGEYeEGzfPkYkEqVer2IYJq961X5qtbNUq9WL8/na197IZz/7ZZaXm8RiGWq1Ms3mHG9/\n+6svKdAOHz7C44+v0N9/4OL/yMLCBF//+v28851v+bnW5oWG48Dv/z780R/By1gE9K/Cxz8O27fD\nRz8KfX2XezT/erzoxYgQoh34u5+4vOJ53tuEEPuAW4EXPdLIdV2k1nguXqs2GmSXluiJRC5e37Nr\nB2vZHA/OPcK8W8F1XYLBGv/5P7+fG298LsuhY9M3+OP/7+tABE3zkSuX0I3zeEyiiAiynNnofgxS\nqSxSLJ7GNFO4bpF6fZhIpI1MJk0ut4wQWTZtGqNUWsU0oatrC6lUgFgswKFDXeh6iWq1juuuIYSD\npgVQlA4SCcE99/wG5XKDer2Vv3Hw4F5OnTrCwMAwiUScYlFHiAiqGkMIFyHW2Lv3NciyTKVSwLIW\n2bev9cESi8Ve1um9/xyEENx8843s3bubtbU1fD4fPT09l7wf0uk07373bXz1q99jddUlmdBJDAUZ\n7m5ncTGL35+mVJYpNtdpb+rIskw+n0dVk1x11W6+/e0HEV4bfapLw1tAdxuoTpSeuEwvbUzVnkTX\nG8TjwwSDBRqNGXw+H11dnXR3D1MoFHnDG95MIBDhoYceQFFsVDWI50Xx+y1se4JKxeTs2UWWlx1G\nRjK8730f2pBm9qGqx3jDG17NN7/5ffL5SWS5iOtOIEl+/P4gQtSxrAuEww6qWqOvz+a22+7mO1/5\nCl0bBO/nI0yrRf9SFCO/8zsfZHGxxRHp6en5hYP37r77du6777ucPXsIITQ0zeZNbzr4cyXM7t17\nDRMTs0xPn2N6ehYIYRjr3HbbW1hdXWJ1dQXHWeXqqzdhGJtIp1MMDIxQLlc4d+4kk5NVCoUSqtWN\nJoLg6ST8vTSVFRR5lpStEI9FcJwKrithFPMYbhm17wBbtgxx4sST1GrnSaf9KO4s7aZDxS5hmGGC\nAZBVGymYQNdzXHvtPkKhEAcPRjh27AyGsU69XkXTPPbs2Uk4HKTZtC/pZHR2dvKhD72dxx57itnZ\necbGElx77ZsusXj3PI9Dh56mq2vXJZ2xrq5NnD9/mGKxeFmVN5/9LMRiLanr/6vo6mrJfP/9v28l\n+r7c8aIXI57nrQE/ZVAghOgGPgm8wfP+cZrkJz7xiYv3b7zxRm688cZfeBzxeJxQezvZUonMhkVf\nXdexm018qRQnT56hXKqRSEa4+Yb9uBcucM0tN5NIRNi/fz8AR448xtpanq6uDPv3X8Pua37M6cOz\nVIou3XFBsbZKQ3cQtOELh5CkKrXaEooSxrLSGIaOz6dRrRq0tSXo6roJTfsRW7b0kMl0o2ld7Nw5\nwk03HSSVSnHs2DHOn/8sS0uCcLgT11VQlAiua2GaFyiV5rj11hvJZDKcPHmKhYU1rr12D3/wB7+G\n4zioqko8Hmdubo7FxSVgL1NTC0xPn2V+foJk0s+73/36K+oc+MVGJBL5Z31ThoaG+NjHPkA2m2Vx\n8Voe+uIXWT09i+OkyGYtNA2yIsjJMytks1nC4TCe12TLlr0sLMxx/tQknZEwES2EP2RhWzlSiTDH\nz05iyxE6O19BPL6Fej2LJE0wMjKAJEEu9xTxeA+Vikkk4uO6667n4Ye/Sy53AdsWDAwMMjr6bs6f\nn6BQmCEaddi+/XoeeeQYIyPDdHd3s3v3AE88cYHbb7+J5eUVfvCD7+DzafT27sUwbDStVSR3dRX4\noz/62MV5SHV0UDxxgvhPtN4btArUlwKapjE0NPSzH/gzEAqFePvb30y5/P+3d97BbV1nov+di94I\ngA3sFKlmqlAk1SxZkiVZtoq9lmQ7TrLuduzYWW/8NnnZN0ne2+Tt5M3uzk422U3ZxNk42djjxHGP\nW9xkWZLVeydFUiLBBjYAJACin/cHFFlUsRokkPT9zWAGvMT97of7Hdz73XO+4iccDpOdnX3B9OAz\nMRgMPPjgl2hubqar62c4neMpK5uEyWRl3LgqIpEhWlp288gjK9Hr9bz00p9pa6unoaERn2+AqqqZ\n7NzZiUhq0WsUHPZx6PVmfAMG0OzAaQ4wOHgAIUyYTFnodArZynj6YyYKCq5j+vSZvP76Hygvr6bd\nXYy1swFjwEtvuBFX9kRKS0rZ3VIP4W4aG71Mnz6ORx+9jxMnWnnxxW0UFFThdOaQTCZwu/ezdGn1\nWcULc3NzufXW5ec9B4lEgqGhGLm5w2dGU7El+ow21+zqSvWgWb9+bDXDuxy+/e3U7Mibb8JtI7cC\nw0WRyWWa/wPkA6+cfDpdKaUcNsJPd0auFCEEt6xZwyvPPENfIIDDbKbT66UpGERgJSsmMRrz6egI\ncPjYFkoWz+GLX7wTgK6uLn7965cIh7MwGu3s3HkIq3U7Tz75AN869D+woOAwWYn5s2nwaxjSFWNy\nZFNSUsSuXVtwOksIhTwUFbnQaFxEo4KGhvXYbAZCoT5crunMmjWOxYsXDatyWVJSQm6uHikDWCxO\nwuEA8fgAsVgfer2X8ePzTz21zp8/77zfvaKi4tTnFi5MdS2ORqM4HI5LbnIWi8UIBAKYzeZRUZ31\nclAUBZfLRX5+Pq+++BIf7K8nS5oQQhDQKESzx2EZsnLw4GEWLVqAy6XB42lh4cKVRIYGiRzZTTIZ\npCAvh1mzlqLX63APBgkbJtPb5yMUCqAoWkpKJjBlSgmHDu2momIq3d1B9u51U1/fwpw506iqms6+\nfW6mTJlFWdlUWlrcmEzlFBbmo9HsYfLkufT3d/HKK3/ma197kDVrVjFp0mF27jyE3a4lGKykoOAu\njh49TDRqAOIUFRWSk1M4LFCxbu5cXti5E8fgIE6bDSklx7u60LhcjBs3LmN2uBLsdvsVOVIajYaJ\nEydy550r2b7deyomBFLXEoslSXFxMWazmW984zHq6+v55S8HWLLkHnbs+IgdO9ox2icwFGrHEk8g\nlBAAfcEkM6fU4O3uZnDQRCIhAQshReJwlPPnP29m6tRKKiquY9++9ygrm4MvYKDAmsWMvHyautrp\nCvYxfXYZRUUzsdsLSCQi/OY3r/Dgg2tZsSLAxx/vJBi0odEkWLKkmqVLz92d97PQarWUlubh9XqG\npURHo2G02gjZJ5f0rjVSphrhPfooTJ2aERVGFEZjqt7IV74CN94II7g+5QXJZADr49f6mKWlpdz/\n9a+zb/du+j0eJl9/PZ909uNrD5BvsqHX6ogm4ngGzVijmlM9L1599V0UZRylpX+ZQSjB42ll9+5D\nTLt+If1tQbqbj+FFg3DkYtTmoigmhFCwWBwYjZCVpVBVdRPhsJ9g0E9LSy8Wy3xycqZis01n375+\nOjtf5atffeDUTd7lcrFsWS27dx/D692FopShKBGysgKMH5/H4sWpGgzxeJxDhw6xZ89RAOrqqpg6\ndep5KypaLBYsFsslnTspJZs3b2Xduh1EowpabZwbbpjBkiWLRl3lxlgshlarPa8jFo1G2bdvP/v3\nN/D2e9tok0U4LSVoFD0JrRG9CNPQ0E5vrw+NRsP993+Bl19+i+PH9zGtehJHIieYVZrPwlmziEQi\nvLdxE36TndW338GuXZvp6uomN3cSUibZtu0TXC4rN9xwO21tzezde5jm5j6OHt2KyaQjEmnBaJxD\nMinx+4OYzfkEAh40GvB4WgCB292Hz+fD6XQybdo0pk2bRjQaxeP5GYWFkygvn0woNIhWq8dgMOF2\nbxo2W1BQUMCtDzzAB6+/TsztJiElhZMm8YXVq0edbdPNggXXc/jw87jdR7DbXUQiIQKBFtasmY/5\nZLeyVLPFBBZLMRqNhsLCCWRlbSAeDxHQGQmFOsnW2OiPh7C6iujVG9DoDYwbNwlPz3H6ooOEjZMQ\nkSz0+gSxWBZudycWSxZz59YQCk1AJhPEg34mTogzONRGVdVyCgrGndLT42nh+9//IUVF5QhhRqOJ\nsHr1TdTV1V72d1+x4kZ+9atXSSTiOBz5hEID9PXVs3bt/Et6EOnv72fnzj20tHSSn5/NnDm1lz0b\n+2//Bh4PvPTSZe0+Jlm2DJYuTS3X/Nd/ZVqby2ckBLBedTo7O/lk3Tpa6usxWa1MnDGDpatWIaWk\nsGQv8TwzBxr3QCyK1mpn8uIvEIu1EQwGSSQStLf7KCsbXnwpP7+UxsYNlJWV4nKVoLNPJNEapiQr\nm2PHtuD1nqC5OQuvt4dotAONJswnn/ye7OxphMM+/P4oFks+8XgfXm8n7e1trFvXxLFjLXzxi7cy\nb95cNBoNDzxwDwcPNtLQ0I/H48ZkMjFpUjVWq8KCBbM5cuQI7777EZ2dguzscYDk+ec3U1NzjLvv\nXsvQ0BDbtu1g374GdDotc+dWU1dXe8k3mW3btvPGG7spKZmJXm8kFouybt1BpJTcfPPS9BnrKnLo\n0GHef38zvb0+7HYLS5bMYebMumFOSTQa5Xe/+yNNTWF0OiceTy6JhILUObE5U2MgFGqlv38vDsfJ\ntvUOBw8//Nds2rSJrVv3M+OGxXT2unn6/Q/p7QsQ0eQSkAVs3ryBGTOqmTAhTnNzIz09HZjNfSxc\nuAaNRseECdM5cmQ3fm8/0SFJ3vgCClx1NDbuJhIJEYvFCQR8JBItBAIhtmw5BEAgcJijR284tZwI\nqWWPmpoJ7N3bSEnJdVitqaXJrq5UWfAzl6omTJhA5d/9HT6fD51Od1EtAKSUdHV1MTAwQHZ29ojr\nXXM5DA4O4vP5SCQS9Pb2YrPZePDBuzh06Aj19S2UldmYPfuvzoqjSQV6RwDIycmltHQSPl+QaNSO\n0WgjEunH33eMSHcWm31daGIe8ockJquLtugQmqSeiG8/8bifwkITihKiq6ufLVv2YDLlIOUAJSX5\nlI2rYOvW4+TnD49YbG5u4NixBFOm1JwMYB3gxRc/xul0XHbMT3l5OY8/fhcff7yVEyd2kptr57bb\nbqaqquqiZXR1dfH0038kkcgnK6uAzk4f27e/wP33X3qW3osvwg9/CJs3f/5SeS/Ev/871NTAq6/C\n2rWZ1ubyGPPOiMfj4Y+//CXlWi3j9Xo2b97M9tde40+lpcy+6Sai0RATJt5A5cQa4vEYOp2BZDJB\nZ6cbnU5HIpH4TPmLFs3khRc2UlRUjtvdgEajJy+vgHC4AYPBisORJCenjGAwiterIxjUEggIotHx\nbNz4PKWlZTQ2HsRgKCE/v5pIxM7rr+/F4+nhjjtux2Aw8M1vfpX//u/XiUQs+HxBWluP4PH08uMf\nn8BqzaGhwUNRURW5uQbsdjsORz779m1j+vQjfPDBZnp6jOTlTSYSifHyy7tobm7l7rvXXvQSTTKZ\nZN26HRQVVZ8qJqXT6Skpmc6mTVtZuHD+iM+6OXjwEM899wF5eVMpK3MSCg3y0ktbCYcjLFjwadDu\n4cOHaW4eoqKijvb2dqzWMrRaM15vPQZDHnq9nXg8lYZ9elDku+9+yEcf1SOlnUOHvHg8MdzudnJz\np1FePh5t3I+iVLB3736WLVtBUdE4tmx8Fq97kKZP3iCm0aDLdtF48AATrVPQZUUZX+hifzBIr3AC\n7RQUWAkEoKPDi9NZS3+/BbNZQ35+LW+/vY1JkyYNy0BZvnwp3d0v0tKyHSFsSBkkP19h9epzZ0Io\ninLR0+/BYJAXXniNpqZ+FMVCMjnIjBllrF172yXHaIwEBgcH+clPfsmHH+6kp6OFuLebfEceNpeL\nkusm8sTfPMBXv7rovPuPGzcOp1PS19dJTk4htbW1HDhQT39/PXa7lr07t2AT0ynOngoCugeP0zJw\njCml42EwxNBQFCkdGAwumpr2YDYPIsQENJoy4nEjXq+etrZmWlsPUVIyvFKy399LV9cAWVmlKErq\nkm6xZJGVNYENG7ZfUQBySUkJ99xz12Xv/847H6HRlFNQkOqJY7M5CQazee21Dy9JznvvpSqPvvce\njNKVw6uKzQbPPgt33JGqPzIawwBHdjWrNLB1wwZKFAWz0ciWLVuo0mq5raKCkoEBxPHjBHuaaG8/\niqJo0OuNCCFob2+grm4SBkPq5l5a6qS3t32Y3O7uFiZPLqG2tpY777wBrbYFh6OL1tbXUZRW4nEb\ng4M68vIm4/W2EQ7rsNmy6etrAAYQIoGijKe7O4RePwOz+Trc7lZaWo5w+PDYZkOoAAAbNklEQVRx\nfvSjP/Cv//pzmpqaKC8v51vfepSaGjuhUCuFheOIxSoIh6s5eNCDxTKZZDKbrVv3kEgkEEJgNLp4\n772P6O7WUlY2BZPJis3mpKJiJvv2dZzKXLgYwuEwoVAco3H40o5WqyOZ1BEIBNJhqquGlJJ3391I\nfv60U03gzGYbJSU1J5edoqc+e/BgI1lZRQDodDqys81YLGZstkJisWaE6MFqjVJbO/FU9kF/fz8b\nNx4kN/c6Dh3qwGyuIBg0Eo1OJhjU0doaR6PJprX1IF5vmP3732f/7j8yJSvJLRPKGafTM1Gj5cg7\nzxLu7cPX56Orr489DS3EkgYCvgH6+93U1pbR27uNeNwOOPD5/LjdDSenzPM4eHB4lVGLxcJjj93P\nww/fwtq1VTz44FKefPLhtASlvvHGuxw/DuXl8yktnUFZ2Q3s3etl/fqNVyz7WhOJRPjOd/4fb77Z\nRshrxNYTZJwcjz2QxbiklUBTJ7/4xR/weDznlaHRaLjvvjswGjtoadmO3R7B5erFbo9x9NA+kols\nFEVhwO8jEU9QaJ+EVrhoanoHozGf7GwXFRU5VFdPZerUxQwN6cjPL6a5+QDNzS0Eg1FCIT0nThzH\n6TTh8Zw4deyhoQDhsMThMGGxfNpbyGZz0tHRczVP3WcSjUZpahpezwTAYrETCFx8aeetW+Gee+CV\nV1JP/yrnZv78VN+ae+5JpT6PNsb8zIi7sZHa7Gz2Hj1KoaJgP/kEb1IUsk0mprpy6Nd10tIyiBBm\npAxSWelg+fJPlx7WrFnOM8+8RGtrPwZDFuGwD4cjwqpVqSfM2bNnUlNTjdfrpa+vjx/+8Ge0t2cx\nYcI8LBYLDQ0R+voMaDRxCgtLqaycxM6dO1EUI8lkH4GAgsUiCIU8dHQUMG3aPKAYrzeHZ555gyee\nuAuXy0V9fQe1tbeze/cWbLYJWCy5eL3ZdHd7cLkq6e/30dvbh8uVTyIRw+Ppx+kcP+x8CCFQFCdt\nbe3DUvk+C6PRiM2mO1kY6dNAvlgsilYbG9FdfSHlTHm9Q5SVOYZtTy036fD7/aeWGEwmA/F4Krgz\nJyeHwkI7yWSESCQVtGe1mhgaOsZjjz12amaps7MTIey0tXWhKHZisTCBQASzuRwpOwEzNls2ZrMR\np3OQJUsq6DlWz7yCAqLhMBs2bOPokTaccS29CT/RpCQo7US8RooLC3DadFjsGsrKTFRXz8Hvz0dR\nDBiNWWRlVdHf347LFSYQCJ313RVFOdW3JV0MDg5y8GALJSULTm0TQlBcfB1btuxg6dIbR1Wsyd69\n+9i3z4fLNZOufb/HqSvCaMwjEumnv8fP+MnlHHX3sHfvQZYvd51XTn5+Pk899RXa29tpbGwkEvGi\n0Uyk8YiCxVgIDBJKdJEcAJM5il6xYs92cuutKzlwoJ3c3PEoioaBgTZiMS1CBCkrm4BGYySRSGCz\nVRGLSWIxHVK20tIyiNHooL+/jWSyjZqaZcP0GRjoo7T0/PpebTQaDRqNIJGID6v4KqVEyourc3ng\nAKxeDb/7HSxYcOHPf975h3+AW26B730PfvCDTGtzaYx5ZyQrO5vAwAB+v5/C04KuYlJiMBhwRKPM\nXbkEl8uF3+/H4XCcVX/C5XLx1FMPcejQYXp6+ikoqKCq6rphLc51Oh35+fkkk0mysysoL/dgMKRO\nr8PhwufrxecLUlHhoKVlB8nkIENDjWg0UZLJFgYHBzCZzBiNZQihEAj0EYu50Gpz2bhxO4sXzyMS\n0WI0WgiHh9BqU/NwBQWT2Lv3feLxWkBDPB4jGg2TSHiYNKmS9vahc5yVGGbzhduz/wVFUVi69Hpe\nfnkLRUXVGI0WotEwbW0HuOWW2hGfVWMwGDCZtEQiQxgMn37vRCKOEJFhwby1tVPZufMtEolCNBot\n8+fPZNOmLUQibvR6I729+5k2rYYPP9xDf/8gK1cuw2g0ImWUYDCBTmdgYMCH0ehgYMCLXq9DUbRE\nozEsliyGhpqorV3D+mP1GHQ6DDodVVWV9PZG0SWT+HwePHRjNtQgpIJvsI9Iop1l825j8+btlJZO\nJBYbJCfn06l3rdZKV1cTlZXXpkZMOBxGCP1ZlVl1OgPRaIJoNDrstzHSOXjwGFptNslkFGMyiaLo\nAIEQBqLRIAatnsSAn4GBC88AKopCaWkp77+/CaOxHL+/hSy7k77uMGb9OGKJgxiNGhLJfoS+j6lT\nx+Ny5aHXm9m+fSednW6iUcnAQCtWq6C4eCo2W8pRDgZ7cDjyMJuLWLVqCjqdns7ObnJz5zN9eg7N\nzW5sNisajZbBQS+BQDOLFmUugECj0TBrVhXbth2jrOzT3kY9Pa2MG3fhgnbNzbByZSoeYuXILwQ9\nItBo4PnnYeZMmDcPbr010xpdPGPeGZm5YAHrfvc7rDYbPp8Pu9FI38AAOrsdh8NB48ngu7ILlLAz\nm83Mnj3rgscLBAIYjU6qq13s2bMfvb4Es9mJEHsIh7vw+ysQohxF6aGg4DqSyQG83t2UlKyktXUI\nvV6wZ88n6PUGDh/uIx73097eyc03L0LKGFJKCgoKaWzswWCwotdbKSzMw+/fy8BAkIEBDYlEM6tX\nL8DlyuMXv3iNWKwAnc5wUj8fev3AsL4cF8OsWXUAvP/+Fnp6EhgMglWr6obFW4xUFEXhxhtn8uab\n+ykvr0WjSfWdaWs7zNy5k09lRUCqzsiyZdNZt24L4CQej5BM1lNa6qK9PYrDMYNYzE529gx2724h\nFHqTL31pLXZ7Aq83daPW6bTodEY0mnoUJYtIpBchcujtPcKiRXlMnjyZj/T6U52GY7E42dn5WKx2\nWhqHsMWCxBINxBOSeDDMdXUzmThxOh0dRzAYTOTmhujtPYrVmlqH7+9vYMoUGxMnTrwm59PpdGIy\nybNmyvz+XgoLnaPKEQHIzXWi1SYALVGdgWQkCDhJJmNotQpDiQiKQc/EieUXEnWK9nYPBsNEhNBT\nVjkVX99mInETiST0hg4TiRqxOLRYrQXs3v0udXXLMRgSlJdfjxBahobKaW/voL5+PdXVKwmHB0gk\n3EyZsojBwU6sVitTpkyh9mSyzIwZ1XzwwXq2b99MMqmQk2PmwQdXDatCnAmWLVtMV9dLHD++DUVJ\ntUDIzZXccccXePTR8+/X05N6wv/ud+FLX7p2+o4FXC74/e9TzfS2b4cMD4GLZsw7I1OmTMF76618\n/MYb1Pv99AQCFBUXM722lvr2dgwlJWmtpZCTk0MiMUBZ2Q3YbA6OHz9GMBhk7twq6ut76e5OkJur\noNNZSSSMCOFEiB683u1EIlF6emLY7WVMmDAJjUZDIKDF63Vz7FgT48fn0dp6nIqK62hpeRevVxCN\nJpg2bRpGY4CyMi1Ll86noqLiVFzA6tXX8/bbm0km7UgZx2QKc999f3XJqb1CCGbPnkldXQ2hUAiT\nyYRWO3qGz/z51xMOR9iwYTNgQsows2dPZMWKZWd99qabFjNjxjTcbjebNm1Dyhvo7OxGp8vHbM7B\n7+9l374jzJs3iyNHNuP1ernvvrX8+tcvUF9/mKEhM9FoIxUVU5ASEoku8vIMmM1GHn/8QfR6PbOX\nLmX3G29QXVSE3Z5FKOLGK4yUzlmBf88ObMLFUGSI0gnVLF62kmQyQX6+FUUZZObMhXR0NHHiRBPx\neIzx4+M89dRj18weWq2WVasW8sIL67HbJ2C1OvD7ewmFjnPnnaOvJGZd3XRKSz+htbUX4RzPYGgX\nsUALMhnHmVfCoW43NcsXXlIWicuVQ1dXDCmjuFyVTJwySGP9AQKDx9BpC3Hk2Hjk0ftwufL5+OOX\n+fjj5wgESrHZ4phMMZYuvRm3u4233noFr/cTKiquY+LEpWi1OqLR4FlBqQaDgVtvXc7NNy8hGo1i\nsVguuYbQ1cBkMvHII/fQ0tJCf38/NpuNysrKzxyrQ0OppZkvfxmeeOIaKjuGWLgQvvUtuPPOVGfj\nS7zcZwRxnuKnGUcIcb7CrJdFKBTiwIED7NiwgajPh9BomDBjBktXrLjkG/P55Ot0OnQ6Ha+//hZb\ntrRTXDwFvd6Iz9fNwEA9VqvC7t0hgkE9yaQOr7eTUGgAiyXJsmVFDA0NsXlzkMrKhQihEI0GGRw8\nxMyZ1WRnD/LII1/iuedepqMjQjicxO0+jEaTZO7cOhYsqKOurvacTc0CgQBtbW1oNBrKy8svu9T2\n1UYIwdUej6FQCL/fj9VqvWCsSzwe5wc/+An5+fN4991XMZvrTnZmhf7+RpYvn09vbz0PPHAjEyZM\nIB6Pc+DAATZu3MK+fUdpbu7G5Rp/slR7iFtumcOcObMwGAxIKdm+bRs71q0jFgyy88BRTDnV1M1e\nTmPjfjZu3EZ2dgm33bYcrVaD232A+fNLyMlx8NZbW5DSiRCgKD7uuGMJNTUzrup5OxeNjY1s2LAD\nj6ePsrICFi2ae9FxSKdzLex+IbZt285vf/s6jY0+utz1JAdPUJiXTV5ZOcvvuJ27775z2AzahWho\naOCZZ97B6zXS0hIiK8tFW9suOjoOU1o6k5UrF5Gbm1qq6Ovr5MiRP+NyzcXhyCM7OxuNJlXnaP36\ndzAY+ikurgHiGAwB7r33r6isrDzZb2kIvV4/qh4M/sK57J5MppwQRUktN4wAf2rUIiU88AD4/ang\n35EQxnXS5ue06ufGGTmdUCiERqNJS6xDa2srb765jo4OL4oCs2ZNZunSRezatYdNm/YwNBSjtDSP\nFStupKGhiY0buzCZcgkGg5hMJnJycmht3cVDD91EOBzme997mnDYhBA6DAbJ9Ok15OeX4PXu5H//\n768jpaS1tZVAIEBOTg4FBQUXVnKUMBJuSqcTiUT4x3/8GWVli9i8+X0GBrKxWFLr915vM0uWzMTr\n3cc3vnHfWU3dpJSEw2FaW1vxeDwcOtREe7sXIWD69EpWrrwJm81GIpEgHA6TSCRYv34Tu3YdJZFI\notfHGRoCrdaGosSYO3cKN9+8BJ1Oh8/no6WlBUVRGDdu3IgPIL4QI8XugUAAt9uNoigUFxcjhMBs\nNl/2DMOePXt5++0NNDR00NraikYTRogylixZOaw77sBAH+3t67HZplNSMmmYjJaWHdx+eyouK9Vx\ntxKTyURDQwNvv/0xvb1BdDqYP7+axYsXjqq06nPZ/TvfST3Jf/hhqrqoypURjcKKFTB5MvzsZykn\nL5OMSGdECHE/8AhgAJ6WUj5zxv+vmjOSLjweDz//+R8wmyfhdOaTSMRpb2+gslLhoYdSfa0TicSp\npxav18tPfvIsOl0l2dkFSJmko6MJl2uIxx9/gEgkwr/8y9PY7dMAgdlsQ1E0dHY2UV1tZu3aUd58\n4AKMlJvS6Tz99LP092eTTCbZuPETLJbJKIqBSMTNlCk51NXl8YUvrD7v/n6/n//4j/9GUcrJzS1G\nyiSdnc3k5AT42tcePOuJNplMIqVEo9EQi8UYGBjAbDaPujiMS2Ek2j1dxONx/H4/BoOBgYEBfvrT\nFykrm4eifPqY2tJygAULCtixo2HYtaGzs5H8/DCPP/7AsOyk48eP86tfvU529lSysrKJxaK0tx9m\n5sy8UbVMdqbdf/Qj+PnPU0XNxkD9vBGD358KAL7uOvjlLyGT/upnOSOZ9JOel1LeCMwHvpZBPS6b\nLVt2otEU43TmA6DRpGp6NDX5aGtrQwgx7GbjdDr5ylfuIifHi9u9kY6OT6iutvDAA3ej0Wgwm82s\nWHE93d2HCIdDDA0FaGurR6/v4cYbR36g6Fhk1aolDA01EosNUVMzhWBwN+3tb1BaOsDixRWsWbPq\nM/ffs2cfsVgOeXklJ9OqNRQXT8TjSdLU1HTW5xVFOXXj0el05OTkjGlHZKyj1WrJycnBarVSVFTE\n/PmTOXFiB15vN4GAj9bWg+Tnx7jxxkVnXRumT7eeujaczkcfbSUrayJZWakCdTqdnvLyGezZ00x/\nf38mvuYVIWWqzPtPfgLr1qmOSLqx2+H996G7GxYtgoaGTGt0bjLmjMhPE80NQDAdMtevX58OMRct\nv7XVg812doqaEFa8Xu85ZRQVFfHYY/fx3e8+zne/+zXuuuv2U+29169fz7x51/Poo7dRVhZBp2th\nwQIXTzxxT9oaU13rczQajvVZckpKSnjyyb+mpsZKUVGchx66id/85v/yox99n+XLbxo2LX4uOW63\nB4vlbNtpNFn09PReki6XwliVky5ZVyrjcve/9dbl3HvvjeTl+WhpeZ/lyyfw6KP3YDabT10bvvOd\nr551bTid9nYPdntqmae+fifwl266tvNed67Gd0nH/h9+uJ4nnoBnnoGPPoLLCDnKqP4jScZn7W+x\nwJ/+lMpMmj8f7r0XXn8d3O6UM5guHa5ERkZXkIQQ/wA0AM9c6LMXw7W+0RYW5hAI+M76nJQhsrKy\nPlOWyWQ6K2blL/LHjx/PvffexZNPPsQtt9yEw+E4h4TLQ3VGLl1OXl4eq1ev4m//9mHuvnsNkyZN\nOufa/LnkFBTkEAr5z9qeTAZwOs+260i7+Y80OemSlakbmBCCadOm8fDDX8bpNLJw4Q1nBcZeqCN2\nfn42g4Mpp6OhYReQilFKJgMXvO6ci0zezDdsWE9lZWpp5nJTUFVn5OL2VxR46ilobITZs+GnP4U5\nc0CrBbMZnE5YtWo9BQVQXJyyR2UlVFXBDTekMpwefhi+/W341a9SzmNrayroOB3f46qHYAshXMAf\nztjcJaX8spTyH4UQ/wx8KIR4WUo5rKrQ97///VPvFy9ezOLFi6+2upfE/Pmz2Lv3RQKBLKxWB8lk\nkq6uZoqLjResW6Ly+aCubgaffPIcfr8Tuz0XKSU9PW4cjtg1qwuiMrZYvHguv/3tu6cK+KVq5hyl\nqqpo1DUrFAL+/u8zrcXnC4cj5ZQ89VTq70QCIhEIh+Gf/gm++U2Ix1Pb4/HU9v5+6OuD3l7o6ko5\nj88+C01NqW1FRalZrZKS1DLQ4CCYTCknp6YGVn32ajZwDZwRKaUHWHLmdiGEXkoZBWJAEjgrqOV0\nZ2QkUlxczP33r+RPf1qH2x1HyjhVVaXcfvtd50yxVfn8kZOTw0MPreHVV9+ntbUeKZNUVuaxZs3d\nIzbFWmVkM3nyZO6+e4h33tmE399Ge/sn1NaOZ9WqmzOtmsooRKNJOQ1mc2o551ITNMNhaGv79OX3\np5yTUCjllAwOXpycTGbTfA9YTCpm5A9Syv844/9jM7xeRUVFRUXlc8qIS+1VUVFRUVFRUYEMB7Cq\nqKioqKioqKjOyOcMIcScTOugcm1QbT32UW08dvi823JMLdMIIYxSyvBVPoZBShlJk6xZwDzAAfiA\nLVLKnWmSfS5HUwDvSinP7g53eceYBsSllEdP23a9lHJrOuRf4NhWUuP3IsOjLijvisfO5YyNdIyB\ndNk6XfYUQtQCPinlcSHEzYAeeEdKmbzArheSmzabZ8LeV2LrkWTjdNh3NNvySn+zY82WZ8j7Gynl\nzy5r39HojAghvgx8E4gDrwH/IqWUQoiPpJRnZe6k+djvSSlvSYOcH5My/AeAH7ADN5EaXE+lQf4Q\ncK5BOUNKecUV1IQQ/wbkk8qGygMellJ2Xy0bCCEeJlWpN0iqLs1XSGVhvXxm8PMF5Fy1sXOpYyNd\nYyAdtk6XPYUQ/0kqKN0EhIFBYAAokVI+eLFyTsq6YpuPFHtfqa1Hio0v175jxZbp+M2Odluetv9G\nQDI8E3YqcFBKuehidBiGlHLUvYAtpNKSBfAE8DrgBD5K4zE2nuflTZP8DZey/TLk7wYc59j+QbrO\nz2nvq4GPgdnptMEZx9tKalnRBLhJXRAEsPlaj510jY10jYF02Dpd9jxdd+DAae8/zoTNR4q9r9TW\nI8XGl2vfsWLLdPxmR7stT/vc3wG/BZactu2diz3+ma/R13f6JPLTcvL/KYTYDfyJlKeYLnJJearR\n0zcKId5Pk/xdQoingfdIeaRZpDzs3WmSfyswdI7tK9IkX/lLrRgp5X4hxFrgOVKe8dUgIlNTh0NC\niF/9xS5CiEteMkvD2EnX2EjXGEiHrdNlz9MbqXz3tPeXMwWbFpuPEHtfqa1Hio0v175jxZbp+M2O\ndlumPiTlj4QQBuARIcTjwPOco17YRXO5XkwmX8BjQPkZ24qBX6TxGCs5t/c6M43HqCPl4X+b1BRm\nbabP7SXoPhdwnbFNC3z5Kh3vfkB7xjY98L1rPXbSOTZGyhhIlz1JXQzPZafbM2HzkWTvTNs6HTa+\nXPuOJVtm2o6ZtuV5ZOmAh4F/vtzvNCpjRs5ECPG8lPKvr/Ixfi+l/PLVPIbKxZMue6Rj7Khj49qQ\njvOs2ntkoNpS5UzGSmpv4TU4xiUWyVW5yqTLHukYO+rYuDak4zyr9h4ZqLZUGcZYcUZUVFRUVFRU\nRimqM6KioqKioqKSUVRnREVFRUVFRSWjjJUAVpeU0jPaj6Fy8aTLHumQo46Na8NIsZVq7ytnpNhB\nteXIYUw4IyoqKioqKiqjF3WZRkVFRUVFRSWjqM6IioqKioqKSkZRnREVFRUVFRWVjKI6IyMIIcQK\nIcRRIcQxIcT/yrQ+KlcfIcQzQgiPEOJApnVRuTYIIUqFEB8JIQ4JIQ4KIb6eaZ1Urj5CCKMQYpsQ\nYq8Q4rAQ4p8yrdNIQg1gHSEIITRAPbAMaAd2kOozcCSjiqlcVYQQC4EA8Dsp5fRM66Ny9RFCFAAF\nUsq9QggrsAtYo/7Wxz5CCLOUMiSE0AKbgP8ppdyUab1GAurMyMhhDtAopTwhpYwBfwBWZ1gnlauM\nlHIj4M20HirXDilll5Ry78n3AeAIUJRZrVSuBVLK0Mm3elJdc/szqM6IQnVGRg7FgPu0v9tOblNR\nURmjCCHGAbXAtsxqonItEEIoQoi9gAf4SEp5ONM6jRRUZ2TkoK6Xqah8jji5RPMS8NTJGRKVMY6U\nMimlrAFKgEVCiMUZVmnEoDojI4d2oPS0v0tJzY6oqKiMMYQQOuBl4Dkp5WuZ1kfl2iKl9ANvAbMy\nrctIQXVGRg47gYlCiHFCCD3wReBPGdZJRUUlzQghBPBr4LCU8seZ1kfl2iCEyBVCOE6+NwE3A3sy\nq9XIQXVGRghSyjjwJPAucBh4QY2uH/sIIX4PbAYmCSHcQoiHMq2TylXnBuBeYIkQYs/J14pMK6Vy\n1SkE1p2MGdkGvCGl/DDDOo0Y1NReFRUVFRUVlYyizoyoqKioqKioZBTVGVFRUVFRUVHJKKozoqKi\noqKiopJRVGdERUVFRUVFJaOozoiKioqKiopKRlGdERUVFRUVFZWMojojKioqKioqKhlFdUZUVFRU\nVFRUMsr/B18y5d10ojlGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8611575a50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, y = sklearn.datasets.make_classification(\n",
+    "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
+    "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
+    ")\n",
+    "\n",
+    "# Split into train and test\n",
+    "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
+    "\n",
+    "# Visualize sample of the data\n",
+    "ind = np.random.permutation(X.shape[0])[:1000]\n",
+    "df = pd.DataFrame(X[ind])\n",
+    "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "Accuracy: 0.781\n",
+      "1 loop, best of 3: 372 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "# Train and test the scikit-learn SGD logistic regression.\n",
+    "clf = sklearn.linear_model.SGDClassifier(\n",
+    "    loss='log', n_iter=1000, penalty='l2', alpha=5e-4, class_weight='auto')\n",
+    "\n",
+    "clf.fit(X, y)\n",
+    "yt_pred = clf.predict(Xt)\n",
+    "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save the dataset to HDF5 for loading in Caffe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Write out the data to HDF5 files in a temp directory.\n",
+    "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
+    "dirname = os.path.abspath('./examples/hdf5_classification/data')\n",
+    "if not os.path.exists(dirname):\n",
+    "    os.makedirs(dirname)\n",
+    "\n",
+    "train_filename = os.path.join(dirname, 'train.h5')\n",
+    "test_filename = os.path.join(dirname, 'test.h5')\n",
+    "\n",
+    "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
+    "# To show this off, we'll list the same data file twice.\n",
+    "with h5py.File(train_filename, 'w') as f:\n",
+    "    f['data'] = X\n",
+    "    f['label'] = y.astype(np.float32)\n",
+    "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    f.write(train_filename + '\\n')\n",
+    "    \n",
+    "# HDF5 is pretty efficient, but can be further compressed.\n",
+    "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
+    "with h5py.File(test_filename, 'w') as f:\n",
+    "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
+    "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
+    "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
+    "    f.write(test_filename + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's define logistic regression in Caffe through Python net specification. This is a quick and natural way to define nets that sidesteps manually editing the protobuf model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def logreg(hdf5, batch_size):\n",
+    "    # logistic regression: data, matrix multiplication, and 2-class softmax loss\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    n.accuracy = L.Accuracy(n.ip1, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip1, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/logreg_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/logreg_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(logreg('examples/hdf5_classification/data/test.txt', 10)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we'll define our \"solver\" which trains the network by specifying the locations of the train and test nets we defined above, as well as setting values for various parameters used for learning, display, and \"snapshotting\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from caffe.proto import caffe_pb2\n",
+    "\n",
+    "def solver(train_net_path, test_net_path):\n",
+    "    s = caffe_pb2.SolverParameter()\n",
+    "\n",
+    "    # Specify locations of the train and test networks.\n",
+    "    s.train_net = train_net_path\n",
+    "    s.test_net.append(test_net_path)\n",
+    "\n",
+    "    s.test_interval = 1000  # Test after every 1000 training iterations.\n",
+    "    s.test_iter.append(250) # Test 250 \"batches\" each time we test.\n",
+    "\n",
+    "    s.max_iter = 10000      # # of times to update the net (training iterations)\n",
+    "\n",
+    "    # Set the initial learning rate for stochastic gradient descent (SGD).\n",
+    "    s.base_lr = 0.01        \n",
+    "\n",
+    "    # Set `lr_policy` to define how the learning rate changes during training.\n",
+    "    # Here, we 'step' the learning rate by multiplying it by a factor `gamma`\n",
+    "    # every `stepsize` iterations.\n",
+    "    s.lr_policy = 'step'\n",
+    "    s.gamma = 0.1\n",
+    "    s.stepsize = 5000\n",
+    "\n",
+    "    # Set other optimization parameters. Setting a non-zero `momentum` takes a\n",
+    "    # weighted average of the current gradient and previous gradients to make\n",
+    "    # learning more stable. L2 weight decay regularizes learning, to help prevent\n",
+    "    # the model from overfitting.\n",
+    "    s.momentum = 0.9\n",
+    "    s.weight_decay = 5e-4\n",
+    "\n",
+    "    # Display the current training loss and accuracy every 1000 iterations.\n",
+    "    s.display = 1000\n",
+    "\n",
+    "    # Snapshots are files used to store networks we've trained.  Here, we'll\n",
+    "    # snapshot every 10K iterations -- just once at the end of training.\n",
+    "    # For larger networks that take longer to train, you may want to set\n",
+    "    # snapshot < max_iter to save the network and training state to disk during\n",
+    "    # optimization, preventing disaster in case of machine crashes, etc.\n",
+    "    s.snapshot = 10000\n",
+    "    s.snapshot_prefix = 'examples/hdf5_classification/data/train'\n",
+    "\n",
+    "    # We'll train on the CPU for fair benchmarking against scikit-learn.\n",
+    "    # Changing to GPU should result in much faster training!\n",
+    "    s.solver_mode = caffe_pb2.SolverParameter.CPU\n",
+    "    \n",
+    "    return s\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to learn and evaluate our Caffeinated logistic regression in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "Accuracy: 0.770\n",
+      "1 loop, best of 3: 195 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:03.232779   655 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:03.391911   655 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/logreg_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/logreg_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:03.392065   655 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/logreg_auto_train.prototxt\n",
+      "I0224 00:32:03.392215   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.392365   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.392382   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.392395   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.392423   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.392442   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:03.392473   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:03.393473   655 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:03.393862   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.393884   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.393894   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393901   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.393911   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.393924   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.393934   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.393945   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.393956   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.393970   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.393978   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393986   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.393995   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.394001   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.394012   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.394021   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.394029   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.394311   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.394323   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394331   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.394348   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394358   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.394366   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.394374   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394386   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394395   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.394404   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394424   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.394443   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.394450   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.394462   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.394479   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.394489   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.394497   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.394510   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.394536   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394543   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.394551   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394562   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.394569   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.394577   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.394587   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.394603   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.394624   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.394634   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.394641   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.394659   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.394665   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.394673   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.394682   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.394690   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.394697   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.394706   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.394712   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.394721   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.394731   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.394804   655 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/logreg_auto_test.prototxt\n",
+      "I0224 00:32:03.394836   655 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip1\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:03.394953   655 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:03.394964   655 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:03.394973   655 net.cpp:411] data -> data\n",
+      "I0224 00:32:03.394984   655 net.cpp:411] data -> label\n",
+      "I0224 00:32:03.394994   655 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:03.395009   655 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:03.395937   655 net.cpp:150] Setting up data\n",
+      "I0224 00:32:03.395953   655 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:03.395963   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.395970   655 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:03.395978   655 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:03.395989   655 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:03.395997   655 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:03.396005   655 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:03.396016   655 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:03.396028   655 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:03.396036   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396044   655 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:03.396051   655 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:03.396059   655 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:03.396069   655 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:03.396075   655 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:03.396085   655 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:03.396100   655 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:03.396109   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396116   655 net.cpp:165] Memory required for data: 360\n",
+      "I0224 00:32:03.396138   655 layer_factory.hpp:77] Creating layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396148   655 net.cpp:106] Creating Layer ip1_ip1_0_split\n",
+      "I0224 00:32:03.396157   655 net.cpp:454] ip1_ip1_0_split <- ip1\n",
+      "I0224 00:32:03.396164   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396174   655 net.cpp:411] ip1_ip1_0_split -> ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396185   655 net.cpp:150] Setting up ip1_ip1_0_split\n",
+      "I0224 00:32:03.396194   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396203   655 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:03.396209   655 net.cpp:165] Memory required for data: 520\n",
+      "I0224 00:32:03.396216   655 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:03.396225   655 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:03.396234   655 net.cpp:454] accuracy <- ip1_ip1_0_split_0\n",
+      "I0224 00:32:03.396241   655 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:03.396250   655 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:03.396260   655 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:03.396270   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396276   655 net.cpp:165] Memory required for data: 524\n",
+      "I0224 00:32:03.396283   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396291   655 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:03.396299   655 net.cpp:454] loss <- ip1_ip1_0_split_1\n",
+      "I0224 00:32:03.396307   655 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:03.396317   655 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:03.396327   655 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:03.396339   655 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:03.396349   655 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:03.396356   655 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:03.396365   655 net.cpp:165] Memory required for data: 528\n",
+      "I0224 00:32:03.396373   655 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:03.396381   655 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:03.396389   655 net.cpp:226] ip1_ip1_0_split needs backward computation.\n",
+      "I0224 00:32:03.396396   655 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:03.396404   655 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:03.396412   655 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:03.396420   655 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:03.396427   655 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:03.396437   655 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:03.396455   655 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:03.396473   655 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:03.396482   655 solver.cpp:280] Solving \n",
+      "I0224 00:32:03.396489   655 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:03.396499   655 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:03.932615   655 solver.cpp:406]     Test net output #0: accuracy = 0.4268\n",
+      "I0224 00:32:03.932656   655 solver.cpp:406]     Test net output #1: loss = 1.33093 (* 1 = 1.33093 loss)\n",
+      "I0224 00:32:03.932723   655 solver.cpp:229] Iteration 0, loss = 1.06081\n",
+      "I0224 00:32:03.932737   655 solver.cpp:245]     Train net output #0: accuracy = 0.4\n",
+      "I0224 00:32:03.932749   655 solver.cpp:245]     Train net output #1: loss = 1.06081 (* 1 = 1.06081 loss)\n",
+      "I0224 00:32:03.932765   655 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:03.945551   655 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:03.948048   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.948065   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.948091   655 solver.cpp:229] Iteration 1000, loss = 0.505853\n",
+      "I0224 00:32:03.948102   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.948113   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.948122   655 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:03.960741   655 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:03.963214   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:03.963249   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:03.963276   655 solver.cpp:229] Iteration 2000, loss = 0.549211\n",
+      "I0224 00:32:03.963289   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.963299   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:03.963309   655 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:03.975945   655 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:03.978435   655 solver.cpp:406]     Test net output #0: accuracy = 0.7732\n",
+      "I0224 00:32:03.978451   655 solver.cpp:406]     Test net output #1: loss = 0.594998 (* 1 = 0.594998 loss)\n",
+      "I0224 00:32:03.978884   655 solver.cpp:229] Iteration 3000, loss = 0.66133\n",
+      "I0224 00:32:03.978911   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:03.978932   655 solver.cpp:245]     Train net output #1: loss = 0.66133 (* 1 = 0.66133 loss)\n",
+      "I0224 00:32:03.978950   655 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:03.992017   655 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:03.994509   655 solver.cpp:406]     Test net output #0: accuracy = 0.694\n",
+      "I0224 00:32:03.994525   655 solver.cpp:406]     Test net output #1: loss = 0.60406 (* 1 = 0.60406 loss)\n",
+      "I0224 00:32:03.994551   655 solver.cpp:229] Iteration 4000, loss = 0.505853\n",
+      "I0224 00:32:03.994562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:03.994573   655 solver.cpp:245]     Train net output #1: loss = 0.505853 (* 1 = 0.505853 loss)\n",
+      "I0224 00:32:03.994583   655 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:04.007200   655 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:04.009686   655 solver.cpp:406]     Test net output #0: accuracy = 0.7372\n",
+      "I0224 00:32:04.009702   655 solver.cpp:406]     Test net output #1: loss = 0.595267 (* 1 = 0.595267 loss)\n",
+      "I0224 00:32:04.009727   655 solver.cpp:229] Iteration 5000, loss = 0.549211\n",
+      "I0224 00:32:04.009738   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.009749   655 solver.cpp:245]     Train net output #1: loss = 0.549211 (* 1 = 0.549211 loss)\n",
+      "I0224 00:32:04.009758   655 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:04.022734   655 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:04.025177   655 solver.cpp:406]     Test net output #0: accuracy = 0.7824\n",
+      "I0224 00:32:04.025193   655 solver.cpp:406]     Test net output #1: loss = 0.593367 (* 1 = 0.593367 loss)\n",
+      "I0224 00:32:04.025545   655 solver.cpp:229] Iteration 6000, loss = 0.654873\n",
+      "I0224 00:32:04.025562   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.025573   655 solver.cpp:245]     Train net output #1: loss = 0.654873 (* 1 = 0.654873 loss)\n",
+      "I0224 00:32:04.025583   655 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:04.038586   655 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:04.041016   655 solver.cpp:406]     Test net output #0: accuracy = 0.7704\n",
+      "I0224 00:32:04.041033   655 solver.cpp:406]     Test net output #1: loss = 0.593842 (* 1 = 0.593842 loss)\n",
+      "I0224 00:32:04.041059   655 solver.cpp:229] Iteration 7000, loss = 0.46611\n",
+      "I0224 00:32:04.041071   655 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:04.041082   655 solver.cpp:245]     Train net output #1: loss = 0.46611 (* 1 = 0.46611 loss)\n",
+      "I0224 00:32:04.041091   655 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:04.053722   655 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:04.056171   655 solver.cpp:406]     Test net output #0: accuracy = 0.7788\n",
+      "I0224 00:32:04.056187   655 solver.cpp:406]     Test net output #1: loss = 0.592847 (* 1 = 0.592847 loss)\n",
+      "I0224 00:32:04.056213   655 solver.cpp:229] Iteration 8000, loss = 0.615126\n",
+      "I0224 00:32:04.056224   655 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:04.056236   655 solver.cpp:245]     Train net output #1: loss = 0.615126 (* 1 = 0.615126 loss)\n",
+      "I0224 00:32:04.056244   655 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:04.068853   655 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:04.071291   655 solver.cpp:406]     Test net output #0: accuracy = 0.7808\n",
+      "I0224 00:32:04.071307   655 solver.cpp:406]     Test net output #1: loss = 0.593293 (* 1 = 0.593293 loss)\n",
+      "I0224 00:32:04.071650   655 solver.cpp:229] Iteration 9000, loss = 0.654997\n",
+      "I0224 00:32:04.071666   655 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:04.071677   655 solver.cpp:245]     Train net output #1: loss = 0.654998 (* 1 = 0.654998 loss)\n",
+      "I0224 00:32:04.071687   655 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:04.084717   655 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:04.084885   655 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:04.084960   655 solver.cpp:318] Iteration 10000, loss = 0.466505\n",
+      "I0224 00:32:04.084977   655 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:04.087514   655 solver.cpp:406]     Test net output #0: accuracy = 0.77\n",
+      "I0224 00:32:04.087532   655 solver.cpp:406]     Test net output #1: loss = 0.593815 (* 1 = 0.593815 loss)\n",
+      "I0224 00:32:04.087541   655 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:04.087548   655 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at output or the `logreg_auto_train.prototxt`, you'll see that the model is simple logistic regression.\n",
+    "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
+    "That network is given in `nonlinear_auto_train.prototxt`, and that's the only change made in `nonlinear_logreg_solver.prototxt` which we will now use.\n",
+    "\n",
+    "The final accuracy of the new network should be higher than logistic regression!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from caffe import layers as L\n",
+    "from caffe import params as P\n",
+    "\n",
+    "def nonlinear_net(hdf5, batch_size):\n",
+    "    # one small nonlinearity, one leap for model kind\n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2)\n",
+    "    # define a hidden layer of dimension 40\n",
+    "    n.ip1 = L.InnerProduct(n.data, num_output=40, weight_filler=dict(type='xavier'))\n",
+    "    # transform the output through the ReLU (rectified linear) non-linearity\n",
+    "    n.relu1 = L.ReLU(n.ip1, in_place=True)\n",
+    "    # score the (now non-linear) features\n",
+    "    n.ip2 = L.InnerProduct(n.ip1, num_output=2, weight_filler=dict(type='xavier'))\n",
+    "    # same accuracy and loss as before\n",
+    "    n.accuracy = L.Accuracy(n.ip2, n.label)\n",
+    "    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)\n",
+    "    return n.to_proto()\n",
+    "\n",
+    "train_net_path = 'examples/hdf5_classification/nonlinear_auto_train.prototxt'\n",
+    "with open(train_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/train.txt', 10)))\n",
+    "\n",
+    "test_net_path = 'examples/hdf5_classification/nonlinear_auto_test.prototxt'\n",
+    "with open(test_net_path, 'w') as f:\n",
+    "    f.write(str(nonlinear_net('examples/hdf5_classification/data/test.txt', 10)))\n",
+    "\n",
+    "solver_path = 'examples/hdf5_classification/nonlinear_logreg_solver.prototxt'\n",
+    "with open(solver_path, 'w') as f:\n",
+    "    f.write(str(solver(train_net_path, test_net_path)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.837\n",
+      "Accuracy: 0.838\n",
+      "Accuracy: 0.834\n",
+      "1 loop, best of 3: 277 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit\n",
+    "caffe.set_mode_cpu()\n",
+    "solver = caffe.get_solver(solver_path)\n",
+    "solver.solve()\n",
+    "\n",
+    "accuracy = 0\n",
+    "batch_size = solver.test_nets[0].blobs['data'].num\n",
+    "test_iters = int(len(Xt) / batch_size)\n",
+    "for i in range(test_iters):\n",
+    "    solver.test_nets[0].forward()\n",
+    "    accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
+    "accuracy /= test_iters\n",
+    "\n",
+    "print(\"Accuracy: {:.3f}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do the same through the command line interface for detailed output on the model and solving."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I0224 00:32:05.654265   658 caffe.cpp:178] Use CPU.\n",
+      "I0224 00:32:05.810444   658 solver.cpp:48] Initializing solver from parameters: \n",
+      "train_net: \"examples/hdf5_classification/nonlinear_auto_train.prototxt\"\n",
+      "test_net: \"examples/hdf5_classification/nonlinear_auto_test.prototxt\"\n",
+      "test_iter: 250\n",
+      "test_interval: 1000\n",
+      "base_lr: 0.01\n",
+      "display: 1000\n",
+      "max_iter: 10000\n",
+      "lr_policy: \"step\"\n",
+      "gamma: 0.1\n",
+      "momentum: 0.9\n",
+      "weight_decay: 0.0005\n",
+      "stepsize: 5000\n",
+      "snapshot: 10000\n",
+      "snapshot_prefix: \"examples/hdf5_classification/data/train\"\n",
+      "solver_mode: CPU\n",
+      "I0224 00:32:05.810634   658 solver.cpp:81] Creating training net from train_net file: examples/hdf5_classification/nonlinear_auto_train.prototxt\n",
+      "I0224 00:32:05.810835   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TRAIN\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/train.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.811061   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.811079   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.811092   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.811121   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.811143   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/train.txt\n",
+      "I0224 00:32:05.811189   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 2\n",
+      "I0224 00:32:05.812254   658 hdf5.cpp:32] Datatype class: H5T_FLOAT\n",
+      "I0224 00:32:05.812677   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.812705   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.812721   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812729   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.812739   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.812752   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.812762   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.812774   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.812785   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.812798   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.812808   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812816   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.812824   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.812831   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.812841   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.812849   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.812860   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.813179   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.813196   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813210   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.813230   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.813241   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.813251   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.813258   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.813271   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.813279   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.813287   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.813294   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.813304   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.813313   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.813321   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.813336   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.813345   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813379   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.813401   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813417   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.813426   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.813434   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813446   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813457   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.813465   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813473   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.813480   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.813488   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.813499   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.813508   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.813515   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.813524   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.813539   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.813547   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813555   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.813565   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813585   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.813599   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.813616   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.813627   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.813642   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.813663   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.813671   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.813679   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.813695   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.813704   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.813712   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.813720   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.813729   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.813735   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.813743   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.813751   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.813760   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.813772   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.813787   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.813809   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.813905   658 solver.cpp:181] Creating test net (#0) specified by test_net file: examples/hdf5_classification/nonlinear_auto_test.prototxt\n",
+      "I0224 00:32:05.813944   658 net.cpp:49] Initializing net from parameters: \n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"data\"\n",
+      "  type: \"HDF5Data\"\n",
+      "  top: \"data\"\n",
+      "  top: \"label\"\n",
+      "  hdf5_data_param {\n",
+      "    source: \"examples/hdf5_classification/data/test.txt\"\n",
+      "    batch_size: 10\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip1\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"ip1\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 40\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"ip2\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"ip1\"\n",
+      "  top: \"ip2\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 2\n",
+      "    weight_filler {\n",
+      "      type: \"xavier\"\n",
+      "    }\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"accuracy\"\n",
+      "  type: \"Accuracy\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"accuracy\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"loss\"\n",
+      "  type: \"SoftmaxWithLoss\"\n",
+      "  bottom: \"ip2\"\n",
+      "  bottom: \"label\"\n",
+      "  top: \"loss\"\n",
+      "}\n",
+      "I0224 00:32:05.814131   658 layer_factory.hpp:77] Creating layer data\n",
+      "I0224 00:32:05.814142   658 net.cpp:106] Creating Layer data\n",
+      "I0224 00:32:05.814152   658 net.cpp:411] data -> data\n",
+      "I0224 00:32:05.814162   658 net.cpp:411] data -> label\n",
+      "I0224 00:32:05.814180   658 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: examples/hdf5_classification/data/test.txt\n",
+      "I0224 00:32:05.814220   658 hdf5_data_layer.cpp:93] Number of HDF5 files: 1\n",
+      "I0224 00:32:05.815207   658 net.cpp:150] Setting up data\n",
+      "I0224 00:32:05.815227   658 net.cpp:157] Top shape: 10 4 (40)\n",
+      "I0224 00:32:05.815243   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815253   658 net.cpp:165] Memory required for data: 200\n",
+      "I0224 00:32:05.815260   658 layer_factory.hpp:77] Creating layer label_data_1_split\n",
+      "I0224 00:32:05.815270   658 net.cpp:106] Creating Layer label_data_1_split\n",
+      "I0224 00:32:05.815279   658 net.cpp:454] label_data_1_split <- label\n",
+      "I0224 00:32:05.815287   658 net.cpp:411] label_data_1_split -> label_data_1_split_0\n",
+      "I0224 00:32:05.815299   658 net.cpp:411] label_data_1_split -> label_data_1_split_1\n",
+      "I0224 00:32:05.815310   658 net.cpp:150] Setting up label_data_1_split\n",
+      "I0224 00:32:05.815318   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815326   658 net.cpp:157] Top shape: 10 (10)\n",
+      "I0224 00:32:05.815335   658 net.cpp:165] Memory required for data: 280\n",
+      "I0224 00:32:05.815341   658 layer_factory.hpp:77] Creating layer ip1\n",
+      "I0224 00:32:05.815351   658 net.cpp:106] Creating Layer ip1\n",
+      "I0224 00:32:05.815358   658 net.cpp:454] ip1 <- data\n",
+      "I0224 00:32:05.815367   658 net.cpp:411] ip1 -> ip1\n",
+      "I0224 00:32:05.815383   658 net.cpp:150] Setting up ip1\n",
+      "I0224 00:32:05.815398   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815413   658 net.cpp:165] Memory required for data: 1880\n",
+      "I0224 00:32:05.815435   658 layer_factory.hpp:77] Creating layer relu1\n",
+      "I0224 00:32:05.815450   658 net.cpp:106] Creating Layer relu1\n",
+      "I0224 00:32:05.815459   658 net.cpp:454] relu1 <- ip1\n",
+      "I0224 00:32:05.815469   658 net.cpp:397] relu1 -> ip1 (in-place)\n",
+      "I0224 00:32:05.815479   658 net.cpp:150] Setting up relu1\n",
+      "I0224 00:32:05.815486   658 net.cpp:157] Top shape: 10 40 (400)\n",
+      "I0224 00:32:05.815495   658 net.cpp:165] Memory required for data: 3480\n",
+      "I0224 00:32:05.815501   658 layer_factory.hpp:77] Creating layer ip2\n",
+      "I0224 00:32:05.815510   658 net.cpp:106] Creating Layer ip2\n",
+      "I0224 00:32:05.815518   658 net.cpp:454] ip2 <- ip1\n",
+      "I0224 00:32:05.815527   658 net.cpp:411] ip2 -> ip2\n",
+      "I0224 00:32:05.815542   658 net.cpp:150] Setting up ip2\n",
+      "I0224 00:32:05.815551   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815559   658 net.cpp:165] Memory required for data: 3560\n",
+      "I0224 00:32:05.815570   658 layer_factory.hpp:77] Creating layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815579   658 net.cpp:106] Creating Layer ip2_ip2_0_split\n",
+      "I0224 00:32:05.815587   658 net.cpp:454] ip2_ip2_0_split <- ip2\n",
+      "I0224 00:32:05.815600   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815619   658 net.cpp:411] ip2_ip2_0_split -> ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815640   658 net.cpp:150] Setting up ip2_ip2_0_split\n",
+      "I0224 00:32:05.815654   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815662   658 net.cpp:157] Top shape: 10 2 (20)\n",
+      "I0224 00:32:05.815670   658 net.cpp:165] Memory required for data: 3720\n",
+      "I0224 00:32:05.815677   658 layer_factory.hpp:77] Creating layer accuracy\n",
+      "I0224 00:32:05.815685   658 net.cpp:106] Creating Layer accuracy\n",
+      "I0224 00:32:05.815693   658 net.cpp:454] accuracy <- ip2_ip2_0_split_0\n",
+      "I0224 00:32:05.815702   658 net.cpp:454] accuracy <- label_data_1_split_0\n",
+      "I0224 00:32:05.815711   658 net.cpp:411] accuracy -> accuracy\n",
+      "I0224 00:32:05.815722   658 net.cpp:150] Setting up accuracy\n",
+      "I0224 00:32:05.815732   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815738   658 net.cpp:165] Memory required for data: 3724\n",
+      "I0224 00:32:05.815747   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815754   658 net.cpp:106] Creating Layer loss\n",
+      "I0224 00:32:05.815762   658 net.cpp:454] loss <- ip2_ip2_0_split_1\n",
+      "I0224 00:32:05.815770   658 net.cpp:454] loss <- label_data_1_split_1\n",
+      "I0224 00:32:05.815779   658 net.cpp:411] loss -> loss\n",
+      "I0224 00:32:05.815790   658 layer_factory.hpp:77] Creating layer loss\n",
+      "I0224 00:32:05.815811   658 net.cpp:150] Setting up loss\n",
+      "I0224 00:32:05.815829   658 net.cpp:157] Top shape: (1)\n",
+      "I0224 00:32:05.815843   658 net.cpp:160]     with loss weight 1\n",
+      "I0224 00:32:05.815867   658 net.cpp:165] Memory required for data: 3728\n",
+      "I0224 00:32:05.815876   658 net.cpp:226] loss needs backward computation.\n",
+      "I0224 00:32:05.815884   658 net.cpp:228] accuracy does not need backward computation.\n",
+      "I0224 00:32:05.815892   658 net.cpp:226] ip2_ip2_0_split needs backward computation.\n",
+      "I0224 00:32:05.815901   658 net.cpp:226] ip2 needs backward computation.\n",
+      "I0224 00:32:05.815908   658 net.cpp:226] relu1 needs backward computation.\n",
+      "I0224 00:32:05.815915   658 net.cpp:226] ip1 needs backward computation.\n",
+      "I0224 00:32:05.815923   658 net.cpp:228] label_data_1_split does not need backward computation.\n",
+      "I0224 00:32:05.815932   658 net.cpp:228] data does not need backward computation.\n",
+      "I0224 00:32:05.815938   658 net.cpp:270] This network produces output accuracy\n",
+      "I0224 00:32:05.815946   658 net.cpp:270] This network produces output loss\n",
+      "I0224 00:32:05.815958   658 net.cpp:283] Network initialization done.\n",
+      "I0224 00:32:05.815978   658 solver.cpp:60] Solver scaffolding done.\n",
+      "I0224 00:32:05.816000   658 caffe.cpp:219] Starting Optimization\n",
+      "I0224 00:32:05.816016   658 solver.cpp:280] Solving \n",
+      "I0224 00:32:05.816030   658 solver.cpp:281] Learning Rate Policy: step\n",
+      "I0224 00:32:05.816048   658 solver.cpp:338] Iteration 0, Testing net (#0)\n",
+      "I0224 00:32:05.831967   658 solver.cpp:406]     Test net output #0: accuracy = 0.4464\n",
+      "I0224 00:32:05.832033   658 solver.cpp:406]     Test net output #1: loss = 0.909841 (* 1 = 0.909841 loss)\n",
+      "I0224 00:32:05.832186   658 solver.cpp:229] Iteration 0, loss = 0.798509\n",
+      "I0224 00:32:05.832218   658 solver.cpp:245]     Train net output #0: accuracy = 0.6\n",
+      "I0224 00:32:05.832247   658 solver.cpp:245]     Train net output #1: loss = 0.798509 (* 1 = 0.798509 loss)\n",
+      "I0224 00:32:05.832281   658 sgd_solver.cpp:106] Iteration 0, lr = 0.01\n",
+      "I0224 00:32:05.859506   658 solver.cpp:338] Iteration 1000, Testing net (#0)\n",
+      "I0224 00:32:05.862799   658 solver.cpp:406]     Test net output #0: accuracy = 0.8156\n",
+      "I0224 00:32:05.862818   658 solver.cpp:406]     Test net output #1: loss = 0.44259 (* 1 = 0.44259 loss)\n",
+      "I0224 00:32:05.862853   658 solver.cpp:229] Iteration 1000, loss = 0.537015\n",
+      "I0224 00:32:05.862864   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.862875   658 solver.cpp:245]     Train net output #1: loss = 0.537015 (* 1 = 0.537015 loss)\n",
+      "I0224 00:32:05.862885   658 sgd_solver.cpp:106] Iteration 1000, lr = 0.01\n",
+      "I0224 00:32:05.883155   658 solver.cpp:338] Iteration 2000, Testing net (#0)\n",
+      "I0224 00:32:05.886435   658 solver.cpp:406]     Test net output #0: accuracy = 0.8116\n",
+      "I0224 00:32:05.886451   658 solver.cpp:406]     Test net output #1: loss = 0.434079 (* 1 = 0.434079 loss)\n",
+      "I0224 00:32:05.886484   658 solver.cpp:229] Iteration 2000, loss = 0.43109\n",
+      "I0224 00:32:05.886497   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.886508   658 solver.cpp:245]     Train net output #1: loss = 0.43109 (* 1 = 0.43109 loss)\n",
+      "I0224 00:32:05.886518   658 sgd_solver.cpp:106] Iteration 2000, lr = 0.01\n",
+      "I0224 00:32:05.907243   658 solver.cpp:338] Iteration 3000, Testing net (#0)\n",
+      "I0224 00:32:05.910521   658 solver.cpp:406]     Test net output #0: accuracy = 0.8168\n",
+      "I0224 00:32:05.910537   658 solver.cpp:406]     Test net output #1: loss = 0.425661 (* 1 = 0.425661 loss)\n",
+      "I0224 00:32:05.910905   658 solver.cpp:229] Iteration 3000, loss = 0.430245\n",
+      "I0224 00:32:05.910922   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.910933   658 solver.cpp:245]     Train net output #1: loss = 0.430245 (* 1 = 0.430245 loss)\n",
+      "I0224 00:32:05.910943   658 sgd_solver.cpp:106] Iteration 3000, lr = 0.01\n",
+      "I0224 00:32:05.931205   658 solver.cpp:338] Iteration 4000, Testing net (#0)\n",
+      "I0224 00:32:05.934479   658 solver.cpp:406]     Test net output #0: accuracy = 0.8324\n",
+      "I0224 00:32:05.934496   658 solver.cpp:406]     Test net output #1: loss = 0.404891 (* 1 = 0.404891 loss)\n",
+      "I0224 00:32:05.934530   658 solver.cpp:229] Iteration 4000, loss = 0.628955\n",
+      "I0224 00:32:05.934542   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:05.934553   658 solver.cpp:245]     Train net output #1: loss = 0.628955 (* 1 = 0.628955 loss)\n",
+      "I0224 00:32:05.934583   658 sgd_solver.cpp:106] Iteration 4000, lr = 0.01\n",
+      "I0224 00:32:05.955108   658 solver.cpp:338] Iteration 5000, Testing net (#0)\n",
+      "I0224 00:32:05.958377   658 solver.cpp:406]     Test net output #0: accuracy = 0.8364\n",
+      "I0224 00:32:05.958395   658 solver.cpp:406]     Test net output #1: loss = 0.404235 (* 1 = 0.404235 loss)\n",
+      "I0224 00:32:05.958432   658 solver.cpp:229] Iteration 5000, loss = 0.394939\n",
+      "I0224 00:32:05.958444   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:05.958456   658 solver.cpp:245]     Train net output #1: loss = 0.39494 (* 1 = 0.39494 loss)\n",
+      "I0224 00:32:05.958466   658 sgd_solver.cpp:106] Iteration 5000, lr = 0.001\n",
+      "I0224 00:32:05.978703   658 solver.cpp:338] Iteration 6000, Testing net (#0)\n",
+      "I0224 00:32:05.981973   658 solver.cpp:406]     Test net output #0: accuracy = 0.838\n",
+      "I0224 00:32:05.981991   658 solver.cpp:406]     Test net output #1: loss = 0.385743 (* 1 = 0.385743 loss)\n",
+      "I0224 00:32:05.982347   658 solver.cpp:229] Iteration 6000, loss = 0.411537\n",
+      "I0224 00:32:05.982362   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:05.982373   658 solver.cpp:245]     Train net output #1: loss = 0.411537 (* 1 = 0.411537 loss)\n",
+      "I0224 00:32:05.982383   658 sgd_solver.cpp:106] Iteration 6000, lr = 0.001\n",
+      "I0224 00:32:06.003015   658 solver.cpp:338] Iteration 7000, Testing net (#0)\n",
+      "I0224 00:32:06.006283   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.006301   658 solver.cpp:406]     Test net output #1: loss = 0.384648 (* 1 = 0.384648 loss)\n",
+      "I0224 00:32:06.006335   658 solver.cpp:229] Iteration 7000, loss = 0.521072\n",
+      "I0224 00:32:06.006347   658 solver.cpp:245]     Train net output #0: accuracy = 0.7\n",
+      "I0224 00:32:06.006358   658 solver.cpp:245]     Train net output #1: loss = 0.521073 (* 1 = 0.521073 loss)\n",
+      "I0224 00:32:06.006368   658 sgd_solver.cpp:106] Iteration 7000, lr = 0.001\n",
+      "I0224 00:32:06.026715   658 solver.cpp:338] Iteration 8000, Testing net (#0)\n",
+      "I0224 00:32:06.029965   658 solver.cpp:406]     Test net output #0: accuracy = 0.8404\n",
+      "I0224 00:32:06.029983   658 solver.cpp:406]     Test net output #1: loss = 0.380889 (* 1 = 0.380889 loss)\n",
+      "I0224 00:32:06.030015   658 solver.cpp:229] Iteration 8000, loss = 0.329477\n",
+      "I0224 00:32:06.030028   658 solver.cpp:245]     Train net output #0: accuracy = 0.9\n",
+      "I0224 00:32:06.030040   658 solver.cpp:245]     Train net output #1: loss = 0.329477 (* 1 = 0.329477 loss)\n",
+      "I0224 00:32:06.030048   658 sgd_solver.cpp:106] Iteration 8000, lr = 0.001\n",
+      "I0224 00:32:06.050626   658 solver.cpp:338] Iteration 9000, Testing net (#0)\n",
+      "I0224 00:32:06.053889   658 solver.cpp:406]     Test net output #0: accuracy = 0.8376\n",
+      "I0224 00:32:06.053906   658 solver.cpp:406]     Test net output #1: loss = 0.382756 (* 1 = 0.382756 loss)\n",
+      "I0224 00:32:06.054271   658 solver.cpp:229] Iteration 9000, loss = 0.412227\n",
+      "I0224 00:32:06.054291   658 solver.cpp:245]     Train net output #0: accuracy = 0.8\n",
+      "I0224 00:32:06.054314   658 solver.cpp:245]     Train net output #1: loss = 0.412228 (* 1 = 0.412228 loss)\n",
+      "I0224 00:32:06.054337   658 sgd_solver.cpp:106] Iteration 9000, lr = 0.001\n",
+      "I0224 00:32:06.074646   658 solver.cpp:456] Snapshotting to binary proto file examples/hdf5_classification/data/train_iter_10000.caffemodel\n",
+      "I0224 00:32:06.074808   658 sgd_solver.cpp:273] Snapshotting solver state to binary proto file examples/hdf5_classification/data/train_iter_10000.solverstate\n",
+      "I0224 00:32:06.074889   658 solver.cpp:318] Iteration 10000, loss = 0.532798\n",
+      "I0224 00:32:06.074906   658 solver.cpp:338] Iteration 10000, Testing net (#0)\n",
+      "I0224 00:32:06.078208   658 solver.cpp:406]     Test net output #0: accuracy = 0.8388\n",
+      "I0224 00:32:06.078225   658 solver.cpp:406]     Test net output #1: loss = 0.382042 (* 1 = 0.382042 loss)\n",
+      "I0224 00:32:06.078234   658 solver.cpp:323] Optimization Done.\n",
+      "I0224 00:32:06.078241   658 caffe.cpp:222] Optimization Done.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!./build/tools/caffe train -solver examples/hdf5_classification/nonlinear_logreg_solver.prototxt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
+    "shutil.rmtree(dirname)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
+  "example_name": "Off-the-shelf SGD for classification",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.10"
+  },
+  "priority": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/cifar10/cifar10_full.prototxt b/examples/cifar10/cifar10_full.prototxt
index c16f7dca49f..83cf0d86b35 100644
--- a/examples/cifar10/cifar10_full.prototxt
+++ b/examples/cifar10/cifar10_full.prototxt
@@ -1,11 +1,12 @@
 name: "CIFAR10_full_deploy"
 # N.B. input image must be in CIFAR-10 format
 # as described at http://www.cs.toronto.edu/~kriz/cifar.html
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
new file mode 100644
index 00000000000..7dd3ecb9d8e
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_solver.prototxt
@@ -0,0 +1,28 @@
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
new file mode 100644
index 00000000000..a57b280fd1e
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
@@ -0,0 +1,28 @@
+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid_bn"
+# solver mode: CPU or GPU
+solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
new file mode 100644
index 00000000000..fba69b814ad
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test.prototxt
@@ -0,0 +1,212 @@
+name: "CIFAR10_full"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 111
+    backend: LMDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 1000
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+
+
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "pool1"
+  top: "Sigmoid1"
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "Sigmoid1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "conv2"
+  top: "Sigmoid2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "Sigmoid2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 1
+  }
+
+}
+
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "conv3"
+  top: "Sigmoid3"
+}
+
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "Sigmoid3"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool3"
+  top: "ip1"
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
+  param {
+    lr_mult: 2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
new file mode 100644
index 00000000000..1a810751177
--- /dev/null
+++ b/examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt
@@ -0,0 +1,240 @@
+name: "CIFAR10_full"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 100
+    backend: LMDB
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 1000
+    backend: LMDB
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "bn1"
+  type: "BatchNorm"
+  bottom: "pool1"
+  top: "bn1"
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+}
+
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "bn1"
+  top: "Sigmoid1"
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "Sigmoid1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+
+layer {
+  name: "bn2"
+  type: "BatchNorm"
+  bottom: "conv2"
+  top: "bn2"
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+}
+
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "bn2"
+  top: "Sigmoid2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "Sigmoid2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  param {
+    lr_mult: 1
+  }
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    bias_term: false
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+  }
+}
+
+layer {
+  name: "bn3"
+  type: "BatchNorm"
+  bottom: "conv3"
+  top: "bn3"
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+  param {
+    lr_mult: 0
+  }
+}
+
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "bn3"
+  top: "Sigmoid3"
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "Sigmoid3"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool3"
+  top: "ip1"
+  param {
+    lr_mult: 1
+    decay_mult: 1
+  }
+  param {
+    lr_mult: 1
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/cifar10/cifar10_full_solver.prototxt b/examples/cifar10/cifar10_full_solver.prototxt
index f30b3986142..882daa2d2b5 100644
--- a/examples/cifar10/cifar10_full_solver.prototxt
+++ b/examples/cifar10/cifar10_full_solver.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 60000
 # snapshot intermediate results
 snapshot: 10000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr1.prototxt b/examples/cifar10/cifar10_full_solver_lr1.prototxt
index 59bc5721f4c..55f4be44053 100644
--- a/examples/cifar10/cifar10_full_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr1.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 65000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_full_solver_lr2.prototxt b/examples/cifar10/cifar10_full_solver_lr2.prototxt
index d4ed5d8e041..7c3d2da31de 100644
--- a/examples/cifar10/cifar10_full_solver_lr2.prototxt
+++ b/examples/cifar10/cifar10_full_solver_lr2.prototxt
@@ -21,6 +21,7 @@ display: 200
 max_iter: 70000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_full"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick.prototxt b/examples/cifar10/cifar10_quick.prototxt
index 1ad190e185f..cf3b2a358be 100644
--- a/examples/cifar10/cifar10_quick.prototxt
+++ b/examples/cifar10/cifar10_quick.prototxt
@@ -1,9 +1,10 @@
 name: "CIFAR10_quick_test"
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 32
-input_dim: 32
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 3 dim: 32 dim: 32 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/cifar10/cifar10_quick_solver.prototxt b/examples/cifar10/cifar10_quick_solver.prototxt
index 14b4401ba16..5de276f722f 100644
--- a/examples/cifar10/cifar10_quick_solver.prototxt
+++ b/examples/cifar10/cifar10_quick_solver.prototxt
@@ -20,6 +20,7 @@ display: 100
 max_iter: 4000
 # snapshot intermediate results
 snapshot: 4000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/cifar10_quick_solver_lr1.prototxt b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
index d3af70c05e7..f8f1efd54af 100644
--- a/examples/cifar10/cifar10_quick_solver_lr1.prototxt
+++ b/examples/cifar10/cifar10_quick_solver_lr1.prototxt
@@ -20,6 +20,7 @@ display: 100
 max_iter: 5000
 # snapshot intermediate results
 snapshot: 5000
+snapshot_format: HDF5
 snapshot_prefix: "examples/cifar10/cifar10_quick"
 # solver mode: CPU or GPU
 solver_mode: GPU
diff --git a/examples/cifar10/convert_cifar_data.cpp b/examples/cifar10/convert_cifar_data.cpp
index f4c42e4d2e7..e1b89f42fb6 100644
--- a/examples/cifar10/convert_cifar_data.cpp
+++ b/examples/cifar10/convert_cifar_data.cpp
@@ -16,6 +16,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 
 using caffe::Datum;
 using boost::scoped_ptr;
@@ -52,19 +53,18 @@ void convert_dataset(const string& input_folder, const string& output_folder,
   for (int fileid = 0; fileid < kCIFARTrainBatches; ++fileid) {
     // Open files
     LOG(INFO) << "Training Batch " << fileid + 1;
-    snprintf(str_buffer, kCIFARImageNBytes, "/data_batch_%d.bin", fileid + 1);
-    std::ifstream data_file((input_folder + str_buffer).c_str(),
+    string batchFileName = input_folder + "/data_batch_"
+      + caffe::format_int(fileid+1) + ".bin";
+    std::ifstream data_file(batchFileName.c_str(),
         std::ios::in | std::ios::binary);
     CHECK(data_file) << "Unable to open train file #" << fileid + 1;
     for (int itemid = 0; itemid < kCIFARBatchSize; ++itemid) {
       read_image(&data_file, &label, str_buffer);
       datum.set_label(label);
       datum.set_data(str_buffer, kCIFARImageNBytes);
-      int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d",
-          fileid * kCIFARBatchSize + itemid);
       string out;
       CHECK(datum.SerializeToString(&out));
-      txn->Put(string(str_buffer, length), out);
+      txn->Put(caffe::format_int(fileid * kCIFARBatchSize + itemid, 5), out);
     }
   }
   txn->Commit();
@@ -82,10 +82,9 @@ void convert_dataset(const string& input_folder, const string& output_folder,
     read_image(&data_file, &label, str_buffer);
     datum.set_label(label);
     datum.set_data(str_buffer, kCIFARImageNBytes);
-    int length = snprintf(str_buffer, kCIFARImageNBytes, "%05d", itemid);
     string out;
     CHECK(datum.SerializeToString(&out));
-    txn->Put(string(str_buffer, length), out);
+    txn->Put(caffe::format_int(itemid, 5), out);
   }
   txn->Commit();
   test_db->Close();
diff --git a/examples/cifar10/readme.md b/examples/cifar10/readme.md
index 4a95cee9e8f..5d8d81e3efb 100644
--- a/examples/cifar10/readme.md
+++ b/examples/cifar10/readme.md
@@ -22,9 +22,8 @@ Prepare the Dataset
 
 You will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands:
 
-    cd $CAFFE_ROOT/data/cifar10
-    ./get_cifar10.sh
     cd $CAFFE_ROOT
+    ./data/cifar10/get_cifar10.sh
     ./examples/cifar10/create_cifar10.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
diff --git a/examples/cifar10/train_full.sh b/examples/cifar10/train_full.sh
index 4285a5d6468..ef112e1f6db 100755
--- a/examples/cifar10/train_full.sh
+++ b/examples/cifar10/train_full.sh
@@ -8,9 +8,9 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr1.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate
+    --snapshot=examples/cifar10/cifar10_full_iter_60000.solverstate.h5
 
 # reduce learning rate by factor of 10
 $TOOLS/caffe train \
     --solver=examples/cifar10/cifar10_full_solver_lr2.prototxt \
-    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate
+    --snapshot=examples/cifar10/cifar10_full_iter_65000.solverstate.h5
diff --git a/examples/cifar10/train_full_sigmoid.sh b/examples/cifar10/train_full_sigmoid.sh
new file mode 100755
index 00000000000..9cff06d3e34
--- /dev/null
+++ b/examples/cifar10/train_full_sigmoid.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver.prototxt
+
diff --git a/examples/cifar10/train_full_sigmoid_bn.sh b/examples/cifar10/train_full_sigmoid_bn.sh
new file mode 100755
index 00000000000..011387c996e
--- /dev/null
+++ b/examples/cifar10/train_full_sigmoid_bn.sh
@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+
+TOOLS=./build/tools
+
+$TOOLS/caffe train \
+    --solver=examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt
+
diff --git a/examples/cifar10/train_quick.sh b/examples/cifar10/train_quick.sh
index 2830c40945c..6b7d228879b 100755
--- a/examples/cifar10/train_quick.sh
+++ b/examples/cifar10/train_quick.sh
@@ -8,4 +8,4 @@ $TOOLS/caffe train \
 # reduce learning rate by factor of 10 after 8 epochs
 $TOOLS/caffe train \
   --solver=examples/cifar10/cifar10_quick_solver_lr1.prototxt \
-  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate
+  --snapshot=examples/cifar10/cifar10_quick_iter_4000.solverstate.h5
diff --git a/examples/classification.ipynb b/examples/classification.ipynb
deleted file mode 100644
index 0babf79f304..00000000000
--- a/examples/classification.ipynb
+++ /dev/null
@@ -1,397 +0,0 @@
-{
- "metadata": {
-  "description": "Use the pre-trained ImageNet model to classify images with the Python interface.",
-  "example_name": "ImageNet classification",
-  "include_in_docs": true,
-  "priority": 1,
-  "signature": "sha256:a2b12abaa1eb252f436d59833c08ab97948c8a7a0513197f31afad0a0690e318"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Classifying ImageNet: the instant Caffe way\n",
-      "===========================================\n",
-      "\n",
-      "Caffe has a Python interface, pycaffe, with a `caffe.Net` interface for models. There are both Python and MATLAB interfaces. While this example uses the off-the-shelf Python `caffe.Classifier` interface there is also a MATLAB example at `matlab/caffe/matcaffe_demo.m`.\n",
-      "\n",
-      "Before we begin, you must compile Caffe. You should add the Caffe module to your `PYTHONPATH` although this example includes it automatically. If you haven't yet done so, please refer to the [installation instructions](http://caffe.berkeleyvision.org/installation.html). This example uses our pre-trained CaffeNet model, an ILSVRC12 image classifier. You can download it by running `./scripts/download_model_binary.py models/bvlc_reference_caffenet` or let the first step of this example download it for you.\n",
-      "\n",
-      "Ready? Let's start."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Set the right path to your model definition file, pretrained model weights,\n",
-      "# and the image you would like to classify.\n",
-      "MODEL_FILE = '../models/bvlc_reference_caffenet/deploy.prototxt'\n",
-      "PRETRAINED = '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
-      "IMAGE_FILE = 'images/cat.jpg'\n",
-      "\n",
-      "import os\n",
-      "if not os.path.isfile(PRETRAINED):\n",
-      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Loading a network is easy. `caffe.Classifier` takes care of everything. Note the arguments for configuring input preprocessing: mean subtraction switched on by giving a mean array, input channel swapping takes care of mapping RGB into the reference ImageNet model's BGR order, and raw scaling multiplies the feature scale from the input [0,1] to the ImageNet model's [0,255].\n",
-      "\n",
-      "We will set the phase to test since we are doing testing, and will first use CPU for the computation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Classifier(MODEL_FILE, PRETRAINED,\n",
-      "                       mean=np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1),\n",
-      "                       channel_swap=(2,1,0),\n",
-      "                       raw_scale=255,\n",
-      "                       image_dims=(256, 256))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's take a look at our example image with Caffe's image loading helper."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "input_image = caffe.io.load_image(IMAGE_FILE)\n",
-      "plt.imshow(input_image)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x7fda204c0e10>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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IKqyrUEsKkeG59FMkhNGdvjnnh86+C5dLZ+zbU4KSKciGT6otrpz5YIggA9gbUTM5aVFq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71Uxl70yVqVOiQqaGuxTtFYg28yxgW8BAhgTal9SbknQjWuaCXT+5ypVklRfIAMcUVHm9FWdNZt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\nxSrNEpvXKuWZlFekVvjYGkGf3L7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lN7sbnh6+QNTCZnvNZrtlXRe0s5TcFBBGyubsLhm/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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda517da210>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Time to classify. The default is to actually do 10 predictions, cropping the center and corners of the image as well as their mirrored versions, and average over the predictions:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])  # predict takes any number of images, and formats them for the Caffe net automatically\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda20473950>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "You can see that the prediction is 1000-dimensional, and is pretty sparse.\n",
-      "\n",
-      "The predicted class 281 is \"Tabby cat.\" Our pretrained model uses the synset ID ordering of the classes, as listed in `../data/ilsvrc12/synset_words.txt` if you fetch the auxiliary imagenet data by `../data/ilsvrc12/get_ilsvrc_aux.sh`. If you look at the top indices that maximize the prediction score, they are cats, foxes, and other cute mammals. Not unreasonable predictions, right?\n",
-      "\n",
-      "Now let's classify by the center crop alone by turning off oversampling. Note that this makes a single input, although if you inspect the model definition prototxt you'll see the network has a batch size of 10. The python wrapper handles batching and padding for you!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image], oversample=False)\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])\n",
-      "print 'predicted class:', prediction[0].argmax()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n",
-        "predicted class: 281\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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iLd0g8Pv9sFqtsFgsMBqNqK6uRmtra1yZrKwsOJ1OGI3GKdelmccgICIt3SAIhULIycmJ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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1abd8e10>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Now, why don't we see how long it takes to perform the classification end to end? This result is run from an Intel i5 CPU, so you may observe some performance differences."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 355 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It may look a little slow, but note that time is spent on cropping, python interfacing, and running 10 images. For performance, if you really want to make prediction fast, you can optionally code in C++ and pipeline operations better. For experimenting and prototyping the current speed is fine.\n",
-      "\n",
-      "Let's time classifying a single image with input preprocessed:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Resize the image to the standard (256, 256) and oversample net input sized crops.\n",
-      "input_oversampled = caffe.io.oversample([caffe.io.resize_image(input_image, net.image_dims)], net.crop_dims)\n",
-      "# 'data' is the input blob name in the model definition, so we preprocess for that input.\n",
-      "caffe_input = np.asarray([net.transformer.preprocess('data', in_) for in_ in input_oversampled])\n",
-      "# forward() takes keyword args for the input blobs with preprocessed input arrays.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 210 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "OK, so how about GPU? it is actually pretty easy:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_gpu()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Voila! Now we are in GPU mode. Let's see if the code gives the same result:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "prediction = net.predict([input_image])\n",
-      "print 'prediction shape:', prediction[0].shape\n",
-      "plt.plot(prediction[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "prediction shape: (1000,)\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 9,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fda1ac309d0>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAAAyxEEAGA5ggAALEcQAIDl/h+B3FfVQOwSzQAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fda1ab53650>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Good, everything is the same. And how about time consumption? The following benchmark is obtained on the same machine with a GTX 770 GPU:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Full pipeline timing.\n",
-      "%timeit net.predict([input_image])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 174 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Forward pass timing.\n",
-      "%timeit net.forward(data=caffe_input)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 34.2 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Pretty fast right? Not as fast as you expected? Indeed, in this python demo you are seeing only 4 times speedup. But remember - the GPU code is actually very fast, and the data loading, transformation and interfacing actually start to take **more** time than the actual conv. net computation itself!\n",
-      "\n",
-      "To fully utilize the power of GPUs, you really want to:\n",
-      "\n",
-      "* Use larger batches, and minimize python call and data transfer overheads.\n",
-      "* Pipeline data load operations, like using a subprocess.\n",
-      "* Code in C++. A little inconvenient, but maybe worth it if your dataset is really, really large."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Parting Words\n",
-      "-------------\n",
-      "\n",
-      "So this is python! We hope the interface is easy enough for one to use. The python wrapper is interfaced with boost::python, and source code can be found at `python/caffe` with the main interface in `pycaffe.py` and the classification wrapper in `classifier.py`. If you have customizations to make, start there! Do let us know if you make improvements by sending a pull request!"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/examples/cpp_classification/classification.cpp b/examples/cpp_classification/classification.cpp
new file mode 100644
index 00000000000..6b67c537a47
--- /dev/null
+++ b/examples/cpp_classification/classification.cpp
@@ -0,0 +1,265 @@
+#include <caffe/caffe.hpp>
+#ifdef USE_OPENCV
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+#endif  // USE_OPENCV
+#include <algorithm>
+#include <iosfwd>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#ifdef USE_OPENCV
+using namespace caffe;  // NOLINT(build/namespaces)
+using std::string;
+
+/* Pair (label, confidence) representing a prediction. */
+typedef std::pair<string, float> Prediction;
+
+class Classifier {
+ public:
+  Classifier(const string& model_file,
+             const string& trained_file,
+             const string& mean_file,
+             const string& label_file);
+
+  std::vector<Prediction> Classify(const cv::Mat& img, int N = 5);
+
+ private:
+  void SetMean(const string& mean_file);
+
+  std::vector<float> Predict(const cv::Mat& img);
+
+  void WrapInputLayer(std::vector<cv::Mat>* input_channels);
+
+  void Preprocess(const cv::Mat& img,
+                  std::vector<cv::Mat>* input_channels);
+
+ private:
+  shared_ptr<Net<float> > net_;
+  cv::Size input_geometry_;
+  int num_channels_;
+  cv::Mat mean_;
+  std::vector<string> labels_;
+};
+
+Classifier::Classifier(const string& model_file,
+                       const string& trained_file,
+                       const string& mean_file,
+                       const string& label_file) {
+#ifdef CPU_ONLY
+  Caffe::set_mode(Caffe::CPU);
+#else
+  Caffe::set_mode(Caffe::GPU);
+#endif
+
+  /* Load the network. */
+  net_.reset(new Net<float>(model_file, TEST));
+  net_->CopyTrainedLayersFrom(trained_file);
+
+  CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";
+  CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
+
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  num_channels_ = input_layer->channels();
+  CHECK(num_channels_ == 3 || num_channels_ == 1)
+    << "Input layer should have 1 or 3 channels.";
+  input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
+
+  /* Load the binaryproto mean file. */
+  SetMean(mean_file);
+
+  /* Load labels. */
+  std::ifstream labels(label_file.c_str());
+  CHECK(labels) << "Unable to open labels file " << label_file;
+  string line;
+  while (std::getline(labels, line))
+    labels_.push_back(string(line));
+
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  CHECK_EQ(labels_.size(), output_layer->channels())
+    << "Number of labels is different from the output layer dimension.";
+}
+
+static bool PairCompare(const std::pair<float, int>& lhs,
+                        const std::pair<float, int>& rhs) {
+  return lhs.first > rhs.first;
+}
+
+/* Return the indices of the top N values of vector v. */
+static std::vector<int> Argmax(const std::vector<float>& v, int N) {
+  std::vector<std::pair<float, int> > pairs;
+  for (size_t i = 0; i < v.size(); ++i)
+    pairs.push_back(std::make_pair(v[i], i));
+  std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);
+
+  std::vector<int> result;
+  for (int i = 0; i < N; ++i)
+    result.push_back(pairs[i].second);
+  return result;
+}
+
+/* Return the top N predictions. */
+std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
+  std::vector<float> output = Predict(img);
+
+  N = std::min<int>(labels_.size(), N);
+  std::vector<int> maxN = Argmax(output, N);
+  std::vector<Prediction> predictions;
+  for (int i = 0; i < N; ++i) {
+    int idx = maxN[i];
+    predictions.push_back(std::make_pair(labels_[idx], output[idx]));
+  }
+
+  return predictions;
+}
+
+/* Load the mean file in binaryproto format. */
+void Classifier::SetMean(const string& mean_file) {
+  BlobProto blob_proto;
+  ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
+
+  /* Convert from BlobProto to Blob<float> */
+  Blob<float> mean_blob;
+  mean_blob.FromProto(blob_proto);
+  CHECK_EQ(mean_blob.channels(), num_channels_)
+    << "Number of channels of mean file doesn't match input layer.";
+
+  /* The format of the mean file is planar 32-bit float BGR or grayscale. */
+  std::vector<cv::Mat> channels;
+  float* data = mean_blob.mutable_cpu_data();
+  for (int i = 0; i < num_channels_; ++i) {
+    /* Extract an individual channel. */
+    cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);
+    channels.push_back(channel);
+    data += mean_blob.height() * mean_blob.width();
+  }
+
+  /* Merge the separate channels into a single image. */
+  cv::Mat mean;
+  cv::merge(channels, mean);
+
+  /* Compute the global mean pixel value and create a mean image
+   * filled with this value. */
+  cv::Scalar channel_mean = cv::mean(mean);
+  mean_ = cv::Mat(input_geometry_, mean.type(), channel_mean);
+}
+
+std::vector<float> Classifier::Predict(const cv::Mat& img) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+  input_layer->Reshape(1, num_channels_,
+                       input_geometry_.height, input_geometry_.width);
+  /* Forward dimension change to all layers. */
+  net_->Reshape();
+
+  std::vector<cv::Mat> input_channels;
+  WrapInputLayer(&input_channels);
+
+  Preprocess(img, &input_channels);
+
+  net_->Forward();
+
+  /* Copy the output layer to a std::vector */
+  Blob<float>* output_layer = net_->output_blobs()[0];
+  const float* begin = output_layer->cpu_data();
+  const float* end = begin + output_layer->channels();
+  return std::vector<float>(begin, end);
+}
+
+/* Wrap the input layer of the network in separate cv::Mat objects
+ * (one per channel). This way we save one memcpy operation and we
+ * don't need to rely on cudaMemcpy2D. The last preprocessing
+ * operation will write the separate channels directly to the input
+ * layer. */
+void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {
+  Blob<float>* input_layer = net_->input_blobs()[0];
+
+  int width = input_layer->width();
+  int height = input_layer->height();
+  float* input_data = input_layer->mutable_cpu_data();
+  for (int i = 0; i < input_layer->channels(); ++i) {
+    cv::Mat channel(height, width, CV_32FC1, input_data);
+    input_channels->push_back(channel);
+    input_data += width * height;
+  }
+}
+
+void Classifier::Preprocess(const cv::Mat& img,
+                            std::vector<cv::Mat>* input_channels) {
+  /* Convert the input image to the input image format of the network. */
+  cv::Mat sample;
+  if (img.channels() == 3 && num_channels_ == 1)
+    cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 1)
+    cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
+  else if (img.channels() == 4 && num_channels_ == 3)
+    cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
+  else if (img.channels() == 1 && num_channels_ == 3)
+    cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
+  else
+    sample = img;
+
+  cv::Mat sample_resized;
+  if (sample.size() != input_geometry_)
+    cv::resize(sample, sample_resized, input_geometry_);
+  else
+    sample_resized = sample;
+
+  cv::Mat sample_float;
+  if (num_channels_ == 3)
+    sample_resized.convertTo(sample_float, CV_32FC3);
+  else
+    sample_resized.convertTo(sample_float, CV_32FC1);
+
+  cv::Mat sample_normalized;
+  cv::subtract(sample_float, mean_, sample_normalized);
+
+  /* This operation will write the separate BGR planes directly to the
+   * input layer of the network because it is wrapped by the cv::Mat
+   * objects in input_channels. */
+  cv::split(sample_normalized, *input_channels);
+
+  CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
+        == net_->input_blobs()[0]->cpu_data())
+    << "Input channels are not wrapping the input layer of the network.";
+}
+
+int main(int argc, char** argv) {
+  if (argc != 6) {
+    std::cerr << "Usage: " << argv[0]
+              << " deploy.prototxt network.caffemodel"
+              << " mean.binaryproto labels.txt img.jpg" << std::endl;
+    return 1;
+  }
+
+  ::google::InitGoogleLogging(argv[0]);
+
+  string model_file   = argv[1];
+  string trained_file = argv[2];
+  string mean_file    = argv[3];
+  string label_file   = argv[4];
+  Classifier classifier(model_file, trained_file, mean_file, label_file);
+
+  string file = argv[5];
+
+  std::cout << "---------- Prediction for "
+            << file << " ----------" << std::endl;
+
+  cv::Mat img = cv::imread(file, -1);
+  CHECK(!img.empty()) << "Unable to decode image " << file;
+  std::vector<Prediction> predictions = classifier.Classify(img);
+
+  /* Print the top N predictions. */
+  for (size_t i = 0; i < predictions.size(); ++i) {
+    Prediction p = predictions[i];
+    std::cout << std::fixed << std::setprecision(4) << p.second << " - \""
+              << p.first << "\"" << std::endl;
+  }
+}
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
+}
+#endif  // USE_OPENCV
diff --git a/examples/cpp_classification/readme.md b/examples/cpp_classification/readme.md
new file mode 100644
index 00000000000..a086db1a035
--- /dev/null
+++ b/examples/cpp_classification/readme.md
@@ -0,0 +1,77 @@
+---
+title: CaffeNet C++ Classification example
+description: A simple example performing image classification using the low-level C++ API.
+category: example
+include_in_docs: true
+priority: 10
+---
+
+# Classifying ImageNet: using the C++ API
+
+Caffe, at its core, is written in C++. It is possible to use the C++
+API of Caffe to implement an image classification application similar
+to the Python code presented in one of the Notebook example. To look
+at a more general-purpose example of the Caffe C++ API, you should
+study the source code of the command line tool `caffe` in `tools/caffe.cpp`.
+
+## Presentation
+
+A simple C++ code is proposed in
+`examples/cpp_classification/classification.cpp`. For the sake of
+simplicity, this example does not support oversampling of a single
+sample nor batching of multiple independant samples. This example is
+not trying to reach the maximum possible classification throughput on
+a system, but special care was given to avoid unnecessary
+pessimization while keeping the code readable.
+
+## Compiling
+
+The C++ example is built automatically when compiling Caffe. To
+compile Caffe you should follow the documented instructions. The
+classification example will be built as `examples/classification.bin`
+in your build directory.
+
+## Usage
+
+To use the pre-trained CaffeNet model with the classification example,
+you need to download it from the "Model Zoo" using the following
+script:
+```
+./scripts/download_model_binary.py models/bvlc_reference_caffenet
+```
+The ImageNet labels file (also called the *synset file*) is also
+required in order to map a prediction to the name of the class:
+```
+./data/ilsvrc12/get_ilsvrc_aux.sh.
+```
+Using the files that were downloaded, we can classify the provided cat
+image (`examples/images/cat.jpg`) using this command:
+```
+./build/examples/cpp_classification/classification.bin \
+  models/bvlc_reference_caffenet/deploy.prototxt \
+  models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel \
+  data/ilsvrc12/imagenet_mean.binaryproto \
+  data/ilsvrc12/synset_words.txt \
+  examples/images/cat.jpg
+```
+The output should look like this:
+```
+---------- Prediction for examples/images/cat.jpg ----------
+0.3134 - "n02123045 tabby, tabby cat"
+0.2380 - "n02123159 tiger cat"
+0.1235 - "n02124075 Egyptian cat"
+0.1003 - "n02119022 red fox, Vulpes vulpes"
+0.0715 - "n02127052 lynx, catamount"
+```
+
+## Improving Performance
+
+To further improve performance, you will need to leverage the GPU
+more, here are some guidelines:
+
+* Move the data on the GPU early and perform all preprocessing
+operations there.
+* If you have many images to classify simultaneously, you should use
+batching (independent images are classified in a single forward pass).
+* Use multiple classification threads to ensure the GPU is always fully
+utilized and not waiting for an I/O blocked CPU thread.
diff --git a/examples/detection.ipynb b/examples/detection.ipynb
index 2ccf21f09eb..6a03c996245 100644
--- a/examples/detection.ipynb
+++ b/examples/detection.ipynb
@@ -1,933 +1,8392 @@
 {
- "metadata": {
-  "description": "Run a pretrained model as a detector in Python.",
-  "example_name": "R-CNN detection",
-  "include_in_docs": true,
-  "priority": 3,
-  "signature": "sha256:5d53dc49c9b6b93c1a2714c99043a763029ec98aebfb44acfa8d9e61781c9499"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
-      "\n",
-      "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
-      "\n",
-      "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
-      "\n",
-      "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge\u2014no joke).\n",
-      "\n",
-      "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
-      "\n",
-      "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
-      "\n",
-      "-Run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the Caffe R-CNN ImageNet model.\n",
-      "\n",
-      "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!mkdir -p _temp\n",
-      "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
-      "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "WARNING: Logging before InitGoogleLogging() is written to STDERR\r\n",
-        "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"R-CNN-ilsvrc13\"\r\n",
-        "input: \"data\"\r\n",
-        "input_dim: 10\r\n",
-        "input_dim: 3\r\n",
-        "input_dim: 227\r\n",
-        "input_dim: 227\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv1\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 96\r\n",
-        "    kernel_size: 11\r\n",
-        "    stride: 4\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"conv1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool1\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv1\"\r\n",
-        "  top: \"pool1\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"norm1\"\r\n",
-        "  type: \"LRN\"\r\n",
-        "  bottom: \"pool1\"\r\n",
-        "  top: \"norm1\"\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv2\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"norm1\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 2\r\n",
-        "    kernel_size: 5\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r",
-        "\r\n",
-        "  name: \"relu2\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"conv2\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool2\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv2\"\r\n",
-        "  top: \"pool2\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"norm2\"\r\n",
-        "  type: \"LRN\"\r\n",
-        "  bottom: \"pool2\"\r\n",
-        "  top: \"norm2\"\r\n",
-        "  lrn_param {\r\n",
-        "    local_size: 5\r\n",
-        "    alpha: 0.0001\r\n",
-        "    beta: 0.75\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv3\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"norm2\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu3\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv3\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv4\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"conv3\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 384\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu4\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv4\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"conv5\"\r\n",
-        "  type: \"Convolution\"\r\n",
-        "  bottom: \"conv4\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "  convolution_param {\r\n",
-        "    num_output: 256\r\n",
-        "    pad: 1\r\n",
-        "    kernel_size: 3\r\n",
-        "    group: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu5\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"conv5\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"pool5\"\r\n",
-        "  type: \"Pooling\"\r\n",
-        "  bottom: \"conv5\"\r\n",
-        "  top: \"pool5\"\r\n",
-        "  pooling_param {\r\n",
-        "    pool: MAX\r\n",
-        "    kernel_size: 3\r\n",
-        "    stride: 2\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc6\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"pool5\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu6\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"drop6\"\r\n",
-        "  type: \"Dropout\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc6\"\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc7\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc6\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 4096\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu7\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"drop7\"\r\n",
-        "  type: \"Dropout\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc7\"\r\n",
-        "  dropout_param {\r\n",
-        "    dropout_ratio: 0.5\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc-rcnn\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc7\"\r\n",
-        "  top: \"fc-rcnn\"\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 200\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\r\n",
-        "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\r\n",
-        "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\r\n",
-        "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\r\n",
-        "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\r\n",
-        "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\r\n",
-        "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\r\n",
-        "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\r\n",
-        "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\r\n",
-        "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\r\n",
-        "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\r\n",
-        "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\r\n",
-        "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\r\n",
-        "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\r\n",
-        "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\r\n",
-        "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\r\n",
-        "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\r\n",
-        "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\r\n",
-        "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\r\n",
-        "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\r\n",
-        "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\r\n",
-        "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\r\n",
-        "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\r\n",
-        "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\r\n",
-        "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\r\n",
-        "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\r\n",
-        "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\r\n",
-        "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\r\n",
-        "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\r\n",
-        "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\r\n",
-        "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\r\n",
-        "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\r\n",
-        "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\r\n",
-        "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\r\n",
-        "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\r\n",
-        "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\r\n",
-        "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\r\n",
-        "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\r\n",
-        "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\r\n",
-        "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\r\n",
-        "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\r\n",
-        "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\r\n",
-        "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\r\n",
-        "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\r\n",
-        "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\r\n",
-        "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\r\n",
-        "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\r\n",
-        "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\r\n",
-        "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\r\n",
-        "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\r\n",
-        "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\r\n",
-        "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\r\n",
-        "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\r\n",
-        "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\r\n",
-        "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\r\n",
-        "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\r\n",
-        "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\r\n",
-        "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\r\n",
-        "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\r\n",
-        "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\r\n",
-        "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\r\n",
-        "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\r\n",
-        "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\r\n",
-        "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\r\n",
-        "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\r\n",
-        "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\r\n",
-        "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\r\n",
-        "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\r\n",
-        "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\r\n",
-        "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\r\n",
-        "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\r\n",
-        "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\r\n",
-        "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\r\n",
-        "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\r\n",
-        "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\r\n",
-        "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\r\n",
-        "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\r\n",
-        "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\r\n",
-        "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\r\n",
-        "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\r\n",
-        "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\r\n",
-        "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\r\n",
-        "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\r\n",
-        "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\r\n",
-        "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\r\n",
-        "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\r\n",
-        "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\r\n",
-        "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\r\n",
-        "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\r\n",
-        "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\r\n",
-        "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\r\n",
-        "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\r\n",
-        "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\r\n",
-        "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\r\n",
-        "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\r\n",
-        "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\r\n",
-        "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\r\n",
-        "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\r\n",
-        "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\r\n",
-        "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\r\n",
-        "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\r\n",
-        "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\r\n",
-        "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\r\n",
-        "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\r\n",
-        "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\r\n",
-        "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\r\n",
-        "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\r\n",
-        "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\r\n",
-        "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\r\n",
-        "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\r\n",
-        "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\r\n",
-        "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\r\n",
-        "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\r\n",
-        "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\r\n",
-        "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\r\n",
-        "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\r\n",
-        "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\r\n",
-        "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\r\n",
-        "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\r\n",
-        "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\r\n",
-        "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\r\n",
-        "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\r\n",
-        "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\r\n",
-        "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\r\n",
-        "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\r\n",
-        "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\r\n",
-        "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\r\n",
-        "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\r\n",
-        "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\r\n",
-        "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "GPU mode\r\n",
-        "Loading input...\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Processed 1570 windows in 102.895 s.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \r\n",
-        "your performance may suffer as PyTables will pickle object types that it cannot\r\n",
-        "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\r\n",
-        "\r\n",
-        "  warnings.warn(ws, PerformanceWarning)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Saved to _temp/det_output.h5 in 0.298 s.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
-      "\n",
-      "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
-      "(We only ran on one image, so the filenames will all be the same.)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import pandas as pd\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
-      "print(df.shape)\n",
-      "print(df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1570, 5)\n",
-        "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
-        "ymin                                                     79.846\n",
-        "xmin                                                       9.62\n",
-        "ymax                                                     246.31\n",
-        "xmax                                                    339.624\n",
-        "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[R-CNN](https://github.com/rbgirshick/rcnn) is a state-of-the-art detector that classifies region proposals by a finetuned Caffe model. For the full details of the R-CNN system and model, refer to its project site and the paper:\n",
+    "\n",
+    "> *Rich feature hierarchies for accurate object detection and semantic segmentation*. Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. CVPR 2014. [Arxiv 2013](http://arxiv.org/abs/1311.2524).\n",
+    "\n",
+    "In this example, we do detection by a pure Caffe edition of the R-CNN model for ImageNet. The R-CNN detector outputs class scores for the 200 detection classes of ILSVRC13. Keep in mind that these are raw one vs. all SVM scores, so they are not probabilistically calibrated or exactly comparable across classes. Note that this off-the-shelf model is simply for convenience, and is not the full R-CNN model.\n",
+    "\n",
+    "Let's run detection on an image of a bicyclist riding a fish bike in the desert (from the ImageNet challenge—no joke).\n",
+    "\n",
+    "First, we'll need region proposals and the Caffe R-CNN ImageNet model:\n",
+    "\n",
+    "- [Selective Search](http://koen.me/research/selectivesearch/) is the region proposer used by R-CNN. The [selective_search_ijcv_with_python](https://github.com/sergeyk/selective_search_ijcv_with_python) Python module takes care of extracting proposals through the selective search MATLAB implementation. To install it, download the module and name its directory `selective_search_ijcv_with_python`, run the demo in MATLAB to compile the necessary functions, then add it to your `PYTHONPATH` for importing. (If you have your own region proposals prepared, or would rather not bother with this step, [detect.py](https://github.com/BVLC/caffe/blob/master/python/detect.py) accepts a list of images and bounding boxes as CSV.)\n",
+    "\n",
+    "-Run `./scripts/download_model_binary.py models/bvlc_reference_rcnn_ilsvrc13` to get the Caffe R-CNN ImageNet model.\n",
+    "\n",
+    "With that done, we'll call the bundled `detect.py` to generate the region proposals and run the network. For an explanation of the arguments, do `./detect.py --help`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
-      "\n",
-      "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
-      "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: Logging before InitGoogleLogging() is written to STDERR\n",
+      "I0218 20:43:25.383932 2099749632 net.cpp:42] Initializing net from parameters: \n",
+      "name: \"R-CNN-ilsvrc13\"\n",
+      "input: \"data\"\n",
+      "input_dim: 10\n",
+      "input_dim: 3\n",
+      "input_dim: 227\n",
+      "input_dim: 227\n",
+      "state {\n",
+      "  phase: TEST\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv1\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"data\"\n",
+      "  top: \"conv1\"\n",
+      "  convolution_param {\n",
+      "    num_output: 96\n",
+      "    kernel_size: 11\n",
+      "    stride: 4\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu1\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"conv1\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool1\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv1\"\n",
+      "  top: \"pool1\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm1\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool1\"\n",
+      "  top: \"norm1\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv2\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm1\"\n",
+      "  top: \"conv2\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 2\n",
+      "    kernel_size: 5\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu2\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"conv2\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool2\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv2\"\n",
+      "  top: \"pool2\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"norm2\"\n",
+      "  type: \"LRN\"\n",
+      "  bottom: \"pool2\"\n",
+      "  top: \"norm2\"\n",
+      "  lrn_param {\n",
+      "    local_size: 5\n",
+      "    alpha: 0.0001\n",
+      "    beta: 0.75\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv3\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"norm2\"\n",
+      "  top: \"conv3\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu3\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv3\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv4\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv3\"\n",
+      "  top: \"conv4\"\n",
+      "  convolution_param {\n",
+      "    num_output: 384\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu4\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv4\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"conv5\"\n",
+      "  type: \"Convolution\"\n",
+      "  bottom: \"conv4\"\n",
+      "  top: \"conv5\"\n",
+      "  convolution_param {\n",
+      "    num_output: 256\n",
+      "    pad: 1\n",
+      "    kernel_size: 3\n",
+      "    group: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu5\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"conv5\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"pool5\"\n",
+      "  type: \"Pooling\"\n",
+      "  bottom: \"conv5\"\n",
+      "  top: \"pool5\"\n",
+      "  pooling_param {\n",
+      "    pool: MAX\n",
+      "    kernel_size: 3\n",
+      "    stride: 2\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc6\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"pool5\"\n",
+      "  top: \"fc6\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu6\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop6\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc6\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc7\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc6\"\n",
+      "  top: \"fc7\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 4096\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"relu7\"\n",
+      "  type: \"ReLU\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"drop7\"\n",
+      "  type: \"Dropout\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc7\"\n",
+      "  dropout_param {\n",
+      "    dropout_ratio: 0.5\n",
+      "  }\n",
+      "}\n",
+      "layer {\n",
+      "  name: \"fc-rcnn\"\n",
+      "  type: \"InnerProduct\"\n",
+      "  bottom: \"fc7\"\n",
+      "  top: \"fc-rcnn\"\n",
+      "  inner_product_param {\n",
+      "    num_output: 200\n",
+      "  }\n",
+      "}\n",
+      "I0218 20:43:25.385720 2099749632 net.cpp:336] Input 0 -> data\n",
+      "I0218 20:43:25.385769 2099749632 layer_factory.hpp:74] Creating layer conv1\n",
+      "I0218 20:43:25.385783 2099749632 net.cpp:76] Creating Layer conv1\n",
+      "I0218 20:43:25.385790 2099749632 net.cpp:372] conv1 <- data\n",
+      "I0218 20:43:25.385802 2099749632 net.cpp:334] conv1 -> conv1\n",
+      "I0218 20:43:25.385815 2099749632 net.cpp:105] Setting up conv1\n",
+      "I0218 20:43:25.386574 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386610 2099749632 layer_factory.hpp:74] Creating layer relu1\n",
+      "I0218 20:43:25.386625 2099749632 net.cpp:76] Creating Layer relu1\n",
+      "I0218 20:43:25.386631 2099749632 net.cpp:372] relu1 <- conv1\n",
+      "I0218 20:43:25.386641 2099749632 net.cpp:323] relu1 -> conv1 (in-place)\n",
+      "I0218 20:43:25.386649 2099749632 net.cpp:105] Setting up relu1\n",
+      "I0218 20:43:25.386656 2099749632 net.cpp:112] Top shape: 10 96 55 55 (2904000)\n",
+      "I0218 20:43:25.386663 2099749632 layer_factory.hpp:74] Creating layer pool1\n",
+      "I0218 20:43:25.386675 2099749632 net.cpp:76] Creating Layer pool1\n",
+      "I0218 20:43:25.386682 2099749632 net.cpp:372] pool1 <- conv1\n",
+      "I0218 20:43:25.386690 2099749632 net.cpp:334] pool1 -> pool1\n",
+      "I0218 20:43:25.386699 2099749632 net.cpp:105] Setting up pool1\n",
+      "I0218 20:43:25.386716 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386725 2099749632 layer_factory.hpp:74] Creating layer norm1\n",
+      "I0218 20:43:25.386736 2099749632 net.cpp:76] Creating Layer norm1\n",
+      "I0218 20:43:25.386744 2099749632 net.cpp:372] norm1 <- pool1\n",
+      "I0218 20:43:25.386803 2099749632 net.cpp:334] norm1 -> norm1\n",
+      "I0218 20:43:25.386819 2099749632 net.cpp:105] Setting up norm1\n",
+      "I0218 20:43:25.386832 2099749632 net.cpp:112] Top shape: 10 96 27 27 (699840)\n",
+      "I0218 20:43:25.386842 2099749632 layer_factory.hpp:74] Creating layer conv2\n",
+      "I0218 20:43:25.386852 2099749632 net.cpp:76] Creating Layer conv2\n",
+      "I0218 20:43:25.386865 2099749632 net.cpp:372] conv2 <- norm1\n",
+      "I0218 20:43:25.386878 2099749632 net.cpp:334] conv2 -> conv2\n",
+      "I0218 20:43:25.386899 2099749632 net.cpp:105] Setting up conv2\n",
+      "I0218 20:43:25.387024 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387042 2099749632 layer_factory.hpp:74] Creating layer relu2\n",
+      "I0218 20:43:25.387050 2099749632 net.cpp:76] Creating Layer relu2\n",
+      "I0218 20:43:25.387058 2099749632 net.cpp:372] relu2 <- conv2\n",
+      "I0218 20:43:25.387066 2099749632 net.cpp:323] relu2 -> conv2 (in-place)\n",
+      "I0218 20:43:25.387075 2099749632 net.cpp:105] Setting up relu2\n",
+      "I0218 20:43:25.387081 2099749632 net.cpp:112] Top shape: 10 256 27 27 (1866240)\n",
+      "I0218 20:43:25.387089 2099749632 layer_factory.hpp:74] Creating layer pool2\n",
+      "I0218 20:43:25.387097 2099749632 net.cpp:76] Creating Layer pool2\n",
+      "I0218 20:43:25.387104 2099749632 net.cpp:372] pool2 <- conv2\n",
+      "I0218 20:43:25.387112 2099749632 net.cpp:334] pool2 -> pool2\n",
+      "I0218 20:43:25.387121 2099749632 net.cpp:105] Setting up pool2\n",
+      "I0218 20:43:25.387130 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387137 2099749632 layer_factory.hpp:74] Creating layer norm2\n",
+      "I0218 20:43:25.387145 2099749632 net.cpp:76] Creating Layer norm2\n",
+      "I0218 20:43:25.387152 2099749632 net.cpp:372] norm2 <- pool2\n",
+      "I0218 20:43:25.387161 2099749632 net.cpp:334] norm2 -> norm2\n",
+      "I0218 20:43:25.387168 2099749632 net.cpp:105] Setting up norm2\n",
+      "I0218 20:43:25.387176 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.387228 2099749632 layer_factory.hpp:74] Creating layer conv3\n",
+      "I0218 20:43:25.387249 2099749632 net.cpp:76] Creating Layer conv3\n",
+      "I0218 20:43:25.387258 2099749632 net.cpp:372] conv3 <- norm2\n",
+      "I0218 20:43:25.387266 2099749632 net.cpp:334] conv3 -> conv3\n",
+      "I0218 20:43:25.387276 2099749632 net.cpp:105] Setting up conv3\n",
+      "I0218 20:43:25.389375 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389408 2099749632 layer_factory.hpp:74] Creating layer relu3\n",
+      "I0218 20:43:25.389421 2099749632 net.cpp:76] Creating Layer relu3\n",
+      "I0218 20:43:25.389430 2099749632 net.cpp:372] relu3 <- conv3\n",
+      "I0218 20:43:25.389438 2099749632 net.cpp:323] relu3 -> conv3 (in-place)\n",
+      "I0218 20:43:25.389447 2099749632 net.cpp:105] Setting up relu3\n",
+      "I0218 20:43:25.389456 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.389462 2099749632 layer_factory.hpp:74] Creating layer conv4\n",
+      "I0218 20:43:25.389472 2099749632 net.cpp:76] Creating Layer conv4\n",
+      "I0218 20:43:25.389478 2099749632 net.cpp:372] conv4 <- conv3\n",
+      "I0218 20:43:25.389487 2099749632 net.cpp:334] conv4 -> conv4\n",
+      "I0218 20:43:25.389497 2099749632 net.cpp:105] Setting up conv4\n",
+      "I0218 20:43:25.391810 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391856 2099749632 layer_factory.hpp:74] Creating layer relu4\n",
+      "I0218 20:43:25.391871 2099749632 net.cpp:76] Creating Layer relu4\n",
+      "I0218 20:43:25.391880 2099749632 net.cpp:372] relu4 <- conv4\n",
+      "I0218 20:43:25.391888 2099749632 net.cpp:323] relu4 -> conv4 (in-place)\n",
+      "I0218 20:43:25.391898 2099749632 net.cpp:105] Setting up relu4\n",
+      "I0218 20:43:25.391906 2099749632 net.cpp:112] Top shape: 10 384 13 13 (648960)\n",
+      "I0218 20:43:25.391913 2099749632 layer_factory.hpp:74] Creating layer conv5\n",
+      "I0218 20:43:25.391923 2099749632 net.cpp:76] Creating Layer conv5\n",
+      "I0218 20:43:25.391929 2099749632 net.cpp:372] conv5 <- conv4\n",
+      "I0218 20:43:25.391937 2099749632 net.cpp:334] conv5 -> conv5\n",
+      "I0218 20:43:25.391947 2099749632 net.cpp:105] Setting up conv5\n",
+      "I0218 20:43:25.393072 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393108 2099749632 layer_factory.hpp:74] Creating layer relu5\n",
+      "I0218 20:43:25.393122 2099749632 net.cpp:76] Creating Layer relu5\n",
+      "I0218 20:43:25.393129 2099749632 net.cpp:372] relu5 <- conv5\n",
+      "I0218 20:43:25.393138 2099749632 net.cpp:323] relu5 -> conv5 (in-place)\n",
+      "I0218 20:43:25.393148 2099749632 net.cpp:105] Setting up relu5\n",
+      "I0218 20:43:25.393157 2099749632 net.cpp:112] Top shape: 10 256 13 13 (432640)\n",
+      "I0218 20:43:25.393167 2099749632 layer_factory.hpp:74] Creating layer pool5\n",
+      "I0218 20:43:25.393175 2099749632 net.cpp:76] Creating Layer pool5\n",
+      "I0218 20:43:25.393182 2099749632 net.cpp:372] pool5 <- conv5\n",
+      "I0218 20:43:25.393190 2099749632 net.cpp:334] pool5 -> pool5\n",
+      "I0218 20:43:25.393199 2099749632 net.cpp:105] Setting up pool5\n",
+      "I0218 20:43:25.393209 2099749632 net.cpp:112] Top shape: 10 256 6 6 (92160)\n",
+      "I0218 20:43:25.393218 2099749632 layer_factory.hpp:74] Creating layer fc6\n",
+      "I0218 20:43:25.393226 2099749632 net.cpp:76] Creating Layer fc6\n",
+      "I0218 20:43:25.393232 2099749632 net.cpp:372] fc6 <- pool5\n",
+      "I0218 20:43:25.393240 2099749632 net.cpp:334] fc6 -> fc6\n",
+      "I0218 20:43:25.393249 2099749632 net.cpp:105] Setting up fc6\n",
+      "I0218 20:43:25.516396 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516445 2099749632 layer_factory.hpp:74] Creating layer relu6\n",
+      "I0218 20:43:25.516463 2099749632 net.cpp:76] Creating Layer relu6\n",
+      "I0218 20:43:25.516470 2099749632 net.cpp:372] relu6 <- fc6\n",
+      "I0218 20:43:25.516480 2099749632 net.cpp:323] relu6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516490 2099749632 net.cpp:105] Setting up relu6\n",
+      "I0218 20:43:25.516497 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516505 2099749632 layer_factory.hpp:74] Creating layer drop6\n",
+      "I0218 20:43:25.516515 2099749632 net.cpp:76] Creating Layer drop6\n",
+      "I0218 20:43:25.516521 2099749632 net.cpp:372] drop6 <- fc6\n",
+      "I0218 20:43:25.516530 2099749632 net.cpp:323] drop6 -> fc6 (in-place)\n",
+      "I0218 20:43:25.516538 2099749632 net.cpp:105] Setting up drop6\n",
+      "I0218 20:43:25.516557 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.516566 2099749632 layer_factory.hpp:74] Creating layer fc7\n",
+      "I0218 20:43:25.516576 2099749632 net.cpp:76] Creating Layer fc7\n",
+      "I0218 20:43:25.516582 2099749632 net.cpp:372] fc7 <- fc6\n",
+      "I0218 20:43:25.516589 2099749632 net.cpp:334] fc7 -> fc7\n",
+      "I0218 20:43:25.516599 2099749632 net.cpp:105] Setting up fc7\n",
+      "I0218 20:43:25.604786 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604838 2099749632 layer_factory.hpp:74] Creating layer relu7\n",
+      "I0218 20:43:25.604852 2099749632 net.cpp:76] Creating Layer relu7\n",
+      "I0218 20:43:25.604859 2099749632 net.cpp:372] relu7 <- fc7\n",
+      "I0218 20:43:25.604868 2099749632 net.cpp:323] relu7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604878 2099749632 net.cpp:105] Setting up relu7\n",
+      "I0218 20:43:25.604885 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604893 2099749632 layer_factory.hpp:74] Creating layer drop7\n",
+      "I0218 20:43:25.604902 2099749632 net.cpp:76] Creating Layer drop7\n",
+      "I0218 20:43:25.604908 2099749632 net.cpp:372] drop7 <- fc7\n",
+      "I0218 20:43:25.604917 2099749632 net.cpp:323] drop7 -> fc7 (in-place)\n",
+      "I0218 20:43:25.604924 2099749632 net.cpp:105] Setting up drop7\n",
+      "I0218 20:43:25.604933 2099749632 net.cpp:112] Top shape: 10 4096 1 1 (40960)\n",
+      "I0218 20:43:25.604939 2099749632 layer_factory.hpp:74] Creating layer fc-rcnn\n",
+      "I0218 20:43:25.604948 2099749632 net.cpp:76] Creating Layer fc-rcnn\n",
+      "I0218 20:43:25.604954 2099749632 net.cpp:372] fc-rcnn <- fc7\n",
+      "I0218 20:43:25.604962 2099749632 net.cpp:334] fc-rcnn -> fc-rcnn\n",
+      "I0218 20:43:25.604971 2099749632 net.cpp:105] Setting up fc-rcnn\n",
+      "I0218 20:43:25.606878 2099749632 net.cpp:112] Top shape: 10 200 1 1 (2000)\n",
+      "I0218 20:43:25.606904 2099749632 net.cpp:165] fc-rcnn does not need backward computation.\n",
+      "I0218 20:43:25.606909 2099749632 net.cpp:165] drop7 does not need backward computation.\n",
+      "I0218 20:43:25.606916 2099749632 net.cpp:165] relu7 does not need backward computation.\n",
+      "I0218 20:43:25.606922 2099749632 net.cpp:165] fc7 does not need backward computation.\n",
+      "I0218 20:43:25.606928 2099749632 net.cpp:165] drop6 does not need backward computation.\n",
+      "I0218 20:43:25.606935 2099749632 net.cpp:165] relu6 does not need backward computation.\n",
+      "I0218 20:43:25.606940 2099749632 net.cpp:165] fc6 does not need backward computation.\n",
+      "I0218 20:43:25.606946 2099749632 net.cpp:165] pool5 does not need backward computation.\n",
+      "I0218 20:43:25.606952 2099749632 net.cpp:165] relu5 does not need backward computation.\n",
+      "I0218 20:43:25.606958 2099749632 net.cpp:165] conv5 does not need backward computation.\n",
+      "I0218 20:43:25.606964 2099749632 net.cpp:165] relu4 does not need backward computation.\n",
+      "I0218 20:43:25.606971 2099749632 net.cpp:165] conv4 does not need backward computation.\n",
+      "I0218 20:43:25.606976 2099749632 net.cpp:165] relu3 does not need backward computation.\n",
+      "I0218 20:43:25.606982 2099749632 net.cpp:165] conv3 does not need backward computation.\n",
+      "I0218 20:43:25.606988 2099749632 net.cpp:165] norm2 does not need backward computation.\n",
+      "I0218 20:43:25.606995 2099749632 net.cpp:165] pool2 does not need backward computation.\n",
+      "I0218 20:43:25.607002 2099749632 net.cpp:165] relu2 does not need backward computation.\n",
+      "I0218 20:43:25.607007 2099749632 net.cpp:165] conv2 does not need backward computation.\n",
+      "I0218 20:43:25.607013 2099749632 net.cpp:165] norm1 does not need backward computation.\n",
+      "I0218 20:43:25.607199 2099749632 net.cpp:165] pool1 does not need backward computation.\n",
+      "I0218 20:43:25.607213 2099749632 net.cpp:165] relu1 does not need backward computation.\n",
+      "I0218 20:43:25.607219 2099749632 net.cpp:165] conv1 does not need backward computation.\n",
+      "I0218 20:43:25.607225 2099749632 net.cpp:201] This network produces output fc-rcnn\n",
+      "I0218 20:43:25.607239 2099749632 net.cpp:446] Collecting Learning Rate and Weight Decay.\n",
+      "I0218 20:43:25.607255 2099749632 net.cpp:213] Network initialization done.\n",
+      "I0218 20:43:25.607262 2099749632 net.cpp:214] Memory required for data: 62425920\n",
+      "E0218 20:43:26.388214 2099749632 upgrade_proto.cpp:618] Attempting to upgrade input file specified using deprecated V1LayerParameter: ../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel\n",
+      "I0218 20:43:27.089423 2099749632 upgrade_proto.cpp:626] Successfully upgraded file specified using deprecated V1LayerParameter\n",
+      "GPU mode\n",
+      "Loading input...\n",
+      "selective_search_rcnn({'/Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg'}, '/var/folders/bk/dtkn5qjd11bd17b2j36zplyw0000gp/T/tmpakaRLL.mat')\n",
+      "Processed 1570 windows in 102.895 s.\n",
+      "/Users/shelhamer/anaconda/lib/python2.7/site-packages/pandas/io/pytables.py:2453: PerformanceWarning: \n",
+      "your performance may suffer as PyTables will pickle object types that it cannot\n",
+      "map directly to c-types [inferred_type->mixed,key->block1_values] [items->['prediction']]\n",
       "\n",
-      "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models\u2019 input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
-      "\n",
-      "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+      "  warnings.warn(ws, PerformanceWarning)\n",
+      "Saved to _temp/det_output.h5 in 0.298 s.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
-      "    labels_df = pd.DataFrame([\n",
-      "        {\n",
-      "            'synset_id': l.strip().split(' ')[0],\n",
-      "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
-      "        }\n",
-      "        for l in f.readlines()\n",
-      "    ])\n",
-      "labels_df.sort('synset_id')\n",
-      "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
-      "print(predictions_df.iloc[0])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "accordion      -2.622471\n",
-        "airplane       -2.845788\n",
-        "ant            -2.851219\n",
-        "antelope       -3.208377\n",
-        "apple          -1.949950\n",
-        "armadillo      -2.472935\n",
-        "artichoke      -2.201684\n",
-        "axe            -2.327404\n",
-        "baby bed       -2.737925\n",
-        "backpack       -2.176763\n",
-        "bagel          -2.681061\n",
-        "balance beam   -2.722538\n",
-        "banana         -2.390628\n",
-        "band aid       -1.598909\n",
-        "banjo          -2.298197\n",
-        "...\n",
-        "trombone        -2.582361\n",
-        "trumpet         -2.352853\n",
-        "turtle          -2.360859\n",
-        "tv or monitor   -2.761043\n",
-        "unicycle        -2.218467\n",
-        "vacuum          -1.907717\n",
-        "violin          -2.757079\n",
-        "volleyball      -2.723689\n",
-        "waffle iron     -2.418540\n",
-        "washer          -2.408994\n",
-        "water bottle    -2.174899\n",
-        "watercraft      -2.837425\n",
-        "whale           -3.120338\n",
-        "wine bottle     -2.772960\n",
-        "zebra           -2.742913\n",
-        "Name: 0, Length: 200, dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
+    }
+   ],
+   "source": [
+    "!mkdir -p _temp\n",
+    "!echo `pwd`/images/fish-bike.jpg > _temp/det_input.txt\n",
+    "!../python/detect.py --crop_mode=selective_search --pretrained_model=../models/bvlc_reference_rcnn_ilsvrc13/bvlc_reference_rcnn_ilsvrc13.caffemodel --model_def=../models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt --gpu --raw_scale=255 _temp/det_input.txt _temp/det_output.h5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This run was in GPU mode. For CPU mode detection, call `detect.py` without the `--gpu` argument.\n",
+    "\n",
+    "Running this outputs a DataFrame with the filenames, selected windows, and their detection scores to an HDF5 file.\n",
+    "(We only ran on one image, so the filenames will all be the same.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's look at the activations."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1570, 5)\n",
+      "prediction    [-2.62247, -2.84579, -2.85122, -3.20838, -1.94...\n",
+      "ymin                                                     79.846\n",
+      "xmin                                                       9.62\n",
+      "ymax                                                     246.31\n",
+      "xmax                                                    339.624\n",
+      "Name: /Users/shelhamer/h/desk/caffe/caffe-dev/examples/images/fish-bike.jpg, dtype: object\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.gray()\n",
-      "plt.matshow(predictions_df.values)\n",
-      "plt.xlabel('Classes')\n",
-      "plt.ylabel('Windows')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "<matplotlib.text.Text at 0x114f15f90>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "text": [
-        "<matplotlib.figure.Figure at 0x114254b50>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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TkxP4/X40Gg1MJhO43W7BnK1Wq9wo/X4fTqdTXt/x8TGcTicSiQSazabsvIFAAIqi4OXL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V1fX1cul9PNzY22trZ0dnZmhgGqG77++muzMaH7WFm5c5Dxc6RSKYMbh8OhhTs6c6pJ0/+t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\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zj9o3yJlgigksRhlDLSvJO+twOLRhIT8b2RcRx/A0JJmOiWK90+mYlonMC3Sm0+lYiAAa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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x114254ad0>"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "df = pd.read_hdf('_temp/det_output.h5', 'df')\n",
+    "print(df.shape)\n",
+    "print(df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1570 regions were proposed with the R-CNN configuration of selective search. The number of proposals will vary from image to image based on its contents and size -- selective search isn't scale invariant.\n",
+    "\n",
+    "In general, `detect.py` is most efficient when running on a lot of images: it first extracts window proposals for all of them, batches the windows for efficient GPU processing, and then outputs the results.\n",
+    "Simply list an image per line in the `images_file`, and it will process all of them.\n",
+    "\n",
+    "Although this guide gives an example of R-CNN ImageNet detection, `detect.py` is clever enough to adapt to different Caffe models’ input dimensions, batch size, and output categories. You can switch the model definition and pretrained model as desired. Refer to `python detect.py --help` for the parameters to describe your data set. There's no need for hardcoding.\n",
+    "\n",
+    "Anyway, let's now load the ILSVRC13 detection class names and make a DataFrame of the predictions. Note you'll need the auxiliary ilsvrc2012 data fetched by `data/ilsvrc12/get_ilsvrc12_aux.sh`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's take max across all windows and plot the top classes."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "accordion      -2.622471\n",
+      "airplane       -2.845788\n",
+      "ant            -2.851219\n",
+      "antelope       -3.208377\n",
+      "apple          -1.949950\n",
+      "armadillo      -2.472935\n",
+      "artichoke      -2.201684\n",
+      "axe            -2.327404\n",
+      "baby bed       -2.737925\n",
+      "backpack       -2.176763\n",
+      "bagel          -2.681061\n",
+      "balance beam   -2.722538\n",
+      "banana         -2.390628\n",
+      "band aid       -1.598909\n",
+      "banjo          -2.298197\n",
+      "...\n",
+      "trombone        -2.582361\n",
+      "trumpet         -2.352853\n",
+      "turtle          -2.360859\n",
+      "tv or monitor   -2.761043\n",
+      "unicycle        -2.218467\n",
+      "vacuum          -1.907717\n",
+      "violin          -2.757079\n",
+      "volleyball      -2.723689\n",
+      "waffle iron     -2.418540\n",
+      "washer          -2.408994\n",
+      "water bottle    -2.174899\n",
+      "watercraft      -2.837425\n",
+      "whale           -3.120338\n",
+      "wine bottle     -2.772960\n",
+      "zebra           -2.742913\n",
+      "Name: 0, Length: 200, dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "with open('../data/ilsvrc12/det_synset_words.txt') as f:\n",
+    "    labels_df = pd.DataFrame([\n",
+    "        {\n",
+    "            'synset_id': l.strip().split(' ')[0],\n",
+    "            'name': ' '.join(l.strip().split(' ')[1:]).split(',')[0]\n",
+    "        }\n",
+    "        for l in f.readlines()\n",
+    "    ])\n",
+    "labels_df.sort('synset_id')\n",
+    "predictions_df = pd.DataFrame(np.vstack(df.prediction.values), columns=labels_df['name'])\n",
+    "print(predictions_df.iloc[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at the activations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "max_s = predictions_df.max(0)\n",
-      "max_s.sort(ascending=False)\n",
-      "print(max_s[:10])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x114f15f90>"
+      ]
+     },
+     "execution_count": 4,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "name\n",
-        "person          1.835771\n",
-        "bicycle         0.866110\n",
-        "unicycle        0.057080\n",
-        "motorcycle     -0.006122\n",
-        "banjo          -0.028209\n",
-        "turtle         -0.189831\n",
-        "electric fan   -0.206788\n",
-        "cart           -0.214235\n",
-        "lizard         -0.393519\n",
-        "helmet         -0.477942\n",
-        "dtype: float32\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254b50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The top detections are in fact a person and bicycle.\n",
-      "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
-     ]
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Find, print, and display the top detections: person and bicycle.\n",
-      "i = predictions_df['person'].argmax()\n",
-      "j = predictions_df['bicycle'].argmax()\n",
-      "\n",
-      "# Show top predictions for top detection.\n",
-      "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
-      "print('Top detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "print('')\n",
-      "\n",
-      "# Show top predictions for second-best detection.\n",
-      "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
-      "print('Second-best detection:')\n",
-      "print(f.order(ascending=False)[:5])\n",
-      "\n",
-      "# Show top detection in red, second-best top detection in blue.\n",
-      "im = plt.imread('images/fish-bike.jpg')\n",
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "\n",
-      "det = df.iloc[i]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
-      "\n",
-      "det = df.iloc[j]\n",
-      "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
-      "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
-     ],
-     "language": "python",
+     "data": {
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+       "fUvSpCS5rvt/JH1PUkXSHziO8zdc1+1Iel/S/5L09yT9viRHUsd13W94/nly8zX+vqR/LGlW0seO\n",
+       "4yR+2T/gb8p62Mxf8XJd939ImnQc5+/wMcdxnul6c7Eikg5u/vtvShq7+bw5SQ3XdX9f15v2Q8dx\n",
+       "kpLGXNf9Q12XEN9wXbcnadNxnL988/ecm+8hx3Eeua77M9d1/6mkhqTiV/jj/lrXw2b+1ay/JOl3\n",
+       "bqC5LyT9M0lVfVmC/K6kv+U4znNJy5II9/htSc8dx/lE0l+V9C8lFST9T8dxPpX07yX9o5vP/YGk\n",
+       "v33zNb6Q9BdvPv4vbhrFVUk/cV3386/yB/11rgduxsMamfVwMj+skVkPm/lhjcx62MwPa2TWw2Z+\n",
+       "WCOzHjbzwxqZ9bCZH9bIrIfN/LBGZj1s5oc1Muv/AuHZAPr9VeA9AAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114254ad0>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Top detection:\n",
-        "name\n",
-        "person             1.835771\n",
-        "swimming trunks   -1.150371\n",
-        "rubber eraser     -1.231106\n",
-        "turtle            -1.266037\n",
-        "plastic bag       -1.303265\n",
-        "dtype: float32\n",
-        "\n",
-        "Second-best detection:\n",
-        "name\n",
-        "bicycle     0.866110\n",
-        "unicycle   -0.359139\n",
-        "scorpion   -0.811621\n",
-        "lobster    -0.982891\n",
-        "lamp       -1.096808\n",
-        "dtype: float32\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "<matplotlib.patches.Rectangle at 0x118576a90>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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CKjRVWzPaXKeal4TgOZuNMVKh8oy93V3Qmv76CKUUa6M1nh8dMlpfp5cXjCdj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1y8chlzqcXWZgcjAykhSALe9oY38ofPPMdxWxDtdBmtbnH+sXv5zPZnqM+7nDtxD8P5Rf78S1/k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b9V2kEn/LVRlfCHm4HigqWxpN25ZTPfBB6aa2Shz9LGXRqAbLnFg+itBzKDyLY53z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fXVV/mVf/dLHB0d8a//1b9iZWWFH/6hH6Lb7dLr9Th//jz9bo9WENJtdxCVvm/nHnqExx57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\nwNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x116bcb210>"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.gray()\n",
+    "plt.matshow(predictions_df.values)\n",
+    "plt.xlabel('Classes')\n",
+    "plt.ylabel('Windows')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now let's take max across all windows and plot the top classes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "name\n",
+      "person          1.835771\n",
+      "bicycle         0.866110\n",
+      "unicycle        0.057080\n",
+      "motorcycle     -0.006122\n",
+      "banjo          -0.028209\n",
+      "turtle         -0.189831\n",
+      "electric fan   -0.206788\n",
+      "cart           -0.214235\n",
+      "lizard         -0.393519\n",
+      "helmet         -0.477942\n",
+      "dtype: float32\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "max_s = predictions_df.max(0)\n",
+    "max_s.sort(ascending=False)\n",
+    "print(max_s[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The top detections are in fact a person and bicycle.\n",
+    "Picking good localizations is a work in progress; we pick the top-scoring person and bicycle detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def nms_detections(dets, overlap=0.3):\n",
-      "    \"\"\"\n",
-      "    Non-maximum suppression: Greedily select high-scoring detections and\n",
-      "    skip detections that are significantly covered by a previously\n",
-      "    selected detection.\n",
-      "\n",
-      "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
-      "    who sped up Pedro Felzenszwalb's code.\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    dets: ndarray\n",
-      "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
-      "    overlap: float\n",
-      "        minimum overlap ratio (0.3 default)\n",
-      "\n",
-      "    Output\n",
-      "    ------\n",
-      "    dets: ndarray\n",
-      "        remaining after suppression.\n",
-      "    \"\"\"\n",
-      "    x1 = dets[:, 0]\n",
-      "    y1 = dets[:, 1]\n",
-      "    x2 = dets[:, 2]\n",
-      "    y2 = dets[:, 3]\n",
-      "    ind = np.argsort(dets[:, 4])\n",
-      "\n",
-      "    w = x2 - x1\n",
-      "    h = y2 - y1\n",
-      "    area = (w * h).astype(float)\n",
-      "\n",
-      "    pick = []\n",
-      "    while len(ind) > 0:\n",
-      "        i = ind[-1]\n",
-      "        pick.append(i)\n",
-      "        ind = ind[:-1]\n",
-      "\n",
-      "        xx1 = np.maximum(x1[i], x1[ind])\n",
-      "        yy1 = np.maximum(y1[i], y1[ind])\n",
-      "        xx2 = np.minimum(x2[i], x2[ind])\n",
-      "        yy2 = np.minimum(y2[i], y2[ind])\n",
-      "\n",
-      "        w = np.maximum(0., xx2 - xx1)\n",
-      "        h = np.maximum(0., yy2 - yy1)\n",
-      "\n",
-      "        wh = w * h\n",
-      "        o = wh / (area[i] + area[ind] - wh)\n",
-      "\n",
-      "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top detection:\n",
+      "name\n",
+      "person             1.835771\n",
+      "swimming trunks   -1.150371\n",
+      "rubber eraser     -1.231106\n",
+      "turtle            -1.266037\n",
+      "plastic bag       -1.303265\n",
+      "dtype: float32\n",
       "\n",
-      "    return dets[pick, :]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "scores = predictions_df['bicycle']\n",
-      "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
-      "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
-      "nms_dets = nms_detections(dets)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+      "Second-best detection:\n",
+      "name\n",
+      "bicycle     0.866110\n",
+      "unicycle   -0.359139\n",
+      "scorpion   -0.811621\n",
+      "lobster    -0.982891\n",
+      "lamp       -1.096808\n",
+      "dtype: float32\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.imshow(im)\n",
-      "currentAxis = plt.gca()\n",
-      "colors = ['r', 'b', 'y']\n",
-      "for c, det in zip(colors, nms_dets[:3]):\n",
-      "    currentAxis.add_patch(\n",
-      "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
-      "        fill=False, edgecolor=c, linewidth=5)\n",
-      "    )\n",
-      "print 'scores:', nms_dets[:3, 4]"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.patches.Rectangle at 0x118576a90>"
+      ]
+     },
+     "execution_count": 6,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SWO+oFhVKacp+iXUpTAeE1IwnM2bVgsYYZrOKpmlojGPQLwnWdUpJCJU8FYlQGm89WkaY\nR2uNt5a68R1soJRCqITpKjDOdh4ynXKgU8ptWKqU6uAK71z0V5OHZb1F+NYzjwrRtUZBRG+K5NcG\nmcJ9SIuWmLniXM4MAeRZVKrOOUwTDbJUOayE6xAXe1VVKaLLY7JNy3TPy+uAgHUO4SM23X43Lm6L\nCy7i8AlvJbTjmxG8oVdmzGZj/uT73+Wd937EYjan3y+xTUMIjjzTaCVRCqo6OhbT8Zgf/eD7jNbW\nyMsc5yI8dHDwFOctvXLIK6/mFEWfvNBYH6jrGp1lCQMPOBtxb5ESm7TzJNmgqNxdxNQTHNLCWD7E\niMJrtUwCdjmApOyTtx4fAhAEOotJdQh45yM+joyQTMoZCiFQWXYOImnhE1hGQq2ClziWTz/FOcJH\nbwaxVOYIVCZTAjMg0vX6FrqRkpR9wTrbGfLWOHRJ1GSA22hEKpXgFd/dezte7f1oJUHTRQbCr8zV\ndDOqS5YLvBDJUD5fvjQFfv/9Dyl6PZqmYTQaolXGdDJHyjPKomR7cxOpNA8Pn0SvTQjyMmd9fS0d\nQaB1jhQRjpjPa7Y3hkxnsy5pFbxHZzpl8UPE3ISgzHNMXTMaDCjzHNs0DPb22NraAkApyXg8TmFT\nYLqYMZ3NqKoFRVEwunkLnWVdoubg2SGz+ZxBmSOUxixmFEXOy3df6LDf+fiM6dkJ8/mCkyxna2sL\n62H/0SOGoyGboztRATlDbQ2Z1jQ2VsNsrY04m5xR14LJ2QnOxmqcpjH4ENg/OMKHgA2B0fo6w36P\nPNcopaibhl7ZS1U0iul8wfHphP3DA1SmWRutEUIFIaQIyFMvqliJk6IJFywq+BhVJBenFAXBpaRj\n8lh9UDifcChilUpMwEav1KcJGvBY53DOI4Uk03nEXFOC2rtwPoUjYqJNpaqcLkHWHr/DZJeQRrfA\nV7L6sWKh+09U1FqS6xKtNVUTvezgo+fjUrKPELDGopToEkztAo0RQ5a8aUubkG6vyRjbXYsNKZkn\nRIR9lKSua/K8QGvF2ekx999/l2o2ITjP+HQR4T3nyDIdYUVgbW3IjevXyYsa21iaukJryc72JmfT\nMXW9YLGYMRmPqeuaO3fusr29h1YF5BqlJcZEOC9qpdbWWUjedUgVObGCJRrY9j4kJNjM4oLHqogH\nhxDAtRFQi9+Kc/k4oSXe+qTYZZwPKamtRLaETFjiwMv8BV0ks+rpCyFonIuxXMoTCRkdBeEFISnR\nVsFyrgolIHxyMkKsiIrPJqPsaUKzCtd4XMKp24jReUdjY1SV6UFKeEaD45OSjzpFdQUN1lqkkhEo\nSZH/KozmkhUTIiZGf5z8hXOh/Hkk1oH/5Z/3Sq7kSv7/J//dP/vn2LBMBvuQIBrVwhuC4KMDETwI\n5TssXaToXMtYKYSgKyVsK2iWSnl5fJdwcq2X0ZqxK2WF6RBt1BDzHynf4z2CWDXUetvRa/dLOMkv\nS2T/m//qPyf8JdaBX8mVXMmV/KVJpgVSlF1y2gV37v3gBUGBCPG30j7W0ac6f4i14bFi1mPaSqU8\nQzrdojC0ddxKZehMd4rWORuhJBert5RaltYCKccSOkMQ+07kitcd8N7SVkTpBEkBCcJ5vlwp8Cu5\nkiv5iRalFDqYiNakogYv6Lxhn5LsLdYcXCw9DmrZLNc2UhkHqCVUJHTqlbDLBKZDdknY2EwkUmlu\n0+HhwBL6iSB9PDfRk1eCzquHlGiHlOBe9h+IP4Pt5EqBX8mVXMlPtIiUs1it0xcsu2ZlCDghugSt\najtx20+KpTeslUolqW1lSMARG7fa47cdtW1FDoBEYBP271bQ4VUvOwS6nJG4oJdX+wCUih67Mebc\nPV0mVwr8Sq7kSn6iRYmAF21VSlhWrsQXEEAmJELLVLXEOYgDIKQqG6XOK0yJwKWW/q4WPP4FsFIl\nE5AyX6FMWF7Hsg8idF3aQi1pOFYlJlqX19Ye43nypSvw//V/+e+x1jGbz6gWNXVVM1/MaWtw33zr\nDQ4PD3j8+DGvv/46xhg+un+fP/zDP+K1V1/l3Xff51d+5R9x9+5d3n7/BwD0+316ecHe3t45CyZT\nXWZs5xapXEp0SfLYCSVSV1RKdAiBDgafkgsQ6RHaz1jju+4+7z2Vd0zmc5xz1E3NcDhCCJjPFzjn\nePDgY27cuMFwMGQ8noCSVPMFIXhGgwGZVDz4+AF7u7vkWvPBB++jlGJttMbbP/oR6+vr9IqS8WTM\nxvoGxliEVJycnvGzP/+3eXZwyMcff8CN7RGzyZgyyyJOB7z61a/x4cPH3HvtTf7wu98jz/o457l3\n5xZnDx/zwrXrbN+8gRv2yAYjzLRGVg1FphjXYz47eMTdu9fJnMLZgNcZ0xA7WaUN9BFI7/GZZJEa\nSAQB7yzO1rFxR8RGliCiJySz2LJvjKdQhsrDOGySDW7ip2N0dUjwNddu3aQUksXTJzDqU+QZtm7w\nzuEFuOBwxOfXL3oUShOs41k16XDFWIsbS98aYxBSYV2gLPss6or7H/8Q53cJ+TbGV2TCc22Uk7uH\n/PIvfpOjI8PJuMe08fRyhRSCpomlZlrHZo+6rkFEHg2V6teDEF2ZWqYz8J6mbgA4m0741V/9VRAC\n6yz9PEMBxlqMc5GuINOJdiAqh5anpyh6HJ+c8sq9uwRnyZSkms944/U32N7ZwbrAxw8+xVjH3/y5\nv82/862fQUnND3/4Q4bDIRDrjpVWWNt2m0aagTzvAUul1ZZ9tq+1a2cJW8Q11uqj1Trvtmon+ECp\nlg1RQsiuEaj9Tvs7BMjk59XTP/mn/+nnXhPBR96S+FfnHbcQR+g6dT3CRQqB9hwQk5aC2LwUUj1/\n52EHE2v5ZaqJV4mSISzr24OPa98CISVNfQix8Y3leJyrqFm551UlLqTuxnl1zJ8nX7oCHw4GOOfo\n90qKosAZF2u+6xqlNcY7bt26xdbWFsPhEB8sZf46L730FTY2NnnzjTdwtmJ8dsTe3h77+/vM53OG\nvX5Xv6sTd4dM5YVCtN2J7UNRqYKtrZUGJfWySN+JaDmTpvcp2SFl5JvwzkeehxDwsm2JX5azWe9Y\nLBZYa8mLnMFggAc2d7YZn51hhwPef/99yqLAhIZrN6/hjGVWL9i5tsejh49YVBVfufdSbD4Bil5J\nALZ2d6mM4XQ+54OPP2Jnb4/eaMTO9VtIofjaG69xdnLCs6MDnj35jOODA/Zu3MA0DaYJuBD46MlD\n5GLOWrNAVTNG22s8Oz1mejLl+mgTrSVf/+o34N2MtV6gEDmmEYSyR7NYkOuMzAVK5/DVAhToPCOo\nIpakWYHTEIKjaWqCiCFo4y3B2Ggsix6Vd/jBGvefTmnmh5TeUpqGYa8kOMlGoXFrJU01RVaBTGly\nHVu3vQCkwkuwOhCEw+PIMtU1ZRhjopelNVmRJ+w0LspcaO6++DK13eDh/oTgHVmZcTYZs1nCtDG8\n88F9it4LiKLs6s+lbEvb0qKVbQ2ww7iGQEDJDIj8NEKpVD/tUUqzu7vDz//8z3H//oc0TU01HiNl\n6kXwLnYHepe6hUkVDLFeuZnNGAyHPN1/hrUNa4Me62tr3Lp9m+OTU45Pzrhz+wXeu3+fN954i2vX\nbzKdTNjY2EjOCl0pW0tApZRC6VjyVld158j0er3ULZvWT1v6lioFO4WePFMlYyIuOH9Occ2qRewz\n0BpkbFZBtBQDsWtTahXX6ecKL55TuSY/n+wTgGvLTaN3lgyOOEdE1v6OG4nFqpTQfkdE50NJiQwy\ndRZHiMM7v1TQMnLHyMRj0q79i8RUgQjRAAjv8Zz3tuPvZe34stPz+fKlK/DYTgxlkSGI3BwEiZYQ\nvCErithAohXOW3KlGA6HbG5uYhvDq6++xHw+J8syyEZsbGzEm07eSp7nSy6LsBJihaU34dyS9a/N\nIMdmvrbNdtnui4jt7LmSWBvQWbTWXeOJc12NKAisd2ReMer3Mc6xsb6O0ip5bTrWbGc5d+/coSgK\njo+OYmOJ85jG0DQNdRVrhV++9xL7+/t8eP8Dbt2+TVVVZL0SkWk2dza5fvsm167foNfvMTk+ApXz\ne9/+A376a2+yNlyjcpbgPdPxhK3NTaazBqEkO3u7/PDx97j58otsvXCDP33nPSbzmldfe4M6wP6z\nx0zslKdPHpLfGDI3grwYYZzn4OiI67fuMChL+s5BppB5DDtdEw2jVxnONbFNGUmWZyDbjH8syTqz\nnrK/ztjlOCeog8A1DmcAIaieTTFbPYZZwVqpWMznBO9ZGENjDTLPkhKX1N6hUARjwbnkacVFqnQe\n50p8BSUF1phEzLTJZObwziBEjQ+OIsvxcsDjZwtmjcRpQ5bB0ckMgURnGXlWYl3NbD5P1A0xkaUT\nIZfwHm891jqci4ybzjusd9Sm4Rvf+AZlWfL48WP85jbeO2ofIwofYm02Pnl6AYz3NM5R5AW3b9xg\nNOyzvb0FzjI+O+WH777LeDJlY32TZ8dHvPLq6+RlSV1bev1BciyiV4mI9dNlnmHb5hQpCDh0Hkmg\nrLGEYJGSlblNqtl3BFxaA0svWIhIZtZ6xKQ1QiYxzkbGTSWJZfZRa7YNRCLxyqikniK+Hf91qWjI\nxIX68LDsCSA5X0l/dx2Q7byI/3fpNZHmStQXWkT+IoKP0V5IkUNXFrjUEyoZVqFS2Uq6+rbzt60L\nDyEq8tVa92RjsM6i5LLT+M+SL12Bt9wXzjmUEmgtaJo6egJS0tjY/qx0HDSTrJqpGwKO2Xwau5aC\nwZklLaRMvyNxUuoUY6UdW15u2c7hYmlwLQJcy9TGsikhnadlUhOJA4HULi6VJNc5QkbvLMsUIRFK\neUCEwGB3r2urFkIw7PUhxMaYuqqRUnLj+nUCMByOGK6N2NjaYDQa8elnn6G1xnrPlhCMJ2eoPPKE\nKF1wcvqYLO/x8YPP2Nre5sWvvMTu7bv84N0PIEh+6utfI88KDo/3KfolR7Mxn372KX0pcR72Hz2k\n8oZgGz778B02y4IfPHmfQT7kb/3s3+FP73+MEYKDZ/tce/lVnn7wAUdPH3Lt1jXmzZwN1tne3qIs\nSvL+gMViRm9ji9HaCKljO/3J8Snj6ZRdpykHQ373++9hzwQUigzHq7dfZGt7j/c++YzpmWX31iZZ\nfciibnjy+Al3bt+KjRbWE5JHJ2WBqR3OOBSS/qDPxsZGIk+K8+rTTz9hPptDCJRlyfjkjP3jmtpq\nvFuQFQ2z2Rk7117CLQJP9iuycoe8P8LJGWWvj5SRYmBuIjwkFCyaOU3TkOVZJOkyhkGeE5wjy3Ok\n1Cyqito0OOcpix5SSoqiiAZZZbFa2Ec+Dyk0bRu59xFSQWksMFpfYzIdM56eUfRyemXBo/0nketG\nCgbra+w/PeAf/Ce/wqyqUZMpmxtr0ckgYsMSl4iVHFIuvVSwHVRA4vqIOT0RuzcTxBk6jzFGI0v4\nUXRJu6i8IrwltYg11EqiyCIj5Ar84qWA4MCDS4V7su1IfI4zOpnP0CJbwZRDbB5LjKJStd2QLd3C\nKpyR1rIgNexA13kJCQKLSl5IGRt9QkDpRC3ZWp0AwTkQkV7gXCMakZJLSIlWCXqDVJfuE1Feiv5J\nkFMae/9nKPEvXYG3EoLD+ZalbwV7E61lP88GByHyY7fzDVgNPVbvu2tpTviWWMEk2++1/24z18vj\nBJTKzj2Mc0o+TfguSPIOfKI+Xfm8SiQ+QoiuvThi8hFnVzIyIbZRmbWK4WCItTZGGy7Sge7s7vAK\nr+C946VXXmKxqJAJEvjR2+9S9Po0VcOj+iFvvPUms9OT2ESsFY0PrG9usL29xWxRMz45osxytooS\nXzUMi5w8BJR1bGxuUElBoxVfefFV3vmDCdP9Z9iiZnY05tOPPySTkmANp4fPeJhr3vnhn7AxKHnn\n3SMe7j/k737zFxh6E+lHVY/aGvzCM6si78fG9hb90RrFYEBfjTg8PuXDH7xLs36LbDMnD4a9wZAX\nbr7I977/AVNVs7nR46XNDdSs4q2/+XOUZcH+wTOmTcOsrtFln8OjU/rlAC0yjqcThhs77OzdZjKZ\nIITghRdfYHf3OmcnR9z/4APGJ8coIbi+u8NsZtDlgKDH9Hs36We7SL/NYnGKkhl5NsApQzWPc1Np\nSVAB42ojtV96AAAgAElEQVSyQpMphWwytFJkKifLJNIDqMh1Uyhq05BlGUpHKGwymfCbv/1bAEzP\n5qg8klAJAbmUSGNZL3o0i4p8OGQuAtnaiP39J6z1+oTgefDRR2ztbMZGFAG9QT92SQbLW199i2cH\nJ9RWcHx8TAiR7jVgGY1GOGepjUElQjNrHUKCDwZBwnzTvPTOJbbEONe1FBEqCgFBlhgxdeI48ZHh\ncUkhSNAS4RKMIJKhEi23SNuBG7tBrbdApNGwjUM8R4EHQYKY/BJ2CG0NdoqcO0V9Xo/ItO6iB7zk\n/m6/0jlpSR91kZBfNg45T8eQ2UUaK2t/NVkZcf/ILyMS50ykxY5ObJFFAi1oecz/imPgQZA42JeE\nMElrJ4+2zQAT/duVbHNsm22VqV8Js1pXeQlthNCmKtJ5L7zfXc8FxR5f8915pRAxxGNpYVc/LwUd\ndtcigvGKQnesFjp0yfLSHScS8MjEW+1D5CZemJqyLLsQK4TI6tbXPYajIVVdEYTitdde4eDgkN2t\nTUotyZXm4EmJFILeoEd/Y43BaMTP7u4gQjQazaziw/c+5Nr6Bq+/eJccie6PyPMep4s5qpfhzk4w\n0zN6hWJ44wb12YKDp0/YvnmHneEQKyTe1ty+dZ1Br+Tw7Ihv3ftZTKY5NHMKoZksGgKeUgpM0zCZ\nOY5sRdMYVJax1fPUwWNFYFadsR4Kylxw/93vcXw2jSyLvZxnsyly0GO96DE7OuXmKy+z99ouc2M4\nmk750/fu4xoQRU7Z69MXCp33mcwqqibimeOzCY8+e8Cr977CZPMIN5vSL3N8BmFRM+oP0eUIITIW\nsxnHx0c8ePABzmrKwYi8Z6ib2EE3GpWsb/SZzU8hNOQ6YzAYsrG+xaA/4OysZjGv6fcHkWu65X0R\nAqU1nz16yG/+xm9GaKRpGK6NMNagQgbOokzDRlbwxu0XKPKcDz77hPF0TO0rghdUxydkuWZ6Kjk6\neIrSisFgiHOGTz/5hLLocXx4hFSRt3tRNzS2iRTJxnJy+ozgPTIrUD5DKk2vV6Bz1XnPtjGARAmB\n8ZEOlrACOYqsS7CSug2tNUT4oCXFSvt9WNnVVMt2Ta02viQcOSpEnSDNRDX1HG80rVJiFNBSK9Cx\nD4b0v5j7EjjT+vYgnG3d7+VaTlE1gLVLAq24zFPiVop0/TISdCUKhqXubjmYAquOZYSV4nstZa/O\nMmR6L6IFgSAkwtON0fPkS1fgWZ4RA4eWbpMuoSikRCSa0RaiaBWp7wamfbhLwqOW96LDsFNCpT0u\nAOJyjoHWWz/n7fsARH7maJpFh19BnDqdFReksFCcU+zqglVuXw8rFtaHsEyIeY/DUZlY4VJPpyit\nojcTPNJF8qZqsUiKH8qiYG005PDwkL29LV66d4/jw2OaxkTyKmcRmeLOjWsREnIOJTW9tTVGm0N+\n6q03aUzD+uYmJ2djbiE4PTrk8Wcfk/c0P/31nyZoxcP7D2gWNXdu7XFc12xdv8ZnDz4hzyNz38bO\nFru3bzKbNtQ2ElqdjmcIAnmWE7ynsA6TNtsoioLxfMHx4SmuCLzx1Xtk/YKjD9/h3u27nB49w9nA\n7TsvsrWracaPOTg6QgCD/X3WNjegKPj+j97m48fP6PXXmdsx0+k+jw8ecu/eV+j3+xQ6kpJV0wl7\nWxuYesHLd18kcxVvvfE6//uv/Qt6eoud3hYil3zy4Albm3c4tRX1/Bil15mfSh5++hQnFXt7m2SZ\ng7MpZ2f7aOWpqwXWeL7x9b/BL/3iL5Kpkv2nxzw7OKSqKqaLislsTp7lLOqaf/Ptf8vZdAohIFXG\nop5Hz14KhG/YG4342p0XceMZ28M+w1deQn72EQ9Ojxj2RrjaUWpNr19inWVna5tPPv2Mre1dmsqQ\ny4wPP3iPu6+8iUOQ5RrvXWSx1JChQUBRlsxrS10vcMHh56ZrQ7dNZL8s87KdwCuOSMz7LOd1olEN\noSMoC6El9Yq82DIGznEHHyGpqnpJ2iQSdwigRUDiMcagxSU79iQRrk0EthzaRIcwcY+s7k6kpcb5\naJDaNRidOJ/YTJfeeoy+1bn12v4dISXVKXIhBMK1lMhLQipW6tFXd2VSSnbXK0Lc10CEtjv08+WF\nz5MvXYHH8pykYH3yuoXsLHcLXsQssewUb8tBBzEDvJq9lVIiV+5/NWxq8a94rPOe9upnVz13JRLp\nfwvjJNgmhOVnSMxkXshEnhPOKef2IbaGRLZbi8mVkiwfcdwYKQgckJfFyvWFmEzxsfW2aSxaqUSi\npADP1tYmw0GPIpMcHR/EhadLVC+n8S4ysalAcBalAgjH7ou3uHPzGqIxBA1zFVi7vYubVextrXF9\ne8SL925Q2ZprG7uI2rG5ts763i57RYbIc3bW1pDGM68WHM8m9DbXuLZVxMlKTMh6axChNToqcnWr\nDIVm5g3D9SFvvfU6JpfU1Sk3twds9zLWB7tMqDh68oyiGLJ/dsz+2TGjXp/HRwccNwtCliMGfSgK\nTpqa2WzCbDpHIXh2cMhsOmE0HDKfTNldH/G3vvUNbmzf4+DJI/74D7/D0f5jZmcP+dmf+RqnpzWP\nHz7i4YNHbHxtD+wcSUUv38DUmlADRaDXK8Fb6kXFoKe5eXOb3e0tjg5OCK7i8OkjBv11tra2WVtb\nZ+/GDZ7sP+NHb7/NBx99zPvvf8DTp09jkt1H3um485Sglyu2d9a5NVxDmIY7O9vsP91n8/Z1CuEZ\nFArhHKXSrPX65GVOIJBLxeZoxOT0hF5vyGI+41//2r/in/zTVzBOYKo5ZZljqimVqSHETU+sNZAq\nTpqmotVibdIy+LgtISlobOuaAwJdxDlqrU0VM23XYiK9cpYslYviUoCNIDgX6XGlIJdFjEqdTQpO\nobWMHPZCpF2sLlfgzaIh7+UdzGGtJcilIm132EFE+tm4BWDAOZ8iYd9BmqKFc3yM2H2Cc7oSStdW\n4qStEx2d0lZpg5OLUX2bg4Ol89YmRmO0sXQC42f8EsJ5HvCf5EtX4LiYARZSdliZEKB16wUX0Zte\n8YpXYY5lGc7So45lVuc93uV4RgXcJk8vlulIuaIs2zKxVK8a0sUJIvwRJ8d5z16uPkCxfF0lMvqQ\nWNvcSga7fUZSKPDp4bZQkVsy73V3kDwMrfNoyd2S0tVbQ1kUcQyF7rwFCRRCxI0KJotunKR0CHPG\nvK17b+JY+8ZEknslGeztMbp+HQhopdm9c4+mrrvxiK5wwvCaAZvsUPZ6MWvftjO3WfUUYnUtyKkK\nwHqHlJqXX5gxr2u8g8W8Yjqb0tMzXnFjtq/toFSD6fVY395mMZ9zOp3S9x4BbOU5w1u7zBZz1l++\ngdQaUWmCFDx6/AgvBceZ5Obt2wDMa8+nj4+Y2YIPPz2lsRs4NcSIMUenDTYUPH32jJPTCtgkyIKZ\nndAQEHqB9QvkIkDdMDmbsGt2OPpswunpHBcWnN6Z07u1zWQW+PCTx/z8xqts7W3Bg0PC8JD7+5/g\negFTGfpqiDIlLhPYsGAgFC8MelwvFLI2nEyPyHfXMMOC4d427sMjyn4f7TOub25wc2MNguFofMLm\nzS1++NEDzhZjgixoDg753tt/wk9/8+sc7D9mPjukKDTWg9M5jYJqcsaoV5JnUNsKowUY6PmcPCuY\nNwsqM6Ec9mjqhlxmCBM5vp2KVV513dDvDTB1g3eBqlqglKLo9Qgh7hnptEc6iUajpEobiwS8qBNP\nOAgnwTqahNVrrdGZJi+yS1VIf3NA04xRIToDucgJElCOys/xElTQ1JVHk2NDE5OTwZNwCrRMRFcI\nZErAqiDIdA5S4XzcNxeR9ht1BuFT5J9yYE1PpGRn3L7N2Qip+CZ68lJFvmMfAqUVUYGLgPSSyN4Y\nd+whCEJMlJ2v+rlEvnQFvkqS3llMsdIG2yrulTDtYvJx9XsXP9N+blUuWsjL5Nx3xNIOrrbnnk+G\nnMeqLjvnZeeOcM/5z7YVKavjcFlN6DJkPf8Tj7EM+9ptwi56Aavnu3Dgrq7WJYL5VdFadxsztLuW\ntK+315tnGU7Kz52rPe5FA5x5H7dZ0xkbIfFZ+EDdGPIi73ZvMaahGpbcunEjeluNiR5XrPtECIFx\nlqqqmC3mmKnn9gt32Nx+Ax8CB8eHbGxuoqTik08/4vTsmI3NEYP+ABc0Tw8OqOYNWVbESgPhaZo6\nJh1FDOsFnrVshKwlg2GPk9MFkPHg00fUixn9ok9v0OPo4DHbO2u89+BT/uVv/A7Z5jqvv/V1nu2f\n8t0/+D7To5r5wZSt/ggzXlDmyyaOYa/H1miNApBInh0+YXE65sZoiLeW9eGI2byi3y/oFRqtIBMZ\n6/0+h7MpX33jNT58tM/h2Yxqfsrv/97/zdHhI67tbEeFqHNsUyFFfMb9Xg4hUDc2diwGj0TEbdRE\nLIncGJZUpkKFWFVhnUPpHO/j/rBKCRpTEfBIHXn8Q4jjJ1OTjLMG68A6lbZ9A+EgiNRinjZskQiQ\ncdcnIaMnbtOGHBdlUc2wtiIXcSOTCE0GbDA4bHSkhKDMC3JV4KxJu/YkrFuBbueji5scQ4zyrV/u\nuQsxD9c6Z6sJzhDiPrneL8sR4z6sOpJrudhE1q7pdkN2KSIlbYsgCNFuNh3hl7/yScy226hVBm15\nT6vYuxLAFQjiojJb7Vy6rKJkVSG277XNPatNBrAMGT/v4S/loqH4cdey5Kp252EUcb7W82LmevVz\nSzjn89fRvraaFV8lkl895mon3apBWWKV58+7OjYXr6/F81pu7otj0TTNOe/hotH9nMFJYXVH/UZs\nkop825KyGFJVFXmuWRvGxKw1cVf0LMvIdUZjDMaaWO7lo2clTaxyqJqaLM/RPRiPJ8yNQSFYW+8R\nfMV8dsb1W9sY4zgdn3Fydszh4TOqakaWazIZUHj2djbY3tqIi9wHzMxSzRrq+ZxBr8B66K8PKIcl\n+5NnPPnOUz46mDKpTugP+3z3j/+E3/2d/4eMdV6++zVC5ZkcPWNt2GMxO0OrjDzTbPYHlFlOqGuM\ncQxGa5RZTr8o2RquMx5PUT3Bte0Ra4MSRUAL6Oc5a77k5OSEl27d4Otf3WJtY4e8KFjf2qTXGzCZ\nzvE+dhxr58AbzMJi0ei8D9ajZWxAsiHukuOcw0wqMq1jN6OKTVLWGZQQLBZzhJBkWeQEsdZ00ZkP\nFhnatRmDS+8M3ll0gv66vgwcTmh80ARnui5NgEwVXCbO1mS5JlhP01RIoVFKYEKNk3HfzuAacukw\nvgER9+qUEnTizfchRnEIEEqgpUShqFP5pJSRp9v5uEmMlDolO2MeLoRAFgRCRkZC72J5oHQubp6c\ncHoh465EKtPJc291Xcq9dU1JETVY5b6/TL50Bd56h3BeMbZyuXfpO4X4vPdDCN32RKuKpFU07Ya8\nq+dYPedlnu9FpbhqGFoFeJmiunjui8ry4rEvU76r17N6/It8C6sJ2D9PlLFMyFws0zx/LasRhhCC\nPM/PXd/F8Wif0cXPrB7r3Ni0O4ojuz0vs1xRyCxtpqBQg9je7VOzg+iVcTu14HHGoTNFXmQdn3Os\n3rMorVF55MG4Ptjj2rVdlFLYxlDNF9SLBdWiAh3IVM5gMGI6nbOzs8X6xpDJZMxsPmdne4OXXn6F\n6XTBJ08+wzaeejLntZdfZmO0xh9/948xjUGX67z81bco1no8OXjKrXub3PrKy3z797/Nxw8Omc0d\nh0cnbGyUrO/eIWSSZ/ufMtoscWcz9obb9LMc2xgUYAm88vprPHx2yGJRsZjMKIWi6JdsDgsyLMJB\nJgsyXeAyj3EB7R1nT5/wD//Dv8/9jz6KdeqzOaYBlIKgEd6graUschqXEdQAZ2qUc/jgWTQGEwKF\nUhRK4hZxi7eqNnjlycsewURj3W6pJ5VMVH5po2IRG9q89bFsFpn4ueOzFCHg01YhaW8eBBYJaTOX\niGEvqtml8zhTntl8gZYZUkSPt64tXjiyIkYCzjmCa3C2IS8LPD72XxgLFiAgExwihOi2DPQidupm\nWRYrWFzbZp/0R5ds8+RSp7kfcGlbQ5K3Hgmq0lrHI0XaDco2Xb5gWdfWOlbqc5H9RfnSFfhFqADO\nL/ZVj7j1/JYcC5/3LC8ec1VBrb63qmxWkwyr20aterjtsVb/Xv1eey2XedIARVF037/Mm36edJUu\nK0py9T6eZ1zaa7zseBcV7sWqm1XD0B6nfX9V2V8cn1UjApdHM8+7LxNbLmPhaNrhO4RAcOc3mPXO\nETR4J85RI2gtUalpyqVuQk9A5bGd34sY1vt0fSEEyBTr6yOyrU1msxmyyDCNYWtrl1defoWqWpDp\nWCI5r+ZIoSiLAcenY154eZvxZMb8rCIXPT598IQ3v/kzTOcNZ/MZv/nt71MMS7wS7F7bZH//YyZn\nljzfQgjNxtoG0/ExAkEvG/Dinbs8O3jEje1tNvtDZIB5tUBkGllkHJyeIYucRdMwny7o5QV5Aev9\nEu0DmVBIHzHbrV5Ovz9kbg0//fWvYecT1socXfQZ74/5+OEBjfV85fYeL9y9TjU7Rumcjx4ec7Ro\nkM5xbSig1yPrj5Ba44zBTaf0pEZ5RXAeIwXz2Yw1rWPEpRU6yzDO0jQmbpq9kouSou2GBtRy71AX\nTCwuEHQ/QkRW1+A81rtoCJ6jyxbzKbrMCc7jfB3r9QuFcZGCIO4qFD3fPJOx2xto93JtczKxgCBF\nnT4ST3kpYrQS4sbnzqbfqeM6QkdRjcaSQ5m6U1PbfNrpR8h2vcQkpc40UomU6lrurdlYgw9xV6fI\nsXT5PbfypStwoPOIV3HV9oYuYkA/TnFdVGKX/b3qUf44xbIqq7DEquK5CIGsKvnnKdbnGaqL57vs\n71Xl2MplhO+twrsMcrk4XpdFCZflEC564hfvY9UAXoyQnnfuc/9u3w9dNI0UkqaysR447VjSnoMQ\nIokWoJRGBGhsot9MSkNqhXdxzPK8IC9yGmM6jHHQH2DqGiUk6+vreB1hm2Ze0+/32QzrGDtnPvGU\nvTW8gzzvMegPKPoNQUgMGYY+L33Lczwx/E///F8wm1UEE2g+PQXveYf3UVpRzS3rwwnj0xnf+ua3\nCAONNRWSNU5OnqHXAuu5oMw11jnmtsE5FblbZIXxnnll2NnZoarnKGkJjQGRQYJEMq3Js4y1Xo7L\nBT/12ptMzILtjXVqp3FuwrXrd5BZwZ2bO+zu9agnmkdP9nl2eMIsjMiFZPcrtyk213l4csLR6Zgb\nW5tsjbaYHTyjV/bxucWLQJHn3N3ZZTKZcDo+w1hDCJH2QukMQqwsgUjKhfeEIAkqVqUIEciFQiQP\n3AlBEDpuvGxr8jxWlzgfW9kvk7IsaVIfSBAe62q89fhA3KPVK4SPe1xmOlVGKYVHdEnFiNXb2CkS\nlrxJNsSNiLuCgNSwI6TEB0/jDMInGLG23Q72AM7LblNvIeWyQccBQial7tO2ey0aAVIt+XWW9ASX\ny5euwFsvTWv9OajjMvz1IpTQvrbqAa6+f5nSeB4OC0uv9Hmy+p2Ln71IXrMaWawma1ePdZmX/Dwo\n47LXVw3c55Kjl0Axl53nz1L0f9a1dR4ty+fT4urt8S8+l89dQ0cfsSzZJHjy1CcQUfFo3L1cblnV\nXY0P3V9t05T3FuFU1zBhzII2qg0hYCob27RFXLBORM+/1+8BAek9eTFga2OUPCKfdguX4GpcgJCV\n3P/sKb/17T/i7fsPmFeWQTlgMj1GWU8uFLrqo5VkJDyL4yNGWvPd3/8ttnd2kVqxtbvN1HjWdm4z\n2/8Y5RsGhaaXZzglmJsm3a/EBkewnqIsyIVGBkGRF2RB01M9CJLttU2GGxts3trl9OiMWnmCUkwN\nvP/hR0xsTm0djz/tM/o732SQZbz99vs8PVwQeoJmvuB+4Vi7fp0z2zCeznnt3it866tv8Ue/93so\nKTl88oh3P/0UDzwaDtnb22P32h5BCA6PT/jow/usra1FQ7i+gWkaGuPplzmNSWWvUiGFRzoPwVHV\nNSLLccJS9PpYV1E3dfJEPcUKbLcqSmuktQgRkFlGlmWRisJahNYEF9jb2mK9P2R8fIydz1BKUDeR\niTHypgQylRgr7ZLPSHgfu0tF5LdZ2AVZ6mVwLu4r2u4fqoqsq5ZrN4NGgnWeMisTJ1PEuat6luZr\nwKXdeKhTkZaIG17/RCQxW3meN3oRL11V0qvvX0xUXualryqfNjmwahDapOll8uPw4edd/6rn3rXz\nr0A6sd34817F8zzXyxTuxeu9zHBdvOeL710W5Tzveaxez0WYafUzz5PLqAqgVdjxX4GAWNmNuzte\n937M/kPka47XlMLvEFKFQfytsuzzEZY4H9W44PFC0FQ1pDKy6IFBCJaz8YKyLMmyAmQeSfxHt/Eu\n8Nu/+dt85zvf4/jkjOZkgqsbni5irXXVVOzefZFr/Rf47MEnVGZMPiqY1Gds3FrjbH7G7dv3OFtU\nDLf3WCwqfJYRVKAhoEKkvTXGoRAEVDJkjhBkLLUNkrox9Ab9OBpBsrOzR280IDgwxjPzFUZAPtrg\nb3zzG4hinXndsDaIxtH6wLxuIqQRPC995QXeePMeP3jvfR4dHLAwNd+ZVYyfPGZzMAClePTwCbku\nCSIwnSx49dVtTk8mvPPeuxyfjrlx4wa3brzAydEJb//wPRbzBWWRc3z8jKzs8+/+wi/wwYcfcXS4\nzzDLuHvnJtf39pjVFQ8PDljbFGyMepydndHvD6jmC8re8NI5ZYOOWDaRY2U2n2CtRxcl89mCemEY\nlgNeuHGTF2/f+n+Ze+9gy5L7vu/TfdI9N7wcZt7k3dnZiN0FdhcZBIlMAQQl/UGVZNliWSrSIiVL\ncrmKtKtMy7IlymJZJVGUTZkumiAlywwimECRAAmABJGXi81p0k56YV6++cT2H336vr7nnftmIJJe\n9tbsve/cc/p0+PW3f79f/wJ/+I2vA4paTZsIIgS97gDHdVGOdiB0Cs7XKegrTVOyPCPwPLIkLswb\n/QMnIc8jFdqCRRTcu3S0VZbja3WeciDLU52U3HEt9e+BGtj1PaKCDjXT8ef8ENOUsk51krgPJaeY\nSWI5B0cCVQDjT9jNJ3Gtk9pSBofxw77RL2PXx60zxt9vuMMqcCz/bb+vvGlVAbDnmek+DLBl3fzd\nqHdMe6pUMGDb1DPS5dmS0kFdijwXo+/lw5xRHcI6LzH29IU6Rdv+a+coo28UAsgrNnDT19FoCBAQ\n4CKQoGShq81AOIReAyEcBnGGEBm7ez2++s1LrN1Y5cWv/zHZTht3kPDkiVO0WnWGKmIgU4ZuhtcK\nqM+e4G2PXuBLX/s9rm1cpjHts9e9Tc2pcfXqFe49cQ81JWkFDTpBg/Xbb7CyNAfSIY1iXM8jVwrX\nlTqaofSQjiKOY/xaiNTnkQgBvuNx5doljq2cRNZDgvkZvNzTCX49n2R/j/3tHaSULM+cYndri8Gg\ny+LSIqlqk5Ozs3GDF+kwP79Eoxmyt7vP3MwUge/x2d//HCrPqdUbzM4tsra2xtKxJZaWlviPv/M7\nxEmKIx3WVzdwhc/m+m1cKTm5tEKeZniuQ7s/4MrVa/SHA5TSG+elV1/n/JmzfOu551nd2uLMfZDH\nIXNzc0RRRC1sMYiqVSiDJGcqaNDt7pFlCYHvARlZppDCY3X1OnOtab761a+zsrxAoxGyvb0DQjKM\nEjy/RpbnKFHEegFyJ8cREkdmRFGC62jViOt55IX+W8uFbsFMCKTDCNCFI0Ho2PPavPBgTfuBjmVv\nHP10qCdtZpimOsqhMUF0nGqcMuVPBOBCiDeANpABiVLq7UKIOeAXgTPAG8D3KaX2jqhj9GmL4lVi\n/J3A1T7YLL4cAjnzaU7Nq0zryvUfcq1ncrD1SfXYKpfxA9FqbrV80DmJK65S4ZSvm5Jl1cBYtTEe\nBeBHbWD2ofJ4O8fbWK7fwcWE5wRZ2AVb75UClHaocmURL0Kp0TPauMGoUMZ6XWwearQ5jjaBUhuk\n0X06RSZzVYi0rkOcZNSaU/zYj/0TTp++l76c4qVvPctCUOfCY+fw+kOivX0ePXuWTGRcWr3OzqBN\nmg6ZaSratzd578OP8PmdW7R395luTZHHAplJdtY2eOqxt3L98mVOnlih3b1NmikdXEo45FGK9D08\nKZHKxXMkjtBOZkrkCEeAkyE9jyyPuXVjld3OPioMec9HP8rc8iKZVAhHcmJlRYfhHQwYRkP8mQZJ\nK8R1XU4cP0nUi1COgEBCLnBzwdKJ46ycXCFKh4R1j3pYp93usr+xwcm5eZrNOmtrtxBCkWUJAu3E\n0+t0GPb7LM4tMN2YZn9/j+Ggz87OLk6zQYqiHtaYCnxmfI+V5SU6e3vMzUyzevMG7omTzM8tcfrU\nCr3BkF6/X0mTb3nsSe5ZmGdrc431jRvc3rpN4Lj49SleeuUSm1t71J+Y4tzpk+xsbYDU9u21sI6Q\nKVEU4dcCarWQTqdNnmZIR+vW0yTVUo+E4TAiigqfgCJUcOB5KAVRFBF4vnYnLIwbzNmOH9RGJrdR\nFBWxlBTCKeLBCBM3hcL23SHLFEkaodSfrQ5cAd+plNqxrv0o8Dml1D8TQvxI8fePTqqgSr3x7RYz\nWGUnEbNQq1QpYwuZat16FddtA9hRoF/Vn/IGY29YVX2yP8v1TnrvJLWG/Vu5n7Yuv7wZHlXK/TcA\nXrbYqWpHuQ1SySKEQOHQIRgdNiphFOTmoFMwLmOZesc3H6VAChPkbNTqQo2VHB5TdD7DPC/sfLVR\nMP3+gObUDGku+YH/6of4oy9/g6/84m9y8thxTi/Nkud98hp85yc/RNYbEuDwFz74UbbWN7l17Qb9\nmwOUDOj3+jz4xEf4xsUXePbqZVS9STuO8IKA3/7653nXu97O7vY6K8dPMNjfwRUubhASq0ERpjin\nUQvQ7su5TvqAwvEl/ahPHPUJnRonTi3z0COPc/32DvVWAxxJmqeoOEbmKYFQNJo+WdNnqBTSDzh2\n7PZhAhkAACAASURBVBi+dAiEYLezz16W0PTqDNp9XOEy6HXY7ewyNd3EcRxO1peZqU8zHAxRjqDb\n63D23Bm2tnfodvuEtRq1WsBUs6WDqm3cZn9/j9xNmV+YRQhFPQxJ+z2CRo2pVpNkOOTc6ZOst9vM\nhjO0212ee+4FTp46zTCOWFxcrqSnr3/zW9QfewuNRo3F+UUuXr5IlGSELcWLL74MSnD16huE7jmi\naMgDFx4myxXdXo+wVieKE/b29ul0utTrIa2pFnmWaRt3VViTKMX8/Hxx6JkT1kLSNCWJU+Ik0Qep\nUV+buxZRHYXhAKSO2hjUQoQCz3FJ/BxwyPN0tP5c1yXJdKYoWVi/KPVnn5W+jFSfBN5ffP8U8EWO\nAHA4bGM89lkwqaL4z7pRiy5CX1W5GrXkqMO8SVx9mfMfB4JxULc51ipOuVwOq1YmSxZVFixHccTl\nOsy95QPWo/T7VderdPP2u8rjUNXmKhWQ3Z6xQ878ALi1ktqhmF40tFLQAeQ2UUtQKi9iKx/Es9Z6\nSO18YYDf/NMR6+w2aA4oCjqoxMFNA7zMw8UnFzm1mscwyXjptVf4yX/102xut/nuR97HvffcS9iq\nc2NjjYXjx+gvLLAvd3jL2fN85cXn8Xb3aWY5F07VacxMsdvr4ePxgNsg76d8qbdGNu3DIGI293n2\n+ZeRJDz14IPEvQGNsE7c22UY9xCBT6Kg5riQaSsfPwyLtGMecZ6xdO4cO+027/vod7Nw7DinpAuO\nC7LIAlQETVK5CXGgo0PmuSKv5WRphkTQcmbwoghyCOemEAKaeY3F5RlOn1gu7PJdwkBnhUryVKcM\nzDPOnVxkd2dXe2lKhySeYzgY0m63kYHDrfUhJ5dPM7+0yMVLl/A9l932Pl7g88zF18nrISvTU8zN\nzxexVXKE47JUXyTNqtfA1VeusPra08zNzXPu/L1Eucvmzj7DtV3IM6RQ7OzdZnt/iuvXrvLUQ29h\nSrls7/bZHQy5dGOVfpIwM7/IVCOlGWYszs9zbPkMTnOf5aUV1q6v029HJIOEmalpHn7wIU6eXCFT\nOX/8zLM89+xzSNXXMf9dVx90IlC5YH7+ONMzS2SpZPP2PrOzC8ggYxgNcBzB5u1VfWYy7KJ6baTK\ncD0JHiRxtfepKX8aHPjvCSEy4N8opX4GWFZKbRS/bwDV22ZRqmyw7WIDxVGAATooTWUjK7hmG6Cq\ndLnlusvXqrj5qt/M+4/KsDFJ+jiKmz6qmN38qI2o/J7y96MkgLHDRyFGOUEn9d+Ucl5F8ymEKGKL\nTZbC7D64spxrUIAz3jatHtO7vy2JlSUtfb+pN9BBkByJyHIyElSu4z3fWlvnp/7lv2bYz3nsgUc5\nefoU++19BsmALI4JazVu3Vrl1MlTrJw9izsc4mxvQ6fD7e422WCAX6+zENZ58Phb+M6G4rnP/jr7\nwwiVurhugJcLciEZRDHNVkvbpqcpzVYDJQWpEAwGAxwhCFyPJM0J6i7S8YiiiHvvf4CPPPAgvTim\nlybaA1KafJrag9CMgU6Y4lR7yxaskk5Fl44sqJJEx9kxDnJK6YQDgePTbNRHY3zqxApJkozNxXA4\nJIpiBj1t7+0HPo3Qp9fvs9du02w1yPKMjfV1er0es3OzLC8vsrx8rIiVHVMP6pW0UXMljqyTZpLN\njTbddsq1a5s6JC01Br0OG7e2mWnN0mousLm+wcb6Oi++8Dz9XCDCBo50GXR7LM4vMDU9zQMPPshs\nq4U330VkDjeyjFdee42Z+jT7u/sszM/j+R61sEa93mRp+TiDZJdOp0PYahX5anU00cz36CQxt25s\ncPbsfVy9eoMba9d52xNvI+oPWDh+FiFymnmC5wlQKRsbq0y1GrQ77YlrAv7kAP4epdSaEGIR+JwQ\n4lX7R6WUEgcBu0vlHwLwc7/wEo8/+jCPP/ZwpYeetLjuMkjah32a46rWF5XBQghxWF/OYf2ufd1+\nZ/mzDGjltsKBWV0ZwKoOAb+dMonjt61e7rQJTIq3YvehPNY2AJZVQUd9L28A5T5UjV/VnNj32ht/\nWY1TNS6TpKh8qF3SHaGQbgak5EIQ1pr80x//CZzM48PveS/kDr1Bh0G/y+0r6yzOL3Dl+Rc4ceYs\nG2+8wa+89Aqzgc+9y4vce+EcA+8ke902WZoRN2ZoNGf4K4/+Jd5Yv8nvfeNr9J2cnowRiYPnaa9D\n13URmSAIaihS7fhSxARxXFcHL0MwTDPCUOKHdaZnZxGOQy2s6QPQVGHsiJ3igPdgc8sLVVI2NhZw\nYANtxtjYNodheJh4hI5rnee5jldSWDoZUz5D70EQaLO7KUAIojhm6vw9CEenIFNoO/7z507pFG4C\n7RbvSG2dkysGRTyccvnkd3+AWi0gqIU8/8LLbKxuM+wNEAj6gz6tRot+Z8DG6ib33XsPi8eX6cYR\nF/p9UsejM4hIENze3GFz7Ra3b10n6u6ztbXJ2QenmZmaY3tjn8B1dJiD6Vm++c1v8OUvf4lTp09T\nq9W5cuUKM8dnmVpYpt3rcfv6Lfr9AWfOnGWoMtqdHVQItTmPxXyWrurhN0OE7/Dp//jbKKV4/3d8\nB889+y1EnrKxeovp6WkGg2q9vyl/IgBXSq0Vn5tCiE8Dbwc2hBDHlFLrQojjwO3qp/8hAH/z+3/1\nEJdnihDac+tOnJ0pd3I7rQKk8d+rzdzK7v7lOu4GdO1FY0DM2K9XtXESCFb1qepaWW1zVJk0rmVg\nLXtnlgGxav5M+4/qQ3mDLF8z77PbMQmEy/eamDdwOF5MuW1BOo2QMUoOyMWARCVkyiONY/7HH/tf\n+N1f/xxhHlL3AtZ33mBj7RZ112O4t0WrNYszHCKkw8MPP8Dl116j5wieuXaZpC558IELvOWhR3j5\n1Ve4vL7Oyl6dv/HO9/P0F36PYRiQ+D7z9Ra3Vm+xNlXjsfNn6Gz0SZJYx69WDsM0xfUChONr1QWK\nJI5wVIO/8w/+PoM4IUoTpOuCEjiOPiuQSO3WWFIxlmnMHovMGitThsPh6ADPPpA3nLfv+4RhOMYg\n2TST5zlxf0iWZ8RRrF3vk7g44NMSQ6vmUZtu4hUOPEmWEkXaXnsSCYmsR57FrK3e4o0rLzActlma\nrxcHix553uf4sWk6+2u0mveyubvDqxdfYzCMmZlfIghrnDp1lvXbtzl37z10ux0cR7K3PEOc30Yl\nMVNhSJ8eT3/ja7ztsbeytblJr98lSRMeevhRWq0WTbfJZ3/rc3z8E59gemWaer2uJSZHstZZY6rm\n88df+yIPP/wQZ88ss7p6hYuvX+Sd73gbYdhASo/pmTl8P+Dc+UdoNJoIKfnMp//DxLXznwzgQog6\n4CilOkKIBvAR4H8CfgP4G8D/Wnz+2lH12EGerLqBQm8rnUNAUtEWff0Oqgjz3b5m/yvfO0l9YNdb\nrsf+rVzKruU2h2LXbW8Yh/pY0beqUuVpejcbzaRnbJAuO02V23fUWJfvN/+MSqv8jPk0dGJvGpP6\nZb8/rYimaPfFbpeLhxA5uSN1ICzpkqsanjPDa6+8TOi2iHf26bZX6WVbNFyYrvsk/SFxb5+1W9eQ\njSbPv/YSjWaT2zu3adZ8ht0O6y9d4Y3nLvKdH/su1HAAt/ap73f5x9//d/mBn/3nxG5I3wsI61oV\nMxs4tNyc6WaLwbCLUwuIen2UI0mLlF5hvUatEdKYnWW30yGoNyDPdEhStPfjKIMVBxY7piilxmjS\nHqeyJ69SOo62WatGPw2MMSZJkoykv/J8SylxPR01sdkMSRKt3zXRLXNlg702LU2ShNiL0flHq89l\nAl8Sp7vMzHh88ANPkWWKTqdLt9dje3ub4aDPcNjH8+eYmXZRLpy59yxJnNLvR2xubvP53/0Mx06c\nYO3GFVKVce/5e6jVQ86dOE+/G7GXdjl3+hRnjp8iSWJ6vQ6d7j7LS0s6CXGWcfP1S5xdWuG1Z18k\niVNmZmbxPJed3S1m56eZnmny8LmzLE81cPJ9Vs6v8NiF03Q7faamZ8lyuPhih5mZFp32NrfeuPJn\n6sizDHy6mCQX+HdKqc8KIZ4GfkkI8TcpzAiPqsT2UJwEUFXqiTIYAqMgRuVSZWFSdXBXfpe90CeB\nXxnIDOGWB94GvjKHXAb+SklkwvhMmuA7WdWU6y//XrV5VT1f3nyr5suxzKqqxtEAiQ3IVaqlsgni\nJPVVuW9Vm1GVdOI4CuUqlMhJlCBRHtJp8tWvvIhSIYEbsrl7hbSzhROk1DwXmcY4ZMRJn9PHL9AX\nLutvXOFd99/HVL3OqWPH+Oq//3Xm77mXP/7Nz/L801/nHe9+kkdnV3jm5Rd58p3v4i889W5+88qL\nDLKI5vQU6zeuMRhGrKwskmd9/tsf/RGWVk5wY22Nq29c5+rFS9q6JerjhCGPPfkkMwuL7LX3kY6L\n4CBwks6EnmrLBuewpKu9AydbVZnxEkKMEoSPdOilWEBVND+yiy7uGxZqlrCmTetc1yWN49F6MOcb\nQupnW63W6PkgqI5GuLQ8R5bX2d/rIBxBHCc0ajXIUpJ6nXtOnyIIfFzXxQ9cEuXSmp4GBSuez/3n\n7yXPn6LdbePWfKI4Yre9z3QrZHluHjHn8eVrX+XSxWsszC9xzz33gDhGLdQhhtv7ezQbdZo1l2PL\nx5menqXTHTA9M8fW1jaLnUVcD5577hkuXnqVOI1w0oR+b0AQBNTqDfbbPVqtGRzP52Z3j3anx3ve\n8z6CIODnKntdzM23q3P90yhCFGmegS/87q+Ufxv725XeoQVdBsnRgpSHOW0j4h0FZFWgbd9bxWna\ndd4JRMrvKNdVBqoqjnHSJnI36pWyBFFubxWY2WqJ8gZbVlfcaXMrSzX/qf0w91VZ2EwqZfA2bans\nc+qQyYSg7jJIE7zaLF/6oxe48cYui60lrr/8PLvXX0WmbaYCncUm8F260YDm4iJzp04TLh3DrTUQ\nSrAyv8jVV1/jEXeKG6+9xiNPPMoL117jxuYNHj57nreu3MMffPbzZPMzfObV53m5s0VrapatW2uc\nXp7lkXtP8cB9Z/nE93ycmJxcOgjh4EsX33GI0oiEHCkFaabt3Z0isYfJFgX6HKmwtTk0XneiV/ue\nsurqqOer5kWhdKZ5pUCpkRetNiMrIMGirZwilDSicM6Ct7/3Y4fe8/U/+gye8EdOXY7jkaYpcRRr\nBhGlowGiY58nriJLUu1Cn2SFrbc2Ye0P+mTkOJ5Lb9Aj8ASeEyBwSWN9YD411eTcvedACLZ39njx\nxVfo9oak7oBOr8/M1Dyu66OUQz1s0ul1iaIBrakGjWZINBwQxtpRaGdvl909HUOmN4i4duMGrelp\nOt2uTvxQq/Orv/yrKLMjl8qb7olpOK9Juz9qnEhs0awqsFPZXM9wAebvsvhfJToeasIEwLGBpMrZ\np3z/JO7Gbqv9vjIA28/czcIpg5ztvDTJCalcR9VGZb//TrFjyvVW/W1fq2qDzb3neY7njWdmsVUr\nVaUcL8YETiuXVAxxHYf99gApa/zGr/0WjpzBzV3euHSJdNhjfmGanY1t+j1JrebRH0aEzQYLx5a4\nfOsaj58+RSZgY20DN4XNjU3+eO8yywvzvHjtdVbOnuT0w/fyjWee5qkPvJ+/9tjf41/+xL/g1Mw8\nnUCy2Ys4vnKSPO2zuHycj3zsu3F8D5FnKKGzl0dZqoNGCYWSaJWJMIfyAjgI/AVaMhVCGJ7prs5G\nqjbaqg3cnqfynJSfEwh0+sTiGVGkEyz+p/Fbe0SiFMLVQcr05gNiwvwO4pjU5CUT4OUKkHieOwoQ\nRWE2iu8z3azpiIF5jkpzslQHrMpVznTWIk5j0iylUdepCdMkx3FqZKleN2FYY39/hxwYDobMz83Q\namXE7HNy5RhSeqSpIooSWq0QoTIiR5AlOc1ak3rQoCECNjc3mZ5f4dQ9D9KPBrQ7XSKhbcjdRota\nPWRvf//IeXrTARzGrTvKXKnKjtZ7j4GcOqyqsDl1W1dXZXlhxEQYTw4x6d5JLv13S9A2IJYXxFEL\nyH7HUaqgKuA0/Sqbb9r1C3HYzNIeu6PUIVWlPG5V5Shdn71ZlqWIu+EGjQWQsbgobwCjDcpRpEnG\n4uwxnvnmizSoEQYNXnr9Vbp7ezR8RVD3mFtcYv3mLo7ULu5nzp5jp9vFRbK1cZv7HnqM22u3CRt1\nwqkptrdX6e9HLB07Rj+NOdFa4m/90A+zvrfL7c1VPv7X/yoyrPGf/YMfhtYsflij29njuedf5O/8\n8A/S6e1r7z4pdYyNXCFzQIpCq30YTMtezXcDutZAohljfQhqXS6eHdWi682rGZwRJz2aWx04zKTR\nG/l3SHEQnEwexLlRRTtQRdjtCRuP5/k4JAUjr8iy2DRP04SjzVeVvkCvnWj/Gim1dY4jka7AVSCd\nkIbQERQdR2dkyjJQSkeqNPb0cRKT5Sl5njI9VSeOU8i0Z2WSZITNBgM5xFE5swtz7Lfb+H6ISKHb\nHZDPNPBrTfb299jtrKEEDJMhcwtLOK5DphSe73Pi5Gl+efJMvfkAbttH2+BbFdDJ5rKMLs4UpRS+\n61eCozsSKw8TcBkUq6xQqjiP8vU7AZRdbCAsu69Xcbl2e8t/3wn47DIp9Gy5zeYQyu6PvRF+O32t\nuvcoDrzqWnnjLVuTVB2amWIkj3J/q1QEwnFpei0+/7tf4NXnLkLi0KjvUMt6uCEk8YAsqSFFE3fe\nYW/Q56Hz52l3BmRJSkvWyLsRrVrI4tw8wyylNj3FFgnJoE/naof16zfYvnmbpeUTXHjoIT6/8Ucc\nm5+h4Yd8/1/7z/l3v/1Zev0BQS1EKfjSl7/Mw489pPNXolAi0xldshyVC0zuVN3/g/gz43OvKKLF\nVKrQyiVXapToFw5oMS3F1cc4SFWcPZXpebRuhdkU1GhHUEVyYaW0XfmoDil18gMERx3lOVI7zGjG\nRAP/qHoh9AFooUASQuIkRVC5XAeoylXB/ee5zvOJJItTUJC7OZ4XkGUK1/VwXB3qN/A8pPQQClyn\nRpJkMIzxPI8oGuqEzIEOXSscl5o7TZYphoNE56aN20zVJTWvxTCKcDwP6c7R6fZotppI16PT6ejU\ndEeUNx3AbTvUsm4VDjjwqoOTcuyPqljbZTG8zJWXAeYoVYa5difucxKQ3O29R1nl2CobqAbwO4m/\nR90/SbVk3lk1JncC8qPAwpTyIab9bFllc9RBcVXfzMZVNjcsz70Skjeu3ODLf/BVziyeZnZmmsuX\nLtLrt5lfmMV1BYNBTM2v48/VWaifYJjGiCSl7gbEaU7aHRC3e9y8cRMZ+PSjIe39bfJej7pfJx4q\nBjd3WH9jjb/1j/4H3vOhD3FzY53XX3qVxx55lM9+5Rl2ox5imHHm3L3s7bfxvIBhMtCJtNHcqpTa\nAQlDj0XIXARkKrP6qdUsUkgccdhppzKcMeigXhX3ltVpRp89qZh16bquBkTDRNhryrEsZex6RW4i\naGtz4gnv0PFXQpIs17FIlBq7N5dFmIaiS0GR7ENISS6Uzu+Z5zp6IBJXCIQXIBEM8ggpnFEskyxL\nyVVCmuqEyCrNkWIISuI6ddIsA88jc8DxAzKlQxS3pqfJUpiRLmmmkEnnYNyKoFqDYcRic07nga15\nzPgzpBXSjV3edACH8cBOQui5NUl5ZTHxqcrJkoPMFSMRryAgRwoyEYwGRYiDe1SuRocgchRTg+K+\nMlnYqhmzwE21xt3brkMwXoXt1VlwGpjnlXWPrjfLxtMpmfukPGhHGXAmnQOUgW8SUI1aOgL1gz6a\ntuj36u/6U3M1smIVGc+9Q0WOe7getYkoVd4w1IgGDpaubkt58z26uFBkH9f0YMRgfSClQUmCkNRE\niy//zi9zfGqZWliHuiTP2zToISOJrE+xN0yYwef0/fezON3g4tNfo+VJVNQn7Q/pbm9x5emnqQ9j\n4r02c4FHJxPMhC3qjkdreZqkOyTe3+bf/Pg/4b/47/8bGkFAf2ONjWGfpePzzKaz3Lp5iUcef4S3\nPvEWsjzDx9GxMYo+6HEt5qvgXJUBLnEwxkqh084JRSaqzfAmSXqj68o4R4kDPbowduW67qpSxYGP\nzXDR9hGzxjitj77faYaFMzKTzJVWz0hLFaNJV4d6zZUiLtKgKXOsK8BRFJJERpYXK1cIvCLbjuNp\nBkPH5wmK9ZJrBBUCgSROEozGPo5TKPT5UuqQv0rpjVEIgYcOaeC4LqBwfYfpcAqloGHhx53OK950\nAB9XG5irBwt+XGzTP+Wq2PmkRBTP5UqSF3altrgthNaxoTQgZCYdkusipBiJnLbId0DQRk1hA7Ju\nn5EIzPtGLZ8AllVR+A7eeXhcMiuDteZO85EdbNk5ogqY7X/2u8pjj2UrbFdj5uMAxIsNlcPSEiP7\nhvFiw/pRsWBAp6MaX6oHG2T52iTn3qqN4cB0WKepMplfHM9BSgeUJE0zPNfjl/7tL7K5tsWplbN0\n+30uXX2VmcDFyQQq6iNqoXZjbzY4u3Scy6+8wM6tW+ROhp/HOtpj7ugATZ5PmqYErk8uHTr7Heqt\nFoHv8tgTjyH2E67v7fDFX/k13vXBD1Hb6+IEglbg0c6GrBxf5ud/4VN87yf/H7a2buN7PkqJIo6X\nIlcH6g2tILFGqawmOoKJq3LEGqkSx9ZCUc8EVZ55rymTrLTG5qjgGIS5tyT9jZ6Z3HzzNlxHe5se\nUh0ZLFAKJXWatBHVmvpzdZgmhebSpaV2Au2lqvXs9pmQjvMtOIjz73vuqA/GlNJeL2mOjjhZJERO\nkuRQEpS7KX8uANx0unK3GQEHo8GQUhY7lxbJVJZDlusAMJglrzktiUCKg7qVVAdxo61JK6dHs0X2\nsrODLcLbAGmuVRH1pAmp4qbLnLR2RjlssneU7r28GVWpRPT1ymZNbIsjDvdlUt9sfeadrFWkpABn\neyOtvrfqdZP6pxWfEi096dCd5PrgLYoifD8kiTJuXr/G5q0t5ldOsB8N8HJwewn9tMvsTEiaJMR7\nbd75+HupL53k5osvsP76Szi9rk6c7DnU/IA8h86wzdLyWXZu95ibn2JqeV7TbpyyvbrOpV7EW08/\nwIlwilR4bL78CnNuwKf/319hb3aG65097rvvNE8+8VaG0YBGIyRNCzWQcFBCx3mRQo5c3ieBZPla\n+b5JYRCq5kuV5rM8/vb6qUpoPUnNaX6zP7+dYoeMMN9tyzN7Q7DXxaQ1Y0sM5no5F2wVdlSpdu0+\njvuBaM9V87vv+4eeuxsQf9MB3Oi0D4BKjDl+SClH4G0G1uRcHA1moUOLRuEcPaSQOu2R0oHvXc8d\nuQHr7NKHPf9gHAjK74QDr7Esy6jVamOTDhN0itZkVwGqed4EhbL1/Qe/54ZhGSuTwNRwqXfa0fUY\nH140VeoXIQSo/BARTyQ0Ne6uXq6z3I6JIFxx790Wx5VoFC/mReivKoepVov9vQ7LS8f5rd/8HVqN\nGU7fd4E0V3z51z9DI0rwXYe9TptGLcRPMuL2HsH8Ap3ObZKkS5L2iVVOq9YgyxJ8xyPudfCUYtjp\nMBwMCMIai6fOMLy1xvrmGmoY85XVXfzmFCeWn+TJj34XshvxK7/8y5AkZPGQ1ZvX+dTP/wybt1fx\nfG2VoOVxUSipbYnwMNNRRXNVpeypWqYXW11lH26b56rCQZTpoVx/+XfzDlOPce67m5DG9nPla1VO\ngmZzKdPbgaSrDtHrSCIp9eGwerN6rdlmrqavWabGNh6bITT/7qb/bzqAm1JWSYwIJ1cjO9axf1IW\nzFVBQApc5+CkPc/TEUh7ng+Ig8hqgMptfXq1Q41pl/ksLwTjxWZbzlQB6qTd3uzctmkfaIJOkmSi\nl1yZ8EzRGgdDSOrQYqlayFpyrdJJH+bgASQHhHsnIJ1E9FXPZVla3FP+ZfLmcjdFb2TmH6B0clnX\nden3IqZbszz79HPs3N7lsYffwRDFzu4u8wvzyK0d8rxHEmX0sgHZIOPi668RDnrMLDQZxjPsZG3y\nPGOQpzTDBmmsEElG1O5Rkx797Q61xjSXXnyFJ0+f48l3v5MchTvI8MMGm7M1Xu5skbQ7vP8vfZKL\nG+u8/Lu/Ra2WE0cDPN8hTiJkEf9EqUKCMKrE/PC8lufOHrND81lB11WbvhBiLMRDlfXU3c5RFcBN\nWn/fbrGfsxlBu+4q9Y4dq8j0y1g82b4j5XEcp+tx5zdzb5lZFEJY2bEO2j0pPeJR5U0H8DKh2YAI\n4EhXuwMrk1uyiPEgpT6EEIIMLSC7KsM4LDlCjmI3SKkD4ShRCNJCFIluD95pE3aZcI3Lr53D0hZ5\nbCIog2zVbm6+G7WR/duo35aDkz1WVd/tTa0sxt2JqxUCJjh5jb2rvKHZ/ZpkwpdanE65vup2VIF8\n1b1HR520ix4HC8CLEkUJjbBFliie+eazLMwucm1zg6npaW6+cR2ZZcwvzpNFLk7ssLm9hZAuYdOn\n2ajhCWjWQ9ajBKEy3BwcJ0U5PrudNq0kpjYzT601xcyZJdxUsbm5h9jY5sJDF5jxGkTDBDXX4vz9\nDzDYa7P1lVfYWl8niYf8vb/7I/Q6ezi+g3Qc7XKeUdBtAQSFMXWZTsrqr/L5y1Hc+STJy9CyzR2b\nv20p2qaTO20sNt2XwfPbKTbI2uv3qHVX3nRstahpY1U8F5vuy/cb56kynoxivRSaA3Pdju9j1tGd\nYp+Uy5sO4GVAMAMHxSFmkUxUKcMxF2fFWabtYiVI1yNOUxCSPM/wfZ8kSnCVsWYpgh8VAG64XcHk\nE3jzfvOv7L1nBt9ub1UpH3RWAa+9+yulRhH0qriH8kKxo8LZIq59EGzy8ZXH2zznOJPDwZpysMBM\n/WaxGZ101aLTmXSMNYm2DDq8YY2LkOObX5XFifbmuztHovKBp1IKhIMjIUlSnv7aM2xvbfPIg4+y\nG7h0b++Q7u9T91w62ZDZqWnoCpZP1ulLaCweY2t7m2lPcmxhgc1wmt7eLp0oxvHquIHHwqlTH4X4\nVAAAIABJREFUnHzofkRrhtW9PdyFRZz5bfrbbTb391n7ytc4MbPIUw+/jdBpIndjFoNpHjpxL//h\nN3+Nj3z4gzz5xJO0u9uAZhYUAldIhHJ0QguhtDkh1WBcpjchDoeUMBKPuaSfN+B6+NDdpk1z3T54\ng8Pr+fB8HKaTSc/ciSbLpcz9VgEwHEQGtZ8re3ErpVWaJpSuKWYd2MBtNjAbew/GXzMLZmydIrem\naZ9db9VY3KnfbzqAw2Gu21yDg7jIucoQonBUEJDnurOvX7nK+QsP4IV1hr09wqCG43oIHLI4pl4L\niaNIm18JgWPEoSzTh5kwpnsvv7983S6GGzGfNgdRHnhDCJMI2OYKyty3mWzb9tsQkB0u1SasMhdh\nNkM7kt8BaB7mtsrzMVr8+TgXZX6r6pexJS6Piz0+BxvDeEzvqnI0qFePrSOcIsiZBishtC21EhIH\nybeefgaV5Ozv7NE6f4LN7U0W6iGpShnkOVudfQIlSF2Xex9/CyfOneX5P/omg51N9rp9GrMLRInC\nJcdvtJiZnyNyHQbDIY1Zl0Gc0hvGpK5DX+U4mSIj5+b+NvNrN3ni1CnmgibdqM/U8hIf+PCHePy7\n3kFq0nRlCVmWEdTqWlJSFBnTM6SOElIJXGXuU4/NuIhfHtuDMVQjMLJpqTyPNvNg00SVFFs1h6Zu\n2xfEnsc7mdCN5thxKttX7qsN4FXMYhUN2XXZ0UOr2jZ+ZlW9qd2p2GtpUsgLu7zpAD5pok1JUw3c\nJomolALpODpnHYKvf/Ob/O//18/yvu94Px/+wPtRSUamJKQ5Nb9GFCd6UGRBaMKYorljkdXMoFc5\nh5QBzQZau91VwFTFedt/mw3CJraqE+841oF5HOlgLGgMKIvCgUMD0+FgTVWLzW5D2UOzvBhGwAu4\nzgEHdpT0AiCdcWcju66q9lWNsV234RjL95QXo13yvLDdHQkmApSObPf6Sxfpd3usLJ9ib3uXy+tX\nYK/HvB/i+g6uW2cv2iPOFK25RVrzx+j0E1ZOn2XhHY+ztrrK8Qcv8OLT30LGKWm/g+e4iCxnuLXN\n4tIJgmFKLVVEvk/Hc/GlIPcVsSt4fecW09cuIa/MM/fAWcR9y5xuP8Ds7DxSKpIUamEdz/fo9nQy\nA6E7DiiEPEjYUJ4/ex4POOvDemZ7LA9+0+usPL72c1Xrwn5/GTTL7bKlzPJcV62Vo7jRKvC1N50y\nU2NbfxyWSMbxqMycmf5WeTVXMUP2OyZhgV3Kjo1/7gHccKY2yMDBZOvdFRDa8F0pvSDTPMcL63hB\njUcfexw3CPjZT/1bTpxY4WMf/DCzUy2yJLEmtxgQtHeXw4Hnn+M4I+60igu33bXhYGBNTI2qCSrv\n7jbnWxbBzKf9vjKAayeawnYZCwwZXyjmmgFJ0w4TyMpuj02Upt13Wpxlk6lJxAxGRzt+IFzmiA7+\nFqO8pko/BMJYkhdjIIwK5O4PuFSeg3kPWoIQ5ORZxhe+8EXmZ+fZ2dpmbmae3qXr+Ar2HJewHqJc\nj0bYoJcLls7dQzTUqa6aYUhDZaSu5NiZM1y+9Aa7N2/i5Tm99j5B4JMPerRch+OzU4SejwhrtH0J\n/T7dQZ99V9HPXL7+hV1wBBdaHnK6TubCseMr7O5vkgkdaCnLE3w/0Ic4QhWO5ZI8V+TqsPRRtY6q\n1Ff2Rlqe+zKw2nUZkLElwkmgVMXpGyalDMxlo4K7PccZ7+f45lXefEybKiXGCsag3G4bl8q0XAW2\nRzGFVcXO3FVl4XPo/iN//f+hJEkydlpcLlp3R7FotQ13lCbMzMzQHgxoNlsMkj3mFhaZX1jg6pUr\n/POf/Ek+8ZGP8s4nnsSVLipLQWkurEonWgYY/V5x6Lo9EVLKUUB6m/DKYpS9SVQBvH2fLRGYUl4c\nZqMxC8BOczUiXilwnPHA+2bDqeIKytKAAeXypqUbdHjBTeKOVJ4dWlD25mH3zXU8cs1e2jWMAc2o\nDlktalfRj9I+eCMvvVwpXOHy/AsvsHrzFvedvQ+n6dLdbzOfCYak4Ag6m1soJMzN462cwJuaQsWC\ntJ8xd/Y4O6tXeeFbzzLthJxcPs5g4zZOntLp7NGcWsaru0RqSDcd0FntEEU9tjZXCXf2yQPBMIQW\nTZr9jPiFi+T3nEaeWeZdb38Hzz77LCdPHdfZzsOATr+L5wXFWaz2KpVkIF2EPCwFVnG9qjinqALG\nMqBoCXV8HMv1lzdxQyM2yI/mwKI1Q1Ou6x6SuAydmn9Vc1xVjHVMFedunrV/t/Xd9mZXtYFVlUkb\nXPm8yx7nqo2wqkzadCb2/a7u+jMseXFAeTg4lf50RU6SK7IsRaQZwhW4vk+838XzHOZOL3Nte4NF\nxyNWirddeJDH7rufF196iYuXL/HdH/0oszNTuFIisgzPdVFZhkwTHWs8zw/iIDhuMXiF7XAJzG39\nl616sYuJZmieq+ImyvkxyxNW5bVp/jbvtrl/+58tLkrHwXEKLlzIMdWMAUezqE0fy9EYy0CvMlV4\ntjLKBF/0tuif3d7DXH6Zsx8Re1at11boOB+i0AKoPAc13i67veVxk0LTmJkD1wkYDHI+//tf5dix\nM3T3u8yEITs7azRqIcP+PkIlOG5Kmgv225ucPHeSXnuPYXvA6ZPLiHjA9uuXCfpDnvmDL/Lxj3+C\n7dkWWU+QuDAcpuwPN1HBa7TjhKg9JPA8nEyiHJ/Q9UjiAUoNiRBsb23Q39pFztap12fYuLnKsXMn\nkKmEOMULA1IBfgZu7hSxPbRllfHYtTe+w+Bq5vtgfMu638P0dng+yrGH7PvLtHonRsZmQspgZRgJ\nu85JUp75zZZeJwFgeZ2Ux8p+v93HSYBeBvAqXbfNgNgbWBWXX9WeP/cqFKNrS9Nxbtb8y+KE2NXi\neOC4IPVid2MQjsOJc6f53B9+kSBKCOshe+02meOwfOwYrdkZfuYXfp53v+tdfOzDH6K/38ZD4joO\nUmW4ElJl4rSJIp6EfpfKM+3lae2ITmHOZbgHY6cNk/Vw9nebAy2LocAY52E2CLOxZVlOmqRjdYGW\nYMxYmXbZC9uWONIsIU3TAqQVjmO4/YPxNu8ui8eakMToHbbOetyc0gJlddiGdhJBpmk69n4hbJv+\nA24sVzlSjUtKpg1VqgMvcEgGMfPzc+zv90jijI2NXbLMAxnQrAf0dm4TdXZIlCSs1SCP6OUx3Thm\nevk4ocq5dfkinW6fhg+b66ska7dZnpmiGw956YVnaDZrrG9v0d3rkiW7uJ5HLagzMzfN+nCA4/sE\njSZpCo2ZJtMupHGffjciDSVho0Hmh/huDTfJkdLBlx5kEcqDBB2F0M0dFIKssMgyslq532Ua1ON/\nWG1XxWXqKg5z8nY8eXutVpXDXP2B6mTS/WVp8yjpzi5VAfGOapttymeesdtpX7P7XN4Yy/XbNGn3\nuSqtX5mJK/fFPPvnXoVih3oti2BpmiIzSQq4jlPoMnOyPCPLBVkmmZ+bI41iBklEy51leWWaoF5n\nJcvoRUO+6zs/xMsvvciP//FP8Je/53u4cP48/V6XmuMSFZk4hCNJskSf8Ls6IplwHLC4CBuc4yIF\nlK3qMGaGBnDLE+z7PnD4hNyUqskcEycRI/23XezY1gfjOKp1BKiGGMIwtLggoyYZ54ryXGcYN+UA\nVBl5ixqwrEptV+5DuR67b+ZveyxtEdR+l/1bFYCb6/Zm2m53qddrXL9+i3rYIqg1eOZbX+SRtzzG\nxvVbiDTm5toqLgIPIIckzag3ppH1nBOnzrHT7dPf77E8P8/WjRtcv3yRRpaS5gluvcatmzeZm52l\n02lDnhEPhkgp6e7vsbC8gNOqIadq1NNZOmmMErC8vEB/dxeRuSSdLs995eu89fgnyNOMzRu3uP7M\ni5x/5AKDXOAlCuE55K6OrOcoiSpMP0Wpv+WxsT+rD3kPb6h6bg67optSPguqKmWp9SjVgKnfMAZl\nACzPebnYa8BsEuaz6n1VMUeqNkCb6TCgX5WsuVqCOfjNvM+WLMr/zL3lM7g7bV5vOoCXD9PK+RNd\nPLyiT0Kf2OAKD6EUjpT4SGanprm+vspDC6fZ2eshOwMGcUy92cBzAp564p0olfNLv/pp3vWOd/D+\n73gfcRIjAx+hFJAjpQ5Fmed6gwABUh8aZlmKFAfxWgzBmByBtm2srXO23Y3jOB47oLE53qpiE0We\n5+RZPgLmMpGVDyvNdXvh6X6lY1yQlA6OMx4r3TxjmyeOqYKycf293cbyYre5F3tOq9QqNriP+lzi\nesy1KIoOEb7ruofUSgBBGNJud5ibXyJNFH/0h19la3ObcGWKs+fOsn71Cs2pOYbtXbKoT5Sn5Cg6\n7R5Ti0t4fh03GjDTlAS5Yu3WDfw4xpECT0rCICDOM+0yH/jkmUKGNYLAJxoMtbouz0mjIa4jieNI\nJyAWCrKYwc4eapBxK0lZ//2Ad7/rvXzsQx/hH//P/4if+IX/g73bXRrCwUmh70EsFH6Wax8IFKjD\nm2d5Lqt+K9NZifoqrZXsgzWbYz4qi5VN8zBZFVJFE/bnUUBmgN98t9U05XVgt8GmLVsisf/ZDFkV\n01C+ZtPemORYklhtxqcsAdxJbWKXNx3Ay/qlcmekdMApdjyJzlKvBEIq4jzDSSXvfce7uPLsS+x3\nO+i4vC5T9YB6rU6qcnb3dojTiPe99/088+zT3NpY45Pf873UfB+Rp3hCO0nk8RCvkAgQLsJxQcgi\nCH1JF6zU6FDQcN9Zlo08Nm0O1XXdkW7Z5kQmiXD2Qjnw1kSnfcoPsgvZxfxtOOSy+AoHcVzMPQb8\nbXHREJmRMsptlGKcUy4Tvf3eNBtXoZhDX7uMVGUlr01Tl8012gfFZc6prH80i2uYDAnrDQb9iHiY\n8fWvPc3p0+fJs5wojlm7fZuw2SRwJWESsrO7Sz/OEbUGzfljbOx06fT6nF5ZQcZDRDQkUBkiF/S7\nHbpxHxl4qFxxcvkYvXyPVAe+BjK6e3vIOCNNlTarTHNUBuurt/nwRz/ItYuXIZMM6wHB0jw397c5\nc99buHDPfdRrdWr1OjJJ8aQkcRSp0IfUXmE66nCYhsbVJnd3eGYXfd9hicfzvDGuumx/bZeJlklH\ncODmPVXgfVTby3Ri00QVZ10lzZXHz/yz1R92X8vWO/a18uFl1fjYDErV73fivE150wG8yvjd/kxV\nRlYE6LXDRTqugySnhuTMykm++sUv8eFjx9lvtwl9nb1aJQnJcEjD86kHPsN8yOOPP876xjr/9H/7\nF/zFT36Sp972GP39bZw8pRkGJHGkQcI1mTz0ghGIQ5NWBq7yyboRvWydW3libJ2iWRDGTnWcE6U4\nVx2f8LITRFUbQROXIUZ7IdqHKnabTH3lRZpn+RgHU+U1Ouq/Gl+ctvhZFkHNpmcnX7A5FvtsoGph\nTBJPHd8hS3J8p8bFN64xMz3Hwtw8eQbXr1yh3+8hpGCq0cRNYdp3YRATzszhNKbYXl1jutVCqYyr\nly8i84QgcInilDzL2NttkwiF7/sszs5RbzToRAnRMMINA3rtDrXZRTY6beZWjjF17Dhud0jdcTh2\n4ixnTt+LK31eunmD+9/6BDfWVol9yVueegIR1BkMI2qtFmmni8gh92GIpJYLhIKsAtfKXOy3C+J6\n7A7fWwZlm07KxdDYQX1Hq0HK778b9YFdqizIJkkfVYeNVaZ+NqM1JoUWdFo+JyozFZPG2tC2zcHb\njIfdhzuVNx3As8xWn4zrjg04ucgil17RUaWIlcINfESqOD6/iKq5CBVDFpPGCgn4ns/CsWX2ux2k\nL/GCOTrDHksLC5x/8HE+81u/yd7uNh9837vxhWLQ72ovt1xnrXaKg00d7F2QkY+BQxAERS8sQE0z\nvdkUnLfNDRwG5epT7eFwONKnw+FYDza34VboxQ0HlWVZIWKDdr3WnnsmNIgjC2lBZZUmhoYjHyfQ\nHMf1xsTociyMESHnhz0nDdB6njc2JqMwwVaMjUkRI6u48izLSCy7/1FbcoVKBZ6EqxevcmLpOMNu\nj7m5OTr7O0iVMegP8YXCczIiIQnn5jj/yGP04pTZNMdTOfvtPeJ4gIpjPN/F83wGaVxsjDE1zyPJ\nM8jADwJc16Xd7bCznfPU299Je2OV4w89iKrV6Vy+RR6lPPf8y5w5cYKF6TkePn8/i815Fp88wdVr\nV7n/3U9x5dXX8AOPtd0dph0XmYASELsZtVRn2FHu4YM0W8K6E5hUqT90MpFxl/lyKasKyvfa1lj2\nPJVprGozNjRzp7aX22PuNcyUTXt2PVWMYlUb7N/KNFfFeBrd+6S22SWxchfYn3c6tCyXNx3AzUBU\n6YOF0ElQHYQ2BxMglEAiSIX20vSFQ5xneM06m7fXaTVbJFFEUKvT7/fY398nCGs0vSb7e/t0+12Q\ngsSp8YH3fyevv/YKP/VTP81f/6vfx7GlBRwBg16XoFZjGA012LheoYMe32GrCFcIoc3sGOc+kyQZ\n4yLt52yOw1a9TDrlNvcY8KzimO1nTP1Znh2qVzEe5tIG2ao5sRei+c0s1ipuxQZUu91mPMqgbXPr\n5nvZG87+e4zjL42D67okeUwYhsjc5crrlzl39j4G/T6dnS2kilmca6GGPoNOl66KiJGcOnWO+uwc\nne0dLjz0AKEjeO4rXyJXGX4YEMUJoS/JE4VbC3BiRXNmiihL6bZ7NL2AWhAw7Tp4QYAUkpWVk0RA\n5gfs9YfkueLqzVvst9s8+djj1FxJcvUqJ9/6MKnnstbZ5aHT97C9c5soCHVWnjQnT1ISAVma61Rg\njNOIGd8yXZYBzh7bchGiCFnBYYCxQdWmlTIATtLjlhmXsmqjCuTvVIwKblJfj3qfvcnZ9djctl2q\nNpwq0J0E5DZQV0klk75PKm86gJvFaB+OmCKEAFdCXpgbOgI3F0glyIqYFkEuyVyHhZMrrK+vs/To\nElEcs7W3SzSMaTSbZCg2t3cZRhGNZpNmM2R30Gdrd5/7z9+HKy/wr3/6/+SHf+hvU/Ncjh9forO3\ny/T0DPFwoF3IrR39qEHO8hyVHfaktEGmCqhtsDScZFmEM+NT5upNvZNEx4M2i8qY40bcLYNk2Vuu\n3B7Tf8NNV4GD3W/TDpuDMXWYzcgWS219vj1O5qC4TCv2oWkURXrxOTl5pPjKF77A7PQsLz/3AqdP\nneT1y6/gOQqnXmOuMUVrbpYb3R2WF45x/oGH2Or2afcHLC3O0964SRIPWVxaoLu7zzDOyAZDcFz6\n0YD6zAzzy8fYu72FFwSE9RYzU9N4nsf23i7rr1xm8YHzbO12CB2PIPBx0oyUlD4x17ZWWQlPszTX\nYG5uhpnePkQJm9dvUW812Om28Ro+ypE4QhJIiXQhSw9H8SuDThUI2LQ2CSQO0tkdFJu+7M+qOmwa\ntdtStY4mcb93w3mb+ybFKyqvh0njY9pkM1fmTKuqXfbGYNN8+d5J16qkgDs9X1XedAA3emNTbKCQ\nUpIqEEpL/SZ7dK5yhOviCYkYaDO4k/ec4cVf+n1Onz1LnGU0ZmaYDUKk4+I4LlmaM1vYFUtHcGKx\nwfLcLFtbO0RpytueeAe/+uuf4Ym3Pk6j1cILakTRkCQeUvNCFAfWHmWzR3vyHKljthggKZsfGc6y\nilMun5qXJ7zM6ZoihBgzpTIqnYNUaQW3LsdNNvM810lYGSdcA8p2/VXSkSkmznr5ur1YTBvLnL7p\nY3kB2pydabPpv9kwqsDL0M1IN+9miMTjd3/nd3jikbezMDNLb28XJ0sY9Nq4kU9/a5NW2CCrhSwd\nW6EWNth+4ybNVp14OOD61SvkSYTr+YT1JnGcMxzso9wM4TosrxxnmCbkjiSoBWzu7pImKcePHydX\nis7qOufuu5cocJmuz5AuzTPc3iYnY5D0uXTtNV545VkurF7mexcXaDk+uYBbN64wV28wPT9FnEt6\nxAgBXpQxNCqG/M7cXxnEbG61GiS1zXgV81FVf1k9IoQYnbfYqhC7DnOf7VNgz+PdtXO8DVVtq+LA\ny4yi3QebnsqcddU7qtQv5WJbo5TbVW7fnTbfcrkjgAshfhb4OHBbKfWW4toc8IvAGeAN4PuUUnvF\nb/8d8F8CGfBfK6U+e6d3GCcOOz62GUApHbxMkYqclOLwIIdUpDpBaAbCldxz4T4+c/3n6AwGhPUW\n0q/h1Ou4rs+tG2v0Ol2kEDTDOjNT00w3fYTnsPzgA+y0+yweO8Hyyim++KUvENYDzp85CWlEzZck\nqVY+2qqAPM9Htt1gHxyBKADEnhzzu0nUUD4AKjseGOI3ZotldQIccKm2+aUuCv01x45/EUXR2GLL\nMp0ntBb6h7h8O/KavTDt9xjCLUsGdvvL+QBtyx17szIWMlWius19Gw58Eidom1QKIYjTPqurq7Tq\nDTzHxQ1DLl28hpQpjkrxXZ84iui3E2I8pHS4dWuVsFZjcX6BrVtv0N7ZgSQmynNmZ+cJ69PsuQ5b\n7T2mZ2YQjo4XPlVvUm9Ocf3iVXrdHo2pKRCSIJRkIsMJfBJStve36N1ew5GKPB/gpSlODi/94R9w\n+fItfvCH/j7LC/O0zl/gc5/+DT7+F7+HPTdlV0XUFTjDhK7McDwXN1OVwGJvkGXJ7G7EdCHGmalx\nGq8GnTJDYn+a32z9vPktCILKM56jDkntYsfpr+5LdRyTcvtsZsGsqSodunmufK5lz8OksZ60GZrP\ncrjeO5W74cD/b+BfAT9vXftR4HNKqX8mhPiR4u8fFUI8BPwV4CHgBPB7QogLygQXnlDKh3V2yfOM\nvLAAqTkurqsgR3MIQO7phKLHgyYzZ46x29nH8wN6gx6ra+vUvTqZdJhZWsRJM2pCsre/y/ZOGyEl\nSZbjeDXqrRYqV7znPd/BV7/2NGma8tCD96EcgepHkOdkEpLCu9BxHVSutCec0Lpk5QhQOXbyb3ti\nDAAZ7tae5CoO14CffeBRJo4qgpDSLALtrGPu8QO/SOAKQjq4ngCldLSQ0qKypQ2bE7NNAW2QFRwc\n5ZZVLvbCt6UM+74yl1Kl0xy9a6SfLa4pkEI7GulolYIsS8hzhe/Xefm5l0mGKc3pKZ575Rt4WYzv\nKJpTLYhj3Bz6WUorCHRW+dU1Tpw7S9Lzae9sEkiJH7YQWc6gHyFdj9mVFeR0i6nFBdrDPoFXpx7U\n2Vhbx6u5KFJ29rZYOXmCOIl56dZF+psudSck3t6lkUlUliKlR4ADIqeTRWwNOnzqUz/HA6dP810f\n/yiPPvVWvGFKPfDxlEdGTi7BdTiI8pJlqAwcxyVV4PguudDR2B3AKYJepYxLjJO4QEOK+pIaqVOU\nKvwjzDwITfw6njylokZ16TnW9GmYHnvzLlsl2cVu66QDPu2N7FgUKKw+VKtjqiRcGFf52GpJ+x77\nvnGu/YCJw0oEfrBhqNHnJInBvvanwoErpb4khDhbuvxJ4P3F908BX0SD+PcC/14plQBvCCEuAW8H\nvjapfpubKotsUGS+EUCuyNMUVXTcKSZ04GYIBHPthO/4yx/mlS9+k/vP3Ut7EDFXb9KUITvRgI2d\nDaZdj6nGFMeOz1GbOk0aa3Drdrts3N6k2+2SCcXi8ZOs7nT4g1/4JX7wb/8ArWQPX/x/1L15kG3J\nXd/5yTz7Xeve2t/W7/Xr13u/3qVGEhIGCWQQYMEYGRyYATzYYDtiHOMZ22MHYcfMIHvGwTgmvMDY\n4TEYGxASBgzd2AIktNIttaTeu9Vv32qvuvvZT84fp7Ju3lP1WgwxE81kRMWte8655+TJ/OUvv7/v\n75e/hFiWS/4d38PKgSSDrEySlFKQCnAdC6cSGSKlPFjyrjnn/badcWxqZZYkCY7jlE64ND2Y7eFo\nOsMMsyvvZe70M11oYa4ULX833SXEpB7MZ1RRhvnMqrKvCvhRkQTmJGF+atRfHUD6vFmnrMgRolwP\nAKVPBEuW2SdtSZKmZHmC77uganz95Uvcc9cD7PX7hKMhS7aA/Th3OwclbOyay0KjTjrsYecT0tE2\nr71wlesXL7PYnKPmN8jyjCRNyaOEuOGwfPYsx4+d4pWXX6EReCTjCaP+AFWkCMcijAf0Bx7pMGJU\nZPjz8/jteTqtBr20T6YEdgY15WAXOZkqmLgOj7zzCd740pcYFCFLD51j7es3sYo6rbkWW8ketutQ\nyxRFmiI8F5GV+Qld1ydwfcZJREEBKkfkOTLPUUIcrCw+yvrT8qG/mwpROzR1CoajrKRqMSdr/f0o\n6uKPU74RTVHKpnl+OnlM/58NAzxKLnW7mJFN+pxp0RxFUZX3MieYKZKfjqGC6d62YmbS+0bI/Hbl\nT8qBLyulNvb/3wCW9/8/xqyyvkGJxG9bqmZ5lQ7QKxh1w+nogqowZFnGkw8/zLO/83ts9TbJUvBE\nDVWvsdRZZLnuE9gW/fUNouGQW5tbZacXivn5Be44eRppSYQlGUVjwiRie3ubX/q3v8QPfugDzNUD\nJAJH5RBlCEti+y6oshFdVebLTvLsyE4wzTN9rBrTrN9Zhyea3v2jFKBuL01BVBWpybsrpWZCu7ST\nUghxsDmz6WCtLqwx38fsO33cVO5aeR9VqnHl+tN0curnHhXXXRQFSFnCw/0BKkTJuTquS5JE5FlG\nvVYnzwt++zefYeXOu7BzSbzbw1KKSGX4QmFnOcqywLM4ubrK/LETfP3SBTqdeSgKdtbWII6JVR+3\n0cCVDnGeM4xilC1pdjsMoglWELC4vMiFl14iyzJsJRC5IpvE7K1v081dlmwbPy/3tSwWmmyNdvAK\niZ8XCNulkTnc1Vjg6iDnlS9/iYfe8xSNVgdcn54NfhISpTnM15js7HCiMUcch6RKEVtg+Q6xyinC\nIY4SB3uX5pYksij3AeUwajSjo46yjEx5Owo46DFZPV8FJtqSLLtuVqZNP9hR8vZWaFXLsom4p8q5\nlI/qe5jhveZ9q0pdUyhVaqQqv7er1+3G7P5ZtOya11d9Uf9fKfCDopRSopqjtXLJN/gjOE/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W6Ho01Mx7k+XnVkV+t3SCYqsljKn0N1IirrMAUn5rlqgjX9XHPiMIMFTBk9Smb176Zcuv4ryPPp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wenNsjbnpcteZYHjsvsSnvQ2N63I73aBJkm6Ge5gS0tZwG2//+gtcCoL8R7bNYSxS5SGPqx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\nEqcl9b0u1vyhKNZYn87kIrmBtXVFXTbkaUqRO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-       "text": [
-        "<matplotlib.figure.Figure at 0x114f15d10>"
-       ]
-      }
-     ],
-     "prompt_number": 9
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
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+       "wNe2trWt7QVq/w/Uvjt8hhUJzgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116bcb210>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
-      "\n",
-      "You should try out detection on an image of your own next!"
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find, print, and display the top detections: person and bicycle.\n",
+    "i = predictions_df['person'].argmax()\n",
+    "j = predictions_df['bicycle'].argmax()\n",
+    "\n",
+    "# Show top predictions for top detection.\n",
+    "f = pd.Series(df['prediction'].iloc[i], index=labels_df['name'])\n",
+    "print('Top detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "print('')\n",
+    "\n",
+    "# Show top predictions for second-best detection.\n",
+    "f = pd.Series(df['prediction'].iloc[j], index=labels_df['name'])\n",
+    "print('Second-best detection:')\n",
+    "print(f.order(ascending=False)[:5])\n",
+    "\n",
+    "# Show top detection in red, second-best top detection in blue.\n",
+    "im = plt.imread('images/fish-bike.jpg')\n",
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "\n",
+    "det = df.iloc[i]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))\n",
+    "\n",
+    "det = df.iloc[j]\n",
+    "coords = (det['xmin'], det['ymin']), det['xmax'] - det['xmin'], det['ymax'] - det['ymin']\n",
+    "currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor='b', linewidth=5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That's cool. Let's take all 'bicycle' detections and NMS them to get rid of overlapping windows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def nms_detections(dets, overlap=0.3):\n",
+    "    \"\"\"\n",
+    "    Non-maximum suppression: Greedily select high-scoring detections and\n",
+    "    skip detections that are significantly covered by a previously\n",
+    "    selected detection.\n",
+    "\n",
+    "    This version is translated from Matlab code by Tomasz Malisiewicz,\n",
+    "    who sped up Pedro Felzenszwalb's code.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    dets: ndarray\n",
+    "        each row is ['xmin', 'ymin', 'xmax', 'ymax', 'score']\n",
+    "    overlap: float\n",
+    "        minimum overlap ratio (0.3 default)\n",
+    "\n",
+    "    Output\n",
+    "    ------\n",
+    "    dets: ndarray\n",
+    "        remaining after suppression.\n",
+    "    \"\"\"\n",
+    "    x1 = dets[:, 0]\n",
+    "    y1 = dets[:, 1]\n",
+    "    x2 = dets[:, 2]\n",
+    "    y2 = dets[:, 3]\n",
+    "    ind = np.argsort(dets[:, 4])\n",
+    "\n",
+    "    w = x2 - x1\n",
+    "    h = y2 - y1\n",
+    "    area = (w * h).astype(float)\n",
+    "\n",
+    "    pick = []\n",
+    "    while len(ind) > 0:\n",
+    "        i = ind[-1]\n",
+    "        pick.append(i)\n",
+    "        ind = ind[:-1]\n",
+    "\n",
+    "        xx1 = np.maximum(x1[i], x1[ind])\n",
+    "        yy1 = np.maximum(y1[i], y1[ind])\n",
+    "        xx2 = np.minimum(x2[i], x2[ind])\n",
+    "        yy2 = np.minimum(y2[i], y2[ind])\n",
+    "\n",
+    "        w = np.maximum(0., xx2 - xx1)\n",
+    "        h = np.maximum(0., yy2 - yy1)\n",
+    "\n",
+    "        wh = w * h\n",
+    "        o = wh / (area[i] + area[ind] - wh)\n",
+    "\n",
+    "        ind = ind[np.nonzero(o <= overlap)[0]]\n",
+    "\n",
+    "    return dets[pick, :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "scores = predictions_df['bicycle']\n",
+    "windows = df[['xmin', 'ymin', 'xmax', 'ymax']].values\n",
+    "dets = np.hstack((windows, scores[:, np.newaxis]))\n",
+    "nms_dets = nms_detections(dets)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show top 3 NMS'd detections for 'bicycle' in the image and note the gap between the top scoring box (red) and the remaining boxes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(Remove the temp directory to clean up, and we're done.)"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "scores: [ 0.86610985 -0.70051557 -1.34796357]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!rm -rf _temp"
-     ],
-     "language": "python",
+     "data": {
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+       "GuSEjnyS7YmSg/GILKPrgy4Jwirh3/cdnkAX+YhOnz4NDNUApWrQCF0DxnFVYIyC0WG/8RDZKwEa\n",
+       "9t2Br2xlK1vZH8aU6kAd7XgqU7NkpbIYo9f0b7UkqtF7OwJO1HW7h/sIJEtyDJO5ILDnLo7jj6ef\n",
+       "+8eui9rHZc+xw9bj1Y3llSbl93GUfmUrW9nKVvad2MuN0u+LA1/Zyla2spX94e3mc5orW9nKVray\n",
+       "V62tHPjKVrayld2mdssduDHmfcaYbxpjvmWM+flb/f77ZcaYXzXGXDLGPDq6b9MY82ljzO8ZY37T\n",
+       "GHN49NgvxDX6pjHmJ/fnqL+7Zoy5yxjzWWPM140xXzPG/PV4/4FdF2NMYYz5ojHmIWPMY8aYfxjv\n",
+       "P7BromaMccaYB40xvxF/P/BrsmcY5bv9H3DAE8BrgRR4CHjdrTyG/foPvBM4Dzw6uu8fA38r3v55\n",
+       "4Bfj7fvi2qRxrZ4A7H5/hu/CmpwCzsXbM+Bx4HWrdWEt/kyALwDvOOhrEj/r3wT+I/Dx+PuBX5Nb\n",
+       "HYG/DXgihHAhhNAA/xn46Vt8DPtiIYTfAa5ed/cHEck64s8/E2//NPDREEITQriAnIBvuxXHeSst\n",
+       "hHAxhPBQvL0LfAPRUj3o66IEGCIsKefNgV4TY8ydwPuBf4vSeR7wNYFbX0K5A/j90e/PxPsOqp0M\n",
+       "IVyKty8BJ+PtM8jaqH3Pr5Mx5rVIhvJFDvi6GGOsMeYh5LN/NoTwdQ74mgD/DPg5YDyOedDX5JY7\n",
+       "8BVm8WUsSO53s/X5nl07Y8wM+O+ICPbO+LGDuC4hBB9COAfcCfxJY8y7r3v8QK2JMeZPA5dDCA8y\n",
+       "RN977KCtidqtduDPAneNfr+LvTvlQbNLxphTAMaY08DleP/163RnvO97zowxKeK8PxJC+Fi8+8Cv\n",
+       "C0AIYQv438D9HOw1+WHgg8aYJ4GPAj9mjPkIB3tNgFvvwL8CnDXGvNYYkwF/Afj4LT6GV5N9HPhQ\n",
+       "vP0h4GOj+3/WGJMZY74fOAt8aR+O77tqRmaJ/x3wWAjhn48eOrDrYow5pmgKY8wE+AngQQ7wmoQQ\n",
+       "PhxCuCuE8P3AzwKfCSH8ZQ7wmvS2D53kn0LQBk8Av7DfXdxb+Lk/CjwH1Egf4K8Am8BvAb8H/CZw\n",
+       "ePT8D8c1+ibw3v0+/u/SmrwDqWk+hDipB4H3HeR1Ad4APBDX5BHg5+L9B3ZNrlufH2VAoRz4NVmN\n",
+       "0q9sZStb2W1qq0nMla1sZSu7TW3lwFe2spWt7Da1lQNf2cpWtrLb1FYOfGUrW9nKblNbOfCVrWxl\n",
+       "K7tNbeXAV7ayla3sNrWVA1/Zyla2stvUVg58ZStb2cpuU/t/6S2bnP6vZqYAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x114f15d10>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "plt.imshow(im)\n",
+    "currentAxis = plt.gca()\n",
+    "colors = ['r', 'b', 'y']\n",
+    "for c, det in zip(colors, nms_dets[:3]):\n",
+    "    currentAxis.add_patch(\n",
+    "        plt.Rectangle((det[0], det[1]), det[2]-det[0], det[3]-det[1],\n",
+    "        fill=False, edgecolor=c, linewidth=5)\n",
+    "    )\n",
+    "print 'scores:', nms_dets[:3, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was an easy instance for bicycle as it was in the class's training set. However, the person result is a true detection since this was not in the set for that class.\n",
+    "\n",
+    "You should try out detection on an image of your own next!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(Remove the temp directory to clean up, and we're done.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "!rm -rf _temp"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Run a pretrained model as a detector in Python.",
+  "example_name": "R-CNN detection",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 6
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/feature_extraction/readme.md b/examples/feature_extraction/readme.md
index 6c8917e27e1..5612b020a38 100644
--- a/examples/feature_extraction/readme.md
+++ b/examples/feature_extraction/readme.md
@@ -10,7 +10,7 @@ Extracting Features
 ===================
 
 In this tutorial, we will extract features using a pre-trained model with the included C++ utility.
-Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+Note that we recommend using the Python interface for this task, as for example in the [filter visualization example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
 
 Follow instructions for [installing Caffe](../../installation.html) and run `scripts/download_model_binary.py models/bvlc_reference_caffenet` from caffe root directory.
 If you need detailed information about the tools below, please consult their source code, in which additional documentation is usually provided.
@@ -51,7 +51,7 @@ Extract Features
 
 Now everything necessary is in place.
 
-    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 lmdb
+    ./build/tools/extract_features.bin models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel examples/_temp/imagenet_val.prototxt fc7 examples/_temp/features 10 leveldb
 
 The name of feature blob that you extract is `fc7`, which represents the highest level feature of the reference model.
 We can use any other layer, as well, such as `conv5` or `pool3`.
@@ -64,7 +64,7 @@ If you meet with the error "Check failed: status.ok() Failed to open leveldb exa
 
     rm -rf examples/_temp/features/
 
-If you'd like to use the Python wrapper for extracting features, check out the [layer visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/filter_visualization.ipynb).
+If you'd like to use the Python wrapper for extracting features, check out the [filter visualization notebook](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
 
 Clean Up
 --------
diff --git a/examples/filter_visualization.ipynb b/examples/filter_visualization.ipynb
deleted file mode 100644
index 7125907f35e..00000000000
--- a/examples/filter_visualization.ipynb
+++ /dev/null
@@ -1,620 +0,0 @@
-{
- "metadata": {
-  "description": "Extracting features and visualizing trained filters with an example image, viewed layer-by-layer.",
-  "example_name": "Filter visualization",
-  "include_in_docs": true,
-  "priority": 2,
-  "signature": "sha256:64c88129e2eeaa956e4c8a26467ff6119f24ea3d7ef15f8217326249973bea8f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here we visualize filters and outputs using the network architecture proposed by Krizhevsky et al. for ImageNet and implemented in `caffe`.\n",
-      "\n",
-      "(This page follows DeCAF visualizations originally by Yangqing Jia.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, import required modules, set plotting parameters, and run `./scripts/download_model_binary.py models/bvlc_reference_caffenet` to get the pretrained CaffeNet model if it hasn't already been fetched."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "plt.rcParams['figure.figsize'] = (10, 10)\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'\n",
-      "\n",
-      "import os\n",
-      "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
-      "    print(\"Downloading pre-trained CaffeNet model...\")\n",
-      "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net(caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt',\n",
-      "                caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-      "                caffe.TEST)\n",
-      "\n",
-      "# input preprocessing: 'data' is the name of the input blob == net.inputs[0]\n",
-      "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
-      "transformer.set_transpose('data', (2,0,1))\n",
-      "transformer.set_mean('data', np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1)) # mean pixel\n",
-      "transformer.set_raw_scale('data', 255)  # the reference model operates on images in [0,255] range instead of [0,1]\n",
-      "transformer.set_channel_swap('data', (2,1,0))  # the reference model has channels in BGR order instead of RGB"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Classify the image by reshaping the net for the single input then doing the forward pass."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net.blobs['data'].reshape(1,3,227,227)\n",
-      "net.blobs['data'].data[...] = transformer.preprocess('data', caffe.io.load_image(caffe_root + 'examples/images/cat.jpg'))\n",
-      "out = net.forward()\n",
-      "print(\"Predicted class is #{}.\".format(out['prob'].argmax()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Predicted class is #281.\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The layer features and their shapes (1 is the batch size, corresponding to the single input image in this example)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v.data.shape) for k, v in net.blobs.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "[('data', (1, 3, 227, 227)),\n",
-        " ('conv1', (1, 96, 55, 55)),\n",
-        " ('pool1', (1, 96, 27, 27)),\n",
-        " ('norm1', (1, 96, 27, 27)),\n",
-        " ('conv2', (1, 256, 27, 27)),\n",
-        " ('pool2', (1, 256, 13, 13)),\n",
-        " ('norm2', (1, 256, 13, 13)),\n",
-        " ('conv3', (1, 384, 13, 13)),\n",
-        " ('conv4', (1, 384, 13, 13)),\n",
-        " ('conv5', (1, 256, 13, 13)),\n",
-        " ('pool5', (1, 256, 6, 6)),\n",
-        " ('fc6', (1, 4096)),\n",
-        " ('fc7', (1, 4096)),\n",
-        " ('fc8', (1, 1000)),\n",
-        " ('prob', (1, 1000))]"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The parameters and their shapes. The parameters are `net.params['name'][0]` while biases are `net.params['name'][1]`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[(k, v[0].data.shape) for k, v in net.params.items()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "[('conv1', (96, 3, 11, 11)),\n",
-        " ('conv2', (256, 48, 5, 5)),\n",
-        " ('conv3', (384, 256, 3, 3)),\n",
-        " ('conv4', (384, 192, 3, 3)),\n",
-        " ('conv5', (256, 192, 3, 3)),\n",
-        " ('fc6', (4096, 9216)),\n",
-        " ('fc7', (4096, 4096)),\n",
-        " ('fc8', (1000, 4096))]"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Helper functions for visualization"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# take an array of shape (n, height, width) or (n, height, width, channels)\n",
-      "# and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)\n",
-      "def vis_square(data, padsize=1, padval=0):\n",
-      "    data -= data.min()\n",
-      "    data /= data.max()\n",
-      "    \n",
-      "    # force the number of filters to be square\n",
-      "    n = int(np.ceil(np.sqrt(data.shape[0])))\n",
-      "    padding = ((0, n ** 2 - data.shape[0]), (0, padsize), (0, padsize)) + ((0, 0),) * (data.ndim - 3)\n",
-      "    data = np.pad(data, padding, mode='constant', constant_values=(padval, padval))\n",
-      "    \n",
-      "    # tile the filters into an image\n",
-      "    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))\n",
-      "    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])\n",
-      "    \n",
-      "    plt.imshow(data)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The input image"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.imshow(transformer.deprocess('data', net.blobs['data'].data[0]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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677sZausTsgLUkjKCiPQkQVnziTnp+Sw1UCw93swJ5bPOU/SdS6jIeRmAIGsn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zz12uoZORjd9bOyd5k6RErREhFTry5yfn93K41a5SKoDVfwWBxnO8/9yV1DbQsi0Wr+nhCR\nAavoDx+JqI6y4he7v1y2jegz5cXXIiLy7EtbmVw/OML0VUyrOhQUBLTSntBpW1K7TgExDb3tb+yA\nupCifatyGoXbx5pQjcMB/SRhxXiH8MytoXoD7v802fXH4o4x0kqrq9z+Nlz+D9J6vHJtnZIStpaO\nM2FcizIyQkROWmp/srZO0NfXhGbFGAsdE+Uhf6GVy0NPRHCU39ekmK3ntKZ6bUWkei5TX1Ss7Xp6\nELQLRq4gbZLhGlmJfUYRAUudqIfeac9jB2OBizLQF1gpfkb/r2juKkG2n1i9W6UzVuhDpLqcJo89\n/LTUmwnwKmEQhjb/qExDQr/NcE8mKgAZ0bez0rV/ltv8JyghnykjIEC9EpKumDE/BYX5/w2VQ9ai\nztCEOVY/TUJuLl0RULDHfHL33g6FYoA//+LPlm3/+3/+v0VEZN/YmOgwx2ZEgH57/w7XSkRp9Kcw\nsT4e6LxbuHaKCSWegKrwvVbf1ZgkEQr1ST0RiRlz5pywxAykM2Y791Eg54Dvh1TE1IcodiLu8wjU\nqafnpIK4rOwfA7HKc1KlT4A+EaZSAbEbOrvG5uT+roBMNZQ5UomdkNDXbOXOmQHJEfdkpPZvUZRx\noue0AE0f6ccZiOKNPoCpAKCBdAhLkqSA8WN61kxApEYqCmnbx0h0nmC8pSZTwSjep8IjUj58+PDh\nw4cPH08M/yLlw4cPHz58+PDxxPhsqb3j8U52GyMit4qok4ryCGhzICPb/dGRd4+U2xmR0kliUgqP\nlFBuUP0BBOUychBjQJ81QO6m2VIRynEPQoO2lbDHOjYzUoZzwgS8I7aR3gggeDXNDAjiXJUX2L9B\nluHgrnUUTllEOE+DbBXGDmL6HkxLn11+s2z74d0/unMD6XwmcnYA+Lpnsj+IpSHBowE0syY28gQ8\n39QG2b+9/j2+R5pJUAyOSTOkRponTR4rS0cg27Oy9X6PdC+p006TGlRSGkeNqYkB/uO7fxARkSK3\nfpeuHMl1hgZPSppRJfoLG8pqejaaCW7vVFvMjt9WOHfSJZo7pJtIvb8LXap6Auk4JNQdPs6yWhlh\nO4diMqdxjuO37pwmNoh2JPNuolQZSMZ1S2RXQOXlFikbIlavobr84aNpRv3iK6cjdVMZAXp34dIi\n3/7++2Xbm5cuPZOepZuQsqJUUYe21YKSgKYkVfGe6b5O2EfbWd/NFKqf7V5rX+A0lpJRIyLWqkac\nEsVZR24WvV+2j67XMWHfU9VjNhdfJHBIl2xE6oM1m2qYyyZcUYB5SZ0VdkzYRz9NiIA9LIbndgKa\nUp+osKA6uf6cZtYnooXKQG2CfqwpsIlUt5WIPZO2WIT0YcBq14vOG5HYS9cnxwejAESrHPuzNMok\nbluIgpnmg/W/ce/mxImOf3HhxnNGzhKDXg9RC/Q302j9b4cCnZxU2UP0jwLp9pTSs32dYrf2/TRz\n329pTOj8P5CO1gHq6Ww4LymcKnIapyhuyULVOKP0PFTRq8oaWw2EZ0rjlriGrGAdJ3eMnKgF4+jm\nLk4Bq/I7TXtGaVnmXSKn46EYUVHIhHn3PAXtjv9ATiEdUnYzOXrEeI51NBmNSKVqKnSmcTVpYQOd\nsM4ZFZnQqxzjRM+9Cj8JM7vHSep+k+X2PJ8Cn9rz4cOHDx8+fPj4o8RnQ6TGYZKqthLqOHBv4XNA\nPmAjFHZHezO8e3Arkobe0pUgHuRUOo9yyYFWaVHg3v5PuiKnEvJgwmppthWEAkZMLA/h4ccE4MtL\nt2J8IMXc5NJdDwFXMoE0Ch68RLRaKgu3qhpbQj9yfV0mEi9WDiO9VSsp/ngytfGLtUMEEiLWXT1z\n56lKsCERTJWczWXA6uc30GolAuqnnnciIgkQqRMRZq/vILFAC+2y1NUJ+3q5f1NVXZ7o+oHcXT0z\nlGiNVcKP775btk1YQYesxIv+MdNqWuBF9d37v102BRBNLqNX+Iqt9NZQAt+uDRHQkvgoImLt4Fb6\nEfl63T+4FeluS6XuQF0Y9ZtAZA4SEDypXbXxQlKb1/ZkBXBtzYhWTYvEA62IR7RtTwUIShq/v3Nj\n8eLCxtDNtetPX//SHAD+8Vd/LyIif/VXVtjw/bcOfVR5CxGREoT2lojlDQjKq5WtiK1kHytN9rrT\nEn5ChGRZEVPfBcLKKLFy7FU5XMRQqpBxpwzl7CCvKkldRGTAMjxkpBvzSrkxBf4JBPx3P5on3Osv\nXX/qiCgfgTTcEXIbAJ3ke6zXY9dNpfYYQ4zIKpowMSKFv48nU4CeMSZu7m2eKko3dsuMJBGANseY\nY3ie6tBfk53Nf6IeelQ8o+M4GmiOBRI9l4YI90d3LunW9heiACVQJepXJo3w/lfO17FJrU2uH1zf\njQn9TEPXxwJCOhSBTzPrp3kKBwCS2ElAGi+AUjDaEKnXYEDq8MgIvNjaeT4cnXr36UDEerTj2FHf\n1RL7mQt6cP2iLgZ2Bl2tPoDsrKFIq53nhNsZkbNHisxCTGNMJVNYEmZB4Ak5C4FEJsiICLlihJAC\nSdhrcdTzJRI5np17chaY1pr2sXYq0cYjKZurTMSM7BD7+sWYd0eS6ZlAYm9Jgb5DW/T0nNLihZR8\nBTNIJwUxIVIMN38iPCLlw4cPHz58+PDxxPhsiFQQRFKf7C00RU6XqkolhO9aQKWOLRCbkd4qd2tX\nYjsQH+cA8bVptLz1JFjt6wpuIEJKq6t/e4PNt4oq8UrL/Rv1toIrQqxqEvteVbnjry5spaU5/E45\nCPTGncTue1o2LyJyB8+tkPYrqXubJyqLBLie+9O7Zduz/kv3PRaTi3XVj9Jk8mGaBwioDcQzaJH7\n5pW+ngaJv2WKJpaUD6/B+aFVggoSMuqlnKAgBs+LbMVXa7ffmPgjz69cW+/31q4n+D/JSPcTPIuZ\n+kQO3lhCCNv769+IiMjFGvwB5j6g3V9cvl62VZWWHzObBtfAPAOs4G5IfLRcuc/TgkqiUR49wn8v\nIPQjAvx0as3pXt3SA+r/ipwkGSE3WFWPhPCNQKLY4zGD/IDyXHpCkJRz01Cp+zPwUX74zvhQKXgQ\nLGrX1iorYPdJUZKe0JSyhNcb+kbb2spPeRllwfIjitgRRwM6JhFtUy5PQMKJysNj8csJ16aciogQ\nKeVShOS1GUG4c2IyGVb4H28JEX7mkL2ZOEoPKNPOid8TqMcg6wZCAkTvTcuinjingBBpBYwYTXt4\ncCi2+uWJiAwQWIyYiIf2qY/GUVIBzgDcr5DGelm6c+lJpiLrgDpFdp9C8HDGyhCxEEhHQNIV6abE\nIWmeUJQGbZeR0GMAlczD0cbVEZIxx8qeJy0gk4w0bjqgidFsXMII7T/QNXbg3yj3jO1HlcMbECKm\naMVMc11zAveIEDGZdN4llAacHxWEFREZIbd5gvxA29t91UTMQFIHijDP/DhDe27oXoeYRxmRVcHY\njp5FoyKxZxxaiGTiWZysbK5pwFEOAuJDou06EkTtcKy6JjQJQyEj3qhyGWeWmNFxjONz5mClvCkS\n6W0gdULgu1Rosziz42/XuMfEZc6BUg8k3NrXniPlw4cPHz58+PDxRwn/IuXDhw8fPnz48PHE+Gyp\nvTwNpe05ZQeCJZVGj4Dn1MtHROTh4IjSAaV7lGScEwEwXDmo7lRZuiuCOqnClDF9v4HHXRoaOXhE\nmiEtufzZQeYpqR1PWtZO8GwK5t9IpPgY0GOkZE4iR/YotQ9DLpd1sHia2rYE6QtO2URIQQ2pwe23\ne0d8TUIiSosqIOP9mdp1BHQ6U3pMFZOjs0QiIF6CR9uTlu5TqTOkC4QUY9VHbSTIVCKQ3AHF832t\nQbwus1/a90HKfPHSSNG7zpFHP/xsxQta/k0IuKQJ/Pzo3ql3408//5OIiLwk/8PNxhUgZFRY0KN5\nOkpPDiiXjWhdkgAqDwnG3+9d6mcd2v5GpIVGQPER4fMJCMAR9Ynj4NTW+4pSW2h3EnGWJD7oh8u2\nedB+TLIfIEOr/11zRvZ3x//40dLjGbD4oTfMPEbqd0vE+gaSJTGl78PwcTpUFbjNh86+YwroBPHj\n9FKWldC+Q5UNEGyWhNINWrxQkU+YKo8HscpvkF8c0kKcxdO5pjpaeqiGXxtLkmjZf0xp8RZODSmV\npI+z+vnZ90oUiGgqlDLGS8okoPPU1GtHyvr6W1WiFxEpUTzR0/XHSAdnkfXJCGrwKvUhAY9/HJNS\nhinS8SEpy09ILYYF3Tv0tbAjSkHuUvUhSbeMGJMhxkR3S9IIkRKWiRw9uXlyXVr/O+2dZMJEKcge\n8hikfiEjPh8p3aUOBTHmxHJF/Rbjc7WyY+VIn7GKfiBwxSD6RI5x2pylzLBbSm0O4q53cSeY7L4G\nGAssoaEpUHag6GCKymnhWKVz6Jx6jFMu3tKamZEoACmI7ylSoQGlu1WJn2xql3HK0g3qhdtR+l5V\nzoPZ9tfj855kEqRXUjikCWiuK2O9fitsGHDvHo52rBZzcra2H4exKuBT8UBb4xooVdgwbeRxeETK\nhw8fPnz48OHjifH5EKk8k45WS7rqYgFFXc1f3xiJc0TJfkYrHRU/Y6HHDpIJGfkKRQnenIGSRLTU\nUzdv9mFanNFjdoTHeY62Skoyh2LwW6u+zZ7JFMRKVAWCQ6p+NYigTOI8wP28pDd9XRFNXNaLF+cg\nses5QIohJZSsH/GmPQMtoHpZJdTO7LWmlakkvjkpxDMz/AEHb1r9B7qsCZi8C+SMVtPH+gHHcr9N\nya9qOLm/08BQygwiiWVq5/ls44oN4ti+99MHh9xU7eMS1ozQjBIo1eHuWxERuWnN12sNRKqjlXYH\nKYyht/u/3kIsridiM8p6UyKl5/Czi8lPcMDqa+6x0jvzkHPnuVlbqf3p4BDWE60Wo86tjqOIvAZB\nbN1GJP8wK3Jq90mJ/x0UaU+DIS0XFy+xX7snJyBM642tyJV4HcUsUuj+romovoHv3ulkK8erZ04Q\nsYLHIwsjxpBmYAmTECjuSFITYaRCi48J4BH1cV1FB8KIaKQ7wX9JmBHb2FdsRh/uCFU5Vq7NCkKa\nTkCsCioAUN+zmgj9+jkjDEqULRY/Q5qncIkTo2Q4zx9/MPmFF89d3y1X1nfG1p3TsxeGuqrAIQuX\n5jhuIIp+2bHUkyynPiQonhlHku64c/1kCkxqIMxQzn+ysRNnVziGHSSATI0iYfeH6+Wzn1G8EZb2\n/QpIVyuGfg4Qoh1akokAUbkdrf8VOUQv6Z70g/tto/8SYTkBqvRAHnobzDsDCV2GqfucxR8DnYtp\nPm8gZ1BGTNQGwg2k5QzpDh+P4RSFKqRmsxRbNIy+ArIeZy5UgP8lCVyrIKr64IksdoqSALnmAqwI\nRH1G/xVp5vPUQo2YvStVOJaQ8B6du+/se7q/BOhXsaY2Cdy5s3B1ixoHqmuRCITyIKUsERD2mao9\nFDDre5KdGBhtfhwekfLhw4cPHz58+Hhi+BcpHz58+PDhw4ePJ8ZnS+2FyUqSkrQZQFgMSDtCANWr\ncraIaVXc3JtfU9U7uDcPSW06UlIepQp0m7INSXdC4N11Dy8/EZG4cZBtT6qzOeDRmPQ5Tgdomkx0\nniC0Vx2TIjW1hhQfQdEFYO+6I9Vj3B1O98WL3gVrFiE9RNy8oUB7zgZjK7SbQzF5JNhbee8hkV4H\ntN0cUBoJqT9OrQRIxwY9pxsBmU5E1BxVMZa6XeO2nfbuXHpKhUzQc/nVb3+9bHsNFfmvv/xy2bbb\nOuJ5ubU0Ros2CzlVCeJjnBmxVvvbGkrcx5OlJ1qk8Y4nI7GfGhQFRNYma6Qj5zVp4SCNMJ3s+Nvd\nM5wHparQtgH0q0ZKD8aTu56hsTbZZG9ERKQiFfv6hFQA9edpAwXqwK4nREorJ08y7QNa0MHpsRHX\nPxPErWMoYRX5URXTrT+tL925s/9bi/Q1k7c1paaE2pnWdj1SqiHpLikHoDsju7v2iVkzCOMujVnZ\nHunjnMjeIKAqsTpkcr4WYND4jxctNNK7AgVgtbLU8v7gcgs9+bolaLuqsnuyKPq3lEYBGThTFXGq\nIqhrFGCUVJSzKFHbfPrxnUttv/7iF/a9lbpH2A1YlW7OjFPbX4TjLmrWrKxfurGWFpSyQ3qcVey1\nQGW+Nb2n4BlSxUTUVv9FCa1PTurZ1rrfbjfmbHD/7W9FROS2tfm/RIHSQ21zdxS5ea8mHbFQ0E8o\n3R3ieRKz/yL0BTWl3DbMrYAWHGl2haCUtNQnYnyP9bA1GxYMRB9BSp09KVPMz4GKLFH314IdHhOa\nKotICykDzSNhzTKkxzjdFaqHHZHteyWq0/MkVdcEFBFFpLulRRwNpQI1Vct+rjrtT1RQo9cxkt6a\nUjA6UnS3FDSe4QmlApGCJVMQaXTuopRxXgRn5+F2jHQvzV3jpEUmTED/w69KHpHy4cOHDx8+fPh4\nYnw+r70wljCxVYiWa2eBrVZaEPq2VNY6gtEW0qkf4ac1ElE5RYl5lNhvOzDQQiiX8lt9ECk53BAc\nXYkM5AO0XbtznkbaL1YVPZX1a5nqROX/EdRRFcwaRnvlb3O30mQ1VT29iTyPejAKA0KOFKWbyVet\nhaJsTKQ8LZ1eY0XYiq2MZ7RdTyTyNlAPI/IrxOo4IljhEmrXbUUl4Vg5U/WrrED8T4hsfgGyqZa3\nhmKo0py7Y53ufr9s+3DrSvEvdkaYffPK3euLwlYQ1SunRl43do3FWhWjDeHpW6x00MdyZoKiOOBw\nNPRnwL1Yr58v25rakWETIjHmIDFH1HfXQBEmRg4712ZB4lCSjhCpuYI0ApXVD0AuSypXP47O16sm\nFWF1Yu/YQwoeXuNsiIyWP+u4KkvykMP354GIpViJh6H1kxQr/IlQyrv7W3zPflsACUxJqVrBlAFI\nWEDWBuqvNdFqVd3kzyQJsG1kTzogmxNti0Il2xKapcTqQN3lHyubp6RiXdUobKFlbQP0a0Vk8wor\n3KwklAxQQE99chk7rc0xGQjNClzmRJhPgFycSFaigYr88+fWJ++vXdFES15/2dodK2HkUuVUSOJE\nSfMhVvPrnUmNpK/tydQAACAASURBVJDkYLXxFkTlnPzyBpxTNNt8Np4cYhS/Mu/GCQ4UVHezFA2p\nmjUTlrPctcXx1vxHc6B6l8Ur2y+KgSY6fle7Bo35OYH5rGfvRs2KNOrXSbIOS1dklNQdv6V5skN/\nnqhdFW2dBpsnAvWTJbXvBMU1A+Q8BiKHj5O7nxEjUngWRrmdp6pzJzEjckBzCJHXqoVcyCcPz8CG\nJCZOB6jCb9xvJzp+mCiCY4jkufeGixUKH6aDPSf0+Uj1PDLhGTcOdgwdEzOI9R2hxOoNuD+RrIsi\nzbldv47FPKPiEWTCSNjdiOWEMEr3hzEnj0j58OHDhw8fPnw8MfyLlA8fPnz48OHDxxPjs6X25ulc\nCXYWBxmGBLEqeZSNJzXNdfNAcCsUU+eYUgCR6l2QuW3l4N4QxFYS0V7IqQGdk5o2shnihL97goxV\n02mmc29A3u1JxbXXNM5ihkr6PLjEiGDHAXhnQmmUETmNmDWb1HCWoOXbvTtWR9t2SEvOo0L2lGI5\nOWh3lRmxU9N4TW8w+rFy8PzFzlJAei4hqaJ3SO3NrGYNGDkhHSdNt4Szg7OTxNITA1ILL7cGGd8i\njTv31CaDEkZt227jzq8sSBcI6rUhmWAHKe4FbkVaEhFSSa+k2aWeyhfFF8u2n2AuHVP/C3FPZkpj\naoogIvLwSnWT0McPpE90/+BI7qvO2isDYXigttZ0HBt5qxwbdZ3FBHfsWFvnnNDaNpYyOhzc/q5e\nvLHvo/AjjEgzCQr8L67s3t3eO6Pl7e5i2aak9HXGyt74rFeDXDLIBmTP6uA92ieh1FqE33AKeg50\njLHhOIiyCWtFKckcRSRENq8ql57i9EwLtWU2jY2hn1XVTAvAfELEbiXvs2lsD5I5mysPSLcO+Cws\nqTgAGkjsDqDabqxEvbty4zigOSlL1bSWpdKRUh3tt+0Rad4V5gsikY9K9rc9SFy49Hx1MH2osnAp\n7bPUqqa0WLEa7TPdmwm2JI7IHpaunxxAnBcxE/Smtn7ajW585In1tc3KEdvb0ZwtEuQPJ0rZzCCl\nx2Jke/1rQkq7I9XxEBQQJqwH6DMdteEA4nVIdAtNI7JSvZLdZ0pjCRwIwKGWIKD0KOYQ4pXLBL3F\nPLO+G4OMHdLBQqQgs4zMpfEx63LNSCU2J3LZuHN/b1daHEKm9RifqskkYq4krBiu7X+xYWqBu4/z\nYGNHx8nQcdvpXOz+YWL/EZny+yOrw7trXZMqfZypfiRL22N/VAA0gjYTsn7dxGnLx+ERKR8+fPjw\n4cOHjyfG55M/kMlgADGV75lKeBXhGYmw3bTuzbWjcmFVew2prFL955goqIiFEjWL7LH3V0RlsAuh\njxCJAcuEiVYaIVYnMUktCFRciScoAd6SB6g9t6S6XcMHMCUi4oLmkIhwqIgUrciVRF4WtnK92H7t\njknXk2P1vd05ZGKmdeXumUM1Xr7+F8u2FATo9x+s1Pi33/0DDmqrygHeWDMpoCvaF7EqMxCDiRjo\nKXzsFAgoCRHqgLqoIraIyGl255lQCX+syud0/3VVFZLa7YjuHhLCGWB1XALBahuWB1YjLFInXgij\n9jXB6qw9cqmta7sgJ6+pRBFRW6UpP77InBL1SKXxR6y6r29JbRynEqWGtCrxO6XrUo/DeSZlbS1a\nYBVpHWMgdLetrQy1GIPJsYoYXd+a2vTLlw6R+Omdrf5joHkBrdxVUTkk5FABGz0llh/QMnDuV81J\nfeUIJYA/JiPcCk4lpLbe4TpyQkSV5BrhROaJESQUm5DUgmqSUJMQEk3+kyCU10QKnzFOSvITbZWU\nTWXiEQjCRyCd25X1dfXVC0aSVcG2M5kCtKPKK4hYdw4JildwNhjtggagaSedazI7vn6voLkmROFD\nQFIbWngQUvHC2ACxqqx4I7r4BjsxlOL+979x54GJ71DZPHl94xDxI+QlRETaEPIndK/DyLVxltF8\nnmiWgBBxXD8BgpICbV1h7phpTCr6H525A0DqIGDkFP6Pn1AbDwilV8SYUepxsZSAD2lkfUOfO1Fm\nE9AKLg8Zoe8BSNkBuX1o8QQjVzkQ5ohkQjqorR/JuzXC/lo8d/n6E5xvRONUnRUykslQYn3GaDJ+\nc6I2VvI4S+cowjyhGKqmMVkDpe7ouVKs4N1JnrCrVLMfNCcDCR+poEazPgnfp0+Q5zk8IuXDhw8f\nPnz48PHE8C9SPnz48OHDhw8fT4zPltpzsCUThmHyODGM7mDHI2mhdJBbZRLppnSphZjJdosCt+0v\nUoNCpBhaUh1XxdSECHOBkshJtGaAOnlGBrkZ9I5SglFHwJiqsCtiirETYOeECG69qkPPlsYZlABP\nub1B1ZZDS8ut1+74f/nmXy3b/ua/+q9FROQK5qUiIrf3Lh1TdY7EnJHJcTu6FMSrC4NiFdI/neyc\nNLV1OJACO8jrPZFIC6SUAmq7CnBsz9pauLYQMG7VGLF9UCXeyY6fIn0SRpyy0r5AaQwcIidS/jWM\nZFvSm9L0ZZaCnE5pjG5y7VT3pM4M9Xw2iFaV+0yM7DqecN0jaXtFJ3yPNWOU5OzatVzbua3QnANB\n1qfK9ac8tL6TI803U3omw1iYJ14ruW0R/VZJ1mOviuVU2AE4u6fjV41rw5nSndfI8nW9wfOrrdP0\nqUgp/gJ6RJyWV+J/j77RksbSopnEqRWkTxqSXU5QMJDQtaoCf0CpQs3G1zXNCYuRsRqvksIx2vNM\nRw1w/9n3MCb3DzbWNX1zPNo4yZN/lscUkRpzWzASeRwaWKop9+NPdq2pXs+ZZh1UrBMmFsujWNLd\n5MqgKtIhzXs6JhZSPmt2IaXTUJtkIArnl1ao0sFoOCEj97BEOpry4jMI2mP6ctm2funa7H/7t/+L\niIi83f+8fLbvoI81WJ9odJ5gg/LQ3eOOUqBJ5vYbkJFyMEPZnYzpo1Gv27VD09FcM5//K0IpYBJD\n6pEyYheHaFCiOqWv8RzjIged7xtoYbGLRKL6UKS3lqO/zJTG1UItNvzWYo+QnEKunjuHiKudKeBv\nS3fvouDv7betI/yn+C1fV415n2ks+uyaUnLPAB2Di32UbD+R47KaBYc8nwfnHbpvqbALx4rJyD4D\nsbykAiBN33ecl1f9LCoKCUDRiYmon6/IIeET4REpHz58+PDhw4ePJ8bnUzafx4V8LCIygWQ+0TZV\nOc5zIxsneHPviUS+cMLpTbdp3OokJWVTJV4WeIPvBiMs1pArYBJdDMJ6QGTzBO+ecUCrWnhjrQoj\nVkYgYE7TezpP+P8ATQlopZ9g1dEP/KbvVlAdlTWHKM5lwnYOkunFytCnHc7lcmUoyVJ+j/Lmh72R\nPjssJ45EjhXISjzck2I5SsJPFZG4gbSFdE6rXD2U7Ny7EMQ+8mnqQMqMRqygGiMxCxYBHZWedljB\nMym6OjoUKyMSr6ptb1ZXy7aPx2/d9RzNO+9q7ZCTVeHabia/xA6k2JGU7bUWYqL7n69AouQS5kUc\n2dCEDovoKaRChQxfxKqeCaO7S/QTsf5fgYAstF/t9yEhojKrTxwrK2NFelYUAaV8IBLsAxbg+xOh\nH7cfXV948dJUpA971z6brbWdErB5TCpRNYxsf4MSO6Ewzh56K4xX3scAD8WcSrh1vyOtNFdaoEAO\nAHpOTEDX5lH0qSW0TPfHbaiSCCNJISuqVFXkgAAF6LYlNA2/SQkuGoDwZSy7gnvbYH9TYde6ee2k\nKOqOFPvhl9dT2yVF8ngbEL5zhA0o/ZlSN/oT0LqEnAC0eKDrqFwdSCQXhaSYswNCieYAiBQhIqI+\nogn5iW7cmP3ym29EROQ//I//YflsSN38sG8Nud6f3LaU/AJ1nHY0d19AaiQlArqiWKEYEh3FKIAB\nOZu9XhckklDFAQfTZ46ISIN+FFNBk/reTVRYkOn4pz6hWZSmV29KQp/x/ZAyIilkbeLEClAm/Gai\n+5RE6pfH/p+uP82T3eOicM+M51evl23XN9c4l8cFYAqYBROjam5/p6M9O0rMkw0z+9VJ4gziQ9Yh\nIZkaVeAfdJza8dWfNKYiCkVnGWnVNEVASJMivBF5/PbaFoywzYxiPQ6PSPnw4cOHDx8+fDwx/IuU\nDx8+fPjw4cPHE+Ozpfaq01HigFVP3TvdQErgJbR4mhOltiKFW4nsC6iyJ/J4A+h7pjRGnqvaqYNC\n9wdKu4BEHhCENw9K4rTzVr2XkJTNQxAbVyuDtuMIRO3a0of7ysHRqSgRltWZ8QerrYO82BKJW5Vq\nGRzdw1Dy3QfT8Xnz2pEHA9LR+HjnNFgq7K+q7Vrfvf9WRER+Li0VOUI9/Lsfbb8Pe5cWK9cGIyc4\n+TixlJXexQOlNqolHUZ6N4CAY6SPClbxxfdaThnO7rj1g7XrQ+6uKyJi4xWIrbudEWCTnx1hcmLN\nHEC6mhYcifQ4fYIwrmmufmCyM4xP6XuRFi9QGjNAakENekVEjrjHIb6/IjPODP21WFERA8YMK1F3\nULtOCbIecYye1koD+ukUUAoOBQ8D0j1MWJ9xF3sy1I2Qbh9aItZ2rk1qMl7N1zBDJfJ6A7XnY23p\n2/XWpaXWSAvd3VvKJkZKicnpHY67Xhlh+HB0pNyIUoAT0mg8Jygpm7MISjZXIq7qNImIBEjfBJR2\n6VTlO+DUHkjBZNp6goF0SfpAM7R1WkoBqo7OTMbUqgul6uVpbJSBDnpfAbWrKpWzOrqahnNaVPvx\nSAasqhAfp1SAgbZVE+aJ0mMhzrMgHbMZhObm+sOyLVPtK0ptDWi7pLB5ctaigNlSUCqupDUR60tL\nz3/79rciInLqrE9U6FenyrYdD+76v/ryl8u2EqnvuDQF9v24x/EttSeTS/MHIOUn1DaiKvrUiZRY\nzmlpm7s5jYi2ppTVDP5CkNk9mUCQb2uYdpPen1JGVitKY2I887ECUR1D0vtDIVPd2Hl++OgM4Z9f\nmFNDDzP7YaBUNdL2VYfUXmd9IgXJu68i2oYUMI3dBq4J01lRCFJw3E8xZ7ECvKbXe6Tx2obnZE0F\nUsp6ETKkOQFjNsupoAxzXEBjYsY7QM/PgrMn7uPwiJQPHz58+PDhw8cT47MhUsMwLN5DIiLKCWMe\n2ojVZBiRrx7etIealFDxm7o1YlsLUhqTLZVQqDIJeWor8+bkVikDrWDjFIqtROKdsaqraEU+As0K\nuawcKxdeffSzroTc9ydS4p4GSCIQgqYVn7oKERGJQVTP+E0bq9p/+KffLNt+eudKhjMqpy9Wbj+b\nzQ4HIF+3g1tNzne0qjq5hlXfPhGRRAn4tCJbFTg/QmREfcUGO36IlQMfdyHSg6i527I6L1bas92n\nqgP6Q75md3cfRUQkL+1YL0DAXJe2+r3auJXm9clQtwHehhMIvhOt4LRyeKRSd+2eXChRA3Vjr79R\nVYyJsKiIUUoGeEqKrdF2U81wif5LREf1BhTqk+jjHZWER1pWTb5SVX/EeVq/y0DiVLXfiRAxRRo6\nKqEugFbs90bYjzGOTtRPtpduf8fOVv8xOvQDyWlsgFzUKk1B5OQV7mFLJF5VW+97W+nq9TCJWtGk\nkRA2/TzPyScQ56Ll5+ceeo8lEVQLgCVBdL+sWK2yD9vnhohqqbuQd90A9EHdBkREptp9noM8PRPr\nVYtMUiLMHw/3OE8qilFfNzYUxfVEVErOVHyN5gTUOQfpnEvzQYQeaLWuZOuSSNHq5ykRkbhx70ZS\nwJbEjc+Q5umpcm378wc3h7FcxekEVLOyY7VAH0cqQMpjt9/nK0Nacjw7xtHQ7AiSJHFk93Nq4J25\nyEBQSgL9qiNETMngHRWAaP/k5q+AmLGKuCJ8EyEi6p3YQ8W/P3M2VESQpDYwdgM6mErGjCSnM+Hv\nnqRDfvqg3pE0dwCJa3v7rbonxHDKSMibT7NEbGGnvq9M4lZ0KiafzhnP6ZAQ2RT+fDHNkzP2o6h2\n2/JYx790AlofEZMp4Qz9k5Dupz7PRyoAaoDS07QjBRUyfSo8IuXDhw8fPnz48PHE8C9SPnz48OHD\nhw8fT4zPltrbxGuJI4PTVGyaUyGaMukrg4LVmJcVm6sOMCYp5jZI/U2UgktAilNgkZVYQ+yvIxJd\nDwiaDULnUYntpBgLEuntrSlgX164pp1GUgAP1PBY85iU4gE8280G+yoRm00WIyWHZmS8if21RJT9\nzfffish5aiMH2fmv/+VfiojI1Xa3fJYkjtB6bAn2ht5Kllhbq9xNQmTbdtBUhJ3TlLrPI8rV5r2q\nstu2aFFZxvVTenSbuTs1kGZTAl2wsbJrrQFB16QtNYwupbJKjZR8sXbbPjx8v2xrGpfGqGqXHsxS\ngqyRlhzoXmv6NhFL46xzp7tSrk2zq1e4v7I+sYJSdEAEdEWom9qlwDTFLCISLKnvx4UVTMQcVTOK\nlfJRDNGQtlWC+7RhuSmkERRaD2kMLeRZUlGeYjeuItKxOTw4va3N1kjBAa7/wLpk6DITpQqPB3e9\nSugeKD1cQxX8gQoLNjCXHkgzStP3IaXWLC1Hejc4Lqf7NW2nn3WU2leC67mK+fRom5LYe0rZqd5d\nFHFfd38PYr9N0SdiItuOSD0oAZyN1wV/s8mqat8dSAG8wvdWGxvjo/ZnSu3kpRtja0qLjzCc7lt3\nj9OQTJ4xZ3MBSKbFFlyA02p/IhKxFqiw4TqKIgJKy7770ZmkvzuhiISdJRpoMVFqJ0WqjGkhX712\nit1lSsRm0Caalo3pNS9kbTKHUO+HQFJGc22PuZbvvyqajz2lxWclRVMKEM+YgXTxItUqK1g/EX2x\ndr+dEk5Pw/B+JLeJCer4lDJtoYqeRkysRn/u7Lf3R/fbdWKaUWsQ2Vdr0yWsKuj9RdBxI707CVuc\np23qe9WRMgpACCrL2fNMX0GI0pIt2nb0fMSLQd2CRG+Hkhwp6PWKdKRwy3pKtyqjoaFxqqfStGwa\nrQR46qcsKvWJ8IiUDx8+fPjw4cPHE+OzIVJ5HApxXhf13JlWqweQLhN637vcvBARkWll3ky/+/H/\nExGRurLVvK5OGyq///DB/X2xRan7RG/wjXvHbWkFu5SEZ0SOhPJz31BpJMiQD5m9fScg1I0Dkejg\nKxSD4FzXtjLoQc6OZioNHtSbyPYxK4k7MERAwaG8MGLnixdOsfb6o5WaHw5A7vCWHoxUwqxKwKRY\nngMR2hIBXNA+GZdrY1XXE/oV4PwSKuuNFaWi61E0ZYTaNavZ9pOWZltHSXD9I9NkQUqvekM/9kCn\nXobWnrvSoRkpoUlt5xCjBp5gSWil5qoivMrf2OUD9dkWdk75yq36o8yOdagdSvNAKFmAFS5Ld5xA\n8uywcn14sD6kiEhS2n16fenOj1EdLBZlJLVrLeQoaDUt6M81k7cXhW6gJYy0KImVvNnaGis88ibb\nLcULtk78+N5JTewuni/b7g9O2iAhEmkFHz291qIwIur9/R3Oya5VSb4tqf0rYpCw/5eW/5N0gRLV\n93ubJxTsU3I6q5OrEnhI+1Cl5oiLPRRFI0SkBNJzLifhvpdmXGSi441L4oFmh4rW2H47KHpnmfUJ\n9b/jwpYOc0tX27VmkCdJIr6fINvTGl+dJLTdc1L2DkDAHk6kbI75ZEeyBno9MxPLNRJC0/G94cGQ\n2woFB3cf3bn/9Pbt8lmKMdb1dF24/7udjV0tgJnF5rOf79wxToNJbJQAkdOIJGaAus5DenaOIiKT\n3ndCVZTjHJKKfgz0iT3iVA0/JOhs6nGPU0KkQvU/RMFIwkgrENTQ+mmrHnohZ19QlECFSgE8BEfK\nsAgKGmKakwYU9JQru+/rHWQaIGFQrEjqBQT0riZUFdI5WkQlIlJBpofPU31KZyrUwe5kIO/IHues\nj46IfPWKElIzJPXTYUzSlCQAsySkZ0ff6/0kOREgsmlqP2Zf2k+FR6R8+PDhw4cPHz6eGJ8Nkerb\n7sxdWQ17ePWnpbabtSEtwaxu7YaShKGKKdpqfoW8/TzZ22+N3OhRhThpValu0knGZY7unKZPlFXr\nW6uIrTBayr1W9QPOk/yCOl2luNVCTmXFY6hCZ+Thh2PQoaSDmFoW27FSlHMzcrUq3XXkvzBX75sb\nx6G4A0p1QSWdK6BPp8i2zbMKhxJHB6gapZ5lAL8jIb8wLZOduf4X7+1RxB6L7vMGXIkTrZZa9Uai\npb6200CrhXwRxGTxN3COaKUzY+nIwqkPg2uLECu9mMrKt2uHtIzEx4s37honEtXc7lz/DIg3NM6O\n13NgrzfluhCXJIL/4hS4+9lPVNYfKs/EkMsM/SlK7Dw7IEwHEq4N4L+YEcKzLt31hJ21iXpWKpqT\nEarQtFj1EySitycl/sARUghvvvqTZdse8gBVReXX4OYkG0MO7oASbMHXY2HKmxu334JWxooYbS+I\nj4YxU5Y2TzzAk3C9No6ctn8/POZBdYuoaP3os/hTSBeNNfU1436aqncgienOvUNxXjw3lE55jRPt\nL4bY5Tp3+6gOhqAEs3rjMUtEvT7tWvWMJ0Ldw0hX2tZ3lOvHEgdKtdPziGhOjIH0ZIRcKq9spHuX\nwc/0LO2gWYeIvQ7d3x8/Gr/rh/fubwUia0Ka5wDisySqqDy0i0vjgynqsCfO4fVHSLzwUw9jNiio\nnVJ3vGBh0z72a+T2nwYt9adnAnbH/m8D2j+i/pRi7opp7tRdq/9lyGgRELaeEFkVMBXyydTnKKNp\nGYjIY2vjqVXuD3HEihVQbxJJzSF3EOAeVq3drxzPjJEaViV5ktzm+hWeE+qN576IsUP9X2VChjNu\noJqcun9jeoYEmAsnep9QeZ6e/XyxbSaUMFPeKj+n1buXeFFx/IcxJ49I+fDhw4cPHz58PDH8i5QP\nHz58+PDhw8cT47Ol9prTKAGnTBL1t7HvqE/U/nC7bNuuvxIRkZFSdur7VVL5/bp0vy2onP32iPJP\nYHcpSQ2sNgpjG8S32q5xLIOxexB6TwcjR6qOQjdZanFQPz2WMwCkrV5axOFdfP26htSJY0DmBBlr\n+oB9nRQqPksPIN2XE1H71UuQpgcH2fYEsRaJu/6XF5ba+fnOeewNHaVAAXeucit1T0COTBNLi2Ql\nUpABpVZwafveUhXqE5avoFgt7HWmyvKk7A6S4Vg9JiIOJAD88d71mYhSa1qJvXlBatMggCr/dQqt\nr4WA9qOYfLgWry2D1nv0j76x67q+dtB3Uxk8niG1OFMfS1L12oM0wY6KCKBovE6JiImUJntohQGU\n9VlZuFbFcttYIAUeEVG+R+olRFqaM7GqFM2pLS3Fngj2XqOvVY2l8QqQoU+VSRcsHoOUAhpRvLCk\nSun4t/eu/P3PLv/Uzhf9PqOxHhXuutjXS9NyLZX6a7qjonuipPETpBY4xaAq68NZGku3PSYbc8pm\nVAI6zVPTwnxlORGQbWdOQbh/tfx7TTIlfatl6KzO7I5RUGpT1aEHLttGemSgMn1NwWwvbJxuL9zY\nTlG8ogrfIjbvRCQ1YarPNE8piZhShqJpdqZKIFUTruzc//1/+o8iIvJx/zsREUmIiN1AiuRI9/Cr\nL1z7cHqs69x+b65tPEcoEJooBd9hyEZi6cM00D6m3oSUnoXLAtM9QrR/xs4Gk0pd0LXGmCcpTVRi\n7GSUvms6TWNhrNOgyNCeM6XxGvTFbcYPFNAiAn7Eu+OXK0utrzUr2Fl7ToFrszSlwifQPBaaAz27\nP/yMuVasn6jUAU01MmIuHqjv9Op1Sv0kXNqWcB51+YAUT0xzok5xA3uowqFhjjg9F539KyKSp0rf\nsfm8xtwZj5aC5mfLp8IjUj58+PDhw4cPH0+Mz4ZI1X2/kH9FRNIab6u0IlUiJhM2V4XzSWtopTmi\nxPysXBGrzzmzS0waIEJ4cU1IrGwHsnW5tbfqFG/uM60gjnCaD0Z7g65rh0QFAYm0gWysZEoRkR5C\nb4p6RcwvxNs0cV2ljYA0kFhZilVqTmTjAud+IGLveHKrpDWt9NR/KwGaNFEZbodVDZMT4wkE9IaQ\nJoifxbmtaqbBIQftYIhchpVuFPIqFagfCWy2nfttivuUhIzIuTZmEu88uX1MRLasISHQ0yrpulGR\nOiq1BQE4pFLWcuXQqSRjPzXst8L1ULm0YKU1jLaCO8D3q6Uy/Uqd3un+Lx5nEfVTOJZfBO7cHvYk\nPojS6CEiQUSsDHMq/81SJXbatR7h9XYgSYQNdl2QJEWK64mAJvSEjJTw1esJ6Qm0iGDklR5W370d\nq8jcb0dGbkDkZ5HCLY5RY6xnRCw38cvHoppCSNtybvS3yqiwTIR6vR0ORkDeKckd18++ig3OqSwM\nEVWBW/b6U+f6MzQLv71csa+au/6Ofjugn6Y0TnU/+r2WxvUFSPYqFikikoGUzl6PERDmnPqaegJy\nkY/upywN9bp87pDrfOsEGVl8tcf5TnT8BPMPTb8y4dESkHTHgk7R9dd3rtjjcLJtF5fuuP/0Iwp2\nqFx+xJgYz64fY4z6hPansba+U8AzlbvO8ejmkYr9VEN3jXHi5vAossKGDkhHED7u/z3V2qteKnU/\nmfAcmWP6Lb4XD/ZbJaqHQEkSknBJRhWVJaFjyFVIReMUSE87MpoKOZ/SUOIkdf0uIpRGxawDwlki\n3LsigPxFbxkJgSTF7QcjoKeY12K6T4pSMSKnCYOI2jMCmlZmNHcC9WvE9fWeGnYGwtpS5ijK3d8F\nzZMpxFljEsRVJKqx6VzaE9C3jseOlz/w4cOHDx8+fPj4o4R/kfLhw4cPHz58+HhifD5l87yUbjDI\n+gHwNaHjC1Q9kAHfu3eOgMgEcFXPDSndJhM8+ehdMUlUKRhpJ1KMTVTbifDpOXJwIqvDTpnb70jp\noQHkwLq16zkcHFaY5USADfA3dD8mIkJruoch6x7aNgVBnCNgyTAhaB0KzJutpdvev/vRnRuRkneA\n6pVsVxPpNEgdZBu0pBmENA+nO1RFmUmMqtXRdOSrhuuJc2u7BKnEkNJ9yaLfhHtCnmMhtJVSgsKV\nWJmTErFqRlj1vAAAIABJREFUyxwotdZC+fh9S9pipVPF320sVRGlqkDtricl1eXVhfOf+uG73y7b\n0tTB3k1rqcUDNJM6SpWqr9cmMmJ7iX03g6XAglQ1kNz3h570mUDADinr2EOrZe4pZQqvsfWOUnZr\n96NTZ0UR94OD9IPB+kmMdFeAdA8rdodIfY9U2KByLmVmKbihc8fa7CwF0UNHLaeUgaZU2IFA01jr\nnUstnfaWRtXhyX6RquM0sWL4rBp0RDZWUjQRwFXviNN9qim1+PXRBKQpxZb8OqtPHL8ftA9zrv6x\nZtO8pBFsnMQYC0yeV3L73Y3TPVrRZw28y7ifztB2Czi1B+Ix699o1pZTkEWpvmrWJwLsO0K/2u8t\nFdRhPjkjB2OOjUmzTNB35pRU0dU7sKX5BM34d//0j8s2TZtu1i599P72Rzs+vBtfvaRiF7Vrs6PL\nCWMyTYiAjzmpJa06Td+0B3J0KGJ85u5XnFFxAtLoASn7DygKmokq0uEe9uR/GYAoPXGxAVJGHVFV\n1GtPPefC2J4hZeLuVx+wFpibYxMS96v2quxv80SMwquQxxPmzo68+9YoHmjXNsftUIwVo09ckrbj\nBqnHHfX/m2vXT8uVzbU5vDhD0upTtgP7hKqP6t3RzmmAA4LEULZvaQ6BK0a2IloKuicjRWOn6X6i\nCrTuGw2zN054FpJWJesGfio8IuXDhw8fPnz48PHE+GyI1LPLS/l4oDdDlJMP7MwM1ClgZWV1zqYX\nxBCr6YYUix/wVsvu621z7vQ+kIP4gLfp26O9/WcoP08TdnoHKZpkFeYj3oQHKyHuUZ7PjuAxCKAT\n1KyJ17uUlSYG4Cx25hMpdmdYLWYJqbOCqF3GtqpsLt0b+z25b1cfnGfV1c4hUxmtlu8rh1zMo+33\n+TOH4OiqVURkB6/DF8+/WLZ1b93K4e21rbSvP7j9HEsrP35euuNOubXJGKuvl7vWjBqlAOrVUftX\n6mpOjNEEq7M8sRXZAJJlH1jb7UEeZ++2Ev3t+YW7rlX5yq61cKTbL//13yzb/v73/05ERB4Ov1q2\nNRWUdYlsGgIJiGnlmqIkfJrtPGtFndCh1+SNpyTmmkq9Z3H7aEgnRFGUuLB2yoB2TlQSrUrlHZWz\nxyFWX1gaxlTE0AI5YZRKh1PLBQhARNsTe126excySRPLxLKg/QH1XcFNvqL+qs71MemEnIBI9ESi\nBf93QWZFrE2U9O22Qb2dUBKVTDC5BOsvigjPtILVvwNCxEMg5hPdk7JUWQO+fKgoc1X3QqgnAiy2\nhfhi21BpPqQ4avKaC8SNey0EEREJgcRxm6iMRUeSDKoUndB9j4Ec1UBJBqr91sKabGvq7Ev/IFeK\nSQtF6Pq1hH4mZu++dmhXSwT0n26+xY7Rr0lZXbMJlzsjxyt5fv9gsEJTq0wDFS8AsWsHcgDAOJ0G\nQpNalUSBN2Fq56vNmVLBRqN2bXRfY/SJjp4xsWipPymQq00jSYLoL3JAbfOZryOKfQrbR4xjzDSv\nTbXbcSd2rUoUr48ktdC7e1xX1v8eQveb119cLttmOC5cothhnRNagwGYhXafXl25uXO1fbFsS1BI\nEhLEXkMy5f7WUM8jigsuM1KqR/98e+f6/ZxaY6/WWmxBKHGHAgAqYpBB/XypUGwPpXTKBKwxx+ZU\nZDF8yjOSwiNSPnz48OHDhw8fTwz/IuXDhw8fPnz48PHE+GypvSxaSZhSiqOGeWHIZG8HMc6Ej4e5\nkmMtBRKoBgzBwyeQgUOh1Aag+hA6TouCrIjcAO4OCOLNQTbNCoNH1dx1IGK7GiMztBqqufJEsKxq\noCgRjvRcEkDMRcHpThyrI3g4cdfQk7ZRqirjdE6vX3wpIiLVyVIwJ+g8vX37g4iI7HYG3TaDg0zz\n1EiEV6NL3339xTfLtgKaIZcbg/Zvi4/uWmdKmUAd9sPB2q5LXZpvc0FaXRt33XWtxsPLRxKBZD8Z\niiydEuAp3VLCXDWhex2ifWbSoFH1/LalVAmud3/vtuWJtfXXLxws/frqm2XbF2/+hYiI/M//7r9b\nth0O/9ldQ0WkbKR+m9TOcxWp2jgR9ZEXGJHi2pLuzxaGu5wy6CrXnl1t8HiKlMWGFIs1o9cTAbmG\nMXfP6XOkYBJNKTZETkVKIaB+ejy4/rTdkNo7xknVPk73sQZajsKDkcbzBsT/Elo8OaXMJ6SCBtqv\nmrweDqyF4/oTm4ZrSomJ6iUMmSfSpRpAPM5x/e9uLRUdYCcdGxmLGmkT3QBpxoII+KJFIUTsHpEq\n25JW1hHk+oqU+rcbd569FgBwB1BiNxPbcYzxLN2Nj+iXmmdMWKkexG+ei2akvtSEVvXnRERm1Uxr\nrK3XO+gs0X5lBY4C6SNpV2ipKOU//dPfiojID+9/vWy7OzqagWnb2X5ffQVaQk4aeCgUeP/B7l3d\nuf7HYt+CoiFWG9cmi8hIuUPqMUYhSExppDR2fSGh58+imE0UFE3zJ0QtKSAuldN9srSxHSMAeX1p\nT1KxH6HFlFB2NgKlhZgiUiJVnrBBPOaChu7d6eA+r0+UbgeVIogtzX4JOopErm2Gzjgom9jd/1fP\njO4xD2j/Nc31hbvGnHTZLmb399WlFeX8/MFpi33//Xu7bjxjnz+DGTiNvwTzykTPyeboxtXxgdJ9\nSPNrYZGISH9yn18RpUI1Dc+V8skk+hPhESkfPnz48OHDh48nxmdDpGSOJY2oXBqITBDYm98EpfA5\ntBXMgFV1mhOJUkttGyI74q2zJWXbCArRXat+YbYy2B9Q1nu0t9B84/ZBVlciWIg3Dfv6YJWa8Bss\nPLEIkdJFn5LcQ/KQ0srtvOQSaqz+iESnKr/p2q61bt0bfBFv6LcgNhKhHDxdGbFKvLm+WT5T2YWK\nEJFv3rjfhpOtIL589Y07D0I6SijrRlRqXYBQ/O47W80fQVj8BS2d3qQOTVOOe9VZyWsIxeZ1aOhX\nilVff7Ky/hFk5KKw/lRhlR4S2TIR9XoiUnrvEBEldP704/fLZy+e/1JERL7++s+XbZvIdYb/5t/8\nt8u2unXn8rsffr9s64CIHicqIUYZdZrYeUZAWw4VVJxJnXyH/pSQ6nKPAghVWBcRmVHCG1Cpt5Lt\nOyrASNH/Yypdn4Ec1VjxxezhiA57eLB7sgPZNCdvsAFIcBnxStv9vSHkakJBRUwSFyXQjrFxiGgR\ndvR99299NMXkAsTjoTYCsBZ0DCQdkuK62H8vw6qzJ/K0qoersjkjOHUNsv2ZrAGOOfK8ghUsoTrR\nrNvsnNTX7vrjWzsneGH2jDqjaCTCvJawKSdwh5mlNjB2xo7U1rHqD4kArrIHq4z6CVDas7ZTJEyL\nPQi5z8h/bTmWImIhkah1QqO+NsB3MSAC+AnI6oe7D3aFWP3P6MNvXtoEnEJOJqBim7sPbr8391Qu\n37kxsyVJmAiFHyPhBzMQoyQiVwoUvgyN6+t9ZmM4VoSHnlN6j6MzRNC1xeXWkI4JhO6IGPh6jQH9\nVntbCAI6y+QEQN0jKkCa0Yfzgsc1xjpd6wMQc352yd61E3vXdijQ2RE6HMXunnV4dlQzIf2x2/Zx\nsvbPM9fup4OhWlEFqQ96dqsDQpbZM+bli1+IiEhZGEr1/dvvRETkw63rJ9uC0HfIKdzdULELUhv9\n3vqkyhilgd0TAZ98Qw4oBdwWYpI4Gkmy4VPhESkfPnz48OHDh48nhn+R8uHDhw8fPnz4eGJ8ttRe\n05ykY2YxUnEJsQMnVaCdDVoPAMEyZJlB7TUJDbJWRdlpJrgbh4s0fUCGsiqj3JDGiZLT2aBzVmNc\nItEu+ijUnEGiujSkI4QUXYpUQUumiLMqqpPu0QxYPs0MYlSDzDm0lFnduhRdEphmR9+46y8Ixl/D\nmLQbHCmz3ts+phGGskTi+3jzTkREnm1NWykCLDqQjo8qVe+2pPcCrY79DaVRHkBipaKACOm+DPfu\npiVi5a07v+ILa+sSqZ2Hg5HtW5BS58bOKU/ctaqhq4hInoOoXF0v25R3rLozqn4tIvK3//B/ueNT\nyuwXX30jIudmpC+e/am71tpSpR9/dmmhjr44AA5PSCU3S5CORRqpJz2hA5R9O0pBm2ev9YkQ6ZOO\n9Y6WdAwlq6D3Ms52jBZqyAN+m/GF1VCY3hiMroTqkPqJGm4T/3VRNE5jSm0s6TNS4EaaY4SOXE6G\n1iNSgO1AqVicXkNiTEEEsjmde31Os3bnXkFZm7TKOh3juAZ2TOiXtB/NSar7dOZGCyNZ+m0BUnzf\nWbqhwz0eKbXYIKVyubM0hqpyrzeuDzeUHlFSOPfTCWNxpjRyh5RxSGnJHGm5iQynN+AtsHCzNk+n\nRrE1aXFhTsoy2++IH0dErA8wt49kWhyhz16//7hsi6HKXoaWvosGRzLebEDOJheDGCTvE431I/rp\nQEUcapbNKVBVFJ+pb8RqpM5uC0jzqN4QpzbDhWbBDYZCJVLWH5CqiyllqIbc9DWJ8T1W29ZsrGb7\nqFstrhAtpQIvMNbokSTpYiRO4xT/jrX15x79frWm679w/WS9oZTyfE6LCclIuYJ+1DxREQX0m+72\nRsGYMcYvr4yC8uqVO+nLS3LAQLp/ldv3vv7C0SueXTm3iXkkZX88u55TrceXWxT2fGXbCijvR2I3\nQNP9GanSazHGRH1iGAf5H/77fyv/pfCIlA8fPnz48OHDxxPjsyFSp+pBevKyOQHVCCNbmaiH3bFq\n6Jfu71VJJNaFtE1oBlaTCb0rDlj9Ktk2JKRJlFg32xt8DaJqS6ufOnVv1QmrPSsBMLdX4qZWQjut\nsEEUXUMVl1XEFbmaeyLxgsTKMhEq3dBTCbcibLcP5kkVTyidJ0+8onTnFx8eH79GGSiT3X/48ScR\nEXnx/Otl282DQ124JP724AiAGZV/pyBIPrsylOqhVWKnfS/EymqD1UpC9+RY6arO9lHmrvy5TQwl\nqUC2Hycq6wZKwHIOSsYfqXa4hspuV7k2ZCHunz84kvP/+n/+T8u2P/ulQ5/WJXmIoQDgDSQnREQO\nB3cvkpFJrCAK08pxjetpVu6ziBBJhQYYfRqBxJQl9Wv0nY6/p6gHkaIDeBbGLDeN5W6MtgupYKBc\nu1VnSOCLqoLv7w3VW0PCgNWpc/h1sSdc0z7g+FRkAXRAzzwghe0RZdqblW37+aM77o7KlUf1KyP5\nkQoq60w2viflaw2VTBhRYt4SIq1r+IEI+Ipgsdp7gHEdUpl6qD6ddD0zytrvb41YrertO/K66zHv\nRdiWlbb6H4HOJYQ0qU8giZjL6egI2Ow1mKqzApGIG8ytu0uTQokKqKfDL5THuqIFCZHYI4znmY1S\nIRkQEXLWAqX49U/fLdu+fed8LNvR+tOzF+66Q5DDw4lQ7ZM733uSpFjQHyIxRyhUykgBPMF1zCRx\nM0GmIi2oUCdUtXegigOhv1AWZ//VbA3CNhUbBYPrn2FAzw6g6CN5NxbwsEvOED7IGeAec79KcJ5F\nYfNaCSmINT1/EiDdDT0n9nuHdPbP2afUnXua0TyBrBBZPEoMhDVA9iUm9GmVumd2nlO7Yv79+iV7\nx+KYJNQQYsyGo83xF1CtX5HNR9uo7A+eq1TEpahiS5IwAqkJVvYvkJ3ISH4h1qKkkDElzEkMBfKc\n+YnwiJQPHz58+PDhw8cTw79I+fDhw4cPHz58PDE+W2qvHTuZhYw6ZwcjP9yToS1gv56UWAfo4syk\nu6KaLgERUDWjMRIsrzCmkg5DIl2mqtnBWlBKcqeUQQMtjiC181T4kN9KVQGcZalTpCCjwv17QanI\ng+KepLp6Dy2qrifIGAgjI5GqwDx2RnZO1UgzMAh4UQpGWiJJieDXqRI8a4e4f3/3wz8u2zZIaYVk\n/HqqbrE/S4vEoYPnS843rHCedI0HaKvs8NmW8kiHHkTE9Ztl24tXTj13IAJufXLX3dRG7J1AKCzz\ni2WbavWkpLcVRO43AeD7jooDchhzPpCO0f/xH3/nzuOMMOnSIjGZXG4voUHzYCkLTQGGpE8yIS2x\nLd11dYOp+UbQzCopY9Kg6bj/H5AWnYioHWUgdhO03yGlVpN6+aipDaQCRiLnqqNARJphqo/FkPnd\nnSOU7iiNliHNsN9bOi2DBk9A55mCyB9D44rH8Lp039/Xpk8TQauqqUzZvMc01lEKfsAE0PaWWjse\nXf8sckoZ9CC551Axp1SUEs/DiOcVJdvathiaRs8u7Ppz1S/jtBRSdhdELB9ad059a2MnhaL/iLku\nJA0bNR6OiILQglges/EttLVOD9ZOMdo2jS2NV5aYE4mAr3pUeqyaNLtipH4jSs9PILmHifV/5ULP\nRzv+x/eub3/300/Ltu/eOu21MLV7HKdagODOo6qtrY9wIIgotZshtRNQYUlSunN/+cwKcNbQKiJe\nt+SFO+fNpY1nLYqYkZ6aqLDgcuu+p9p5IiIZ5pOM7pMqn1eVpSAf9m4uqKk/p7gO7pMVTMq12Cmj\n9Liqgq/Xdvw8VX04KnZSRXV6/nQoGmhbeu6O7t5FRJ8oQDwvSuIgYD8DqCchjdOlAIHPU2keXJMh\nquNFautKX6DUWYa5oCBz9TUuTSk7XJSj4yO/sDbMoXsV8BuO6q0R3UGvg1P1OhQCKiiIkj+MOXlE\nyocPHz58+PDh44nx2RCpSSZJSB1ZVzpta0S4hwf3d1fbqi7QN2wqIVYCJi00RLA64zfSCZer5ZIB\nlbUmWpNKaIF6YnGpd4i36YiUXRO8EQ+0+pzBKF4RiVFLhkso0KaE/qTwhos6lgtw13XkctXOrVYi\n8nCaA0UV7K26qRxKM4WkFA+0Qd++1xsmTOM1nN7WcxA1Z0IQfvPjr7CNVrAD5BcyWs2D2KnkTBFb\nYTAS2ffufh7VXiq1VcAlVjXPrwyRen3pkJubwry5IKwrBa0LAOZJOtqqSrvWamvXPQ9uxZjBY6+q\naAkF9GO7spXWXe3u048//HbZFsauJPdia15Tl6X7zV1FKsq47raxkuD0EqW2o1thZqUhaGHoVq4z\nEdYTIB0j9bUtZB3qxla/DciZIyOX6P+no11ji/6cF+58L4jYrB5vTJhuQPINSEJBuciXl6ZA/+OP\n3+Iz66cXWPUPhJIlQIljyCTkBam4A3WLuYgDl70/GkpSA7FuKuvrUD+RKOUxgQ5AJNK6Q5sB4Zno\nuhSJYK/PT8UFiNpahi4iMnWu7e7vDZEpIAWQkbJ9dUCZNqGOMVbCqvCdr20OiTBndhU5AASKFtj1\nhxgL/Wgo7V4cmlMUdp7qWVkSAff+6Bq5g6xBTIrlWqgTsdIz0ISACoBmkIGbB0NYfwIixb/9BdwD\nWrF2UiBApWZOkzlbbF5iDiNEVO/PTFUcKyhmX+wMJdRnR0kOCOuN+96arl9RUZXJiAmtyIFqsdp9\nhvG3JgJ4DBSXnQWur10/2RNKt0KfyQh90d6myFSS2rWqU0DIhSW4TzMjKOj3EWMlOJVTa31C5REy\nQhNL7aeklB7ASWHAc5eV0HWccK2BSggouioiEsChgewXF4kRlgSZ1DuQMkYxCPdZuX10rcEiyfEY\nuZ1pH+qX19I8qWAjz3GJ7ocV6P/wFOARKR8+fPjw4cOHj6eGf5Hy4cOHDx8+fPh4Yny+1N4wyYpU\nbwdRIq59px9AGCOT0wGE0vFMM+axCbFC9Ky3kqqmCGD8nnRv1LS2IHh6u3InMxMUPTdID4wGbfbQ\nnhkD0rECZF5sXtrxAd/mgMcbSmNmgJOz2NIez6BPcrg1yF5E04ikWAsdqZ5VbJHTGWeDcRUqLwFP\n5yvWvYHJqTDZ2p3vn/7JXy/bVEfkd28ttdYgy5CTsnCk7c9kexAqZyLWKme2gpFzvrHvX2xVM8VS\nawMU2/uKdE86hbZt2+WVI/Syts2udND6QG2iBPEI9zOkdGeD/VWNEdtzEFrH3qDwm48utZlHrM7r\nrqMg0+gBxPostvRRB22lWVRPxlJ7XQ8SMaWbE0Ds82h9ooQS825txNqba3eNH452rRv0nYkI/QmU\n6hXaj2gA6sjpx8d6KgmNie3GpUrub41Yrlo4q5WNpwBjLCe18xRpESV0JyReE0HjK6D08Auk0Xoa\n/wcUGXQ9KbYjt8tpkQEE1cPhdtkWqrlxj7HI+Yl/luLhj7UQQUSkQNHG8USE6VhTdjQnYEwweT/S\nog2aT9RcOFTdO05jgKuQ59auDcjL00B9onT3s++ZggBzYRp/qpUzdDYXlejjAXIwdPdlQlo2pH4d\nIC02kQNFiN4zUVosBAH4amt9PAm+ERGRrqf0Hcb9Cmnu+mjn1mjhEVELDujjrGWv5G1Oy6kGWJpa\n++fQu0rovquRdJzov9Yn9Vuc7k1BLE85PYfPQ6KAXO7cdW1Y7R3PgozI8z0KHlaYf7m/KAF+pPlf\nDY8TIpsnqpVIRRE9KBplaKniLm5wfOunqscVky7gspsGf1ABVIK+EHJ+emkf0pZC8czQcgGGu59M\nlFc1fm5j/VzbibXNdLzw0B0XvSlrp2kp0CD6Rnh+biI2TmOSoI95558Ij0j58OHDhw8fPnw8MT4f\nItUPMtGqMgUReyZUJVLVX3qDr2e3Sm9YsRmox0iy1NP8uKxRdHUID53NpamoZ3gLzkgxWktI+ZzG\nDjIJxJgbgQipb5mIyIzS3aMQAVVLy1XCgeG3Qd+CjQi3Qt37+IGYbthFQqufJIJPXkteWw3OnT3B\n0BSdmg6GtjLIcij2Jnas7YVbEf7Lv/g3yzZVYt7T6vvbO0ee7FtS5wVBNSeZhI2uogkluL5xvlsq\nzcCq36WgDY8m63ACsf7th3fLtv7gvheQirESCtdXdo+1nHpkj0eQJlOsqp5dkecZFIg/3tP3gRIF\nhOYN2MepMuRqs4ayd2r3f9AiBxp1TePaLo5BgB3t+FfbPxERkZvDr5Zt0QyyK6101UOKFePvQ7fC\nTwMqCcfnFxd2Tu0JRGGgvmdEcKzIzrzZ1q49545QUqwcb+/snmxA4r26sFL7unV9Jjlb6bo2SRIl\nnZNfmsov0OpTUdLN2hCZI/r9w8HGTg/0gcvPUyAMPRWZqBegInEsNaD805l90LAyzYkAr+T12xsj\nVqs/YUx9vYGyd89zFzrDHNLKGcv/CfeYybEF9sfjP8K964lsPuCeXV5ZAYAW8rCf6Rpee+xTp+Xx\nqrCRxey5hnFKKFkI/8upY6QZaBa5DWxRsl9TQc0EGYFnl1fLthcvHIp/sXPb9jeGIGpfGwl/OoKU\nzXNdAISBi5e0L4xEAD/cuz55T9IxK5yn3sOyPCtjEhFDYUSsiEDHoYjIvcp+BNx3ocBN7gU63lg9\nP9YsyiL3b8ftgbpWlSF4Uagq3uTsMD6WblACfJqSe8EEJJoKvwKMk4RkP3rsL0RfUxkSEUPuztAv\nzLEt3WvRAimCb7Jl7qJJUZUbmOGt8gsYOympveu1jmd+hf+sDYUI6zR0F99Fuk863ZDCg0yebO7D\nhw8fPnz48PHHCf8i5cOHDx8+fPjw8cT4fKm9rpeRVKRnQIYTKSuvgcF1BE+uoNXBcHewpPEMfysA\n2WcpE6qhQQLTToYH9agtGUoO0BRqSe14VlVoOvdRCeKk7RSAovnQmyp2C+i7UoIhQ+ZIGQmRndvx\nHudrKbPqCFV2IluukI5gZd19AM0MVpGd9HpUM8cOr2m+mExrL65cau/LV79cts1IH1xtLWXw29Ep\nnyucLyIyq+EmFQqMgNt7gtbr2v09LIbGBgUXF+7vmztTQt43zvD0vrM00jw7+L4YTR9GAJknn9BW\nCkmldh7cPXnxzPWrTWZEzDBy+31/Y+e0gsTuOiR1bJhlDhORrSfcY0oB5Gq+21k7qeHw6eR+W+SU\nioXG0nbzlV0DvscjN5g1jWvH0tQzK2bHgNGL3H6ciRY5oABBLBWoqYB1ScbDSOkyr/TjjUuLrFbW\n/praCyg9EOBYMfV7TeUpKfiM643fplSwMCH1zPo0JdJsZWmprQp6VzxPqFbMOD5OGRTQz0o4Zafn\nQQNFTy+n9NgEojRnIjSNVJAuVoa5iwnws2paEc1hxr1TEnHbWRonAbUgiq2tNQWR0bmX0IpiHa83\nL5ze2WpnabQAOncB17PohWC88PyjekdclDBpuoeKPSYIecWfImDTnLQCaT8nbSulUhyRHuuJiK8k\n+i0pgatWFKcblXhdN5baOxxcGl31mUREWnzO27QTKlGZ9QE1jcYkdiW2pzTW0/hcd0nEUl+cWtSm\n7okqogUC2l7T/PizmIqolL7CxVPBFJ19JmJ6SwXltpRQPZ0VVCCNRs/dRaNMVb9pAoj0ukKa10B3\n4AKwONAxTmk0/JbbTmMcWW/Q7U/T8nytSqzn/erYCZnao9qGn2gTLigJP/G90JPNffjw4cOHDx8+\n/jjx2RCppp4kX7GvHsiupDqc4o2wY3nmxdeHyorlsQJtshDm7KczJBampayXVjBYYTW1bXu4cauU\ne1JRVqSrIPQpUvJeRmrfeIOvJ/vtae/KdNUvaUVEZF1gTETYi1CGmq+oXLQGYZmUbaNllUhv2rqa\nFG4AqCJjxTdxXTN84AJaaT67dCvYNSEN93u3gptolVaqAjqtNHstNaXDx/Dpq6hNmgw+XYNrk6E3\n9OO4d/u73xrSc2xcGw4jeYiBdzyQKnWewjuOtnWT+00w2TEOkB9IQnduGV3/EIHYmBGqougIr8iA\n5szEbByAvnVEgEzweU8IR9+qJ51rk+c7WhkOiqBa+8dY9c8DoZmtO6eOrjULHcn75frVsm0LZKkk\nNG2CenEOUrqWcru/3c3bP3xcthUYUPcHa/+r589xLXZKumJuOjsnRSezjI4Pomgn6uFHq1XRMmjb\nh/6dnKlNK2HVpB7U54ClU9pWVZTtnmyAeumY3KyoNFyJsoQ0qaPARIUlKmOyIv81vf6YJyAQZZnQ\nHwNFjogAnChiBiJ6QIUF1cFdY0oFA9GyIrdDKRJQkExCDESs2FoBxvwpYq3eSKAprBif4V7PvNAH\nqjtKlSLqAAAgAElEQVRTFcXYuHPeUz8ZR1WKt9+WpWtvLvG/v3djcilXP2v/x2v/AOOqKMnrcueu\ncUv+j6rePnIBDk7m4cHmGPUWVESCMx16Loxgffzosg4sYaCoxkRjfUSfYeRMJTmYqK1+sto3mHSt\n13AGkASPj6XoDCM9hcopfOJYA3nXqQQJt7Qq5Suhm6+hgdfiHBGqunC4yYED/7aEEi4oHZHSA3mM\n/igqF8R6T+jsgFwFdF2d9uGePDE/IQmhf7JPoN67YX6czfkvhUekfPjw4cOHDx8+nhifDZHqZDzL\n/Qa6CqO3QF2kTGIrMs2b0sJ12cZcKhVsY1+naRHpQrkkcSVaCPINZPnUPeB7NZUGwwsuXpNIGvYT\nEMKjPlXzQCWh6n49ogw3I6FFrP6pqlW2L9wbdEmlqfMapfa0+NYVdkrih2nu+E19bb5u2sYqCMrt\n3zbK6bFjvbr6Uv5/9t6s15bsrBL9YkWsvtvt2ft0mSczT7ZON2kKU6qLRFkiLV3pykKyZF2QkCX4\nAwgJjPzkN9sPCOGryxtClniBJ+AFZHElQEKUTZVNYZw2dqYz8/TN7lffxn2YY8Q3Vq7tdLGr8MHU\n/F7OPrHWimbGnDNijjG+8ZmZpdJNjvphf8Opa79aSPV//Pig2JblME4TM03qYeqymt5KAnKSz7Ba\nWPjKZJaE1Rd1VGZmk1FIhU6lJt+CKI2s3ItagCv+iuEGjcUmYj4N7X7rQUBdFqKHoyVHRVa6WaGN\nEU0HbvwKHz9nDq/bGSRLaJQETe1shr83q1jhDqX+5Dj03frSEZw60pVnZbUpoG7O+1p3i+NJNYIw\nUxSYsDcY49xw3VLxfQSnVXE/sDPoVir1dY1Mkqn2IezntCf9BFoS1QhRazEFqqPoZzFOJTWcVgQK\n9FTRrxqSfp4VprdaOy+MuzwXbSZ1gzi+6ia5+k4E6XWgVVbkWGFv7jjSk8L+o9dzpIOWFWVBn4gS\nVMp+j4miVrDOXZrWhgz7mMoKvpZReyJaJqAjFbmfFeiQFoI+0HQxL61rZIjSq4CKZ76sab+Cpmbg\n3yM6WxhomutQKmK6ShsJNU6cAjlk38gE/R9D86YIEpGYutTLI9JUFePUOQ1LpYYa0dFOR+oZYhxT\nX6UI2hKo4mjkqEq/P8A+HP1i3TdFk4jOlGX883uKeKTpqploSY0h0dd1DFHLOF+x1Qj/ls7RIy3k\ne0Ogb4r6EdlJZD7h+RHBmco8SYR1kYqZM9iWspqZUkMlliwpjpFIO7F/qiVEjj5WJ0rnp1s8/4tr\nkfNVk1LaFCn6xnuyYgiKi1Qz24navZwTEZGKESNGjBgxYsS4YMQXqRgxYsSIESNGjAvGE6P26puL\nFYEja8NVxcWcoseFOMGewh24Ji6urKc3GjtkN4DYetYXwRjr+QEqzioO3VGAVxMocgYIfjwWB3Zw\nimkq9fdwfovlqjzPzAoX8xCo04aPBlIbiwK7qoguK7AiaLfEHRfXPa4IZI6f1GsOT48nYePRgTv2\nlkoU4DI1Wa5hAcGqiPhKAFB74th9926wIjg+c/sBA/S8EAH6CLW7pkLV0gF3q+p0F2HmEe51PpE6\ncKDRFrmnsLPG2zJxGpP3VQWgw3ngPjOhanI426eSTr9RDtQirQEaFYfnm6AKykIPLIsUb3HHhqCz\nnCllRQGwiB0BaWcl398AafrLCt2ZfR+VBSw8Ft4nWKZusFT7D4htJSW8gXGUiXieda1GE7GEMPxN\n+xExcc5xnqIXL1K3E6UxaT+ingj4fCx1tTa77nLOIKVDeH4p6eK0q5jPnUZhu5YE3E9LrGunNinh\nfo5GTgHRdkMdoAuxLRIwToQyoqA1EyqGniGNmt/Xdjv0xbMzHyctuGOLrtWyfD3VeoZ7Us+EWmKt\nsTnrBXp/yUCFLaT/zWEPsCFCeR5hNJZab6jJWCqr2B9jXEThE1DpGfZSkWs1Uu9NHye06Vge3Cs2\n3X8QKN1TocDodr2Q65+gLypVw6QVUloz4ZZJqc1lXk0wZ+ZCWT14EM5lxWoCx1W6bzjgWPBrpGv4\nGYT9Sg+RiqyKhQDpxl7P9Rb9PsTe0nd4H9WSoLD/0GOAbqTbvwq7OV9m6ToVVcqVbkYFBvMg3bVK\nWbHag1jSgNKsyXzi42SOc/IgpUbLAzOzei383e16XcUW5qRU2rrfD/IBdWqn5ESrB9TQPkxO0Tac\nYO7QcZUWiQJCy2O/A6nKQamCCtA5n6vFw/tLzSMiFSNGjBgxYsSIceF4YohUeWNpiQp7IU6el2VV\nBaPL7qa/GR4cYYUjKdENvDnmYtzGRaym6ZdY9RuIVLPlqwUaoqWiYq3WwrZW4iuYEk39lop0QdhW\n9eZs4s++lBriMRIcY7oitofoVVKjz2A1kC79zXxWwWqhIWJ31JCrSVp7HcaFo4GvCCtMT8Xbfa5p\n2KzXVPJjPToJqFNJzP8ePwyI1FhSWIv1SSp2FkA6DnpeJ2sbYsyK7C/D6ms4CCviNPHjb0CIma1U\nP4f5ZuorHaZVV6u+SqbYsVFx9IviTbUzSIFsNZthf01JF+fKeTqTNgRyUxNhM5GOmbR1uxaOn4hQ\ntoFzSuX41PaPp7DrEPPDMVbfJb1+oj9iyJfPWQfL9zsZBaRjVpKVe0LjTjXTg6AVu9O2LqwbZGWW\nVUL7NHS1ChNbFdv2escr5xuOT4RRxN5YYVP/OxW0jKjTSr1IohVqlofVpDSJzSAezwRhnU9pdOnf\nS3AMJqVUa5IGTYGt3EOa3o6GgnTBkqMsFeQTzE/5TOr/cV27lHuH05sJ6sZ+YhXWH/R5imbCuo8S\nhd0iQN9q7+L7fj3zIWod7rqZLk+5oYaYU6KjRCZ8TCQCzhXb0iCyn5W9TY774bfv3L/t+8Xlt9o+\nx1fGmItW0vlx34u6eoI+AmEuCdJcR91FdSkYjcM1TAciyifCIW03gwdMIpgCBeDtPOz3PPPJmqD/\nNfQZis7NzJZLoonrps8zNXhmToim2lMoXyQneb+idUAqVhNEsxTB4XnSmDacE+wnVpTlfO44wzEB\nKqymlymOUdS/lME2wlgbjRyRWxC5lUSdxRxjR0xyHz9+ZGZmxyeO5nIMbmz4HF8YazMBZUXEH/5V\nk16iyFVhXSbTcNyZXGtaMEwyntCRtE6m/n1eREQqRowYMWLEiBHjghFfpGLEiBEjRowYMS4YT4za\nS0olM/WdqAa6ZyYGUQt4C9W6Dk/v7wXI9NEdqY2E37TEKXyEWnczpQUAN2aAjksLP1YHwuJUvXAA\nHy/Fi4Z16rT+XgkeLGJsXtBIzY5DhlOodsejsI+awLMT1nUSeeAIddiyxI/Voi+TnCch7Zl4MNFb\n51J317eB2uRpZhURtqMNr+7790uAdg8e3fJzGsHtuO+QaYL6eNtt/y205tbKGrINsGxDfHTyAJFf\nbcFjqeKQOaHduiQALHBOZRWR56SAzL9n9CCSpATWyTKFaXEvQFmpYJztmYjvCeuZVUUcegRXcopp\nzcwOh4HSbLb8+lnrrSQ18TIK1QuMX2g01uuaie8Jz1poWcLS6i1Ed+ZcfGRqhUBWaiLSKyldnwoo\nlK/V3R9pMQn0xVSOv9EN92wm1uZHh4HaazSdAiwEnWKLPRr1cSyKWdfr8JXk/pNtVBE/e7SKstkm\ni/nI3hvqos3fkBbZEHpyjDqF6hhNz5zx2GmMUT/QElf3rhbbTkBVNMRvifrwqmyrkDIRWQLrnqW4\ndzMR4M9nEOJKUg5FwUsRZS8hwM0kAaZcCzR3onXNCid7mQuSNs4TNI7Mq/SFyyXZI8d6vNz0fpLg\nnLTW3hz9M9X6m2haHc/VagO/xf0XGqmBvqD3P0HtTKUHKZ9QfyLWZNR5IlmwdqFcP9qni+QI9RDi\nIVScPJnQsd+vtXKOsHwCT62BCKtnI3qbiaAedRrpWaceU4XY2i+hSCzZ2PT2b3fCuY8GfqwJ/PNy\noaA5Z6gA/ezsDNu83/F6mu3Wyu/MfJyoZ1i5Ej7v9V3YPR2vf4/JJurjRRpNqTq2wRz9ekWwz3tt\nHgnaU9suRwfpdH2Ml+htJRIECvVN+mSW+hx0XkREKkaMGDFixIgR44LxxBCpYb9UOAebmVWRmr8Q\nIewcq9SWuJNf3Q8oxcljF2cOsThst/239QbEZnqFU6T9s+KzIAiVJlIeBSVLiFzIm/kMb8T6Bl+n\n/YC8E9fwBlsqSZ0wfJ7D9XoykrzyxL9VRMrabHIJQMeaDX+D72CludH29PKtVlidvHz92WJbscIv\nsYaZH2sJQWWrJRXk4d4+EQf40rXwebcqKwgI25tVX7mW4MmgtevSLHyv03ERIc+lqMOW+bs9V5Dl\niqxq56z+Le2KtFtN4Z0BkRyKJUZRETz3TkFB+YL1ouReF+64IgAvIxVc03XHRAJlWUIRt6If+Tni\nad4DikL1MwqqVWzKum4arQbE+1prrVg6+7YJUQ+t/1X8QRdvsWvAKnQu/aSEBJDpxBEZpj8fHLgl\nRhPi5ZKgD9yNVhTg6rfZDP0kP6c2l9aQZPso+kLrkJI0QPWcavJELFQ42mp38X0iQ4LW5etC1PE4\n3PeJiOLpYq73ibX7tE5gC6v5RIpcFjYqIsAnAsMEkJmk+pfQX8eSws3vVURsXCBSspLmeeZ1QZMz\nXH/N0eSsKAoQ/khkXBv2sVIPjeJkqRhAke/lS17rsT+gi7bMO+iLivAwKYK2Hquu0rgueXSVYA9S\naUmyDZMhpK/TTmAkNin8OFmsoz5EvTQBg3PCVOafpa07lrfbSKyRfjjAcVUUzmsbiSs36xMOTwKq\nq2gZ3dtbgvRmQG5KMneyOZttRw4pop8tvD35DK5KUkIbyQCJZGWk70FOx9L/mSAyUUQqC9eTyX0d\nrZgmhCjc6/UaURO0WpVxj/tO1/NE2ByK95UlYu/sdrf8Gmh7pNYtOCetdjEDc6TI3WIea+3FiBEj\nRowYMWL8q0R8kYoRI0aMGDFixLhgPDFqb3C6tJlU+a0sA2SeCxXWy4Ngs335SrGtVQ+w3LUbTmPd\neit8b750eLIBN/C5wIkNCCUrQOz0M0J75ZpAhhQbzwTuB1Wk2yja1gKReQGtr4t9ZzPA8gInV1Dc\neDR2ainbgBN3U+BpQLuXO88U2/a2r5uZ2XbXPZMa8DmpimCvDrqFxV2V2itRbKc9ApvG4k68UQ73\n6XJLnY3xdXF7Z9vVxcejgvZvVKW4aIn/hJ0sRFjLHdfFs4lnrLTwaBjOb6zUDmjZXGDsJeiT5Vwh\na1AwLF47FeoM7dOQYqgL0MMKY7O4pkL2bfinqN8R6WAtmkk6gF5Y/b5TZuPZepHNFAJcdTGnsF69\nZQrvGxHqVsoU2/r1V3ht2FQS36UFfc9ScUKGeDuRcXp6FvqEehH1IXLd3vRxSlf6wdBdtDkm6ugT\nU6HbKbZXd3p6wSiPSeG9UmuktirqbI3xWSqr30zok2NQq+OJCHFxv1QwPitEtn78TSR06H1aoN81\n1ZeqRPd4pfFAn4pX2Qz7oShdqU1SajW5Ll63OnAnLG4rY3KJBBVRL9gCySOZiNdLHG/0dMuVRsUm\noT1moKweP/AC1dOiuLK3SQlU0ULoTor9m0LLkYOlEHnacxopxxx/duYVGxag2a5fu15s45yhiQJT\nC9eqxdr552y+fk6c907P3B+LY10LBJdxjy9f9edUDQXkNSml8HGSPtG0QPdOxu6tRxotKWQP3tdY\n8LkqBeprcNFfyLWeHYdnotJ97EdLccXnDa025dnZhgeeFjwuEh/WpS0diPKzktPN/KU8/gpPLXV7\n534mQl/PZiwWLfektIr5LEbS/yB3GYm3oY4FBun+vtDi42HoE7mM5yxjEXjfppKf8yIiUjFixIgR\nI0aMGBeMJ4ZIVaxudXHiHsPZmim3ZmZNOFUPTkXEuB3exK/v+Up3jlVkb+hvpEsssaeycmhDvNbB\n2/dCkkiPT8KqKhWUKIWIepL78etwB6+3fKU3x0prIDYJiyKtXuoF4fiNrW2cm7+FD/th1dXsSP27\n8hLbfB/Xtm+Ef7duFNt2t4NTcVlSjbmIzWX1wTf9pIHvrTj2Fr/066LVgKwGyvWw+qmLiHI64UrP\nr79WpiuzuL1jlahpqrQbWLopQ/EZ64DpyojCT7WuSLE6a9RVxBnOqSIiyiVU+zNp9wFSXZnOqyJm\nojMqROS5ZCs1tGDTIce3RK8S+4N4dV1y6VtHgv71RwHVaTZ9tdrG3yqALlbTK0JxoK/S/4hIaXsW\n1hF0kVZUByt4RUQyFPvLp9qGYezOU0V6wn1qNtbtBKpSPYCICRHGiqSQ87q0rxGlWEq6MjuUXhdr\n92mVrApQr1bL08TP4EadJKHddQydQOy7IenSzDsopb7fHuwPtsSJeTgIfWZTkJYBhPVbUn9sTJGx\noPMl1DgkwqVi5zmQXr3/7ON1QU4plC43vO+U2kF4W6r78Zn2vchF0A6UhH1iBWkHWrSSag70l4kD\nZmaHp+Hv0sKv6z6qIihK3kR9wMUKIgSXf8whdUE6KUDvSmIN7UFSSWwpECNBn+ZAWNUpfjIh+uHf\nI7KcVzH/y/jvn4PINDqhvdTtn3Ute8fHxbYCaZTr5xyj00WrFdqEDvCKrvA8M/0BTr1/6u1P5FQt\nGXjd6nSSACVrqMUIE3DE4oSJMku2tTwnakC969vOiBDhX60rCKRH5inON4qSn50GxKh35ug852zW\n7qsJqlfDXKzMAdtV6zoOca+n0teWsC7KFLmH7URd2u5HRUSkYsSIESNGjBgxLhjxRSpGjBgxYsSI\nEeOC8cSovWpaWYHs58MAt7XE+KkCqHyRixBzFt79GgKPXr2+b2Zmb9+9V2yj34rNxCkczqo72wEC\nbJjQUyhoupCCijTPbXTEiRt+U7l4Ec2XKKRZcQFgqx62dQRa78JbpFYPkO1crmtML4yxQ8asKDoX\navHKZvCFagndQKueeSoFmgHZl8SzZoFj9IbwsxIvlBRwZk2ugS1XFyEqi7xOJ94mhKxrJaFgAAuX\nhMiiK7mi0oR7HQIWJ+iM1J6+79P4Rag9iGgrQi3xqCu0YCN8Ppm4Z8sQAkUWzazXhHaoUgjq7U/K\nainXRSh8KTDyDDSiesAQ0lYBOotf87Ou3NdmB27rMk7ojj4Y+DXQW0qLwW5uBBpHacES2nZFFE0B\nbOEEvC7EVdqDcP9YvHjoc1QVWswdw0UUPw+/UW8d9skFKCstEEwbG/VwIVVMkaiZiHOln7C47XLh\nY6wGaq+SiUsx7+ci/HYgTtB0J1faYasDIa5QhizgykLNZmZt0OeZyAdGpOqERpijWLGL6M3GIzg1\ng26pCrU/IRUo/DDpEXWbbqBodrUpxYiRgKKu9OU2fJ6mLsDN4ZWV1EjxiYg9Xx9/Gejj3Ssutm7A\nIf7RXa+KwFM+PHSh+P37QaCuCSUVJO8UVJTMF5xD06r2tfD9o5NH/kUcjH5OGlpwlxSd+mL14cbN\nfq8JC1c6l81stWg9JRLqAXcEL6hRz/tTVojItSoFqhJM/N4tikQJehF6v+Y5Tc/xk5vJ/XfPNvHb\nwxjvnfq9ppQhWxGA0+1c6WbMXUXbeQfcgbREVBE2F/G4HwvPhFSlGvB7VPoW45gUp5kXRubc0Wj4\nc6rB6hCp+k4xe0bmExacFgqQ/ThJdDzzWShedDIHnhcRkYoRI0aMGDFixLhgPDFEKitllkpK4RLi\nsEomwtoGU6L9TXMAe4C5LCrrUIDud12AeHAQap0tRZRMiGk4DG+ftYa/cW5DPL6o+AqGK5dSWRAZ\n1KbSlOy0xLpS/lZNEbU6LNPtl2/TWktojtT8haR/n/bCivjw9HGxjW/JJXGdZdprMvW36gX2p2Jv\nrvqXWC3oO3YthzVCJs7mWM02G1IvDyJKPX5i4XOtCVbiSl9W84NR+K2u8EpErs5xOJ4CrUhEiMtQ\nF2dWnpqKAJoC0bNez94bKsolYtOBw3VaFWd1oInVzPfLtO6+OJsTnJhrqjeQtrk4pbsAXhAm/M3r\nSSRdeQ4rhuVSEwbgLDxWZ22IPcVtvtkI16M2CfNCKLteO6wQuwsiM8ZYywXVKUTBJXEiBtJRq62j\nr/Olt12O5IGhoFktrERZp1DroFEoOxQXZVq2q2B3PAifl6X+ZBMO/Wcjv9ZWK4y7nlQUGB2H1PYM\naKYiGGUgArqqX0KUrWhuA5YpKkCuwVE6F+SqWg37U7Hrgqn+cz/PSoK2w3G1XlwF97Mk45qIXCqr\n72LVn8nquxYQ/jQXS4hluBeJoFSzMwrFMdYb7g7NunolFfsD6bt84wX/HsTBU6lJeDg6MLNVO5XJ\nPKAjtZJP6DPMXWcnPVyXz/8l9Lvx0UGxjUiwInK0FXlKKjsMUCdSlfJl7K9Zd4RjOAjHnQAl6nQ8\nOYHjRJMyiNLOJP2eFQ2qMtcRaVGLE6I0ihITdeY8PR6tJ9vMpL+MYN1RLkufADpTFqyEKJqK5/lb\nfU4keLY1ZN7n30TdFOknwqN17TgW1OpjXgjLxU6jQLq07mu4/r1dd8Wn7QsTENSJvbCVkGfSlNco\n95r2IxUR1vOdIJV5lwi0WsFY8v6YU0SkYsSIESNGjBgxLhjxRSpGjBgxYsSIEeOC8cSovclksALZ\nVgDtLWdSZHMJL4ipuFj3UFzVHO6vQViZCYxdhwfVZHhUbGuUA/XQygJk2Grs+GcpoHgpmkwPnky8\neBJ4lag7Lh2bV4XFcFafz9e2kVpqthxOLuBp8aKaQ7A8WhGgh3+UxiLMvCq2RcHT2brobwbxup4b\ni3bOqwKxT8LBRkrj4RqUnuPfc6EHuG+Fe0npKLVFUTApPT0nNrGWiyyoRaFWSVnOF+vUnsLI3HdN\nrjHP6V9EwbK4vfPIqnadL1d+Z+YC9FzIUtIM9L3Rz1dE6YDvCWcrPTEB7K7i/Ol8/d6V4RivMDZp\nJPWg4bGUbiVEz+Ouairhti70KJuiJC7qbRShLslvSRkk5m1dySjs9O+RPiFUP5M+VKlwTDgVWKYT\nvVAGmTGxQQqO4xjqtk5/oKMDd6o+OArC5xHE68/fvOnfRxv2e+7Pk1lo/62uUCEsmqz1xjG3JSI2\nJ6Wg1AoLqW5IVQIWTWVjj8YD+f56MeYaJBAlGVcNUNa50D0GijYRv6UEIvfTBz5PDgZBUtDsh/Zq\nOsNipVaYM7WbFLdT6Q9IBK7ffLnYdO/eHTMza0lVhBZcuVUCwXs3RFKAzmFMrJBuUrS/Ujb8niYP\nsDC0Fv6eY3xwrJk5LTcBVTcWyu7wMFCKOoeRFtOx02yQ7lqv1KDJG+Mx5Q4yJzVAs2Pez2W+YKKK\nUmYUx6tnE/tfRea6ZpPPSe8TnW6gLdXHa2nvTQDyqDeYbOHB7y1FlsH2SWUb5ywt+M55X59dc0gP\nSqZzd2iLFJ0jl2ug75g+azI8Y5OSXgN9rKT/I8lq1faP85Ru/J8Qm9++fds+/vGP2wc+8AF79dVX\n7ctf/rKZmR0dHdnrr79uL7zwgn3iE58oslvMzL7whS/Y888/by+99JJ99atffd+Dx4gRI0aMGDFi\n/CTH+yJS5XLZfud3fsc+8pGPWL/ft5/6qZ+y119/3f7gD/7AXn/9dfvN3/xN+9KXvmRf/OIX7Ytf\n/KK98cYb9kd/9Ef2xhtv2N27d+3nf/7n7Xvf+96KgJgxX05tMPCVRlKB2+7S3xaHPa4g/HtLvK3e\nv+dvtZcuXTIzs0bVxa7Nanhznlb9zf363lNmZra1GVJY63UX5zaw0tWXcCJI1aojR8zJnojYlO+q\nFVn95QahqLzVUjNc1JCa+kr7vPTzGuoKXspc7DmHAJSu32YuYtYgEjEXoSzF0ERddFWT4LibIrZd\nUvQoIuoK0pTVHZhLMTlU4ZSeSE2uJhzIS7L6oMh4NF1f/fH7uYpoM7oei4sx0S/Jv+UKp7qyIgv7\nW8pK56wX0InlnCnHvtIcAonROlCLwu3Zr5UiylRWRERMUkUEINBN5B5P0J8pjlREii7WimrxElfq\nYKE5a1LDkCtcTbXmyl3bmO2jqc7v/b629RQWIirsZvsrSlQr023bEaElUv3VMZlu+HU4FWutxeUU\nSI+sBlm7UpNIygWqKaJk/FmueF/jPJRLe05nRBjD/x8+9HpxbdhJzFbsInBOYvXBe6hIZ6UW+tpk\nKHYqCVFarT9HOxU/zyruY4q0/sLfxFwAq+gHUV2T6ypTIL3pqHuSh/3OHz/w7zXCvDuce5+4fxBs\nBK5thxqCtb4gYg0gZzKlE2ldQalyVmVwpG1/O8y7qaB07Pd0kzbzsVUBMrSQPZ9BqN1tios1a8ip\n1Usxn3pfqwO5nWv1CjaZtDHHDudiRf/nqDXZFFuJFuaVhw/dfoGC8sHIrQaabTAiYvVAl30dJ0dH\nj/FvQL90DtvYCN9PtE+gX+vcxTGuKD2njLIgMlUg8Tp2WPdzJtddCLCTdbTmvdUR9FxSSewhclUX\n6wLO08oELDDu87VUGLeJmIldBJ8nOtcRfV6KrcES+8t0npim2P86Sl+WZ1yu3g7nxPsiUvv7+/aR\nj3zEzAIE+/LLL9vdu3ftz/7sz+wzn/mMmZl95jOfsT/5kz8xM7M//dM/tV/8xV+0crlsN27csJs3\nb9rXv/719z2BGDFixIgRI0aMn9T4Hxabv/POO/bNb37TfuZnfsYePnxoe3uBON/b2ytWcffu3bNr\n164Vv7l27ZrdvXv3f/Epx4gRI0aMGDFi/NuI/yGxeb/ft0996lP2u7/7uwXczUiS5D2iLFv7/Nzt\neXUVuqN7rjBGk1GAFkciTpuAAlIfl6PHgZ5pXXcYu9kM0F616tt2t4Pz7kYzUGXqO0FqLdHCr6Q0\nkhVlo5mt1Idd+8zMhY8qImRbJOf8uGBFZB9tQMFKrdALRAWY9EpSZ1seaz5SHw/QV/TkUBoLjsla\noLcNIaLSU2lGHytxZ6agcsUdFte40DbOcZ5SBBQiT4pC9brSctjHSISgpOcmIhileDIV348+6I+T\nMsQAACAASURBVIj5XIWa9IBRagUwMv5dCBUwwfcm4gVV0Gha5BPQst7VMs5FacykoJaEqgLNRVZG\nXbTpgF0RiHmerxc35n5bkrzAPrNCAfJvTZTA9ygKXag49JwkhqzgviVRAbvLEu9PHcD3uVALp6dB\nS7mzcanYNpufrJznaOjC7s1mqFiwEGqDNGIqlVcT9LGK+LhVsnD/l3PXb1IioO3PQrOzlB5bfl3s\nizr+SB9VtZAsKAUF/xPc/4n4/TSqbH8Zk9NwnqWaU+ol0KZMDqipYzM94zQBAWN2Z8dptMKxOvE2\nsSz8vax40eKz4yAyH/ddZmAzFnw/h87g+CitC5FXyD2cYEnuUwnicR2nCa51IjIHitYzzE/tpvTr\nc9y+OZ9oIWsmCqmwvKBvpPFaoEDpT2Tm3k+L9vpcy8SeTO4Jxc5XLl8uth0eovDtSJKn0BYdLUKO\nOT4pCQWFdj+DK/r82JMj6Hu32fHnMAX76nvEcx6M/NnJotK1itBt6NvqlcXxsTwnUWc0Hq99xueO\nPtfoO7Xiys7rk25CCYrSaPR003Gav4+zePGsEwnGeUlWpC9zeZ4v0f/OK0w/ScTbLXv/V6Uf+SI1\nm83sU5/6lP3yL/+y/cIv/IKZBRTqwYMHtr+/b/fv3y80SlevXrXbt28Xv71z545dvXr13P3eeuuR\nGbJsupt1a+2vW/nHiBEjRowYMWL8uOO/fP3r9rWv/72ZrWYfnhfv+yKV57n96q/+qr3yyiv2a7/2\na8X2T37yk/aVr3zFPvvZz9pXvvKV4gXrk5/8pP3SL/2S/fqv/7rdvXvXvv/979vHPvaxc/f9wQ8/\nW9RjMzOrYzW5XBFHByRCxYkl/J2LAD2FOy2tEcy81lh3w4XaG206n+MNdiaOzVjVrtTQStfT2r1e\n0HpdtyzzbeVy6T2fukCbddo05ZTfT2QFV9g6qMEq0s7HJV/BUYw4FOTmvJRYAgsUxaqFQw/iyDv3\nvV7hEKu5dstXUFxp6Crdr09QAiIcIkBm2reKnfmbOsSB6nrNmlxqYcA4Pva6Zh3U9VI04eQkfK5o\nCmtyjcd+jClTjPF/HTDniQ6JSGi9NIrIVYBehbA1XXHEBUohiFwV972BVXey4TshIpEJIjgAEqVi\nU6Ywa72yHIsUXf1xVal1tZh2Pi7TMVlF50xKUCdifE8QhBQu462695My2mcxU5S0uLJiW60S+hMF\nwFNd/c8hcJVrYB9a0fAX/ViQNgpwZUyOgLrO5RjlMs4T844igvy+zglbcMCfnZMUoe1/ehZQBHX7\nX6DdtU5hG6JlrbVGFJcASy73iy7SOq6qEGqPJ44+NJtPh2td+JywBHJQ7ToiSNuPo0NHPW7dedvM\nzDba4dzSqo7rogCjny9v7NTbZIlqEyVJHtq+EhbV9++9U2yjy/nRsTuVs5LClavXzcysK2gJ5y5F\npM94PwVNzpHEMpNKERuw6WgIwtUo0uR9nBJtYaq/VkJgu+vcTaRfv0ekSSsQEB3b23PkiqLsodg0\nzDCMduHs/eCBi9jv3nuI6/I+XMFcozUkKziXuSLshXhc6gqC7VnIGGfVAJ3jiSxxfl61Pwj9oyb9\nn/dppSYhfqSbyDCtzLu0DpJzHw34PAvfq4sTPbviamWL9bqurFRSEfSxQRsfQYk5jfz0a6/ZT7/2\nmpmFxLPf+X/+X/th8b4vUn/7t39rf/iHf2gf+tCH7DXs8Atf+IL91m/9ln3605+23//937cbN27Y\nH//xH5uZ2SuvvGKf/vSn7ZVXXrEsy+z3fu/33pf2ixEjRowYMWLE+EmO932R+tmf/dkVLlTjL//y\nL8/d/rnPfc4+97nP/cgDN5qZjXpSwwzoUzIWQ0S8fWpaKzVHWUNq3ZUDX1yWy6lCD9CSKuFMxadx\npBpIcgWtr7BcdZQEfeAb9iJ3VIFIVGkFOQr7Sc/hVplKqRXH03NMPZkKrlqFZZHW69uIEtVEjzVK\nhzg3rYkW2oIolSI9TaBOC0H6zk7DKlX5aSICJYFfsjJ1U5Jqj2Okso0I2EhW8+TIeS6qKRpC50TD\nQT2+6qa46itJ/Tem/aaybTSg6eS6RogrUjWwLCwpxBCPtcbUTJZ6KNWNsGbiXEwiC+O6FZsGpvMv\nsC8//ibQj4m0Vx/nonWwaOaYrPTdsL+emEmyPzdlRU4dFLvCiqkd2ma4Yn4Z2q4h1iHVCmxCZAU7\nnqGGl/STKvrfSvp5FvZ3cnyCfYmtAFarWn+wZOtaNmoqFlITkG1REd3ISS+MCSJNZmZnqNPHVHdF\nGhboQ7qPK7uhL9YbrjPKgL70Tt3Ukjqbupj5jqBX0VRqruYVpa1t4jc4l6mgGsQttrZcD0Xj3t29\nK8W2BAbDqay+ORb6Dz0BKLcwJjY3vU8MoM05A6o9FZ1NCt1QooggELvFXI1GoXNJvE9s7AY7he6m\n10TlOG03vT8RJXlw/344vrRNHUhHd8Pbn213KEarRDjUkJP72VCdL5CIuSCRy8JMOHymfbJALs8x\naFSUsA2UXGu4sU9q/b1WC88uGTv1BvtYE/tyRO4RErvuoG3MzEZAffcuuXPqFmwntjZ3i22cJxTN\n5xyn8zktWFQHxucSbQUUQaJuLZE2YVuo0Slry2o9ywnaeiJ9vJh3BLnt9UJf7EAbpjZBrB2qJsVE\nrtROBl3denOfE8cjMGEreixq4/y3qnU+L2KJmBgxYsSIESNGjAtGfJGKESNGjBgxYsS4YDyxWnvL\nLLFBLjV3loAR5wIP9gLEVm2IsDwFtSdu43QRrgmNVwWkOs/FgXxa2EKHfcnVZ3R4FWqJlE4itda0\nxhijBrGfZgTTlVodkEmbkFpKpV4caZ+ppGEWomh1py5SPSWtHx+vpHzi3CtlEZsn4TwJz6qwt4zj\nlxv6fdg1yLWSRlNR8iZg9rE64XK/ogom9afCSqasVpGGrsLqJgSTI0mhJwSr1Bb3t1w4FEsaU9Nv\n3b13ndrjvwrP++/EpgN/LqT93dHav0cxrLYTqVx14GUCQp/XINRaHwJLpX3SKmjZkkPNtKJQsT2h\nb21r2lMofcm0X94HJimEcwvf29xyC5EKxPHthvfrrU44l96R1+RrQgC/nDhkXy6FY03EuoGQPimm\nkkn9SbTTQi0xcAuHst8qzkWrEqQl1jXzbZQPTBbaTqgxCRp9sWLXEb7XEMf4Wg00ttI9oBaaFb+v\np8chUaJ71em2CSo5sG+aeb+brYiXwznU6hBHV/x+LSFPWIgAvtUALT/zbWUIype5OkuHvytNv8bD\nO0HkTddtM7PN7fDb036gyrQPZcOwrdyW2nAYT2nZr2sBmjPdFAF0GfXnZI7to44gkw7MvD4cabTh\n2Cm7wRB1PYVuZ9/lvTEzyyDiP+u5/QWH/1ykInMkm0xk7mCnyTA/H2ttSojtO22n24p7eE79U6WK\nKeLWkmqHh6Gdul3fXwu0YHczJErt7Ttld2kv3JuDAxegs+5nVygzUsblslJS4dyrQlORWlX3dM77\nKgvhs4qJDWKiX1gDTEXYz4SOWk1tYsK/CxF209Fc78lyykQBfxby3I8hAVD7k+J3KzIkuq37limk\nAlrrcjgMc5bKNwaQlGSSUKYJCudFRKRixIgRI0aMGDEuGE8MkRqPhjaeqjgRot+K1KZCPa1c3iCb\nqCafNFVEiUr3Itgj6qN16GiSyYrrWi+OKe4rtfEgNtSUaCIcVXnT1rRXRiEszUTEiDdmHkPRkgIJ\nkTd9Vvo+34xMUkMhylNEiqnzan9A88E6VmuptOuUhnxyfKIqC3lbZzvpORFNUXEgkT1FmHi9Kp6m\nsJApxCpipLC4LoJZms9VzjHQGwzE1A/RXlmlhbY4PXWxYbPYHyuIi2ARK63zxOF9qT82m6+LUile\npzVDOAZWblNZ/aDtap2wmh+J6PIEBpYTSY3Ppuh/Tb+vQ/Q17ZMUVmpa/clpQEmyy/69IWrBcR9T\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7N24EL67Fm0KZgwKrrCQUce72vsMi6EOZi5ncQd+jpy5fKz47PAptc3ri44pzx2Dg/Y+U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\nAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306e1cd0>"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer filters, `conv1`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# the parameters are a list of [weights, biases]\n",
-      "filters = net.params['conv1'][0].data\n",
-      "vis_square(filters.transpose(0, 2, 3, 1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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HsnuPT6BhpP/ARhMFuOtruPHh1MmwX3/7sWOQp1rDDRtjzpC3VCAbERyjo0ToTZ5v1Jly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\nRK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b6d0>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first layer output, `conv1` (rectified responses of the filters above, first 36 only)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv1'].data[0, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ms2YoOpgSpqZhCsFvW62WPg9sBU7MjEwmo30ERsrKK5fP5z2z0cjIiMlIueZ5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wGNgv//Iv22uvvWb/83/+T/u1X/s1+6M/+iP77Gc/az/6oz9qX/va1+xHfuRH7LOf/ayZmb3+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dxu119/vW6kvD73MssD3mkyjxIf9gQk3w+IS9I8DIrForYvK3Czmw/PQNtw0l+0KasFY8OD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\n8lJsiPg+dMyicuh8ku+UxXQm+WFt1ieM1+LiopdYNGldEYnXhExKZ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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d91150>"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer filters, `conv2`\n",
-      "\n",
-      "There are 256 filters, each of which has dimension 5 x 5 x 48. We show only the first 48 filters, with each channel shown separately, so that each filter is a row."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "filters = net.params['conv2'][0].data\n",
-      "vis_square(filters[:48].reshape(48**2, 5, 5))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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0vzuvmqNWAfPO3EFL6ws5BltgaNfyy/LKGnOdQ8BnrH9o3PTtiEPGRv98b9eu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\nZsxo8/gdQWq+jUUreVymxRalLOrMsuefbf+Ots2sAAB+YKnms61ChvM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NAAAg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\n1k/7BsAeRj8v6JfrgWVZePb8OqPQWYG7du0ayyKM6HsB22sjRnPK9dhaT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dZbb+3kdrwB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\nxcV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c3/Gb9+s973lPRIy/53ekP5xi5p9yhg+A9wtOIb9/t9fMvG9TU1O92CV+gyy07dgO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7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\ni8T/A2aklexSCttuAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630658d90>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second layer output, `conv2` (rectified, only the first 36 of 256 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv2'].data[0, :36]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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44sWLqd6jk/OahTXn6zYWLlwYeU2rRmu6rL29PbLeq6++6uJDhw4Nux//99rc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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d73990>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The third layer output, `conv3` (rectified, all 384 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv3'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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Okec+WKymeN9jDGirKHE81xm3v7CvTg1LbQRziVSkkiRJkiRJOrJDKFLOM57x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\nbZbwMQj4m+t2jYZrq6B4Xi+i19yv0P0ugbY/LCiZbdsu9UM7od5ro+1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tmZrYJHWYfAy0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C/MCPAMUmVb+sXzmRIFU/rJTol8H1sc5YD/w/vo4ptQh6HecjifX9cv6R+FZQLQDlAn83LFp8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\nVOIHKWOMMcaYSv4PwNFhvU7XHUcAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064ffd0>"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fourth layer output, `conv4` (rectified, all 384 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv4'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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3h8fOeeE1eY0XPP+LLrpop+0BXo57YShweMucH/93qLvk/U8WFooX3rJnfXn2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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d25190>"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fifth layer output, `conv5` (rectified, all 256 channels)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['conv5'].data[0]\n",
-      "vis_square(feat, padval=0.5)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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QkQIAAIjEhRQAAEAkUntoq8MtKb3izJYeyDJE3m1/c+sivW7dujC2d+/eoqaT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\nxEaKiIiISKf/AMt+wa3UEUvkAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d1b3d0>"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The fifth layer after pooling, `pool5`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['pool5'].data[0]\n",
-      "vis_square(feat, padval=1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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GTMu3v7/fiGn7PS8vz2q72udN3M9bTUFBgRGzPW9tc9Gm9Gu0c1nT3t5uxOrq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\nIqDoAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd621d7cd90>"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The first fully connected layer, `fc6` (rectified)\n",
-      "\n",
-      "We show the output values and the histogram of the positive values"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc6'].data[0]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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SpqSP+cc+lmx+eVKtbu+Xeh+HP9Nzm7kk7cAZ5jhz+tx/v7RsWdqlSMfWrft+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\ni4iIiEgxDlhEREREinHAIiIiIlLsO5hP7FaK+KtUAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd63064b890>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The second fully connected layer, `fc7` (rectified)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['fc7'].data[0]\n",
-      "plt.subplot(2, 1, 1)\n",
-      "plt.plot(feat.flat)\n",
-      "plt.subplot(2, 1, 2)\n",
-      "_ = plt.hist(feat.flat[feat.flat > 0], bins=100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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oo7xIJvWCXOztt6XLL+/92bBhuT4cTjIZ6c//XFq/PtzYomC6nm64IdzOy6tX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\nGERyBQAAYND/B6hFJTD2mK/NAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd6306fb450>"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The final probability output, `prob`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = net.blobs['prob'].data[0]\n",
-      "plt.plot(feat.flat)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "[<matplotlib.lines.Line2D at 0x7fd63060d990>]"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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+acS6dRH/+q8RU6akPhoAJpqx7Ja6I2ENDQ2xcuXKWLRoUVSr1Vi2bFl0dHTEqlWrIiJi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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fd630627290>"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's see the top 5 predicted labels."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# load labels\n",
-      "imagenet_labels_filename = caffe_root + 'data/ilsvrc12/synset_words.txt'\n",
-      "try:\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "except:\n",
-      "    !../data/ilsvrc12/get_ilsvrc_aux.sh\n",
-      "    labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\\t')\n",
-      "\n",
-      "# sort top k predictions from softmax output\n",
-      "top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]\n",
-      "print labels[top_k]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['n02123045 tabby, tabby cat' 'n02123159 tiger cat'\n",
-        " 'n02124075 Egyptian cat' 'n02119022 red fox, Vulpes vulpes'\n",
-        " 'n02127052 lynx, catamount']\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/examples/finetune_flickr_style/assemble_data.py b/examples/finetune_flickr_style/assemble_data.py
index b4c995e8eae..09bfa2618a4 100755
--- a/examples/finetune_flickr_style/assemble_data.py
+++ b/examples/finetune_flickr_style/assemble_data.py
@@ -9,6 +9,7 @@
 import argparse
 import numpy as np
 import pandas as pd
+from skimage import io
 import multiprocessing
 
 # Flickr returns a special image if the request is unavailable.
@@ -27,6 +28,7 @@ def download_image(args_tuple):
             urllib.urlretrieve(url, filename)
         with open(filename) as f:
             assert hashlib.sha1(f.read()).hexdigest() != MISSING_IMAGE_SHA1
+        test_read_image = io.imread(filename)
         return True
     except KeyboardInterrupt:
         raise Exception()  # multiprocessing doesn't catch keyboard exceptions
@@ -48,6 +50,10 @@ def download_image(args_tuple):
         '-w', '--workers', type=int, default=-1,
         help="num workers used to download images. -x uses (all - x) cores [-1 default]."
     )
+    parser.add_argument(
+        '-l', '--labels', type=int, default=0,
+        help="if set to a positive value, only sample images from the first number of labels."
+    )
 
     args = parser.parse_args()
     np.random.seed(args.seed)
@@ -56,6 +62,8 @@ def download_image(args_tuple):
     csv_filename = os.path.join(example_dirname, 'flickr_style.csv.gz')
     df = pd.read_csv(csv_filename, index_col=0, compression='gzip')
     df = df.iloc[np.random.permutation(df.shape[0])]
+    if args.labels > 0:
+        df = df.loc[df['label'] < args.labels]
     if args.images > 0 and args.images < df.shape[0]:
         df = df.iloc[:args.images]
 
diff --git a/examples/finetune_flickr_style/readme.md b/examples/finetune_flickr_style/readme.md
index ecb9d3d2e6d..9ba4c9217ff 100644
--- a/examples/finetune_flickr_style/readme.md
+++ b/examples/finetune_flickr_style/readme.md
@@ -14,18 +14,18 @@ Let's fine-tune the BVLC-distributed CaffeNet model on a different dataset, [Fli
 ## Explanation
 
 The Flickr-sourced images of the Style dataset are visually very similar to the ImageNet dataset, on which the `bvlc_reference_caffenet` was trained.
-Since that model works well for object category classification, we'd like to use it architecture for our style classifier.
+Since that model works well for object category classification, we'd like to use this architecture for our style classifier.
 We also only have 80,000 images to train on, so we'd like to start with the parameters learned on the 1,000,000 ImageNet images, and fine-tune as needed.
-If we give provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
+If we provide the `weights` argument to the `caffe train` command, the pretrained weights will be loaded into our model, matching layers by name.
 
 Because we are predicting 20 classes instead of a 1,000, we do need to change the last layer in the model.
 Therefore, we change the name of the last layer from `fc8` to `fc8_flickr` in our prototxt.
 Since there is no layer named that in the `bvlc_reference_caffenet`, that layer will begin training with random weights.
 
-We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `blobs_lr` on the newly introduced layer.
+We will also decrease the overall learning rate `base_lr` in the solver prototxt, but boost the `lr_mult` on the newly introduced layer.
 The idea is to have the rest of the model change very slowly with new data, but let the new layer learn fast.
 Additionally, we set `stepsize` in the solver to a lower value than if we were training from scratch, since we're virtually far along in training and therefore want the learning rate to go down faster.
-Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `blobs_lr` to 0.
+Note that we could also entirely prevent fine-tuning of all layers other than `fc8_flickr` by setting their `lr_mult` to 0.
 
 ## Procedure
 
diff --git a/examples/hdf5_classification.ipynb b/examples/hdf5_classification.ipynb
deleted file mode 100644
index 19d27372754..00000000000
--- a/examples/hdf5_classification.ipynb
+++ /dev/null
@@ -1,1074 +0,0 @@
-{
- "metadata": {
-  "description": "Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.",
-  "example_name": "Off-the-shelf SGD for classification",
-  "include_in_docs": true,
-  "priority": 4,
-  "signature": "sha256:741422697d76b1667287180dc7c6360cf105ee774b1e2def800dc8fe80f78f67"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Caffeinated Logistic Regression of HDF5 Data\n",
-      "\n",
-      "While Caffe is made for deep networks it can likewise represent \"shallow\" models like logistic regression for classification. We'll do simple logistic regression on synthetic data that we'll generate and save to HDF5 to feed vectors to Caffe. Once that model is done, we'll add layers to improve accuracy. That's what Caffe is about: define a model, experiment, and then deploy."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "import os\n",
-      "import h5py\n",
-      "import shutil\n",
-      "import tempfile\n",
-      "\n",
-      "# You may need to 'pip install scikit-learn'\n",
-      "import sklearn\n",
-      "import sklearn.datasets\n",
-      "import sklearn.linear_model"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Synthesize a dataset of 10,000 4-vectors for binary classification with 2 informative features and 2 noise features."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "X, y = sklearn.datasets.make_classification(\n",
-      "    n_samples=10000, n_features=4, n_redundant=0, n_informative=2, \n",
-      "    n_clusters_per_class=2, hypercube=False, random_state=0\n",
-      ")\n",
-      "\n",
-      "# Split into train and test\n",
-      "X, Xt, y, yt = sklearn.cross_validation.train_test_split(X, y)\n",
-      "\n",
-      "# Visualize sample of the data\n",
-      "ind = np.random.permutation(X.shape[0])[:1000]\n",
-      "df = pd.DataFrame(X[ind])\n",
-      "_ = pd.scatter_matrix(df, figsize=(9, 9), diagonal='kde', marker='o', s=40, alpha=.4, c=y[ind])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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ikQzBoItAYBQYobj4OhIRLz7VxtP7R7lm7Uz+n3vvPZ0qvJ2dO1vRNCeQRdNe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dJjiJgJ+tTFAgoOoKGjIvuDGOqs00PHJUVAGxWLFQ7SYAmbgJCYNjl0OY5wWzQHGcM+EygI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\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YTGe2SZKELx7HaDQyNjaGMhpFrVQiSVKi/ocoYk5JwWuxEAqFWLV4AT1HjjA82E+KR06f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\nIAIgiiLDqRQXTpx4ywUjvu9z7OmnOTg6elXKv6VpJGMxyqur6Lt2XdXKMG0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16fV9Hnvo\nIfL5PqrVDXK5VwxDVbVBIiG9KR5Vv8g4fBg+//k3Z+zJye1OHdOE1zSN/txDeKNluH/qP0AQngbu\nem1mRBAE/9OffopodDs1W69v4boLfOpTv/0qcyfDMHjppaMcPz6F53lcf/0uDh06+GO9Zmzb5okn\nnuLlly8CARTF4a67bqJY3OSzn30W0/RZXl6gp+cd9PVN0G7X0bQlyuU5CoUs6XSOnh6J++//MHv2\n7P6h8efn53niiadZWSkRDodQUDnz3HMMuy5zi0WaJZ+0JG/rWPgGDnF05jiIhwaIQBQwgMuEqJBg\nDIUwIjoqORoEgWUhxJbSRTqRZ0tocP/73slt111LOBzGcV2en5tjwQgzOfkejhw5Squ1PWmbpo7v\nX6YXk14ZphsrnD63wKAbREYggEmEBgEcFpFQ5R7yrknS7zAQkEiFFGwB1l2TGV8kNXIdttKD1QYB\nkBMG/+d//9+4++67f9p74SeWhf/iAw8Q29qiP5/HsCweO3aO6ZU2Gx2BXMBmZ3+UXV1JXnz6adRi\nkUXXZe+ePYyOjdFeXaXYVtGjk4RC/aiiQnagj2e/80WuzXfY152j4XkUdR2xXKYvm2VheYtW3SEg\nS6hWFSUcwDc1Bn2RVU3H8R10oImHgYhJEB2ZFiDQS4AM3Yg4NBHZII1GCIt1fDJAAWgDJtsZsGkE\nEoEw6XCcmCmgyBKWYbPpqNQIY6KgBUcRPI+Gs0osPkx39yCC66JEFMb3JvmLv/gjAoEAf/zHf0Wh\ncJBGo8TykW8wGk9jWSbxuM5tt91Cu9NhxvP4nd///Z/qer1REASBv/r0pzGrVQQgkM3ySx/+8BUB\ns1fDcRwkSUIQBFRV5Xvfe5bTp2fwPJ+9e8e4557br5YUvvaVr/C9P/sz7hkevhq0zCwtsdHpMG3b\nxF2XUVEkGY2yvLKO68tctC1S+T5unjiAIjcptitcOnMGwbZRVJUxBCQEjuIjEEZCwaOXtgBLvoCF\nQkLw2CGYpDwXjxoZNJAUTrgaJiAjE0JAQGAXEhEEPFx8WQIlzAVdZYY8spjD82oEadNNh4zgYMsi\nmhChToFg7np8R0DR1xjN1Ll9zxB3v//9fOvJJxHrdbr6+1mbmaGyvEzBdVlptwnKMqlQCC0cploo\n8Huf+ARrlQryrl1MTRdx3QKJRA5Na+A4G3ziE+9900TQfprn/ecFS0tw8CBsbr557be7d8NDD23r\njvy84co1f92a+1uaM/L9QAQgne5iebnI7Ows+/btu/p5KBTizjtv5847b3+9IV4XiqLwvvfdy7ve\ndRuappFIJGg0GjzwwNfQtDzhcARJClCpuDjOLKlUBsfxiEYHcZwy6XQUSfKp1xv4vs/6+jrtdptk\nMsn581P86Z/+HbquMDw8zvBwPx2zio3I+vIqVsNFcUUW9ToBQEbAIEgHGRuLPOABKlAnSIgEEhJR\nfGIIBImi0kHDRZCDhKMyjrdFJupzeWWZucsz9A8NkRod5cb77mOipfGZBz7D7Nnz9IW7cR2L+foy\noihSsjq80CkTdAyi+EhIdCOSxSYBuEAJlw2nRF4KIHgWirhdX3ckhVokQG8yT9eeWzh0x4fpdDps\nbS0wPAzvete73pib4HWgqirlpSUmryxWoUCAX3nH9dy8q86ff/3r7MkNYpXqPHlyik6zxTWpLD2K\nSAQ4/eKL3D05yYvzJfpGc3Snc1RbTXBcbrzzA5x86UtEukUmhgZYW1/Hr1QIOQ59ERFaNbyOQdH3\n6I4kkYGnVI0cIh0gjUcXChI5bCRqGLQwcVlEpk0HnSgOI3jkAQUZBRuD7QB0kO0HMoaEhguWieAB\nUhjXHIqDRgAAIABJREFUaKF4MgoCQcGh4Wu45iUUySEnhkl5ZWRd4fprb0UQRObXVvn61x8mIoDd\nWOaly/O0OxL1i+doKHEEocOtt+67qjXh2fabdr2+j06nw3PPvcjx41P4/vbG4bbbtnkKg7ZN/oq1\naaXZ5OG//Vv+9ac+9UOt37K8PWXZts3f/u2XqFQi9PS8A0EQuHRpieXlL/G7v/ubhMNhpk+eJBmN\nXs2yaJpGbbPIesumJIcIpbqZ6ZRJt7fwUfA8l6QkYmZ6OKU1qJVX6XQqNJwovtFkHyJtPDbxcZEY\nJ4yBzCYiSZL0oLGIgeVvofodTFzCbFs32K7FfkBHRieAT4ctAERkwMOj4gikfYkAIiG2kD2bCHHi\n9LMd1qyDYFF2exgZvRbZ92nVavSEorQ7Ooubm9i2zejICGc3Ngg3m0SjUZxMBq/RIBQOMxQM4gPR\nUIhmp8O5y5cZ7u9Hdxx+7/f+FSdPnmF5eZPJyQw33viRH5Lbfxs/HocPw733vrk6IHv2bJNYfx6D\nkR+Ht3Qw8u1v/9XVn3fsuJ5oNEmr1b76mWmaTE9Ps7CwRjIZY9++3eRyudcb6nURDoevZlleeOEY\nudxeisUSnuciigrRaBeNxjKGUSESydLb200kkuaGG+7GcWy+/e1nOXHiPJWKiyhGmZ4+xcrKBsnk\nzfT1DVGpbDA//xhDQ4O8fHKGdydiWKJFy66TwcfDZRkfSCMTYAMLHTAJYxDCQmIdhyQJ6rRx8Whj\nYwKKEMCLFxiMTrLWbhOJw6/dcw+WbXNsfp7YwABPPv08jz12grXFFo7ms2GexHdrBHBI4RO6sisP\nILAMWLiM4qKwvTjWAQXI4pCIpGh0LOpygHXfoybBfe97HwSDmF0ZVlePIIpw003j3Hvvu34kN8R1\nXaampjh16tK2o+j+nezZs+fqIvOT4PXIrIIg0JVOo5suWlNG9mNkwh6CGWS1VcePGAyKItW6xsNT\nWyzWJdRVlUZnmfG+Lk6fu0Aw2Y8fGGGq5LOolunOh5BzOWZWVhgOh6kgsGLbKIJA3bKwPJ8SISqE\n8ZHo0KELBQcVD5MgkEOmSYAUHTJY6Fi0kRDwUfAZAFaAEBADLAB8OkANl6zjojsNBgIyuutTRkD2\nRTooZFAZdAUiYoooAqXaZV54YYmh/p2U2zWe/twCH33ve3jXQB+fe/GrnJ6tEiREOmQSicY4dmyG\n2UsXERJxdn3gA1iW9aaVamzb5rOf/TIbGzLd3TcgCCIvv7zA7OyXgG2O0/eRSybJtFpMXbjALYcO\nve54ly9fZmvLZ2joFRXknp4xVlY6nD9/gQMH9hOUZdxcjk1VpScWY35pibImo4kR4ukxUqEcltLN\nzOrz3JBI0pPrRlSbTC9ewHFMzI5O0w5gB7qwPIdLuJhEcUkQwuUiOnl8fIJ4fpAwHRRsXHR8LApX\nrqvOdsawCQi4SHTw2PaVmsejlwAGIg4+bVdjAw+BNApZfOJUMRExsP0YEbdKDJXV5YvcNnEt1USc\nlOMgWlHKpQWajQY7h4Z46cIFyqKI2OmwoGnETZPRbBZUFZPtzc6u3l6WlpdJpFIMDwyQyWS45543\nbxPxLwGHD8NHP/rmnuP74me/aHirBCOvm7a5777fedX75eXjdHdvE1M1TePBB7/MxoZLNFrANNd5\n+unTfPzj72Hnzp2vN9yPxdLSBv39k3hekDNnpjGMdQQhh64bhMM2uVwfpdIFEgmd5557jEIhx6VL\n87Rau9i16yZKpS2q1RzNpkUgUKdUCjIzfY52e4OpqRl0NcERu8iQbzPiS/jIaHgMEGSVTXbioyEx\nS4gcSTx8NpFo041ECx0JiyZ9iIBP03fZsgzaXotYOs9E/wDTK5tMDnSzsNzg60cepFR1cK0xFDuD\n7izQRZlBXOJsT0YuEAQUfHrY7taZArJXfqexTQ3eEHy6AmHWfCiGgwgC3HX7IZLZLJk9e/jQRz+K\nZVlIkvRjFzPP8/jqV7/JmTNl0ult/siXv3yM8+cv87GP/eo/eo08z2N5eXnbayQQYGF9nbH+flzX\nxTAMFre28OM9mIEwflMnKMsk4gm2yiqbukbKdChZBcb9QWLhCp4dYGGpzcb6OoYSIWrbiIbK/rHr\n8XyPIy99k515gRsnJvjKqQsImkM/IQzXpdVUiUhhdot5HM+hik2DND5lJrDIINBCYQ2LawgioaAQ\nJkcLHyjiM45OHoEKPpuABDjAIh5Btks3OiYNoGZ51FFwKKATw8QlSg2fJppXR9BaDEoBJM8lpbfR\nagsI2SGy8TgL8/PIjQ539AxSDKfYWl8gsrmGYqmsizZeKoruOPiWxb/+d//uTQlIZmdnWVuzGR5+\nJas5MLCT5eUzr3t8PBikUan8yPGmp+corm/QLleJprvo7Z8gGAwTiWRZXS1y8KBC18AAQUliamaG\nUq3G6WIVlRTtcI7J/p3UNzZIiRE8L4GgKNgInFyfZ4fn4lkGNT9LkgiOvo6AS4AAOgHq6PhINBCp\noRFFJUwUnw4KJcax2Qmk2Q5A1oAutgP8DBISLuBjIzBNHJ0gUSQ8dOp4BPEQUXBJEKCbCAJVpulC\nJe+6WBjo7iZnF3V2DN7AZkWlo5cY8Q2+8tnP4oWitLp7uPnD7+Pi0aMIsszW4iLdokjdtlFlmXwq\nxXAmwzOlEvVQiF++7ro37mL/C4Vpbrf0Pvjgm3uePXvggQfe3HP8LPCz7KaRgceAa4HHBUH4L77v\nH/vBYzY35ykUhvA8l83NWYaGIlcJoy++eJRiUXmVtbiu9/G1rz3Bf/pPoz/1hJrLpVhdbTE5uYNC\nocDRow7Ly3NEIhGy2SSzs8/iuk1Cof1cuLBMs3mcen2VVstnaamFqtqsrNRx3QCN2jkUf556u4Hr\nFPC9CIJfoOI0KEgqVQQcZGwUfDp0YeDhUUJhk24abJdvWkSwSNPCI0udnJBClwzCgkBIUBiIZtiQ\nPW7efwCwWS/P8/ixM1xYNCg1BHyvC4FNXC4yQp0DeCSBBNuBxgwQYXvCDAMttgMR8crnw8AsoPs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JUuMre6yvC11774GuX1dQpISFK8m4+EoNWp0OM5ZGWDjAiIgoj1loMLXDs8zOT4OIePHmXJ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xf38zLpcas7kCtVpDc3M7IyN7MBj0ZGYWnCKzTpdCT8/AhDECsGTJYtLSUvnyy4M4nTYW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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x11dade4d0>"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Learn and evaluate scikit-learn's logistic regression with stochastic gradient descent (SGD) training. Time and check the classifier's accuracy."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Train and test the scikit-learn SGD logistic regression.\n",
-      "clf = sklearn.linear_model.SGDClassifier(\n",
-      "    loss='log', n_iter=1000, penalty='l2', alpha=1e-3, class_weight='auto')\n",
-      "\n",
-      "%timeit clf.fit(X, y)\n",
-      "yt_pred = clf.predict(Xt)\n",
-      "print('Accuracy: {:.3f}'.format(sklearn.metrics.accuracy_score(yt, yt_pred)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 499 ms per loop\n",
-        "Accuracy: 0.756\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Save the dataset to HDF5 for loading in Caffe."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Write out the data to HDF5 files in a temp directory.\n",
-      "# This file is assumed to be caffe_root/examples/hdf5_classification.ipynb\n",
-      "dirname = os.path.abspath('./hdf5_classification/data')\n",
-      "if not os.path.exists(dirname):\n",
-      "    os.makedirs(dirname)\n",
-      "\n",
-      "train_filename = os.path.join(dirname, 'train.h5')\n",
-      "test_filename = os.path.join(dirname, 'test.h5')\n",
-      "\n",
-      "# HDF5DataLayer source should be a file containing a list of HDF5 filenames.\n",
-      "# To show this off, we'll list the same data file twice.\n",
-      "with h5py.File(train_filename, 'w') as f:\n",
-      "    f['data'] = X\n",
-      "    f['label'] = y.astype(np.float32)\n",
-      "with open(os.path.join(dirname, 'train.txt'), 'w') as f:\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    f.write(train_filename + '\\n')\n",
-      "    \n",
-      "# HDF5 is pretty efficient, but can be further compressed.\n",
-      "comp_kwargs = {'compression': 'gzip', 'compression_opts': 1}\n",
-      "with h5py.File(test_filename, 'w') as f:\n",
-      "    f.create_dataset('data', data=Xt, **comp_kwargs)\n",
-      "    f.create_dataset('label', data=yt.astype(np.float32), **comp_kwargs)\n",
-      "with open(os.path.join(dirname, 'test.txt'), 'w') as f:\n",
-      "    f.write(test_filename + '\\n')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Learn and evaluate logistic regression in Caffe."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def learn_and_test(solver_file):\n",
-      "    caffe.set_mode_cpu()\n",
-      "    solver = caffe.get_solver(solver_file)\n",
-      "    solver.solve()\n",
-      "\n",
-      "    accuracy = 0\n",
-      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-      "    for i in range(test_iters):\n",
-      "        solver.test_nets[0].forward()\n",
-      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-      "    accuracy /= test_iters\n",
-      "    return accuracy\n",
-      "\n",
-      "%timeit learn_and_test('hdf5_classification/solver.prototxt')\n",
-      "acc = learn_and_test('hdf5_classification/solver.prototxt')\n",
-      "print(\"Accuracy: {:.3f}\".format(acc))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 240 ms per loop\n",
-        "Accuracy: 0.752"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Do the same through the command line interface for detailed output on the model and solving."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!../build/tools/caffe train -solver hdf5_classification/solver.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.141863 2099749632 caffe.cpp:103] Use CPU.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.418283 2099749632 caffe.cpp:107] Starting Optimization\r\n",
-        "I0307 01:34:29.418323 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 250\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"hdf5_classification/train_val.prototxt\"\r\n",
-        "I0307 01:34:29.418416 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val.prototxt\r\n",
-        "I0307 01:34:29.418583 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0307 01:34:29.418598 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0307 01:34:29.418608 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "I0307 01:34:29.418692 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:29.418853 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:29.418879 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:29.418905 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:29.418918 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:29.418926 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
-        "I0307 01:34:29.418992 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
-        "I0307 01:34:29.420812 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:29.420841 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.420852 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:29.420866 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:29.420872 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:29.420882 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:29.420894 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:29.425689 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.425709 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.425724 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:29.425731 2099749632 net.cpp:380] loss <- fc1\r\n",
-        "I0307 01:34:29.425739 2099749632 net.cpp:380] loss <- label\r\n",
-        "I0307 01:34:29.425747 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:29.425756 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:29.425767 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.425781 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.425789 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:29.425801 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:29.425808 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:29.425815 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:29.425822 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:29.425829 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:29.425837 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:29.425843 2099749632 net.cpp:218] Memory required for data: 284\r\n",
-        "I0307 01:34:29.425961 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val.prototxt\r\n",
-        "I0307 01:34:29.425984 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0307 01:34:29.425997 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: \"Accuracy\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0307 01:34:29.426126 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:29.426311 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:29.426331 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:29.426343 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:29.426354 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:29.426362 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
-        "I0307 01:34:29.426484 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
-        "I0307 01:34:29.427692 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:29.427711 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427721 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
-        "I0307 01:34:29.427731 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
-        "I0307 01:34:29.427738 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
-        "I0307 01:34:29.427747 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0307 01:34:29.427759 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0307 01:34:29.427768 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
-        "I0307 01:34:29.427777 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427784 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:29.427791 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:29.427804 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:29.427813 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:29.427821 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:29.427831 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:29.427845 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427857 2099749632 layer_factory.hpp:74] Creating layer fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427866 2099749632 net.cpp:84] Creating Layer fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427872 2099749632 net.cpp:380] fc1_fc1_0_split <- fc1\r\n",
-        "I0307 01:34:29.427881 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_0\r\n",
-        "I0307 01:34:29.427891 2099749632 net.cpp:338] fc1_fc1_0_split -> fc1_fc1_0_split_1\r\n",
-        "I0307 01:34:29.427942 2099749632 net.cpp:113] Setting up fc1_fc1_0_split\r\n",
-        "I0307 01:34:29.427955 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427965 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:29.427976 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.427991 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:29.428001 2099749632 net.cpp:380] loss <- fc1_fc1_0_split_0\r\n",
-        "I0307 01:34:29.428009 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
-        "I0307 01:34:29.428017 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:29.428026 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:29.428035 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:29.428048 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.428056 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:29.428064 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
-        "I0307 01:34:29.428076 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
-        "I0307 01:34:29.428084 2099749632 net.cpp:380] accuracy <- fc1_fc1_0_split_1\r\n",
-        "I0307 01:34:29.428092 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
-        "I0307 01:34:29.428102 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
-        "I0307 01:34:29.428131 2099749632 net.cpp:113] Setting up accuracy\r\n",
-        "I0307 01:34:29.428140 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:29.428148 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
-        "I0307 01:34:29.428154 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:29.428161 2099749632 net.cpp:167] fc1_fc1_0_split needs backward computation.\r\n",
-        "I0307 01:34:29.428167 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:29.428174 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
-        "I0307 01:34:29.428181 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:29.428189 2099749632 net.cpp:205] This network produces output accuracy\r\n",
-        "I0307 01:34:29.428324 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:29.428342 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:29.428350 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:29.428357 2099749632 net.cpp:218] Memory required for data: 528\r\n",
-        "I0307 01:34:29.428388 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
-        "I0307 01:34:29.428412 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
-        "I0307 01:34:29.428421 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
-        "I0307 01:34:29.428431 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.471674 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.4532\r\n",
-        "I0307 01:34:29.471724 2099749632 solver.cpp:315]     Test net output #1: loss = 0.694067 (* 1 = 0.694067 loss)\r\n",
-        "I0307 01:34:29.471853 2099749632 solver.cpp:189] Iteration 0, loss = 0.692695\r\n",
-        "I0307 01:34:29.471878 2099749632 solver.cpp:204]     Train net output #0: loss = 0.692695 (* 1 = 0.692695 loss)\r\n",
-        "I0307 01:34:29.471890 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
-        "I0307 01:34:29.483834 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n",
-        "I0307 01:34:29.486868 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
-        "I0307 01:34:29.486896 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
-        "I0307 01:34:29.486922 2099749632 solver.cpp:189] Iteration 1000, loss = 0.472665\r\n",
-        "I0307 01:34:29.486934 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
-        "I0307 01:34:29.486944 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
-        "I0307 01:34:29.498821 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
-        "I0307 01:34:29.501900 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
-        "I0307 01:34:29.501941 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
-        "I0307 01:34:29.501988 2099749632 solver.cpp:189] Iteration 2000, loss = 0.6863\r\n",
-        "I0307 01:34:29.502003 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
-        "I0307 01:34:29.502013 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
-        "I0307 01:34:29.513921 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n",
-        "I0307 01:34:29.517227 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.6964\r\n",
-        "I0307 01:34:29.517300 2099749632 solver.cpp:315]     Test net output #1: loss = 0.604707 (* 1 = 0.604707 loss)\r\n",
-        "I0307 01:34:29.518105 2099749632 solver.cpp:189] Iteration 3000, loss = 0.617542\r\n",
-        "I0307 01:34:29.518154 2099749632 solver.cpp:204]     Train net output #0: loss = 0.617542 (* 1 = 0.617542 loss)\r\n",
-        "I0307 01:34:29.518170 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.531672 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
-        "I0307 01:34:29.534873 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7424\r\n",
-        "I0307 01:34:29.534920 2099749632 solver.cpp:315]     Test net output #1: loss = 0.601764 (* 1 = 0.601764 loss)\r\n",
-        "I0307 01:34:29.534950 2099749632 solver.cpp:189] Iteration 4000, loss = 0.472666\r\n",
-        "I0307 01:34:29.534962 2099749632 solver.cpp:204]     Train net output #0: loss = 0.472665 (* 1 = 0.472665 loss)\r\n",
-        "I0307 01:34:29.534973 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
-        "I0307 01:34:29.546567 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n",
-        "I0307 01:34:29.549762 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7364\r\n",
-        "I0307 01:34:29.549789 2099749632 solver.cpp:315]     Test net output #1: loss = 0.60818 (* 1 = 0.60818 loss)\r\n",
-        "I0307 01:34:29.549815 2099749632 solver.cpp:189] Iteration 5000, loss = 0.686301\r\n",
-        "I0307 01:34:29.549828 2099749632 solver.cpp:204]     Train net output #0: loss = 0.6863 (* 1 = 0.6863 loss)\r\n",
-        "I0307 01:34:29.549837 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
-        "I0307 01:34:29.562142 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
-        "I0307 01:34:29.565335 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7476\r\n",
-        "I0307 01:34:29.565373 2099749632 solver.cpp:315]     Test net output #1: loss = 0.59775 (* 1 = 0.59775 loss)\r\n",
-        "I0307 01:34:29.566051 2099749632 solver.cpp:189] Iteration 6000, loss = 0.664614\r\n",
-        "I0307 01:34:29.566086 2099749632 solver.cpp:204]     Train net output #0: loss = 0.664614 (* 1 = 0.664614 loss)\r\n",
-        "I0307 01:34:29.566097 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.577900 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
-        "I0307 01:34:29.580993 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7524\r\n",
-        "I0307 01:34:29.581015 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597349 (* 1 = 0.597349 loss)\r\n",
-        "I0307 01:34:29.581038 2099749632 solver.cpp:189] Iteration 7000, loss = 0.456775\r\n",
-        "I0307 01:34:29.581050 2099749632 solver.cpp:204]     Train net output #0: loss = 0.456774 (* 1 = 0.456774 loss)\r\n",
-        "I0307 01:34:29.581059 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n",
-        "I0307 01:34:29.592854 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
-        "I0307 01:34:29.595973 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7568\r\n",
-        "I0307 01:34:29.596002 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597265 (* 1 = 0.597265 loss)\r\n",
-        "I0307 01:34:29.596027 2099749632 solver.cpp:189] Iteration 8000, loss = 0.673885\r\n",
-        "I0307 01:34:29.596040 2099749632 solver.cpp:204]     Train net output #0: loss = 0.673885 (* 1 = 0.673885 loss)\r\n",
-        "I0307 01:34:29.596048 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
-        "I0307 01:34:29.607822 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
-        "I0307 01:34:29.610930 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7432\r\n",
-        "I0307 01:34:29.610960 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597777 (* 1 = 0.597777 loss)\r\n",
-        "I0307 01:34:29.611558 2099749632 solver.cpp:189] Iteration 9000, loss = 0.66526\r\n",
-        "I0307 01:34:29.611583 2099749632 solver.cpp:204]     Train net output #0: loss = 0.66526 (* 1 = 0.66526 loss)\r\n",
-        "I0307 01:34:29.611593 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n",
-        "I0307 01:34:29.623009 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
-        "I0307 01:34:29.623209 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0307 01:34:29.623319 2099749632 solver.cpp:248] Iteration 10000, loss = 0.457922\r\n",
-        "I0307 01:34:29.623333 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:29.626454 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.752\r\n",
-        "I0307 01:34:29.626484 2099749632 solver.cpp:315]     Test net output #1: loss = 0.597362 (* 1 = 0.597362 loss)\r\n",
-        "I0307 01:34:29.626493 2099749632 solver.cpp:253] Optimization Done.\r\n",
-        "I0307 01:34:29.626502 2099749632 caffe.cpp:121] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If you look at output or the `train_val.prototxt`, you'll see that the model is simple logistic regression.\n",
-      "We can make it a little more advanced by introducing a non-linearity between weights that take the input and weights that give the output -- now we have a two-layer network.\n",
-      "That network is given in `train_val2.prototxt`, and that's the only change made in `solver2.prototxt` which we will now use.\n",
-      "\n",
-      "The final accuracy of the new network be higher than logistic regression!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def learn_and_test(solver_file):\n",
-      "    caffe.set_mode_cpu()\n",
-      "    solver = caffe.get_solver(solver_file)\n",
-      "    solver.solve()\n",
-      "\n",
-      "    accuracy = 0\n",
-      "    test_iters = int(len(Xt) / solver.test_nets[0].blobs['data'].num)\n",
-      "    for i in range(test_iters):\n",
-      "        solver.test_nets[0].forward()\n",
-      "        accuracy += solver.test_nets[0].blobs['accuracy'].data\n",
-      "    accuracy /= test_iters\n",
-      "    return accuracy\n",
-      "\n",
-      "%timeit learn_and_test('hdf5_classification/solver2.prototxt')\n",
-      "acc = learn_and_test('hdf5_classification/solver2.prototxt')\n",
-      "print(\"Accuracy: {:.3f}\".format(acc))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 333 ms per loop\n",
-        "Accuracy: 0.818"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Do the same through the command line interface for detailed output on the model and solving."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!../build/tools/caffe train -solver hdf5_classification/solver2.prototxt"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.589234 2099749632 caffe.cpp:103] Use CPU.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.872560 2099749632 caffe.cpp:107] Starting Optimization\r\n",
-        "I0307 01:34:31.872596 2099749632 solver.cpp:32] Initializing solver from parameters: \r\n",
-        "test_iter: 250\r\n",
-        "test_interval: 1000\r\n",
-        "base_lr: 0.01\r\n",
-        "display: 1000\r\n",
-        "max_iter: 10000\r\n",
-        "lr_policy: \"step\"\r\n",
-        "gamma: 0.1\r\n",
-        "momentum: 0.9\r\n",
-        "weight_decay: 0.0005\r\n",
-        "stepsize: 5000\r\n",
-        "snapshot: 10000\r\n",
-        "snapshot_prefix: \"hdf5_classification/data/train\"\r\n",
-        "solver_mode: CPU\r\n",
-        "net: \"hdf5_classification/train_val2.prototxt\"\r\n",
-        "I0307 01:34:31.872687 2099749632 solver.cpp:70] Creating training net from net file: hdf5_classification/train_val2.prototxt\r\n",
-        "I0307 01:34:31.872865 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer data\r\n",
-        "I0307 01:34:31.872882 2099749632 net.cpp:257] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy\r\n",
-        "I0307 01:34:31.872891 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TRAIN\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TRAIN\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/train.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "I0307 01:34:31.873246 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:31.873276 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:31.873292 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:31.873332 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:31.873352 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:31.873361 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/train.txt\r\n",
-        "I0307 01:34:31.873443 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 2\r\n",
-        "I0307 01:34:31.875783 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:31.875816 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.875829 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:31.875846 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:31.875857 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:31.875875 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:31.875892 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:31.882478 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.882505 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0307 01:34:31.882524 2099749632 net.cpp:84] Creating Layer relu1\r\n",
-        "I0307 01:34:31.882532 2099749632 net.cpp:380] relu1 <- fc1\r\n",
-        "I0307 01:34:31.882544 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
-        "I0307 01:34:31.882555 2099749632 net.cpp:113] Setting up relu1\r\n",
-        "I0307 01:34:31.882565 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.882583 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
-        "I0307 01:34:31.882609 2099749632 net.cpp:84] Creating Layer fc2\r\n",
-        "I0307 01:34:31.882619 2099749632 net.cpp:380] fc2 <- fc1\r\n",
-        "I0307 01:34:31.882632 2099749632 net.cpp:338] fc2 -> fc2\r\n",
-        "I0307 01:34:31.882644 2099749632 net.cpp:113] Setting up fc2\r\n",
-        "I0307 01:34:31.882663 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.882678 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.882694 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:31.882704 2099749632 net.cpp:380] loss <- fc2\r\n",
-        "I0307 01:34:31.882712 2099749632 net.cpp:380] loss <- label\r\n",
-        "I0307 01:34:31.882779 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:31.882796 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:31.882810 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.882833 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.882844 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:31.882860 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:31.882869 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
-        "I0307 01:34:31.882877 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
-        "I0307 01:34:31.882886 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:31.882894 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:31.882904 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:31.882931 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:31.882942 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:31.882951 2099749632 net.cpp:218] Memory required for data: 3484\r\n",
-        "I0307 01:34:31.883157 2099749632 solver.cpp:154] Creating test net (#0) specified by net file: hdf5_classification/train_val2.prototxt\r\n",
-        "I0307 01:34:31.883189 2099749632 net.cpp:257] The NetState phase (1) differed from the phase (0) specified by a rule in layer data\r\n",
-        "I0307 01:34:31.883203 2099749632 net.cpp:42] Initializing net from parameters: \r\n",
-        "name: \"LogisticRegressionNet\"\r\n",
-        "state {\r\n",
-        "  phase: TEST\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"data\"\r\n",
-        "  type: \"HDF5Data\"\r\n",
-        "  top: \"data\"\r\n",
-        "  top: \"label\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "  hdf5_data_param {\r\n",
-        "    source: \"hdf5_classification/data/test.txt\"\r\n",
-        "    batch_size: 10\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc1\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"data\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 40\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"relu1\"\r\n",
-        "  type: \"ReLU\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc1\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"fc2\"\r\n",
-        "  type: \"InnerProduct\"\r\n",
-        "  bottom: \"fc1\"\r\n",
-        "  top: \"fc2\"\r\n",
-        "  param {\r\n",
-        "    lr_mult: 1\r\n",
-        "    decay_mult: 1\r\n",
-        "  }\r\n",
-        "  param {\r\n",
-        "    lr_mult: 2\r\n",
-        "    decay_mult: 0\r\n",
-        "  }\r\n",
-        "  inner_product_param {\r\n",
-        "    num_output: 2\r\n",
-        "    weight_filler {\r\n",
-        "      type: \"gaussian\"\r\n",
-        "      std: 0.01\r\n",
-        "    }\r\n",
-        "    bias_filler {\r\n",
-        "      type: \"constant\"\r\n",
-        "      value: 0\r\n",
-        "    }\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"loss\"\r\n",
-        "  type: \"SoftmaxWithLoss\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"loss\"\r\n",
-        "}\r\n",
-        "layer {\r\n",
-        "  name: \"accuracy\"\r\n",
-        "  type: \"Accuracy\"\r\n",
-        "  bottom: \"fc2\"\r\n",
-        "  bottom: \"label\"\r\n",
-        "  top: \"accuracy\"\r\n",
-        "  include {\r\n",
-        "    phase: TEST\r\n",
-        "  }\r\n",
-        "}\r\n",
-        "I0307 01:34:31.883535 2099749632 layer_factory.hpp:74] Creating layer data\r\n",
-        "I0307 01:34:31.883548 2099749632 net.cpp:84] Creating Layer data\r\n",
-        "I0307 01:34:31.883556 2099749632 net.cpp:338] data -> data\r\n",
-        "I0307 01:34:31.883569 2099749632 net.cpp:338] data -> label\r\n",
-        "I0307 01:34:31.883579 2099749632 net.cpp:113] Setting up data\r\n",
-        "I0307 01:34:31.883585 2099749632 hdf5_data_layer.cpp:66] Loading list of HDF5 filenames from: hdf5_classification/data/test.txt\r\n",
-        "I0307 01:34:31.883664 2099749632 hdf5_data_layer.cpp:80] Number of HDF5 files: 1\r\n",
-        "I0307 01:34:31.884842 2099749632 net.cpp:120] Top shape: 10 4 (40)\r\n",
-        "I0307 01:34:31.884860 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884870 2099749632 layer_factory.hpp:74] Creating layer label_data_1_split\r\n",
-        "I0307 01:34:31.884879 2099749632 net.cpp:84] Creating Layer label_data_1_split\r\n",
-        "I0307 01:34:31.884886 2099749632 net.cpp:380] label_data_1_split <- label\r\n",
-        "I0307 01:34:31.884896 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_0\r\n",
-        "I0307 01:34:31.884909 2099749632 net.cpp:338] label_data_1_split -> label_data_1_split_1\r\n",
-        "I0307 01:34:31.884919 2099749632 net.cpp:113] Setting up label_data_1_split\r\n",
-        "I0307 01:34:31.884927 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884934 2099749632 net.cpp:120] Top shape: 10 (10)\r\n",
-        "I0307 01:34:31.884941 2099749632 layer_factory.hpp:74] Creating layer fc1\r\n",
-        "I0307 01:34:31.884951 2099749632 net.cpp:84] Creating Layer fc1\r\n",
-        "I0307 01:34:31.884958 2099749632 net.cpp:380] fc1 <- data\r\n",
-        "I0307 01:34:31.884989 2099749632 net.cpp:338] fc1 -> fc1\r\n",
-        "I0307 01:34:31.885000 2099749632 net.cpp:113] Setting up fc1\r\n",
-        "I0307 01:34:31.885017 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.885030 2099749632 layer_factory.hpp:74] Creating layer relu1\r\n",
-        "I0307 01:34:31.885041 2099749632 net.cpp:84] Creating Layer relu1\r\n",
-        "I0307 01:34:31.885048 2099749632 net.cpp:380] relu1 <- fc1\r\n",
-        "I0307 01:34:31.885056 2099749632 net.cpp:327] relu1 -> fc1 (in-place)\r\n",
-        "I0307 01:34:31.885064 2099749632 net.cpp:113] Setting up relu1\r\n",
-        "I0307 01:34:31.885071 2099749632 net.cpp:120] Top shape: 10 40 (400)\r\n",
-        "I0307 01:34:31.885079 2099749632 layer_factory.hpp:74] Creating layer fc2\r\n",
-        "I0307 01:34:31.885088 2099749632 net.cpp:84] Creating Layer fc2\r\n",
-        "I0307 01:34:31.885094 2099749632 net.cpp:380] fc2 <- fc1\r\n",
-        "I0307 01:34:31.885103 2099749632 net.cpp:338] fc2 -> fc2\r\n",
-        "I0307 01:34:31.885113 2099749632 net.cpp:113] Setting up fc2\r\n",
-        "I0307 01:34:31.885126 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885138 2099749632 layer_factory.hpp:74] Creating layer fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885149 2099749632 net.cpp:84] Creating Layer fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885155 2099749632 net.cpp:380] fc2_fc2_0_split <- fc2\r\n",
-        "I0307 01:34:31.885164 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_0\r\n",
-        "I0307 01:34:31.885174 2099749632 net.cpp:338] fc2_fc2_0_split -> fc2_fc2_0_split_1\r\n",
-        "I0307 01:34:31.885182 2099749632 net.cpp:113] Setting up fc2_fc2_0_split\r\n",
-        "I0307 01:34:31.885190 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885242 2099749632 net.cpp:120] Top shape: 10 2 (20)\r\n",
-        "I0307 01:34:31.885256 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.885267 2099749632 net.cpp:84] Creating Layer loss\r\n",
-        "I0307 01:34:31.885275 2099749632 net.cpp:380] loss <- fc2_fc2_0_split_0\r\n",
-        "I0307 01:34:31.885285 2099749632 net.cpp:380] loss <- label_data_1_split_0\r\n",
-        "I0307 01:34:31.885296 2099749632 net.cpp:338] loss -> loss\r\n",
-        "I0307 01:34:31.885308 2099749632 net.cpp:113] Setting up loss\r\n",
-        "I0307 01:34:31.885316 2099749632 layer_factory.hpp:74] Creating layer loss\r\n",
-        "I0307 01:34:31.885330 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.885337 2099749632 net.cpp:122]     with loss weight 1\r\n",
-        "I0307 01:34:31.885346 2099749632 layer_factory.hpp:74] Creating layer accuracy\r\n",
-        "I0307 01:34:31.885360 2099749632 net.cpp:84] Creating Layer accuracy\r\n",
-        "I0307 01:34:31.885368 2099749632 net.cpp:380] accuracy <- fc2_fc2_0_split_1\r\n",
-        "I0307 01:34:31.885375 2099749632 net.cpp:380] accuracy <- label_data_1_split_1\r\n",
-        "I0307 01:34:31.885383 2099749632 net.cpp:338] accuracy -> accuracy\r\n",
-        "I0307 01:34:31.885392 2099749632 net.cpp:113] Setting up accuracy\r\n",
-        "I0307 01:34:31.885401 2099749632 net.cpp:120] Top shape: (1)\r\n",
-        "I0307 01:34:31.885407 2099749632 net.cpp:169] accuracy does not need backward computation.\r\n",
-        "I0307 01:34:31.885413 2099749632 net.cpp:167] loss needs backward computation.\r\n",
-        "I0307 01:34:31.885419 2099749632 net.cpp:167] fc2_fc2_0_split needs backward computation.\r\n",
-        "I0307 01:34:31.885426 2099749632 net.cpp:167] fc2 needs backward computation.\r\n",
-        "I0307 01:34:31.885432 2099749632 net.cpp:167] relu1 needs backward computation.\r\n",
-        "I0307 01:34:31.885438 2099749632 net.cpp:167] fc1 needs backward computation.\r\n",
-        "I0307 01:34:31.885444 2099749632 net.cpp:169] label_data_1_split does not need backward computation.\r\n",
-        "I0307 01:34:31.885452 2099749632 net.cpp:169] data does not need backward computation.\r\n",
-        "I0307 01:34:31.885457 2099749632 net.cpp:205] This network produces output accuracy\r\n",
-        "I0307 01:34:31.885613 2099749632 net.cpp:205] This network produces output loss\r\n",
-        "I0307 01:34:31.885632 2099749632 net.cpp:447] Collecting Learning Rate and Weight Decay.\r\n",
-        "I0307 01:34:31.885639 2099749632 net.cpp:217] Network initialization done.\r\n",
-        "I0307 01:34:31.885645 2099749632 net.cpp:218] Memory required for data: 3728\r\n",
-        "I0307 01:34:31.885685 2099749632 solver.cpp:42] Solver scaffolding done.\r\n",
-        "I0307 01:34:31.885711 2099749632 solver.cpp:222] Solving LogisticRegressionNet\r\n",
-        "I0307 01:34:31.885721 2099749632 solver.cpp:223] Learning Rate Policy: step\r\n",
-        "I0307 01:34:31.885730 2099749632 solver.cpp:266] Iteration 0, Testing net (#0)\r\n",
-        "I0307 01:34:31.901005 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.5944\r\n",
-        "I0307 01:34:31.901049 2099749632 solver.cpp:315]     Test net output #1: loss = 0.693021 (* 1 = 0.693021 loss)\r\n",
-        "I0307 01:34:31.901177 2099749632 solver.cpp:189] Iteration 0, loss = 0.693163\r\n",
-        "I0307 01:34:31.901192 2099749632 solver.cpp:204]     Train net output #0: loss = 0.693163 (* 1 = 0.693163 loss)\r\n",
-        "I0307 01:34:31.901203 2099749632 solver.cpp:464] Iteration 0, lr = 0.01\r\n",
-        "I0307 01:34:31.920586 2099749632 solver.cpp:266] Iteration 1000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.924612 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7556\r\n",
-        "I0307 01:34:31.924646 2099749632 solver.cpp:315]     Test net output #1: loss = 0.511002 (* 1 = 0.511002 loss)\r\n",
-        "I0307 01:34:31.924684 2099749632 solver.cpp:189] Iteration 1000, loss = 0.38536\r\n",
-        "I0307 01:34:31.924696 2099749632 solver.cpp:204]     Train net output #0: loss = 0.38536 (* 1 = 0.38536 loss)\r\n",
-        "I0307 01:34:31.924706 2099749632 solver.cpp:464] Iteration 1000, lr = 0.01\r\n",
-        "I0307 01:34:31.944727 2099749632 solver.cpp:266] Iteration 2000, Testing net (#0)\r\n",
-        "I0307 01:34:31.948729 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7824\r\n",
-        "I0307 01:34:31.948763 2099749632 solver.cpp:315]     Test net output #1: loss = 0.489214 (* 1 = 0.489214 loss)\r\n",
-        "I0307 01:34:31.948799 2099749632 solver.cpp:189] Iteration 2000, loss = 0.532582\r\n",
-        "I0307 01:34:31.948812 2099749632 solver.cpp:204]     Train net output #0: loss = 0.532582 (* 1 = 0.532582 loss)\r\n",
-        "I0307 01:34:31.948823 2099749632 solver.cpp:464] Iteration 2000, lr = 0.01\r\n",
-        "I0307 01:34:31.968670 2099749632 solver.cpp:266] Iteration 3000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:31.972393 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.7956\r\n",
-        "I0307 01:34:31.972411 2099749632 solver.cpp:315]     Test net output #1: loss = 0.454184 (* 1 = 0.454184 loss)\r\n",
-        "I0307 01:34:31.973024 2099749632 solver.cpp:189] Iteration 3000, loss = 0.541374\r\n",
-        "I0307 01:34:31.973057 2099749632 solver.cpp:204]     Train net output #0: loss = 0.541374 (* 1 = 0.541374 loss)\r\n",
-        "I0307 01:34:31.973067 2099749632 solver.cpp:464] Iteration 3000, lr = 0.01\r\n",
-        "I0307 01:34:31.994829 2099749632 solver.cpp:266] Iteration 4000, Testing net (#0)\r\n",
-        "I0307 01:34:31.998638 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.798\r\n",
-        "I0307 01:34:31.998663 2099749632 solver.cpp:315]     Test net output #1: loss = 0.456348 (* 1 = 0.456348 loss)\r\n",
-        "I0307 01:34:31.998705 2099749632 solver.cpp:189] Iteration 4000, loss = 0.490437\r\n",
-        "I0307 01:34:31.998718 2099749632 solver.cpp:204]     Train net output #0: loss = 0.490437 (* 1 = 0.490437 loss)\r\n",
-        "I0307 01:34:31.998725 2099749632 solver.cpp:464] Iteration 4000, lr = 0.01\r\n",
-        "I0307 01:34:32.021085 2099749632 solver.cpp:266] Iteration 5000, Testing net (#0)\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.024950 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.804\r\n",
-        "I0307 01:34:32.024981 2099749632 solver.cpp:315]     Test net output #1: loss = 0.46184 (* 1 = 0.46184 loss)\r\n",
-        "I0307 01:34:32.025017 2099749632 solver.cpp:189] Iteration 5000, loss = 0.467703\r\n",
-        "I0307 01:34:32.025028 2099749632 solver.cpp:204]     Train net output #0: loss = 0.467704 (* 1 = 0.467704 loss)\r\n",
-        "I0307 01:34:32.025038 2099749632 solver.cpp:464] Iteration 5000, lr = 0.001\r\n",
-        "I0307 01:34:32.044390 2099749632 solver.cpp:266] Iteration 6000, Testing net (#0)\r\n",
-        "I0307 01:34:32.048216 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
-        "I0307 01:34:32.048239 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423084 (* 1 = 0.423084 loss)\r\n",
-        "I0307 01:34:32.048790 2099749632 solver.cpp:189] Iteration 6000, loss = 0.480104\r\n",
-        "I0307 01:34:32.048809 2099749632 solver.cpp:204]     Train net output #0: loss = 0.480105 (* 1 = 0.480105 loss)\r\n",
-        "I0307 01:34:32.048827 2099749632 solver.cpp:464] Iteration 6000, lr = 0.001\r\n",
-        "I0307 01:34:32.067795 2099749632 solver.cpp:266] Iteration 7000, Testing net (#0)\r\n",
-        "I0307 01:34:32.071524 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8124\r\n",
-        "I0307 01:34:32.071542 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423947 (* 1 = 0.423947 loss)\r\n",
-        "I0307 01:34:32.071570 2099749632 solver.cpp:189] Iteration 7000, loss = 0.447471\r\n",
-        "I0307 01:34:32.071617 2099749632 solver.cpp:204]     Train net output #0: loss = 0.447472 (* 1 = 0.447472 loss)\r\n",
-        "I0307 01:34:32.071626 2099749632 solver.cpp:464] Iteration 7000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.091625 2099749632 solver.cpp:266] Iteration 8000, Testing net (#0)\r\n",
-        "I0307 01:34:32.095410 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.814\r\n",
-        "I0307 01:34:32.095432 2099749632 solver.cpp:315]     Test net output #1: loss = 0.423586 (* 1 = 0.423586 loss)\r\n",
-        "I0307 01:34:32.095461 2099749632 solver.cpp:189] Iteration 8000, loss = 0.386258\r\n",
-        "I0307 01:34:32.095474 2099749632 solver.cpp:204]     Train net output #0: loss = 0.386259 (* 1 = 0.386259 loss)\r\n",
-        "I0307 01:34:32.095481 2099749632 solver.cpp:464] Iteration 8000, lr = 0.001\r\n",
-        "I0307 01:34:32.117184 2099749632 solver.cpp:266] Iteration 9000, Testing net (#0)\r\n",
-        "I0307 01:34:32.121587 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8208\r\n",
-        "I0307 01:34:32.121608 2099749632 solver.cpp:315]     Test net output #1: loss = 0.419969 (* 1 = 0.419969 loss)\r\n",
-        "I0307 01:34:32.122161 2099749632 solver.cpp:189] Iteration 9000, loss = 0.468262\r\n",
-        "I0307 01:34:32.122181 2099749632 solver.cpp:204]     Train net output #0: loss = 0.468262 (* 1 = 0.468262 loss)\r\n",
-        "I0307 01:34:32.122191 2099749632 solver.cpp:464] Iteration 9000, lr = 0.001\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "I0307 01:34:32.141635 2099749632 solver.cpp:334] Snapshotting to hdf5_classification/data/train_iter_10000.caffemodel\r\n",
-        "I0307 01:34:32.141860 2099749632 solver.cpp:342] Snapshotting solver state to hdf5_classification/data/train_iter_10000.solverstate\r\n",
-        "I0307 01:34:32.141978 2099749632 solver.cpp:248] Iteration 10000, loss = 0.441529\r\n",
-        "I0307 01:34:32.141995 2099749632 solver.cpp:266] Iteration 10000, Testing net (#0)\r\n",
-        "I0307 01:34:32.145747 2099749632 solver.cpp:315]     Test net output #0: accuracy = 0.8148\r\n",
-        "I0307 01:34:32.145771 2099749632 solver.cpp:315]     Test net output #1: loss = 0.4216 (* 1 = 0.4216 loss)\r\n",
-        "I0307 01:34:32.145779 2099749632 solver.cpp:253] Optimization Done.\r\n",
-        "I0307 01:34:32.145786 2099749632 caffe.cpp:121] Optimization Done.\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Clean up (comment this out if you want to examine the hdf5_classification/data directory).\n",
-      "shutil.rmtree(dirname)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
diff --git a/examples/hdf5_classification/nonlinear_auto_test.prototxt b/examples/hdf5_classification/nonlinear_auto_test.prototxt
new file mode 100644
index 00000000000..53eda6ee8a0
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_test.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/test.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/nonlinear_auto_train.prototxt b/examples/hdf5_classification/nonlinear_auto_train.prototxt
new file mode 100644
index 00000000000..fc0688fa652
--- /dev/null
+++ b/examples/hdf5_classification/nonlinear_auto_train.prototxt
@@ -0,0 +1,54 @@
+layer {
+  name: "data"
+  type: "HDF5Data"
+  top: "data"
+  top: "label"
+  hdf5_data_param {
+    source: "examples/hdf5_classification/data/train.txt"
+    batch_size: 10
+  }
+}
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "data"
+  top: "ip1"
+  inner_product_param {
+    num_output: 40
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "ip1"
+  top: "ip1"
+}
+layer {
+  name: "ip2"
+  type: "InnerProduct"
+  bottom: "ip1"
+  top: "ip2"
+  inner_product_param {
+    num_output: 2
+    weight_filler {
+      type: "xavier"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/examples/hdf5_classification/train_val2.prototxt b/examples/hdf5_classification/nonlinear_train_val.prototxt
similarity index 87%
rename from examples/hdf5_classification/train_val2.prototxt
rename to examples/hdf5_classification/nonlinear_train_val.prototxt
index 8795e8facb6..8f7ef04f58a 100644
--- a/examples/hdf5_classification/train_val2.prototxt
+++ b/examples/hdf5_classification/nonlinear_train_val.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 40
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
@@ -72,8 +71,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
diff --git a/examples/hdf5_classification/solver.prototxt b/examples/hdf5_classification/solver.prototxt
deleted file mode 100644
index 65a6eb9e9fb..00000000000
--- a/examples/hdf5_classification/solver.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-net: "hdf5_classification/train_val.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/solver2.prototxt b/examples/hdf5_classification/solver2.prototxt
deleted file mode 100644
index 32b9feba346..00000000000
--- a/examples/hdf5_classification/solver2.prototxt
+++ /dev/null
@@ -1,14 +0,0 @@
-net: "hdf5_classification/train_val2.prototxt"
-test_iter: 250
-test_interval: 1000
-base_lr: 0.01
-lr_policy: "step"
-gamma: 0.1
-stepsize: 5000
-display: 1000
-max_iter: 10000
-momentum: 0.9
-weight_decay: 0.0005
-snapshot: 10000
-snapshot_prefix: "hdf5_classification/data/train"
-solver_mode: CPU
diff --git a/examples/hdf5_classification/train_val.prototxt b/examples/hdf5_classification/train_val.prototxt
index d5e8dbfa169..13ddf47524a 100644
--- a/examples/hdf5_classification/train_val.prototxt
+++ b/examples/hdf5_classification/train_val.prototxt
@@ -8,7 +8,7 @@ layer {
     phase: TRAIN
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/train.txt"
+    source: "examples/hdf5_classification/data/train.txt"
     batch_size: 10
   }
 }
@@ -21,7 +21,7 @@ layer {
     phase: TEST
   }
   hdf5_data_param {
-    source: "hdf5_classification/data/test.txt"
+    source: "examples/hdf5_classification/data/test.txt"
     batch_size: 10
   }
 }
@@ -41,8 +41,7 @@ layer {
   inner_product_param {
     num_output: 2
     weight_filler {
-      type: "gaussian"
-      std: 0.01
+      type: "xavier"
     }
     bias_filler {
       type: "constant"
diff --git a/examples/imagenet/readme.md b/examples/imagenet/readme.md
index a6bdf49ca4d..65174d601f2 100644
--- a/examples/imagenet/readme.md
+++ b/examples/imagenet/readme.md
@@ -91,9 +91,9 @@ Resume Training?
 
 We all experience times when the power goes out, or we feel like rewarding ourself a little by playing Battlefield (does anyone still remember Quake?). Since we are snapshotting intermediate results during training, we will be able to resume from snapshots. This can be done as easy as:
 
-    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+    ./build/tools/caffe train --solver=models/bvlc_reference_caffenet/solver.prototxt --snapshot=models/bvlc_reference_caffenet/caffenet_train_iter_10000.solverstate
 
-where in the script `caffenet_train_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
+where in the script `caffenet_train_iter_10000.solverstate` is the solver state snapshot that stores all necessary information to recover the exact solver state (including the parameters, momentum history, etc).
 
 Parting Words
 -------------
@@ -102,4 +102,4 @@ Hope you liked this recipe!
 Many researchers have gone further since the ILSVRC 2012 challenge, changing the network architecture and/or fine-tuning the various parameters in the network to address new data and tasks.
 **Caffe lets you explore different network choices more easily by simply writing different prototxt files** - isn't that exciting?
 
-And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb).
+And since now you have a trained network, check out how to use it with the Python interface for [classifying ImageNet](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb).
diff --git a/examples/imagenet/resume_training.sh b/examples/imagenet/resume_training.sh
index d1febff8d39..bf7945c0fd0 100755
--- a/examples/imagenet/resume_training.sh
+++ b/examples/imagenet/resume_training.sh
@@ -2,4 +2,4 @@
 
 ./build/tools/caffe train \
     --solver=models/bvlc_reference_caffenet/solver.prototxt \
-    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate
+    --snapshot=models/bvlc_reference_caffenet/caffenet_train_10000.solverstate.h5
diff --git a/examples/mnist/convert_mnist_data.cpp b/examples/mnist/convert_mnist_data.cpp
index 2749e4521b6..16d28093dd5 100644
--- a/examples/mnist/convert_mnist_data.cpp
+++ b/examples/mnist/convert_mnist_data.cpp
@@ -9,9 +9,13 @@
 #include <gflags/gflags.h>
 #include <glog/logging.h>
 #include <google/protobuf/text_format.h>
+
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
 #include <leveldb/db.h>
 #include <leveldb/write_batch.h>
 #include <lmdb.h>
+#endif
+
 #include <stdint.h>
 #include <sys/stat.h>
 
@@ -19,6 +23,9 @@
 #include <string>
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
+
+#if defined(USE_LEVELDB) && defined(USE_LMDB)
 
 using namespace caffe;  // NOLINT(build/namespaces)
 using std::string;
@@ -102,8 +109,6 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   char label;
   char* pixels = new char[rows * cols];
   int count = 0;
-  const int kMaxKeyLength = 10;
-  char key_cstr[kMaxKeyLength];
   string value;
 
   Datum datum;
@@ -117,18 +122,17 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     label_file.read(&label, 1);
     datum.set_data(pixels, rows*cols);
     datum.set_label(label);
-    snprintf(key_cstr, kMaxKeyLength, "%08d", item_id);
+    string key_str = caffe::format_int(item_id, 8);
     datum.SerializeToString(&value);
-    string keystr(key_cstr);
 
     // Put in db
     if (db_backend == "leveldb") {  // leveldb
-      batch->Put(keystr, value);
+      batch->Put(key_str, value);
     } else if (db_backend == "lmdb") {  // lmdb
       mdb_data.mv_size = value.size();
       mdb_data.mv_data = reinterpret_cast<void*>(&value[0]);
-      mdb_key.mv_size = keystr.size();
-      mdb_key.mv_data = reinterpret_cast<void*>(&keystr[0]);
+      mdb_key.mv_size = key_str.size();
+      mdb_key.mv_data = reinterpret_cast<void*>(&key_str[0]);
       CHECK_EQ(mdb_put(mdb_txn, mdb_dbi, &mdb_key, &mdb_data, 0), MDB_SUCCESS)
           << "mdb_put failed";
     } else {
@@ -166,7 +170,7 @@ void convert_dataset(const char* image_filename, const char* label_filename,
     }
     LOG(ERROR) << "Processed " << count << " files.";
   }
-  delete pixels;
+  delete[] pixels;
 }
 
 int main(int argc, char** argv) {
@@ -196,3 +200,9 @@ int main(int argc, char** argv) {
   }
   return 0;
 }
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires LevelDB and LMDB; " <<
+  "compile with USE_LEVELDB and USE_LMDB.";
+}
+#endif  // USE_LEVELDB and USE_LMDB
diff --git a/examples/mnist/lenet.prototxt b/examples/mnist/lenet.prototxt
index cb42610fe1e..8cf78e62c89 100644
--- a/examples/mnist/lenet.prototxt
+++ b/examples/mnist/lenet.prototxt
@@ -1,9 +1,10 @@
 name: "LeNet"
-input: "data"
-input_dim: 64
-input_dim: 1
-input_dim: 28
-input_dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/mnist/lenet_adadelta_solver.prototxt b/examples/mnist/lenet_adadelta_solver.prototxt
new file mode 100644
index 00000000000..16176c0ffae
--- /dev/null
+++ b/examples/mnist/lenet_adadelta_solver.prototxt
@@ -0,0 +1,24 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 1.0
+lr_policy: "fixed"
+momentum: 0.95
+weight_decay: 0.0005
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_adadelta"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "AdaDelta"
+delta: 1e-6
diff --git a/examples/mnist/lenet_stepearly_solver.prototxt b/examples/mnist/lenet_auto_solver.prototxt
similarity index 66%
rename from examples/mnist/lenet_stepearly_solver.prototxt
rename to examples/mnist/lenet_auto_solver.prototxt
index efc6a335d8f..481c84491f7 100644
--- a/examples/mnist/lenet_stepearly_solver.prototxt
+++ b/examples/mnist/lenet_auto_solver.prototxt
@@ -1,7 +1,6 @@
-# The training protocol buffer definition
-train_net: "lenet_train.prototxt"
-# The testing protocol buffer definition
-test_net: "lenet_test.prototxt"
+# The train/test net protocol buffer definition
+train_net: "mnist/lenet_auto_train.prototxt"
+test_net: "mnist/lenet_auto_test.prototxt"
 # test_iter specifies how many forward passes the test should carry out.
 # In the case of MNIST, we have test batch size 100 and 100 test iterations,
 # covering the full 10,000 testing images.
@@ -13,16 +12,13 @@ base_lr: 0.01
 momentum: 0.9
 weight_decay: 0.0005
 # The learning rate policy
-lr_policy: "stepearly"
-gamma: 0.9
-stepearly: 1
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
 # Display every 100 iterations
 display: 100
 # The maximum number of iterations
 max_iter: 10000
 # snapshot intermediate results
 snapshot: 5000
-snapshot_prefix: "lenet"
-# solver mode: 0 for CPU and 1 for GPU
-solver_mode: 1
-device_id: 1
+snapshot_prefix: "mnist/lenet"
diff --git a/examples/mnist/lenet_solver_adam.prototxt b/examples/mnist/lenet_solver_adam.prototxt
new file mode 100644
index 00000000000..4b5336b1a04
--- /dev/null
+++ b/examples/mnist/lenet_solver_adam.prototxt
@@ -0,0 +1,26 @@
+# The train/test net protocol buffer definition
+# this follows "ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION"
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# All parameters are from the cited paper above
+base_lr: 0.001
+momentum: 0.9
+momentum2: 0.999
+# since Adam dynamically changes the learning rate, we set the base learning
+# rate to a fixed value
+lr_policy: "fixed"
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+type: "Adam"
+solver_mode: GPU
diff --git a/examples/mnist/lenet_solver_rmsprop.prototxt b/examples/mnist/lenet_solver_rmsprop.prototxt
new file mode 100644
index 00000000000..924b72d306e
--- /dev/null
+++ b/examples/mnist/lenet_solver_rmsprop.prototxt
@@ -0,0 +1,27 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.0
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet_rmsprop"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "RMSProp"
+rms_decay: 0.98
diff --git a/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
new file mode 100644
index 00000000000..26c4084a374
--- /dev/null
+++ b/examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
@@ -0,0 +1,19 @@
+net: "examples/mnist/mnist_autoencoder.prototxt"
+test_state: { stage: 'test-on-train' }
+test_iter: 500
+test_state: { stage: 'test-on-test' }
+test_iter: 100
+test_interval: 500
+test_compute_loss: true
+base_lr: 1.0
+lr_policy: "fixed"
+momentum: 0.95
+delta: 1e-8
+display: 100
+max_iter: 65000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "examples/mnist/mnist_autoencoder_adadelta_train"
+# solver mode: CPU or GPU
+solver_mode: GPU
+type: "AdaDelta"
diff --git a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
index cc0ed9e310a..065cdb20ddc 100644
--- a/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_adagrad.prototxt
@@ -14,4 +14,4 @@ snapshot: 10000
 snapshot_prefix: "examples/mnist/mnist_autoencoder_adagrad_train"
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: ADAGRAD
+type: "AdaGrad"
diff --git a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
index 2a59fd45c8d..c95e3fe7e49 100644
--- a/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
+++ b/examples/mnist/mnist_autoencoder_solver_nesterov.prototxt
@@ -17,4 +17,4 @@ snapshot_prefix: "examples/mnist/mnist_autoencoder_nesterov_train"
 momentum: 0.95
 # solver mode: CPU or GPU
 solver_mode: GPU
-solver_type: NESTEROV
+type: "Nesterov"
diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index ef7f5da67d5..b87a0f53c7a 100644
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -38,14 +38,16 @@ Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.
 
 Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
 
-    layers {
+    layer {
       name: "mnist"
-      type: DATA
+      type: "Data"
+      transform_param {
+        scale: 0.00390625
+      }
       data_param {
         source: "mnist_train_lmdb"
         backend: LMDB
         batch_size: 64
-        scale: 0.00390625
       }
       top: "data"
       top: "label"
@@ -57,14 +59,14 @@ Specifically, this layer has name `mnist`, type `data`, and it reads the data fr
 
 Let's define the first convolution layer:
 
-    layers {
+    layer {
       name: "conv1"
-      type: CONVOLUTION
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "Convolution"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       convolution_param {
         num_output: 20
-        kernelsize: 5
+        kernel_size: 5
         stride: 1
         weight_filler {
           type: "xavier"
@@ -81,15 +83,15 @@ This layer takes the `data` blob (it is provided by the data layer), and produce
 
 The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.
 
-`blobs_lr` are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
+`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
 
 ### Writing the Pooling Layer
 
 Phew. Pooling layers are actually much easier to define:
 
-    layers {
+    layer {
       name: "pool1"
-      type: POOLING
+      type: "Pooling"
       pooling_param {
         kernel_size: 2
         stride: 2
@@ -107,11 +109,11 @@ Similarly, you can write up the second convolution and pooling layers. Check `$C
 
 Writing a fully connected layer is also simple:
 
-    layers {
+    layer {
       name: "ip1"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 500
         weight_filler {
@@ -125,15 +127,15 @@ Writing a fully connected layer is also simple:
       top: "ip1"
     }
 
-This defines a fully connected layer (for some legacy reason, Caffe calls it an `innerproduct` layer) with 500 outputs. All other lines look familiar, right?
+This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?
 
 ### Writing the ReLU Layer
 
 A ReLU Layer is also simple:
 
-    layers {
+    layer {
       name: "relu1"
-      type: RELU
+      type: "ReLU"
       bottom: "ip1"
       top: "ip1"
     }
@@ -142,11 +144,11 @@ Since ReLU is an element-wise operation, we can do *in-place* operations to save
 
 After the ReLU layer, we will write another innerproduct layer:
 
-    layers {
+    layer {
       name: "ip2"
-      type: INNER_PRODUCT
-      blobs_lr: 1.
-      blobs_lr: 2.
+      type: "InnerProduct"
+      param { lr_mult: 1 }
+      param { lr_mult: 2 }
       inner_product_param {
         num_output: 10
         weight_filler {
@@ -164,9 +166,9 @@ After the ReLU layer, we will write another innerproduct layer:
 
 Finally, we will write the loss!
 
-    layers {
+    layer {
       name: "loss"
-      type: SOFTMAX_LOSS
+      type: "SoftmaxWithLoss"
       bottom: "ip2"
       bottom: "label"
     }
@@ -178,7 +180,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
 
-    layers {
+    layer {
       // ...layer definition...
       include: { phase: TRAIN }
     }
@@ -190,7 +192,7 @@ In the above example, this layer will be included only in `TRAIN` phase.
 If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
 By default, that is without layer rules, a layer is always included in the network.
 Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
-Also, there is an `ACCURACY` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
+Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
 
 ## Define the MNIST Solver
 
@@ -283,5 +285,5 @@ and you will be using CPU for training. Isn't that easy?
 
 MNIST is a small dataset, so training with GPU does not really introduce too much benefit due to communication overheads. On larger datasets with more complex models, such as ImageNet, the computation speed difference will be more significant.
 
-### How to reduce the learning rate a fixed steps?
+### How to reduce the learning rate at fixed steps?
 Look at lenet_multistep_solver.prototxt
diff --git a/examples/mnist/train_lenet_adam.sh b/examples/mnist/train_lenet_adam.sh
new file mode 100755
index 00000000000..a32ecf2d9c2
--- /dev/null
+++ b/examples/mnist/train_lenet_adam.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt
diff --git a/examples/mnist/train_lenet_docker.sh b/examples/mnist/train_lenet_docker.sh
new file mode 100755
index 00000000000..32cf1c8e4a3
--- /dev/null
+++ b/examples/mnist/train_lenet_docker.sh
@@ -0,0 +1,119 @@
+#!/usr/bin/env sh
+set -e
+# The following example allows for the MNIST example (using LeNet) to be
+# trained using the caffe docker image instead of building from source.
+#
+# The GPU-enabled version of Caffe can be used, assuming that nvidia-docker
+# is installed, and the GPU-enabled Caffe image has been built.
+# Setting the GPU environment variable to 1 will enable the use of nvidia-docker.
+# e.g.
+#   GPU=1 ./examples/mnist/train_lenet_docker.sh [ADDITIONAL_CAFFE_ARGS]
+#
+# With any arguments following the script being passed directly to caffe
+# when training the network.
+#
+# The steps that are performed by the script are as follows:
+# 1. The MNIST data set is downloaded
+#    (see data/mnist/get_mnist.sh)
+# 2. An LMDB database is created from the downloaded data
+#    (see examples/mnist/create_mnist.sh.
+# 3. A caffe network based on the LeNet solver is trained.
+#    (see examples/mnist/lenet_solver.prototxt)
+#
+# For each of these, a step is executed to ensure that certain prerequisites
+# are available, after which a command that actually performs the work is
+# executed.
+#
+# In order to provide additional flexibility, the following shell (environment)
+# variables can be used to controll the execution of each of the phases:
+#
+# DOWNLOAD_DATA: Enable (1) or disable (0) the downloading of the MNIST dataset
+# CREATE_LMDB: Enable (1) or disable (0) the creation of the LMDB database
+# TRAIN: Enable (1) or disable (0) the training of the LeNet networkd.
+#
+# As an example, assuming that the data set has been downloaded, and an LMDB
+# database created, the following command can be used to train the LeNet
+# network with GPU computing enabled.
+#
+# DOWNLOAD_DATA=0 CREATE_LMDB=0 GPU=1 ./examples/mnist/train_lenet_docker.sh
+#
+
+
+if [ x"$(uname -s)" != x"Linux" ]
+then
+echo ""
+echo "This script is designed to run on Linux."
+echo "There may be problems with the way Docker mounts host volumes on other"
+echo "systems which will cause the docker commands to fail."
+echo ""
+read -p "Press [ENTER] to continue..." key
+echo ""
+fi
+
+
+# Check if GPU mode has been enabled and set the docker executable accordingly
+if [ ${GPU:-0} -eq 1 ]
+then
+DOCKER_CMD=nvidia-docker
+IMAGE=caffe:gpu
+else
+DOCKER_CMD=docker
+IMAGE=caffe:cpu
+fi
+echo "Using $DOCKER_CMD to launch $IMAGE"
+
+# On non-Linux systems, the Docker host is typically a virtual machine.
+# This means that the user and group id's may be different.
+# On OS X, for example, the user and group are 1000 and 50, respectively.
+if [ x"$(uname -s)" != x"Linux" ]
+then
+CUID=1000
+CGID=50
+else
+CUID=$(id -u)
+CGID=$(id -g)
+fi
+
+# Define some helper variables to make the running of the actual docker
+# commands less verbose.
+# Note:
+#   -u $CUID:$CGID             runs the docker image as the current user to ensure
+#                              that the file permissions are compatible with the
+#                              host system. The variables CUID and CGID have been
+#                              set above depending on the host operating system.
+#   --volume $(pwd):/workspace mounts the current directory as the docker volume
+#                              /workspace
+#   --workdir /workspace       Ensures that the docker container starts in the right
+#                              working directory
+DOCKER_OPTIONS="--rm -ti -u $CUID:$CGID --volume=$(pwd):/workspace --workdir=/workspace"
+DOCKER_RUN="$DOCKER_CMD run $DOCKER_OPTIONS $IMAGE"
+
+# Download the data
+if [ ${DOWNLOAD_DATA:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./data/mnist;
+                     cp -ru \$CAFFE_ROOT/data/mnist/get_mnist.sh ./data/mnist/"
+$DOCKER_RUN ./data/mnist/get_mnist.sh
+fi
+
+# Create the LMDB database
+if [ ${CREATE_LMDB:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "mkdir -p ./examples/mnist;
+                     cp -ru \$CAFFE_ROOT/examples/mnist/create_mnist.sh ./examples/mnist/;
+                     sed -i s#BUILD=build#BUILD=\$CAFFE_ROOT/build## ./examples/mnist/create_mnist.sh"
+$DOCKER_RUN ./examples/mnist/create_mnist.sh
+fi
+
+# Train the network
+if [ ${TRAIN:-1} -eq 1 ]
+then
+$DOCKER_RUN bash -c "cp \$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt ./examples/mnist/;
+                     cp \$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt ./examples/mnist/"
+    # Ensure that the solver_mode is compatible with the desired GPU mode.
+    if [ ${GPU:-0} -eq 0 ]
+    then
+    $DOCKER_RUN sed -i 's#solver_mode: GPU#solver_mode: CPU##' ./examples/mnist/lenet_solver.prototxt
+    fi
+$DOCKER_RUN caffe train --solver=examples/mnist/lenet_solver.prototxt $*
+fi
diff --git a/examples/mnist/train_lenet_rmsprop.sh b/examples/mnist/train_lenet_rmsprop.sh
new file mode 100755
index 00000000000..621cab238bf
--- /dev/null
+++ b/examples/mnist/train_lenet_rmsprop.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+./build/tools/caffe train --solver=examples/mnist/lenet_solver_rmsprop.prototxt
diff --git a/examples/mnist/train_mnist_autoencoder_adadelta.sh b/examples/mnist/train_mnist_autoencoder_adadelta.sh
new file mode 100755
index 00000000000..4be0ebddedc
--- /dev/null
+++ b/examples/mnist/train_mnist_autoencoder_adadelta.sh
@@ -0,0 +1,4 @@
+#!/bin/bash
+
+./build/tools/caffe train \
+  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb
index 75c9889fb5a..a6092db0c40 100644
--- a/examples/net_surgery.ipynb
+++ b/examples/net_surgery.ipynb
@@ -1,464 +1,6910 @@
 {
- "metadata": {
-  "description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
-  "example_name": "Editing model parameters",
-  "include_in_docs": true,
-  "priority": 5,
-  "signature": "sha256:f21c804f76329e70847ccb87e28a91e5d8a375f5da0ba6dd85d3b87a05bebd72"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Net Surgery\n",
-      "\n",
-      "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
-      "\n",
-      "Roll up your sleeves for net surgery with pycaffe!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "import Image\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# configure plotting\n",
-      "plt.rcParams['figure.figsize'] = (10, 10)\n",
-      "plt.rcParams['image.interpolation'] = 'nearest'\n",
-      "plt.rcParams['image.cmap'] = 'gray'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Designer Filters\n",
-      "\n",
-      "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the net, list its data and params, and filter an example image.\n",
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
-      "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
-      "\n",
-      "# load image and prepare as a single input batch for Caffe\n",
-      "im = np.array(Image.open('images/cat_gray.jpg'))\n",
-      "plt.title(\"original image\")\n",
-      "plt.imshow(im)\n",
-      "plt.axis('off')\n",
-      "\n",
-      "im_input = im[np.newaxis, np.newaxis, :, :]\n",
-      "net.blobs['data'].reshape(*im_input.shape)\n",
-      "net.blobs['data'].data[...] = im_input"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "blobs ['data', 'conv']\n",
-        "params ['conv']\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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wOD+7rZYez/FQzmvBLddIBpl0ZLwxxp9nDRd1dKIXWRtjJ2NlZaU4fDbAGQCw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lUint2qvVaolWPu/c52sIw9DlCoYViATzjiKAfhyYLJ0bNr1eLxnQrVYr0ebu3buSzsOR8CRz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\nfKhKpaJarZb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OPk8YQq7YY0jd++IoDzLQHWE3XjC24BXQQ2rJeVjbK5s7qkdfHWFx9Gw2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\nJ30z36Xk8TmIYK3hpNqRMuWiTHmxFng2mzV4XrPZjHfv3iWV4uLiI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eYRUa6eoFaRyd8m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\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CizFwuhBPlujJi4SBtEIz5MjGQeAsrL1erxRH3CeEjNFRgo2yo7WIeg2WvLkRCMZ5f38f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\nl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j4jBfpSej2/+81P/g8v8pauFrLQIAAm7bnizYtvZ8ZRe8QuakOoysi7JJv6+Y7WbUB4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\no5hg26exMZZ+TpELSPFu2uPx8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-       "text": [
-        "<matplotlib.figure.Figure at 0x112797f50>"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# helper show filter outputs\n",
-      "def show_filters(net):\n",
-      "    net.forward()\n",
-      "    plt.figure()\n",
-      "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
-      "    for i in range(3):\n",
-      "        plt.subplot(1,4,i+2)\n",
-      "        plt.title(\"filter #{} output\".format(i))\n",
-      "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
-      "        plt.tight_layout()\n",
-      "        plt.axis('off')\n",
-      "\n",
-      "# filter the image with initial \n",
-      "show_filters(net)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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4URgkf3g6BJoVFgCwUMY2oJwMJk7RC2ERFMyLr/xYR7A+Ed9++2392I/9WFJo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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x113e04090>"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Raising the bias of a filter will correspondingly raise its output:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# pick first filter output\n",
-      "conv0 = net.blobs['conv'].data[0, 0]\n",
-      "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
-      "# set first filter bias to 10\n",
-      "net.params['conv'][1].data[0] = 1.\n",
-      "net.forward()\n",
-      "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "pre-surgery output mean -12.93\n",
-        "post-surgery output mean -11.93\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Net Surgery\n",
+    "\n",
+    "Caffe networks can be transformed to your particular needs by editing the model parameters. The data, diffs, and parameters of a net are all exposed in pycaffe.\n",
+    "\n",
+    "Roll up your sleeves for net surgery with pycaffe!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import Image\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# configure plotting\n",
+    "plt.rcParams['figure.figsize'] = (10, 10)\n",
+    "plt.rcParams['image.interpolation'] = 'nearest'\n",
+    "plt.rcParams['image.cmap'] = 'gray'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Designer Filters\n",
+    "\n",
+    "To show how to load, manipulate, and save parameters we'll design our own filters into a simple network that's only a single convolution layer. This net has two blobs, `data` for the input and `conv` for the convolution output and one parameter `conv` for the convolution filter weights and biases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
-      "\n",
-      "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "blobs ['data', 'conv']\n",
+      "params ['conv']\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ksize = net.params['conv'][0].data.shape[2:]\n",
-      "# make Gaussian blur\n",
-      "sigma = 1.\n",
-      "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
-      "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
-      "gaussian = (g / g.sum()).astype(np.float32)\n",
-      "net.params['conv'][0].data[0] = gaussian\n",
-      "# make Sobel operator for edge detection\n",
-      "net.params['conv'][0].data[1:] = 0.\n",
-      "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
-      "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
-      "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
-      "show_filters(net)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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6rjiupf4Cj5aQOy8AIySOjNHfLJs/HKpUOi3GzfPTY+lRaO6ds8MGg0PE5N69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\nXi/AHOsJhfHw8BCsD5/n83m1Wi1Jq+A8gMTh4WFcy8fRgwJhcxgzZ46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lxGYlBsKBKwod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\neJp22rYCTlKa3xVsVpDoJqXjI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-       "text": [
-        "<matplotlib.figure.Figure at 0x112bc2190>"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
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+       "n59+FodZjgAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112797f50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Casting a Classifier into a Fully Convolutional Network\n",
-      "\n",
-      "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
-      "\n",
-      "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
-     ]
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Load the net, list its data and params, and filter an example image.\n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net('net_surgery/conv.prototxt', caffe.TEST)\n",
+    "print(\"blobs {}\\nparams {}\".format(net.blobs.keys(), net.params.keys()))\n",
+    "\n",
+    "# load image and prepare as a single input batch for Caffe\n",
+    "im = np.array(Image.open('images/cat_gray.jpg'))\n",
+    "plt.title(\"original image\")\n",
+    "plt.imshow(im)\n",
+    "plt.axis('off')\n",
+    "\n",
+    "im_input = im[np.newaxis, np.newaxis, :, :]\n",
+    "net.blobs['data'].reshape(*im_input.shape)\n",
+    "net.blobs['data'].data[...] = im_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are initialized from Gaussian noise while the biases are initialized to zero. These random filters give output somewhat like edge detections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!diff imagenet/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
-     ],
-     "language": "python",
+     "data": {
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+       "31Tbg5N927d927d927d9+6baHpzs277t277t277t2zfV/i+IAQDEy/wsagAAAABJRU5ErkJggg==\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x113e04090>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "diff: imagenet/bvlc_caffenet_full_conv.prototxt: No such file or directory\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# helper show filter outputs\n",
+    "def show_filters(net):\n",
+    "    net.forward()\n",
+    "    plt.figure()\n",
+    "    filt_min, filt_max = net.blobs['conv'].data.min(), net.blobs['conv'].data.max()\n",
+    "    for i in range(3):\n",
+    "        plt.subplot(1,4,i+2)\n",
+    "        plt.title(\"filter #{} output\".format(i))\n",
+    "        plt.imshow(net.blobs['conv'].data[0, i], vmin=filt_min, vmax=filt_max)\n",
+    "        plt.tight_layout()\n",
+    "        plt.axis('off')\n",
+    "\n",
+    "# filter the image with initial \n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raising the bias of a filter will correspondingly raise its output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pre-surgery output mean -12.93\n",
+      "post-surgery output mean -11.93\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# pick first filter output\n",
+    "conv0 = net.blobs['conv'].data[0, 0]\n",
+    "print(\"pre-surgery output mean {:.2f}\".format(conv0.mean()))\n",
+    "# set first filter bias to 10\n",
+    "net.params['conv'][1].data[0] = 1.\n",
+    "net.forward()\n",
+    "print(\"post-surgery output mean {:.2f}\".format(conv0.mean()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Altering the filter weights is more exciting since we can assign any kernel like Gaussian blur, the Sobel operator for edges, and so on. The following surgery turns the 0th filter into a Gaussian blur and the 1st and 2nd filters into the horizontal and vertical gradient parts of the Sobel operator.\n",
+    "\n",
+    "See how the 0th output is blurred, the 1st picks up horizontal edges, and the 2nd picks up vertical edges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe\n",
-      "\n",
-      "# Load the original network and extract the fully connected layers' parameters.\n",
-      "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
-      "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
-      "                caffe.TEST)\n",
-      "params = ['fc6', 'fc7', 'fc8']\n",
-      "# fc_params = {name: (weights, biases)}\n",
-      "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
-      "\n",
-      "for fc in params:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
-     ],
-     "language": "python",
+     "data": {
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+       "+7/r/Px8jRFjHlAkJycnAXZms5n+z//5P9rZ2VG/31cu91wS/Xe/+52Gw6G+++47lUol/fa3v/1l\n",
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+       "bqter6vX6wUAy+efT+qm0ByB7GQuSc8kA/Ep6DBcRLjGOp1OABp0C0zv9fW1Wq3WxjW3tYBYaaUU\n",
+       "WQy8RkE58EibB2O6i4j//Tog9JRtcSYCpYqC5n3/nMWIsqehnL1YlIMgSWufp0yI99vZF5Svu3/8\n",
+       "u84s0CevnAiwQUg6COS5UpDg93A3C699Q3g/nd1wYOMti0lJmTKfo/Q56QuWhYM67/s2mxcNKxSe\n",
+       "T/2EhaCeAIGqOzs7QQ/DMmDJSSsQR0YN1gen1+Jnr1QqYT2NRiNJq7gurBssc9wXgChX3jAmrNX7\n",
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+       "yQQAMB6PrTFfLpdI0xSDwQDb29v/2uPl/8bMFu9rlm3YTjCY5QRPPgPsiHivMhhlW8XSAveX4fPM\n",
+       "ElG9XsfDwwPOzs4se+Q+829vb9ah8vX5/2WwzeDFfYbSNMXe3h7CMMR8Psf+/j6SJLFdVtmOMRAr\n",
+       "isLue95HRVHYEngeHMh7lJu1MUhmWxzHMXZ2duz1mVXjnBVmjhgcu2VeTrTn5+Z9zdVILy8vNieH\n",
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+       "zfPcdqvlBnJ/86OnSXHU687cdoMNtyNzsyBu4OGO8quBDP++W4JxV5fw9/l9t9ThcrMQ7ByrnTw7\n",
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+       "AmRZhjAMbT4IJ1YyK8Dab1EU9j5YvgG+Z9qz7MFOhUE4/2+slXMZJScsukuD2aE3Gg30+31kWWaT\n",
+       "dFmmKYoCcRyj3W7bKO3u7s5q54vFAo3Gn8PMtra2EEURgO8SJ1dAMbOSZRmSJMFyucTR0RHKskQc\n",
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+       "w+npqa3E6ff7uL6+xnA4tH0mmPpvtVq4vb3FZDJBu90GANuwi/uZcE5LrVazn+H15qFvP4VbnvOz\n",
+       "cGUR2wTOdeA2/rwX+Bn5DLME4GYBWBbic+IGLNyXZz6f22ohzm1ie8jyEjtkZlYY1PF7LK8wU8Hs\n",
+       "DOdBuFkaljFZVuPnYhmZmbMgCGxSLU8qZtnIDeA4x6LX61l5lgNR7jDLVUYciLJcyu9z1Q8DRban\n",
+       "HARwPhcACwhZvmIWpt/v2yq0x8dHHBwc4P7+Hru7u7ZUmXufrNdrdDodO/n46enJBjd85jnp+PPz\n",
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+       "BSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUnIiIi4hUF\n",
+       "JyIiIuIVBSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUnIiIi4hUFJyIiIuIVBSciIiLiFQUn\n",
+       "IiIi4pXfPRZNtgyLF3IAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x112bc2190>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
-        "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
-        "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ksize = net.params['conv'][0].data.shape[2:]\n",
+    "# make Gaussian blur\n",
+    "sigma = 1.\n",
+    "y, x = np.mgrid[-ksize[0]//2 + 1:ksize[0]//2 + 1, -ksize[1]//2 + 1:ksize[1]//2 + 1]\n",
+    "g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))\n",
+    "gaussian = (g / g.sum()).astype(np.float32)\n",
+    "net.params['conv'][0].data[0] = gaussian\n",
+    "# make Sobel operator for edge detection\n",
+    "net.params['conv'][0].data[1:] = 0.\n",
+    "sobel = np.array((-1, -2, -1, 0, 0, 0, 1, 2, 1), dtype=np.float32).reshape((3,3))\n",
+    "net.params['conv'][0].data[1, 0, 1:-1, 1:-1] = sobel  # horizontal\n",
+    "net.params['conv'][0].data[2, 0, 1:-1, 1:-1] = sobel.T  # vertical\n",
+    "show_filters(net)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With net surgery, parameters can be transplanted across nets, regularized by custom per-parameter operations, and transformed according to your schemes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Casting a Classifier into a Fully Convolutional Network\n",
+    "\n",
+    "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n",
+    "\n",
+    "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,2c1,2\r\n",
+      "< # Fully convolutional network version of CaffeNet.\r\n",
+      "< name: \"CaffeNetConv\"\r\n",
+      "---\r\n",
+      "> name: \"CaffeNet\"\r\n",
+      "> input: \"data\"\r\n",
+      "7,11c7\r\n",
+      "<   input_param {\r\n",
+      "<     # initial shape for a fully convolutional network:\r\n",
+      "<     # the shape can be set for each input by reshape.\r\n",
+      "<     shape: { dim: 1 dim: 3 dim: 451 dim: 451 }\r\n",
+      "<   }\r\n",
+      "---\r\n",
+      ">   input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }\r\n",
+      "157,158c153,154\r\n",
+      "<   name: \"fc6-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "---\r\n",
+      ">   name: \"fc6\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      "160,161c156,157\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   top: \"fc6\"\r\n",
+      ">   inner_product_param {\r\n",
+      "163d158\r\n",
+      "<     kernel_size: 6\r\n",
+      "169,170c164,165\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "175,176c170,171\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc6-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc6\"\r\n",
+      "182,186c177,181\r\n",
+      "<   name: \"fc7-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc6-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc7\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc6\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      ">   inner_product_param {\r\n",
+      "188d182\r\n",
+      "<     kernel_size: 1\r\n",
+      "194,195c188,189\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "200,201c194,195\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc7-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc7\"\r\n",
+      "207,211c201,205\r\n",
+      "<   name: \"fc8-conv\"\r\n",
+      "<   type: \"Convolution\"\r\n",
+      "<   bottom: \"fc7-conv\"\r\n",
+      "<   top: \"fc8-conv\"\r\n",
+      "<   convolution_param {\r\n",
+      "---\r\n",
+      ">   name: \"fc8\"\r\n",
+      ">   type: \"InnerProduct\"\r\n",
+      ">   bottom: \"fc7\"\r\n",
+      ">   top: \"fc8\"\r\n",
+      ">   inner_product_param {\r\n",
+      "213d206\r\n",
+      "<     kernel_size: 1\r\n",
+      "219c212\r\n",
+      "<   bottom: \"fc8-conv\"\r\n",
+      "---\r\n",
+      ">   bottom: \"fc8\"\r\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Load the fully convolutional network to transplant the parameters.\n",
-      "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
-      "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
-      "                          caffe.TEST)\n",
-      "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
-      "# conv_params = {name: (weights, biases)}\n",
-      "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
-      "\n",
-      "for conv in params_full_conv:\n",
-      "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
-        "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
-        "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
+    }
+   ],
+   "source": [
+    "!diff net_surgery/bvlc_caffenet_full_conv.prototxt ../models/bvlc_reference_caffenet/deploy.prototxt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The only differences needed in the architecture are to change the fully connected classifier inner product layers into convolutional layers with the right filter size -- 6 x 6, since the reference model classifiers take the 36 elements of `pool5` as input -- and stride 1 for dense classification. Note that the layers are renamed so that Caffe does not try to blindly load the old parameters when it maps layer names to the pretrained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
-      "\n",
-      "The biases are identical to those of the inner product.\n",
-      "\n",
-      "Let's transplant!"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6 weights are (4096, 9216) dimensional and biases are (4096,) dimensional\n",
+      "fc7 weights are (4096, 4096) dimensional and biases are (4096,) dimensional\n",
+      "fc8 weights are (1000, 4096) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for pr, pr_conv in zip(params, params_full_conv):\n",
-      "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
-      "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
+    }
+   ],
+   "source": [
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe\n",
+    "\n",
+    "# Load the original network and extract the fully connected layers' parameters.\n",
+    "net = caffe.Net('../models/bvlc_reference_caffenet/deploy.prototxt', \n",
+    "                '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel', \n",
+    "                caffe.TEST)\n",
+    "params = ['fc6', 'fc7', 'fc8']\n",
+    "# fc_params = {name: (weights, biases)}\n",
+    "fc_params = {pr: (net.params[pr][0].data, net.params[pr][1].data) for pr in params}\n",
+    "\n",
+    "for fc in params:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(fc, fc_params[fc][0].shape, fc_params[fc][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consider the shapes of the inner product parameters. The weight dimensions are the output and input sizes while the bias dimension is the output size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, save the new model weights."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fc6-conv weights are (4096, 256, 6, 6) dimensional and biases are (4096,) dimensional\n",
+      "fc7-conv weights are (4096, 4096, 1, 1) dimensional and biases are (4096,) dimensional\n",
+      "fc8-conv weights are (1000, 4096, 1, 1) dimensional and biases are (1000,) dimensional\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
+    }
+   ],
+   "source": [
+    "# Load the fully convolutional network to transplant the parameters.\n",
+    "net_full_conv = caffe.Net('net_surgery/bvlc_caffenet_full_conv.prototxt', \n",
+    "                          '../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel',\n",
+    "                          caffe.TEST)\n",
+    "params_full_conv = ['fc6-conv', 'fc7-conv', 'fc8-conv']\n",
+    "# conv_params = {name: (weights, biases)}\n",
+    "conv_params = {pr: (net_full_conv.params[pr][0].data, net_full_conv.params[pr][1].data) for pr in params_full_conv}\n",
+    "\n",
+    "for conv in params_full_conv:\n",
+    "    print '{} weights are {} dimensional and biases are {} dimensional'.format(conv, conv_params[conv][0].shape, conv_params[conv][1].shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The convolution weights are arranged in output $\\times$ input $\\times$ height $\\times$ width dimensions. To map the inner product weights to convolution filters, we could roll the flat inner product vectors into channel $\\times$ height $\\times$ width filter matrices, but actually these are identical in memory (as row major arrays) so we can assign them directly.\n",
+    "\n",
+    "The biases are identical to those of the inner product.\n",
+    "\n",
+    "Let's transplant!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "for pr, pr_conv in zip(params, params_full_conv):\n",
+    "    conv_params[pr_conv][0].flat = fc_params[pr][0].flat  # flat unrolls the arrays\n",
+    "    conv_params[pr_conv][1][...] = fc_params[pr][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, save the new model weights."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "net_full_conv.save('net_surgery/bvlc_caffenet_full_conv.caffemodel')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To conclude, let's make a classification map from the example cat image and visualize the confidence of \"tiger cat\" as a probability heatmap. This gives an 8-by-8 prediction on overlapping regions of the 451 $\\times$ 451 input."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[282 282 281 281 281 281 277 282]\n",
+      " [281 283 283 281 281 281 281 282]\n",
+      " [283 283 283 283 283 283 287 282]\n",
+      " [283 283 283 281 283 283 283 259]\n",
+      " [283 283 283 283 283 283 283 259]\n",
+      " [283 283 283 283 283 283 259 259]\n",
+      " [283 283 283 283 259 259 259 277]\n",
+      " [335 335 283 259 263 263 263 277]]\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": true,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# load input and configure preprocessing\n",
-      "im = caffe.io.load_image('images/cat.jpg')\n",
-      "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
-      "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
-      "transformer.set_transpose('data', (2,0,1))\n",
-      "transformer.set_channel_swap('data', (2,1,0))\n",
-      "transformer.set_raw_scale('data', 255.0)\n",
-      "# make classification map by forward and print prediction indices at each location\n",
-      "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
-      "print out['prob'][0].argmax(axis=0)\n",
-      "# show net input and confidence map (probability of the top prediction at each location)\n",
-      "plt.subplot(1, 2, 1)\n",
-      "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
-      "plt.subplot(1, 2, 2)\n",
-      "plt.imshow(out['prob'][0,281])"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x12379a690>"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[[282 282 281 281 281 281 277 282]\n",
-        " [281 283 283 281 281 281 281 282]\n",
-        " [283 283 283 283 283 283 287 282]\n",
-        " [283 283 283 281 283 283 283 259]\n",
-        " [283 283 283 283 283 283 283 259]\n",
-        " [283 283 283 283 283 283 259 259]\n",
-        " [283 283 283 283 259 259 259 277]\n",
-        " [335 335 283 259 263 263 263 277]]\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "<matplotlib.image.AxesImage at 0x12379a690>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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m4NvfEI5zItkeCZ5geiiRuN9wtdtQUsGXQtKCLRZNlegjxRowrkn05kqYWYy2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\nA43ph1xlbAoPAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x12666be50>"
-       ]
-      }
-     ],
-     "prompt_number": 11
+     "output_type": "execute_result"
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "image/png": [
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+       "Jycn5z4gD+Y5OTk59wF5MM/Jycm5D8iDeU5OTs59QB7Mc3Jycu4D8mCek5OTcx+QB/OcnJyc+4D/\n",
+       "A43ph1xlbAoPAAAAAElFTkSuQmCC\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12666be50>"
+      ]
+     },
      "metadata": {},
-     "source": [
-      "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
-      "\n",
-      "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
-      "\n",
-      "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "*A thank you to Rowland Depp for first suggesting this trick.*"
-     ]
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# load input and configure preprocessing\n",
+    "im = caffe.io.load_image('images/cat.jpg')\n",
+    "transformer = caffe.io.Transformer({'data': net_full_conv.blobs['data'].data.shape})\n",
+    "transformer.set_mean('data', np.load('../python/caffe/imagenet/ilsvrc_2012_mean.npy').mean(1).mean(1))\n",
+    "transformer.set_transpose('data', (2,0,1))\n",
+    "transformer.set_channel_swap('data', (2,1,0))\n",
+    "transformer.set_raw_scale('data', 255.0)\n",
+    "# make classification map by forward and print prediction indices at each location\n",
+    "out = net_full_conv.forward_all(data=np.asarray([transformer.preprocess('data', im)]))\n",
+    "print out['prob'][0].argmax(axis=0)\n",
+    "# show net input and confidence map (probability of the top prediction at each location)\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.imshow(transformer.deprocess('data', net_full_conv.blobs['data'].data[0]))\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.imshow(out['prob'][0,281])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The classifications include various cats -- 282 = tiger cat, 281 = tabby, 283 = persian -- and foxes and other mammals.\n",
+    "\n",
+    "In this way the fully connected layers can be extracted as dense features across an image (see `net_full_conv.blobs['fc6'].data` for instance), which is perhaps more useful than the classification map itself.\n",
+    "\n",
+    "Note that this model isn't totally appropriate for sliding-window detection since it was trained for whole-image classification. Nevertheless it can work just fine. Sliding-window training and finetuning can be done by defining a sliding-window ground truth and loss such that a loss map is made for every location and solving as usual. (This is an exercise for the reader.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*A thank you to Rowland Depp for first suggesting this trick.*"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "How to do net surgery and manually change model parameters for custom use.",
+  "example_name": "Editing model parameters",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
index 3c951970fc1..f8f5c3c325a 100644
--- a/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
+++ b/examples/net_surgery/bvlc_caffenet_full_conv.prototxt
@@ -1,10 +1,15 @@
 # Fully convolutional network version of CaffeNet.
 name: "CaffeNetConv"
-input: "data"
-input_dim: 1
-input_dim: 3
-input_dim: 451
-input_dim: 451
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    # initial shape for a fully convolutional network:
+    # the shape can be set for each input by reshape.
+    shape: { dim: 1 dim: 3 dim: 451 dim: 451 }
+  }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/net_surgery/conv.prototxt b/examples/net_surgery/conv.prototxt
index 9444c63ab74..8671bb5bf0a 100644
--- a/examples/net_surgery/conv.prototxt
+++ b/examples/net_surgery/conv.prototxt
@@ -1,10 +1,11 @@
 # Simple single-layer network to showcase editing model parameters.
 name: "convolution"
-input: "data"
-input_dim: 1
-input_dim: 1
-input_dim: 100
-input_dim: 100
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 1 dim: 1 dim: 100 dim: 100 } }
+}
 layer {
   name: "conv"
   type: "Convolution"
diff --git a/examples/pascal-multilabel-with-datalayer.ipynb b/examples/pascal-multilabel-with-datalayer.ipynb
new file mode 100644
index 00000000000..94b9b4fed8a
--- /dev/null
+++ b/examples/pascal-multilabel-with-datalayer.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multilabel classification on PASCAL using python data-layers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this tutorial we will do multilabel classification on PASCAL VOC 2012.\n",
+    "\n",
+    "Multilabel classification is a generalization of multiclass classification, where each instance (image) can belong to many classes. For example, an image may both belong to a \"beach\" category and a \"vacation pictures\" category. In multiclass classification, on the other hand, each image belongs to a single class.\n",
+    "\n",
+    "Caffe supports multilabel classification through the SigmoidCrossEntropyLoss layer, and we will load data using a Python data layer. Data could also be provided through HDF5 or LMDB data layers, but the python data layer provides endless flexibility, so that's what we will use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. Preliminaries\n",
+    "\n",
+    "* First, make sure you compile caffe using\n",
+    "WITH_PYTHON_LAYER := 1\n",
+    "\n",
+    "* Second, download PASCAL VOC 2012. It's available here: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html\n",
+    "\n",
+    "* Third, import modules:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys \n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import os.path as osp\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from copy import copy\n",
+    "\n",
+    "% matplotlib inline\n",
+    "plt.rcParams['figure.figsize'] = (6, 6)\n",
+    "\n",
+    "caffe_root = '../'  # this file is expected to be in {caffe_root}/examples\n",
+    "sys.path.append(caffe_root + 'python')\n",
+    "import caffe # If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path.\n",
+    "\n",
+    "from caffe import layers as L, params as P # Shortcuts to define the net prototxt.\n",
+    "\n",
+    "sys.path.append(\"pycaffe/layers\") # the datalayers we will use are in this directory.\n",
+    "sys.path.append(\"pycaffe\") # the tools file is in this folder\n",
+    "\n",
+    "import tools #this contains some tools that we need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Fourth, set data directories and initialize caffe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# set data root directory, e.g:\n",
+    "pascal_root = osp.join(caffe_root, 'data/pascal/VOC2012')\n",
+    "\n",
+    "# these are the PASCAL classes, we'll need them later.\n",
+    "classes = np.asarray(['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'])\n",
+    "\n",
+    "# make sure we have the caffenet weight downloaded.\n",
+    "if not os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
+    "    print(\"Downloading pre-trained CaffeNet model...\")\n",
+    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet\n",
+    "\n",
+    "# initialize caffe for gpu mode\n",
+    "caffe.set_mode_gpu()\n",
+    "caffe.set_device(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2. Define network prototxts\n",
+    "\n",
+    "* Let's start by defining the nets using caffe.NetSpec. Note how we used the SigmoidCrossEntropyLoss layer. This is the right loss for multilabel classification. Also note how the data layer is defined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# helper function for common structures\n",
+    "def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):\n",
+    "    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,\n",
+    "                                num_output=nout, pad=pad, group=group)\n",
+    "    return conv, L.ReLU(conv, in_place=True)\n",
+    "\n",
+    "# another helper function\n",
+    "def fc_relu(bottom, nout):\n",
+    "    fc = L.InnerProduct(bottom, num_output=nout)\n",
+    "    return fc, L.ReLU(fc, in_place=True)\n",
+    "\n",
+    "# yet another helper function\n",
+    "def max_pool(bottom, ks, stride=1):\n",
+    "    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)\n",
+    "\n",
+    "# main netspec wrapper\n",
+    "def caffenet_multilabel(data_layer_params, datalayer):\n",
+    "    # setup the python data layer \n",
+    "    n = caffe.NetSpec()\n",
+    "    n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, \n",
+    "                               ntop = 2, param_str=str(data_layer_params))\n",
+    "\n",
+    "    # the net itself\n",
+    "    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4)\n",
+    "    n.pool1 = max_pool(n.relu1, 3, stride=2)\n",
+    "    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2)\n",
+    "    n.pool2 = max_pool(n.relu2, 3, stride=2)\n",
+    "    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)\n",
+    "    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1)\n",
+    "    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2)\n",
+    "    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2)\n",
+    "    n.pool5 = max_pool(n.relu5, 3, stride=2)\n",
+    "    n.fc6, n.relu6 = fc_relu(n.pool5, 4096)\n",
+    "    n.drop6 = L.Dropout(n.relu6, in_place=True)\n",
+    "    n.fc7, n.relu7 = fc_relu(n.drop6, 4096)\n",
+    "    n.drop7 = L.Dropout(n.relu7, in_place=True)\n",
+    "    n.score = L.InnerProduct(n.drop7, num_output=20)\n",
+    "    n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label)\n",
+    "    \n",
+    "    return str(n.to_proto())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. Write nets and solver files\n",
+    "\n",
+    "* Now we can crete net and solver prototxts. For the solver, we use the CaffeSolver class from the \"tools\" module"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "workdir = './pascal_multilabel_with_datalayer'\n",
+    "if not os.path.isdir(workdir):\n",
+    "    os.makedirs(workdir)\n",
+    "\n",
+    "solverprototxt = tools.CaffeSolver(trainnet_prototxt_path = osp.join(workdir, \"trainnet.prototxt\"), testnet_prototxt_path = osp.join(workdir, \"valnet.prototxt\"))\n",
+    "solverprototxt.sp['display'] = \"1\"\n",
+    "solverprototxt.sp['base_lr'] = \"0.0001\"\n",
+    "solverprototxt.write(osp.join(workdir, 'solver.prototxt'))\n",
+    "\n",
+    "# write train net.\n",
+    "with open(osp.join(workdir, 'trainnet.prototxt'), 'w') as f:\n",
+    "    # provide parameters to the data layer as a python dictionary. Easy as pie!\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'train', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))\n",
+    "\n",
+    "# write validation net.\n",
+    "with open(osp.join(workdir, 'valnet.prototxt'), 'w') as f:\n",
+    "    data_layer_params = dict(batch_size = 128, im_shape = [227, 227], split = 'val', pascal_root = pascal_root)\n",
+    "    f.write(caffenet_multilabel(data_layer_params, 'PascalMultilabelDataLayerSync'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* This net uses a python datalayer: 'PascalMultilabelDataLayerSync', which is defined in './pycaffe/layers/pascal_multilabel_datalayers.py'. \n",
+    "\n",
+    "* Take a look at the code. It's quite straight-forward, and gives you full control over data and labels.\n",
+    "\n",
+    "* Now we can load the caffe solver as usual."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "BatchLoader initialized with 5717 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: train, with bs: 128, im_shape: [227, 227].\n",
+      "BatchLoader initialized with 5823 images\n",
+      "PascalMultilabelDataLayerSync initialized for split: val, with bs: 128, im_shape: [227, 227].\n"
+     ]
+    }
+   ],
+   "source": [
+    "solver = caffe.SGDSolver(osp.join(workdir, 'solver.prototxt'))\n",
+    "solver.net.copy_from(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel')\n",
+    "solver.test_nets[0].share_with(solver.net)\n",
+    "solver.step(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Let's check the data we have loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QFN44TWaiBFYSW67ATBkn5xuRuVUkhi6l+hUo07CNwKdlJCYkCypcljbHvoibNJJFCsBL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\nYb2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9ZrNesbnbo40lp0iYJsaL3LuYtFBPqcjKkfKAJzDEnsuU8UmhjEWlwMIcUjfP4v/H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ms0IlK897IZMylGQxwVP0Mx2XSjDQxEDb7/jHP/8jP/74HVkNx1YjNFM1dm/7R56fQIVe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\npfLk1EJeqVQqT04t5JVKpfLk/AsNeRHC6zkzzAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fac306356d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "transformer = tools.SimpleTransformer() # This is simply to add back the bias, re-shuffle the color channels to RGB, and so on...\n",
+    "image_index = 0 # First image in the batch.\n",
+    "plt.figure()\n",
+    "plt.imshow(transformer.deprocess(copy(solver.net.blobs['data'].data[image_index, ...])))\n",
+    "gtlist = solver.net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "plt.title('GT: {}'.format(classes[np.where(gtlist)]))\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* NOTE: we are readin the image from the data layer, so the resolution is lower than the original PASCAL image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4. Train a net.\n",
+    "\n",
+    "* Let's train the net. First, though, we need some way to measure the accuracy. Hamming distance is commonly used in multilabel problems. We also need a simple test loop. Let's write that down. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def hamming_distance(gt, est):\n",
+    "    return sum([1 for (g, e) in zip(gt, est) if g == e]) / float(len(gt))\n",
+    "\n",
+    "def check_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = net.blobs['score'].data > 0\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Alright, now let's train for a while"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "itt:100 accuracy:0.9526\n",
+      "itt:200 accuracy:0.9563\n",
+      "itt:300 accuracy:0.9582\n",
+      "itt:400 accuracy:0.9586\n",
+      "itt:500 accuracy:0.9597\n",
+      "itt:600 accuracy:0.9591\n"
+     ]
+    }
+   ],
+   "source": [
+    "for itt in range(6):\n",
+    "    solver.step(100)\n",
+    "    print 'itt:{:3d}'.format((itt + 1) * 100), 'accuracy:{0:.4f}'.format(check_accuracy(solver.test_nets[0], 50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Great, the accuracy is increasing, and it seems to converge rather quickly. It may seem strange that it starts off so high but it is because the ground truth is sparse. There are 20 classes in PASCAL, and usually only one or two is present. So predicting all zeros yields rather high accuracy. Let's check to make sure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Baseline accuracy:0.9238\n"
+     ]
+    }
+   ],
+   "source": [
+    "def check_baseline_accuracy(net, num_batches, batch_size = 128):\n",
+    "    acc = 0.0\n",
+    "    for t in range(num_batches):\n",
+    "        net.forward()\n",
+    "        gts = net.blobs['label'].data\n",
+    "        ests = np.zeros((batch_size, len(gts)))\n",
+    "        for gt, est in zip(gts, ests): #for each ground truth and estimated label vector\n",
+    "            acc += hamming_distance(gt, est)\n",
+    "    return acc / (num_batches * batch_size)\n",
+    "\n",
+    "print 'Baseline accuracy:{0:.4f}'.format(check_baseline_accuracy(solver.test_nets[0], 5823/128))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "### 6. Look at some prediction results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oaWdkMB8GUkqEcSRME+Nmk/2wAgHT5AIwnJ4wbDdIENP29nt0VhgHZLJ3ZRoN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OO8yWu6zq7RYpL+o9HK2ny/UivVbRNDELHNYhGiNK2V2hdVFcPD7aY6722SeDWM2XeD2Xnt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\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KJteAqh6jvRi8GYUhYJhlDTdYUinxIQopk6pI+XWbVqVU0ztoTWcdO+OEQ84aJ+qtqXmH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wInswnXFye8fnnBD2+ueXY+Y9YMjZSz68nIKMmTrglLXWC5jQFamsI5ciuB6GHfdqzWO+4eNrz/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\nndiSd5M5/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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc077450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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SvKcDPnh0NgrylEdRhKqwVGW/KrVSGq02wCtOTKq59VCGgTs5MJfCVDJD9Gyc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\nEBi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EhlEArk0dqVxB4TTGASbTLmteWketE9MxovNAVTiev3jF88++ZLFa8u2vf8vXv/41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RRY1llCiUUucAYVRI6pFEQVhIuCQJTODTBai46fyuf7iqvNIrfkrImpNACdp+RzKMTyE/I0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\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sqYRGZJ4mcs5Y55jOI8pqEhnnGlpjcArGZWFcpLdPq0Y8JUYRo6hzUpTaudJJiewSAomC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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc193450>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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M928NahTbCL6x5f1zgjiH05owKLGLjGPAWsGIgEKMkewctTGoMaiUgR0MKoJSeEyRwn0V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AppPEqMVhBOIr0noiQRGzL2CKWx+oxGprSxpM8kzkYoJNo6/NCRm5aq1yg9I05mSB3T\nmz6kwHuFUgkX528RUYJOJtx+tabbtvR5R7acY1xH3xl0pLl6fsKPfvaC09OToNXwsDw5I6827PYb\n1usdTd1irCcvCmIV0fU9wzAQaU2WJrSNoih27Hb3rFZnaO1QytJ3NXGacnayonv5gvq6xZgepcNu\nSoxQYqQV1liGfgAfsOAsSsYZQvBYyqYaKQS7ouL/+fXvuN00aJnx8Xd3/ONv3lHkPd4Hlbc8JF+h\nR0gzcLClDLi2dYHppqRksAMIiGPN5GTG6dmKk8Wc8r6mLhvsYFmkU4Z2oHMNrTHHgabzBIm+YCzi\noYY9xkeO6IAPZnVD3YHyCB8TuWAFLVEh1WyEfI9lyTu8f8S/nTPB2sCZYxMn8Djxb4h+KMTBbmZ8\n8cfhoh87UxG653E1dKPENkwrIUAR33/ccPfQTj+GQ3zu5w6z4sPO4HMUvz/sNPbkJ4CgqjxYkR5f\nIwdk5wD1yMfbOHguPIp1xiX9W13/08c7ZpF6eyzCUsojdh7w9oSu1zgc3dCBDB2LEIqq3rDP78mL\nDWenlwihONAwhQhy78BiDFvKvFjTdTVaJUzmp+hIE8cxs/mcOJYkScx0ugQ0bd/TDJ6yami6jrqs\nmUymaOmDf3hX0/Qt6Tyld45UWOZxStPVKAGJhmK/xtaGONa8/eILXr/5gslsETzJ24qiyRlch9Ae\n5QTZNNAHoyKi6Ru2+w3TdTa+JkeapuRFTlPX7HVOvttxdnrBdDKjqTuKomH9sKfvDf/uT3/KZDbh\n0/VH8rri7PyC589fYs2AimKy6QKpdbBvRSHQ1HXHp5sbvv7wnvuHNcNgiHSEExFWJkgRI5xEWYdy\nHuU82nqks8S2I7ENyluMnNCrGT0ZQbsZOjTnHNaHEPG6g7z25DU4H+EcNF1OZAZS7xGpxiEZGk95\n37L7UFBc57R5SyoDPKVkxMtXz/nZL7/gZ3/5I+anM3QsKJsc6SMethtu7u5Zr3PqtkcgcMZgIkNe\nFXz89JHVdMZkklGUEXWzZ7e7QUpDN1Sjyhqm2QwtU+plGwq4DPJzMw7plAqZAQ4X1J3ejbayCtyA\n9IESOF9lmMHTDAO//eaG23WFa+DTb++4+7imaodjFyyFJlIJSsgArVhzZMMdPvfmI70AACAASURB\nVIxChElb7z1CBlbIfDZluZozX8wZOovBhKFwpBCDZfAHKPZAjjh86t3IsDt8zv1YaAOp4lDczWCQ\nVqHkuMUXAVoZCYkBrDxACp4Rag4DVWctVVUToCjAa4Iq1H62Fv1AhTwMAwGcEzgn8E5gnUOPRlnW\njviUDJ7B/hB9JOS3uudwDg7lMfx9iHPjOEB9/N1Pi7gYn4t4IgoCP5pTPa58T22pvnscF4zDzxyx\n+m+/3kfq4mPxPFwG31KJPtk1BLWnAh3OR/jjnyxSwVPmES4StF1HURfsmwemrEiGjLYvyctbyuoe\nY1pCpsHj+Qse2gfVGTg3sNne0PcNs/kJJ9KRpVPSWGFnM+S4TUVo6q5nV9/xzadP3Lx/hzCWzEe8\n+eIl5+crHh5uyesd1htefvGauNwg8z0xCQ/GoZ1nEQv6agtOcvFixU9/+VOev3pFZw13+zW399c8\n7O6YTWJirfAG6q7i9OyU04tTiqoISTBdTRxFnJ6esDxZoCIo9xV312v2my1/+RdTVovTMUmnYbet\ncECcTRjcwH/6z/8XcZzy9u1b4gjKskJHCauzc2bzBVk6RakMzEBbF2w3d9zcfqKqK7IsY5ZGNIOi\n8VNinWAHgRoMwlmU9cTOIWxHNFhkp+hcRqcTjJLgWqLeoHuDMwbPAD4kYvWRY7fv+ObjntevnxHF\nPbv9HVonTGenIBUfb97xd//33/J//h//kS///ncU6w3CBe+QbDbl9GzFr/7ql/zl3/ySL37+itqs\n2VdbHjbvubur2O623Nzdsds29GPi0WQWRHr363v+86//C//Df/fXnK1O2BV7eluyzT/Stlu6pqLt\nO5RMmU/P0KIFfw3SIzToSI6iHzdeZxybF2/dWIwF0gcPbxkJlqsZTWNp2p6i73hY1xQ3Dev3e0zX\nj8IbA0qjRPDX16MavO2bIzbqvD0W8sMvFj7AIZEeM3RVjJpqEhGjEkFb9zgzBAvog198KN9j2/lo\nwiXw6MOcLlQo/DjjCyiLwHqwcsSOwzT0uDyE+wWhlPchwkMQCvluW44Ln8a7KNQD8W+okDt34GAG\n3wMhA3whlR79UgSaQ1cethyH7YsaDeG9VzhHwB0FgQrkx+EnB6fCYMhzoC/6McvzUJq9t+FnDvc5\nElbGkKYRWnkslPB9oPxx1T/819NiLp7cFl7DYUArnzzU08cUx07ZE8QMjOwSY4P9rx8GxOgfIZSm\n6wuaNqeqK9b7Ndt8S1G1RNpRFCX73X8jzx/wdmA+OSHSCXiPGVqUVHjJceczmJaqLsiLPXm5ZV/l\nJOmMk+U5SRQxmFEqPO5YyrYO3XtbcLqYcDpf8Sc//vGY0pNzv16zy7dYLLPVEmuCJ46OBIvZhEmU\n8vzqOc556rKlybuAP5oW09dEyiDcQLnd8/7dFikFWZoAjB4UYUB+slhxtlyx2ezJ9w1OOpbLJUPr\nKXyD0Ir5csHy5ITNpsQMjqbtsd7z2y9/T1mdMVtOqMqWT9ef+PXf/i0XV1dBXHR/zfr2I9lkznJ1\nQZJEKAGLScaPX71CvY1YzJZ44/lwX/D1fU8fxfSdD+ykoWba1cRdia3WVPkDQ1ezmk3JJnOUjhkG\nR9Nams7SGhNCQKzDWiDyPIiS/0jNVHb85E8vma0c1jZ07Z69c/z9r3/N3/2nv+Ob//f3VA97/OCY\nzWf81X/4c97+7C3P3rzg7Z/8iOXZCcTQ1GvW64IPnz7x4eM9ddPSdC3pTCGH8JmMM81gLEXX4HYb\n/vY3/5VnF5cs5kuWJ+fMZpNxoGTpTYfqwWpHLzoG1XH+5pRqU1HXPWXe0zfmKHUXQhDFGqMsZjCY\nYRihJ4upej59uWa6ysimGltZdjclm5uSpmvw1iJ8yPVJdDyaWw04OQa0+OGYZ2C8JRqtAbxzTKMp\n6TTj5GLJz3/1M9IspipKTK/pSkFTeybTlEjGKBkjigp6wAp63+FGj6IQK/fo4HTAFpR/0rmPhVkh\ng/G6GJktfhhrUKhDUgiSOCabpIFyaC3WGiazCReXS+aLlN/+ww1FXgZ7788cP0xmp7UjQ+PQQ/OE\niRFerBhftCOkAwUK0OPKF9gfYpwi+4CLjUnqSj4xm+JgGnXowZ8Ofo/lflwIAFwYQvBtnedhhf0+\ni0U8Mlw+04l/9/tHW9o/PDn13/lCHhkt4UK1psX7UeGqIspqQ1FuqJqabbFhVxbB/1vvqMqc/W5L\nmsQs50sWizO01nR9Td/VTKcnxPEEsHRDTV5ueFh/4n5zx67YjF4PMcNVy8nihKou6NqW3vRYZ+ld\nT1nnOOeYpxknqxWr0yW7vKCsKox1DIMNocvOQiSJdUyyijmdnrDI5jy/vGKz2dJWA30b2ApD29I2\nOaZvSSPN6WLJ3d0dTdfQpgknqyXIkJikhWQ5X3F58YKugfzuhrzOmcwjdBSzPFkxm2fEWUzTtxRV\nSdd3oVuTkOdb0hSS7JKyqumagSxJiZMIrSTTbE7b1ljrUDpGqgVpEnN1cc5iPmcymbFcrGirCuJr\ntsOGfa+prME0Bb56YGYKVqZgaNe4/J6u77iYXHAZQ5YkdNKRO0PhLA2WyjpqY6lai+ih9A1fmZo3\nby44OU+4eHZB3ZYhOWm948vf/o53X71jc7NmaDqms4yrV8/5y//wK3765z/l/MUly9UJg3fk1Za2\na9nt9zw8rINYaghsjTRT6CRcq9NJStW0dL2hqAtu1rfEieby8orpdE6WZXR9TZTEOOXZdwXka6qi\npGxKiCROCuqmo+8DtKKVxAzj519FIEVIUnJ2TLEH1w2sb3ZYZ5jahKbv2T/UVEWDs4FWKAkaBRHQ\nUTwG4yze25DUhBj53o5obOacJxitLecszpdcvjzHDB3bu1ucsaTpBHU6J0kNne6xA3Rdx9HX3zqM\nCylbRyIFj00iY9WQB28jETICxFhADmlnYJ/0f4GdNpllvHh5QaQl+b5kuynJJhkvXl7x+s05rpfc\nfAhh4p87fphCbhwoUEqMeHZ4AxBBKu68R2s10vQC9ODsCImEpRbnA8wQdC6Hrl2gZISMA83p8wPI\nw2LhRwqUClg1Di+eLBpShQvr6WLD9x9OCHnEyD7/U///Dz9eLkJYBMMoUunC4uUi6qagrArqrqcs\nK6oyx/QND33OMAzs9yU/+8kvWJ2csVyeIhTk5Zp8v+P5858wmyuE1GyLNZ9uvubdh99yff+RusoB\nT101NHXJy6uXVFXBPt+S1znGDWTTCVKNXu9KYwVYeqp6R9eWLGZzTG9odwPXd7csTlacnZ5zeXLB\nPJ2zmM6ZLxYU5T/QdYa2aemajqZqKIqKtm+ZT+dc/rsr6qrk+uYW6zyzyRwhDV1Xo7xmNltxdvGK\ntvV8+HjD9YdboknE1dUVb764YppqBmv5/fuv+ebmPdVQIhNPFGuSzOPp2e5ydruCWTpDiIj1dk2S\npjy7/BFRnOFFYC94Qrf79osvUDIhSaZEOmb9cMdi1zCbdbRbiexrXLXBPXzFTDY8iwY6VTPIik72\nvJgOvD73LGeSvoV9JcgbTzN4HkrHfW5xvcF4hekM+b7k4X5DUxtWy0vavme/v+Pm+oGb62t22z1d\nF3jjq6sTfvKXP+MX//7f8/qL10SpwgmD6Uq6Iafuwo6rrAqkkggDeEeSaBIUWkum08kIO9a0zYCU\nljRRLOcJWmmc9RjbgxQ0Q8/Xt9dk+5q+asnXW4rNQJ23dHWH0j50plZgTQiA0aOnkZQj7Xh0pLLO\nUeYVvenQa0k79JhO4A2BskcomaHTDU2cVYFJIvEkOkYIEd4r49FSEo05vavFlPRkQrxKECnU+5KH\nT3dYFXN++ZKLy+fs1tds7YYmbtFaIJwmkgrVRzS+B9/jxXAwBAEvcCJg3wgdmC+CwLQTIMdgHHcs\n5u5QPQLjTDjmJxN++mdvmU4U33x1w37TIIVkuTzhRz/6KdN4wu+WKd989f6zNeIHKeRDkK8FnBXF\nUa4vwZhhHFZEqHG4ZIwNYhmpiHSM94+iigDNyPBSfLCwPHa8BxbGcUJ8SLI+4Nmjw+BhLyBGvqj7\nNoMmuCR+7njajYd/Of77B+4hDruCf2aSOh7HUA0hUSIm1h5BRFUN1G3JYPbsdmv2xZ5msNyvH9jn\nW5RzpFHI7Dw9Oacoa776+h3X8Q1vXr9GYNjtr9nXG+J0io4zbu6uuV/fst3f0w8NcSKZJhlaCvb7\nDabr0UqT1wVFXaASSV/2VFXLu9/eslrM6VyHTWBzv6atG04XJxRFgTGWt2/fss9z8t2eWTTF92Eg\n1I5yeq1iHu43/N3f/y0/7r7gxYtLzi7ehKgvHfHm1TfMZxkeSW8cOtEslydUu4o8r7i+fUCpmDdv\nXmNFzz/87h9RUYSUmkrFvP9wzcebW4qiRGnJyeoULRWvXzzn7ZtXvHr7kn/8hy8pixKvWk7OXjOZ\nTdnXd6xWZ0ynS+JkQppmGNuSprPAzwe6vqHvg03A66sL9g8VYn+LfrhGb9fomYdEE6uURbbEZ4IX\nly85X6RkiaQXlqEbMMoSC08yh9Q5bNHQ+gSdTlhdLvizP33L61dXDL2hHzoG02Ftjxksznp0FPHy\n7TP+5n/8a/6n/+1/5ur1S1Q6wWJo24Lt7p6bh2+4uX2PjARXL5+T5zXr9Zq2dcymE5SEJNYslxMW\ni4SyTtjtShZLjY4bivoDn+5+TzcYhPZ4F7He7dhXBZtdjW8NdANRaokzx1DAJEnxvaCtDMY1IX/W\nPZrkubGBMiP8KQHbu6OOINDuxiI4unpKcdg3j/TlgypyFBNKKUnjGD0uGskk46d/9WNWL5bITLCI\nY+6rgU3es7xY8fpHb/nVX/2CcveK//pf/oF8V451ZSAaoSbZO3wfoI9whO7beYcVhNB0EdI6g+f5\nwbHwAN2GmcABRw+NqkbrCBUrqrpCasvVi0nwjxGeyWzJr/56wcXZhGfny8/WiR9IEHTwfAig/zHW\njHGr4X1QGsYj51IcxBgaJaOxAz7QF8evR9dDKdWxAD/R+BwRqaOi9IhRP2WUPw5UnxbiIE46MGae\nHuJ7f/O9r5/cesTJ//nz8/Q8He4rhUZJkFFELQvarmO3u6VuSgYT6Fh125CXBRMVkeoJSZQymS3Y\n5SXbzRacRynPfJbiXM92e09nLA7Jbrenaku6ocFZQ6ymxFGEEpqyKdnt92TphG5oaYcG7TVt17Ld\n5KzXDwyuwyeW2lTURYXtDHVZU1Y1cRpzeXaK6XrKoubm5pY4iUmzlNlsSl8OVFVFXdd88/4d6Szi\n/GLFxXTObHGCQHF59QwhPfu8YJMXJC4jm2Rs9jlND4MVvH31mvOrC6qh5Nf/9Bs2ux1dZ6GBDx9u\nWG83eGk5PV0RJwnSgfSSSTblzeu3aBkyRLWGF8/fkGYTrO3JplOmswVJMkNJjfbBUlWJmLqtKKoc\ngSCLYuZao+pr2F+jizumpmWqYqIkNA3zWUocx1yeLzlZJcRK0EcGa4N5kvVBVJLKhiJvMWnM4sWS\nP/nFK37282dcXi5RKiaKUkDSNg2mG/BjIX/z47f8/M9/wY//9GekkwVCSKx1CKmw1tI0NVVT4ZCk\n2QRjQ0iDEJ7lfAneEmnBYjohdQYhQwTedJKgFKy3d3zzfkNRd8TTCO81Xd9jhoFqV6GMYhqlCN0h\ndYAe0izBK8HQhet6GOcAMPr4Pwk8lwdXE+uDuOdAEhg/E8KHuqCEColYIzVPjYESUkjsqB6NYg2D\nCU2dliyfrXj+xRUq8nRFjbeWdDrh/OqEl2+e88VP3lLuJ+z293z89I5iX4NxKEBLsE6HxYeB3rkx\nHegw02LcLRDq0aHOjRXBeoJXi5d44UdCW/B96YeB7WbH0DWA5+xywcM6R2qC8+TzJbMs4eLs5LM1\n4wfikQc1o3MeIc2IGwGIsfMew0ptsKFUUqN1HBJuRm65lI9Yc6Aceg6qyKdUQsG4pRm7/sPFI8fk\nITt6HRyYMGHBCHd86qsyMiO/dxxW23+JAhQei/m/riM/TLeD3F9pidYx1hoeNrdICTpOUWoSBpa2\nhzgmy+bMpkuSOOJhqNkXG7puII7g+bMLzs6X2KJnvblju88DHh5J0hjqosP2Gmcscaap6pZdvkPX\n+2MEVTf0bHd7tus9zhrqoeIuN6z3a4QVuM7yrvrAyWrJy+Uz5lnC6XxOWzZ88/5rRCLJZhknywXV\nfcP2dodzns12x+3DmodtzsXzkKPohWOxOuNhc8/twy13Dw8k2ZQknfLVN9+QRAnG9Hzx9hWTdMpk\nvkCpiN1+z+31muKuoW8GwBFnAu0d2llsZ9mtH9htz8HH/OIXvySOA2Vvki1w3tG0BUk6Q+kouNiJ\nMMSK5AThFX2/JS92RFoTOYEvStzmFnbX6GbNaao4mcZMZwnGDMy1YjqNODlLWM6DOnJIHEpnTLsW\naweEE8S6Yl0M6KsZb/78ir/+X37O69cnLJYZSTpnMjlBynvKoqZvDc55ojji1dvXXL14FqwN5IG5\n4UnTkLupVdAMDIMNsxalmEwykjhitVzR1i14S6ISPBLpW+zgg5JTZTzcb3j/cce+6kgWEc5ZYi2Z\nJxFNa1BEZPMVvd3hfIcx4XoVCJQORdXY4HSa6JiD54gemzCPo7ehO8f7oGgUjxRlKR4LuZYaLcF7\ncxx8OjsyybVmNp3SFiW9M7TeYKUgylKyacTuYY2KBK+/eMaf/OwZz1+dMZnNGWzO5es5P/rTE+4/\nbSkNSCsRvkcrSaI1RijsEHYRYhSNIQ4Wd24MbnbHXGFCbz7SKEaZvQieL0oLqrLk9199g5Jq9OBf\nUtQdUSqJMoHXGVev57z94vln68QPUsgjnYZthXV4OwS5LOBdKKRJEnHAuB/VjOOJGjlzB7EOhxM1\nQhyhLj+lAgKE3MrBDgxDT1gwgnJLjp7oh9+h5MFP4WmhFU8e7/vDzn/N8a8p4offefB/ETIowYT0\nxLFmOp2yXC7ph47eOtomR0jHajHjzfOXnK+ucIPj46f3LFdLVqcndG2LMJa+a6ibmK7ztLWnLAaS\niSfVEVGkmS9m+B7yvCCKZ0znM4gE+80DZrAIJ2i7nnyd09Ydy9WSeBkjYkFf9cHsxxqariftG4wf\nSLMJyxXUnWNXNZRDiXEGKQi4eN3S2QFb1TzcbfjwzUeuLl+HIADbk+drhFI8f/YcFSvqrqfrG6bL\nCba37PItn24/UdUtX339NZtNQd00eGdJpwpBkEmvzmb81a/+nKvLczZ3Dxgs0/kMKQRpMmM6mYcm\nQSq898FTWkbBT1schvHj9SAgSRMW0znbPCe/u+Pmy6+p727oyx3eNaTTGYupZJEoai2RJliWCsTo\nOZIg6PFEKGVpW4NUkjkxl1dTHnRLbSuQAfc1ZkAZR74vGTrHYn6KGI2a8BKHoe0r8vIhOEGanr7v\niaOIbswm7Y1jlxe0bZDLR1qRTaZ4JHXTYoeOxSSiaFuKvMMazcnyGZdnK5SUrDY5ve8ChOAkWZzx\n6nKBLGC77vj44YYoswwWdJJQtx3SgsehtECNtFmlFJHSwfBKKaz3mMCPHWGTsOt24449EuqYsxkr\nTTS+J8Pg8DKAGcNggk2t9Zh6jJuLII4lm4ct8muPnnomsebi+YrTc8HPf/6W2UxwffNPfPj0jqrY\nMT9Z8OLNCZuood4bqqKnMyGKUglJNDLCj4KgQ4zbWHdC8Ll7bCQJcK6UGqHAYXDCcHm1Ik0Ths5g\nhWe7K+mNwRhHFKWk2YLpdEZd7bje3POTv/h+nfhhbGwZ/VPGNy0Q4cFJj5bBZwAYYZDHlA9/ZLR8\nW2n52HuPPGz/bcDkcLIfC+i3Ze9SHuF0EEFA891ie2CJ/qHjjxXnf2m3/sfu58fF6sCbj3RClk6J\n45S6a+j6Fo9kMUkhjTldnCFFRNHsWW8f0Mkly8Wc+STkSiZJRpotkKrAE6iczgVPB2MMWaTpup6m\nrojSGUmW4L2lNz30FmkFth3AeGIVMVvOcVEQInXdgDEDfWdojaPsOrZlyc1mG/DYIqcxhroLVrK5\n1BT7gt70RDPNJMtIE40ZevLdmq6vqdqSpq1g3DJ7H9gOgxmQWtI2Lffre379939PXlRc396R5yXe\nG6JIMV1kRFGwS1idLLl8dsWLZ1dordjt90Q6Ik0z4iglijOk0MdrVPnoaM729H3x4/uupERLRVvl\n7B9u2d9d0xY7GDp0GAPhCXBh1RoG54iiQGFDKnQUArelEkjlA99CQuoNy0UcoJXVijSdY+zALl9j\ndgU3t+/Z77fBKRGLE6Hc3W3ueH/9e/TcobWi6weG3rCcLxlMOI9129C0LW1rMX3wn8lShzGetuuQ\nWISWdENQCbeNZ7+tmcQJSkrSJCLSkqrtMRZ8Flw3JUGPYH0f/Lq1QKeKoTeIIZQzJSVSCqwN2Hak\nNT5JjoPA40QqVD6EkEhPgE+EIpYRiYrQIxsFRk+T8f7eGbSKkM5hup7ODBgp8Naw2+fYqEdmA8/P\nT4hE8IFB9AxDztAMPNx9pChKbGf54qfPmU4Krt/tqOsGM+L6iBAQEYnAjvHH+nCImgwowbhhH2sE\nwTZgdGcN14/BecMwCLq6Dwwga3BuYDJJiCLFMDTYvuL25iNf/tM/8r/+79+vFT8Mj9ybY8Pr3YhL\ny1EUIIN504HTfTgpfjSj8d4H9deBDgiPlJ/xONx+UIoCIMQx+edgihXQq+/GqT1yvZ8e307W+fbx\nx7rs7+LiRyHPUxXodxSc373tW7cToKJIJyTxBCE0bTfQdj3zxYyr07OA+aoFD7sddw93lM2ebK9J\nI8Xq8pL5dEWaLfFySrLeECcJ00mGjqHpGsqqYjGf0NQtVdESpSlZN0HgsIPF9zawHJwkiVNiKYkn\nCUVTUhZN+PD0Pd1gsV5QtAPX6x3+y9+x2e4pihpn/cg4cZi8odq3CCVZnk+5mJ1xdnLCdBpRFTv2\nuzW7coehJ4kStIjYbkuaocMLjx0sTdex3+a8e3cb6G5DD1KRxJo4ipmvZtiZRUvFycmKOEkROiKd\nTvC7HO+Do5/WwTrhoMQNqOaYn/jU0Mz7YEHrxuvSesp8Q12u6dsC7zoiLVEipfdQ9JbOd9zu6xGO\nk/TOYaVARlEI1o2CYRnOY91AbDWzScTszXMuvviCxfQMYw279SceNnse9rd0VY+pCPMJ7TDA1x9+\njzoZaOSGOIqxA+AlL65e4HzDvthQ1hXWW6TwVFXDbl+FFJ1ZhhSO+SxGpQqDo24Hqmrgyy/fUe43\nnF+kQWruNW2R44SgTxVV1dB1BqFhPh+DJATICZg+nCNnXfBXkozGVJ5Ia5SStH2P8MEjRQoZYAl/\n+CyMUhshiJUOhVwE+iFCoFUUZmXeIUUQ6UjnMdbQGkMvPKYV5EWJVR2y7plqR6wS8JpPn77BujOy\nNKIu9nx6v8EOgv/+b/6MLMuoq5b7j6Oew4UFQwlFIiVmxMFDKLwM9Y1Hx8TQgcmR5cYo5raBeecs\n2+0WLTXCSU6SiCRWTCcRs8kUJQy7zS3LuOebr/6Rv/vP/+mzdeYH8iPX46okEFIcceuw7TgEKI/J\n0UqNq/RjR24DueSY/iFF2KZ9rjgeCmDI+9RIHaiOztsxjWjstv2xJT9um5/W5n9JEf9cMf9cus/n\nLQG+fzw1yQpMnUe7AucMwsNiOsPZCwYzkGUZSaTBQdcO3K1bBIbLswt+9cu/4dnlCzwCYweqtmVb\nfMSYnMvzCV+8PkNquLm956uv32OsIEomnMRTnDNEAhazFfNowvphw7rYMhiP9R6koL77FDxGkoiz\n1YKq6smLhlrUWKCoW3i4oSgqurZHeoVpe5T11MoSzSJmi4yTkxl0lqqrULUmTjLSJGO1PGGySBl6\nw26bI6OYvqnDkI6wDdc6wrgGREhniZMYpUBphY40aRoxn055+/oVpycz0lhi4og4kkg8ph9CBFnk\nkDjAYl3w/BaR+s7M5NEVT+mQgD47XfDs7VXwwiFje1fSlg2DbbjLW5zvaRqLEJBmhsFpvFQwFnAh\nLdIpZKSw/UCSpTxfrTj7i1+wfPOSbDZFKI8TEnYb4jhi6Ay97VCpIp5rEJaWjvt8g/8EsYxZTk+4\nOL1iMpuHIOayRAlJGsUMPrgfNnWPc46u65jNMoQS/Pabj2x3DVXTgXPk+z2SDhkviaKY2XTKZlPi\nR6uIZujQc8U0S/Da83Czh0EyjTK88tR9S5OHWLYQJmEx0oaUnlCxA2wiBFnkqIcBMzJaDhRc68eP\nqjiIccbPr1QIb0eLg4BVG++o3UDNgMWjjGS/2eKZMz+bsZheMJtMGYxgty9pmppIStY3e4p1RT94\nvv7wgWLfUDQ1gzE4Hwg0By55JASRlHgnQ7oiYizs4doQHGLcAg/d+gFrhgC5iCDz73uDV4JEa6aT\nCamOMa2h9R2bu4IP2QOi8dxdlxTb/rO14gfzWpEyXLzaRxy8xx/ZIk+9UkYl5jgQMdYeB5MHRWY4\nPCH95rF7PviTHAaFQjyGpUJ4w4VQPPqdH57feHn8KxGRz3XWT78+FO8/REH8XGF/vO0xgds6y9B3\nwX50uiJSYfAZxwmTbAJAXhRMHu5JqghlDKfLU85OntF2htv1J/b5nqLYMosly/lJUDwWOx62Chlp\n/DhISqKINIpYTBdM0inlEC7RAUv/ZIg8NBatNZPJJBSYxpApSTRNafsBoSRppOi0RKcJs2xBfrfH\nDB1eKpwElUQs5iuykwwpNUiJEx6pFdPJjMk0YzfsqZpuDC3QCKUwXR8S15MU5/OwpzoEdBDSY8qy\nZLWcMZ1lnJ6smM3CB6ZVkkka4t28tdRlwdAPJEkS7jt0NG3L6vRZsIP1Bwj0CZtIaeI0Y3EaBCZS\nxuQbhxMJMqtp8i1N2zMMQTRijaFsB8rWYbyASB6zY423tG1LUzeINGHybIWPFc3QMBQ9q9U50+mK\ns9MWIRVtdUdRr/HeomKBnMAgDEXbEBUlJ7MT4jRjOp9Tdx3N0I/kPoUfq7GacAAAIABJREFUO0Lr\nLNY7rHXUdRDAtF2H2xmsDWyQ1cmEaNRMXN9uybIUIT0nJ1MQgjhRDBYG7xCRIJvEpGmGh8BuMkHs\n1TY9URzYBJ5gyyGkQioZ8nm9QDvBJM1wCIxrsc4Hlgf+2MWPnDXk4T2QIRLNjgQG5x3Ge1pnGIRD\nRYooiWi6jmRIWCpJ2wiW8wmzZcbH25z19Z5y17K9z8n3HYNzGN0TRRFGeqT2CBl2aNY7BBbpJUqE\nDAHpR8adOEC7nqef6ODFbseFzI3e7hHeidEQMISFe2eQEprY4Pwdpnds7+64v75mu6k/W3t+mEJ+\nxLNDWDE8hgsfC7AL27AQqXSwknQMw4DWOvxRYRE4Pt7YXYfO6fBBe/KLvUAKFQYn3ocLSGgORfLb\nx2Fb9M+8ls8U36fd9nephAd+63cL/R+CWp7edmDsODvQ9y3OOubzU7JkirMOHUVk2RSPQKoZy+UN\nRbWhLOqjtFipiKoqqYotDBVXF1Muzk9IZnOu17fUXYvXkljGaKlIooiL80tSnWIGS1U39HZARMHJ\nL0ALYUo/9JZWdHRZh+s6UuGZn6zYlyVOwvlyFSw/veLZyXM+VILCFIhY0foOYwRaTnjx+g1SSvb7\nPVEcoyONkIphsORFxXq7o7YhJi5JM/qmRylNmilCL+0QuDF1PPCU90PHdJqitEYqTRJnpFGMVsE8\naTbJ8M6y3dwjpGA+X6Ckp2triqIcqYcZj01GmCmEuYUkijOWp1dIofA2ZrJcE7cSrbMQ1+VynOvJ\nYk3TWjpj2BcdrXHBFVOGD/pgBsqqpCxapAtX9vb2E6K8ZzIJObCz6YrzM8UweB7kjq5rwTtUFFBa\nK4Inez9YprM5JyenZNOM67sbjGmQOsY6STf0tH2P9QYVheHu0PXkeR1gRwFpGrNaJJxdzIlVQlnW\nvHv/kdWyZ7mc8PLlGQIRsPV2oO1bdCSYRRmr1RJTOkwRfL77bmAYLFEc3gOlVCh3ArwMUWzOBBl/\nmsQ4B4OxGG94OvU67oLFIVwmCG9658bEJI/xjsE7em/xSqCTmHSehQQnYZGR5e6uZDpbcvZihVhL\nHrYN775cU6yLwALCc5tvePbynJPZHJ0KhHJhRz/+DpwlkRKFPGb3hrxngXVPKBP+oFR3B6A4WFWo\nCHxgo0mlyYsSWQVhY5IpnAdrOj5+6qj2FVXefLYO/WCmWaHjleHCGzVShwLnrGUYBg588UdP7mDt\nqNT45skDyd4/nrFjJ+6OBfJYFsXBkjZ0e2LkjB/8Vp52/8fB6fee9x96Pd/+/nOF+gAhHb5/ipc/\ndTT83HEUBuHxbiCOI+JIEyUJNgoGP1EUh3QaD3Ea0VuJUJpXb14GBzobshZXy4wkWhCrhPPlkiTJ\n6GxQ3CohmCYpwUg1ZBc6AzfrNXd39/SuwQtDHCvcECKwwnnUVG1LWxeYzjCVimen5/zyl3/J79/9\nnrvtA009hFgsD3lRYS2kWcrkPGFTOJqq5cPXN6hYk6QRQ9tz9vKUpun46usPJGnEw3rD+083VGPU\nViQFiQzRdm4ImKgdzZnatkNrQZbGrJZzssmUtrd88+Gay9PnnK8uUFdBNIUQ5PmWT58+AI7nV8+Z\npnGAQZTDD4HJIXUYynVdT9u2oZPUCiFj0ulFSLinoGwED7XkoYmoK01XGhLX8er1kt7EGOu53+7Z\n5QVnZxPSJJglHVz5QLExHe9++xv06Yzl5Skvnl1RVgVl2XF3d892f4uS8Oe//DNMZzDO47oaL8H0\nlr7pUF5gh57N5p7f/Pa/oSPNdDqj7T3OK5AaLx3T+QQlE7YPewYHWInyHlpPy8CaCuuLwNiIJWfn\n5zx/dsnZckUWpRRlwTfX31DUNXXdc/upZJqEMJOmbmnqMAR3LuTyRnEMXmAGy2BtuH61Ps4b3GBJ\nVMQ8nWCaMX9z7ITtOJuQcRgcCsDbkABkcUQ6CgEjo8BIEGjMIlVkUUI8i+nMwHZzjVMtQ7xjW+wY\nhEFOFLpL6W2P6QzaKprOoWTH4Mfkr3GCafHgLcqPMz5UsB8mNItBzgl4P05aDgCRAgJE3PcDWkVM\nlglXV2ckiaAqWsqi5/mbF3zxxSUX5zPW+R2f3t3j7b+hqDfgCQSigMCB1foQEPzosR0wcw/OIr1D\n6wg9muDAI9PkKUfloPoMNMMRInnCSpHHYZb4Vqf7xzDrfy3z5Ls4+NPiffj+c4PPP4adh+cbknuk\njIKvjNI4GUzF5CF124TzmSYRkywligJG15uGZijREcxnExIZkSQpDiibmjwvMZ1hFmdcnJwjkXRt\nS1PW2MGQpglVUWC9CfmBxuEGGz58QuCMp+8t2JZsMSNKU6IkBSmDYs9DP3iGdgzxrdoQbjympzjn\nqdqGu/WaKFa4wTLJptjeUZZ7dntH0w/IJKXZb2m7Gu0dp4s5cZQc01j8OMbWkQ42oV4glaTpGigg\nkjFCRqNYJixwVVVxc3PN/cNtOGfxC+q2RApJHKd0bYuOW2IV0/cDRVmzy4vQWeoIpGS7b9jcd6z3\nitWrn7BSe7bXO7p1hxcFk9Tx4sUJCE9etuzWJQ8Pe86WU85OJwjnUFIymU4wUuDygevNmqmyTE5W\nzBcnAW6wA14KpA5F+fzsnI/vb9kUOfuhxltHIiNWkzlplAa7g7rgYX0PUjHJarq2ZzqNScZrBC/w\n1qKkwo7eRQrPNE2IEk1ZlYFdJz06kaSTEGrRVB2da6maiq43WCvpOk/fNGTnMdY46qJ7JBgcd6UK\nqT1d19ENwZJWyclxFtb2fZg9aMUsjql7HwQ4QoTd+QjFWkKDN5iO1hkQEEuFG3qcC9259IFhomTE\n8nxCHAvaqqHYhGJd9zVeG6qmxwmL1y6EVDiBdx5jDJ3pGawdU4LkcffvRYjxEyNL3CExPtg4HIZs\no8FGaDoPg9uxHnkHMtacnK/40Z+9pWsKhNoz2AAlpdMply+fw7Rjt9sh7v4NmWYd6pQYt6VHgbs4\n8OvCCVBKoXV4isIahBBoGQVvCNSIiT/FycfL4HsskkezrcNQRQrxZKh8KLI8eZzDc/xXAuV8m3ny\nLXHSH4FM/iW/R4wOkZHIOOxoPGF4HOCowAiydqBrS9JIMkkjnAmBsnWbs83vmU0SUq1QPrgjt8NA\nXpXkeYntHSezFT969SOkkKzvH/jd+iuyNGV18oz9l3vauscaj+0twjiwwWvCWwFe4QePUjFeKnbF\njrKr6Z0lRgSP6aJjt92jrUQlkm4/AEH45ZRnu89DMbYhm3UapwgFRV5ClDBbLrnL97RN8/8x92Y/\ncmRZmt/vLrb6Ghv3zKrsquoWuh8GIwjC/P8Q9CJBmtEIGnX1kpnMJIOx+WrrXfVwzSOCLGZNqQeD\nLCMIkOEe4R7ubsfO+c63EO3IoirShTsmPNfhkRLKqiIaR3QpnGNwHdZbLpZXyVtc56jco/Iasz/y\n4fqaw2GHUmfkhWa33xIjrHXB0LfovEYXM4bBsD923G2PeJERURgb+OHDPduHHdE4vv39P2BXW478\nyO6mRw4H1jPNm1fpZz9sjjzc7Li/37OaldQFZAJEjOgyJ5cR5QbsBmwICKWZL84ZXHJFrOYlIjtj\nvpizOj9nsUxK1RAD3gYqnXO5XJGpjOOh4dPtJ4YxXUCPhw4hFFV1RlVWLKqa3a6na0dESBJ3ZKTU\ngrP1DKklH24OKKXJCo3WEikjZujZ73f0TUNvDWMMDGOcsmJTyIfpk9+KECksPcaQLDcU2OhpzIgx\nI0okVWwyuwr0dqAgJ880VZbjvMMG99i0TYRLgvcYN6T3NoIWmhAF8QT5iQlunVLqZ/MMoudwPzIe\nHM3O8LBrmV0opBITnJvcCROSHx4fzYkUrCylQqakEQRiEuF5HAklC9FPhTzVkoicGC2QwK9TqlBi\nyCmdszpf8813b7i5/plhGLDWMQwd/eAIMl1MdQki/yuysX0KTX4qdF9CCl/eJ546KzkZR06FS0mJ\nkHJicjz5qEiZKEzJWzyxUh7RF2C6pPK8aD+/9b/l+Noy84mB8/mF5i9hr3z+s+WjGOpE0TxNFCm0\nNWJNx3b7gc3mmtEcWK3mNP2e433LTx/+he/eveLF2Zq8rokyEJGIUJLnBbNywTcv/4bV8iJRu5xg\nd34gCAhKJJ1xlCDBxoFcaLIsw0eREm1UpJYKhWAcRzYPd2QKZkXO4dhQqwKhI/vDJlG/vMAES1EV\nzGY1ZZ7RdgPOOYQUDM6RZSl8IMhA1+4ZNhvCMKCiAJVRzxaAnMKVDZJIpTNWRYnHQ/DkMkPrlCTT\ndS2b7QPH4wsW8wUfP37iw88/Yt3I7e0t+90usZxEZD6fkWU5MUSUztFlRT9aeu/po2RzNOyOI5t9\nz48fNzS7A4UIzM/XLJdzvvvmJR/+8Se0nbNaF5RlwXxWIITk9atLjoeGf/l+ZFbCvFDkWqLzHK80\n8/U5v6tmDGHEWcuH64+8/3iN856zszVv374Fqfjp4zX73RbT9+DT0tlaz+5hj9IFwzDSdS0iajKZ\noeTUieclecyIo2Y4OI67jhhjcspc1bx9eYZUkmPbYUZQAcQImVe4g0HpipfnK7q65m53YHN3w2As\nQTjyUtHujzS3PYftgVlZpR2Xd8S+J8TA4CydGQjekwtJPyT81wZP7wwmejKvknfK1LiE6BLlNHia\noQMRccEyBIsSGXLatzkCQZDC3JWc/PMdNzcPaCTSqESRDQ6ZCURXUJcVpcwZhSUKg1eRssopqoKi\n1phFQI0OZR3y0fiKybgrjf0nrsrpXDyhAdMKb4KLw+N5q6RA4Onbgc3NAT9GqqpAXUGW1TTtgf/0\nn/4jqugw3rG4KL9aF361Qv44WsC0MHoqfic3wefLz1NX/RhRNi1Ev9ToPGetCCEfqYk877jF5/f/\n73F8jYr4l3XdfxkOn74Gn/FwRMSMPW37wLF5T4hHsjxS1Tn7Zs/d5o7N9oZXZzV+OSdKxaFtMGPq\ncQqdUeUzlvU8mfwLRV3VzBcrNrstm4ctfTswujEV9umP8x7nAoJIoRWlyogRvA/kSrN68YpL57i+\nuWfoDE4YZrMZtncYY7E4ZKYYnUXakWEYcd4htWTfthjnEmKfC3KhiARmrsB5Q29GWmPx48Bx1+Cs\no9CSWaF4+2JN2ySKoi4ExoNzyZjt/v4TN6slmf4Nx8MBY0auXqzYbh7ouo4PH35mNq+JMZBnBa4K\nqKymmI9sDgM3u57r7cD1ZuR+37M99NwfDEPrKKPl9m7Lu1dnXJ7PqWtNHLPkOzI1I7Oq5M3rC/65\n7XjYHPnh/S0vz2rqKsNGSXlxRnm+5nfrJcduT9u3/NM//ZFde0RpjdKBVbek7VseHh6o6pLz9YqH\n4z29i/T9wPHY8/KtZrHU+GjpNwEzBmRQFFIjHdjOYVqP6wMiCOqqYLmsWC3ryX/e4UwgONAxKTLz\noIhjxA0OJz3Nsee47+gbgw8GLaHIFX7vCGNEywweldmk6DhvGZzBTYvyQMCHNIG5Cdu2LmCCIxOT\n/wypIUMm+MwGmxgk05Ytn1gsPjhM9DgVUXmOzDTlvGK5rvHaJfvbIBFZmxSai5yAZOxd2rEYl1xW\n86Qd8AYMaUEdJFOCUKIQPiaAxSfa41PmAJww3TjdR0zy/RMtUSrJ+cWSusrpDi0ImNUz1uslUhT0\npme/2zK6liyPLM+XX60PvxpGfhqRPlNgftGtfs3f+zmt8MuO9zmEISb45OkC8eXD/2UGVv8tx2c+\nKV/ALKfbvwarfLkI/XPPVTB5yYTksjaMDV2/xdgN1UyR5zNmsyW3mw903YEYXRrrRIaLmofdATMa\nRFApdVwptBIEa5A6J8syZvWC65tbbm9uMeOI9QYn0mLUx4B3DudS4c2zjFympRVI1sszXry8TKwk\np3jf/AwhcHF5zv2n5P1yUrrZ4GDsiYTHqePYdrT9gJKS8/MleZ6T5QqhFX2wtMGxbTrGZqA/tIQI\nSkmqQvPyasW9CgyhJwjPaKYQklnkeNxzd/eJuprRDR1SC9ZnK+pZTdt1bPfbZNEaQZBh56DKHlWP\nfLg98uNDz8fdyO1uYHscObQjvY04B9Y6rj8+cLUsOVtWzGYZQ5uKkfVph5NnihdXZ9zdbDnuW97/\nfI+IZ8xmJdtm5JvlGa/X51z8zbfsDvf88OP3/PGf/4m8zql0zWg69rstIUT2uz3vXrwgBsvtwzXd\n7kjXDTSqJ89yZosSqSWbdkSEQI4mF+B6x2g93aHHj2m5+OLinPOLGXWV4UeLGxM0VWQZ86xklucU\nhUSiGLrA0DXc3D6wOzQ448nLSJEpSpmxHy24FEF4wokFMuHZzmC8JTDBnFNnF2NMnymYXq9EhnjK\nFzhFOTB5C3oCPgXIkJhULnjGmNKD6rpEZIpyXjBblugyI1oYMMhCpezOVc3YOUw7EroRp0DnGXlV\noIXCDxHbWfrWJigR0qPHiDhBJo/ncMLAE7EiteKRtPObNp/pMzXlMSitePvuBVeX67QDzAvqecls\nURKDQnUS4xpu7nrmy5x6OftqHfgVWStPhTucttBSpuR4EkXv1LKfbv+SRXJiuXwOz3zOCDlJ+r8s\nmP9W2fzXji8vKF/7Xb/2tS9piP/GByd4i3UDzlmc7dFasV69pKrOKMsVeTbj+lNLXe6pX2W8fv07\nZotzjuPIvjNsHj7R7vaMo6fQBUK6tFTWkhgFi0XJbFaQFxmL1RzfHnHjkGArKUCBVs9HWEkIoGXO\n1dVrMqU5dg229+z3B4ah4+XLl/RdA0NAzTNiBWTpwz+vlmRK4wkcux5jRwTJrzvPNAKBVxJZZ2Sx\nwtsASlCUJURJWRaofMEwKlqj2feSfjfgjGVeleirnNV6jc4z/ulf/0hnOox1/PGff+Ln61uapqEs\nCrb7I8tDiw8SoSr8duDj8YH/+K8PfNgbjk4QRE5vBf3o6MYB2/eooeVDf+CbF0vOVwVnZzN2bQU+\nTA1IQCrFYl7wm29eQYDvf/iR26akCJGb+wPf/k8rrt79hsvXr7G+p65LLi7PcdEghceZEW8ts9mK\ns2/PeXVxiZKCy5/fc7/raZqO3dhwc71h0VeMLr2G716/4nJ1we31RzabB/a7Hc2+ZRws83rGq8sX\nvHlzTp4LHu7uMWakriS/O7/k1cUFhc44HBsEOU1j+HT9wDi0eOcplOb15Zqi0AyN5fq4Z/NwpO88\neVkQXRLReOfTFBcDEYUWSQgkosB7l5a5MDUciQChZdJ6eFKwhCfVC6FSimq0qT44AjY6gghkhWK2\nLHEi4ILjeGh4tXyBl569axOzBEUMJ+JDYrfoXHN5ecXF2Tlja9lvj+y2O8LREa0j2NS5x3gS5T85\nriJSdkKMTPbaJ6+kKRpO8EiXlBKkkrz79lvevrkkuJ68mBOIGGfRRcbl/ILFumC/NzSHPW17+9Uy\n8CtFvYVHQOB5IQ/+VLBJS84IPvqJhXFSfz5d+R4ZKZ8dz5gh/DLi/eiHMH3Pv/X4shD/UlH/JfHP\n1+7z5yT/Xzw6CE8IFu/GKXW7J2LIdESrhMsrnTGfFVz5BXm2QuUV26bnp5uPbLb3HDYHukNLlmms\nCzgfyPLkUGdHgxKRxazk4mKJ6hVOTKwBOz1XEScedBqFFYoooGl73r//iHOe47Hh4e6eECOr8wUv\n3pwTnOXQNPhcwExgcYzjiA824Ygi4tyYOPJSMrY9RgpQkhgUwUVyIbAkWTZapWARnS4wUgmW6wUj\nkbvbBzAW2/dcf/iEFpLb9R2D7YkB7Bhom4Hdbgt4dKlx0dP2I/ebhvVVgRkkP+22vN8MbMeIV4lx\nNVrH6ALd6LDWo2xk2xnuNgfOz3OKKkPnGa5NPHHrQIkIQTCvNW/erAnZSH15hihK3HzO1bffsLq8\nIgJD31HkGf/w93/PdndH0xwxxnG+PGOxOMP7gAsgs5yz80vq97d0rmccR374lw+UyxypI15GsqtX\nLOY1t1HRtCPb/RFvHN54hjhy93HDxWrFcrZmObdY41G6R1c5VVVTaIVzjiKbo4XhsGlZrSpGM7Lf\nN6gsNWCjcXS94dAOtK2hsAYVIDqHn3jYT4xh8ZikM5kcJiqmTKK0SCSTiXM+evuobk4NW0rbySb3\nQR8DJibzqUJClqcFq/eernH0raPINYvFnG0xJCEaER8tQoNaVpTzFX/42+/49u1rfvzhZ3w0NJ1C\nickbPZ7QglPHLSeeFIgoOBnX+iAfK8uJbHHC0lOATap9x+NAP1iqKkMX6fdERYJM55WUGbOq5nho\n2G/br1aCXy3qbWJEc/KsYCro3vtncEIq7N5NBpAniEGkK9qXnf2XMIp45Bw+8cIfbxP//xeNXx5f\nFtz/Gp/8y9u+pCP+hY/67PHThj1El6xK8Tg/YF2L9T1x2ONjJOIocsdyrsmynGbouN0cef/ze5zp\nGTuLGRIk4TwMxpNXEEPAOk+mM1arFZfmkuHekw89eZdjO5P2FAJODnWRtAtVUjAMPf/Pf/lj8oIZ\nRszQs7qoOL864+rFOaYbCCrShRFVaEQAMw70fZcW1VrgrEVMW37Xj3giUUkIkkwVzLOc1jpGkR5X\nKAlKgArIDOZlSZCAMbQy0h4brj9+ommO1MuKvJZkMscZwXE/IAkUlcJN9q5C53iR4fWcg9V8f7dn\nM4AhSfadsYzOYpxP3jI+4qOksXC/77h42CcOs5LYkNgcZogI5/A2oJXi8nLG/NV3iPWakBWcNZY3\nv/2GxWrFodtgnWU+m/PNb3/Hhw/fc3tzzX7fcHV+QZaV3N0/sDUG4wPrs3OqrCSLkmFwfPjxBj1T\n5POMxVnBOLTJj7wdaJqephvIpi6yM4affrjmxdUVF+cXzOdLRuuRQ4GuCqpyTqEFIgrm5Zx57omj\nozov2B73NGOLzNN55vH4GDHe044Dox/JokSHiIt+EsZMMYYTq4T46BGIElAoTa5yfPBoqVMBj2JS\ndwZkTJ22jKmjT7v5gJkk/QiIMtUXKTWCHDtI5mXF4rzm7vrIGAxaBxweNORFzmyx4PzinKsXZ9zc\nf0JXCl0olEyfsRNzJYSnYh5IrCk5qV+JJMjnhIULhYhP0EogOYg6F7j+cMdsVvLu2zOMGZLfjgDj\nR9zgMYNNVNqgGI5/RfRDG6aNb4RTcGmiAz5xL5kM5d2EvwopUjTcZDObPIB53CKcoJmTb8pJdASn\nJedpU5x+/HOq4ZcF9ZeK+5dF+zRNnP7//LY/Rzl8fnzt+77knJ+er3jWrsQYCTicSyZMWhUIqTDO\nsG/3dH2LFC15dk89yzm29wx9S4ieQ3/D7uBwZuRyXuO0YusDQqaxt+sH8rJiMTtjtXqTxA7VksYF\n/vj+R7pmwA2BsbFIBTqX6EyQKUle5Kzmc6pixtg73v9wg/NMG3yHUBV5nlHlJdYHmq7jaFpkrxBK\nIKOiaxJ7Ia9yBGqK84uTUlMgI+gIl8sFq/manz7esO0bXEiMCaU0Os8YYqA77DGj4dvXV4zrBfcP\nG/71x/eMYYQx4GVGa3tEUFRFwXq5JBLZPhyYVSWvX17wD//u37OPK67vA7cDmDAFBNgk/zfWYs1I\ndFNRkRqfZTRWsG88uY9ED8bC2HtGGUEJvLG4XFOu5ly8ek1xsSQWOf0QOD8/py4rjIU3r1+jVMbl\n+WuCGSmzgv6yp65zNrst73/+F/K8pipq1os1RZaizmKI+D5R5kQRCVbw8cMdD9fHlP94PBLHgFXJ\n/lnGwGgMh6alGTrOLypyk6PrjFdv3/Hm5StKJdjff8R1LSa3LKu3xNKT5ZbtRvP65ZpMSWZacv+v\nBza3LdtDh/GeiCeGiMUlVgmpAIkY8dHhg8BEjycma1udXA59EMmzPQaESed3ID4uRTUCZLoI+BCw\nEx/fRxicI2rJYrXg9Zs3LOtzzmcltfT8s/oZpx3FXBNcjmktXdPT2wf+r//7H/nw0wc2my3NccRZ\n8H5iz6iI8ImwEYMgTMHvKdxGnVpUAmmPJKZIyShkCm2O8XFxG6xnc3dH/25NPX/N/fUNZrREKTma\nntFanPX40eGNRY5/RV4rYSrkJxrg49JzWgLIyXvFOfforZIsPtNiIcQA/uknfCbs+YWC+ksd73OI\n5cvu+JcWlP81COTf0uV/WdB/yavly9cLMgQeG3rafkvbHZKCLmqkErjo2TU7Pt7d0TQNUgaaHvoh\n4lykLCsWqzWvLs+IQoHMML6n7XPyrKYoInawKJUzX6zxUdAbgw2es6szREgrJ5WBjZYYPSaMzLIZ\nWUi5q9GlDNZ6VpFnWXrfPLx++ZooNd9/+BE3WY9Ws4pmO2K8RWYR75NJURQRZzVxEkpoHYjO4NyQ\nunYBWaFBgAuG/WGPMYboAplU1EjqsuTybMWxOWd0yQfftAZjAuvFmr/7/R+YVSXH45H9tuXVq99y\n+er3GHnJT3eOD9u00Ax+khx5kRa91mKNwVsH03TphORoArvWclnI5BsTA2b0DFLgVfLjL8ucbLHk\n6t1vKVYLdFkitWa5Wk7eNcuJyiYQUVOXa9R5ASItBTPdMpvVLBfnVEWFHUd0ppBT4xM8uC758Pu2\np9HHtMCzHuE9GoExNgUnBHAuXZBiDFhvUUqyqGu+ffuWeV0jwkhcl+hlQd+M3NzuGd2Y1MZFQdOP\nSEkS52QSlSk0CXZj8kBJq0wSk4XJmmDCkJOHScr3LFRGlRf4kDI9U/5mSjny8ZmZlpRkUjJ6N03s\nqXASBHYMBBsIS09VQTmL2DDwsGsoKo2LimF0OCNxNr0Wxh/Re4hxYGh7+sZgB0u1LIlOY43F+/EZ\nU+VEwXtixsXn1S2eWkrxmN35SH2OafI11tG0Aw+bPTEIZosVi2rN0N5x8/EGvKDZN5jhr6iQx0l1\n+cTjfsK0T/xx79NCJMaI1jqJgFR6cR754vGpiD8tDuOfsF1Oxy/V168Vzafv+bxbf85V/+91/JIP\ny+lVelzkhkgIIhUk29N2W4axT0ZHKgcZGb3h2B75eLflcDiQa5mB5ijIAAAgAElEQVRoeP6UNL5g\nsVxxvjwHkbE5tNw87AgBlMwRQie3wiwjz0ryvEoMAhW4fH0JxjL2feKjjxEbXPJsDo4QBVoLMqGY\nVRWXL85QlSDTORLF61dvQGd8uL/B9z0CSSaTcjeFC5zCEtJrIckmPDWSZ4lm1vRHbHAoLVFapJHe\nWvaHnofbLbnKWNY1tdYs5zOKIqW/uyZgjcNZh3WRbF3w6sVrqiKnyEq2+5Z3v/lbyvW3/LyR/Hg3\ncHsYk+LOT8k1MSa3ROsINmXNxmmSjEJyNJFta5lPcWeCmMRaBpwEm5y/ELMl52++Ia8WZEVBVWVp\ntHYDWlWUeUq28tYhRU5ZaLJCcTjs0FKzXqy4uLxEq4z9bktRFsm4TDkiAjcYxv1Aj0MpQZFr6lmV\nrAUiRB+IPg3EzjvMODIOA93gKfKci7NzXl5cYO1A1w24MFDP5kmluNkxtgNDP+JsYLNriDLijEfP\nC+aLkplWGG/w4an4MrHK0oIyJthhglckKX1eS0WmFEqki6cXKUTZBnB+gvRkgt2UVODt50yvALb3\nODzeOJQIoDqOzcj2Zsd8rZGhZNcNeCfSXx8x0dCbjqKP+MHgBw8+Ui0LvFXENiI7m0goE6YQJ8/d\nk6HXiSkZI48MxVP5Sf34ZBAYE9R8PLZcf7rnYXNMex5Vc3G1QIsM043Jx2g0yRbgK8evU8in8YLA\nlLry5P99Uisaa5BSonUy1xHyCVOHZ93wVMxPX1cSEPKzAvwl1PElZPGl98mXx3MY5VTInzswPr/f\nn5sAvtapf02m/9WvP04OaQMeg0uKtmGfQhzsiCBDyhydZWQ5POy2bA4P7LsNu66l6S3CR3Su08/w\nls3ugWVVsFosyHROP/Z8ur1hURusjYzDiPdQFBUhwuXynOPyQNM2rJYlwWQoAcY5pLVkSnO2PsMZ\nT3vskQLOVitev3jBN799Tef7xH6Zz1FFhco0xjqsD/RDw+FwoKpn1LrCMSL6kAq80uQqwR91UaC0\nYjCGZhjJqhw1hfZ6QRrjI0lsZA2jUinF52LN6Cwfbz/RtWMSlE2+H844rj984uXVC1brK/7n//CW\nfPWOjwfFf/7hnq2JDC7gXXgMDo7BE3wAH5OcIU4MK1Ihb2zgvrEsVMQZhxRJ2OSjx5jAYYiM88Ay\n5hTrC8psDhHGsWPf3OOjZzE7nxqQSAgDgzmmMI1BcDgcGLp+Cl2Qk3Tds14vWS5n9MchFcNR4Yxk\naD1VXZDpEjt4gnAElSTiISSRjZKKm+09+YeMd+qKv/vD3/Lu3beURYk1Lc1xx48//sD5eklVzji7\nXNH3A0NjuL/9yOJ8jdAl1ku++e6K0oO73fJwHOg8uFPjNVEIfUxLbUhJ8zqmAq+EIjiPExYhkmI3\nU5pcKoxQeBmRapo8hHx2rqdzx3uPNYYoBVEqotMIX9D3DfvjkUPb8j/83SVelegbxXYc8DZF7CmZ\np/fHd9AFMlEwn2dY6bBMeLpMU6aIiuBJ08Z0fj4vtqfO/Gk5yqNjI9Pv//DwgFeO1rYoAd4Gbu4e\n+Na8JS8U33z3lvv7DSgQ+de9mH6VQj46k+SzCHJRpGXQqVhOnhw+hCR8UOoRE35eyB9FP9OfRxUo\nT/zNL4vqLy0fnxfxE9b+/Phal/6lN/UvXSj+3M/5pc77l74vBJcS032CMKztOLa3xBAZjWW32zPY\nDh8dwQR2+yP3Dzt27Y6uMxgbCCainCWTgkxpht5zuzlg/TVSFjxsDuy2R46bgcPsyNn5OZeXrynL\niizLeXV5xd39Hfv9juPxkNRws4puu8e7gFCSTOWMvmccLNZYyrOS5WLJvK7xo8VHx6450G4fuH64\nw0kSbcxbvDOITKOExOPQU+BIpiEvBEWZvMX3+5beOlyErMieTJN8SuKxNuBcAB8YjOPQdXy8vcVY\ny+AsKs8odEGVZylMQgR+/OknUDm/WV/x+t0f+McPR364aThYT28s1iXecCCNx96H1M3GUywZnLoy\niBgbOHrPre0p+oElAqUlQsI4Ru72I26lsdSovCTLc6JzOBsYxgNtf6TvjqxWZ8QQ2G4fcMEQgicE\naI4dUgheXL5A5Tn75sD95h4/eY4EwuR1HhE+INFI9ONElZUZ5HpKsA+EkM4fG5LkXKmM2WxFVS0Z\njccHgQ+CYXQMJlLWGYvFinp/oJpX1NUSQoYfI9E5ygpKnVjf8dSWPgNEY0ystPTZBz11qZEnUZAL\nyckSn/59moYm6gIxJLfHUbjHTMxA0kPkRUY1L4kyKUJv7+6RhcGPlrJQVGXJKBxKB+arPAUstyKx\nsXwiWSgBWakpq5qYOzYbi2uhmFdIJ4kmMvb21GIBUwSGAKb6JIWalp3+8XkTw3TVEVgbaI8dD3e7\nRK+dIuNu7++YL0u0TrTLUwrU145fpZD34wCkyCels0dSfXi+BJ2KpZByYmeEz7rU54VcCvkofz8V\n8K/VxD8ppDDNPekCEk4n5Wmh+hVq4Ofd/ecd/fP7/WlhfmLM/NLzOf38r309TmO5MT3WjQgRca7H\nmI4YUyBA026JwhJF8lvpup6uHXCjT8uYqLDWYY0lqyrWyzOCc9w9NNxsW4iaoTG0u45gAm3d4byn\nrBfUsyVSpVgyrTU+BLaHHVpryrxK+wzrUQGCi0k5FxXehVQ8x5F+6HAhiYk6N/Bpe8/tfoOXE9VK\np/fcR0+wqcPO0BM1Lb23zgecG9juG6LOKOeJSeGNIViHHz3OBryPWBeQRKzzbA4HRm9w3iGEpNA5\ndVFSFzlVXSCkxowCJzQm5vS+5v3NDT/dtowUEy4akSKbCgPJJ/sx+OD0LjHBLgEXofOWu+ORc0bW\nJUnMFASDddztLOJS0bsc6yPWDUQ7MAwNZmwY+j1D36B0xBjHTx/ec3ZxRqY1oxkZx56yKKirgig1\nxow8bDcYZ9IzkRA1kw1DYn2VVcH6fEG2ksRSYGOgPSaZfILpUgGz1uOdQIoc7yX3D3u8HxkNxJjT\ndB7kQBQ9XoIuK/JqwTg6xt7iesvBRtpdizMxRQk+EvCexICPROT4bNlP8mX3py53arDcREGWJ6w5\nhMeYtSgCnsQjj4DWGWVZUM9KdCUpKuhNQ+zTcnSRFwhyRAxIGSlyDVUGPtUCayzBhQSPKIEqJGVd\ncTi0BASqzJAmwSIJ/Zvqw7MaEeVpt3HKkpzcE1PFQcQkkIohTb6HfUOZZyiRJo37e4vxNbO6xIzJ\nLyk5tP7p8asU8nbokFKm0IJH69j0oqTuWpFn+ePS88S3PBXzE2vlyU9FftYhPx9jfhkumZYOp87A\nJ756jJFKlY/Kqy+Xm59THiFOhvzAo63u6Vmcfq8vmTKfPZt05v/igvN0pxgjxowMQwcioFSCocps\nzWAPRNFS1BHnBC5ADBYpIlVRMpvnHLqBfewZm5BsZtcz/vDbv+P9Tz/waXPHpmsgaELvCZ1NWYZS\n07cpyPjT/YayqGnNkc6NyDKjGXuqviVGMNbiXYAA/bEnzyoWswWNOvLx4zXH457OvqJalyzOl8zP\nFsh2g1eeoAIqk2RZSa5nBCfxNuCMTdioBIJk8ILd2NJ2A0pnXF6ccfnyCtvs6Xxg8COmtyRvJUWI\nDq01UQoetjuOR4mWklJqsJ7RtjAMrNevefX2Wy5efUdRXdCZgv/1//ie7+869gM45fEmQSpe+c9s\nIhIkkcbtkD4QqZCHiA+R0TpC01HnBlULZnVB1xiafmBzDKiD5+Fg2O4OdKLH9HuaZoePQ2Lr4Njt\nbtjujvz88SNv3nzHarniYXNDpxv6fs/dreHy5TtiiDRNi/OT177SBAlRRhAp7ejs8jV//+9+z+V3\n59y3D3z49IkQLMZFvI1EB33Ts73bcbfcctj3VEXD9z98T6YhegNuzof3d/T9B6QUnF9d0I3QmJHm\nONI+9HS3LT93A+ZgGQYmb+6kOThtxcLE7hAkiEue5hpxsqqazmatiC45bhZZnrScMWImd0NHxHJS\neia2SKYz8ixHa8HZRcnyoqSsS+5+GnBtwHgY+wiFptQVm27ADh4FlBqwMLrA4AWekSAleXaGjBkg\nGOyItJCMQE9inwkWOqEDEpxLt/oYU3QdYgr1PrFXAkIEnPWExiLqCmJyMM1HRfQWVgHTWjCgQ/bV\nmvor8chjCjyWyV7VeUfw6ZfJshyt0vLipOpSUk9dTqLgpU5cTgX9mfnWiW4InMrl5111KrwiNf04\nZx+xwXEYiECmM2Ispivf0wL1ZI0r5XNq4+lxpo9lfCryYqIdPRbxL5a7J3z/+YXidF/5aIz1fByN\naFWQ5wFEevNDdFSloihrZvUZi9lAb44c2xs2u1sILVXpObtakm0ygov0TUuV1RRFwTCO7I8th0NP\nP0zLOhPABsgkbW+IdweyhWF7POJjZLSGfgq1KHVFnVVUKkNFz9lqwXK95vLqgnm9otv3dJsDrWoJ\n0vHQ7Mlcyxg81WyeKHveQnAJb44CB2RRokiKOZ2lKaJtRnSuMEEQlaKYl+SFQotAUVX0x462Hxmm\nzjmElOYSYsC69FJGF/AaqiIjLzKEg6ExPOwsYiWR8YzjR8t2P3Bz9BMuHglxJHqfGCnCkonp/Zw6\nRR/CtPeaitT0vnoCQkbqakaRC3RuCTJn21luDwqbXzCoJfe94P/9ccvv39RcLC+ZzdeAo+n2fLr+\nmSyH2WzGd999i8BzOOxo+z3t0DP2A/tdy7Yd+PnTLT/9+Inh0NO3Q3IPVYIgYMCTa8XF2RkvX70m\n6kCelyxmS8wC9l3DEAd8FDR7yzhsGY8Rs5PMqjnXP39In/PosWagaduE1UvFfPkBEyObpkfIHHfQ\nmLskeXejxbn02p1ghaR4IGkCBCgSaU8LlRSeIimE1dR5P0KnWnxmoIUdpjT6iJnUoBJBhiK4gB09\naoj0Y0AOFicCRZ1zsa64WFY4a+nagfZgaLY9wUYynRSmUoJQ4K2j1iXn6zW/++07wmDZ3W5RVZ5S\niIybIJRpUpB5+r1icmZM5SFx56OIIFTqwmOiY6bzOEx7rwCiQukU91dV6WKEkGRViVIRvm5H/ivx\nyK1Fq5PHQuqGzWjwNhH/Zf4U+EBMTBbPk/z+6e+TcgpO2+DTIfjsRp7DLemFttZgrcFYQ9/16Ewj\n63q6WkpOPUEqtn5Sl566/+dd+ucd9PO0o+fPLT2n0/elDu9pL/D0/VophNCfwzVCoHUBQhFJ4a4x\nBhQlUqUuxvnA7viRYdzh/IjSllxFilJRFJoik2RZJJcpSmbX7NkfW9p2xLnUQXrnicGTiSxdvHwK\nOxj8yKFr6HuLCwGlFHlWUOmCUmXkUjCblZyfLVmfLSmzGmykrkp0LnDCcRw6xAhIwfnFOc6YqUA6\ntEwinxhSgRQh2YiekmKMGSavaMjKjHyWEYVj7BsqleGcox/HBA14n94DlSxGk0umwIZIEBEtA0Um\np7QoaPwMxhp/zPj04ch2P9JFjUHgQyQ4m8IAY3rNkRKlNUIwRaUlDFhw+lykz16IkSgF5WxGkUfI\nIps2cHOIPAwaXy2I1YI+5ry/6Xl7taasUzJRiI7ReNrOoZwjzzXVrGaz29D1Lbvmnu2hZewN2IgT\nN9zebHi43yJMEtMsLlZJRNMYeq2QUSOQmNHT+Z5utPheEo1m7AVdG4mWRF/1A8ebns37BhUlx90R\n75PwLOAJLjU6SilEJhFZQSznlDOF7BXhGBiHZB+c8N2p/xapgPupoJ9oeVoItJBoqVITJ9PXTk1U\ncjkFgqDQOQiBDw6lFV6EyWtoEtwISZhwfukDo/fIMeHMF8sZL88XXK3m/HT9QHccGRqbLHxRSKWS\nUtaFxIwhkmcZs6KkzDQagfKCXOc45QkyIoVP53UUIFSy8ZpQhBMzJ0b/WLN4rC3i8eIGqbEtq5Ki\nLJFKsphVVHWBLjQeMO3I2Ixfram/DkbeNmhZE6skXAg+RbjZ0VIU5Z/gzc/ZIo/4NKTsu+n/T8vO\nE9423euR3ThtxqfEoXTxGOi6hqFvGUdDVddUec5JmBQe2SzhcRF6el5CPMXTpWxIHm97fj/5zG/h\nhKSevjdOWOopjuy0I4g6ca2VEp/9HKmSL0UIT9F08USXEBGlBM5C3zms0xR1wegM95sDbTsxW0LE\nS0tvO2KrUuL8mDyuVZ4TZMDapFKbzWtev3jJy2+uOPYHPt1+YrfvOBxH7BCIeRK6iFxS5iVSBIbh\nyGYTGdtbun1PwHN2vsTh+PHTNVIKjE0iGj95RkcfWNYLpNSJ09smHxGIzDJNFiEbMhwWKSVZoVDK\n0w9HumZPrTPargV8UoAGh/cBleUpLDcAUuAj2BiJbiQCBTP07JLq5R8Iy2/4uLHcHh3tkHInrZvi\n4ryfjMncZKMKRVmh8xznEx4vQkqICadpS6TsK0jL2KyssMLxxx/33B0jjcihzKgXM+p6weByeqtp\nx0Ac99gwsjtu6X3P7mbPYAwgiS7QdAfu97fsDi0xSqqiIsrIcBxQQnC2WvD6PDkvPux2KBvodwNd\nG/nw8ycGb1i8XNMOI/ttw/6hZ//Q0R2HhBFPE5ILDqPdk8+Jd/hok/d8mPZReIKUqConLwqMiYhx\n+v7osXhs9MnWgcfAnAk6iY/KzgxFRqKqqgl6kCJBQi54yqxESUnfDagsJ1cZzqXIx+SF7wjWpBxZ\nCWQQi4gvA7IKSXGqBOvznOUqn5KF0rkZiFgBmRaIXDJ0PW1jGAdPWeZoIenblv/8f/4X7j/tCC4S\njZ+M4TRKRXxIAc/isfN+qjmniT3VgVPTloT98pQXLCDXBZcXV8wXM6SSrBZzyrpA5RJjLTfjDdtm\n89Wa+usIgrx//HvqSpMcPyWMf401IqRIVqbPOvLntz9BFGIqck9pQ5HUpXnvpp2DIATPaHr6rmEY\nuhRq4CzD2FM7O0Eh6YqZTuL0BjiXZptEm3zisH/1YvMZHZGpo0/JJiGcinh4hqGfFjtPv8/z3zPB\nLfJxyZpmSZEk+sFhnYUoyPMZ89kLTDD0zchm09K0I9Y6illFrhVagvAdq1WNlKmgRyXBJBzz8sUl\ndVnR2Y777QNSedarirquKIqOrh25OF/gomWz64lRELxjGBp6YzhuR0xryTUs1jNUrulGi4ueqiiJ\nwU+TmebV1QvWizOG3nA8fMKMA0pIqroiK3JsSAupdb1guZ4zXxaMY8c4jDjhKTNNqEqCBWMirerp\nzZjEdoHkXx0jQU7FwQqinhPnb8iW3xGWb7CxomkMx3ZKjdeWKE5YeEB4jwgO4R14h0IkGE4IPNPE\nqyQhJhogIU6+IXEKgpizyAsePm7J64p36wsWF2esL+cUyrHdHfjpWpKRs8wH7je33O/u2DZ7Pt7d\nst81uCFBizaMtLbFmPT56U1LpiLKw7qe8d3rd7y9uGBVlyyKmswp8pjz8dOevrfc/Lxhs09wWtuM\n2M5jRo9wpOcd/FSIA9EFrIAopvF/ggSfdjsRQsQNA3a/Q4tI7gfyYIh4vAi4qfueQgEnPsNkaBcT\nXKJRaCFRgtSZ5/k08QSscyhlCUql9PqYJsKqqrDO4KzlxGRJU75MWbxB43qBbQTSwhgj137LVrYw\nCsYQ6MxA07WMg8FLB87RHjq8V9RlzXq1pNSasR1pNi19m5CDw+4AZBBUqilSIEKapEBMEYip2Twh\nCUy1KMaYbAlOHfoJRQ1pP1EWJYtlldSgMeKcZeg7ZITFbP7VmvqrFPLHwGUfsNZNy8Jk6ZiK1Rf4\ntkhRTafjMzbInywip9H21BlxWkgajBlBkFz7QloeGjvig6MoK6IQGGtx3qVMz4m5kgy9/OPS9XRo\n/Tn88byQf8kzF+LRXYbnkE0IT3i61vKr9MfPpfvANMKnVO6A9QZrR7xLHWN6/IwQMpyVjANYkx4z\nzzRlli5yzhiKUhFiAUpgrMe6NGFUswqpJO3Y0W86Vsuas9WCs6JG6SPb3ZGyzmgOLU3TIr0klwEy\nTZbVeN/hnSGfVdTzCp3lVHmFwyGlpO1a+mFAIDhbrDhbrdnF43SBDyidGDJRRLJMcb5a8fbtbzhb\nLyhyOO63OJcWSP1wII+aUlU0/QgyFW0fIz6AdzEVPRFBaJAVsn6DWv8NnP2WUZYMYzLIGvqeoe0J\nckTmOSgFRIR3SOeQLnlgZ1ISimJaaqW4L5lqyBTCm95rFUnOgFXN2UXBcbOnXq44f/WC5fkSldcc\nR8G/Xu/5FzrMUfJibnj/4XvuD7c4Zbm727DfdIwHj85A5JGQgUATo8C7gHSWeVbxcnnBd2/fcjVf\nkMdIBsjLiDCCY+Nojlv2+4Z4GDDGY0cPPhXZCAhxWjO6ROWLAKnJkkIg4+TlDROckLZS3llM1xCi\nR2EAM3FIEpvktCM6FbA4MX0EoIUiV9nkbJl+bqaSWdYpo9M4h05PMEUNAlmZY9rEREpGfNPUDcni\nw3mEEdhGI0wqDfemJYwHTOMo5hVBJFuCaNOS1BqD8JLVfM767JzVfIZSKQ2pDQ1hIkWMpkfmgizT\nZELgkRPdNX72PE5cloQWTKlBIqI4GfLyJBxykeO+IdMZRVaQ6xypgZjQA2KkzP+KgiXqqiZTiuCg\n6/oJRlCURZZk5d6ipEiKzomRkuKTPu9SU7edjqfu+BknPSSHMYh47zC2x3uf5LtIjDUEAaooKOtZ\n2jBHgZsMeRL0fApKTQXGOf8ZnPI1sdCXt6X/MKWGn57xE1yTirh+irUTT/THL6ePdPvpQuAZ/Ujb\n7TGmI88U1rcM5sjh8IDpLZqC1VKznOe4cWBsO6RK1KggBUMY8AqqmcbszeOk1A89rlAMGGzXU1YF\nZbFkuT4HVeIJdM2R1gz01uDaQBXgYr7m7TffkYVrDmrDbFmQFRneBdpDiyo1HsfxdkfvDWVZpe72\npOKTkkxrtJAE5xn6nkU947s33/I//vv/gBKC3cMNL+Yp3kzpjP/9f/tfcMIhFwWfNpsUnBvthFMK\njPCMbQ9SIYoa6rcUV39PfvYtfSzphzEFBJsRN4yEbkhugnWFyJOTo/KBaCzBGIgOC4xaI4sZSslk\nGRECGZFMRHxMPagWgI+orODi1Uvevj7jbLVgPq+JImJCwY/XHfcffmb/qedDPXBZtbz/+I/sx3uK\ndaIr2jYybj1ZFcjnmrwoUVEgVZYak+7AxXzO3777Db959Yo8RMb9nplQuGJGW40IJxl6R9uaFPcT\nSZmmEwskiIiQSa8QhH0stAlmTAZQkJwKEeFpokQjJsWmjAGiT24qn+HWYroQiMkh8LQDSzubsigo\nqwJjDNa4tFdWEiE1MRqcS+dNlhf0tgNrKeozghRYEu/80VU1ekw/oDHJxXPwKJHi0oZRYbuA6y3W\n6alxzKgQ4CIywOVixtvfvubNNy/AOzKtsIMhDsnQ7dA5pBJkdaI4liEyHGNKz1JJjRwieBR+uoAp\npVINiwmOUkjkRL7gpJ2xkWZ/JLiAsxGtNLookerU3Sdh2deOX6WQzxcLcp1O2tG4KVlb4D1Y4yAa\nlEpjSCpuTxj0iZNyYq6c7CNPRwggZcS5pw9RGn0kSmXTEsOnpU2IyXc7y8jzAiHTYkxLjVJ6Ys6k\nN0ap9LHOZXK9y/P8iYaWnhDicRT/Au6Z4CIh9XR7TCN+lASZggjENKojpo05ny9QT9+Xir8nxClw\ndmjwwSBVBGEIYUDLyOX5JX3fE0KD1oFX5xcsyxId4f3dB242O5p+TFFYImIHQ6Ez5DzBPnmusc7S\ndV0ajKVCZyVSpMDnPM8xOk0RKlO4TOBEpLOOzWHH6AxZnrOcrzk/f4XOCuyoOfYbRt8TZEU29pRl\nzcXqBZkq8PaA6QYyqZjVFYvlMgUgy/+PuTdbkuvK0vS+PZ3BpxgxEJySlawulUmmC73/E+hKJlO1\nsqqzkkkSJIAYfTzTnnSx9vEAq6mb7guW00DCCHiEh/s5a6/1r38AWymUTlSu4uLikmW7oG2XxBj5\n+osviTGhTIVyFVOKjH4kJvBeVsPW1Jj2muriHe72z4T2FTFY8UnpR6Z+YOx7kduPUtDluG9QTg4V\nipSdlJn6EcyRylU4VQ7/lDEJdNagjewPEmiC+LNYw+XNDZu2xmrFaew4HTz75z19vye4gMonhsMv\n9LGXz+U0YTzEfWB8HmldS63le1q0CH1C4mp1xbdvvuSbL75kWTmm/ZbD0z2/fnzglw9bfvyw5f37\nOw7HnpgyFLe+8/6HMifGVIpLuZBRZRlZocgCGZRD6mXvpFHGYVxD8j0+io99UA6UE4n9zE5RhaGS\nhZIHYF2Nsg3aiIdMIjJFj9MWaw3L5YIYpPTXxjEGg8+B3k8Ya6mriimMzK9KI/L8HDUmW6q6pmor\ntNPooElDYgyCZVgnosOx68mTFNi6qtjePTEVyCXEgJ+CpE2NnowWPUGQXFnXtsRRIDuVFdM0kXjJ\nCxVbCVMOxFRQlJnoUCx9VaFgZ9DWsFgtWF1ecHu7YbE09KeeJ7flOe9+t6b+IYXcOodzTqxOR1/G\nLEXO+pwAb+0cJJHBzNvLXOiHsqT4nAYoGFTB335D65OLUSuDszUxQESWMcZYjLKFb1oV3q6S4q6t\nFDnKdI2sReZu3Dn3At2oWek5Y9/x/P1jjGfO+9mKoLw+U07vafJnDF4sLNULvp/nn+cFjokx4sPA\nFHpSHLHakLHEeJB8SutYLq95eH7EHLeMQ8+yueH2ekNlHB/3T8R8IASBWmIM+H5iuVizrIXJYbRh\nGMWQylihYcac0EqzbBaki0vsfPEJkQMfB07DxHa3I44DC1NxeXnD1eVrXNMyTRH7nDgcFaOPZKVZ\nNisW1YpuGJhGT22ky1mtVmyWS0IYsUbCfrfPdyzqBc5ULFeXLJcrUox88813dF3PoR9xu23hJINz\nFpUhWkfVLrCbd9iLb8mLLxhyRR4CeQoM+yPjaY/vOkLXEbsT4XRCW0s20rHlEMmhsGFSJo+eqDqq\niw3OViRt0NFTq0xtM04ptNViiYCmIaJioG1bqtqSYyCmyDpDJc8AACAASURBVPP2yN2nB47HHc3S\nok0kZk9IgTB54tFjTpFwDISDx7eKprG4RQs+4jCs6ppvXn/BN2/fcbPZwNgzHo/sn5/5+Msn/vbj\nIz/8uuXh1DEESXpXad5Lleuq3Ju5FBqB71KBbzUGsWHVpQk5CzULNRBtxd+n/FwZWTrO5l0hRsQM\nC5kkQDp7rbC2QtmajBXoS+kzy8VqzaKqmIaJVEyzjLaMIXLqB1rrqAqLJav5bi9QTlIQLZv1ksVV\nTTKZ8RAJXURbLdXPQjYKnzwxBEzW9H2PDxOP95lp9AyTRNKJnUSxf0ia5BO4TOUagksEE1FR3s08\nKzc/n6jLNKJQ57i6WUyUsxTz4hshzWVTs1guuVjX6KBRt5b1+uL3a+r/UCX+n3yMfsIYWUikLJ2B\n0hZrDN5HvA+fJQJJRz1DJMYorHJnyt9czP8jt1tgDXmeLv7B1hoWCycpJMFL6nxKYntqLLqczs46\njBEpc84Zo+f9fDgX8nM6Ubl0lLblc8vE+NLphBCEbWLn1ze/VuHSW5OwZrblTb+BUVTJGxVJdjw/\nL0ZJ2J58T1U5qnpJiJ6n0z0KaOuWqqm4vLhke3zi7uEDx/5EfarIybDvPCEYrGqxyTENnukw8Gr9\nimbREnNimEZySBjk/fAhcOqOvLq6Yb264mqzYb+5wJoP5PxIWinuH+85dR1Nc0T1nvWy5vb6FevN\nNVhDvXJs0hofJp6f72kXDY2tyR6eHrcM3cir6xu5gNuGxtW0VU1dWRSJH//2F2q34PryLVeXb9ls\naparGuf+iZ9/fc+H5x94fNpx2B+JY2C5aWSxbBxxscFsviK1bzgOmhzFGTGFxOnpiXH3TBpHpu0e\nfziQhw7qBqyRRMgoC++EsIxUjJg+cJEzzlQY57DRs9KRC5vZVInWiDdlCoprO+HGHpMVWWlSmeAe\nHrf8/P4D2+2Otd7QrCquLm+5//GB08ORcPLk7YQaRVW6/XUHSbFeXTIcetrFgnevr/kvf/ozb29v\ncSpzPBw4PO/Y73pOx8jhFNl1gSEEQum2dXFjFOXkbDCgSs8kS3Uph+XPyqQhy8hMVNLAkCl0QYNR\nlqAqOfxyjbYJ5zTWGsahI0UPqdhJZ7mntHYYW6GsJRTIxRiHNfMsAG1dkX1gCgGVwGmLVp5D11Ev\n11htsNowkc/ZBloXrNzDq9s11+9WjAQ+/v1IX3ncWqEsYDIhZSnEQaDTcDpKQU7y8wcSSSM7rDkQ\nIiTRO9Sa2raMZiJDaUSLyPFckUQgNi/AlVJFsT6nB8E8ISVE1+JTwEcv935IbB92vH73hm++//Z3\na+ofwyOPSSAVIx2dtYaqctS1IwSx3JyLsda6dL8FkyvLQq0tStnPgpxn0EV+LyIifkP5k+gy4b5a\n6wS3KsvGqqox2qCLEAk+Pyi0wB1K3q7zMnLeVMzfWUlnGoN8kPNrf7ETkMeMk6eczt0LiEIwhSwx\nUEgySIyJ4D0pCTYWY+B0OpLxWGdo6hVKG2KacBVMPjIOJ477Z553DxyPB2LKHIeR6fGJ7bbn1w87\ncnLcXNwwDT2xDlxeXXJ1fc3F1S3GVfz957+iph6XHVprur7n4909Ds3rq2vapqU7jhi9YNkmHp8+\nEcaJ7BP+5KlDRudcRD+SrLjdPfHh/QcO+y1OeZSvGI8Tj90Tp9ORyhkur94Qc6KuajarFd//+TvI\nkeN+z9PhI3VtuL664urymuXqEmMtMWV8SnTjkaap+P67f0ArxTidiLohmg0Dr/lp6/i4D/Shg+jL\n4gqm00DqJ/ATJkRcDKixh+MetMLoFbNFqVZyU6MKHbTrqU9HqmHPny/h29uadzdLNusVlVXkJNc6\nrqJqoDFZMOQQ8L3n+eGZ+7sHfD9idU3jVriQGe4Cu/cHcsg4n3HZYJwSu9VeoQbN91/+iW/eveHb\nL99wc3mBTYHTccvPP/7Mv/3lB/761595PgQe9h4fxdkv80KdnVlSs/3BrKpU6GLsNGPoBd8mz559\nzAw7lcEqiEqen5UqSsZI9gNTiMKQChGnFLU2okou0QMhToz9yDBJnJtRCk1iGAOVleVn3/X4KFZU\n3ovPkE7yvftxQJWlv85SeE1ptozRGJtp64rNckUymefa0ywDbqVl8vEaM1nM1QWD6+j3AznIz1Aw\nU2LZdfkgNOGYorBvUmLqex4+fWIchBVm5ti3eak7l/KcJH+asiMoTaBRRqaJrIgkUpprlOPtmzcY\nMh9/uaNZrLi8ueTyevG7NfWPUXaGyBBHglYoEtY66srhnGBiSlGWiv6zBeA8gsTS8XKOepOF5Pz4\njPqntWzZS/czF2BTYqOMERfAmcmilUEpU4gwnzNOZvz7t0vH8+vKhUOSz9PU+flnmuJ/eG5GsG5f\nDi6lNS/9vZjrVLYhpkwoF69cTJ5xGqgqU+AgKwKNNJDoGfyB3emZY3/kaffEOI00dSPjX8jcPezo\nOk9bOZpKEyaFcgbTNOimwTULXFWjrEVbjcWSSEx+4nDa8/RsWNaWyko6jnM1iwU8PnyUpSUG5aEy\nltq6YkcsVrGHXcd2t2MajlxdOPpDzzFEjOnINrJZb3j35i0PT8/SVSnNxfqCHAPjsUMlqKxltVqK\nURaJcRo4HHf03RGVAzcXG25vXrNarbm7/0TIhpCXnKYrno89n3yP7ydylMWuSNMn8eAeR3IM6BQx\n2aPCKL9SDVmfeQZJKbTKVDnB4Zk6D9zqjn/crPinNy1fv6lpVgu0UoQUGKZAFwzeRmyaCNHhx4nd\n/Z7HT89sH3fESROnwHScOG47pqeBsJ8IOeNchasdTVORjWPZrlm4BV/cvuarN295fSW+8Kf9gYdP\nd/zbf/uR//fffuaHn++YkqUPlpCkWAjwUKbDPEcil12S9MOFWTITeOWq10iDMTuFiHyhMLGz0Oxi\nEJ/ulAZymsh5xGQRxbiCby+NxeR8vm98TnQpMiRPUNBUFdZYUiwwYorEMYthVhZYRiiS8ssXvcFn\nnAL5lJRMAnVl8UMgDpl2vWDVtExToI9Z/HxiIo0i+LLaYI2BrGVRHovXilJgShddtAHWGqzW5JQ4\nHQ/4EGVqKYwldeaSv7Rv8p98rhXn2pJnyFU+lxAifT8SQ6SPE4f9ketXS4yVfdbvPf4YHnmIeN9D\nTixXDdY2OGfRylDXNTHCMIxn/xMpyvNJJhzwFDOWmdVh5new4M8znq4L1fFFESqVsni0KAuqiIQ+\ni22SYvwf2SJz5/1Cd+Rc6Av8kcXRUemXwv1CQ5z3Ry/PT0nUpV3XoTTifqcgBhlBF63QMs+jWuGz\nOyesBevqsq3vmMKeYXzieX/H3cMD28OR/eFATpmriw2vb95yOkyk8SNtVVFXmeC3JJXxKtHlxDEE\n9HDCTgNJJYwzRBXEPAiI2dP7E1McyDqiTKQ2FUrVOGNpbE1Mlgrh4LbLJUlppikw+MDxIDYA1kWa\npubXH3ecjrBct7x6d83r2xu+/OILdts9+/2BHCN3dY1RsktBaYyrMM7QTyemODJ0HR8//Mhxd8ey\nsmyur/jm2++5uHrFZn3DaddzPARMl7jSsLGwC5MUnWL0lXIUH/NpRKUASronY0UWnlQxa8oam42Y\ntOWACwNpfKZdZb68bfn67Ya3ry+5vFqDbiUUJWesSziVSHqC8QS5ZTj1/PL3D9z/8shp25PqltP+\nQO72PG9/xh96nNJMyeNax2qzYLNeUC83XF5f8+r2ls1yjVMG3w/0hyMP94/89NMH/u9/+Rs/vn9k\nexTudVQU9oQuOK0pYrdS+FSA4synAa1eOm+58ue/W7r38xJUcO4cIzGNTDES4oGUeyEsZC3UQlXR\naMvSVqysQ8dYBM6KpDU2B1TUnHyH1oq6qVHGCZSVMjEExhIB2TQOYUhmgWmUfhkP0gtkAxpjK5aL\nlqe7A5Vt+OqbDdfLJVM/sd32+GAkWORwEivgrKmVIxktWL8WcY82Gm0NtqqgLGnrWg4chcT3pSQL\nbaMUarYByXL48dm9n3Iqxm3CTCvLsDJJSHfuh4nHhwd+/vEnlsUKYJomTqee/e74uzX1DynkQ3di\n7DuCH+gHh8qvcNaJB0bVoJSwSKCEMJfuNYQJ74eCl2uM9tT1gsqpsoGH+UyeaYhKmfPpOOPmvy3S\n+fz/5kIOnP/8tz4o+Tf/b15GpizK1FTsQ00xnPr/s6WdH8bIhjw0NT6IoCflxOlwYBwGbq6gaRdU\nZTkcQiIojzaaytU4U5FRhHDieDzw8/v3/PTrL9w/bTkNXg7KtmLZNoQp0HcjIWay0fQp0B8G+m6i\n7yL9AGH8mfX6kfWqIatIu2xZqBalYBgGhmHgOASGZFDVktUKDseO5+2W0/HIcXckThHVZPJyRbVY\ncXnzipgUu+2e42GHMQo/aX742zPdmMEYcvYsFg2LRYs1hsvNJSlKtz1NnsViwWK94f7+jr/9/BO/\nPD/z6s1blm2DMwCB9cUFr19/Q8oWZVdCLWNJ8J4QPKuN44sEh5i4P/Z0AUKM4Ed0SpAUKSTSNKFC\nFHN/jHSdWTpSozNWJWyKNExsmsifv/mCP//pNd99ecPaetRSkayFVJZ7WhSLychSceoODMPI092e\nf//L33h6OpK1o6obNreXbJYG2xzowhJDL591U9Os1ty+ueK7777m+uqaSju2H+7pP91xtal4fHjm\n1w/P/PTzI7/enThOiqBrkrakMrbnXBJ0zkyv0pyUwixNTjrfA7NQTwFnaUOWf8kBIOKdUMT2OUth\nt8KnQauMyRqdZmtpTS5/f95boQ21cpBkYdm6mrZusLUStffkpVimJMHKyZ8PIVOiHWPOhDMVWZSa\nwnSyLBdLri+vePf2Hd99/Q0//fQTu/2JqtZkC8EbtHGkIZSfXzGGKLmzrUUpMcXz0Ys9b5T3YxgH\nXCWwoxdQpLxn4jQJnPM7cyqaD4pavPxzplNTFrRZthIpZIZTx/3dI+rNNetVy/F45Kcf3vNw/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Rqq5o10uu1plFbVnWDt+fxMeaIux5fOT5acdi6bDOCBSQIiF6Ygrs9s+8f/839vsH8WTPiW++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\n5DygCKyaFus8cU4M/UQKMM49MSWstTzcv+eHj/+Gj+9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3ixQXIuegiOFpNfSg6XvIowRf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CzKWYN5VyeC4pUdigk+rM1HkDUY6evohkolcTztOZxUEJHygZxnWhF82NBqZJ4L//A//pH/\n+X//I3/89z+xHE/UXHSD5ErNFsR9W6XxSOu5KAC77cSrFzte3e7YBE/t2WgxDxV0Y6gzYVgrkipC\nLjq2qxbdeMMQV3WsiFCppKKv7das0ducx6ZNYlu4K/UZPSibaFWgAb5xPBz48PYtVz/+yIvf/mcb\nDOI6GY7T/sC7Nz/w9qe3nFLi9Tdf4aaJuVRSPRnGe8aS++HcRCX2OWfmpHM3a07kdJ7BWYpqBmJw\nbKaRUmfzWnFK3TS8u+bKab+wf9zTUmJ+/MyfvnvHj7//jqdPD/gYzENFiFGHK4fgrR9wph/qGuEZ\nuql/6SW4SIdLBNfa6lvtLFj2DF/FQQLeM8RRmRfoCL+zJ4vBTa3RWsERzfNFA0NzfRQYxBjwQ0Ca\nDvoYd5Htywtubq/AN57uH8g2ri6VRJFKpZGOsz6nEBnjpNnvkhQKcN4si2GcRhXDUJG5GuwptATH\nfeE0z/gJLq8n6t4hkvXZlcqS1PwqBMWTj4eF6XAieK8TiJKw2Y1MVeHB+ZARH3Tvzgt4he9qqtRT\noi7FjMUEPKSSlP9tFUoWEwUCIUajoqqVQ0PX++l4Wp9pbeaRMg2Muy3iPW1xxDSoyKdUTQwM6qhN\nYSdcM3TM7G4tSXLBM2wGnRFbG614nOtjKlX1rInYuSegbVJt2mJ9nb64HOf48HPXLxLISymIU/61\nGgwFvNcMNpekkIr4FWd1RpvQQ0usaRFwYcCHAe+jus+JZbZ2032IalfaBN9gsOZFEZhLXUURvcmw\nJt0rzGL/hrcv6fi4Juqmtz8+cjw+0KQw+IFSMp8/veE4C5/uT/zwwyP/9k9/5s2f3pGOyzqsOM+F\nbBaY3qFQiVnGIsrjjT4wOXhxveGbV1dsx4iUQs6FXIpOwekJrDVItE/Qm7xWmeAYQjT+qjZfxCqS\n7tymZfsZPul9dO9sKJPGDHsN6wyILv5iGLhrwv3bdwz/9M9cXL7g5a9+w/bmFqKnVGH/8Mif//07\nSq3c3t3y62++5nK7YXCiPP/n0J+9TmlCy5UlJU7zzHJK5HmhzDO1ZFopWkI3EzIFLWfFGnFebACB\nj0zTQAwj211k3DiWw2fefbfwT//nv/H48ZHolRMcB1VsatalC6IjRM5EGt5pgI3B8Mv1jnV/H11P\n+tdzBQOagOCV1eTFmETizo00o61J689AgSVQaoW0oodGCNa0g1ohOsfF3RXX377i6cMTadZMchgi\nc0q8+/BISE4rOadmTWEKbF9OMAolQ6ueUirNC37Q5jVFsX4XPHLSZKjkZT1gShCCFFyLhKLNYaZG\nXSpLKuusSxkHunXBNA6Ijxz2lThZRZUbQ5hIVJYoyOVkuacjtEgtakO8lIxoM0ghihjA2X58POKd\nYtBFoDqdkOTiRBgcgytIW3CoPmBJaUUDaA3xQquF0/5Jp245pxz8EC2G6qg7J2ok1+FehRatKqYZ\nmy4gFds8Zx56jycdbuzAoVtZcn6F0/T1HK49V7Cck8/n1y8SyLU5oAOoggtK9QomAGmCtPOHFZxy\nLm1CUA9a2lyIqlx0Osi0B5pSVRXpYmW02lXw+v1BaC6QijYCEVYs94xKGizDs9vmVIWlzABjYOTE\nkhNDVO+KUoTHpyfe/HjPd3/6yL///gMff9pzeDhqhiPNAnmipgLSdIPYAAexE9eLeqPsNpEXNzte\n3l0yxMCc1ASr1mY0MF0MvimroyCrvLe180EUg7emnfYfqmiGJ0U51k5k/Zxu/dPgAG/3xQLb2oBs\n7fw+RAgYF/sP3xHjhsPnB+6++Zq421Kr58O7D/z0p++pNbOZImX/yFOaOYSgsnF7VbVr1edbnVL0\ncq7MuVJyo6REOp1oVTH/VnRzBaWXI6hXR2vKjnBOxSsxBm5vttzeXXL3+gJP5unTgXc/vKEm9ekY\npmDNQFY+rwbnDo10QVAX7/SN3CN9h1XOINFf2SJz9vBwoSu+tJJ8RvdZ/1QYu7+HXrU046/rPqnV\nBl5vN7x4eYubG8fiSEWpdcdj5pgrOz9q2HFCSxW384TRq7OiqINozola1NsoxgGqNWCdwT6nwjJn\n4hjxU8BFFX81AZc9YQA/QrwU3OxpRXFsL9Hk6EKYRggDqXlqFmoWJFdyK6RayVJpzhGjHiaxeaq9\ntzktBLEALWpbQNQE8Hg84VolJ7UQENTqwI0DfgyMPrCcVHOAiFlTWK9McV5qqSynI8M0EcZRA3Dw\n+BZW8kLfJ947xAWtwAy69B6GQQU/LatiusMiKw7+bL3oJu02uL2/Zc9/bfr3nszPB3H4hQJ5agkR\npYpps8Gyb8uYO0St//8lbl2qmDMaOKfMCXGKHBXEsnLFVl1QHDoOmrX7YSRGpxNK5FzK90lEf9nw\n1L9Xw57Ng8JwRRc8LkR8mNS7YRhpCxzTyB//+Il/+5c3vP3xgXJq5HkhzSe8j5RcSEmbnMEgkdX5\nsfVpIJ7oA7c3E3d3O66vdtQslmm3Ve6vAqCGdLikda8ZMVtVg11CsODSA/GZR94HWD9XLnr+IpN8\n1j1XzLPz45v5jBjlsxQePnzg6fGRH/7tX7l58ZKrly9x4wX7Y+Ljjz8yzwc+v/uB48efTF3bV66a\n7+uQCk8wwcl2s2OcdvhpB5srSkocTied7FKrslVaVjhkgFwWcs2Ig3G7xVFVQ+Aar7+64m//7hte\nfnVLDI6PSyKODrdT/DzY3NHuZLeG6N6cMln+8/6THW/P2FI9aJ9zqOdXh156BuacwmPV1Ll6hGoP\nRp9JXOmT3fI0OB1qIcFDhSiCLImUE6fDCY9jiBO1VpZjVfWi9yy1sNuOjNPIUzrAohPjj0ti2WfK\nviBLISV1bmHjOS1JR55NOqDCjUFVisET4sC43RhTA3J21Kkx7DyXt5GwBOIpczoWXI60U6UulVI8\n4WJkuJxos0KNLTdNrhahzY3j/MTF7VZ9d5pnXhpFMqVmRALORaKPjOOIHwM+GdaftRnuZFy1D34I\nOhkIgdlpRWuMJkQbzs7pOVNzQwIEX/G+rmI6FwNSTGnpdPiL94oIBBeRrCsh+MhmGHGi0M3KroaV\nmKEHcd+fJulvup/CsyrPeX0+Dd3zPbn6uesXCeT7wxPOiXl5bJ+tecMEnwUdAB+MHRA0K64i5KrZ\nmveBzW7DbjNycXXFtNkRh0GHMATPMI0Mw8B2u2FzsSUEz7jZEYbxfPLxZeDq+7DfuPPEd7PCrFUD\nWPAkaTx8fuTy4pbHp8IPPzzy0497Hj4ulJOQZy0bvdcmVPcB14cn56arZQSgzbTdNPLrb+94cXdJ\nDEEZLu2c0dG8sVU03PTSfOWn9saXHVA6Ek+ZLgo99caL/lwP4OYuYB5Rz3nzrM6Rq91orXogotJ7\nLwY/1Mzy+IkPpyfev3uDny6Zi+PDh3uW455A4/DTO8YYbOFaUMOD00PHBQcRhjASpwuGmxe8/t/+\nG/O8sCxpdTyUolSuaQpsd5794yPSMmN07DaBaTNxcTHw+vUlX39zxdX1yGajNNTdxcjtq0sO90+U\nk1aJ6juuzfaVGeIMwRSteHTQ4/MD/0v4pPv/PHere27R3JugfRpM/1GFDt3qDyTWVO8yfxXNKBYe\nB5Xe4x2uVJz3HA862egq7hhjxIdA3AbaBG5wjCEQxoAbPGOY1KclF4PhlFVUU9Y1GANuEKbtgCCE\nQRgAFyM+7jThCh6qMGwGaqss+cTNZsvt1zuuX1zw6WHP8DgzPCXqUW0jchZ1L2QghJGUEmnOlFNi\nTnXtHdFgOSxIrmz9hnYSfFV2Tl8rDs80TYybgeAFmZUt5ix79d7hB+WGt1qYl6MlL5Y09agjXUzk\n1j6D89Casshap38O0SZkCZJty0rFOx1+3q1C0jLbqdDWRqnzbkUSNHP31jjXHF284Cpr1aU2JOZZ\n5L+s6H7u+kUCec1FTyL70J6AHzqU0i+PiHKSnYPtxZbb22tqrez3R2rdc9gvxGFgGDy3d1e8/Oob\ndpfXhDCQcwbABb1hwzCwmQZ8CGx3GzabnTahnmXmf3kprtWoTTNoa60otCKajc4lcf/0QKmO+49H\nfvj+I58+HDjuEzU1bcq0ZjRIDHZVaiLWMAzBr5OOvHNshsjN9ZZvvrrj6nILon2FUtV/Wixr6yV9\n7yXIynTQplKMwTJEpUqWkg0br2cjKsP29I4bLu66LJwzRrD+D6zTmgwWQMQMukwU5KGVhSWdWEqj\nDU+k6jg+niinGS8NfzwxDTqzEN+DmWOtzpw2sTwet7lgOCYufvVbUpJnDaSq3HEvTJuBzSbw6eOC\n1MrgPF4yU4xcX2345ts7rq83imsbvW/cDNy9vqEuC3vjHmt6FtYAvv6pH/yMfjy7L71a+et1ZJxz\nCxJthQWf/QrvzAHCrVBYiFohSG0WyPUG9R5HjN5EStZ0k0ZFpyyxF64uJ6LBRH7ylChIcMQpIl7I\nreCClv45dRMojwueiqxMDp1PocM/FP+VlQwQBg0drTb7jACNm6tLXt1dc3k3ckqJZS60CdKS8dEx\nToG4Vbw8n06kw4wkhRNP86xU0ia44MhLpSVRvDm3FSvuYpuuFQk29CQvHVX3BLTp6ccINJ3jepqV\nvbWasPXSSg/KMHiGbUSiW5lk2sDUXgutN8AdYQosc6YU24leX9dHr7Tgor4pYupN5x212kPvis1u\ntNbXiSnDV5X3ihg8q/r+Iyk7N+OkGHmrPD4+IFdCiOo/rJmvYl+dgYITXr684+b6EhA+fXoghPc8\nfD4wbSbGIXD38gXf/u53XN++xIdBHdUcuBAoRcUzXoQQI7vNyOXVBTHqx//LDehsirdmRIViUuEY\nddBBcJElLyw1cUwn9mnP6bTw/s0jP/35PfuHA2lOtKTDDmggMWoQbqgTo7U5aFU3KcoQ8B4uLye+\nfn3Nq1fXbIaR+ZRJKa2BuLa2BoQeIDTINPNLCQyDiaFaM3GMDqgQiyZSZcXuekB3Ng7OP2NCmMTU\nAo6+P7pvuYPQ3duagHHHw+B0LmPV97fUBEslloUhwBAHLnaTctQ79qt3ns75x7Jyj0fGET+OCFBa\n1RK6dhWsNsunaWQaAylnFYOIY3nYMwXBy46rKxWBlNw47TPTVo3YXry64fHDAyJ79bkxBFNwiO8u\ndai9AILgTYfgjHBip9AzWO7/LzFY/52+ic36Fu1zdD3REALB6VDgktW7H1RsEmIwDnhD1Y+NnBJL\nS3gfuRy3ROfxrqk1ufWWqlO+cypqTzFsRpYlkU5Zx7cNA7IVlppoWT1YaB6xXpJzjeIcrjTKbCKZ\n4HR/tIzgGOOG1zdf8/JiRy176r7SjhDKgCyFgcDF5cSrr17w8WnPn9584HSoXF1csru8IJ0SKVdK\nVytLgBZIreGbSvD6esTM6Eop+CyqVDY82eEYfFC2yhQpTQeO+CXbszPltvTUQeemDruB8XpkmRNt\nEVz1jJsJhkh1sMwnXPSM08A0DTw+HDjsF0qrxOCIQ2CcNkpkaMVsQTShDMY1V63HmU4tVYidzup1\nbTnbl7V0y2Qs/cfEYX99/SKB/OriBpFGLolD2xsVR02teoGvmyQwTRtCuKNd9hpFGIaJeUm8ffee\nWkdojXmeSctMSQtx1LKoZzvSCo62coCHcTQjrrBmUt1yFgeYV4SI+qGnsmjQAKpAblkXkIvEMOHd\nQPAbpB45Ph7JszJS1KmxC0acDjlI2WxV9SGJ8XJ1+IS69t3dXPLNN3dsNxuo2LTvQq5Kmyy1Qx3O\nxqcBTu/XMKrfifcKp+SUmE9H5fE2NdKS1lZ2C421UnDRGW/cP2vJPPvPBCyCaJOz2cHEswaePcEz\nCUPQubsCU7fIdYRgxmgd0lo/Rqd4AU4bqiE6pt3G7I0LVay6KBmRxjBEmjROi/ZElMXioVXKXDg9\nzXy+33Nzd8VuOyjD3wnjJvLi9RWf3lxy/HxkbmBM8PXw0jitlZmzLEmqMTmcZoN9U1aeB3I9Djp4\n3pMpcee7Kp2q4xw+sjY8xaFsDImU0vq3aGU5eeII3gt0ypqxdqZhYDdN64BsJ448V4oPMA408ZTU\nOB0XjsfEUgtN4GKzwY/eDuFAPmYkCb5FSm245pnctPZThikSYkA81CLMqRCGSNwF/vTdn/jxDVQK\n7z/tSUlwbmA5LLhSWYJnGjcsh0Q7NFz2lCQs3gJeDIA2cluuCp15oUiitGQwXyU3pWiONRBRLyYX\nHDFObB1oH9npXE2a+ues6lvWfe84Q1utFDXwKqJYeamImwl+g58mNpcXOBoZoS2aVDmDvwhBIUZL\nLtRLhXWQC/UZecD1A51n0FuHVm35IYZGmAGHQSz/oQL5Ztwo08FHWmsqrX2WxPTmovdebWeHwGrp\n6NTw5vXpyOHwDU8POw5Pe+bTkcdPn8hLZhhGBM3UhiFSW2EcI+PFls3mku12w7jZqlTfXqsZjKKH\nSiZ49UXoKtDaKm2ZKSKaMXjPZtiym67YTdc8fUo83J84PB5VsGCinRXbqo2SCzUrW8VE62CNrY6b\n7bYbXtxd8frFNTFEFbdk5eKWqg5vTdakWDMzp6ZXMfoVF280lVwvC/OyKNWQvojaGoS1QlD50TlD\n0cXSA7gHuz8anVdoQEFd/d5Ob3QdbenUOm0UDsHhJz04e5OPfjhwZoMgmGmWNVRFy/thM5kWoFBb\npeZEq3ovQ4yUUlmWkwZ+c6l0TdkQZc4cj4mLazlzwr1nHCK77YbLmwu2u4k8L+aJ8eUG7wMgvN0C\n13oFYxCUW2/NOWL79QatQFiz+9URqW5s5pxY9aFNL+etElkNXAAn+ABhYKVH9kaaiFJML7Ybrm8u\n1b7BSvEyF9oQCG5Qt70CZa4stVK94EZlnXjvdaJWCPjmlfmTFE70XnTepCmIve+iJkccIs05xm3k\n+sUV9WnP4XHmcEzc3+8pRWnAJak1RPaB6D+TUqUtBScDNTeSU3MztaY2zNg3fDCRV8Mqog45NDJZ\nhUzeE8aBMOiaHoiqtaiZtmSF6ZoytnUugfSTzu5t72tASY2W7bAWbUJ78/AJw6DxoPWxcHRw04gP\nUQ/ipsG8WqIkDoirtckAACAASURBVIUyu+Jz3UtOITTpyZRl61ItwZIvLBp+PoTr9csIgrz6KQ/j\nyNVw++W/OQ/ilLLk1WukZzgaPCrTZuLrb77i+vaKw9OBn77/gX/9p3/i7Z+/p5SmU12cYxwjm82E\nC3D36oar7bdcXU5cX1+w2W5xxl3HWApVbMpHOjLELTGO4BwhDNQqnE4nilR8DGwvL5F2Rc1CScJ3\n//LPfP/dO/aPB9KSlGaYjXkg0FrWrLg2O21lhUOg4tBGy93NBa9eXnNzc4kDUi6ktBic0GymobPG\np0mQzYpAZejm51Aqy6xBPOesrSHnzcK2nRulhtutgdoWVc8YOh7Jc1zcnpP6uejy8qZU1NFidWXP\naKFjuKK5UvbrTHpkDVj9mLB8FkHZQWEcKaWSSlkHj9SipbLzjpQW5tNR4Rz0sBITftBETZScI4xm\nAuUHQpzYXoxc3mzZXo48farP2CRuLXFB1kxobQiLYprQBy/3qqJvOM2unjNYxCCo1X+masavlb1o\nT8Crxw8F+hGANKtS1KNFxXKeYkHDCWyHkZvrS158dcfhcNAG4qJNRL8ZGcNAcIIXhytem5pbtVvI\ntTJFE8LVoglWgFRODFE9T5Y0n8stcQSJjGFgO20RX7h5ecmv//43uIcjn99+5s9//oC0J+bjgrQF\nkYJ3geIix+N7PI4Q1Dah1UbW0bI6iq45Sq0MIaoLZ4y0lllyYnVcRBuNLnjiODBuRsqc8SIMceSx\nJFoqSK6rHbBWx94iSbPfoypWZSwFWnWUXNBehTbFXW1qUhfVNS2EwOiF6jPFZcQ7hmlkHCdYFrKo\neEt5vlqNN0BJR7rnnw+HF5S9FkPA49VK12xLMNjRrSzGn4ftfplmp83b7Fv23LywG27fJ3Rp96Ni\ngyFYlj0RQmTa7Li6uERK5uPbHzk+7jnu98wm291MI1zuGKZIyxPOF3a7yMXFpLQldw4XempqNj4v\nT5S6MLQJkUr0I95HUprZH/dUJ1yWE615vDhudy9ZDoWHz3tSqUaX1c+j5VwfKttRVs7NRnMzicFz\ncTHyzTe3vLi7ZBgG5tOJZUksSbvnq7SXjo9bttqtRs2HvZTMsizqrpiKZm292WbBTQfOainoO6VQ\nTOLgO3PCrf0CtVJQbLpPYkKsMeqdLkIn9vqFPoyjqxr79dx/pI+2+surHyS9OYjzuBDIWT02fHO6\nSYs1o5xjSZXjaVH/DOcZvFe+sc7XI88ZncASlaZJQ8z4/eJmx+Xtjvc/YF75bs2sKiqvltrHbwnO\nDwTsffWg8OzokS9WcD8fz418dd7TYF5NrBZjh7S0SpLSVqxfGUnVsje3UtG8a0yD52o7UYLn8nLH\n7es7fvVf/46HT5/54d+/J5aFFoRcFo7HE2mZcb4xjoqLhxDpgw3EsSJL3jum3bAutDBuSSkhrak7\npHNov1UoqFZht41cX94RvfD+/jMXt5cUAvN+wTe9n6klpUealYBzkPNCSZUxQnCRKEEPzBAhRkrw\nFB+oBJpgh5Kn1ExOlfmYiE2gVJxruFAQl/EexmGjPjJoj8yLJYpStA/gVFw19jmztdHKWTLfqmiG\nTMGFivOazBQjEBADwzASNzoftczVCAOOKmexnahT4DpZKnr9jN3/CFErjeBHnPPEOGp/ELcmS709\n8HPXLzSzs5ecdkJawDhzcE1UIX0CugpXxInhlioEcj7SQmC73bHdTDpguBbSnMEaabWOBJMm6mQT\nExqEHhD7RtRTMKWZOR2Ibaa0gdYq2+kaiCs2l2tCDpVp2NFq5Pi48Pjxkf3Dk7JCeiYqrJlsqXUN\nYudErxdmEAfP5eXEV69vuLragqjv9rIkUsqU0syUydkhgDn6hTWIg6OUwjwnlnk2vnq1Kt9CiQPf\nqwGzi5T+9WcYnj4g47I6a0AKK1xy5laryi0GZ2KknrV/eTg/v+TZ89eK6Mt/7yGx+43gNZB3g6xa\nKnnJ1Ko4tQ+RKkLKlegCg/NEOtSj7zvPWYUn4iA4gwbUsMwPER+HcwNT32RH3bRHkZtOU0KDRQjA\nEA0rt5XUIRY75PT3db8V/bJm4+fMXB0PLcv33l6vQMqIDeCWVmn4tRzXZdUIXkfayWYkO/DSSDnx\n1YtbwmbicJr5XGZOvrG0hdqyiVpEsf0mSG7qRhoUntH5pOoIKN6p0lOUeledZpYBWyfm+tQEgh+5\n2l6xiTDtjky7idtXjoYnLRVyn5Xb1qrb28CKUqtVHcre8mKOkna/suHNmMYkeK24a1WF9Mk5Ylbi\nQAjnA9M7zxQHiuhQCSd28Dez4W0aGbvLaV+bIZqcGQ0NTUTJErXircnepOiQaHfWvkgzR81ObVwX\nsu6zXrmp0Zn2t3q867a6OnD6mVnWOSdYI+bPXb+Q+6FfA/dzIc75crqQmmZMm81ItY313P62VLOf\nbAJ4hmEgxsjsMs05WvAwRNww4EK0BfQM4+p+0jRwjlIyc5pJeVFZf4VSFgQhhp0KBcZATnBc9gzj\nxLJU/vD773n35h2Hh701P2SlOBWbiVnNJFz3vFGWXJ8jCcMYuLre8vrlNbtpVCbCvLDM6vJXa1On\nN8vQNFFSjDJExRVLaSxLZp5nlnlRkyVkPSDPgoKuSHxGYnSod7I/f/3cMncr7awiq2ObcyoNj17r\nimKHVvfFOUMKz+CUZ6yOzqv+60huBbAOUDTb3EiZGyl386xs/jiBMA5wVG/0IQxEEXyr6hljTdi8\nZErSwb46WBfwjVQS81yZF/VxD83YOEb3Wy14m+HzxQ5H55mGUT0MnEVpwaAyWZ+rBmIzWhNrltpB\n3wMVZpSG1808n07KDRdVCtZWjfgq60FbWyWifRG/US75fNjz9s0b7n77ay5urvn2P/2WH58+sZ8f\nKVIYxogvlZIy3nv1qZkLYRoZNpHdMEEbOORFXTOzUj0LOoi79ErMYCC10df75hi52LxgPjywFM/2\n4oLxakfD8Xh/pLQC1a2Oi955e3aRSTyhOoagLBlXhNF7ltrIeaE53e/BB1wwe1dbNWlRlo0fYRoC\nflSmCxJxrmqCIx5x1Z59QJruldqUcBAI6oEkWgJuhg0EO2y9GSR4wVFpxWAWKXYQBPV4WrTxXlM2\n61/FGtSMssOW5noZ/Pnw7ywVO/C1X9YZCM3qd/QQcD8XK/X6RQL5ad4zDCPjMHHe8OZtbZcKg/RI\nU5vbLhZxq2Q4RvVSGacdm4tL/DCq2Y1za6BPuTJtAiKemtHA0M+1NUPWutJ77ajj7miSWPKBx6dH\npI1sNp6lJh6PT3x+/MTj4YHry0eOD5V//Mc/8unDB2qacbUYdNHW01lsfFunGIodHBpk1UFvs524\nurrgYrfFe8fplNTxrWPjXQ7fueLGn9UmjWLSKVVS0vFmKz6NlXS9Qw/0Y75bxPYmU1gX2NmGwCNn\n87X1zmmmPXivVDnvbLizMSxss/am5/PrC5vg9a3oofFFkO90PJQ2F2KkSOK0LBxOM805NU1r6sWS\nckZEZ7LGUnC1WEauWW5eEinNtJYZpy3HQ+LtT4/c3+/58OaBh7efOe4ru8kTB8OjrQxmzUMHpAh5\nycx+YYyDBpfoqDaY+HkK5da+Tj/AWAUifTBDT9VLLqRS1ekxZ7VPkKa8d4TooVJxQYM5VahNfWak\nCeMU1fZ0KTy8+YBkgUHnro5twAWhzom0JFKqxI3S+yKei80F2zgxop7wc11ocyXtjSWCcJof1Rt/\nUisDPDp+bRoJpbA/7Pnn//WvKlSi8eLVNX6EnDM//nnAO5TUUBqyFKKPxGGgBW3aeiu+rP1PaA0p\nghStmujQiHfoTACdZq82yoVGsXutk4nE9srSkh7EDvCmiO5Vsa2zVivtpBBk0JREn70DiY4wjbgY\nEZsS5L3TIRTNUJdFPdE9Qmg6nara5+gHvArszjzx2myegPReyrM9Yiyp8+XW/fdXSY9dv0wgX57A\nXzAMEZFAZ+6ep9LrRhfaM6oRUIsOZTZ6YpOGEMhNu9f9w/oeLLvXcoOUG8fjwjwv7EoxaKUhUqii\nNKzSMo1KjIMGjOOJj/efycVxWRupwmE+8vHxnp/e/cgmfGb/sfDHf/+ep4e9ntZNVql8z8INuaAb\nMSns61cZ+DAOXFyoFH8zRZBGMgVjKWXN5vrD9t4ThqBezVb+5pQNhjn/TGepYJWMQ7OZVaZvb8x5\ntWvtiXQT5fH2qy8+ZUApQ8ZhCkOv5XC1N+hAS0agR7GedHfY4S/Lzr8K+NKzWzQ7j4E4Djivdran\n45G8qJVtSzOFmWVZcC4wbTb4+UTLvYLDxoZV5v2Jzx+fKAt8/rTn08cn7j+pF07az7gkTKNmmipS\n0vsX41ma7wSWRShFmE+JaRwJzgKF0QmlVz2a4Cnb4hmsJtaPUHWtls9NlNWk0vFiHjINEXvtpt79\nKRVi8NSkDCYVf0WmQRlYVRqP7z7oYInJM+eZMHj8EHjaz6RUybnRXMWjY+wu4ghz41RmIg6yMOBp\ncdSpNy1zPJ1UWeoUemit6iSq5hDfOC4HfnjzZ4ZxYrOJ7DYenxr5sEDu3uC2pqwxH8YBt1GaYWsN\nQtBGbgBfCkEqXoRSlYXjnWLL1YRxPkS85NWFUtCh7a0kzYi7mKfPNfCW9LRuUKeHaKtuFTYRYIha\nlebakBDUgmOKlEOvqlXnUsV+Vym06lBGetT5m72YNeis9aSqoYI8cZrciTvvlX68yFkI5qzak+5d\n/2xfPr9+kUC+5CPDGGmywbtpXdit9SETYo27Sm0avFPNijM3xxD0Z1I+In7g4fiZ/elJ1ZyCLQYt\nRZpAqo3TnHjaH3k6PLK7uiUOVzhXEcnUpr/7uBxY8swwRI7zgYfHBz4/PFBaJbWCCxfs5yOf90/8\n9O499fCBp3eJn374wHJa6Go8qSBFJ/74ENaY1R+BUriCYnY0pu2O66tL7q4vGKOnLJo55ZQMo8SC\nsS4GHx3DoKIfgFqKDiOeZ8vGNfCuM0gd6n+t6Bu1VQueGghcsODsAZplltGqFpS6ZQpbHxzBBQKB\nYTRqYtHDy63rsK0Zz5o/2BdWgy7phzVr2dnhojXqN2AAF2zjR4+UyrI/sjwdOO335HSEg05Mn+LI\ntN3QaqadetPRDtIiHO5PpPyJXD/z6cMTT48nWgFas6lJajzW7HDrB2AMDoKqZcMQcIdAWQrLXMjb\nonYCwRqVuFXYcx5icn4v63llhxXVfDbErf4xrJCXNjYRRylCo+CPi3qyJO0fDUNguxuULhsdUjNP\n9584vX/PLIX9lbB9ccVmu+Fz+UzJjZqFnBPDFJjGyAbH08OJ4+OJaVSvm00cGC4j1Xt8TuzzyaoB\nPfxLqZRWcFTc5Cg1sTwdFd6Kkd12oh0WHt4fKE8LLetndg4Gm9YUp4jbeepxIS2FGDdMcWJonraf\nwReqU28mJ2qMNgXH4qzh69VPJYg+O/VWL6QlEYiMcWTYTJDV26k5VTU3yTR9Q1pxWLWMB4IwXUTS\nUihLUZdV10e2GV7eHINEsAEvToRWPeICcRhxkpSFZIFXmWKqkREBSRUXgo6XsxilzppYBSy2h82a\nxBs5QMd0/GxM/UUC+cXmhjFOemJSKbUonawVvPlqq8eBWHMvsMwHjsuRtCRe3L5mt90xjhe8efee\nd+9+5P7+AznN9NOrT8pptVJSQtoGH4Q57TnOD8RxMtZKJdeF/fGBN2//xOeHD9zdvsD7kRgHvv32\nb2lSOJ5O/PTxOx72B+4/7ZkfhcPHhaf3J8VfjW5YUlGcrDVi7D7qIN7KaHF4CXiBOEQ2u8jViyte\nfn3Ly5e3eOcpWdWLz+l+OLdOcPfBqx2tQM6FlLUcrzaYYs3+/BpGz4Ida/4ZzLlOnQ9BrT2D/e71\nJ+0A8Q79IQv6+k/VJht1HxGFcL6kGH4Z2KVXBx1a6i/Q4S5nFhVWhTgR6unI8f0H0vsn8ruP1E+P\nyGGhLplcCtJmNmNkEzfWrNODC3vPysdtPH56IH8+UBg5nQo5aVbaitoHB48NrtY12Jk73j6/anA8\nUk2Ik22CuwRiiBTBbBJYvXm0apQ1WemMHZ2hasHcOa2UYjQevk3A6qwHsTvlB06nohldrkTt6DI2\nNUGLIWjDPy+0pOPNJDnKDI7GaW60psEjnRaaCGNQnn0+zJRT4nK3JS0Lp9OJJSU2l5eE0bObRgpJ\nJ8svRRuPPuDbAK2Z57vjdJyZc6ZkRz0W9nOhtGIGVmI9HlbxEUEZHa1gPSN9ZiF4GAebsYlqBqzJ\n3yu4nIvSKXE2aatXO2cxW/QBFyM1ZU6nI80pJNUb/OdU4/z/NQtSNFsefGQk4LOQjwuSKq05Tvmc\nnXuj1Yrip7iqRIA4RNsbKBnA8gqFEbVSO9szmMDO9Z5VT/vOOPm6gX7m+kUC+Xa6VOK/lQ5apmkg\nCja/zhvOqrLxkdo0C+gskFbF+MOJZT6xpIUm9RyhQAOLQQqdYaGj2e4RL2zGDSLCkmf2hweOhweW\n5Yh3r9htLglxQwMe9/ecHu/59PCB4zGznDJt8ZweM4eHmZIKOalqs48dc6IZHK43D3swVcvQ4BxX\ntxvuvrnk5sU1X33zgrvbS2jqOV5KNYvYL/Ez3yl9okrAnOsaxL8I/KCL3nVYp8PyTSlazlgnaEOt\n48ne9UaUPwdb+94+XKHTILuh0zlI2ctaUIczgvKX2PgXBmUdAupv2wRLzd5/zon94wP7/YF5Pllp\nrRhts880BMd2DAwBZZLY8++VR2tOZ31Konpt0Kn61AYQWLaYi5zZMNbw1LVjB413VnU1tVsVVcn2\njIpnwaQn3bIGMTkH9dZZCpZ0BFTIhI6jE9yKqeJ0OEIcJl0bzZpi4sgNTrniU2G09V4qFDwlBppz\nlAJyqmoVPQhC0bUpUHNlOS6afTYYY6QsiZYq9ZRpMRHcwBg8QdSXuy6NOpia1Al3NxdMm0CrhZJm\nUtIst4o6Nk5DwBsLRFCWSNx44hZyUJjRuwEvOsU+OIcYq8M149WLIs6l92HotExtKNamfSjQ7Dz4\nqBWWCfCooj78q797B3Q1qxGzBZAKy7FoNuyjcsnFQWmY+b7i8FWIPhDNQA2nsakbZ4k0peraulf5\nPWvlWTthQTqz3a2V6RcbmA5trhjpz8bUX0jZueMsiHmudDOzGl+VVWBv3PtB8SIcm82ENKXmKZfW\nhuqaYg+vC6NP84ghMsRoUmoh5YWnw0fm9MT11R1NYFkWUlqIYeLu+jVfv/wtFxd3CJHP+3tyec/+\neOA4z+QkSEalxadCOiUNpqlYdqiVxAprIKw+49GGswpMU+Tumxt+8/dfc319weubay63W46fHjXD\nXgN53/ysyYNDg0WtTTFxC+T9cq4Hzi8BDssV9f1wDj6+B2qnJWZnbPTOOnQpseJ6GvfOUcoBfRbh\n8+t50HY9Y3dnjvrKWvkLeJwe6JTXxiKV5XTgcTlyapkcoA0B1wa8G/EhsZkmLjYjY1A7YzEwS+9T\nXf0qXNNS1fugbo5ZBUbO1mIpkAu6cfr7dRq0+mkYo9BG5f83p5LxvtnOhkfnPdkPFdCNizXJxHx2\nXK8gvdo9VbO5xSs9V2dXDsRhBBcRF7SZFh0ZR02NNleGpgfwUjx5DNSLETY675Zc2U5bsnMUJ0xt\n0PVVG8uc14lKUxzIPhJdYJSITw18Jo4Q/UAVVT8mCnGoXIyeb7++4upy4nDYc3p6pJWC855hjEyX\njh2emnQoC9GDE4bLgbBzZFH/ozGCb+Zp6D3Fo7bR1QgD6CCH2lRVHdyZvaX2CGoFoUZngSEOOJya\n8jmlxioVta4BVJ+NZdIYd7w0lkMiDANhM9qouYZk7bn18XYqcgumTlflp6CDKcQ0F6Ua1dJsL7x7\nlnmjiUovHH/+MohNAudG53+gQO40pq4lkvPOuNBeMa6cWOZHVQ6ip9ecjqQ862YUjxsd280Fr158\nRUuN/fsH0lPGu0yMWmrFqGKZYRhIqfDx4wOMgj9WhMzT/snUnZ7d5pJXL37N5e6GVzffEuLE/rjn\n/bsP/OGPf+D7n77j4bAQ3YZ80jJ9Oc7UrNm4VOWJtib2gJ1iuu2ZgAaVNofouflqy1e/u+Wbv3nJ\nJkR2ccCZl0jKlWR2sXqPzrBDx5WrjXHLOa+jzuAcPFczp56RclYJ9pLeoTaawXuG4FeZvqAUPR91\noAc2EcehXs5Kp2xnNk1tK1wiYLRHvgjsqzDoLzIKPcAbf/lFj6M5T/WRpTmWpXD/eOTh/onD50fK\nUhmi4+Likuubl1wEYWO9lfOklX6Q6IEVUT+UORcy2gSvWW1bveGupWojszadW6oUWdADUKsRH2CY\nAj6q/4igEJd4GzLQM5Nn5ZBCNM4wVuygaqvXhlYlznKz89BwHOtIwyUXQowMccJNXicM4al45jZy\nmlGYsjX8tGG6fcHr33xFPs08vfvIvP9MWRq1KryCaK8kxpHkTSXbnVPxBBe0U9JQl8RhxOGUUTNU\npsFzdzdytXPc7Dy7sOFD9DyeEstS+S+/+x2vb64ZPfz09hMlQLyI3N8/kKXBGJRWN2oPos6ZUy5G\n+2zmV6Rre23498DoVZDUn7Pzqs4MgJPGEAatyFwfjGH4mDFkvA2EqBSyqJJzJQeAKt2CjpBrSZvl\nYgc+Yl4+9L2m/kFUMcIDdIql2H6qUjXD94HgWdXVamDXKatuhT5tI8DaDLW14P5ir9j1y7BW0kHp\nPVael6rTc2Lc4COkZeGHt39myUcEIY4jx+OBvBxwLNzdfMMQN4zDhjhOXF3NXF/f8il8Qk8/TO7s\nrXPtyaWxPJ7ILrG5hO2lZ5wWRJL6mqOuhmXYUWsmxolxmLi6uGa3uWQIEy0nUqnM+8xpn0mnQslV\nH4SAw5sUHbqVsEjTQQBDxA+OaTtwebPlb//+V/zu73/Ft3/zFTs3MB0SfHpafVV6Nt5s4bhueelY\n1ZM553Xw8Gpj0DNl0GDyLMCq6MB44F3w4JpVNh271tdap7NHj1mfaL+hGCWyQ5btywOkp6FfZOeu\nH0Tn5mbPXFeYYf3+M37YxNGaYz5k7u8fuH/3wOHphIhjvJi4e/2CV1+/ZDtG8sM95fGeYRwYxlGd\n7+x1alUWAc1D89Ra9K/N2agvvbGtwxIddnE2Ps65jsPYfQmEFqhNtP8iSnX1RD3IWs/IZYW31s/b\n9BmohYG+tjgzW1oz+e4qst6SFSbyURko0UdKqjpwpKKWyU0nzKeadULNw8zVVwHaRGsTJQ2kOZNy\noRa1cYgIe5dIWacCpVpWUZstYLw4pjCSRS1aU04QhZaE+VT44Y+f2G837MYN6VFoR6UOXm83fPvN\nHbd3E7tXkX1W/Pyw95SjjperOVMPibosFJvrqQEYXG0EEbPI0DrLrV7wGOxkMJ9oX0WHbsRVRV0N\n5ijK0dLfgV/VqUUEke5po2u4uUIMAz46csmUpVBzXXF3j35/s7kEymoKlrTp7+kMny8YS00Q3827\neLZP9aVtJxnjxdvMUXfGxZXD+LMx9RcJ5IfTE+M4MsRAKQs5L/RNggukMvPD2+/5cP+WIpnb2xe0\nXCnpRE2PjHHDi+vXDHGk0BjiwLTZ4l1Yb06XbjfD1qTBkjOP8z23dWR3eUdwnmSeJMswWBMpczFt\niGFgM2751Te/5bQcmJfE4emPPOxPHB4Ty7GR5kZN3XbS4AfndQqRk5XiFIJaX7oIV7c7vvntK/7r\nf//P/OY/fcuLV7dcyoblzXs+vv2sAoxabBLRc5Uka1XVqmbjKRVj8uhCcWvAeBYU3TnItuZ0OHTP\nVeU5TGJNFq9ZuLcGqI8qeMhWeaRloRbFnCH03k3XVp0hIPnrYE6HiDi/T83Iz+pHHfSsw0OKNGpu\nzMvM4+eZ/ecDOQvDdsvlyyt+9V/+ht/87teU/YmPKZEePjNuJqbLHWleKMdZcVGbb+8M45UCEhQa\nWd+HHXjViUq6DfZx/pytO7D7otlc6bir8fa798z6eeif1YK7DXCota6DPpxZlLUVamTNQDsmT7WM\nPqgSMgwBoqdW5ZtLM/65DRhf8oykQi6O6eIFbhjIS6DmgeUUOM6QF/254CqUPVIzUwzsjydSymSj\nAremVsWbYatBrWaDISPp1Lj/NPP202euhi3fvn7N4b7hSmAXBzbjwPXtxNe/vcS/KLz9+JEf//xI\nPSXyQyNlT15OzMtMXhLVVKTN1m00Ln8MntwsCHttAktT2MWZCyUilKKy/jFE7RuJzh+VVtX0zu5r\nAFX/OmWN1+Y518xCcxVCIwQhnzKyZCjNhNjBrIGhSgWvgbyTKywSoFYOfUO01RYCw/HVDMv2ncE1\nZyFQV7z7FRtftS/u2Z56dv0igVyZXbrjfRjZ+FF9GJZEoXFaDuSaOcxHcl3YXlzRzFYyOthETwyQ\nS2af9tw/feT+4RNLmvWB9SZID2pNS8Zh1KzGAXmpHA+POOfZjRNf3X3FdnPJOGyZtpfrUOfduONX\nr35NTYVlFv7l4U/MT4+quCxKawpeVYhnua7CKc3YHD4GNhcj40Xkq9+84G//27f89ndf86tvv+X2\n5iXxWPngHki528OaqrXb2zk9IBA0wFX7PruH55Bh/QbLClZxoTsPXG614Xw429HiqE0Dp/qPAEMw\nNWzACRbA1S5Ajf97si9rQ9RSXsV2V1MIv0I1nU7HswC3YujeQa0dfDLuv0Ifh8OJY/Y0B5d3N0xF\nYZtf/eZXXF9csDwduH/7gYf7Rx3gux25m24Zh8D7735EcoHm0O2r5XA0fFI9ONAbJqhc3wm5CUUK\nzfVgD9hG7QyWLpbqe1cHULc1ED/PFGsVaoFS0BmVJvFuUm34tceZ2x5iPR6MN9w8VG82C56yqN9N\nDTrHsrbzCMJam062b4AXakkcHu5xcaTMmlXWVCgnHQQuUmk0nmrBSWP2nvLTPa5BqI3JNWJztOpY\nbEp9dwxrpuIZd5F2GsB7aqhMFwOX1ztubm65utlycbvjxdcveP8vn3l6e+Tjd0/MDzPHp8JpaStj\natxEatIZtySjogAAIABJREFUn9VGNYIG4IyjtmQWA+rHgkBuCWFRfjmeLKrc9s5pkJWm82xFAZjo\nAslU5cEmdmHQp+4HrczCMChMOhdIhdCazpUV2yft/2Puvb4kSa40v58Jdw+ZsmRXd6OhMbOjwJld\nLvnAw8Mn/vXk8pwluRzMYIAW1SWyUoVwYYoP95pHNoB97gmcalR1Z2VGuJtfu/bdT0QMQnMu2Wig\nhXbZVoNbinbnRsCybMQipFDItuCyPCtFCR31vFDfK0WvMWKoJe/WkvO/Ix65d2JCBSJmEVl7YN8/\nEHNkGHcYA+fn5xhrON9c8Hi3IxPYbrZYV5jSkcP4yOPxkcfDI/14FOFA3eHKCX8Vb5AqlzWQLTla\npiGxWa+4PHvG9cVnLJdbvGvx7ULf2zQX5TglxuNEvx8YjoNMw5GJei5Z/aGFgpXnLMyMby3d2rM8\nb3n9+RWfffWMz7644vrZlouzLWerDcNxRwyZaaxQSVaTqnzyM9ZuIuZMiJmoSs+nxzYxrdLuHNQx\nzVDqv6+E9LkzPg1RZ66sdzivpvymht7q+6qCp1KHgPXIJ0Mm6dJrh2Hmn2WebjYVZoH5PfAEDqqF\nsBZznGG5XdF1G7Jt6PuRGCOvPntNCBPff/uOu+8/Md7d4cOB+xu4vLxge77lfnFDLFKkajCjGBoV\nTBZNAuXk75OyISJOfFNMVHZgrkdcbQ5SqnbHMsgWJ8MERnByY4xutLJHhJCVv51Ik3TP+Ylx3CxQ\nQbBrq9dUYPYCGqmndBRhzjirJIky21TM+gHrwHqca1ksRCY/ppFh6JmmnpykAakOkSXKzzE5Ew9H\nnDE0xpCVOJCTIcdArsIoJx0pSiFcn695cX7BL3/2GQ9393Tdmpcv3rBcZc4vV2zOJPR6uV5SnAPv\nMV5OsrZtMWRcMIQkHv7OGU3aEXpnKBm8JB6lEBQcMTjfEMtEJgm0UQSLjgqlWeGhzE2E1S5XzPc8\nRqEtq7hGNlCswbctYAhj0ACWupY95ulC1cKfUvkBlCpCRnl2rfPMViQYmZE5SwkKnxnkHpQTqeFk\nqV3nOzLqlfXy7whasbaZTd5jDJQciXHgcLxnyhP9OOBbz/PtSxrfQnLs8wGD4+JiC7ZwGHdMpWF/\nPNKPvTiNoUdhzAwZVF8LdIds2gWLbs2yO8f7ls3qGddXbzjbPmex2GKtp5AYhiNTGPGN59gfub/f\ncfP+lsf7HUM/qmugBedEUKDmSeJFJLxkawvdumV1sWB7veQnv3zFmy+uuXy2ZbtdqtGXJwwjYz8w\naiEXsUUmPsHcMmL8E1IixjxL99HPSBaHtx/I3Gd+alYMtnCilstiqdDBjP2qy2Q1cBIsPmqgQ11c\nZsbR/7S7xkANftb/MC96GeaZE0SU8+k+1bdE1Q/IhtGtFpxfP2Nx/YKQCo8Pe/ph5PrFM95+8z3v\nvv3A7v099A+05cCHuGPRNlxcXtGuO0iBEqobo0BAzhRIkZwEM58/hDazMWXGKUmWpXZsYuhUr60M\ntNJs1VvmYm6NRKblKP8tZYSeGsTwqwQ1gtMB+ImKmH5wTWVvqwPqKH4es3mUFCNtIwWj1xPGfC+s\nxVhP1y6YUiTGiUO/Y5wOlDLJxTYyL8laQAqSSWsMBC1q1nu5RwFsHSBaKydQwBTL5fNLfvbTz/mH\nv/0ZH99/pFue8frNT+n371lvIBNplw3dpsOvW8wi4gK0TcYvl1hVIhfEbRJr6BYdUzAMk5hm+cWC\n1lvSsccEYbj4phPv8CKS+rrBllLmoaLRDbeuv0rftd5DiHLNjaeeG2t8XIpZIikzyDHEYn2DQymI\naT50ceLUVLV5OcFjVuEhIzCkOIVaYbhVwGQ+iqH3u8xzEuWEzY3QnzLD6utHsrHtAYe1jtZ3hGhI\neSRFGMeJECPr5YZUMrv7Pd9/8444Hbm8XLJevyYWw/4YoT+QLfimYbFY0LUtkx2ZgggW5mGnYpTW\nNXz+2RvefPEZL16+BCzdYslqtca3jdyUkkkpcP9wx/74yPnFBdY3tO2Kqc+SI5jSfAMrPbBpTtmA\n5CgFo/NsL1dcvthy/fqMV29ecPXsnK7zrBZnWFr648Bht6M/HATCCJEpZkKSgGm5f3L0n5JQHGuR\nqB1ZRWDmDrv6Y1fWytMu3VrgJLn3XvIOG1VOylDN6M/IjMOkdMgn/uJop6AzojJ3tfrnnH/YeXOC\n9sRHgnmDqVVUlqueoJSeZY3l7OKcizcv2bx6zWF3xHvD4SA+8943GCQwIWdhN4194PF+j/MNzaYj\nThMx5oowYih4V7AhC5xTvF4XpUUiePg46jDOi3IQezr9yPuvn1kLQ2NmuMton5ezYPw5y3GblChZ\nsGmJJjtJ9Cul7U+5+pUqWs24pGUrep11IFwglrr5qzAmjZQ4MvzuSCpZGoVhLxGIpYAe1WtiUd1s\ns96xjBRBVyR6kZwwoZBMEU9+LCkVxkPgl7/9Gb/+xU9YLhZ8/mbN9vKKZ5+95MPbPQ+PH3n3/37g\n//vd13z39oHDLpLGhEkJX6C1GnRsHLFYshHriClOBGXgpCJhNM2yo2AJe1F6ds2CRCKEnikE6cC9\nmOfVYYzYG8j0upQyd98xJZkVGalDMq+QE1R/6KU5SuCNAxLWZxabhhwLccjC9dfGJlEgZZJuhLUp\nMVRfKDklpeqJpKHOFBEdVchODo3VWK8CgbXI183h31FHPoWDWkd6ShaOZNOu2Gyucb6lTQHjOm7u\nPnE33RPCyNXlBZeXGyngppWC5MSsKVFoulawLSMRZ0L9qUdfCW6wTrCxzXLDs6sXYBvkUmWO/QFr\nR7xrZ1tLay3Hvufm5o7vv//E7ccH+n0/T7D1TkioupECFqNM/5vOs9wuWGwazq5WfPb5S168fMHF\nxRZLYdmdsezWYvczBcZxYJokDehpnJvCt5Qi3i0pRomhysxH86eb9Hz6AKn/xUgVrQu7Ns4KhzTe\n0XiHr0Uc5g4+xsSksEot5PI9ZHHZJ45scqRXxac5QRHUfxooOtR52lXMdq5P1scsonCW7XbD65fP\nef7lGz68v4EYlWuMnChMwXUeU5YYdcvrx0g3Bly7wPiRVCacqe8NGmdprDA2pko9K1aP3vJ+pqkw\nTYnUaNDxPIiEyiKQzy4DOIuciIReKF4aRdkpp9NwYb5hRWXbT6yMtcacnBJn06U8H7+llmu3X9Bf\nUlxDTsQisvaSDSUNHO96KQApyXBU8BqBDOdN9IlVAlCR/0hhzJIN2rROOvYqUiqWHDLjfmLYHxj7\nnrxZ8fzlazYXG6xLPOx3fPf2ho8fH/j4YeDhdmJ3e2R6HMQQrkApjtZYbLE03lekGBqLs57WGAiZ\nMiWSCRgMvvXzILExAiNltY6tm2GO0kx479VQ7dS8lCKeLB4P2pFX9W/RzThrwxKLzE2sBb80hAHK\nVOGaiq/nJxh3bRpOcYkUedaKUVplyVToUWAi7cbnQWaFUBS+4Ymb5r8nr5UQByL6IJQG65d433F+\ntmC5WAvW7VvuH3ZQ4Oxyw+dffslqseDd+/dier9oWW08YxykeKq8HCOUMnKVQ0vn56wMq+IUKMXQ\ntku8XxJyZBiPHI47rHV07ZLV4ozlcknME3ePO7755nv+7fffcHvzQH8QWAWQa1pqnRR+dsngW0+3\n6thcrFltWy6vt3zxxWuePX/BarEgTRPedjSuw7fikjgNoxTyGBX/ziJV16N9zkWm+jGRQ9YOQ4rj\nDwIaSpkLjcxK9Ig2f0k5YeJWpMPVFErqi+J7KZGSQCohJvUBUQGR1S4jq6+yQjOlMDsG5nIq5E9f\nFQM/QUC1Oj6BOJDv49uWi/MzXj6/5uWL5wyPB46rFaUYHT4lnC90246ysBA64n7HWBxDLKwXC3AD\n2Q6A+mgjFGHvDM4mTMpgHNUHmiLFagpayBcO05ycHCvjzTzZGKSpLfOJLkfISY76MvAWCGw+yehD\nX5TSWB/o+ifBeutxuu7mFUeVzTBmKbSxSLFJORGJhCIcHQCyIYwyy5E9JJ82IsMcYQfzVjKXDqO/\nD6VgizAz0M7W6o3MITPGiQ/fveN6u+JsteLVZw3FwO3dDd98944//OE9tx8PhGiZjpn+sWfaDQxT\nENvjsUDX0XlRUVpdD8ZbaTCcw+RIHiR83HZifYsr5H6SRCDriN7rSaWu/QzWzgHrlRUk96gQoqQL\nSUyiV2OuJ8uRyiNRTQSWBCRksFx1oXKL8rzpzhYYnODGCufMMv6shbl+7byp6rpAGlDM07txWm9/\n6fXj+JEj0VEpBrGNjQnnEtvNOZv1OTFGHvZHcswsVwtev7nm5fPPScHw/sOem083LJZHLq+27Cfx\np57GkQLzUKEKLgriQVyVigXBQGMUwZCzBu/FV6VCB06xxeNw4NOnT3z77Xe8/e4dQz/pgyz915wA\nZAtpkkFr27Wszzo2lyvOrzc8e7Hh889f8cUXn3F9fUUKifvHHfv9DlMsLsNw2DMOgxRxxb6zFvGg\nvisxKsMhZ/EG0b2kGEO195XOV3YXW8UllRmiXXTOipPrcNZoF1CKIcWMdQZfivrf6KaSJBgjRSm+\n3lmM8Vgrcn+5tqdu1jkHqYZMQC3oZV6kJ1OgWvyl8CsEZCxtu2B7dsGbV6+5ODsnToH720dMMbx8\n8ZxjzDReePnL7YqSDWmKDA9Likn0xeFLQ25XsEyEaSTnhC9ZBCFo9qQpiApGHyjFrEMUKp94njek\neBoYW1sfc+ma03zklY4rxEiaDCnk2TNHBpqVhXR6EmpnVj3cTycqTbLXylLDsqW46OAbLea5UEoi\nkkhGxF8ztIajenUUPX0+rUGmlNMaUNgLKt3NkktiShH6gcZZHOI/UjCUmJniyOF+4NPNJxabxMQO\n7zseH3t+97u3vHv7if39iPVOHCptIeTEmCIhJ8iDrOuuxblW1LYpkUKia1oa12C8ZAWUAo1vKc4S\nQ6CXHRNMoe0a0kyckGFyRjI3ayMjCFFWCEMKaa0X8xcYQynqTU6FDx0xGO4/DZQUMVk2DydttMyq\nTBWPOT1Fa5i1eRpagUImJ0hrLuIzTp5nfB1OStVS3A9OrX/6+pHcDyNTCOQUaJsljW9ovKjGrHVY\nJ4vde4t3hpwk7i1FSybRtBbnC8P0SH94pD8cCf2kvE5VraE4YxZOedM2LNYtL1694vzqUvyFTWHo\nj+z29wzjgcViTdMstFAbQszc3j5we3PP7n4vKTFPrmbV4ZUkB6nlZsHVs0tefP6MxaYhpp6Xr57x\n4sULzs4uaJslxRbOzwrL5RZy4fj4yHg4MI3ChJFCLhzjXItoyISQpfiUKsTRSm4sNs9jsfnIlykn\nA6m6BKQKnABrJwsm5UxMYubvnaNt3Fw0areYMgL35GqBW7FAi2/sPOwVpqQRKfzTQ8LTDrTokXQu\n8k+oooCxBd9Y2mXLvj/y/Ydbpvue97d7zrZrri7PSbcPtK1ntd1gvGSqTsOEyxtyCgRT2EfAt7Dd\n4qLY2zINwqVXNauzim0W8fY2CA0x5cw4BcbJkZKEg8vGGhV60z1Tr3yFwXIsxDGRk1G6p8xUqm3Y\nrH4tkBWnNlb44aYYZdKcTi7Cfjj9f6H8oJDX32NAYu68Fu76vkSiP6/beiqjnujmr+R0x5WqqX8n\nx8J0DOC9hLXo3zEOvJ5YjJOmZr/fMfT3fPyw49OHR467UXFjWRyucSQjSUtON/+QJvIQaJ3AQylH\nNWYTSCnVoOMEeQhifKXMlHrASBaR1WdEYVky4i1aP7MRloypSauJKU80NHjrcVZhnVKDHjLZVEdP\nPZGN8mSJU6IMk+WE556sZXtqquRCURuU6sGjBMP58+WSdROtfj16zMdKk6ERd/Wz/qXXj1LI98eB\nEHoMhe3SiwoKVUqprNV5Q9s12AM8PtwRxknM3F1ktW2xrrAfHglBdknqUVQZFaXUrlzZKr5hs91y\n/fI5q+2GVCIxT4xTz7E/EOLEYrmlaTsSmWEK7PY9Nx8eeLzbM/ajYMQVf0autc3i1rfoWl68uuY3\nf/MrXv3kJdjEx4/f8+rVK549e8F6dY63MqBbd2f4dsG4e2TY75n6njgFguLSKWqqUE1AShIeXDu5\nrPj4fCzPJ7xTtnz92uqhDTyBW58c4KizM6EeK+3QOie+zk8XsQ4Thbcs11VEC2nuJquf+VzMFVeu\nb6u+55lVQy3j5gcYJgbaZcP6Ys2u77n/+ImjXXJ3GDm7PGezXfPp9k5ocotGvEdyoJiMadxs+zml\nROMtTbPAl47iChkNATASitE45XmTZmRTzluFfoz0YyDEVplQ0qlna7DKRhCPa/keOUMKMsso2aoM\nW4p5MQgTyJyuvXTiDucaXONm3cF8awuz6jUXocfVe6UjULmGRrrnYhwWkRWXkkUWXrt8ynxSL3Mx\nl3uStaDod5t9SOrNKBniULAt0IBtpKX3jaVbN7iuzp8a4ph5vNvz7ruP7O56wqAugUUk/9bLQNMY\naKyoK0MKjCGR3All80bi1uqcxirdNR5HUkkyYJythi1Y8TKXWMcExtbcY05sf4NDgi8kaD1gNRqw\n8Y3EoqYJWzQYoj7rmlAuBgAV3rAyKDUGpyfRWfaBUnN1jpMV8snICVoGrpXdciricIK05PdVumfI\np1bgL9bUH6WQf7q/ZxyONNby4uJzjsc903TH5cUzmnaJsY62a1ksO6wzPO7u2e92LJYrzi/OGKIh\npMIQEmfnZ5xvDNPjxPf3k4YUoxJdoc9N00RhRdt1pBR53N9yGB65OLvGtw0XF88YxoG2XZCLDFXu\nHh/47u0Nb7/9xO7xKPQwvZT2KXsBoRhdvzjjN3/zc/63//1/IZvEsd+zPet48fwZz65fcnF+DdnR\nNgtWyxVgKcMElQecarhxEv74jIkXxdQKJanisJSZx3166E+7ueDYEjwsvl2ZTBUp1KMbYiHayvGy\naaBberCW45CEfheTCjOYwbmcpRtLBWIsOFfUX6aWZXlZpcCVunPo96iKxdl7pWLp+Qmu6Axnl2d8\n8fMvecwNOxpSkqFj11raxhLGgbE/MB0OpGyYhoFxGCghYik0zrBoPZ23tNbgTSYkyzB5RjNhjKUp\nhU6P+qkoE1FxaG884xQ49hP90NB1jao/LTEVTbnPs6I1ZTPPEuZhKPIsp1zULK1uYmYOFnHOize3\nd4gGVVknmNn5z3BiR4lSWTaa+tDL7xwFp78XPPfkhS4vZ50W8ie8Zb3muZy4/kVZUkLZdBjjBUpz\nHt8Z2pWoH/3C0W07UokcD0d2dweWiwXjbmT3SbyPJPw702ykyzQlKTwpZIeUkpgKYjSr09N1Ldvz\nM8Z+5DAcGWOYTegKELLkZRoDrfN0vqVt1wI5moxtJZy5jo7MPMioHXH1QamKWUtbGiiZlIQqmOdj\nUcWodQnr9RW5PWCcWjxDsTIPmYVj1s5GetX4+WT4JRTGpxMJg1xzip0PTlJmTuyu0///8PXjSPQP\ne1FjtS3TGDgej6QU8V4MrkJK3D/c8+7dO95+/z0P9/d46zlLhYuLDUY9hfsQWHQLVosFa7Pio/sI\nVMxVCldK4ndeJdPH4Ugwo4QpOMt2dcliuaJpW6EIlcRhv+ebb7/jX/75D7x7e8Pu8TgbUxkdPNXB\nj7OFRef56S8/5+e/+ZLNeUdMI+1iyWb7SsUZii1bh/cNzjXKO86EMcivKQgenvKM4ddfORZR/lVJ\nJYUfqEayKMvKk4Vr1Xkx5yJm+lmsV9WUAotI9o3mJtqupY+Z2I9SxItIiGX4xCxkqAv8yUx+PjbW\nSf+fvuahc6m4L1R6YvWYEi9qha6y4fDY8/brd4TFirw+p2k2pOnA1B8IY48zhdbC0hVs4xiLpU3C\n4HAUGgetSzQkfM7Ykkk54MhqTSuBuV0sDEmAolIkA9QiM4eYAsOYOBxHpQVatVM+DbarpQHUbtmo\nLzZz4S2UeTidsqgp581Yu8qcq3VvmTu3Ys0PbvfTR1nGHPURFwZ21ri5glG4qDJpZGGYJ0UpG4W2\najNgkP9uoHLlZXYg8xBDy2LRsr1wbK7gcX/AesfZ2QZnPYfdwLf9B5xxPNzvebjZc3FxDUtHfxyZ\njpExjPTTCFk2SmdbUhqlGFqLcxqq7iRQG8THxlpVVCaZNMcsbB1KwZaEc4EmBdWkSHCzUSvjqeof\nnsIbPOmagZjTqTnT/+aezBXml6nTA00oyxkjV1hmIKomxRSyMdhi57lbJgoMaovAMuaJqM48eZbr\nD6q/SoVZdJP9y3X8xynkQ39ks17TNi37w0G8G7yT4aOz9NPAx08fufn0id3uCHiMacnZMhwndmPP\nIQTGBK1xtCvPZXeOd2IV6Zybu9rKSxY6XeRwPNIZh28t++ODYvKepllSgHHoub2955s/fMu//csf\nuLu5YzgOclwuRShjohTBUlitOl68vuBXv/mKz3/yklRGnCt0iwVtd8bh0GMM5BSx3UoFR0b46jFJ\nhuI4zS6GQYt4COIwmGMSo6uMVopMsYjplX2CsJR5bDYLfORYl2eDJioVThWeKUExlmQsY4bjYaTv\nI+OUcY2l9ZbWG1otfI21mBosq8W9UjxL+SHr5IcPTpk3VWB+IOpD9bQ7lCmvYXe3px+/ZvHiGV02\nuBVMh0eG44pp7Fl2DZfbBf35AoOht4FDMoSC+r1nGiT70aREjpEyBYryh6XDs7TO0liEt1+SiNWs\nE3FQMUwxcxwibZfn4Imo96ScECwxc1IxVSnIvStlniUUFWzFHCl1HGlEjZhVWBTrkLswmz0V3Uhr\nezZfVyoOXNRJpsz+NEX9SjLMEYimWKxt8I3HeE8giDJYFDhqbVE7ynrUr32kwRpP4zxd61gt4HiU\nArtZdnQ4whg43B7oD4HjfmAcAs+uGrp2gc2OtD9SEuQgYcdYof5lpbA6Z/HezYWt+usDWOvUf6ie\ngo383SwDXhsNbhxEGVpETt81LRSEeaTK2FOhtvPpTwzPEq6UuehSUJ9wNejS9Uw9KclCpRTZOKWQ\nRynkgocJVGwk+Bk0j9hk/cxe7r9VQzp0qlXKDyu1AcxMEXgCyf3560cp5DEM5NQSw8jdIbBarFlu\ntnIMMT19v+PT7Sec73j9+ies1yuMsYz9kffvP/L24/f0cWJ1sWV3c8twdmDz+RJDEXmvM9gkN1pS\n3aVw9v1AN3YstlsWiwX74yP9MLDb77m6+gxrPYfdnu+//sB3f3jLx7fvmPpeHrSCyJRjhCQMkrZ1\nfPb6Gf/5f/1Hfv5XX7K9WHE4HjjfnLFcrGkWC4xb4PQY553AA8VADZEN06iFPOnQMYsgSOPTRJiQ\nRS1ZF3EpcuLTzrj+qso1p7aauViV6ddiW6hgaymFEguRxG6cuN2PPD5OTEEq9PKsYaX0rLZ1NM7I\noDXqAK92pUVsUb1/Qv16AuGcAiTyfC+MQixZxR5Cr1QMsMgmkdJASInu6oJpf6R/HDg+7JieXUDJ\nnJ+vMJ8/Z90mHm4/cRsiwU6kMsp8AaFHGozwy4eJ0I+kMVOSU0xYHvoGEf2EIowc7wSvrj40IRpi\nNBSnRlmIhNxag288xQlH2DiP8S0YR5gC/e5ADEENlhQ6K5JDCyoCyQZjpACkUmcQCANFsyZtxV2R\nolJPNlEhlkQh68/IRX6PAoGppFMh9gu2Z2cst0sGesZxEnFO8Rz6I8PQz8/oaVAt5cM6mIbA46eR\nMESO48T6zOBzYLnyFO+JU2T/4chxP2HwvHv/ie2mY7Nuefl6TT867u4Mn+KecRRWUEyZxlta7zCI\nT4olMw29RCXqPmap20rVO4gFrTUSz3ccxdCrFAmsbp0MqEfN7yw6LXSuVQhDsLxcRBwkxbYeWAU+\nqT+7yuIrXbbO4vS8qoytuumdarEpSjs0BfQkJjx1hVisw/lWVNPKR5/PBJaTP1apm3ie2Sx/+vpR\nCvnrl69YLdeslxu8XeJsRy6Fb99+x2a7xjrH1cU1IWZ2hz3D8EjTOob+yLv3N4SUaLuORbekibBa\nrFlvtkgART3KqxBIi8Y4BQ7Hnm1czxzl41HyOdtFSz8e+PTxgW/+8D2/+39+zx//9Rv2Dzt5ELUT\nzinNLJi2hS++eM4vfv0Fb948F5O6ENmut1ycP2Ox2mCco2vlPbS+wfkWUEpUDIz9gX73wDSNSvWT\n7kE6XmHdmCJtd866mI3RKDtA6Y8SnCwbmPMWq+GxdSMTSMZg8FiFeZKRwJOHQyDsBnb7gRAMpYi7\n31QiMbSYTYv3Fucd3hva1qvjXyJOWZVrwugw0l7MjBcpOsqMKSe1IKoXqpFu9fRkQGEKXbRkinMs\nz87ZLM4YjxP3H2/5r//Hf2HZWRY+s+0C3WVDw4rGJh4aS9+PhCDQ19QH4nES/UBKkBTn1MGqt4bW\nQmMKYxI5fXFAVpsHKyW0qlCjeq87Y8RUzMnAM5VMmCZKiMJmqClLXqiYtdUzSe5FKRKAPCtBjQp8\nqCKfOAuMHBansJlcviK0w1rE63UGTcCp4AtgBE0vxpKKKIPbWOgWKxbNEjbSobY7z35nGIZBbH4r\nvEKiEMklCmtsMpi2oc2Z88UZX756w/l5x3AY+fbwCYt4swhUIlS8oQ8stx1nXYdzZxweBqZhgJyl\ngJtCigGMwdlGZjvZ0FgLjaOkrJFuMgfo2kbK6RBEnFaAbHG2lW64VB+dcvLGRyETHbCWnCU0QgfV\n4n+ffzAIltCaVoy7UtCrDdqaM+t4TR1KnsIjpMBXbP7EKZN2X6DQ+n1mn7n6vbXrNzOAqfCLdXNj\n9KevH6WQX2wvWC5XbNbnrFcXxFh4eHzkfvdIKonNZsvlxRW3949Mwx0pBYapcDzs2T3u2Jyv2J6f\nc3Zxzca0PNte0nYLMCIbjmoVmotiwUUSyMcpzENAAGu9ynMzj7tbfv/7r/lv//X3/PF33/Jw+8DQ\nD8I40K3bZMGNO+84O1vyi19/xa/+6mecnW+YkkAkq/VWfq3OZICWZfjhnHoWY6RjDBNTf6A/PEgi\nfFTvlkbnAAAgAElEQVQ/Ey3kQnqVYl7JhZUPOw9xtLs1ylJyXn4ZK7O42umShTUg3TokI1LuMRWm\nfeA4DDzsDxg83nraRoZQOSiEpN7uzjU0Xj3XFVOt3tViLapv7gmsIh3OaVxT01FAURQE1y5qTFUd\nBefxjmtol2sW23Msjg9v3/LuD3uuLztePlvy7GrJsmkxZx3eFtrW8bDz7Pcj4TAxDZHxIF26M1Vv\nV0n44KyhsYbGyMOUQxQ66UwpQ9+rqDVTzsKTt5X7rZBShjiOpCIwi287QJLes9O1k2UTkGlAjSpL\ndQ73hI2iLIVS9YKVPVF/lHbipii74ocslpNukHoVKQjFchxHcI4FC7qFp2m8ZrSu8QYeS6EfIagB\n3amQB3JqIDX43GANrP2K89U5z89X7M2B9/YBZzzOZazzCnEWpsNEd3Ss1i2LrsE7R+OcFEBriWki\npSRJX6q+pij04OXZK9q4YKwYuiGqzlxEyDNrOoxVwZ4yoRRGnDvd+evKXBjtTIvV4qytsLGGtuuI\nMQgJQjfzPy+lc3kXeqNuLqYGI+gAvX7lDJBofSDnJ4XbzndOtcbUDt01ThqDv/D6UQr5bn+kaZc0\nbUvXtRiTaNuGs+050zSy2+25vHqOt7L7np1fcPd4wy5NNM5xttny4uoFz19+zovzS1rjOe4PajZV\nCEmP7EUNfpQXbophu9zQWkcaRpZdyziO3N09cne74/f/8ge+/v03PNztGYeRGKKEvhrZIV2ROrVe\ntbz58iX/4e//mp/98iv2/RGmxJQjj8c7npXXEuuWlcaEFDRbRQcUSoqkMBLGnhirDD6evEaqYdMT\n1gHaLYgRj9D9iuJosobNKXS4kkUUqqjRVI0zmJyZQmKYInEXmEKCbAkpgC14BBvOkySSl5x0IGYp\nZJZiYIeLVOiWysio2N/sJaLH1aJH1piq2RdUXzf5VChMI8frgjAsrHFMYaK//cT7dx94/8e3TIdH\nppdb6FeUYcXZ+ZpuueTqfE3XLWjbHsOe230gx6KFuQb76k+bL6rw7b2z+FiYMdA6a0IGlCmIZUAt\nBvU4LbNb/YCyc4NRc6ts8EqNwyYJw67NWK5H9pqNOk9I52YawxNNRMXEzUwWnLWfPzzP66cqp6P5\n/G8kn3Z8DDzuHlksWparlqZr2CzXXJ5dYiPk/CBKRyN4LyRKiZTiBS4oQvXLU+T2wy1nywVpdITR\n4FxDtzS41hFSYBgicZoY3x5YbxYsuiVkw3q5xvmGMQwcx0QxjsVyhXceiiNHlCKYT8piRD8y+/wj\n17OYDEa83YtCWLW7zvO9lmZI+OfiFJpyxhmP916LdTUmi0otFS1Lzk4LbJlvjJhtuScbfsGoirog\nehYJZTbkVCG0kyixDr5NUk2vrnVj1AxNG6W6OeAcruvwi+4v1tQfpZD/6x9+z3E4yqWNEWsbnC1s\nt2cMoxzt9ocdl5dblkvHFA+MQ4O7vOaL6y/57PVLrq6vWa7O6ZqOMA4cdzJFt95JRBiSsGGBccoU\nm7BN4HAccJ3Ft7BcrRj7npt3N3z3zS3ff/OB+9sHhqOklaQoMTi1u3EFrq42fPnVa/7uH/+K7dma\nKUy0XYPrNsQSCfk0lJEpvGT+pTp0LZmYImEamMaeOE1MUyCmdJpw5xPL40ROLaf/n3FnWTzOnoZF\nAllk4qRDuShwgnOC6GVjGUPgMAQOfVDLXQmebRW3k8IBY0iMQZLqj8PEoR95drnCbDs2rce20rUJ\nciA4ZDZZaWKnIlMP6RkZsErA8UkQZHWGMXus6IZRYuH2+3d8uttxnBL3Hz8w9SNxKHx6f2DaD9x9\neOTsrGO1WdAuGlIx7I8Tu/uB4+OROAVVL6qqMZ9Uc9KdSbH0FhqbCLmKvorMMFLmOEqsWNso115n\nENZIR5XiKTlJKTjEMOqR20laj9XeSt05izEYgWaRwe+TI7huclUE8AMhELXz1s3jtDvpmmAu5rXD\n1/9Cqpa+YpDB0EdCmHDeM3WJzjWEKc+c71Si/hwdqdZ7VApt22GK5+6mp+QPApFZh1s0khfQeKZh\nAt3YU44SoefluQghMQ0jxYgjqTetrA8VRMWUGONEiKN2wlYphYUcJ+p8qBgvnbNviJqTmbJQSg3l\n1B1zcj6sXuRjmsh6sk0mydfKysBYR9O1rFYrSjlw6NN8D0wRnNsZ7Y7LybGSp4zvIkVaYukspVhd\ng2a+ZdnUr4OTO+hJvFRnXykX0jSJGvYvvH4kQdCRx90jj4/3rLqF8LeRJPFUBBe/v7/jbLuhbSzj\nMHG2WtGePeN8+4LryzO2my1dtyHmwuOUCCHhmobFZk12C8IkykhjDN1qRdM2dKuWVDwpO2yxTFPi\n/vaB777+nj/+6w0f337iuDuSpkhJWaTD2tl4Z1h1DV/97A1//Xe/4td//WuG8cjj4z3bsw2L5QLj\n1sRS8Fb8jI0eBU1h9qcoit2nOEkRH8fZg/w0tJYlU6lMtXcFOMknmSEVq1RKEUJJ9FwKwkGv0W54\nweYSheMUOPQT/SAblTXglTngrFjY5lJmGuQYBsYpMk5ROecNXdvQNBXDFwuBrNeqpjPV91l7R4EA\nVCUafyhDrl7agPScBXKJTO8+MBlHP0am/YCJUpiHw0TsM8d7eFx6uqWj6STZaAoSQTYeIxIto9iD\ndlx1CGuNnNiMsTOdMeU8C54KiSlJTmOrdr+ttTNkJRROcbyLUyTF00MWY9JTkMe5BmPK3CFWymUt\nsnWIWecIyn3QOl2LuHxlvY7175l5vTz5X6VeUIGAyt9PSGqp/ruYCDFiTEMaYXSNQFvF4q0XOfuT\n/j+TMLbQdI7lusUYx93HIyEXulVHt17QHnuYAt55huNAAZq2pVnIRmtdg/WBbCJTjFhvaLoG560M\nX7NAglMKjEEcEJ16pjjNj61OkNZ7jM5o5PliPmzVbILTtVJOfrWTwMiw3kQiEHTTMkZ4+845mqah\nbRuOPZQ50lvKtPDus+6jRu2AdY3pm0g5qUmd4na6odRTWb1DokGttlin42D9q/OwfEqUMP3Fmvqj\nFPLP33xB1zTkbGh8RzHQT0ce9ns+3Hzi7Xdv+ePv/42X19dcX13Q+MKXX3zF1dVLcllw6A+knHn1\nYo3FkkLmsO+xvuHq2XNedls5ymCkW+1abOPFVvZiw2qzwHnD7c17vv7DDb/7v//I91/f0h8FqxMv\nDjPHSllbWC4bXry85Lf/6R/429/+He2y4Y9/+GcOj3fYPNK615xfXXF+8RzbNHrMK5o1iJrmKGvD\nChZcYiQohFPUM7kuRkM5Ta6L0WQfhGNr3ZwLOg84FZuWlBhdVUkKrTEaSuwMMReOU+Q4JkKUXnke\npAFGzYvqCF8m7YZ+SOQy0LQDXbdguVxwtlrgXcG4wHQ4Ujm/ouq0+t6fFjDd2IxF6TSUXIgz9Usd\nFgsqbc8QItl5LJauRKyNTC5KR1wMJcJwyPT7iVLUolVlzWj3BNKlnTrXomwCo54X4k3TGsNkEqEw\n+6dkpMueQqLRlKCUCs5lonP6wGbN4SxzfmqMUQqMi3SdMkqAULniWQyvtISLerLeNn2PlRcxK2GV\nrli79npSPJXw/KTUqL8IJ7ZLxdxnlNY4LM0sLIspU3LEOUvjOmKO6vNfv2vEtZnNZcdy2TINiU/v\nd6yuVqzP16w3K/ZH0VykkDkcj3hrOT+/4PL6Qor1MLHzPdZbPF5OCR5854jZyUkyR6Y0itrWGazz\nLLqOxjtyCnowldI35USIgXEaKcja8s4TwqRDfrDF4K2hse6ESxeBLnLJTGUkFGEXOfVKaVyDd56c\nArmcBp1Z/qJ+D7nGzlhSUUuBOmspEgBjjZx+CkVcFnUXL+gzZ9CTt/mBj79w+u3sF59ynk8Xf+n1\noxTyV89e0jUdXdtxOE4cpj0Px3se9g/cPT6yH3bio7Fc4XDcfPjA1flzCne8/XDPMD5ytlkRU2C/\nC9x8uOW7b7+hFMNyfcbl9TMppMofb5cLcEIVsqUhDoZQMsf7wt3HkZv3e6Y+gIYw1/Fina+sVwve\nfPGS//g//T2//pu/5vnrzygU3nw+MQ3PWS46Fqtzlpst7XKphH+Q43n1Ds9zOEPOib4/Mow9KacZ\nw6tDmfpHY8zccRkdOFonvHfrCs6JlYFz2mHkE29cNSeUUlPgJanmMATGqehQTmCfbCplTZz0KOlJ\ndqebMfAYMo8PR7rWYR2EsGTZOolOc5aig0+j3hj1AtY8w9nD3EkOJEmFHVnwQelL0rzILQg0FPXB\nywUXM03JuBk/1OtMpFq0itmgqhdNnVKcHqC5F5qfC52BmGpSJAWwYvsGYS1MAcUyC9ZmrI1P3Cnl\n/la+8kwzTBORRC6iCJ1STY9Rf5YnUm1NFqUYgwccwnGPVXmqHeZ/n1MsJdtwgrROn1b+/kmAomuT\nCEUyMRMS7TczN54Ye8zqUmPxi46L51viFDnsB5abjqvrK15/9pLbm1tyGPG2IaQNMUaKyXTLFigc\njgFTaeSl0PqGpnXyZy/7uy2Gbtmy8kuMMYxDYIoiVCs5i5jIyLA0FpHbZ21WvHVgioja9PTROEdr\nvczGtDESlriIqJLG7lncyYslJaZh4DEGUQzPA/taF8oMJxaTlQ8uS9XoOirUuUpNk5IFZzGnDbmO\n9f90gqpD2ZrneTqh/eXXj1LIX1y9wPuGEBJ//Pprbu4/cpgewWcOQ08ukavrC54/v2bdLTXIwfGw\n2/H1u2/o+we26yW+sdzfDNx9eOD25oazswvWm3O6RYfV6XWKibaTZJOUCjlmxhiJIbJ/nNg9jBx2\nEzlKmvxsQ2nEJdA7z8vXz/jNf/g5//BPv+X155+z2mzJKfH8xWtymoTO2Czw7VKmgMrzLaUQUiBF\n8S1JardrjCWEUR3dylxMqPiYedJt6T+MJs9Yr3RDZ7SIm5nylGtnqKwXGTwKBBNzZpgiu8PIqOZf\nJ66qdqaqJEyp4B1zmok0tRmjdL7H+x5rDTEk1suGrrGYknCAN4DafBYBSeQzWXWNyBIiLMU2oexK\nCsIeEJyyclbK00mpDtqKoESlKL4tLVABqerZilGUdtRQKPWhq4XZyPHdGPn2Rr3rDTIEs0Y588hD\nHUthTNJdlVBl8+LNImwW+f6xBvyiW0uWQiG2VY5cDKH8UJySSFrYkxZxmWALHqzD0rnTzic4xmhB\nrwXBVMoaP3ja66ZU6/Jc/hWikKN+BhMEOlERixQR+Zxz6o12hiFknOLgq4tG4T3YbBesNi3j2Mns\nKif6XoLVU5HA8H4YpDt1FlcKrhGLAqsNCb5omo5VRllhJBCC2Ng6NfTJFooVbx2Lk4GkEXWo95YY\ne0mAMmJT27iGxrWaqyrwhi4dTltjnUQjARDTRJgQEoBetur5YtVltJQ4n4Dq3MIgz45rHIvFkpIL\n++NhLsRKxH1COazv4SnYVv+t/NPMBl5/+fWjFPJnV8/JJfP+/Xv+z//yf/Hu/VtsAz/79ReQCovW\n8/nLV3z52WuuL6751a/+ik+Pd3zz7jvGcuQwHpjCEfddZvdu5HA3MBxHSc8eDsTQY3D64BZiHAGR\ny0blB0/TxG5/YBgmUqqMkjoekqFK4yyr1Ypf/9VP+cf/8e/56ue/FFqZyTgHq/UGkILhmwXGNkJ9\nLEFw8ARTODIF9Uwvha5dsmzXlV6qjBqhPBk1YardaC3wxYiy0jqDcQbjmQu6qz7HuYbvQk7SfRqn\ntCUPh3HgsR956CeyhDHijAGrTBNE+eewGP37QldUZoauepNgPEzc5czUB5aLhrbz+AY264bN0gvM\nk+XvxXQK9UhWwihclqeoGMBnihNTsKyYo6giE6mE+YERschJ2iz4umxw1dnP2EINSa54Qt0SBPLQ\nr8POxlBSvLVQFtnMHYUGo5AQxGLoc5HZg4m0iEgHVUJWi9lYqrUsFMuMLpsUtZibirDqJmKIJhJN\nUm8g+ayuuLngOiVkZpWCF5xcJaOQzCy1t/NnFvdOKQyuDsb1msm+XTc5ue9yUkm6OWUaOpxp9OsS\nxRSSyRiTmKbA3c2BizPHcuNZnnUchz139x952K9wnWV9tqZ1LQnwbcM4BB72e4bjyGHXY9HsAGsp\nOKxtabwjukiDpC1lCofjwOHQyzMaMxaHd50IxihEX1ifbXBYjmGn7JBC44VWXIx6z1iPdS2NX+AQ\nS+YpTsypWcZiVCSnbjeIPS1ioJYKFDvDKLLJWn0fUSA9FSlVIkLjHNvViuur56SUCZNkieaSyCXK\n99B1mFVXkAgY62ZmUDLSsGCK3scZlf+z149SyI1tSXFgzIGhTCQHXbdgvb6gW3TKwLA87vcY23B1\n+RIaR7aFkCcZtjUN1jdcPltxsTXEIdLvD9x8/47Dw272Dffe03QNBUMIid1hZH8YORx6DrsDnz7c\nYEqcd7uSwZpI1zZcXV/w67/+OX/z27/li69+ivMe8VM4cdArjlW7ulQiUzxy7Hfsdg9M4aCpKo5x\nfOR8e8mrZz+haZf4ZoWxjRbv2olqP5dPXaQ+nTKA8UYYKFYzQw2UnDRVKKvk2aBUXDKGKRmOY6Ef\nJWBYTJCkQ3Z4bF2gFJxBunyNEjKlsk9Kte0WHjmGEgvTkIhTwbhCHCNh2bBZL3jz5edsLq/Y9yPH\n3T3Dfkc+HLC24LwUveIcJSvvVyaHFIUeUtRwigzyIe0MLVDFNpVqWZt30HtQNCJMBrBZKWlyglXM\nWIfIpaBdno7zSiYVI1BGLYdaoKecMWpi5vS0MLucFM3NxOrRPIsdrkGKAE7EWzbTGY8tEFKSYTFZ\n8e/67gwZSzKGoRT9vefEYK7rws+6gtoVVtM0+eVwVBjCEEvSa5JP+HkxVZ9IXWp1rXnbkuMkcx7t\n/KcYeNgduLt35LIgKWNj9zDy9tsbUrDEaNg/PAobKwgsloKYRDnjxa45SAqWc+J66IzHYYgpM0wT\n/TjSjxPjFMgh460MYL2RWDaKfL/UKzauzaw1kgpkndMN/8T6wRi881KsQxS/cBpM8XraUdxb52PW\n2bmhSkXDaZDvR0kKBTKf+CpkIzOZxBgDtw/3s/+N3DqF/2SLnyU/pa63FOUZK7phoMI+85Sq+uev\nHyezs2QOw5H7/SPRJNrlgs3ZGdvNBdY7ocPZwjBOmMOBxWbiOA70YSSWSNd1rBcrlss1m/MzmtKS\nhsg3//JvPNzes7u9I8aEc47lYkG3WIA1jCHw4WbP7d2e3cOBFANhmjA5UoN1hS5UuLjc8tNffMFv\n/9Pf8dNf/oztxeXsGlcHmaV2NBhyToQ00U89IQ3sj/fc3H1LTIN4WpmOkkcWywWpBIx3ON9gnMd5\np97WspBO3iMqAFLVZuPl1+z9rd2dSPuFKge6AK1g4CEXxiEyJUMuTsU9Xif3Ql+0VlgrFoFqvCYt\nAbLwqqeLwg7eGbwTChdJIB2TC1OR0NvV2vP8y5/wxa9+wX6M3H38jt3NB8b7HePhSL/b8/jwiI1G\neLTGiGBGf6SxDmwFKMqfLf6iD1rRzU+ky+rXnWSgKwlLiv8X2Tik0wEQCCdrkXalPpCZKWcNMq4u\nhBXWgJg13q7ojGH+PplYMrEovqpQVzGWbA1Yh29avGtobKFJGRMSORtsSTqQrg+70WGwZkGWpCVa\npVImz3RKMfeqJ7es7IeiNORTKW9si3eeUiKhRKYcSFnpmOa0F9YjvTFyf72zRJyaVMlVCClx6Ef2\n+4mmcbjGk4tlHBKH/cRyuaYkw+72cfb4SakQg1BdRcUo3OocCo0V13BTRBSTY1IjvZEpBtEVIFQ/\n79S9sT4bWCnIWLzVDOC2wVs7zzoqZBGz0H4bK0rSYnX4aJxqHaIoWnWTk82yzHCUnmsE6avgSIX2\n9OQ/z7OQAfgwjYwhMWdTSfuvqm39HPX4aOr31xMbCWMabVJOm7T979TUHyfqLY98ur/h+49vKbaw\nOdtwdXXNdnPO3d09fX/k5bMLSikMY+T24ZFP93c87B9JObFYLTk7O+fsfMvV2RWda4nHwM3btzx8\nSgzHI8M44b3HkCXKrHEUIuPY0+8PHHcHSg6C51YZO+qf4T2fffGcv/sffsM//NM/0q2WIoWOo+CG\nAo6iem+KgZgG+nHP4/4OTMvh2HP/+BHMqCwFz+XFc5q2I6RJjrJOfpZrpLucU2QUt67eMb4xNI2j\nbUUmXzuygkiQY0jqnKicfG9wjaVpPWVMDLueghMjLJugqKLNGqzNOKOduLXCBrGWbCp4XbRr1IVk\nrRZyKyZaepp0XuT/zjkWmzVvfvVT/uZ//o8k33Lz9t+4//CO8aHn49t3fP/Hr9n96wBHKUoO8Z0x\nMJue4SBao54tSk2sCfNU3q7ACylHdZOEFMrcTadSFOKAhLBc6oMo7oCCf1uESmiMbEahCNBQOA2l\ninb8kSI4v96frJTOZCTtPVMoRaLjjPO4zkFjWW/XrJdLGgxpd2TaHXFRYBRLns2TDFaFIfLTxLnP\nUc8SBUnEcVrwi+L8CoPPRafiuBZH6xoWTYe3MKaJYxgkoat+9cyGkKJiVQizWnpCdozBzDh5KuLz\nPoyZcSwsbJJTYJH0nlcvn7Ff7Xm8vSPGQhkmhtgzDaP4rWdZZ956iol0raNrxCwvZNE+hJBmOq58\nrcMb+fSRTERmK9a7+ZTVtg2LxVKj3arl9Am7DilwTJklDcVIw4H1WOOxzpBzwBQZLBeT5fQSArY2\nCDD7AcmmV0kRynCrTQgCS6acCFNAoKOG1nVyB2eBl2688180YKyKnbK+R0v19D/Bgn/59eN05GGC\nlFg1HT978xOG40CDYTz0eDxX2ys+e/kZXbdgSombx3vuHh55eNyTgW7RsVqtWCwWDOOR5ALLrmOx\nWdAuOnFMdBbTiD2r0c6h9WKda72ICGpijV5DMAXfes4vN3z1iy/4+a9/RrdcU0ikPNJYr9FQmqBN\nmjvfGCeOw577h1vGMHAY7pjinrPtJc4tiNHStWtKzhz29xxuPnF/f8swTkzKg48hkUJRH3vFyp08\nVG0nHZLUV+3Fc5FEmiiued43NIuW8+sLvvjqS2we2d0/Ytv33D9MHPpAiAKo1MxOqyILq11YdYST\nrkFW0OwBZ8o8XK3q9Dp09R6MNazPVnzxi6+4evmcxWZJNpbLZ9csVi0xZS6/eMWzLz/j+s1LPn7/\ngftP9xx3R8bjwNhPTMMkizULo6WkBBGx4DV2drQE5iFnypDEy0zYAchxNxcVUiBQi3Tkin1rl54q\n1o5QFSPqdaIDKa2RM6sHhC42O8LkE4SVtblyjeHs7JzLF9ecP7+g27RkEjFMTIcjoUl4k9inIuCH\nfp5ci7kBi8eURs4gxs2QkNGCRlGqpxzZoDWY1mIXnouzDYuFqCBjMLx89hmfvXpNCiMPjzvevv/I\nf/vnf2Z/PM5FShhSFnCyGa9anj/fkjkyjkeFB7UvzYkQAevZnK/o2iW+8fTHPdYkWm08ukVHt1qy\nOFtByOxud9zd3FNnQr6TFChjjIQxD9K1o+utImdmNgOLTFkGvtY1tIsFLhZ8EghwHAaiZtCip2tR\nekbh9Ks/uCwv6auNlXBpbxwyfGKGWEQABfOZRU+lpz9XSKqefyr3W/9U5Jlw1tGoepRcT/GcBtao\nZUN1u5RVPW8Uc1wj1bfxz18/SiFvXcfF5gLzCj57YegPB2IMrNZrrGlYdkueX17j25bd8cB3Hz9w\nPAz0/ah5loJBhXGk5MRkDJNriES1lEOwaydm+Kb6HWMk1slUXJD5uGONZb1dcX55xuWzM16+ec35\n9TWpJGLsKSXSLTqqLLuUNPOipVN1NH7BYrnlMOyYwiDf13aAJ4SBnAJx6jn0A3efPvFw90h/FBmz\ndCFl5iMDYIUj3rTSjWPEnyOj/sy5yn81RLZ1nD+74Ce//Ip/+M//xPRww4dvviFORxrf0+5GDsMk\nHaMx6tmsi1an+5KslLFKlaIwd5+G/IOwZWMtxlthMHiD9YbNxRk/+fUvuXz+grZdCDNic45vPPvh\ngY3b0HWOi8s1775/y+PdI2HIDPuB/d2Oh093HPdHjvuBw2FkOPaEcYIQxctFXRdhPjCQkiVlLaoo\n2q1vXyxhlRY2r8ByogBShTkCXkTQJJ4/9dSQQp1BYSDZ2LL5/5l7zy67rjS/77fTSTdVQAGFQBJk\nN8nu0fQESR5L9it/fa9layzJ9ow8M+pmk0SqcOOJO/jFs88tzKj1mrpcXABJVBE4Ye9n/6NCW8mZ\nL5ykW9ZNwRdf3vLyq1dc3FwypsA4jQxDz9iXxE3DtO45Lnr6vqefBsYQsGWJdTYfIeT0U2QFllai\ngx5HUVAU1kjgW2HFaFNqgo1Em9gsGqpKo21kGDRv3rzlqy++YvvwiR/+8I7dcYfJHMj8Z58n2/mv\nwhnWm4a2rTkeC/opnJ8DUmAYJ3yEZlVjtZPvEiLj2DNNUtxRVgVXFysW6wWnxyN/9D9y/+ER4zSl\nsyirUYUl+oCfIiGXctgMifrJS9ooGTiKoqBR2mC1prAFTmUqePLEMOWmJs0cJfsEkWRJaN6RBbby\neSM22bSXXbdpPu3x9NDMBMIsPfn8uqn8C58wqvyl6gznfc55GYzg6p8t2xk9PEMssySV/PsBcRL/\nD7WQbxbXrBcbvriNcowPnhAmxugpbCXFC0HIgugjYztKc3xQpCkxDSPt6UQYe1xhsqX/SHdss1Ro\n3rs089laRWkidGhsAh3kKJ+UBq1wBbx884KvfvWGxbri8tkNiZLjaU8MHc5Z7LLIk58HAjqZTFCA\nsw2bTc1iecXkR4apz+5IRT8c2O0+UTgPoSEMid3Dgf2243gc6bqRcZKpOsTswktPEiZXGmxh8GHO\nrM71YlEeEKUs1ihcbbj98pbf/pt/xd/8b/8rj3/8BxZF4PDpJzF5lAZz0JJDnh/AmDIsEWVSnwO7\nZmnW2T7MnL5Ilj9qVNa0y48aVxnWV1e8/e57Lq5eYFUJcUTZhn7o2D++R8VIU9Xc/vYrLt+sSO4q\niXgAACAASURBVEmzqJ8znQYe3t/x0z/9ng8/v+fu4wPb+x2fPnzieDgyDhNxTMTB51jSWZ8PMc1G\nGZmAzhnp/+yArc5k6BPKml8UNcM16fw15wuQzvO6/J0dmudSCKNxdU2zbGiakrq2XFzW/NlfvuXV\nV7e4puL3P7xHTwWLVY0rNpg+kI6B8dFzOrUc25ZT33H1/BmLxRI/jvRDi60sz15co0gU1rAoSx7v\ntqiY2KyWLJqa1WrB6qImWc9pPLFtdyTvZ5CEYXK8en3L89sLOv+Jdtxy9/CeyQ9nLTvMxqknB7LW\n0DSW1aJmX9ccT2MWXMq02A8Do/cUTcHYDig0dVVzPJwYeikpN87y+ouXfPtnv+Kf/u6fuP/wSExQ\nO0dRl+jSMAQvnETMUlAti3zjlvTHlq5tiSoyBS+RvElh0RTKUCpDYQ0pedp+yJLfLMFNkTl98NyT\nGhPJCvQRQzzLRFHzJpWX/nmIUXL6DJ/Ba/AkADg/JvMGN39mwjlDTgSplQtJojDO2TrzxvC5Xl+J\nSm0+eSqyQksJJ2DUn0bJfxnVipqPJIILayNdkSbmTIyULc4oKldwvdyw/qZm9F/RDi1NXeOcuAd2\nxx0f79/z84efKXp5oa2zwpInYcF9StIAguRIGxFJizbZJKrG8fKrZ/z2d7/i2+9+RVXXPHt2i9WG\n/f6BonC4qgJtME5s13PKmfeBfuqZ/EQIWYurJfe47yIPjz8yDB0xBu4fH9myY9j3tB9ado9buq47\nt7X7kKNbdSazCo12oh+PmcCT+553dB1JVlMvSy6uV7z85jlv//w7XvzqFYdxhyocZbPEGotRPYVO\nLAvLFOciZTGazCFQKS9a8gCL9Eqrp3Q/os4Ji0qmKSRN0WpwRnF5dcGL17esLmQC9z4wjS3d8MDh\n9IFx+kRh1ii9JEZYL19gbE3hlqRGsVjdcP3yNbvdI117pD0c+I//4f/k/c/vOB5a9vd7+tPI1AfG\nzjP2nmmYEK5CGtUT5AhTjdHCkZioMEFITJEI+kwy2UykqUwQioXbWoN1DmsSfvK0pxEVcwaNlv7X\nsiyoFiW2NNTriuaixllDaRWX65pnzy9oGk1ILYXydKeeUxewJvHmxS03X18SXnREnWj9wMeHB168\nfEldVzx8uqftLNoZrm7WrJdr1osFy7Jie/8JpxXPr64p6iWulDrEn3/6Pf1+oNv3nPqBMHlUUhhb\n8rj4CG7k4bBjeziyP7RMYc5En5ewTHbn6V8jeTLLRcVm2XD/cJTyBBIpWfw0ycScDJvrFevVgs3q\ngmka6NsBo8Xgddg/8E//NPHj+4+0vqNa1RKENkz4fpDSaRLGapRVJC/vgcoSQKUUU5zwOTDsXAwd\nIY2eoETpkZhD2uQ9wcjokZI4KxUqm31kI59TUfNkInBp9KLCSU/odzpvXk+E5KzfJ5PvAs7IQh7n\nTPEkC7lVBoPJV3eWJ4gB6Uwxq1m9bjiHPCgtmfogYWtZ+aSU+5Nr6i+ykM8BPPMWp7WI/2fJWwhB\nbOhasaiX3F7fEJJE7SWVNckx0A2t2GC5o207dGwkQ9hapuBJSSRePgbMGU4Q/BSdKCrHalPz7MUF\nb799zdtv3nD78hnaGBaLksIZqqKirGqca4RMzIlnxjiCn0SPftrSjyfGqWPyE/t2x/64o21b2v6E\ns4bLixtWiyV+nBjiSHfq6U4dwyBhSz7L7vjM/WgLg3UmB29lTPwspUqCTVeG2zc3fPHrN7z97Ws2\nL55Trwra4UDhDMVyRVUvsabHoihMjghQOVs7CSyhMu4+vwB6DuOSY0uuNhNFjzaiYQexPhfOsFgt\n+PLrr/nVb37D6vICYx2THzkctzzsfuDUfiAxURQVZblE6YK6vMC6GpRBl5ayXrC82LC+uaIfDpwO\nj4zqyPWbDfvdnh9+/xPdcSB5Q38Y2T+cOGyPTN0kKZU64Zxh6Eb6fkK8BIkUA9oHdMwbj00YZzDa\nYJXOud/iiNXOUNQli/USZxNd23P3cc80yGCgtZHnoimoVzVF4yjXBeVS1BJNYbjYLFgsa7SGvh2Z\n+oGp7/BDwBYFy8WCF7c3mDBxHI/Y7kQqIs9uNywWDc3ScGprINEsG5ZNw2ax4nK1oS5Bp8C6abD1\nAm0dUwwMPnA89jxujxw7sacXRtPUib7fczwmhmHieBo4HFq8j+dT2Ywni6ZeNuWyMCybApsWnA4d\nVlumGGRqRkj2cZg4HXpubq64uGoorWXoZWgpSk3VrPAEfvzxJ/a7jkSkWpSE3jMNkvNiMyyYcnmL\nD3ONnj47NmUal9OTlacTIvh+zBLWkPtuc3JolpSkpM4ntRmRCJnf0FpDyP1JeUKetfaflzeciU01\nj54zNv6kbsnyA2YRwkwaw9OJYHZ6ngPPmIf+OUJhloKqDGcK0cnMg8QZhf8faCKPcfpsEZdMDJht\nreLwKkrRChdFiXOO3f6eGAObzSUAbd8RHyOrZs2iWlOoCqdLSTKzE3oSSdg0eYJ3JCc6aFQiqYCy\nifVlw1e/esU3337Bmy9ecX11iTWGcewIvsfWS66f3aK1JZLo+hPSvi6t6t57uqHLZc4PnLo9p67n\nYf9A13UkL8H5N1cv+PrLb2mqJd2h5cP0I49xJyTn3MsZ5RbPJydjhDCy1kp63OTPDSaJgDGKonas\nNgt+/btv+O6vvuf262e03cA09UxjoqjWuPWaenOF+3hCqw6tvLgFFUDWwBPxMcspFeeCWGPACNxI\n1CnnPMczcQQK6zTNouLZ7XO++4vf8du/+msWmw0paYa+Z7t/4Od3PzD6Hbe3tywX1yyba4y1OLfA\nmIKQRoR0Fo17VZX4cCKokbfffsmrr67Z77ekBbSnEacqum3Phx/vUD9p+sGTUsQVhsuLmsdPBx4+\n7Ek+DwYxkgYvM5GzuFVJ1ZQUZYFThoDHewkwc0XBcrXg8tkFRaU47I/Y6hOHbUeaxFJeOENRO4ql\no1xWmFqjC7DGsGgqVpsFyiqmKdG1nuO+Z5omisKw2Sy52Ky42KwpdGL3w57T4URdVJTG0tQVm82C\noVvhR8Gagw8kHyhMybJZMnUn9rs9xZhQrmAMgeNpYLvv+PRwpB8DhTWYxoH2hDDQd44wJvp24rBv\nxeRC5npSks0UhSJQWMWyKXh2vaIrLcd9hzU2Ry/ISTnFRN8NPH7a8evvrnAu0Z72HPZHhkGkiW9e\nXLE7SP+tSg5nHUVVcBymcwWdn7zo8UMiThID4UPARwUpZKLa5wXYPEllIwz9gLOyfszxyFJ0nPOd\n53WXOa4hEgOSbKgNY5hduXOhRHyCV84LNqjZPDdDIGpOtM8Q7iw3nTNTVFYCqBmuS2dMnDTLbDMb\noT4nTp9wepPmZE6E5P9s4/hTn18MWplzgyc/yrFOW6wVra3KmlClOKfixfCkDTbaUNiC9XKN0prn\nV8/54vYr1JAYdh2+24u0LptJUsyxqQqM02yultTLgi++esnrL5/z6osXvHrzBgLsHnb8/NMfuXk+\n8PoLx8XVM4bxxLHb8nB4j8ZQFwuWzSVaW3wYQHu2+0/c7z7Rj55jeyQlaIoFF6sNm2aDTQWb5gY3\nHrn3d/g+Mg7+HLsZEXxQayk7KKymKmy2yCdGn6NwIZOKC1598ZK/+Nd/zhe/+Q3V1Zp39+8JfUdT\nL7i9/RLjCqajZ3W9on5nKfaKKejcwJIfvIy3q0waSYEEWK2z+UiLmiYfIJVSIjU04j5cbZZ88fYr\n/vrf/Xt+/Rd/zvrZFZHE5E/0w47T6UBVbLjYXPPm1Zc05UtKt8ZajVJlDstSxNgDEzH2HA53HA4P\ntO0h50UbloslzWrN3f4D93fvaB9OHPctnZ4org2utKwWBV+9eU7xB8sQAv1R0hoTwOjFUr6uuXix\nwRSiJKhcwfX1NcYZ9rs9+/2ecRzADDSrBWW1JiXNY3NgbEd0AK0TtjLY2uBKhassrrJ50Uh0Q8f2\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fOvaPHbcvr3jzxVd88eU3fHp3x/F44Nju+XD3HlJEBUncDCmcQ7XkVTYoowmDZwqJqirl\nOQqKojCoIufYKI2LTkjybGybe1eNNYBwLiGGPH0jT7fSGPTZnRvzMw3zwDTj0CoPMrOEMLc5qYSo\nvJ4iN1NexdWZNBbYxDmHMVpweK1y5dNn8MkcfpU+g36znp8kg5RVGgk/ll0iZUhI9Of/7eeX0ZHH\nHmsLTOFIUXTST7YpztpWuYg5Sxo5dmk0YztwOOzY7Xd8uvvI+3cf+Omnn9E+a0xTyhpoucjWaMkp\nUeIIXC82fPX6a66uXlAVjUAOxoG2TF5cojpF6sIwdi3b4yPHbs/htOPZxQ2Xl9eUteNud6APgFbY\nssIpg9GWYmPpUmR7/8jUBk6PR3764Qc+/fyO/cOWYRgY/MQ0P2xq3tFnHExCrAqtuHl2xde//ZY/\n/5/+Dc+/eIsp15SxZvQjOo1s1jcSrqM0q8UzyrLKD4emiInwsOOnwxGShIeJtd8Tk+RPPG3w6nzd\nRc8ujd5GCblpnWZxUfH629e8/vVbNtfPOe3vmYaRenFBVa3z/QrEGCXI35bohcmzn7jVtJ436Hyv\ntCEpS0gjh8MdP/z+/2WzvmS5WlE2NaWDorQkbTCmwLmEL0aM8ZSFwsSKRleERtFfJwyOmKAoHIu6\noT/1oMB7j6kt1apksW4w1pzNLxc3K0yt+Kcf/5GHw4Ok5A0l2mrKpqAarLAMOtJNHVe316zXF2zW\nG46nLUVVsd6sWa2WaBuZxpFkcqCVDyirqRY1i82KTnds90ced3tO3SBYrcohX0h8bZrzdKKotXxS\n7A4df//3f+DmbslX37zkuz97y939gSkqTr0nqvkhgr7z7B5PbB8P9H1AQrxmAlEkdtZYnBNC3ZBz\nUeqC2Oc0zJyh40pNVRcslyvcwxFFz5wfnHKO9nHb8unnHcuq4MXtK6p6Q9UsePFaczmu2B0W/N0/\nbND6jhgUTrscWpYoS4PSlmmCrh+kwDoqUpDTaVkY1lcNulIM08j204E5e12yS8SVarQwQXPkgghz\n5MkzSShGS46alssk5G2MpDwgRhXkHuRCktntKjCSIkQPn51q5D/NYMiMJCSGVopXYpTic+m1fYIu\n87c8/zCbi86KF4XEZ8QkxHGUCjrU00nqX35+mfTDocMZm4Nk5jyB9LRT5TkxBMHCx2lknEaZYqfA\n48M9u909h8OWbhBsbrlcwhQJ/cTY9ZloyDcpzXtwTtMLUfDSMRCY0FZhTYFImgylq7BOEuaaquHu\n/o722GO1E7doTFTaslxuuLryEgBkLXocWdWN/P+qialesllucMqwv3+k3e0FVpkmJh8yQZNPHbMt\nXkmWuC0slxeX/Po33/Jn/+avePX1VzSbC5QtMcnhlMVoCf+XIC/B1UyWYy2XitieIEJ7akWLTJYc\nfsZ8q6duL7nyKuWojyTmixxhsLpa8frrV3z921/x7NVLbFlzODyitVR4zQUL8r0kWhakPxFmmddM\nsD49wGLYkc1auhc9x+OBED3l1NOsaqqyoKlrfIQ61tRlSXc8EceJ4CylsZiV5GevVk1e9B1Exdbs\nmfL1LpaOclFIMqQVAnq9WXFze40rDNvdI1EL8dwPPUZbQvRoA1VZUVQFSoN1mrJylHXBqZeUQ+sc\n2koBhA8T1hQMk6fvOtpp5NB36P2e3eOej48PPOwPdJMX1Y0zOCsvus/28hTJp0ST27Q8p92JqZ1Y\nNAu++jqw25/YHVop0Z5Ptj6w357Y71radiL6nF6pNGSXo9GGpm5omgrnDGM3YpxEJSctEOfQ99w/\nPLBZWMZulNNFfkflHs+LueTQD+1I345UVUPd1CQCkYl+bNntdzmGImVTm8AiYs7KMuOQ89oRI1Dw\nQVq+EozBY8JcZZhy3pI8RbNGXJrtZ8MNZxm5SpIJZFASV2wUOgn0KfxbPOPhMaVzfQc8Ab9zAmb4\nHNZQ/3xJPVv8E3LySBqrNNM8VT994T/7eb5r5/WPpJ/KZiRzlZnoJK8Tf+rzC6UfiglA5zKGmA0n\nmrmlJcc8xoD3UvN0Ou05nk50p55PH95z2D/gQ0dZL9ms16xXK9r9iV1O00N7UvRIAL0nHyR3IQAA\nIABJREFURYsygpX5ceS0P2CipakXQr4aRzbB0jQLdJR85M1qQ1VUFLZifbnEuopx9AzjSFPV3Fxd\nY5xld9hhU+LFxSXDOFJFTXOjeX5zQ13U7O4fGI8dfhjORz85ZejzEQ1yB6fVlIua2y9f8+3v/hW/\n+cu/oL66RtlSICUszmmwBYk5ClNl4kSKJ2pj2E6R/tTRt7203OeX4POqqqcH8ikeQe5JPkJrjXJw\n+eKKr3/7K7767mtWl9fEWTqms1s25Xztc19pJCUhgOS7zzDNvIDLwxtCwE+eYehFe7+4gBjoB09I\nHXVTUlrDYlGTcE85PO2IxTC2J3SaWC5K1ps1l9OaiCFGRXscGcaRU9dRxkS1LHC1kSAmK+Try9cv\nubhcMEw93adW1EFqtmxnCazRuMrhStEvD6OYrlAC3fkgOvsxDJC8RJESGKeB/enEoesJD49sDx3b\nhy27xyPHY8c0BZwS6CBNyLX0QpCRT5JNVWKTVOy1R89wGlksHnnxwx0/3n3ifrtnyPh/nAJjP/L4\ncKRte8KUSTklrTNReXRSOGOpq4blYoFzmlM8YI3k3CejGH3geGr5+d0HaisFIqdTm8nSdNZCz3Bc\nCkmcmTFRNxVFoen6A4ftlru7O3766WceH3b03UhIsjwpY7BOTH8pipHHaIvRUsYRQyKoxKgCoWsp\nYm78ikqyzWPCWpNNcnNaoShF5pOJFL7kPP88FVtrBJLJGvWAJDGSVB5iPvv6z76RKE+eFvJZJz7/\nKpWhkZT5OWflFDRO4Qke+XwMh/x1OQMIcc1qZfO7Ie+UZAbNVYT//c8vo1oplxhVECbBdeXUoFBW\n4X3IOJKQHa6wKJ0IqSJETxg9wU+QAutVw82Ll5RVQ98PbIsHxrbjPvgz/l6WJcC5o7EqCurSYXVk\n93jP1A9i1fWB3LpAUTeMfpLgIpd4+foFz17dcHF1jatqjNNY7VmUJYUxVMuasT1gqpJfvXhFPwSO\nhwO7zR3X15f0+56Hnx/o255p8kzT9PQIpIwRKpH4GaspSsf64oIvf/sdN19/SXVxiTaOlILwCFq0\ntPBUczbjgJCP5QnuPt7z8d1HpiwYlxD9iFY2t/yI1Vo2lCcbccoYbUKTjKbelFy9uuH5l1/QrFaU\nZQ2q5PLmFQmpc3NuxtRTtu9/ljkxnyfV03RxLqQee9p2z27/Hh8mbp6/ZsrdjFXpgJboO6yKuKpm\n0VxRugVOLfjR/QPvfvxHjrsDG1ewXDSYaWR3ONGdBqypMFahC0O5bihryflu257oE2EZKJwDIuPQ\nczocSBjW60te377BWUfXnvh495FuHGkHj8IS04RRicWi4sPHd+yORzyBcBgoCkXhFP1wYvQTUUnh\ncve4J/gtbdsy9oFxEou7RvK26zIzXnnzJMNrmkkq0nYt28cjMQUmIiOejp4+BKxzHLYn2l1Le+po\ne6kWTDF3rxqHRiITtMpu6pgY+oHoFXVR0FQlZWEZJk/0iMfhMfADd6QpsN8fmCafcfYnbkvlIdSH\nxDhJnV7ftZzuduwfD+z3B7pjT38cJY6CiK4clXOURUFKib6b6Npe6EilqUopXRmHkSmMgEFbi025\nKSkvwKUr6GJHyFV9Russ7UuoHIpFesqTiTmKIaTIFD0mE4iBdE6YnKlRkyfplOb1aF6EZ03JXOyR\neYc81SejsYWjsJLqqHVe8rOv5XPF+ezszIhMFns8nRISOdsGiNFz3mH+xOcXWcgPjzvGasK6gm4Y\nKKoStVhgjcquK9A2R9AmcWJVhYOmRgVFWVYMfSELki0oygJjDe3xILkkMcuBlOS3+BDQIVCQcE6z\nWCy4uLxmHKAqG6pmgbVGpEKZgPRhohs7DqcdfhqomgU3z26wZcnkB477O8kZqSqaasGzzTWhmbhY\nXnHqRnzv+TR4fvrhPd3jif3jlu7USaHr5M/a12yMP/djWqO5urnk7fdf8/VvvufqxUuUE/gi5sot\nm6u7Zmwd5qhLWShDCAx9z6f3H6TKTml8DExeFhA5vguyx4xX52OuuCwTxmhWVytuv3jBq1+94e33\n33J1+wVFtcLYEqNrkoKQSzJ87CSeE4fS7vyGx5StJ3PH5CyrihNC6I0M3Ynt/QemMNAsr3BVAxra\nqUVFUVs4W+OMpbSOpqy53FzQHq847e7oj0dx/1mRqxljKRzUzYo3ZcPl9Y1k8ehI1504Pe6xSnJ0\n1ssN3fBI352oS0s3xOxS1FxvLjm5gvuHO6yzDKOnbztCmFD6gTFE7u4fmYi40gq2Hh3WFBROEWJP\nTBMhwqnt6bueru0JUyJOCj95SuOorKV0loRUDsZk8X5kGgb2vfRcng49XTsRVCDuQX/YMqUhLzaa\n3cOR7tgxDoMUPMd4bjIieM7tQ9agrOSmpCmhjOPyYsP6YoV1BeNwglF+z9Pg+fBxy9QPHLs+96+m\nc6zDPIXKrY6MPjuVg8BlVVnRu57gvWwCKFxVYKMsriFFwhjxYyCGhLZZSWM0dS0a89FL8qOdezVj\npLROTtfOMHqPzuUjWtmcVRKlqCUbhTJlSUzy7If878iS3xRn2445wzlnvYiaDTn5XVOgE0gByGfm\nHiUGJRI5mTSdieunxqZ5jMnQTT4ZG6QgIz0JZTKUk2bRncRK543oT31+kYX84dMDVdVSlKUcbVKN\nteBszijIVQbz0SYG2T2rosCsCzabS1IUrFIpidFxRZYp5ZwFaZSRm5DiDNkkCXhaLrm8fgEUWFti\nrRxn6DuZWmQUIsRA20klXF3XLJoGV1V0XWI7DhhlKYuayjU8v74lhoTTJYkWpbYc9z0PP71jPJww\nQcxFwzDlQK/PLL0INmiUpiwdt6+e8+2/+p43b9+y3Fwg4U/+vGOrrFkl65LkZJkXygRD13H/8RMf\nf37H9uFRAo/CQD8FhikwennA52QJSWPNzTxI6awrNNcvrvj1777j+7/6K66e31LWizyB2JwS6NBa\nEeLAOB0A4RpSXGCd5GhLxMJEYoI0oVUBKeFjL/h6kvqvOE30/YkpRRb6EjyMY0tlKwkuK6q8cEgr\nZlUUbFZr2qtntIcjRSFqHcFeC0pX0CwWvLi9QGnN/aePTGPP44Pmk3OUtqAuKtbNktPpjjCNrBY1\nwffEydOeWswLQ+EsKQasNQyjtMDHFJm2e3anE/00UtRCFFamoLAWqxx1UTEMiRRPuerMM/QD3akn\neCWEno9MShMNuMoSvCdNkeC1qE4O4hY+Hjv6QaCrZBIMGr1vmcY+NygljodWSNYcNDXH9cYovToa\nUEajrQNnGKM8K7U1LDZLlpsVCsNuK2RmyqUc21NL33YMswM5zWUHgTkxUWuVFUsqQyYldb1iWUPw\nAaM/yHNrNYUp5f5PgTB6aRFKOd/HSIBbSsL7GGexSvo6VRCIJwFl4TDWEFTKqamRECfmUm2FRC0r\nglxrYJ69Y4aAtBLnbUop932mM0eV8pCTzkqW/N/lh/ynNswqF4gS5pd/lc9NSDHOsMqTAgYQuCRP\n+FppTLLy9Ul9VlGXyybmRTNvLv9DqVbGMTKNLUU58eL1C7BI008MVEVNUdZoUg4ymujaEzF6jLU0\n9YavvnrLy1evsc4QoseHgXFoqYqGsqwz7pZnXSXZIDZHz1ZlmXHxFcrUaJNb30mYoiCECasTSgWm\nRcPF5gqjNKv1hQQHZaigcCVGF1Tlgrra0NQXkoyoHfVSpqdl/Qd+PLyj3e5ZlEpcnHNQz+fu1WzN\nt9aw3my4ffOG12/f4qriPAkoZVBOjt+ihc+4c/wcc5bJ5+HuE//5b/+W9z/+RHtsaaxm8pFpkqk8\npDm2IJ7NQPNTKrJBRdk4Lp5f8vKrN1w+e0FRLQlJSocn3zH5gUSQa4KiPbUM4wkULJtL1ptb6rIC\nPOPUMk0HxulEaZcoLJPvKYo1rqi5er6krGqO3ZbjuOXxdM/Yt6gIdlFg6pK6WTBMrUy4aUIbzXK5\n4eb5G/ykCdMgYWgq4qeBvpuw1cTNTcWiqTlt7wi9x6TIqinFGegHILJcLPBhzW6/QyuN957H3QPv\n3heE4PFeOiQPp55TP+JcDlHShmGYxCyyWvDmxS1xGjkeDwwk/DCBl4XCKI1VDsMk01aMkoQJdFZT\n146k5UVtDy33D4/s9gfaTkonfH5mUkycupbOj6iQDS9J5YLkmWuLT6qgHAU7xypoayVrIURsYSmb\nCltaqU0L+gyV+RCIRmPrikIp2mEEvGDhPCUl6qz3LqqCi6tLnj1/wdXNc7r2gO9ayrpksVxhywKR\nvkYxvZUO6wpIo2B5OVQqhoD3kZ3fZSVOoj8GopM0zmJRwiTcRFRPsEUI0gSkEUeytrLNFEpUWv48\nzOk8OpEJ09l6n4cqpYl4mbyVQs3T8uwMjfM0Le/urKeXkDmL1o4UksieNVjjpOsmpjxQ/nPHJ3CG\ncpLOqbr5+z7RqZ/97LN14/PPL7KQr9eXkkNus5RnvpFJGH89yfGYpLN+uRZc0hhcUbBaC0Fonabr\nW8bJUBaO6ZQoi0YWPRWyAkS+Rrr8niIgldbZTWrON8U5kWiN/YFp6DFa8eL2FUa7s3VfK4MzJU29\nFqlh0eSwL/le8uJMjIPn4X5H33eiWzeK0U/4GM4EY/oMH9NWUy9rvvjmLa/fvuXy+lmeGITwmLtC\nFTOv8xlrHiMhTEyh4+Hukf/6j//Af/4Pf8vx/oEQAr33DOPE6D1TkAzyGVsPKWbFzExKJmxpefX1\nG95885br21dUiw22qGS6DuN500BLFGnwQtSEEInJM0xHiZUlw1y5wkpnd61Cg0emcZUwxlHXa4ks\nVSPdhz/ip56LxSbLQy0+5rbGJBuRtSWLZS41tgtOh0dO7SOpjahLQ1gqirrGWJMNaImqqri82DBN\nAYxhdbFhGE+S46KuiUkxjiYTrYG77Z3glfl0Jp2q0r4uRhnZaP000Z5O7LZbdAqMfQ/REKcJFRNT\nP+bykQJnvfAcMZyLQXyKdFEIziF4Hrd79odWqvlCPj0lCbQS5ZUijKNMw7m84Cl6VT2d8ZScbo12\nGJcrDtWM1Z75wXPjvPREzjpqjVZgnc15Jx1DgClOxJSy30Aq/4yVdq0XL5+zWC+olw3GKYKztG2L\nKQp5z/Tn2T3SLuWaUgjmwYiyKiqSn/DjrPs3pCgnBG0tVVkSciWg957oA8nPkdhiojJGekATiSGO\nzEURKm9sIIv0rONmnsBzp+usJCE/t+evz/Lg+T15mrBlEyDN90dw+BRiXhP0ecpHnc8x53t0hr2Y\nJaKzugzm5XG+t3Pd3r/8/CIL+XK9Op82QvDE4CV/29pM0vSQFMYWGOMw9YKUPCozwjoH92gt/1yq\nClMvGY6RqmrQ2mQtdF7InROn52eqDBlnn7SfKmMMWmn8NOKnAY1ifXWNNhWzbUwW8oq6WgkkYZ2Q\nLDm3wQfP9mHLu58+8OMP7xiGHlEwwDgfT+FfyJcSrrSsry746vtf8/zNa4qmEQhPqA7gKV0vZiL3\nbKKPYkkexo6ff/qRf/gvf8/v/+EfqRW4lGiHgX6cMj6ek+KYAbn55JKNPAaKhePL777hzTffsL68\nwZUN1pbikETnggEJxx99zzROGG2xtpIS27POeD4tKLQq0M5hXJMTzDogEOMASeGDTGaFKSQ7noKm\naKgKwUrb/oCPAyhHWciRujBWmptshdKBwR+oo6NwJTEVJKUZp5G+8yStWa42XG4uaZZrmUoNHNsd\nzWKFdRVNs+ZwlA0Rndi3B0CCl6zTuPx3WSlcIcXVMwdzOB74OUyUzlAYTcLgh4AfJsZWcH6N+AyS\nkYo+IbhlgRuiyEL7FNm3Ld0gi3iYp2w1Pwfm/Byc24zg/PP59JbBOpTSWFfgylLythH1kiLn2+eW\n+vk7zQu5TkJgaiN9loV2TMoDflY8y/uloVo4Lq5XvHj1DFcI7lwWhRjffODY9me3pkGu17xgKqMw\nhZUeAZ8rGiKEMWKMmHzk2VI4Z6nqkjGI5DFMgZgduma2x2uNVVLoEGOQrtdM3utZOTIT8LPSJV/j\nAOg4OyvJjUNPmPZTJkq2zZ+vuGykKgrsGZNM3z5LV+Vr58X/qRRbiiIsSgnIKTJRiQoO5zurzuD5\n50Tpv/z8MnnkoadZLiiLku1+x+l4IKaJi6tLtC2ZYuR06Fgs1zSLJdY6wWXzRBpTln35kWGaZKIr\nGuq6p65risIRerFpi2V8Nn/o81SotTmH4qizW00wvrJuhBn3nhACxmmMLc47r1IJZ0umqSf5EedE\ng+595Hg88p/+r//I//G//x/8/h//wOXKUZjEOM4yNXmCPz8qaaNYrhe8/OKWL779muZyQTueWC02\nTxtXJjtSNgjIviEl0DHJsbLvOn74x9/z+//v9/jRE5wmxkDX9wQvmeMhxhxbq+V7mxyYlbFPW1jq\ni4ZXX33B9YtbXLHAmhKrC5QCqwsgEuJI2+0IviXEibKocbZCK0O9qCmLhjm90ugShSUpKdvzsRdD\nkw6kNBCnI4fDFkg0zYK3L39F150IcQQV6ceeffvIMLRcbhRNcQ1hwKhIYmQYW/anT2yPn2gKwzAE\nju1Rro+XNMCqWnJ985z1aoUPnvvtJ+4fPrF9fORhe5Ay36KgGzqGqcdRZFu27HfLusTloWB90TAF\nz3bfipNVRU5+ot2PLErHqq5QpmR/OPFwt2MaJqkzS+Q2qKyyt46yaijKQlQvBEYNqTTEThGVypZ4\nLb2PfL4gCIGX0DnM6ZzigZlVUDnGoqgLirqin0bCGInei3QvJGm2n6dQJWl7yQdCL+9WsFLbFsfs\nykyz2V2djV03txtef3nF9fOGcdhz2AqU9O6PP/F3f//3/N//z9+xOxzwUZy82si7Mg4DvhsorKUq\nK2wl03lUCayUKBeFwxVivzdZPkyWO6YEzlqcskjbUybSJ+kUDUnymJSRzUA8ExLREHPBq3R1Stl2\nSIL7O23yqSUQBN05L+hPueOJOWmRnIoYY2IKnNUvSudC5ZzvklQQ2CSfgKWkZu4WUiglhGyIAZ/8\nOV7gCWPnv7uY/yIL+ePdA9Mgtt/H7SOn0w4IOGelaWS5YhomkcMFj+zxiqdBOofeWFFISI6HxbkC\n51yGUTgTM1rNOxrMJ6KZZpSH8Wn308pgbIX3B7q2R5sCYyuMKc6TptaOoqjPWddKO/5/5t7rS5Ls\nOPP8XeUiIlKVblVdrQEQoJyZB54z//8enh3OLHdBgiAXBFp3VaWMCBdXzoNd98zGgM+N4CkUOyur\nMtPD3a7ZZ59IES7fXvOb3/wr/8///Cd+9++/Zxpn0kZRtLq3ql1pW3UEroZSz995zic//5zuZMMY\nR5L3gsOrVmT7i2nOA9kxGnF4VOKk981X3/PVf3zD2++vsNbikycFj09pzQDMuSDcYrXi4WsXZjXb\nsx3P3ntBf7Kj7Ta07QarrcigETioFI1WFmdbcrPD2rbG3zmMbnDWVT5sZPZ7xumGGD3KNJRiCGFk\nHC7Z9o6u6bFmR7c5Y5Eg28bgckMOmbvjDSEJDS2nRI6JHAOhCG875UHc4pIXxewQGebCFER+HudI\nY3uePH0P2+2g6emd4ZHVJBJXN2+5vr7Gh0y/6ZjnkZQjxUsH2bcNZ6cbrBJ72QS03YY0TsRwXB9g\nUWJCCIVJSSrQcT8wTp4lpSZTKFrVwF+NaRypFOY5kkyFUBrD7tGJMK7uRobj8AAKqZmhiHLxngdB\nHdmXiD7BrrUxtLse0zUUK0UvF3EjfFgOFIq2bTBKc+iO4nqZRVw0BYGyQsq1CRGFpNGW1jVsTza8\n9/IJT15sifmOH777A7eXHaYYvvrya77++lvuDkfigyCLjBS+kgqmyJJfxEgFnMJ0jq61mFLoO8uT\n56eEGOU6hfoTF4GlUllSfkCrSgs0EsaR0rKcVWhjxb0xSLanLBV1ZY4JfVDuvyLw6H1fzrI7Wr6O\nlJFSIc/6LBbWd0Og93t4pCy03spEvw+Guz+cM2UVJC2iJIEhDSWb1f9lgUD/+PXTKDt94HgYGIaR\n/e0tPgxYJ97Kxli2uxNiK52DXrfB92bwWmm01VgsGVtPN7WOYtbeLyjkJmfdTojtZYXlV3ijYlT1\n5DOmpShLzLKBTinVbNFqWG/A6la8jktBKcv+sOfrP3zL//0P/4vf/ObfefP67f33sMA5ShYzSxSV\nViLOODk75Z2X7/Php5+gnGb0e2KYCDtPdnV8W4KA79UY8rvOJB+5vbnjt//y73z75Xcc90dOdx3j\nHPHTXDnblbvNYlKva4hJ5a9rhdu0XDx/zLsff0C7adHGrMyUxZXlYYahs02le0o3qI0Vv/h6vVOc\nGaZbDofXhDiibUcMCu9n/Lwnpxa2ipPdIza7U/HQHi/Fd8MaStRM80gmYYwl60TJBT9P6KIJ4UiM\nR9q+X6GCu7sjPimKMsRc8KMnN1LUfIro4Gm6Hdvdltmf0DSWnDzjMBLDWKPuhBPdto7GGk42PY0R\n1ewwB7Qy5KSJoVSb1mo3WkdmEuyPA8fDxOSjYNpKyJcLHY36HviQCCGhnSapgnKG/uJERm40KUZi\niBRRh1NKls+rtwTruyGH8WJ4pq3Gdg396YZkxNPeGUsKgTSp+wDh5Z7XlsZpurbCVUb8s32KgtMv\nz5/SWMTWtusazi+2PHvnnNPzltkfubm6onhQ0fDNdz/w5uYWHx4EYZdK9SvUBb+lcRZjpdkpqmCc\ndKuWwnbreP7ilGkO3N6N7G8mlBHmoPji5CrqkaWhrfm/OfnVF4ZSvQq1YY2l1gv8BKoYQqpgxrqE\nXNwNc73GD6AOUcBVgGUJXOFBoS21FKcKl1DBq3oIL9BYLfZ5bdAWTF6+jLDIrLDLsvrRLuuPXz9J\nIX/nvQ9Eunw4YLTm9HRHt23ZbDYVy9Z02w2qqsmNvr8AAiuI/wdK33sqyJOENupe9lsdE+XkrCHG\nRa0HgryqM9qibACMsZycXOBcVyu+qbh0rik+QnssqNUU//tvv+c3//xb/ukf/5m3b6+IOdE3hoKk\n1udcaU5lKYeC2TeN490P3ufdV6+4eP6Mw3wDOWOVxShXvbzlZiirJU2VcZcERIbDHd/+4Wv+8f/6\nn1y+fkMpkZwCvhoyGa3XlHK5QeqBoiAp6eKshdPHO1589C4ffPExzbahIPuLop04tCmRe9c5R1gQ\ndZGjtGNR5MY0S2BEmhnGW3zYE9OMzhk/CcyUSmIaJzqXsLbHmBatEl27wZqWFG84+D3bbotpHMoY\nbu7eMMeZ/XBHa1timIkx0nQKa0SEEUtEKcFcQwgS8hv2fP/mS7a7LWdnp/R9RpVIKRN9r3nyeEfb\naObBkyn4LHFjrQKjAjoH2nZHdJnWDsToGf0sS9EYpJhXo65+4zjpG4b9iPeJcQyQIBlFRJG8uOih\nFDmIy6FC0SDLP601uc205z1FFUIMTPuRWJsBaULuxS4g74tRGqcMTmmSAts2tGcb7GlDTpk0Zxoj\neyWlKhdaFZIWbHiaZ5Hzq4hz0DaW4BtCLgQl9sSLnUJRBW0N/abh6bMtJztH0zimoPjm2x+4uxpI\nHq5u7vA5o6wjpZEUCxRhuRhTMI1GqSxpTY0lj1FsjRNApt06Ts96druGbtugnWbyE8ErjDfYWYEy\nxKzwJcJiaKU1QSUiSQ7EAoREjln0CwoxlysKMGhaQqZCKxlKJSQoy71vbPUhr7zvgiLWwLpCkmdV\nWawyxBKgiJO5LsJ8MUa66rWxU1TJf+3s61GnSqk7CytMlns8DcEl/ox45BRZErnWsbMndNsO11rm\naSAGuQhGHLLIMTCOR6xrRKFWJeFq2dUpXYefLEYz9VdlFNYgVxA4QRYp5GVH/PB7qr8JjIZzbWXO\nLFsZdf+J5f58Ljnjvec/fvc7fvubf+Xy8hrvg4hETeWwV9mwKMvKOiK3fceTd57wi7/5FS8/+Zi2\n3xIJtE4sAbabx1jbA7Yqx+qSqqrNhEUS+cPv/sA//9P/x3dff0eJAatgHEfJK10CaRd/C6tqjJ6u\nsAp0m46LJ+d8+ssv+OiLL3j+7AO6ZkvbnqC1xscD3ksB22xOhQuORuXaRRQoKa4TDShxn8tR0tFV\nS8mFkFRlfMBxmunaE6ztsNoAiWG64fXVV/T9llAmsg5S2F2PON/Y6hsdKTiZGHSL0pYUC74WyRIK\nJQZULOQ5Ekrg7mYvHFyVMKZgtWRMpjDTtxZTNowYilJMIZDTQKMKKgf8NDKPnmEOjMOIV4p58vIw\n19DsJbRgCqI6DglCVoRU0BmyUpVyWmebLCyf5UaWwHErMFwR64KkFM1uQyqgGovWEOdI9JESAnVl\njapwh1WiEjVGYRqHrb9UDSQOwwFfo9tQCqcbtJWwCO+D7POrn7azFqOSdK0VvnCVclko9Jue3dkJ\nJxenNH3P7APffX/J1fWB2+sBP0QOw0gxGtc1CDlBFnrW2TpZJCmmS9OVkjRJTgRsrrU0nSOngp8C\ncY70XU/sFeOQCYj2MalSG5UIKaOrUZVCk0uqjqIyfS6Eh1Sks9EAKtfPr7hrnXHWcX7poJcCsYCb\n6l6kL8vK+l9ZVdFUtYYm18mgFhcWc7xlEbv4psMiTSoZSkrVd2dRmuYf+708eP00ys79nSzOnGF7\n9pjNbgeqMI2jcGCrz0kpmZQ803igUzsau2x6H1hHsmC3BaUzWmRXq+1jjJkUa/q7QpY7abmg9wjF\nj5cIInZRygGCLafqYyKffv+5MUUOhwNf/eErvvrqa+ZZMDgxwhfYIdfvN6vlZpAx/ezRKS8/e8Un\nP/+cZ++8g2s6tpzhjKN1PcZuUCzh1GW9JqUq2UII7G9u+e1v/o3f/Po37G/39K3DWM04z8IOyIsl\nsCQMaVPVeDUsorGai0cXvPriM774y7/mnQ9fcnr6CKNbnG1RShHjzDjd4f1YDZ4yCrfO9sKLvrcB\nMKpl9p45TBhnadsTFL1E1uVJKJFppujHGNtjtCMmz+3hiq9/+D3nF09QFKY409H3NP9jAAAgAElE\nQVTXTl9jdYvWGucaYQupuoA1BqMbrOloXMDHiZKSeFYXCCFxOI5gFcoqibyzijBJgo1R0FhNckJ7\nNUrjZ8+msVit8LPnOAfGOTKHREBG+gLECplZIw3FOHvGqTCFJOyKUheEWVVfGnNfqFNGW4tRShqO\nJAK2nDM+CnfcdC0mJ1RncM4Sx4A/zqgjK9YtFq2SN+mMoThRbyotdDujNI02TJMn+iCFDFUFQpaU\nwYck+rkiTC9Jq9IiUslQSLXoaRKw3e04uTil223RxjFOkcurW+HajzPz4Akxrd0wWqMt6KJonBOc\nujY7jbNYo8g+gjYobfDJC9yUM8e7kXHwTHPEGItVVg5vRBoXi5RCoxHYpXbcWUFiruEySy9Wi2jF\nzxc15iL9ucfBl6Xm+vTVYr4sPWulqFj7EqUpz7eMz0otsIywdJZ/jyr/V5UVI5z8ZY9XDxrKSuFd\nrHPX4v8nXj9JIf/mm6/ZnWx59OQJj549o+t2EgE1DTjryEkKeUqBFCYRO1CtIjUs4c2lFEn+qRde\n/LYzGPmBQ06okMSsJ0PKIrMOMcrS78GbsQQtCEa+cMvvVWdKKdL/cQ0L3nvevn3L5eU1x8OA1vLQ\nSphC9XfIcsAI80QWRV3vePfle/zq7/6aR8+f0fYbjG3oXV/5v4rFqVC6iFS37dWzmsJhf+A3//xv\n/Pr//Rf+8Psv124hpoQP4rVBUeSUMKb6gMNKo9Jasek7PvjwJX/393/PB599xu7kVG7oUieYIjCQ\nNR3ZyYMue1vp8o2xFcYKwvIAcIq7w55xvmG7M5ycPEGfOkIKfP/mdxynI1OMpGJAtWjVMvuRm/0t\n3735noP3krkYEkZbcg/GNHTdlr7dstucUXLBOYdzEuB7dvqYEAvpzbcc0x0xz+Ig2DVyraYJ3Rq6\nTSN7l/rLaoOfJqbRMw6evu8xCnprOd+dYKzhOM3Esiz7DNZojA4irFKyXNPWkGNmCokYEvMcJTC7\n8rxZhFtVcBSDKHXJ1cdaGzE2W7DXgrjgmQKNMH+arse2CesmgtISjFw5/LqIT49tHdkZstWC+w5e\n7skYK2IgTKWUY9VSiEd8jIUpZlRKD+55YZ8YNDHF+uwJE+bk/JSTi1NU4/CpCl2KYfZiA1GAtmtl\n2gAJH8lFOmajMEoonZumYbdpcU5z29yKJF2LF3cKkZvbO443oLKhFE0pM2kGExUNrkKWBa0sXWfo\nGnHDHIdQp8BJGo3qfy68nMWqNrEEOchOwqwNXiatdrZrZ1hx7SWuQp4ndS/Vr8vSZWehUJSKpy4s\nGVlM1cXqAqWoh/+WQL+l1iSqZ/w9c+XPCCN/9u47bHc7SePpN1jXYJzl0dMXWNvgbLMu0GzTsDXn\nNG2PMU39gepJl+UJWRRhuY6n3faEk0eFLhSMaem6jYTrdg1n2148JfxMiMIRNlZoZQudan3zlmFn\nLXw/vogFmOeZy7eXHI9HYkwVzy41zLisJ3quRVVrRddaPvrsIz7/xc/54NXHbDYnWCMYm9aqZpOG\n+jXN6m0ck3jBKKU5HI589fsv+cd/+Ee++v3XTONM1zS1AxemwQoXKem+VJXMO2OqJNry/P33ePnp\nJ7z78hWb3SnGNqiU7+9dNM72mI2jz3VHoBYrhVIFVZpWb+XgzREfj9wc3nI4XoM6oTk9QWuDn/co\nFK5paZGHGwWxJEY/cRiP3A17lGs43Z1zenpOAfajBAZfnD2n73Z1dwHWVgN/lem7U87PNWMUh799\nvmE/HNCtYdNuUcpglULHQqMMulqljsOMn2bmOTB7obIaI91vQeiC0yzsDbRls+3JZAYvD6xu2hqF\np2rKeu3iUpGvqQtpkozZojVYLRNhRpZXRe7jKUpiFIBrrDAZKiRnGkvOhbkecG7X0bYN4+2e+TCS\np3kZ4ClGoVqLaQzZaOY5sog5TdPKgZsy2jV0G7GclRCMREwRvRYj2cGsHapSFUIq2FbjegO2MIxH\nvv72O7RShEQtQsLRF/OvRbUsE1zJWQgJRoLAnz46wyiYpvneerpS/5TWFCz744BVju1mw/OnT3j7\n+pbhbkIVRaONfG0ybduw3fbsNj3GHki3gWGujobLQF2quCqnmtgj/2eVQZW8qkml0C7Gb7UWqGUa\nX3Zq9eOFKp2vdrSUOq0uhbfiudw7geZ1U3c/3S/GjQLTyMc0MlGJ4cD9x//49dMsOz/4gL7v6Tdb\n6Qi0zD7bk/PKiRWMVehxLbbRGNPIn0G9weS0SimSUmT2E4e7PSEE+u0OZTtK0RjjsLahacRcqzEV\ny5xGlE5Y62hoalFZTr4/fsnHHspjl9Eq+MDl5ZU46mURUKgsU0N+gKdR5OTuNw1Pn5zzxS++4JPP\nv+DJ03ewrpWUosUKk8VDndqFKDEYylE8JYrih2+/419//S/8+p/+mcs3l2I6pGuSUmVRrPeIkiKu\ntIRrKK1oWsfJyQkvP/2EDz7+hLNHjzGuqzdSqdehsly0xVb6pZh9STBFzhVDVQajHCl5fBjYDzfc\nHV4zTgO7bcfkZ4zOjOMBrQ2N62EW6buPE3Ma2U+3HKY75jAx+YFd3mCtJubM7CdCipyfPRc8tWSc\nFb8NpZT4umiHto1kOeZMDJ7DdMS1BrdoBibPtC/MJxs22waDEn59lCxErR0F8RgJSUKOi1LErEh1\nylJFr3zkXArKWNC1iBaEg+0jKYpiVqPxXhZtcpg20kFWbFgmpEUXEVbmxT2rQSxfVcmEOaIajXEO\n01lMCOgQIMihnzUEstDwjF4Td5SWBbd2FhUyKhds62j7jqZpKChCSGQvVN+UpCjJGrQWMy24tdEF\n0xqKSYQ0E4eIVhFtNHMo5CId+6KKlFImKmppeBNt27JpHbtNw9PHJxwPAzd3EtgsXXMm5iQU1gxz\nKJjesjs/5eWrD5jnyA+vrwFhdsjUkGhcIxTkxnH+6ARI3O336/O3iCIXeCTX4qpr17JQgusxxtJb\n136Oh6DqQl2uNyS5Lj6XAr3u4JZiX//WEh6zfO7SGorXkig3H/bewkRagikelv0fv36SQv70xQvB\nIrWpneZcT+peQC6lpLiX+u0pDcrykPaznIrzNDGMA8fjnu+/+Ybr61u6tuPkdIs2dsXDRIhgiH5i\nnI8M04G+O6s89PuLt7w5y2JD6HllpQ/evylyZX0IXF/fMM2TvIl6eRuF8ifwloIiHsVPHj/ii198\nxC//+pd88NFH9N2JFFmo90QRpoprK0VSrkeVAlJKIcyef/vX3/KP//A/+PrLb0kxYo2RB3DF0JdL\npyvdUG486WIV25MN73/4Pp//8i9499Ur6XLRD/Y8qhbrIB4QupGirYUS58OBECec2+LslpwTh+Ga\n67vveH39FYdhxOgOtOX2cEdJCeIkgcjKcXd7wMZCYzSbk57r4/fsx0tQkZxHjsM1Kgb6zTkxKUKU\nsAerbwlx4uLsor5Xhr7fUUrBpZZm02JKIvuBMA+MYcbHgJ8m9pd3HFJGh4mPPnnJrt+w6TfVOtnQ\n7zaoAsM4cHN3RzIInNH0mFSYJs94uMb1Du8DMQYp/ElJ4YmReZwY9yO5CGBbUiHNUYRJzuI6RVIa\nRUYZeW/XBVZZQoClgyuIV7uzDUYZuetzkRR5rUhOo9qmKiKFlTRGjwkaZ7VI87WVrExdSKVS/ErG\nNk2NRpNFY/CROHnhoBfxHdcmg8oVTlDiI67BtJoxDNgh01rD5ukFuRTeXr2lIAdNLrKQLaWgrSzW\nlZE9wenZlscXJ1yc9HQ6cXU5c3m1J8xQaoBESBFI9X4U1fM7L9/l5Wcf8t2btxSnyEbVHYmmb8Sr\nPsbM7d0tn3z0kk3X8d13l5XWSLW4rZ2vytXLvA7OuppiKV3ZYMujvuzTlhJagRO9LOKo3Xuq/O9c\naY1qLfZqGW8rdVA+Z+nY5QuJcM6Sc0QVKfBLPJ1Ri8Q/85+5kv8khbxxqi4CRNKqKw1e5blym+1a\nVNfLVzKr75cSRBwQG9tGuqEnjx4RhpGrb99wOb8WapeqkUtK3BGdyWzdO6gXJ3TNCY1N6DJTsiIr\nh0KWRIsEeTk8lnHrPhxBBBPzNHJ7e0sqGW0NISzcd4VQZPQKqexOOj789AP+9u//G09fvJBuqFIj\nS71pJNBVKEvUG0FStMFoyxQmvvyPr/j33/6er776Hh8CRsvdGZZczlIoMYsb3YP3XVV65rN3n/DZ\nzz7jL//2b3n31Uu67a6Sc5YuHECyTaf5DlTG2Q7ntrVblMITcoAUAE/KgTmNJBXoNiLp17S1A01Y\nXWg3rahy48job7hKt+QykFXi+u41sx9RSHCz7EMsm90FMUTubi55e/U9t9ZV/xXDbnOGM4akYhVT\nCB0zpZmcAhenp3RB3Cb1WeatcQQfOLs4xVBI84QpGZUynbO8eHoBSjHMO7rthjkG2Q80jvGoULFQ\nQmbvR4YpoCKoqhIMOdblmcHYhuQDKYqSsgSxaUUvBbtin87UhXTCGCjVgjknpEOMhXQMdZGvKVrT\nOmHr5FSwfQ8oQskY40QEEwKEhLEBY3VlP9QuMCTB5TVoZ/AhcjwO6KZlHj3zccYoRWup1FiLnQLa\nR0oWT5W2dWw2HU3TgrbEKBa9hSKK1apZKBSssVirafuWFy/OCTGyvzvSWpHR55C43O+5vTkyjzMx\nwiK6y1WlqSpKMY+eH75/y/8YZ7756g1+jFASZ4/O2J70OAuXb/eM+wltFLfXB4xWvPrgPfbHI8Mw\nMk9eCrXSJKUENy8CsfgilF+tJKyi1DqjSKhKdRMph1pFcbB09/Kcm/rcqwVP/1H/LHBRXv9rgcwV\nuYjfjsoghgkGo2rkIpXuq5di/qdfP0khVwvtZtkWa13xIUH6lJJT7aH/1/KDqx91xQZrpfMx1qDj\nBbdvL5mHI9dvr/EhYhu3evgapel7xXTckMIAZaTkSMoiMBAKWV14LCfpgo1V4u7qt1AyIXjGceRw\nOJByRlsNUbrxJZ2EeiBorbh4dMb7L9/no88/o+vPMVZYMcsIWi/IesMs4oKlIygFxuPE//9v/8FX\nX37D9fWdXE+9hDXIkmlRqS15m+LMpmhay8nZho8++5if/9Uv+fwvf0m7OUFbwWCVXnbzrHxj+bpV\nsJATWUvHFGPkOBxpXKFtCiUHYgqgDV13SjERspWllklC45tlyXkYbvBhEKx4H4k5MXhxv9y0u7rB\nrxNUzkzTxHF/y3AUKGy7OeXi9ILWbnBdX6mOMurP057D8Y5pnti2HW3fC+0vC+Nkmma6zYaSMsFP\nEhfnA04Zdl0niknjSDgub67Fg7uUVZiTUmGaIyEIdFKCyMBDjrJ4pxq+pZkcEjlIQvCyyNKqqjIV\naKOFYlZZWivemupiNGTyJFaoxlnMpordUJKy1TgxlbJGAqK1THbZC7ddx0hOFbNG/FOMNrAEHaeE\nnz1RGcIcmaeI0xqLxhnROLTNzBwK3heatqHf9LRtKz97gqw1c0jIUl6EecIEkU7cOk3TaE53LX5S\nTHtFnD3D/og/aG6v7ri9GQjVJ18ZYa2U+syVlCEU9tdHpqPn2/I90zCRo0BI1mlsa6DaPKcQKVFx\nuDtwdrbjvfef8/33r8kxEmcvUKWWpbXOGqWkmC8OMhZdG+11RmdpsOS5VyxpQvc+LIv3kV5x9KWD\nfig5vGec1D1fpaxK2IVC148pJUz1JYLux9+L+ZM19SfyWgm14zV1ZK8FLcsFBiMpJkt5K0V8Oupo\nJMtk+UGtsRUHNzAP4ntCwfuZlAuN7oS3XWqmX1GEGBinA+N4w+yl6z1xDkOL4j4keOFtL9mSS1Er\nlYky+4lxHBnGSZgp9Uam+oZrpddiqJXi+fNnPH/xgs3mFNtuKh97MZeSa6OX7rywYrHLjRF84Obm\nht/+9t95++ZSEmCs8ItZpOLL264XCb6htQatYXe64d0P3+NXf/s3fPYXf8H27Iksfkqm5AA5Vfc4\n+Xtd2+OsdODSgQhDaI6Z4zByeXlN342c7GZBjzJYthhdCIwUsvDUrWY4HPndH35NRHGsjn6tafAh\n8f3bbygYumbD00cX7I83MvHowg+vv+T26pr95SW7Xc9ut6NrGubxSNoEdCf5hjklpnHm6uo1l1eX\n7A8HGmN4dHFO03XsLy/F+MtAyJpxCoRhZD6M+OOESpppCPiUmaPQVuMc8TlQyAzjwDB7UjHkKinX\nOTGHmTlEQom0TkQfJCghQ2J1uFPWYJyT5bWctvK5pZCjeJ7kKEwgrSUOMc6xFiyNdobOtSgjbo5T\nmHE5kUJEp0pV0xrXNMRcp9cgHuam1DHdGYFzpP0Xn5G6mE0xCzXXioDNGrGm7WNLSIk5eXa7LdvT\nLcZY/DiRi2V3cSoHQ0ECt5Gl54xM14lCiIUYgkwDpXB9ecuVpOExDbN4uiTJiS2LB4pSxBwFlhrg\ncOmlqJtE1zWYGiQyjEeGeU+YZmzpaG1DjDPRTxiz4+mzR1zfXMlErRYjLIQOqa0sd+viUzSiD4s0\ndUkqbWUNoayFdyEy3C9Mf7RDW4r7yjWXBkuwhCrs0UunneSpVbmiEYqCBZWQqZxK/nhok/bj108m\nCFrw5pIDy5JRmYf4j1q73xUfKA/5mwsSzVrUtTGVJ710PEZc3+K9D3jKEELE+xEfDtgiXWuKA9lu\nULq774QXIj/30EohrTzuaRIvjHmWTm3VDnHv1wxgrWZ70vPhx6948d774qaIpVRsXLH8XGr9+Vav\nhvVqaA7Hgdev3/L69VuO4yi4X3V6FIqqqqwSqnG+qFy7TcOrj1/x8Wcf88kvPuP9VxLkjLWYIoKh\nnMoDU3vxgtfKYEwL6/Egf9Y0LdvNCWdnF6TsOc57fDiAMoSYuL69IgbQpmGzG0jTDcf9a97eXmGb\nDdievj/nxeNntM6yH285HGZOunM+eP6S11ffMwU5iH/44VuuX1/ihwm0liQgYxlODvhT2UuUpJim\nieF4h9YWWz1Frq5E7n96Jkv0TbdF246Tky0ueY4hMk2BGArWFsY58Pb6VqCCDGcXJ2i3YwqevtuQ\ntCMohU6ZNM5kL+lISit0ccSQST6BT5QkS7CFOpxKoiSNLSIEiiGSohQulSCMgRjFjso10omqUpk5\n1Wp5nkaaqjbWCsI0E+dAmoNkvSotBa7rqe4dNdgjr1CBtrWY5+r9rSLJhArdGcjiTkgsNI3G9Q07\nrci2RTuFj544BbrO0TpL6zRGKwlsSBmVZepwzmJaTdMY+tYxTbNcn5IZJ884BMIc8ZOErKAhqcyc\nIiFmTDEQIipmTLFkW8CJZiCTiSHh48x0N1CKQFedFZxZ7lLF8TDw5R++4urqhnGeiCWvcIXOGoqp\nCEBZ7/uU4/rELSHTWt0//+uOrhphLTVopRP+CQz7vicvtXYp+Zr1qSp1glmKvMApFVIpok7PeeXn\n/cmS+tNAK3VuV0uxXry1F/y5XqhcL5zSD2g7ebGPrN6/i1x5UXaq5f+vMUrGyKmLWrG7ECPTdGSe\nG1AOpxpCGDF2QpsNqgYLi/3qMtoshV0cynKO3N7d8PbqiuNRkn+yaPGlq1jGpFJom5aLR+e8eP89\nLh4/k8zNFTapb/P9WbV+rZQTD85zpmnm9vaOu7sjMaTVAnWRiK/XsOZvGqPZbDuevXjKL/7qV/z8\nV3/Bex99SLvp0dbVAIKFx6zXgI2UQp122nXCKaXcQ1RakpG2m1Ou96+5PVwyzFfkrJmnyJs33xGT\nxtie7ekJyd/h51vmGHFack9CSVjTst3sMM4R5mvaZsPZ7oKb/Z7JR0KY2d8e2O8HcigMY0SbGW0M\nx2lgDqJSVNoJvBGmGlMnEEQcxAbCGseu33Cy3dHlQtdadNaYtgPTkLJnCpljtftNSZZ4Z6c7lLOE\nuzu0a1FolFWUyUvuo/ckIT6j0OQQaydeKAjenXLF7SgSKrzoFzJ1WUr1nqD+vYyuSlFtLbpjpbTG\nnKBSXBtjmJLg4bITrKIbqMIjecZE4CMTmqhsdfX7SQIRkKuATZaGJQqHPQFJgal+M22JZIPssJzC\ntoaud5xsGlxj8T4xa00MiRglHKLYQoOI3+ZZ7AjQipAyo/dMU5CeLhdUBBrxNprngCkFGws6F1CR\n4kpNIKqeAkWyYVPIol5GEXJiiRb0Pslzcn3L/jDgY6qZ9HViLYsnuODRPCimFLH/UFWgo9YCWqdj\nVc2aHsC/hbKqVNcGtL7Kg/9dlp+LEnT5tdS9pbMvC8RaIRoBIcqaLPTHr5+kkGsrXZ7MOtXsvahK\nwV3IP1WCzkLVklimlMI6XiilSJVWJPBCglLtfSpbJCugWsHKVcmEGDkOe7ajxdkzWfzMA8aOODfL\njV5pf7J8XSCeRQiQCHHm9Zvv+ebbr7m92zOOI94HSkoiqlDCSVVas9n0vHhHklM2J2co5eqeoF4D\nkczVg2mBclLtimUEBFWFJp7oxZrVOScbee5HueVf0iicNpyfnfPZzz/nr/7L3/Hqs8+xrXh4pOQJ\nfqoPsMU1LaVoYpzxfq7FXcQihVTZRXG90YxxuKZlnCd+uPqew/yWcciMB8/+5o1gyLqhOz1j21ta\npzDtlqysBGXvr7jotzRa03UbVLkWV8Pgubu74+54J0yAlEFZsDD6DGNAO88Y5JdPkd6J/akqheBF\nALPd9LLUSolxHLk4u+CileXiHCeKVqi2pTu/4HI/MXnPYZpo+47zizPOTrZszrYMMRAHzYxmVoao\nNGOYGEZJDdJKYrqKkqmm1C4ObcgkYhG+vNaFYhWxZIw2WAOTn2qzAo21slQrBatBVx57SjIllZIx\nCXTMGKNwjSOrWcZ+q/ExoqLCOktKEdtoXCvLO3mPVXXRBFjc9cRcS1uZhkulLyqjUM6SjEI7DTqT\nTUY7Q7O1bM4arDP025bHpz3KWEab8FNiGo8cxgnvZ1wAckujNNkJvKRaC06Lx4tR9NuWNATSGGi0\no7GQk0JnK3a8JUuSkxIlaEJhnKNxDbZowtQQUySpTIrgU0QVxeHoAdlj1Xjxms4jNUgQcbk2YkYF\nFKGuZu5ZcinHKg6qi1y1MIruN3U/Cnl58FrhGfmP+hfqzqIIhKyymJPVzVp93rXALFlVeFfVNem6\nIv0/Xj9JIS8YKbqIoKQU4Y6vSsaleKpFjahWietiDakXXnQp5DQz+YNI++uFWLBuKQRayCdZwmNT\nyqSg6PvH7E6e0LSyMNPairFUTgKhyKALLEpPebtCTByOR354/YbXr9/iZ0+OCQH60vo9a6DrOl68\n+w6//OtfcfHkCcY1Dy7EEgQri81SlqSXUhekD1K6Ney2Gx6dX7DbbTkcDkLRWhWqul4X4Z7aGoZw\nerHl4y8+4uTiRJZrRZPSTIyeHH2N/pKvr5TGVkdDGbXdutAR62DxyCg5Cwd8mhiGA9M4onSDc5bU\nBJw9cnGy5eTklP7klBBHSom0naMozd1x4OruitdX35Gy58n5M6b5KGlPvw+8vnnLHGaUhkBCtaLA\nLEDM4OfC4TgyjAOzH2mc5G+WkwuCHzE54wDdNGLF2++gZJqmAwU/vLlinEfGaWZKCdU0NKqh7Vs5\nADuL7hw/XF9xcxy4GjzHnPFZEX0i+kyKArspa8kZsV2udLSUC2GoniiloFqRwuMkpMEoTdEKj4i/\nSl1C2tahjca5yksHnDL3933WGCTlp5RM4xxkCanICmzjxI42SRduud93GKulc60wRIpBQhuswToD\nrYJUU4icBiPmW0Z09HQ7AzrTdJZ+06BUwbmCswv75n7RY6yhoZEFuM/Ms6Q75eiZ0izF1moJocgC\nL8WUCMeRSIGsqrUt1atIoVO1Xq5TRQHy0o1nhbGOorJI9LWhaRpyivjo6/NlUEoW9uuCUtfszQJp\naQCVTJxaO2keY64+LmXtuu/TOe9fqmLZZS229c8XWIuHJXhBDpbuvZp91RqXUiGpeljU6VrVgOj/\nJCDoJ8LIK/ZTSgX8F0k63ONVlemxQg9FLqJe8Wv5HLKc2NN4Tat34hNhxNksJ8EByyJPr0ChUQ2N\n3bHdPqPfPsG5Vtz5dAuIfHkJRcA67oEs+X5SLBz2E1eXd9xc38mmPOfqnZBXXwetNGfnJ7z78l0+\n/uJn7E7PUdrIYvThhpOH49gybWi5meqtkUvCGEXXNvKwmsqpX5dDAo1oVWO4tPCzz5+c8+Sdp7gq\nVbf1htFIETI1ZGMxyVdVpVgqtBPDsIYyL9OIeHAL19VpQ+tasBty4+hMxOTEs0ePeXTxmGbTcXN7\nyexHuq5DW0sp4g1yc7hl9p7DMDN58Yb54fqaaZ4oyG4h6YTqZAFIFLOj2QcO+z2H/S3TuGfb9TTO\nsem3bNqOyRgCAmMYpcSCVilhAnjP4e6O4zwyTZ7hKN44jbV1V5GIUTH5mbvbPbfHgWPIzHNhDjLN\npXmmJMmQNNaSQpTg5KwosZBCIsxR8HKAViABZczqgKm0oWk6CmIzLO+peOk3jREPFiv7gJRjDTmJ\nYpObCzFGjGkIc8WHU6JpHJtdjzWKrnF0bYPWRvJBc+0qUyT6mXEcKUosFow1ZAu6kYSdrKRwhRjJ\nSqAq7aRRMEaSelKcCSEwjBMui+gpxijRgRXW874QSHgbMTaRVRYbAnK1TtbkUC2AjSaqsvogaV1x\n51KhPxQqQ4wJinibZJ8IQbJKndaVGlvZXnUadk2DD6FaCIBg3mKrsOQWpFRhriKHs1VVy6EtMWvI\nKwbA4pa6+pSXpWgvRs/VpuJHnXNZf7uHRqrUtOh7rjnLXkysPRbYeUkjkq/+Z9SRL14p5Cib2cVc\nOPMALK4/XMX5WLtC1uVNKYqUCsHPTOMetxEDH2dbcZeLSRR2y4KgQNGaxm052z5jt3tO3z9CG4fF\nr57dKc0URNGo6sVeMi5RtZDfzdzdjAz7eY2bEuytMkG1xhnH0+cXvP/qPV68/BDXCO+3lFBTelTt\nCuoOoCw4t3wpbSwlZ3n4ciBFgW60XrC4vGKdSkvXqlUSMyKrePT0gqfvPFLp2EYAACAASURBVGWz\n3dUxPdFYwUIxLVp3FY6p8JaSUU48KgIhzKQU6Lqd+IyXLN97ETpW3zU8Ojsn5ZlY8cS0S5xuOp4/\nfYeL80dVIAWH4x2tbWj7nhTB6Za7Yc/V7ZE/fPeWzXYHKMZ5Qimw1tA4R7EFbUHyPRUlipfHfn/L\n/vaK4XjNo7MLtGskz3V3wnjoOd6JgZXiiNOG7WZD8TPT/o7xcCSkhB89+8srMIZGt+RpJOTINGmG\nyeOPM2VK5BCJQ2CePT4EdEqYksXsqXXkIqupFFN1XqyMhkpZlYmw0uqSdHhKGTYnZzBW2uVui3WW\nxjV0Xcvp6RmbfoN1RpKw5olhnOr9J+k6wUeiT6Qg2ZBt59htex6dn/D47Jzz0xNc49gfj9zu7ySx\nJibCNLPf77m+u+HucEAnRdYF5QxN1zD7QJhnxuDRoXqGdw6tCgUj/t1TJE4TJWaaNhCTYpxDxasF\nRsix+v7YQN+3GKdr6Ii4gkucnCheXd9itWIcJ1GwGqF8phjRprJFsiT/aB1FKBPEtXGZaJpWskFj\nzvjgMVqx3WwIt3e1FhSsM2KyZgxtK6lfs89yAK8E8iRzuDHoqNdnROBQ2Xvd+63ktUNf8Oy1zq0g\nMGuhlmVplrpXlgbOrB392sgVqYE5LbWuKmf+k5b8p4FWYpRjsJTKf3qgROSexrNwNlngJa0AUxNZ\niqAJWuOaDdvtE6zr0MZibKXw5Cw3ghI2i0j8pQux1pJTFEtP7aAYSvZiSJ8nsQYwLRTxjqZEYpxJ\naeZwvOL169fcXe+ZhpkUArn+TArpAjd9y+NHF3z0+StevP9uhW3kTV4WsnIa12tSHgqOFnmzFGij\nLCVl5ilwe3u34vGyVEqgDLrIz6utot+0PH5yzt/+1//KL//mL3n0+B3atq9QiYyQkOtbILCN+E0H\nUvISsVa/T2PkFslZ/myajxgttgfGOLpmx6YduDlek0tEK81us2OaZt5eXdFveg7TwBw8XdMRfGSa\nPLOPHEfPGALg0D7hjKU1La4xGFNASZcnVE5F0xt00ZgIjat+KfPMPA3oqkpNWdH2p5w9KthuwlKw\njdD+ZhUZyNxiGbLBFwhuw6ZxdH2HaTbEGJlDZNwHdBL/m43NJDthS0N0ItWSbrxhd3YuXh/TzDx6\nwhyJU2QegzA1YqTpWzCyr5Gp0lRqqq7vH7R9S9PIYeScZbfZiN9LTszzhLYNCcMcpRBY3WD7IjRF\nHyXO0AgkkotmDpH9OOFCIqQs75fWuNZgdmc8efKU7dUlP7x9y3yYmNRMNAltHUl5rDL0TSNFtDYN\nmULIhuNcGGZFDophCrSdcP5T0WAboe7mhNKelCLTGNF6xvUOZTWdbVFOuPdaWYEOKMwh0HZihGay\nJlZKYs6FFDwxCPtMMWPQEvGm5BmJOUiqk7ICidQYxxgisdKatdY18q3aXlQxV84Ji/gcSTOlZYke\nxSp4eT6pRAShJy7FWa2LUnmU7xGDH/Xkqq7EAPEqv196UtKa+CQaXTn0t9qRSiJkyfG0aJo/J9Os\nsm7u/2iJqE31bH5Io5ff1bpVXig/teUpMspo1aHq2CRvlpxmIQSM0qLaqt4cMQXG6UjwAymeonQL\nJRPDTAg1K9KB0S26SndzyYQ4Mk233Nz8wHfffMnd7Y3YguYsixKky+7bhmfPHvH5Lz7jk5/9jKcv\n3pHFa1nGLrl5hCu/FPP6E9eN9bLw1BWfV0ozzzOH/RFfBR+ppqtrCrpUbwat2Z1u+fjTl3z6xed8\n8OHHdJvdinunPFfL2SIqtqKk01CFnCMpe3KO1TyqqYeoZgntlWXzEjJQaFxP22zJd2+JUeLpzk7P\nmEPC+4BPgeNxxM8jZI3SluPkycqQEcOjrulpTENrHK21NK2jEJmTwDoFYW5Y12ByhUl0wzQELl9f\nYdSGs9NE13ZEr4AeZTOhQIxe6H4M+JzYzzClnikVYnEoW8BoIpbjbPFZM0UjToBexlxlDI11aFUF\n2EqCwtu24+TknMY6yIlxGAleQoFjyIzTxOy9MIuSiKhSWtybFlaUdFwaI6EdCUJOTGoiBE1KIkTy\nXjjhJUlzY7QBMhglvYYRoUjMmWGYiCGxP0wSVLFCYZIEtaToDFMkZUNSFkxBuYx2TgqGMdgKgUCp\nbolgnKHQoO0WKu86FoMpAh1pEDiTIIdPqXuNCHiRs2hlMaqIL4wV+12tCiEHEeQYwaaVlilRnuNE\n8EHcIgWLEcjPVsqxWTrqWDUNpSbvpNVyGL3YxMqzFmP1NCqsyVZLdFypBXuBaRZGy72Ss7ZaC41a\nUWGWhx3zsuxc+/Ef4eW16a7QqCxi0wKhKIPTFl3UGkdnEbHWn3r9NIKgUtZuVFUJV1GSMg7LqfUA\nH1f3nPJSFgoPiLXrRI4zKWQM0hFbYwXJyongPQVdb0Lh0c5+4Ob6NfP0Hps4Y2xHThN+3jNNe0Rg\nIiOPVk6oeiXhw8j+cM3bt9/y7df/weHuZnW807pytzGcnm559ckH/Lf//l949fHPODt/zCIkWkKU\nc0kVq5aOROslYLmQ67ubKyNlsbGdppnjMJBSrsvfeuhVGMoog7WKs/MdP/vlZzx/7x02p6fCPimQ\n80yIR2Y/S4fbdIJposmYVXSAllxSjUMhPHW52czKoMm1MGltaRqBS4JPOKM43Z0z+cDN4Y6r6yuO\nh4FpHrm5HWi6nilGtOuwLuCs4mJzikmKxlj6pqVpHXOaSZFqjFVl29WwX6PIyXJzM3O8+YFhD8+f\nJS7OH1EKTEGxPxZ+uJwgzhglD6e2DWNUpNBDVOiSUNbhS8RPmf2cyUqTlSNrwzQdUEXRbbZot6Gx\nGaq1qFYaVRqy19VdQ1GykULZNXQtuMYxzbNQ8nyAmPHzJDhxNckWtah05euSvkhqjjaqOvTp9VdZ\nnTzEjzyn+ABqLJCyQEJ14BW2hXC85SCWz9cyhoFRNcVGo5yIhVyn0W2zGtct1tFL92uMxbnt/fNY\nl4RKKYKPdbIr6Kaj1GUtpiGhybFg6tJ9CTuR1C0qNVKm0pRK7aDF9ld6PXkuJFJPV1GPFHJjhdVV\ncqFkYTulXP1PsuDySzNFrSEpp6qdkIyDUvc/RSmsFuMxg0alur/jvpQvzokLcq2o6taaoLV+3lr1\n7gv8ohyVYOW66JRFgCSg1R2hHCDyfFtd3/c/J2hF24bV0EDr9ZRdvIGpQpnlNFtGkgUXL0WTk2ee\nD+R8K52h6ZEwrUzrXE3crorOlEjJiPTZSuK2wuBsJxBLnjkcX1fZfqZxHYWAD3c4Z1H6BI2hcRtK\nbhiOics3N8zjxPLEqCIp311j+fTzl/zFX/6cVx//jM3JqdyoVOWakGDrT3b/enhQ3f/cBSpnNWeR\ngi+uegu+ds9LFXy+cR0npzseP3sk6SqU1SsiF0/Mg/weixxM4w25FJpmw257hjFO/u00oZHYtFKp\nkqVAY3tCiIx+BqLITorCB02MjuA1t9cHUlYMd5E33x2Y5oCPwkwoaiCmRJwMZu5F3TcDKTGUyF2Z\nsY2lqEyonHx5WJ38/STXujFZ4sqy5+r6G778+pqmbatzYWKOkeMc6uIKsgHrOpSyzGOQaaZCdDlX\n+K4yooqGrDOqYpc+1Ii0uksoyCgsVgUjwd8yTUfByFdWVX0QTc2SbS2dM6jGELz44Vtr5VCuFswg\nBUYbXd0Q668kXvQrg6k+DCUtRaqgbKmy9vtQlpzkY8ZZmt5KLCL1OcvilqmNqY6jdU7QslHKOa+L\nS10B7XU/n+5v3kIRxWXtAay5D+YuzaYuCu4j5pbJWumI0S0x1jCYEqEqZsmy7FtotI22lPp+owRR\n1hW3LpWVZtoGVSKJVHUVQa4hBWvl6ItJSAxFFcwSv1bZYsrIzxdzksi3rDBZJvjlQFmstR+W6D8u\nq0tk5H33XTt2oDyoasKEM6xRdAscoypYUcDnXPcsBlsdEMOPAJv710+07JTTdBlXFA9ProqZ52o4\nVT9UK1ctKCL+GI7XqHwQulDTUpKc1m3bVopSVWOuHWUNdbWOpmnFe1vrFb8SXNyKX3n0Va0WySqA\nbnC2I0fLeMzs72SMVixMmkzXtTx7es6nX3zMq48/5PT0VEQdy1a6LFmby81t6tQmXfr6O6Vu0KV7\nyEWWW+N0ZPYD2mraTSuuhBSscTjrsFbz6MkJF8+e0fQbQkocDnsomuhnfDjiw77S0KBkxc3+ShZ4\n2nGyGzDKEIIINbQSRzkQ5sAyBY3jxDhNKC1D5nEc+ebba2LwWGN488OAKpZx8rx5e02IYcUjUz1Y\nY0giECmQkVCKnLKwQYwwJCSUQ66TNZGYlw5QDs3qFQZpRquhJjJVC1KtKMpUr3AoWqFNpbtWlS8V\nspLCxmohvYy8xokwLJcs32u1ysuLO15JUGb8PIstslrm5GqOajQqG2wuskwssGj3lmKgrV4hBNlJ\nVDaFr4sypVFaHugl1UmrB99kqaqLas+8/CylduEohW0crm3uJS1FjLzqQ1E1FvfPmEAxemVQCPK3\ndPYLRlzLlAaMkY/lLH7oRgrU8nxTqCKzXL9ElukqQpkTWWeKlvfELmIuhYRvFBHZWSNW1mo5JAsr\ndVcr6mK0moLVeyeVWIvyEghOhS1U9Zx3gAgFbaNRPhHyJAdADoQccLrmFLDYF9d/iB+L5e93e3pt\nfGqxe1D3pNqp+h9aG5xukcyp/OAAqOypOmlLI1gp0H9OgiBV7m94So2rwqy4sHQb6YE6rZb7WsRz\nFix7GG5pSkC5jmwBBBLo+k5wyVyo+VXCrzYSLtA6J0kw1lYMTNM0pzWrsEG614FQPDnLTaiK/Nth\nKgz7wDCJP4Q4lQnJf3uy49WnH/Dq0494+vwZpQTRji2jVGFdrkghXx6tJf1HrDsX8W7JQf40Zabx\nwH5/zWG8RTWwPdvQn/RYrWi6nqbrsI3jyZNzzp49Z/KKm5s9+4NIow+HPdM8EIOXxU/F/u72R4Zx\nIoZA39yQYmKaRnwIKG2xtkUp2ZgrJfjnYRhXBkXOBR8ih8MRH4OEDyCYoyoS1ZVyTWIp8tPmlIV3\nXymmEg59LydXUAVI0voZLcs0sTheuPVIR6m1ZCSywAsZY8VXRDtdu0sEMw1z7XbSWhSNMeIzWdV9\npXbkKFBWujWR1Ityt6TlkK1NRf23dQ3r0NXjBiVUuIWuGitVNOdECkkW7SkJvxzxQLe2UhStwRQr\nbBq1QG7L9yu2pou/PPxv5t60SZLjSBZU8yPyqOoD3TjIGZKPwxmRtyL7Yf//L9mVlbf7hjMAcTSA\nvqoyI/yy/aBmHllNcGT3wwqQlG40qzKrMiPczc3UVNVIyfRUSBWWkTMhglAklHLm9Rn2u7dK50V1\n0R1f3Hs3YRNVv/skLSG+37vBdPx9SZjVawCHKxt91a2hdTBjLwNWfViFvBbUhxX1yn4UFt6XtGSc\njhnjmLCuV3Q0jN6RU0TKCXRNNVimd+QYoGPger3O/kNeMtcZOoY2lNp5n2Mm2yXGudcVvC/3Zw7/\nXreIMhRdG2qv1ouwQD1rX6fj3uTjbr8tIDfe83GvXGZ/D3CueYik7q7Fkphh/TEMqDR6u5uVrfuV\nfzLbZj5+JR65Zcqjo7cNLogZYzUckOPEgjmh8XsK1QrtK1Q7IoDT8TmOS0JezgiHl4AIjjXjeH6H\nGBM4s5MXJPcGUfPrtg58H1y4IQaczs8QrKM9esPplHA0dgfAxVJrxfff/YC/ffMtT387jEQVx0PE\nV199hv/1f/tf8PzVSzQlRe7+7gV5wcEEO8FLOjEssAA60Hul2U8vkJAQ44IxSH1qteLD+3d48/0b\n/PT9z9ChyJFqxyiC0/GA8/097p8/gwTgzZu3uF4LjicaHPWmKG2zocVmzCPMKIoNJUgxYb0+sgYa\nilIGRi8AKicOGR47ANQ+UHtHs9JvdEVjCktqlo2j0/lfa+iFgJQzlhvLBcE+oX140B+D3jiNjSsP\nuKrRTL5uy9Adn510VaOojlrm4dm7VQDwilAM/xWkaPfC5rJ6DNx93U2sZriXJ96q5BtH+1k+aCPG\niJj5WWOkfwkzXXBak2W+NGpSDs/YaDu7HDJOx6MFUkFMaa6ZGAmisvnJirH1hlB9P/EwJLTAw2Y5\nHBCULoz0UhmQ1qC9IZkNhlrRLwLEIFBEi1FGqYRwBGMUDIMb3C4pBJg3t6L3OOmmHuLYaAY0MChV\nNOOyVxIFuukulHBJzgmHwwGlk+2znA/YOkfttdIwesVhOeCwHHE8HJACOCZy24yjDsgQ1FFI2XUf\n/4GJR4uYW2YrXBetAdpQe5kH4NCGJp0HhgbrQ3lAV0swPTtWYMJuHqZ1h7Hm1bgJ7uLXnGUlyRt8\nHv2DqAAOsh+MwH6Afvr4lZSd9seYD6NXjNYxWkNMC30tQpqVGVPBBm0bensgJhYi7s6vEHNETAdI\nPjLDSweEmKa1a2uDeFkl3VCjD5lI7Oh3noaH091s5BGnzAAGSqEpT2sV12vHD9+/wZvvf5yzEolT\nD7z67CX+8M9f4Y9/+QPunt2TgdMZNKgy7eaLrmwkGrTkXHCfetRagUqlOjHeQSCoteHd27d499M7\nfHj3iFIbYUrCiXvzx3+eKH786R0OhyNyoigEgWV8XvIcDE0P82GBiZsgmbAi5QOQMSEql+irAkkH\nYutYzWtE+oAatgrrdQyzZ3XhE28jTbxCuBGAwSQWqlTmDrIsJPhEo09Wjf283o1XHgTiTVrLWlU4\nBOF2eC7soFBbT2LXIMaAAVsvEwowHHo4bgwYDjMPFn9LKSX7TBHNRrXFGBGXxCEaOaM3BtfemTw4\n1EYWE3tCIzEIcvSbCWZimNcrBEtxRBEjEJeIWgeiAj2S9aKdY/Oa0Pl6iOIQQf63XV/tA2JJjaME\nA6aUBh0niUyzSTlSnLCkGhVVRKYPEvsBdt/N9E4NDlSvpg0q7dYLYPbJg2Y0TrmXJkBQ1LXgCoHm\nyMHdpwPqyoNodEXrilA7cuw4HE9QchQRAtdWG2NCefv9dDjHVdf8m+uUFOWyCZp2HuCO38EYLhNO\nucnDZzy1+nm+5hMpvVWhuzOiV06+Pm8qvE9+doC5uajygNXfWiA3W1mFIISEshWU9QqtFcuRzdCY\nMmZH2MrL0SpauSKmI1I+4Xh8iY7GLEdp+O4Yp4jMDT+6otWMXhuGBfKUFzqlNbIvluVEGMbscsUW\nIc3yO0pp+PjxET/9+BPe/vzOLEdZx0ZR/O7Lz/CHP/4Orz5/hWU5ccJ7CoQm5oHRgSBsvkgCwBKO\nOGhCBBs3ZBZULIkBo9WK9+/e4+P7B1wfNpRauFCtkbet9HdGCDRGyhFIiZnL4cCAflhwPB2RF1rn\nOqYXhdeoNnLAJWfElJCOB6SckD2r9EWvA6U1bLUiXS+0e60dZd12q02DXHQMm/No97sTNqGd6OyA\nzA01IQTDBaMt/tEJsxCeEQQ1awPDonV0qAlRQkzohhXfDhTxKsgrBBecxRAsUBIC800kxhDwysmD\nAts7uxovpYSUqY6sxcVSNKhajhk5LyiFkJP2htEMShS1A5x3I8NgiMomb460roWZwnW7fkmUcFqM\nJIUGBRIP39EEVTuaKnpg5XKwamMoWS5qnOTh4+ACv+c0uj4GD6IgYN8GzPxrt1KfzdhhSlEKVdww\nyqmOOu+l2sHUTNzTWuM6Ea9iKMZBC9Aw8FivuFw3HJ+TFZNSohq4G8CNhlo7SqjAidbOpVQmCTlj\ntIbreiHTSnhteevYc1JVuJuAB20ditatOrlhygXZ3UjFm8xQ66Xo/DczdK/eLED72t5R8Zmr+w8l\nPZIMNlaWRjW2/yU4T4kMHufU/9Lj12GthMi3FzjfL6kAkiAHZuQxLzamzdzHhI0Xnp68YSEtJnkm\nNzsaDzOKIgadmUxvhE5sgDcN7GuDdrIh8kKhhIwBbRUIAyI8RCREHI93hBbGBdt1w7YWOg8iIID0\npeMx44uvXuP1568whiClI5Z0AjRCAgUKIQmyHC2DiQwUgQFdNaOPatSjA835h6K2DQDQOhdrrSxL\noYZzjmFNoQFgJR0wCQNAihRWRGZ657s7LMuROH/hgo4xIAZmMxIDlnTA4chhDIe7O6QD2T8xEqVD\nV5RS6EAXiVGOWjEKx1NFzzBssrkOOvmp4a+wYDxUjcFDelxvvjiZ8Y7BLC1FQYrJRBJhWgUIBOF4\nNGEXF39rHb0TC3WsW0KgECywWVV7Y0Dq3ZwfCV3AMn8BONWnDzTwEHDKW4oJrkgOPr8zEHLrtSE0\nfpYpOgEDYBdCGsdTxPGwUChjcBx6w3q5omwFFIHwMy55wbIk5CUjL2kmJNQsNMIjQ7GkCLGh4SEE\n1Fqx6kAKgtbZNUjWCwBAUVIMGJ0B2/3zVQWt07cEqohZTL7OKobzLBWtbMSxc7YKYSefPclC4ZAB\n11dMFODRfZRwy2w6pgQEy85VCOWUAYQNqgN1rahbhTZSPwMip1FtBR8/fkRrHbV1VG1QBI7BG15V\nOJTBTjZHuBEm68Zg8fvb+545iwkHx+go4wIfrOL5uM8L3u24/Wd7Nq8TceCVcUjOAzpf72vOdTGs\n0CKi+/HoAEKERKB0jhUcN83T28evg5Hb6e/84HgICPFg9C8GUIkRu72t8S1Dos1sPEJCZlmNZWJN\n3lBU1dkI21P0CIkZA9z0tVZAeahIpCrSfcTdm4Wqx4SgAb0pfnzzIx4+0kJWFYhKBeeXn7/En/7y\nZ/zTn/6E+/MLG0V2ADSa1SyZATqXFmYNNYOZbSRn1igGal2hqiiVfhz76d13WMpUZjo4y1NyQBwR\noSeMUuBoXRiKngt6HbhcmD0zaLh9LbvoMWWkvCCfT8gLR3XFmCEIZr3acDgfcLw7IeaEUSpGbYgQ\nLDnjuBxwOhzZSKYHMFkfnYIO9/2ooxNnN7ghxWxe8mLNZboAekOPE4zCzAKjNRddsNF7n4HI/0f4\nZM+qpSVIvAnkShhEDWOPAozAja4QG5TL9ZSzSTFGs3XL+5dMBGOVOJtTkT8r6kAYjdc2Rh6wdlgJ\nFGiA1IAwHLYhm2JZ7BBOtKRwTQWNQgPUMHcJe+/FqagMsgHRnUPndaJBVxdBs8PSMz+1RmSpJLfF\nnNlQNe+RoawI2BNRpOABXvdAbp6rhBB5WMck5u8y8MQJEPt1BZwlw2Ejow2gK+eediZdrVRa3Vpy\noIPCp8frFQomaU2doSYIkgF4YL0Ntk5NMhTbGC6Ae8+576pBfzowdJvw0I7Tgdff1sB+lDkuznvs\nyIBn5IxHPpOVr9i90D0aeDUQjOHW0IebdIWbtff08etAK+JIE//EnIAss95hE/GGLyukNMV8AMJC\nHnrI9vUMwMsUgEIDuxiGM1IFlpEyR1SNMdALZyyGkEhXc0N5HYAW6GAmDUToCFgvBd9+8y0+fvhI\nWtcYiAI8v7vDX/71z/jzv/0Fv//DH3E6PTPQMEA1zRJNAhDVbo4HYlhjsK0memCWNEKA9I7WV26w\nss7m1UCfi8o3w4DDGIpgi1k00DrAMoVR6Qdd1ort4Yo+BnKKGInYKAUUtjGV9yQlYrQp5ZmlKBSn\n+zOeffYCz188nwyUkBLOhyNePbvH569e43x/h+VwNMwaaL3huhVstWGrBWvZcCkbfaKH4HA4YjEY\np9VC3rQOW/R24IFDD5rBQC52UVO/9dFNbNOn06WX17Q6YJLgGC0rOdskQtGFaCZmG8l4ceuElJJB\nCJ2/e5D/naN/3TI26xomoWoTHtQkIYbsTQFmaFEwloQQlPccNhgiBk4cNBsFiGPOyurBDaXEsF+D\n/6BOEAiIt0HBDrymBRoNCiFyMhOf1jtKbdAgyIONWVFLMizIqguHEFi5DA4w9lxp6DDNBmGXBUK/\nldp2XHx41msN5ygWiNm4FZscVNbNdBPUTgQlVtyd+w/FWqkehdD/RR2SCBGqdbJ5PIedaZRVWvv3\ndAbxYVm7NzVV24RX1PFu7P/ffvAe3/1fBsuF2WC1pAFjNyy1tcBmPQ8T3hhLTtQPzGbCwTSH1Xz6\n+JVYKzOHgas0/WL6XoCbz1g2Hg9H4rfDZbOGnVuA84yEHNE0s5vDkYH/cFqwJBo/5ZiQwgkIGRI5\nVX5ospNxoPcLVAJhjnBELRWPHx/x/bc/4HJ5hIYOkYEcBS9f3uPf/vu/4LPPP0c6nhFissXAcXGq\nln14ZQa1ICPo5t9St0cEROR0AgRo14ptvdBbuvHfdbsQXx2CVmHSehj91PC9ALjywsdRcXQYzfzp\nL0HsUoRzHofxg5tNIfHEA43+2nUDcl6w5IVBtjU89opaNtRtY+ARgZwWjFOC9iN0rMjxjGf3Rzx/\n+QL5yBFlXRWPj1d8+PCAt+/e4+P1EdeVJknH0wl35zPOd3f0rRk8sEot2GpFqZy32LvO0tyx86ZU\n7vVhOPUIgGbCIJFiDognY8aHtmYd1bjep+K9CQBiSqitGo89zLUhKmavykZzChzerIOMHg12WMDV\ni2aB2p0OB1YJkVPul/uMbFnaTBYJWhucOuYeCbIP4HULCh3eLLeG2txPFhNsvQ2wkgtBkJBYKcFt\noc0NMCcgRB4GCsuCGURTECAHS11NJTp4+Iu95dEN8/Xq0hSerTQjHbAnFjNplSlF9NpwrVeMooij\nzworBRdVAaWtqHY4+IQfgZBKmBJyipAeUTshEqjDFGIDJPa4M0VdEuj0qB19NDJ6vOHovYAgXEvT\nImsX5++RmNfYGTFhmmYZLKzWt1Owr2UZv1jjXMJAkoAuAV32Y0KUynQV6ysF3ZOAX3j8eja2uA3i\n8NSAZW3v6L2yCRWTWd2yPIVfb5eqOlZFxQQvn5fKIWDJGelAQyIx+tYclOp4F2BZMsup1iqz8ZgQ\ng+Lx4QE/fP8Dvv/uezw+PgI6INpxPp3x+ecv8Yc//R73z+4RQtrhHcvSRG5wM8XOFzcMjBlVBX0r\nLEuB2QzMYRq0MGWmaQb3fh1NsOI2nZ5h8XfuZR0pgsbNtkVKqKJZ7sO2cQAAIABJREFUlutYNvOR\nYTRAlpDmDw2FtoZqTSsdO23tMSc8vn+Ptz/+iB+efYf78z2ePXuOz169wunujLvn93j+6jOU1lAu\nj7h+fIdtu1ijuyNDgSVhkTOW89EMjAZq61i3gnXboErM/GAHC1RRW8GHyyM/2+h4+PiA0bplmQ2w\n9aBQ2/6WzVoCEEQsGDLjVbMDTjEANqjX/Upu16xEitqSBCApMFjZWbsX01FSxbykxQ6hbhmzIOds\nymGZ15qY+463evPUeesDdogMZvtUd+6sBwBz/U/KmkEXozNQEr4ja2lApncMHNc2ogDhO/7MAKF1\n7GB/o7WOan0amtFhPl8R7P7ZPjf7XrdgNYY9d14QJFSkZg6RIOuoNhtv1owBpd5IN5aWUsQUfW1G\n+qQgAsclYd0KxgagW0C1XW74FyQIlmWBjoHSMWEW9qcs+N+KCizJ5Dgm2b9mP1dmXJPdP8VRB2Wc\nmrMG7K2oVZHqFZL9/iDy9Hmy888/nT7kj19J2flJfTDxLVuYraJtKwUA/nxxupp9Yu5IiG0fhU+s\nHryh4CbNpuDyppgbUnEsW4d2wg/DDgI13G+K3aB4//ZnfPvN1/jxzU9Yr+ts4j1/focvvniF11+8\nwuFI0y4q4uZlhwiphTSqCuYFMWbTguMAKSmuRuMDOBezKyDTKQ83i1Fnabh/yQQ3g9i5is4ml1pz\ncRhDB7PcFrRKRkJvlsHIHvh9Nmk3ihYH9Vpm1DgYuBtjJBLMRQyC47Igh4DjsuD5s+c4n854+fln\n+Oc//xGIAddtw7t371C0oraOVoDt/g5tvQKVQ37zYTGTIzbkUqCl7ylnPL+/w935DlFk2pV2KLoO\nxG4cdOV0mNYbKySDSsUPP8eVwSa4jxIJ0Q85ICZSOkXExrBh0kYDdshiYvUwLrxVP3sl5pTMgbJV\nqlJNwZlSQohcbGrB1pM6pwwCRhEMfN6QPeirzcocIGwxvMqIMiuJMSGnwYw4ei/JNyR9xjnkRQD1\ngQ3+eayiaM5RVzYYrSJKEXZAiB2OVLUOBWDNzmaDEUjpFIhYQmV9FOlqwi4/eIiP10IRFkwvECQg\nCm0FtHdEFUTl0DaRjrQI7l8twMcVA4Ja/DCzBAWYuHROmWuiCWrZjGHl28lgI4s3Api/Ct/LHk/t\ncJiB3nEW3b81w9weyMUP7b6bdDE23uzz+fN2KGjMjP/p41fKyG8en54whp9F8yeB0vWNwqC9WaQ3\ngcwzLBF6JbTeMedydN2zd3vUWnG9XNDKhrYFtL6Rjx0zYlqQ0wEhZQSJaK3izfff45v//IY0st4h\nYyDKwBefv8SXv/sCeVmmbJiKQXb8mVE11HpFKRdzFMyINkJtGJah2jFahWrFCEIufTygt4HD4Yz7\n+xdY8tEafAw03QI2S26xTMb4rLaZvYviZTg3ZaMtrg60qvMz7QyGHW/lvTH3ObRJHyQN8WZjK1jx\nAGy+jorr6PiIR7x//xEBgvTvCf/7//F/oprnyhgdkmETXyJy4nSb4/GI490Zh9MRy+mI4/Eeh+MZ\nOR/RthVBFTlFHBeye7oOXLYNh/MJx/MJvZj1qA4E7SDIMWiXPDFhMEDa2iNmbM3xyOASIPMeeUUF\nZaMtmECttoahirQsOByz52OI2AOh72YXp10fL0hLRlqSCT5cHKVTzOMUPYdL1HBqinMwkwEOtLDn\ng9nd6IMHhIYpQPSeESQyozePFu3eiLSDQHUOEVfLvr0Hos1EVZ2BvHlPxfBtInoyqZxsQLdZKZba\nUE0AFHNm1V2pXG61Yb1u2K6rrQ21YVtcixLI4nLBlZi5mAgHTIRh+PcYiDni7sUBYTlD0fHwYXsS\na9SashxSUXHIB5yWMxIiaq/m8iioVgHfYAYzUdSbrzFmm7mdZ+oT27KlCEfg/fQkFDMsmDsiP7QZ\nnGKGWk9+kK3d8RsK5M7x9sckw4sQ400ZUShkYBZtHHF4eSJPShfPVKBkoSzHgznJuS+DZanjxqeh\nd5RthYSO3lcc7+4gFnxDIIOz946H94/44bsf8Ob7N7YQmY0dl4TXrz/DF19+gePpbHJ/y+TVsw5r\nDrnDmTVsgpVoqs0y24rR6ZsekeBedzktyLKgnwaOhztEzyBkL7Nul9SsdG6SAd9YHhjYZKU4hXQv\n0g5MYeyrhRmAYZ5QZhDi6kKmTczOOzOXLhQ78fT130f7UTXpdPh4xbZysHNeOGWdLn9xSvVjjDje\nnXG6v8P52TOcTg2HE+1xy/VKzNZxbcN2JSVWIFEse2PWNFxsBV5z30guxpgQy/DrYjXf2I2j1Bg3\nzfByXiOOfNu2ihgT7p4FcHC9lf5OkxRnIpljn9EhR+/YVgaucchTvNZbozBOXeG6B3JXp/qBDKWY\nphnUNWdKGqbO6mD/XA73TWgBmHuQFFA7YJta03vsgdycLh3GcVYK/UyAbPYYXp2o0vtk2zZCQwho\nxWiECuTjyTB1Y7l0pZNhNk/wxmRpaJ8Ny92EwNaiKJJVAdN+RMncevfzOygIR9J7APPVU8k6FK3R\nSyXbfo8SuX9HY5LAV3wSvRyqu1V2AtOoBzucaVvx5qX2u2++o0rYym7ITaA3MgNYbTgT5u/QDHv8\n+hk5YJFJJ0Yn1nDBaEZJNAoaboK+/e3KSYnMklJecLq75yQS8zmGv073xeAZkmwDvRec7u5BKpaa\nKdBAKQU/vfkRb777AW9/eksrUgVSjLg7Lfj889d49flrLIejVQyA07D29xYRA6cW8ZwKs3yEKTr9\nUHIOaxDaEqSUEUJCOyhOp3tOtJ9B3P0+THwDpzHtlEunPQZTZ/qB5q2bPmSWup6B+/PEmCCjDQhI\nWyN32HB8DdZsU2gHunDTiAI9BO+JGaPG/tSK9XGzAzYC0dIpCHTYgFlpZBnlBYfjQMsNsm3otaGs\nG1VuEtBbBZRl++nZnQVbG9Fnm8F7BwAhKwY174n4NaTIyeporgFYYDU1bu8+UozvtTfFtlZs14Lz\n+UyRjeHszsgI4hwJfnYIfcBzyii10GjrcsUYDSHRKthNn2iJuwtrdJC94wF1DF5nbyp685ZbwwM/\nG36j79j+gO8xJgqeeusAamlWnelsXPZZxXXUVq2iYRN8VvwANN5i5OwDrFvBtq4GtUTzlxlQCYg5\nk83TB1rhVCGEgHQ8QFqHSMXoG1QU3UYne5aqZhNri2jCEmKfp6wF1x8+Ii+B+gqZONU+3EEFouyh\njN6piXCDKvexAXsHt3DI7d8eg/xrjoKIBsyT5ROw4e8fnjyYU6tnow6iKL+HiZnfZPufPH71QD5h\nJMvGd+gpGuxgGyE6nQfQmxKGi5ocaGAgLwfcPXuOw/GMGBMFLOBCVjB498GJMelwwHLMqCVieFAT\nSvpVFdfLFd/8x3/ih+/e4OHDo7ElFEtOePn8Hp+9/gzPXrwEh0mz7JGwb2Y31AqZ1COBZ8WAjy7L\nKSMc79BrNWEMKY8hsNkZhMZV93cvcMi0/92tTxUyaHk6oMjGLBHHcR23dBihA72R/sTrZ9RCgyJm\nYHY8Wcwn2oODMjMKISJATaCkU8ruGcVoBmfZ5nCOPFTQWoUA6D0haLKXBLt/FgyU3Gbt3KABCh2N\nsEWIyCmZAKagto5FjZveG8w2m+9F+HOmP4tBDGINZSDQrmD6aWCKjhRA7RxhRqFOgjfzilZoBlJI\nePbsGY7LglaLwXvBvGOM2966WbXG/bAcvAe1cNK7xDBhCyipjjBMvbZGw65hKkvbIME/YhAkSXul\n+mSj63RTjDGijT2zbzZnVgez8fW6YV0LPAEBAAyOS6y1orRqlao1WicPTgxuGRPfrbWhbIX9mKHo\naIByHJuYUK8qvYtqKaxOWkFeMvroKJXTr/rYTb1wk32HYPoMEbobQpEDQ5l2qy5Kswapc9i5Jr0B\nqyJzL3I/cp3WUS0B4thE9tum6gkeaOXmb9ch2P+Dd4h5sO4urH57HErhU1nFzgra2CzwnzWDPbH9\nEH45kv96gfwmC9+v0Y3BjGXeOk8jD/jOv5HZBJ3ZQRCElJAPByzLASllIFRiyxLM3hNWSgMhJhzu\nXmA5m3GS0bF0cITWw4eP+Obfv8a7n9+jdb44CnB/d8Sf//InfP67r3C6f0bxj7MZ1BYd37191Li7\nwUEN57Ls28am9WwbGQESM71gRKAhIgQO1o0SuFBNMq19THvLQ864uz9N7K11jp6bbAj1RrJl3IFD\nKABjlHXPIhg6pwWBJQksIJSTZMz8fy5Er0DsvtIx7iZlE8f4xtwTepO16DAMGx7AxxzCq53NUEI5\nigZiqxDybMloYjXWm9M+d+m9SELQaDCUb0JLAmTMDBi+gVTm+w0hItqhC+duQ7EsgmT6heWQDU6z\newoGC9+wUEBtpOAYIAwy+B45tNsYKjD5tfgByOuibR8KIX5tbS3NDa9q0274+mBJi1dYAwoMWgdv\na8G2beitU9EsgrI1FAt8IqTkqd1bn0IFGJPJ7onzpAGyplwrIQrD7q2xaS6NOvb+TVmpm6DTY0LM\nGdmGPKtm4MihJ3ErpICKUw4Z8VIgPXK0BlkiXSpFgaZAHxiDiQ1hizjXmvfWhseRIEAMVA+3hk0H\n6tgQQE/+gGiHkNX/asF1P8O4ZtUx7R3XpucR4CiA85nmPrH71ody1i7UtKhq8I9xoGzNye1++oXH\nr5uRf4KVA7BgDiuFLPPmk+0ln5xI4TZkOm6akBI9QxDF5nV6XeXllRqefoeQjsDowCjQXjBGx/XD\nI969eYu/ff0tPnz4SI8OVaQkePnsDv/yr3/Gqy++wHK+4yzM/R38HULmJzO8ohCmvKrJsGlBzGOW\nwc6dF+0YEoBQ4Y55o1PS3s27O0RByhGn84IXL++Y6dVBPrUxTAAeMEOYFfEQC5xSP9TKaTdz4mOo\nN+sYxAUMpA6TALfZzH47KVE3hzzz1IkjTMHSzIqjHXbD+NX2mXlguKmSdfXHAGyghi90IVUCIe2Z\n/OiG0ztPV9UqI9sM6u+bWT8ztD2bYlDCTDCCUO4/5mkmEB2U6yexwMrjeTifGkY7dZhuOJuE7osD\nOm0DVM1d0BIYDzBNOPcVkzURLBujza0nDSHuQaOb4nFWHLrDWr031KHY1oL1smK9cmA4GTMRZau2\nvgGE7oXDhMYkBPrbWOat0Bv0QEnx876FV9DKneAUYpU+10BdN04fShEBGUsAYo9QKEJKSAdAEJC3\nQjWwdEIWpjKNQo71tg4G8hxI6Q2EF1MStO5MlfCk6rd0AUOMueTrVsgXb1oRB+fk+mzVsW9iLvX9\nB86vuVgK9nNmPjq923eq7i0ZclgciqL0Z4cZnln2pLa2+f7+AYkcv5pp1j84Vvz7cB8Cl2B7swb7\nJvTH31UavGgh+B9L2uBNH52nqh8TLMczDfLjgjEKHj+8wZv//A4/vPkRHy+PqL0BOnDMR3z2/Dl+\n/8c/4fz8BRCpAJ03aQYwP6eeftbJ8YZ/PmZxZDDwOU9EB7zbhBFqMxHGvmljEiyHgPN9xotXdyzp\nm6KVnVbI6eMrYhLkJZFVI4Qr3P2wYz/ooEDKka59PoRBCUEMo4J5xgbY4SmCGAEm45ZBGYtBNdk1\naTPQxEQ2cffDQdvuiugVhOGtKkL+v2WB0YJZn8HFXfWCUQaHOQ62J81CP6imqdHtvRFnqfjpNJBi\nhKiQYw/A50B6NTjl13aIDIOlAGcWGXvF8jGI4HBi9bZvyzgX8hiuTravGH2UgYtrllC+OVbae5hQ\no/0cP0CH8db7IMa/Xjl2TgGU1lDdQdJxcYOoaJrFBmAQcumfJFDOTFHPHr0Rh3lQT3+XYLzqYIDC\nhOis2g4B6XxEjidU0ybAqua0ZBzMekGGHfKW/dMATE0Bq3boAClFnA53uG4FpXT0hieVsAd2Veoq\nqlRsUhBCg0QmDAMdEdEqMiY+A6asvU0sZyW6y+xZqSbeXXHBle8R7D0rlqVQKGKOkK6TNeZ9AfqR\nhhkXGAp+Q6yV//Jxc2LNJNcZ9v4UcVeyX8CLrCkXU+Sf6MZJpgSFZwx2Sqorppn5jDFQ1orvv/4e\nf/0ff8WHD4+oJtAIAjw7n/D568/w2Zdf4HC6g/gk74mR7Z/h0+phFgW6B0AJcS/dPEPwYC8MOq03\nXC+P2Goht9wxNnBqvConssSYCW1EMgnIS2bwOdbFONU6hUYchwW0PsgzbrsPSUSc+GjrzRgZ3gr0\nYOYZBrNDXmtrFs715s0/W/TmfwI75IaVq6oDGgUx7g1a4/wYlTJYD8JYOHY4z58zOka3VwSHOLAH\nXN3fK6xv4J48DKIK1T6ZIRj00e69Yd02o9QFqGG1nhVzCEO3qUP8rDEmxJQZgO1edvdm95Si67Rl\nUAsI3YRDEjmA2Q+oOQBjHsD7+gAIq2zbZoMVLHApr+mwA3GYIjolmTJ+FwnVWu19R1ZKTiDAzaHi\n11EcoFJbtt4k9PV7ezhacHf+dQDMmpLVkmXXtMMEkoYdzhM+dYyBthKqGY1U3RSYOKUhGLWja0Wz\nvtbxeMTLV2f89PYdel+n2nQXEPL33+4v1Y6QAkIP9u2OIdwDXIHeIPcoZOvr5iszYM24FD75HuDw\n3Sy2rGqqjZYUUMoCMaEVJhQO2qnqDkl+8vjNBXL5L/4f4PfBMxHg04vFbCAixUx1ZAiT+jWDrbqy\n7+b1hmW12vD+x3f4+j++wX/89WtcLxtGHwjgNJQXL+7x+svXePbZS+TDESJx/m79Bahot0Cdz3ry\nYcgmwFy8XMe84cNEJWN0XK8XlFpwa7WpA2hN2ahF3OXVAIIvXMuA+sg3UEuwjLehq0xGRG802dfe\nkUKiGrRVrNtKM6OhN8GTi6sJG3Q5B44tE+H7lhsYxct9EfMYV6gM6JCZwfI/rKA8g/HXzcHUfpjY\nhgjCAcXBLsYYnRCEq3apqNqpq0NmsNnpmAzkzvQgZEWxWL+BdobqlNZ7ow3YM1IYJ5xDIBjICZ+Z\n8hICCDfl6FTTlq3sgVxkNqFljMmW6a3ZdB8CN3Ami3r2Topn2epePXnpfrPuyEmPkGgU2BCs6Sno\ntc8DUoOLhRQYN423GdG5Vidk59mH5726/wH80LFZmNajmZPy1AMpPw+U5AU4V7831K2iXAparXbw\nNeSYsUTy8Ee7GX6eIpblgPPxjPfxESLFaiH3Vdn7axQQ3uxIZ8tJRFNm5eNmEpCnafvf/tmt3mIG\ndnOQ6ZOt7h/YD0H/9tCBra6zfzAm35xWw2w8U/Ut8zf//eM3F8jnYx52diGdU/7JR9GbvwHMjIh+\n4xkYT5sQYphm982iY/JSoQPl+ohv/q//iX//63/gbz//jFZoXE+2SsDLVy/w6qvXyOcTu/jKDMrp\njZi/yd7fk810G+e94ONC88zyaaWxMwjKtqLWCiaLVJ86n1iBWe7mJdFbBJiVhwoQuzEjGss1tQAU\n/eCw6KwmzT+YPaqOjnXb0J0P3p1X3VGLM1c6JBofeQA67Od5xu6BOSSEUKHiWRLvjYCTfzxI3S4A\n8uDNl9v4ygyqHAqRMr02HD6jjbZzoPeLzux/TBaEHwiMwaR7TqaNOVaKKHI4IIaIdVuhGpAzPd2d\nm99NsBJShLc4Vdnwq7XMST4Q6x9Ecqq3yxXXy4oo/JoEZ9cA0gJCSMiBVrYQQLtiK9uTazM8oI99\n3TypQgSTwsilwsk3qsaiEfcjOuy2D55Vq0xYZXLYbX3qTVUwDbDmQegGWUAfDX00tN5Qqlkxbw3o\nMKvexXzKvaKxcXBKN8xajfbZBqAdOipK3YChiBKRlwSpBSlFnF+cUVfu5YePV2xrM9uJ/X0G8bF5\nOis+hZt2CYCEFBa0sRJmmnL8ABEOf54s8BuEhRCJCbhsuPpNmxl+wHXx1ynUCAGekXuFMsRmfkqf\nv0MhgA6EGy+WTx+/uUC+J8m20W6l6PMhn7zi5mwVKitzWqZrnzhQHoA0Eo2tYJmjCk8+dLRa8PHD\nB/zP//uv+O6HN3jcVvNCGFhiwPPTCb/7/e/wxT//MyQeDL+HKbp+4bPo0//K7J7LzXPmGY3ZETeM\n3UsrjE4f9bbziXecnIs0Zy7snCJFMs6UsU3tG6b1DvFArp1cY2tOsQSnH03K0cymElKOzG6HziBJ\nVWncHQe10SCpDZRSZyaroOioxm7+50cGv1l+yjxE+R5kZnGqan459Fef3i4hMHAGh1woBCmqWJaE\nyRYxWIZCM94nMlTYkHUFIlkeMgMiG+ZWPCuFMqSGDvTQgVIA2IGqpA+KT75RHrC1VLRKjjTVuBEx\nAu7rwhtuGVhXoBs3IQSjv+0QCRkpY1abtEbmsh/DIBoRoA9OWfLPrxTVdcN3eU05RtGbbhICQg47\nZ3z2AHw9GnQS7Tp1TFGSWqOcLK+KWlaD5ngfS7liLVeshaZXrHYEOWTc3z1DjplqYdsgQxUhcd1J\nCAgDCHVgCCdnlcbByCktkChIS8TWgVI3bO8fIT0iSUJvAehAlgREWD+GDo+jA7ufDADdmWpykzg5\ndVGwV9wzYZzbl90szhH1kD3mc2dUuqlY3B5Cbn7UbcE+Q4Ob4llyAI8Pn1T8/vh1mp1GvwMwP5+H\nradv/iabte/elkP7K/x51jkO5EenSAN8iJj3jRvSWEx6EjAV6/WKtz/+jG+++RvevnuP0hqAgQDK\nwp+fT/j8iy/w6vMvIbJMnNR/85N345nyLMM8iO9fuwnfN59q/6+CysTRSQ9z17+JI8I2a/CBshEp\nktkgXuJbfyCZYCbZ0GN/B31YBtWHSaK5sGPE5KHHSOomsUxWAQEBOWfLwtucxNQbVYuteanvPHJl\nyTqArXY8XlfU1Qcz83cGpXH+xIDVGGLGOvKsiuwNNz3jwzH+btk2DFef2XXwKoH/9VF/fhGnulMx\nX+tBtG4VrdqkI1TUWifurCIWVGUKkpyb3Tql9iFGpKToI0AM91bVCYO5NP625+BwgHqVBHLcQwzW\ni7DBKRaVaazUdtGL94GCwBoKJqrhATpBAr+uHiOMJuiZLCubzmZ7r3uV0YYZWilGHSjbinW9IAUK\n4GJIuF4f8XD5gIf1kV4rSibQMZ1wzEfocVjuJUCkDoT2BZnXpLmym5VEH5Usmigcrh0J4W21oKwP\nSOGIHDJ6jZARECVxsEd2OXwzLyMelmhOgDCbAp3Ahp2zDiVyLTLHeloxCm6D+E0skn2DOuI6p5j5\n1y2w76RrnYfazlDxe2U/0gVOnzx+lUDea5uln9ff7LLTtF8CfXu9++0l8G0iflvazewd6sYL9JuI\nLHdZxvO5o3U02Ud+7QwSxcP7D/jum2/x5se3nBKvxuUUqvLuzyc8f/4C5/vnCMKuvnVz7Gfc4maY\nvxPiWfgv34Qnj5vX0hK0oZaCdeVwiWSbmYGO1qdsDrIxQ8qUHSpWzicT0IQQUIXDljnMN1rJOiyz\n6+ZLPqCj2WKDqTlBqqBgp/VFenxHzQCyiVoHxt2BWLsO7JQswZCBdDjg8VLw9dc/4Oc371E2CmkA\nV72Gib87MwLKYEi3wIyU0nwNxIZ192ST7ukrzyycQZb/9IAbZrbfTEk5LOCSSm/BvxNK6pVDgkUN\neel9rxj8loGfmzoAg/cCK4PeORovdU6T6tu2VxoH86cZChiPezoMOIURipiIyUd3KLSDyUe1+UHU\ng1sXu3+K0+wZpcWqEtVdSaigXN3ZOrzNys/ZFHXbsK5XXC4PuJYrrusVl/WKUQZn0o5Au+NWUOuG\n58+e49n9CxwOR7S6WhbfcGMWDNhn7o1zRX0ITBRek3xY0EYDLhtaJzyjts4VFOTlJQJCM7rem3kP\nkeE0ZCAJTeyc+dRh3vZCnyMe6n4IAmM0tF5RO/1fxMza5pwhYZXhAXz/AztsvArSfVH8EpIw47v5\nsxi7SyfQsz/Pvw6RmzFzv6GM/PrxYc8ETFrvfhYhJpsuE+aIqBjjXLzqcnMAVj/zc9/sLBHDLGPg\nFO3W4ZOARld04YgybZRWY1Aq/OO3P+Cv/+Pf8eHte9StWLlFWtBhSXjx+jOc7u8R82LvBX9X6txi\n4pPdIE8z8f3J+wk+q5F5EPCgqqXielnx+Mgh0HlJSKWClifJYBVO14GVpLez/1RhNLSBrRRcLo8Y\nY2BZFtzf33MTxWjU9YgEh1n6DOTRvGxmE8uudTTOLMtsozp2ZoGcN2llucEYIQIhJaoxE5WubKAG\ny344oeh4OOL582f4/IvXOByW6eLnEIU7DXozlGPKGnorqLXYAc+5rBrCZD9w9Fvk+vJNpYCLoKJB\nNX0oynWl94kF9NmoNTw8+Alh1sdDaVa1JwbR1I78PSH4XE0TagH792GKUiVUx6w7YapybS3NpEDB\n6qO2Kc83nA0KmWP0PCuklTihIw5G5AFVGzPsthWUjQrLUgoVl7WhV8vEa0EpK9owt8rWIBoQNCJa\nf6PbwRHzgnw64nA+orQD4jWDZrOWcKkdqgr2VlKY66+ZknOYGEYExoKKSHGxA57iuOWQYEQiu5YJ\nIhFDA4YImh1mrthUoYaCy5ru7FBFhQA9IDRFHYXv6SZD9qHoc0IQnDliJzuAOYPYmp3O4gHEDlXg\nlgkUbJ+7Q+aw7NzrS68c1ZMH9YW6Qz+fPn6VQF5WNm084+5jd1aTUFg2psSMxTPPyKZYkB1gmR9w\nHoKOAwKAeYWMwY3TB4IGtK6oSkOfsm3kGteOh3fv8d3X3+Kb//gGl4dHfp0/FFGA43HBF//0Fe5e\nvNjNq/gpngRv/1zz3zd/6y9g6bcvffJvy2A4+LaiWuZK3BuGo5qUOAXjHFvZaKo1b+B5Y7S3xoap\n84d1gGwXLjEbdzmrCn9DMbigCjuop2xTqlUNrVUAdd6X6K5+ygAawx4opx3NPBO8CUXoJOeE4/GA\n8/k0Ky0e9moHhjVLHf/vDa0UtFrQGmlozh5xemNvbB6FGLEMy2Y9ixqmbr3JXqcXuwhFOAB8BmjO\neV5vb+ztmdYeyF0JGkaY1gnJBmzc3Gg+PwaisXawRhvu0GfArnLtAAAgAElEQVRD0deQVT2zAWmZ\nnZhYCmB26g3EGxpjKRW1NBtYvKGUFVvZsG0btnXFulkgL2SItOZ+M8yKYftLVBAlI1lAG2ClRuOr\njJgzQjb4y4UsFryDBXPthMKiC8Nsed1aZIUkWA4ZqkzuUs9QGTiejliOTKbysiClBbUDOS1YYkKO\nAW1T1Er7DqVgmuZTdhHd35xkhw4xvj3Az7eDn4ahw9lFtztb9zcuNyXaf/Ew8BcAYZYBV+H+8nMB\nTFERj8LfUCAHYLhgfxIEU4zMElpF7Ik+00Gg2hFGRBgDYpmKTzDBTdD0qeSwQ6EN/gk6EMaYntVr\nbXh4vOLx4QFl26AS8e3Xf8PX//k1fnjzhiOvbHMKGDDPpxP+8C9/wvPPXiKmxOTpxvPFP8ctR/zm\nw/K8/QeBXG/+PR8CsmKsu+1e4wzIbMQMEYgYqhewZ0UilpWHGQSc45xtnFrO2d57mAGRWa77PQQT\nZuzc7Wh/dhETh2P00VFLQYwbA2rvhLWC2JBba2QiYoyIWgO2baD3CGi6OYC5lUWohCxlm7M4x1Bj\n4/gW29eRQq3J1mfw9XvHw5gHhPPlgzYsS4ZEsQbw4J/RObQ7ciBJOBwMmrOEIGAGWVI/zTK5+/i9\nPTkJBp3kJaONMQ2aWA3ADuWbiT9uKWBVg8NUO3y4V0Q6dhbJHAEn++i70QbadUO9rtQfbBvWbcPH\nDx9xeXzEer2ilJVwSKuoo9KW2ILFGDwQBsIeuIQwnsDbf5adYqcOwqA9CG1gS6toQwFEwnHg2vIR\nbqN3SAvo0faxJSgwy4N8yFjSwsPaGr4xCvLBbIBzxN12z/5IFzy7O+PufMBxSXj30wUXXaEtQWG2\nzEIocARW4UN3Ic8TNpN/aMXMsmeFY99/igAAEx+fCAF/wERa7BBQ2+eulSBLneveF4P3/rypLaLu\n6Yb9hjx9/CqBPC30qfAPy04832Dubar1AGXnfxBfDDZ70y9kt2zPgxVfR9Oex8cHbOu2Z+TSgOhy\na8HaB959eMDzDx+xlIbvvvseb376GR8vVxTLWhVj+nYsS8b5/oyYs5E3OgLkk7ByUwLjk6D+SfXw\nXz0Unu7b+a2CVjtHZtUBZx5QCsySLqaEZaF971Ag2KaeDT0hDzpGw1styw4WOLgOB9yO09/mDDA3\nn8udm7zJB4PI0mIT3ftATtm8M2yGpvOENSKvFSFHy9i4EGJkRaBwqbVjvX4d7NC0dRPC/h7ZpAUx\n23l4GbRhd6f3zszMWC6tNUiX6ac+y1+7LmKfc9g2nqISHbjVdqeUOKzbYSfYJo0+3YlghuP96hOC\n7LVUXqr/uJkUeMB2zYOvJzZSmxmGNbOxLailoF7JuS7XDdt1Q1lXbNuGOqjivGxXZuBts5mbNvRE\nxwxWBF7YmNRPql9PSP053da0V3Huac91daPQ9Z8iM17zc/WBCFJMY44ICUhLIqX0kICD2Ki2HYIM\ndrBCyEbJywGH5YB2aHj54g7P747ISbE9bqhbQFfCtF13zxhCFGIDKmxoSwzAEPTh+9Uz4Y65HeFD\nY2zPC4+7+f9/4bHrXfanUP9k/S3YlCnD6t2uYSaI2Bv/LGx+S4H8k8k/HihUFQmZai5rfNLes9mm\nHOjBA4IHcivXBZOWV7aKy/WKtRQ6z0mHgE2wYV3ipsDj5YrHxwtqV/z883u8//CAy7oRflBmhlEC\nDoeMu2dnnJ/dkQfcGjSw3Aq3mJXsVDbAO//yNAh++rBA9hRi2Uu/1hq2dcPj5Yp1a2jdNtmw18nY\nXQLNrwSeUc/MALaIbqfamNjBoofl408yChHQ4wK3Wcp+dDGmWbbiB4IKQhhWTdgmtLFtE4tOESEa\njGE4S8oJKZsvTnQ4xTy5QUjj5jLzM5owJwobs90muM+D9Oajc1A3M8YYWQU45Q5g9ZBcnWqwlVdl\nCsxD51bBGwRA5EG7B629ZnBoZedBMAFgAk3uOM834jvDlGFqIqve3boX07ell45WyacupaKUglI2\n1K2gXgrFM2vj12tBaY2VqXasdUUZBa3XSav1Nfg0DMkevCY+e9uZYoPZUV3X4AYhdVV7h3Z+NacD\nzmdWy0FYdafAPsjpdEY+LVhOC/IxYyjvEXFzQnA057J3pQCGmoGdmtrVgjEAmPOhThWtV9SsErR3\n00lac33uSUVIAeqq0/llA6v0l/evzHv7S4/b5G7fh46iq11FxY6Nu0+L771beb/HuX9El/h1Annc\ng7er9hQs6WLKkBiRjRpIr41ozUkbO3YrTrhdYJYBbeuGtRSUygxW0CwUBSiSNQwCWueGGBLw8WHF\n5VJQtoraCqC80QPA6e6El5+/xN2LO0hU1HrlIANniwghBlL10sQrn5bb3qSbx/uENHZBh32OQX63\nDsV2WfHh3Ue8ffuAy0qJ/p7jUL2oTdHLQK8DcmBATXl5MrncRToiLntnl8i9U0Kgl4XY++JYK8M3\nvXmmMv0/AKCOsQdy5p2zKdnMxwWw9+tQkYAeOGTywxd8tgZWTMy0IFRAjj4QY0Y2pora+2sKhJyQ\nlgWxFfRNUdeKbauAWjPSsW3A4A6aqS3LgloLRq0YSkZSDJEGZpEc8hTN22MQp/Ysffdr0RvBFaGr\nbrj6gE4mzE7j6+jWSwhCe2OORO5oMLEJBaVoAmjoKHXFhw/vIE2hldas7cHhq4Z1LbR87bSJ1aoY\nlfd6ANAlYkRAm2LUiqqFFD5v4InzM/bBw/NeOVLgScX0B3aIcLeVnRO9AqC9opUVgoGAjPvzC9zf\nCWqrCEGwLBnH44LjmQ3RfCRLhUwk4fxYY/+4cRqgxi7isIkQmU2nGM2HiNfix5/e4/07HsrryvWn\ng/NvxcRNvW07/TkkqAw07TjGIxQB0p1pJfPzk5MghJfgROMxD0K1PeJ7/emxeJvB73GKim7eg6Hk\nq7sPjHu0KAYiElLMUGRAow1P/4WY+otf/f/5EWLes9TgOCYQgnfxmRmpBdMQTsDhMDeO83O9sJt+\nFhbkJZh5vVgzSshaiQl2cIxpQJVSxOl05ARxcThkx+tSivjy97/DP/+3/4aQj6gDNMkXWxR9TLMb\nLz9D0Ml7draDy7f9uB+wjDdgbvgJEU1vjIjH6xWXUnF89gy/++Mf8PKriuvjI8plRd1W1FKwLAvK\nVvDjjz8jHxKW4wF39/fkHVvId3kDJKF5BaBA7IPT0rGf/iGosXIECJY3COdaTrzb3q8vK5/xCd9w\no8MHB4fpfcKsszUKbFo1vF8V2gY0k7kyVXdK2GRdL+j9Aee7E2KM6GPg/eMV9y9f4vzsGUYQaGvw\nysEhOG8wzgw6xDnMOyY2LNO89pYlO1VS2XPoQ422djMsYiJfHtx5PZoPY7iBftw1k/Mn2VRs1cRS\ncUzFYysNIWfIkqE5I8pAuTzgp+9/QB4R0fzStbgDIStGjoxbAAj57o8FvXTi5pHNVVfohhAho7tt\nFGZMkFu5usM4Xo155WxPBTPRYNBEDoE2rEZHvV4uWLfV9h4Tm5QyNAjOd8/w+tVnON0dkQ8L4hIB\nq4AgAPpAUOWsEd0TBHcg9MYvYB44l4sNr+jIB+DFizuEEPDx4wMQO52RYzD+uQVGGwAzhuHiBu+O\nBtILNUyI45P6Cvjkz+zt/MOsfH/8w4ocPBJEO9zTxVkrAFHM1gdErC36D8SHv04gTwnEqTDfMJsh\nN+WzHWAaABHecPsO4kjkvYoH8j0bd5Oh4/FEDHIMjA7EMWzqExkM6+j4+PCIn9++xeG64eHhAaUU\nblqApVuIePb8Hl/9/iu8/uorNA0orZudKshbNnaLZ2g8XQeFpMH70xbMQ5rBvHOnQDHQW2Xgs53V\nmg/wDXj79gMet4Jnrz5Dur9DHwOXhwc8fnxgQL9uSEbhfHh4QFwDDtsBY8AyTfJzW/MJN1yGI0dI\nZBNouqfPg4eKwBDiNKfyLE28KQOXtjNMdhswARXLGnwh+n01P+uBKRBi3FfSP2tFTIKUwoSBeKAn\n47d3g3AETvkDKOyBqgl8EiRwZJ6zVp5CMmFuqOBQSRD0TqZLDBFWvTsr1QK5IogxM+xUcB0Eh3hQ\nfTtM3dmHovRGh8Fa59izsm0Y3Wl9FVtbMZQZaFk74vGEeDpBjguwbdg+fMSHnx4ROwcnHPPBSn8F\ngg3Uhu2hENBjQAvkk5N5aJmlg+9+84H9g3iwmkmMPenWqO6T2OGwqFekQVyNaQriQohz2mWkjJgj\nYnyG892JB/LCA1UDDw816FTAam2ocaZ9LTX2upxmOcyrpnk/SxQhU7E7rG8k0arqZhm9aVOYcesM\n1gJh38PsPOhZ7pj67UXz0G7XTf07t1Xp7Wv+cfB++rBDw6qbaZnh72UoQjAih/6GBEEx5R1PNIx4\nfxgsMdpsEg4rhZzHG2NETFygTsoBMN3cQoi4v7/Hkg8YraEjkuECcldLrdh6w/c//IS1cCrKt9/+\nDQ+PD5SPq0J0YMkRv/v9F/jy91/h2cuX2ErHCA0p8wBxC1Y7SQD7KMTvDeccY96gEBfERM437WjZ\nrNq2jc2qWtBan9jnVge2rWHdKl58/jnOJlg4PzvjcHfC48MJ5bKhF1PcVfqxtNowKt9TFEFOCa2y\n/E6J3f58XKiiTGCzDrLjuS6Zd+wz7pJxd/IMIWCxewLLnB1H78NUiAK03uB8WQqNZAZH95vndPmC\nEIBlyeYoyCC5LLxmKVXkJdvkeeB+cOAz11FAiBkpH9Bqp6eLufndBnI/8J+ut72ZKGKHjMUxf65/\nLk8u5nFo61Erp924PL23jrdv3+K6rdhqwXVdcX24oJWC43JAFKDWgrcf3mM5ktdeNiDFBXEZiB14\n+OkB69sH9E3weFmxxIb0/AAz24QAPABM0CQpoY6OIp3GY4MYa+uwhupwUa67IezuhSKzJ+O2GE+C\nlwc8IazAEOP1D9eIj7ej370dMDrQW0HrBalntLrBIQna0AIS6S3CYdDDvNzZudau5oFvQ74tYPsE\nLFjDlM3fhrVUxMjQ7AdciAERYaqXhwnUJCjQ69ybbKpGQCOGDPg0LM/HYdm33FQKexD//xK0/9FD\n983lmfeEsmCEgT5X36ePX0eiP080XoRWCqefz3Kdrnsh0COhlMKmYeCiT0YBCylBhX7Bjk96ubss\nCyl2s0QZU8jgA2t/ePMeP/74DnUrePvzezxerrbQFUsKePH8Gf713/4VX3z1JZbjCRIzRBW9VX6C\nm8xTYfFBgDHoXdGMVaC+WKJAAhu0pVYbdcXS2qXxVKIBrXMcVgOAKMjHBWEkJD1gORxxOJ/xor6G\ntoH18UKa2XrhIOdW0TY2AIcObMVmSqqirStyTtCyQteMfliQlkyRUwrzOsG8JxAiYtpHvrlJUgjK\nuYqq03vF55H21hAaqV7M9HhEsBkpKJV0Pw7FCGjdePCWjXQ7uFPKSHnB4RBxdyecCwpu7pQStA+U\n6zoP/JAiltNxerHwnuxB2w9ZbU9FNiktEyqZJHeFGYgx8nkzvlmzzT22oYOCmnWd9L+tVrx9+xZb\nqaijYysFdStA6ygKnA4LlpxxXE64f77gcFpwvQzI4Q7IBzTw8x3v7hCfR1w+fkQYinheQFOmAZXB\nYcU2Fo3VqUJyRI4C6UCrA3XbMBqJ1CmmG2gMZtI04GPaeP1dYcug3Y3SqfaiqT8w+CiFNOfjepbr\npLr9db45lDBOMGinq0Fi/mbsT+cfZ+14chEisUhVCqKqed5AAkodeHjcEGLDujZoh1ENgZQWIAQy\nbQbXigRl8B7W9JS+Z7vqKYzs5lhWpfiO95yZyejTHtftepuN2gm/2v//5LncKhbEb6peHxnpB5f+\nlgL59fEBLiNWKMrlSpHQMHWcAKUUBmUvZQ3fjDGhx0hznchA3oeap/JuaE+YpM8rybFVNp17DLSm\neHhYsV1XXD4+YF2vqFuduO/peMTrz1/hX/7yZ7x8/RpiqrwxuKBCirYxiPm6mIKOeDoH1lK2bRCA\ndMCYFs2ycc80wGex+RE42UgQkBY2Lw+HI3FuBTMUM6tqreN6fsThckIvG0avaNuG68OF/uKNilmx\nskwdIx0dvZij2qBhWFwIRZVS7eAPBmvBlJ9i/i3xxqfEkIXhVFCyh0Rt+FWMtlGYi7XasW7UCrD8\ndcwmGCYvVrXY4AklfzzlzGbg6IAMin3GwLauSMEChwBhsdmNYsZH4ykd1A9qHjCYnwFgFWaLZfqh\nuKlSNzZGr+SbI1AoVFtDLxW9cGo7PV0GJ74EzAo72pAKHniCvGScuuJ0SsgHer+HwwINEW29IkRF\nPAlCDrg/3iFJwCll1MuKXqk6DonqSuk7ewshIJjOP7Sbvo0dwkEi4QylPsPHlzm7I7pHEYDWNzZK\n+46hw4JTmNDKbkDGBqpl54bz3sIXngg4P75Xm/UZnK4IzAlU3lgXtYYqVa5j3hN9YnQ1VLDVCukd\ntXeEIZBA+M6VoyEaY9v2AV9r3HltBr34euD77U9z8psALPNvHjT+uv83Dz8QnobzPS0c89+q+++a\nC/sXHr9KIH//849W6rHMWi8XbJcrMBTL4YAQI9ZS0HtDjAF35zOW5Qjffb03oPoNDKi9o5RinGiy\nYD58+IB1XW9OwoFgi8QvTq2Ky2PFxw9XtLpBZCDGiKHA3f09vvjqS/zTH/+A4/09SmvYLhsQBvKS\ncE53ZFIosJWKDx8+4MPHB/pCq9m5ehEW/dBJk5ftFiQhAO5v7e9VBmN+Hh3JmiBJIpz2BXD241YK\nrtcrBhTpkCivHwOtNqzXK1opKOuGdV0R1Ahj2tE2BgOMjijAaGR7pErxSqmVrv7WSW/aOCXmE78T\nZmXM3qph2L681YZRkCnCAR8IAevWOGG91tm1V5moI1SA5XhEXBbU3mm2BECDmA0tN6cEHqytXTEk\nkAkViRX7sGXpgDo7SlzEofM682fRWgA6EFNAyhECRWvFXc0hOtDKhmrKyKGKmBPSktFqhQ7CfbUW\nHJaEw/EEUcVhK7iUykMpDaDx5+Ql43g6QENCyh1Ahwrdr3UodFsRUkPPDUUKPnv9HHeHI2IXvP+h\nojTrxWRz+1OwAgk2NWkrs9wPIfAg9u66MYtSjJBR0YY7/dE19Hx8juNyADBwubzHGA1jEL3mnb2p\nyuTpABBIxIBL3MOemGBADEsX2CSkQcKBQpCEA8cJc2HeXx9AIrDkpTW0UuGj5JxKK+Ce6ibRHcr3\noIPwpwRbBzGx19HorTJdS1XRUOYh5Rm5io1b5JOm4dXkeSt2vO0Xg7iDMO5tYw+Rm+/6W5Anr5M5\nDpLr1K/db0qi/+HhcarS+qCfCG1aG2Lt5CODnWnRga02LEtByosFQafxsTXijBWAZU7vHY+PF5RS\noaDgAi1AQp9qyZSCqcX6VJiGEBFzxOGYsNw9Q48J3799h/DwgM2ojCHS2+R0XiEhoNaKjx8/4v37\nD7g8bujdWQ7MwiXadJeUuJCCN3l3XDanZNOMzJPCykf3TMdQ9mFuOKbD8MQUBIcUofGIQ7bp62Pg\n/v4ebSvY1hXXyxXaOOhXVLGlK68tyJNX7Vh6gxhDpTf6sqhlQqnHKajopaOvzdv+Vp1wco7/iTHv\noqFBwU0coPp0+KIN5E2rQTCq5sDYEHMkiyjYNYnehBQGFcOwJQbEoGDoVbTOQ3BeI+HBxoxLkYIg\nibNRLNPuDRA2p2UIYjggQtFCncIeCRkxMeCt2wqiKgEpAs//H+bebDmOJEnX/NQ2d48ASGZV9elZ\nZGSu5v2f6YjMyExXV2WSQIQvtulcqLkHs0+d68xIgYBEkiAiwl1N9dd/+bogOHpRYgLvbWG7LJEQ\nHakGoFNcQ6Lw7evCl29vxDnx4/E0jYMoaYLvv65sa8Y7JbwnpnmiDFgnV9s1lCC4W2KSCNGRW6Nu\nFsqtxXyFpJ+4tQUsd1U77JhQrUiD5e1GLjv78aTUw+ASEVBH8AknymGBZ9aVysl1OVeSckEmp/T+\nbBxfkMiYgqThXLTCzKDbIrjBFPNpIsZo13uxYBOD9cZ9rt2uX6xrr92MyI5RNxRPSm806ag2oth1\np1opKOrt56yaB2qmgy0XkG5/xzI3bXroA2YypG1g8fzEUtHRTsnrOV5tm9p9ev7ylPufh9tph3H+\nOdXTaM2aBtWT8svre5r9GT+zWf7r4w8p5Ou2Xx1YV6Nm1bEAq73ZqCgyilgbb1rDB/Nh6ePovEQt\n5/gzOu9WK/t+WGL56Da0WREiniM8uOCYlom3L28gMC2JZVlIMfHl6xvh7Y1/fDxxwQ/1n42Tvihb\nUaBTSmZdV57rYZFw6szBTobARCy5R3EGc4ylkvmAW9RXGEXcjRH1Z9ocWDG9uGKjuDKgizl65njD\nu+EBcr7IY/FrAQenQZjR3l6Og3p9DZQQo3XOCDkfJn3ujePI7EPmnXMZUEIZ3WiHbF1X6Y3WAVfH\nOavX8zmnjt46NQ+VZT/FNAJtFGDk5YbnhnhoOPydO1iTkA8K3lhJnTTB3qx4hRDwCmaC1dn3nTl4\nghdbTKp9j9Y7PspZL2jd6GmjuScER5on8gbburGXjHeCU7NRjeGF2frhBiko0Q/tgIf6NpGzFdj3\nKbIsE0TPNBuMIQLbngd2bD44Pjr8MIbSvXLUTEPQIDgCDsVNHm2O0jplMwvdM2DaGuThKaOdETk7\nIAvbCxgtMtOl0Mdrz4BXvDMWjxud9ckWeXXbr4f8VF/OIukwG4Nz+j0VqrVWJEQkjJBuJ4QUcNEO\n/y4YF35EFLbW6Ueh7UYIyMXYV63UK2mp9z5yXI3lcXla6QhzGJxsY7fYNXEu6Y1haQe+zTADCBpP\nyou/oCBLtTophy93yvP5wc8fXHUJ5MqbNQjLDsJzoeouz/NzWuRfgDhjwuJPVMj3nK8Xyvys+6De\nyfANHv4hpyBAFc0NkYyPgToCeS044uQOu9FZWyHPF4vAnPD6wNG7Kj6ZCU/0thB9//pOSIH7+xv3\n9zemNFlIQ4r82I5hzGMeyx2hVci1cMrPuxojJU4QfLTDY5gfpRTxIeBEOLbDOkC1LM1aGiUf17h/\nyuVjOL3Fw9WtW+7hS9ADMqCOxDxNpBR/B9FY13jKpcfb33Xg/GdCD5Ri3VjwnnlZmNJECN4WwqWQ\nj4PH54OPx4PPz0/WdTXa15E51o2ym4hqPwq6j0iuXs3Pwg1s2A1RlgpaO2UrSBecOpp648xjbXZw\nnlMzf/qQn0tbHR1g7w3FvOKhU5otSEtv5H0j+cAiEzHMyHD9e+4bPUWCCPu2vUbisbBzTizcoWQT\ndbkwpjTHPCXaUawjr4XgHUnUnDubpdiUo9jP55x1xKNBDcFxf5tItUNTZglobbReCU6Yp4SK8rmv\nxFtgkolSinVuzuGjY993Siv0cc2Ds8kleIIIwRXr3FXHUq+ZzUGwA0Ga0fXqcEsU543u18Grx6kJ\nsC7/Dz+Ut+KR4Vx4+gqd+PcFsbifdhBgHXYwC4J6DMqcyHX/HTnjJ088XU6Hf/65wtNxjeZc7IDK\nhfK5U5479chUlJhGsAYKOtgcavAiiOXa6pm0w3UNdKNTWZPkg0X6jQNbGmNcHBPzeH5e/GvCEDeC\nkV9CNzgnzP9ayF/fR396bUQgiA4zt/OLZ0PaL7jmZb52Yktn1/4nKuTltN/s9kL3QS8qOY8G+1XQ\nz5vaqIcB3/uQzxtefLryXTxy7WMpcnYQRgfbtoNjb9y+zPzl/a/823/7N97e36/DAOeQ6C9Y53zV\nu6pxgHMh+uFl7E7DKFsUtiEEAeNgJx+MoSLCnExJGEOgJxNunDL/fd/Z9if52Ad7wpa5fkjET2z3\nFBUFH4gxME02iqaUmKbEbZmZp/nF0jkXWAPLNIe5kzo2WCaq/G4khMuHxYr+abZvfjdHzhyHffTW\noHWbMErh2A8e68rnc+PxePLx+bAU82pScHPYy+Rc2daNXqtx4veMSLbkHlPRUFtnH7g+Yq9nG2ZX\ncZ5s6gxqHas3nH792K0vd1iaEZ2jZjQ4ljkw3+78+5cbNWfKfiDaxmJ6YKmtQ1XqsTMphAjiHLVl\nNs0cUsj7QXfw9ZdvvN1uLMtEDIHPjw9ya/ToOdd66PAOP5emvcMoAM8t02pDvOf25Q3EUaiU5OA9\nEZeIrzYtNulsx0EuNlkabReaU5pCcA6anhb8BjN3jPoKl7VxV9ubnEVVRdmPw7rLq8M2HDamyQ7F\nUsjFIAeHv4rO2VmeBfIsxie47L2zJiRGnu1p4jbl8jxXfUnrRSwfoKyH7XJWM7LbnislZzqmrq37\nsF4Inuk+o85Ea3EKxN4o2ji2zHxPuOAoApZhqOB0BIF0HBa4EuJ0KZ9bzbR6DDte0Daw9+H9ZC6Y\nzpwTx+J2ADBWI4btxL+iIJ6/s9ndXmnziAIdvveWKZyMGDDqll2XJ5B1TuCvg/RfPf6QQl6HO6GJ\nPAZZfywsz06ko1e8mMnJFbo5qp1e5fbEudzgTjlxHxhoH8WqlWbslPUgTUqUxvsSeFsCMY4T2tmy\nSOVFf5NzBOpG+Yri8N642DGeUnzjSk9jUnBASJYlmWJkWRbrmMfo6GT8W90yGLftxnFslp04TYQY\n8EP278bUIjJMqYInhkCMyYyFYjRv5miiixitI7mmPOE6BH4CoK73QU8wWX8yyTrL0U/Cgz4mpjOr\nc8x+MA7PUgr7cRhfett5riv7Vtj3gy0ffP/4wcfjwbrtPJ8b93Xn698OtnXjWHeObaXtmV4L4mzZ\num/b2CXwOvSDDuc7B+7F300xXAUqO+jltFw1oZGVGIMRajFY6RhhxdM8A8EsfFMffOSGisOFTlTo\na0GzFYYp2KL3zH88Odo6Euhba7TS8CqmBciF5myEbq3TjkovQ2XpDqRUDtdYW6b2wbJpHqXRxMRC\n2rqxR3qzycHbIq5y4rcg8bQ1MEbN2dz9HKpyWRs782iXIOA8TgN0uQRrrTWjBLfOmepjw8ZLUHVF\n5bmfF9Wj3Dsw1FNHVxuGa6Z7OTfmgzaglu3zyfPHp88iO8IAACAASURBVBXxdeM4DlrrpDjjJVGO\ninqzQQ4C3antH3yA4MF7Wsfu5eBsuerPZeRgbYnpKdKc8MMBFCIlC4d2lGpLUbWdzJmD4Z0we6Fr\nY9ufQ+B2duQD9x6LWr3ur5e65fzaKzTODdfSONg6infTOIwr2gum4nTX377g1POH+hePPyYhqPfx\n8Vo09tOPfGzVX0sE448rDMpdJaaEHy9VG/agl0McerEWLl52tZSdY9/pNeDaQewHegi1mHmRDxHn\nguFndBOY+Dgsa61oBF+JyTFPjmVJ+JhAHLWdOPJOPg5CdMxL4n6/c19uo5CH15shguBN8ZdvlHrY\nAnW5mQDGB5yEyxXy7KqdP0dZd4213sk1hl2UQATpch7ijEbMIKoBF3C+vuq4ljLXNWI9hBVDgxCc\nQtBIPIv4eJwdxF2Hp3y1bNGSG/ue+Xw++M9f/8GvP37wuW5s20E+TLS0Ple254Pt+WD/eBoNdH1a\n0a0maZfrIBNEK06E4KIVyKbQHUsYXh2AlkqV0QnXylErWzM+tR9Tam2d47nRGkS/IMkTh3/29x+f\nrFumqfD2JVmvWiuuewbgSjkOtBsWr32oKFUvq9x8ZKR21ufOlgtxWVBn/PNSFTq42qjlE42OHJSH\n7IONBe4IhOTQoHTX0SZoUUorxJtDgqeJUGpDSzVYJQXEQ9urLZ+L0qu9jgAxBeqglOI86p2Ji4bK\nUWpFxCacVio110snIWIobnDBYArtQ2zlr+toXC1joh7MEcfYcaRXNF1rHMdB35V9hFY8fv3Bxz9/\n4/l8GuYNQ79wI4YIw/AOLE9efcCnwDRF/JSQENDm0Ajdd6IzevLpleO8IygEH7jfFlwwqNX7ZLa6\ntVJP+uzQIHQxrcCyLExzQrVx/Gc2dekwtxoAJvKTV81534yX4fWVK+rRoz7i/GwQChXnjO5p36Ke\nd/DvW/pRN/5UEv19PziLxFnEVXUkAw1IAy5JrfeBNhgaKSVimu3PilyycOdONaAQHUzzTBiF9oQL\nxPTh9JLJ24NWDiuUTsao5wyvGhz1PtKJTnOnokqfJsL9jk/CbYnEFOga2AMcUckJpvnGfLtxWxZS\nNMOnMHjR5yLDiaDeE+OMaiLESEoTKaUhN/dXByQn3nZuleQ13oKd0z+/74xswNdEdq5wGG6NP+F2\ncEFTp+wcTiHEiY0yxnC9DhH0FPgY7JGP4/IN2XeDA47jYF03tueDnneidop2Si3UbWN/fnJsK70f\nhCQs9wnv4dtf/srb2zwWiXpt9DuFum48v3/w48cDxRPjxKfItRiDgXW3ESw9FH3SOssc8cGRS6Vt\nmdLgx9Gpx42YHDVvfD42WlPStFCc4hdPnBx1zyOA2Z47rdO9ZwozcSzpe+2EfnoJQXifeEeIy8Ja\nDh77ClLRbB+lFI6tsWvlCA1xagZoz4PpnvBzQAJoFXoVeoNerMOuvVNqp++FumXmr++o2NcFs0wo\ne6OWhgum5N2Pw5aB4iEK09tkAcaPjZILWhq1HlAHc2TYIZzwgRuOmqhBT27Qat3AiA17jrhhtTDf\nZKjqB8+8Ketjs2s1OINUi1F17X4XxNtkGeLEdLsRXaTWbPqN3kkob8vM/T6bT9Fx4BzE6CAKxGAF\nstg+plZFp0RzHSee2zzjouNolV47p9hLxCHRppKBTYIzp1bn7NC04AkZGgZeHdLZMQ049pxOZFwr\npxe7jOfn5zdCmOit0PdihIOB90Mb+zB9fU/OjvxPJgh6SaLHC+gMXjlTzRE759TLqygBFowwDYfB\n4WPi3BhsDGIRb0yHlMzf4fx+OvDKXo3m1ntF63Czc9DPgi8Oqqc7Rz190rVfysscAmV9UtYnt/ud\nNM0vpWa1JJWyrZRtpi7zwMetOJuHh5VP86CwhPoYo1Hjgpkc+YHVX9mC15sJA5y8qvZZ6M5fj19w\nUhXPlvx3h/tYHJ7Q02uJeGJy8LPbHbxUaYIaG6E3arXlZsmFY995PFcez43Hc+exrTwfT9aPTx6f\nPziOHRBKaRylmnz9OEzc0ipeHN0LOB187kAItrA6U6Ja7uzPg+dzY3sehi97U63WscSdptmWiYOn\nXmo1fDhXbnNimsbUZQjFtfgUB3lbeXyuaBeWWydvwnHz1FukV7sRQ/DU/aDu9nym6XZNiy4I0jHx\nztvNAoq7cv/yxld3Y6s3Hs8nmm3h+/HPH2zPTN4L1TckurGXMY8XFJwzR8jeQVuhlQaNwS2WYV51\nevEbhRZngpdeFZ9GCIN35CEiUzppSrYI9bY0D4tDU6d+bPSeL9jzlI0LXB2DKhYf6JR+mt5hxTjG\nZL4qzrFE5bJcHruI7bnRWsMnj3ojDrRitLsUJ0IITNPM/HZjmmecgKewPy3CL6gSupqwr9qHA6Y5\n4JJx5ls3V87agaIkSTRnu6yyVzy2w+pqhz2YuArnUCfIsAZorbFtn+w75sfTu0Fi479rYynwcorR\nUcSvVebv0BCjrcZr96XOD0sFc8K8GrLRhVtzaW3MmVb0rx5/DLTSzmBlLuhAxIryyT91ImZDqi8f\niPPJt2YMgGtDPJ68npiwmMeGO3M1YbBZhn/DT+iACUfsxTfIQa1rf+EMV5GQ0bWu7pPv7p8jASZc\njnyn5aVzhl9PS2KaZnycEBdeS0ZgShPzMnO737jf30A73jmac5ewhSGWeZXy3yNk1y3WDYXrQ06u\nY6b8XcLMT65+OhbCbXTTFpIMp+zDXX/8dNRwv/s3e23UfHDklXJk68b3nd++f/Lbjwcf68H3xwc/\nvn/w+et31s8ftJpJcULE0YA8FnKnQ5WPhvm2ZmG6XTvqfuLTqrF8jq2wPQuosUpKy9RskJvBG95s\nXo+MeM/RTICU153tOFhSZJpn3t4XfBS242DfdlrvHOvB88dm3i97xSchzZ7tZqyleUrG6mjVuigF\nwmRLKxVmP+GSLbN/+eUbedvI687XWyK9T1Tf+M9/gFbH8SwcnysUKI9K0QJTQCbLls1did0weY+5\n/9WSr2tfxfjXLjq0wV6tkIeRHG+MKpi+zPjoTEMQwnXI+yQgnVraUJVGJHr02KjZQo1PuE0uH7PR\nbXYxTYU2HM2M7WzvyhQm4ggYMVXyaFo77I+d9cdBHsEiPgWDazpEF1luE3OKzMvM/cs7cQmoNpJ0\nvnfIh+0epHc0D+phaUiHaQqENCw9joPSFEqnbc0U2N1IFttjx7eIv0fze1FTc4YQaM4MENw4fFqt\nPD6e1vSpGtnCjSZSBZH+U/1xZt9xfX0UJH5PJBTMcZRmpT8NLn0fjBm6mOukYJDNOUGfFEjav6yp\nf4xEf9tgdBMx+otj6pzhzTo29HHEPNn/G9j6vtrpN7pbwUyCTtm4itLLGN9PmfV1LMoLahHrLGQU\n/1NU9LPM/8Xs4Po9cC34SinD/4FrmjB1GWOjf+Ja0PvrjHbOMU0TyzJzu1kh//rtK99++YUvX74x\n3+6kabEb1ZswRuVyK0bPX50shDExtOHt0kqmZrMMeLkG2sXRB5RVmxW/vB2jCHamFI0xc411Y8c8\nipYTwfvI+njy4/tvfH7+Rt4PynGwPVf2vbAdhWduZIG9FB4/HrSajWOdImcIgx/djIrSGN7u7lqO\ncEr8bDy1G6sW88oQ8UzBX86B9TyYWmd9brRS0FaJ02SLUqwzrbXzfGa2rXF/e2OZF9Zstsj0bqHK\nKVH3zPOxM02evMHnr5/M88Tb2w2+vfPvf/vK3/76lb/+5StvX7+iKuSjsSwTy31hnicA/vn3v/Pb\nP/5BSkIKSqbTto1aHeVotFY4tpXn9w/WfBgsED0ECG+R+S935G3Gp4hH8Yc3lS12zXYqwXmbCmtj\ny5l9L/Rih7oLjjBHajff/bAkpvtsewJGSLUCRSm9IMlz/+s3VoW9dFwd74frwzLBGb6PwQW9NkrP\ntGAsGxmpT3Kyn8Qgj2mxg8gH62TXHzt5LbAVvn6Z+LJE5G3mNs/WJdPNhtYHYpqYQ0QIrM9MWDzz\n28I8R/K2Uzd7vkES0WPWBuJoa6bnCrVx5GoWw90Sv3pr9F3ppVo3n5JB36MhbFoQHN5F9AqyOT3D\nZbgyWmF1IhZYQrRDobrBGhqcfpGXIlQFrY2uu/0+OHzyVDoSElOYKdmuX3N/PBee/SfL3T8Ra+Wk\nFvZrhrAC1Xu/RDwJwbtw+ZLbkxj2rnIW8g7akKb0PkQ1vQPmXXH6fJ+PU5GlF8TRbRw6w03/RRFv\n7fV95IIi7OvGPQZkpKCLUHtDh8cFTsdSd3g+yGsMjTHySJEpJaZp4vs/3/jtyztfvnzlttyZB84e\nl4WYJnwMVxoKP6X7XMyJ2kYq+kiNGVTB0upLS6QGKZUzPSZntudGyRltjWU2do2FKvQLi++j6NdS\nqaWxPVfW55M6TPpbtcVlKZWjNNbScfNMd44yPG/EWWCI9lPscU7qxsjp2i+PFh24tg7M25afyn4U\n9i1z7AXnMiLWoWg3pkjOFe+HUMgJ3cvAhBl2ENbP9KOwbgcujeVdsetKnMNFj2YLQmDEDbZWqFnR\nZtPjbYpWxN/f+fLlfXDvI9M84cNQx7ZOOb5QjoPn4wf582DNmf/8+we5mKfQv/9vf6VXoVbY/t9/\nUvdGO5ot7KTj5sCxZaa7h+DQ6C1UecBprXdqV6RZgf350FMAL/ThLe+nAKdMXhTfRupSxRbjQyzj\n3mdcjIQUCc4ToqCuc+SDU5novRB8MGusNg7bIZ6aUmC+2Q6r10yMwjwHcI7gKlKhbJXe7J2N3pFm\nR4yeeQrUblbRrZsNBa7j1S75ODmjHE6BNAUckZ47+TALieCVOAnLvJA/K8WDTIE9j0PLC34OaBQq\nRmMO3tteqhhB0PgrUBSbfHwa9adZh6wyFpcnFu4IMZHSjBOhbBtHLRZWrTqYcOP9GI6f3Q0bjt7R\nOmqNnH9u7Ok4Kdbt6sbtHT7p0b9//KHhy3bK6U8dcbPuCEHDq2heOK6cDE69Fgp0Y6ac9DhLoekj\nj/D3HfXv/u2RmH3hxxcsM36DXrS33jtnqvrr7/9EUZSzU8YKZxkip5GH2Ac7JDg7zbva+OedYw0e\n74Qfv0b+ORnT5TYZL/z2dme5v1lBn2YTJQ1DKeeNaniq5eqgBpr9beEojVzMQKiN56itUo6dbVvZ\n9p1929jWzdwlW2NKidsyscyz4ZjeDT+Zxr4fPD+ffHz/NCc/YJ4iPni6dvNGL41cO1uF2XtcjK/X\n/4TOGIf4TweFODf8ya0o9BPjdrYX6Qi1dva9sG8H23agrRCCmDgEy97Mu9mYumRCmO6HPemY6iTY\nAVhb53nsyOqMutZ0dLDeut9S0E0ptdo+RRUoY5nnCA7u94Vvf/kFXODbt8ByuxFS5KiZbd/QplQF\nQuTH82DLG5/rzn/+55O9ZN6+3vjf/8//FVXPfjR+/b6ybfnSQkgDilI3E8GJhx7cuPGxgtCaYdXN\nhCviBEkBHUUJp6gzAVyYAqXqUHeaavO0nEUVLRVo1GzwV5xmljkyzZHWTQ4vzqAQH2GKCdRR9kpI\njpjM2mJZErfbTEiR7VltulMheo8k6EsiTZHTzEwC5kToGqqWnuR84CgVLQUVGXbJpsAN0V22tuIc\nt7eZmCr7sV0ZAOaOKsToDLpwndK6GdYtkeIwWEUECZ4wR4IzaEOBHJxNhKWCj6OQF8y0ZtBe3djM\n+YCLM9P9jSiOowudHarHqaJDSd6AXoxeTfIw0ot6MSFZH8spfyr0+9h99UEhBVPZSvyX9fQPKeRH\nOcc6RWJAhwBhXVdkwA4/53jWVs2+afh6lGKwCeepORYT3jt8UJwfIMY50pwz00/4sHXkP3lND85j\nb0q78j2NUnfS9k6XwuuAEBNS9MGNbacHxF5N3i5nsXI4CZaGMqKdrgUiplQ35V0mHxufzhNcIESz\ncY0h2q+H4tPHaD4ug5apw1hKRCi1UlqltE53HlwAHwkuUo7C48cHHx8ffH5+8ng+TSxxMkO08/Z2\n59vXr/jgmaYEzkKMc23spfL53IbNbxjJ8/bePR8buZrfs4Q4dqxWmC+HPG+S9Q4w/t1BgbgOUidC\nPnZ2c5DCh2g/g0ItI5i7ddbHyjQFYrixzBP71sh5RbUSu2NxiTiPsdZ73H3GzdbNhEG/q6qGh6rR\nztI8mSw+Ctor+ZmpY8nSMY+Pozb+8dsne6n8/Z8f/O1vf+P9yxvLbWG6zahjeNpbYSil8fePg3Xf\n2faD4iItwrN3/vvf/87+yBRRbl9uSEi4UtjqyvLtC/OXG1SlfW5IsCzUXkGb0MWEiE4cfvLgHF6F\nED15L6g3FkmcE2mJeC/kAnk/qMcxFmp2TedeKPsGm41JgmNZbvzl6xe0VdZ1xbtECInbsvD16404\nbIT3LbNMkRjMkuLtnggJmhRUC+sz8/hQbvdEigHvxV7fct4rSiowB8cUjW3mY6CXSoiBeZm4p4k0\nVfYtU0rlt79/GP6/TLy9zcMwLlJa5/lb4fn4pO4dr44pRRJCEFvqrlpQujVF6sdS3ZN8xKl15L47\ntFT6Xsw7yAHBIWJNTe0VdYKPiRATGrzpjHvlKHl48xtUGOcbBE9uRhNFIEwBpaKlmrZgoL7eWXNU\na2MrhatocapuA+7PVMhrLWOp5myExkYL43Lbsujncfi0ju0KVTvfH5+UUpij4UoyZN1dbRFHrQNf\nfXV/ei4a+6vw2iF4LhMHC0YElXHa6gsrtp93MGDO5cNgcIAtcE9Hwj03avtJXSqC0DjUBAbhXMSO\n0APtMk5hgxOKVMQVfC542fFuSPaDsxth8F2tKxjfw374IdqplDosc33AhYiq59grHx9PntvKum1s\n2z66i0GHrJUUMuVWUdSMvFoj54Nj28l7HuEQSukNKbZcrbVcP7sK5tfixZaVTnC8Yu7OHEaDuE7G\njO1AcOZ/chxGC3XBk8Rf1FCjZAYraN1Rq1CK4NMo2OKoW4bikCooGzp7uJlgSKIpSLVCEyFXJWCH\nVO+KHAU8hBB5+/aNHA2iKtk83psqRzEjpiM/+Pjc+e3Hzv22MC2JNM/mkQ+oHhcF9vvnZiHITc1n\nRIWilf/49Tt162RtTF/u9JiRGgl+Jn5Z0OSp+UCaLfhqq7Rc7TVNJuKx7RnIZEyRNNv1Vtdii8BB\nWxNnBmMpebwkWiv0qnQP8y8WKl623aiM88IUI9FbARIRbreFFGfmeSbFRPBG9/MpEVCCU7uuMbWz\niuIHHv342KjHwdvbjeW+cLvbkja3xtGKKZw/DpbYeevCtBijiG6QWgt6TcVaO8djH572UHrFj/c1\nLRNpSrQeyRwXXKRqqWCtNHLLlsHqTeXpMaZRKwOCFLseddQTnBt00jCIFtDFpuoQImFKdDFef22V\n3OpFU5Rhse3FMc0LtDwsPWyikhgIKUCt9F6hNdsDNZuOVM0oTNzpHOlPUOB/ePwxrJVaTZrqxhJQ\n7IedlhmHXRCGTdbB8BmcClWOVvixflJyRu5fiGEmehuZFYNUei0waGunzHwo/c3bZaRti+hLPXnx\nWE4UanT1LwifMwZNgdbl5Z2MxbPlYouVUg27PE28zsaz5kL0fsi77Q6UwUxBreg1NadDQUH6UAQ2\nevO0Kla7f8aDGAUSw9jaMMqqudjzGsWvVGU7Gp9b4ajFvGh6R+Lw1hBHL8bq6a3Rvd2EJReO9SDv\neSyHDF+szbxTZNA65ZptzG+b4S9u94IbPjDnRPSacs6Fp9ncQtdGLooPlakpcTyH4B0xzaRZUTxT\ntkg/woR6E4akOZE/do69UXLjoDKlGz5NnHsigTHVdbSaBW3rdqPregx/ksB0uxPihD8OZF0ph8Ez\nRY0loV1Zt8x+dNL8ZJo8wSfEj6xYdt6/3Lm9vfHYsnmfuDFCO/MDWZ+7JTnhCPeJFASngfkWaQ5q\nry9oriv1sDBqSQ4XjF3hBFwQ3DQUw02I7kb1mfI4DEIsQ7hV7AC1aEBHpaJeWP7ybsZgHxvbx5Mk\nnihuxKsZjvz2fif4RHCBVrqFe6TAtEz0Ya3hUjAeey1DvSjj3qispeDHtJ0mj0ue2BU5dzW5Uoth\nxF2EsARTlHah9XFfDCJBb2aa1WrlsRazxcAzLW92ULzBx/cfHOuOlmpwZjHzvTYW9yqKLt5onbVT\njmJwitjT7gOecs5fTNzTvK21OvzdGfRpP/zvZXx/m7ibOk73zSkGWgjUavsyFWtUfHD0vaMZC0oZ\nyvSxzRuMPnstUHgZavz+8cdg5INHfj5xnLMNu484Os5ZEWvd8N0Qk3VrvVnqvZpYwXs7DJw3y1hV\nxXVP945SzIHPeQ/lhZe34dViSzB98boZHSKK6kgt4RTQ2G9lLJJOjL63s7s31V4unWPkFfafFrg6\nQLBt36khEKeINBBRRMwwSJ1HwhAnjQWgc2ZNcIZiKHKlQZ1sm96Gi4P3hBRtZ4DQrRVBugkMWrGl\npGpDaYgXMy6KBuE4H9iOepkW4YWai3mabwfSlMlHdnazWOhtWO/adHCqdbszzksfFgm1Nlx8RfS1\nNv6N8XzsuZh6VJwzPj8OxYOYL0acZhDH9P6GxkRqyu0vfxmcXM80edbnE4dn/ceD9VjpBZbbTGyV\nkD19zbQzYaYI++eBdPjy/saUIhoSZS/2UStVMWx6ifgw4VlouXCsGy1bpxtCpAOlFrpWtOZxoAo+\nNpy3CWPdCmH2uOj4eO5I8gPvTZeLo5ZKnGyJ2Cdnvuet4cf3dwJxijQ1+bgTxSWb0CwP0xtb5ZFh\n77Rnoe+ZkDxSrQlan5uR14bbpo7lSZjgdntHvryz/uOJbmZJUF1hSpF5mlCFXDr5sFhBccKyTBZ4\nIgFJnvC2cDweaOn4FI2K5z1pSZSjsOfKx8dqC2HnaA5ElWWacG/K+nnwY93JTvjr+y+8vd253yam\nmNi2J4JQKKQlgMeWqMGohXU70Gr++vPbRKsz3kHPDS8e5zPu8CRVamuU2i1MfCACEWOXtdopDVRN\nz+FjRKsZjM0xWMpTswzXdmR6h7DczE0yBPIJ44oY/DKm7365jFpN6jIWq7VZZGQT6JGzITXxHTaF\nSkAuF8Y/EY88b/tPUW2GNZkxjw76XicMNklXOKotdGqrHPuTmg9qKfxojSlGpinhxGPBLd38H0Yc\nXPAByPaiePNh2I/Mx+eTGM38Kjg/+OXWQYvn5ermBhtgvNFW7Ieh1LDe7Qq1K6V3k00P1oRRImWc\npDC3aUjtzdXutGgVwI/ACf9TYPKVvH0KMTh9VIZZfrcU+qZYFFiMvKQIYymmFjpbR6q9C+Y54Qd2\nH2I0VoyPtMfOqfLXjiW4DDZJL80Ke6nDCdAOYPE2Cdiy2V6TkgshWdpNa50qJtsf/fAofras8kPA\nJd4jLowR1rx0QkpMy2J+KM7xBce9GaZ+0SHFWBTz50wtjf9Y/j98jza5RHPaLKJId/TeqNrwfXhj\nqFCyLb1672zPHfWYRW2oxjpynR6Myic+4Fqw26z2C2aSbt4dRz4AMcZHiINv3E3oFUwAI1FoWmlZ\nTdhTrVNuR8M7QaKJUow6181grRe6GJKmansZShs8bbt+9Gjo3pBnpT4ymgu+K1MKaLN4PV8caTaf\n87ModhpzB+8Uguft6xvN77jaTGk7przjeSBqRclPcbhNdrZ1gyHmkmI0PxQLRplmJAQkRfZ1h2JT\na3nu+BSIU6J3U1j2EcZtzKxozqRhOIcGx3K/M80z2jrhllgfO3R7//JRqHtme2z44MxDBnMfbQ6L\nTNRusJmcFiBCFTfSvMySoNViMX4Euthho60MBpXSpNFbGZO8WQBQG7rnQUE0KmMdSWE62HnWQI29\nmXac7xdZr7dz0+mwUW+oaM+evCtdqtWvn1h1//Xxxyw7t52YEtGqqy09uwUWay90aXhv9MSmmClT\nrdSSWY+dPIx1sqz88uUbcn+/HP7AGQbpTjvOF/bto83WuZgHSAqeFAPRh8s+1Qq5vAQxY7tvzoBY\nh/xTIe8KXd2pa7mKqBMhhiH7xw4A78z4J6YwZPuWbHPi7875q/if8t7xw1/wkj10cFtfKe9dG/uR\nL0bNORGcf6VpH8XAE7x5pDtxZtGbJryL+BgRNUJtH4Kr1ju9KnnPHOtOOYr5cGiDebbRYLCGeu80\nFbRUXOsgZlFrXUcbUI+Noozlk0+RaYpjogosd4jO83ZfeP/yztuXL6RpQgXCdANeAb/29IZ1gBMe\nn0/8LTJFtYLoO03MYCHNFsRNqRY4EcwKNfeGdqHXyrZvuOCImKuklkqXSqWar4qoOWT2QVsdyk2z\nQwioHpcjoNktRFIKxDmCh4JNJ2XPZgRVMCirdvrRTIgVHEzG0bZ8S4wpIRYOoWDe7a3hJlM199Zp\nh8LecRn60aApwQsBM5SSpiSXWNKN+21BW+NwQm2FUIFWwUGcEyoTXjv3twXpnbJl2p5BzSNenJpv\nzRDZiPdodVAKdMWp7YymeyKlG9P7G+HHJ2XdqWvhOFZcsyu0caqmjSrq1Ja25Eorhdo8MQbSbPbK\n4gS/TCyPjfzMPH77tL1I7+zbPnYzJ+vM2MGHdgojxo1z6tafYNZKPQ6DJHu3muo9Tps1ZkOE1ZvB\nXYopaJUxoWeb9ry3a7PLy1artWq+N10ufJzarC50s2SQPhrF39nXjloz8kBFsVokfyL6Ye99FLpX\n56LDy6DWA7TSC7gU6c6TC6yfH+zrSu0vzrJ3jv3I1FpJaR6+KBblpBgueCojnTfZtwsWTHGMwIWc\nHSkEovO0YnSzlBK5DkxNhXlZmJeJFAdzRtSw7a5WBPS0GBBSimao5K3zjSGM59xowbrtOPzJvXO2\nIBrFVnjhijrwuoGhcK1dR30+Va44JXhPbY1ff/3NOtlgS5zTcU5EkGa+EipDhjcKqowTvvVz6Tx8\nL0bHDI6jVB7rxvr5MKpfMbjGDZVb78YkscAHwY19hHTrLUytZvBXECEpTL3bUm2emKaIuIh24du3\nzhwC9/vC+5c7y20BZ2HW1r3wej2bsZcKBs345mfkEwAAIABJREFU5JGbZ3ILcZk48oG/zYT7Aimi\nR6GtG8dztWVqF2pX0uyRbKqCfd2ZW+fr/e3KOy35SZ8qLpjFsASPhIDDkfcC6ukqzLcF5wWf/GD9\nTLx/WYjTja3sfKwPtDVqruT1ID8t9Ue64KoVbxVBZjXlZTTs3vYYI+Ch6NhldNxbwHWhb4X67HgN\nxDBTJ9DecB4Kp5eJY5pnphAIvaPS6ZOZPemjmIef77ilEN6FlCaWaWbyAdegfH1nexw8toNSDt6/\nfjGo7LnTiyUsBWsbh0d7MXuC243l7YYTOLynBuvat80sj+fbzJwmoovsa6bng/yj8SOv1OOd3r8N\nquNolLRzf7/z/v4FzZ1fp3/yI/zGwzkyynFU2o8V70C8ddU9eLRHXLW84OPI1NrpMRqTDADDq215\nr2itNOmUsl5Oqqebo3PREo9wBnu2yv4c+bctj0bK7uNWLLcgOfORpys9d7w3mFTagGBF6TK8VAaL\nzJo/u59zLmbb8D95/DHLTjWf5VJHbt4A8Q3WbTjptCRMbzfUK/ta+Px48Pj4YNs2CwdGmKaJ5+PJ\nbXnixDyQpatJtGu9otIuep0X1BnzxdVyqTFlcMJNnGJdeoiB1KNJfZ1YeEARei1or8Z9BU7l1rlM\ntS+d7meDoSGnr7iad4SIObT5U2U68PhRyC2qUDi3LKcy7IRVdHTkbiThKAYxLMsyGChuQCdysUPA\nBPi12/PxIwnGDSFTP1k2DFvUwVUX59D9wMVInBdaMaGME3u/2gmpKKgOVs9ptCTnskivxes04JK3\nr99GkIWN0LjA2Bjh6aRk1LOYLAHd9W4F8DgoJdtU4UcAcO9DUCGk20R13Qr7LrBXWltpjjGdKT5A\nSgGnjt4UmYwauXx5o33/oJbK5+eTNBlTIRIJEgz+O+0MxnQRoqcXqL1xXxLzEomTLadOCwdlqG5L\nRUvD1U7E4afJirOCHtVyJZ3DzRPiFVynjcP0PAxr7+A9flqY4oS2yrEf5EfG9wqhI6Vf7CoXPH4Q\nnEQc7WhsudCd0m+ePkXkq7OdTbdFvzYll0JrD3SeuKWJ29eZEBwuCX0DNw9+dDOYygVnFsNzHKym\njoverA3mhZQizInJR5wX0hrZhl+N9o6Wgnfgl2iwXLBrquyV548nvSnzMtnuKNp1j4f3v7zjo2N5\nX/h4Hqzrwb4VeiuE5Ehvi0FVw0JX8YQzum90+KhSMO8Vu4eHKlPBEa1DdwL+NMCyBbk4O7gs2ALT\nLDgLZL8oMCfduY0lqzrjyqsb9WEcEHjDwXvBIp9tInZjF3ZiMX+uZSdiznilom2Y/3gxIqXayVYO\nM2CXGMl7pewHx7rx+PFBLZai7tWxPzee8wOakmJCBjuk5DLGfXsBDMUxzNv43kqKAxIZzBgnxoVN\nyfilKkJtSmndPldh2wrbtlrHM/YaRjO0m1BEjLodPDUWw7wH7s1g5ChizIMXmv0qDvL6fLo0/ASo\nwPUW24fzBl94Z4XcKJv8FLhxejQM7/TecQoeu+hs3hwyf+3XGO+cw8Vgn4M3vFMc6/OB0zFFDC9q\nxBmnVoaPd0rmmZ4mwptdYre3G7fbjTSlwYmPTEPkZJqBoVbVTq8Z5+RynsMJ6gLSDOvfj91UlH6Y\nPo0Nv4oS5kTvI6C3KH0/6BjOHeZk3bRaKEPwwTi/0UEQ5m83g+zWg3U7TPyTPFOczC7C2c1e3RgM\n9CUSEy/c3mbudwucyLkSnDkG5paNolk7gZFOkxykALhLPNS9qUt9CiDmNaN+KJiLeXBECYT7zPLL\nFxyF/PmwZWYxUY84awgQaM1ixGwfZe6JrTSOWqjSEJlwMRFu0Yp4VXxW01J0pbp80WWnJTBLRCKQ\nhF2Eo3XUKU36RaLy00jtUh2e330EcRu04aLjFhYkOeTpOPZCPwoK+OjNonpOKM0aj9zYnruZh4kn\nRk/RQktmqeDnwOJu+CXQ005DWLfDdjkduoeqZgMBRipw0RHE00eWp7ZGxuoNXUeG7GnhEUfkoBly\naa9oM/63w/ZxA9A1YoSPI0ijXnscUbEd3EtifWWB6pDzKwE0cDqtnhGGo3fj9Fz5nz3+kEIefaBs\nB/u6I06Yl5mwWPpLyZYNWZ+ZsO3EaWJaZmbvKCGyj+glUUVqR4/C/vFg/9iILuKHW1iIAbp1vx4M\nL4z2hqgYyyR4DE9TKwbW+1hHHL1FVhk9OaLiyc2R88H3H4113ei503IlX6nlYwGXTFUWoolq0mTW\nnD440hS5LROilZgibtjx2o990iE9OpwPdfBnT9hFHYjaYrirsW1kMFvsTR+fnb+6so7hhoqFZsjo\npruqTQGq0Dq1NTtLhxugjGbQB8c0LGC3/YkEIThn3hkScC7h4zKokw6fkvnFLAsxJmKK3O53fvnr\nLxesBJbk7sRCRC5rhC7Uwdl1TjCczOxRz/2BTSRjg8zpPGN7AXHWXWppaG7U/UClk75OPy0XrVCF\nSQje07yCV8LkCUcyLnwzDN07xxRMdGMCNU9uhVwqZcuUteKnxO2Xha9/uXOLEWqjPAvUgHTjVdOs\niH+939Ep0Usxa9QqpgB1Zq1q9FnM92XoB451px+dpJHw5c7X//a/8G//1//Bx//9//DxLCRJ1GiH\nXOuC89F8ULZMK53bfSbOidoKDVvS7tuBb52kSncTfjYYReqBqknnfTIf7nw0dl+Yp8AyT8S75/v3\n3ax9cyXvOwW7j9oyEVIwFe0Epe58/razPx+UIyPYspQAfvaE1o31EmYStsSuxWwXnBa886Q50pr5\n25fd3mmCEJZo1yjmxJjeIktNfP5Qgp/Ix8Fv//EbcktIihANpvXibBpVw2sqGGe7N5w2pjDTEUqz\nIov1f8NewiyS6QZ/ntF3zinihu2IWAevrZlTK45eQDARXKMi3YE2ei80sa7c4S7nRKNImujLetEJ\nRIetyf/4+EMK+T//8WOcTjZ6xTDjNbA+VrZ1NwOm7ZOv3+6E0f30vdBzp+du/hBekObQjDEAnLIe\nhuGmGInRc+zZeLdj22tczBdM0arS3VBF8pMbo1VAUCugp2y2Y51y62rb961Sd/Mf6YNSV6spx3AD\nRjkxa+fxQQwymCLLbAU+TNFgnBSYp8Q8WfqP90atOxe24ty4WBgnuYHFRS2Mo2Ne1EZbMn636Ih4\nO4VQQ8TUWkdKsdFRzNFPOjD4uq0q0eswI7OfuTlwTvnyfiO4N+63mS/fvpLmBXGBMrpKFSu64m3Z\nHGIw86OhTBU5aZcyLljzsumtXaydIP5iED0fn4jzpOUNVSXFyNvtBjByRS16rhcTrpyhIh3rwhTL\n0NRibA8RE2X54ME7mnbq0Sxj1GPFQR1ta6bEKwXtjlr74MM7S6kS4TbfiK3g53h5nbsgiAtMN7M3\nLtlMrnpRu16aCT16q+TW2PdG3k2sIh5TwqgFL7hs8FJpfUTmNf7t/jeWb2+E2zSWdeNZxiFuaXbd\ntm65tcbW6ZScid68ZUIKzDrZvbB1dLb0m9phxjGlhHeO3uqA5iBvFdftteu1EjpEPNF1sjezu/XY\ncZPH+UhKnv3z03ZMIZkIq1jsWmymlbCFY2O535jSTPBKOQq9dhrQSmdbjxHfZ++3894OOxT3dPRq\nIeC5mrVHPiq1FoKLJkRT9+Lf+zhU4J297tb0KcPjx5hfqNni+nHdtmokjNba2Gc5ungqJuK5yIDd\nDdM+O9Acg7QwTOIsItaKtJV+jJ3izOHRILuKjBRyuQgN59cDZlr2Z2Kt7IfFpaXIGa7bSuV47qwf\nB8/Hxro9uN8C0hK1QN4ydS/QxDzFayNLZXseuJAIS2AvZRQspTWT8sMo0rgRWTXYIDpGyHYyD+BU\nYTIWDdptCeH0zK4e2+6BC5fSKUXpxeh3pRhzpPZyWV+a26IVVOcNuonB4snSFIyqFgPznLjN0whS\n9qTkCCkOVkkkjj/nvRXAlwWw0TgRaL1eqk87nsbBpZhnyGnheypbFZoblMA8XBuHB/wJM8XobVl4\njoNf31mmxP1+4/Z2J91uSEzk0qhdGLeBqRD7GW5rPHLt5j2jTga2b4+X943x0sG62Foy27YaVj+E\nNs7ZbqQMQVM5MuU4aKVYgXbmvocqzXZLSBXa0YnOEabhzhfd8Ky279HpuNnjUjSsUgo9N0pp9FzH\nQW9eK6UXQkz4FOiumX0ESqeaDbEYfp9LHRxzZfvcWT839pzpYhTVBqyrmYBph3lJhrmrJU7RzwnK\nDpzcCpocGoQj7+bpXop5gs+BMJlKUXO1cIhBHaybhXzcbwtxDsbe6pFWOhQlFCzarVbLRU2RMFg6\nJjlW8lYtfUjs3mldLjhFvEO1U3rl6BXfG049x3ZYw+MKHUcpOuwrui2mgwyuNIRgTom9jMW+2lLd\nPFWUqRbCNHz9h9iODnU3G+Xt2O3awrrhPiiIZp3cL8Aihoiq+RJ1Zy2cisEi6sQEisYFsF2ThhGu\nopaR2qGJotLR0y546FKcDuWx+DHfv2rN6avvr73R6evkUc3Dz2dMAKeC8IQMOQV3Hv0zmWZ9+3YH\nGV4meWVfdyP174clZR9DmagdoZH3yv48KHslhcj2tFCCfSt0tfPql2Ux35ZaybUiakIBe1FlsD/c\ntUS8xEFtOBai48+MZnywN0wdyjgdX0Zaqieve8h2uw5hkC1T+7nIUK7Pptztr5NZzgPGRvwYhoIx\nOGI0amCcEmlJLPcby80YHt4J8xSYk3k9vOT7ip+iYXyoHSZdjFkzHBhrV5K3ToGulCNzlEbdbIkZ\nYyJ6SzVKwUbpebbc0XlOLCkxRcO195LRGCAGpHR8NRVe74IWO5xzzoQBH+lIFApjb9DHaxBCuCLJ\nTk+b49g5tudg0zSOfRtxYf5i9pyf9+dGpRO9Y5lnpDmOUnj6lVwqujeSRuYlskwRiY5Cp/RCa528\nF0QgLZN1VNGR3j39acG8+34QxA6ephWfAkJjqyvbYyUmD7GS3gSC7T4+n09azvQ6YgDXwrYVcjUf\nFILY/idbLJ5X8IsjipmQ5dLJuVGPTrrbErt5ZS8Hn58f7HLw+fGDvRxocLjZDhYnDn0cJrjpcGyH\nBRm3iuBYnBJ9sOV2M2vWmUQRm2LOpiMEz+220JpRT5/rk8/naiHYKeFSpAiWtCPG5OkYb79pZy8G\nc5aS+fX5K0u6QYeSGyVXJmammAihsT6ffH7/oBV7LVoZApnWhwhwIxVbfItaBqgThw4eeGlCzp3H\nttJFme8L0iH5yP1+532KqNi1H3C0Yh7115Ie7LwK1rEXp2YX3M3sK4QxofRKzRno4Gx/UxXzGOrn\n9O6uxKTWTQegwuUS6cQRTgYLAMNZlWb7oVFrzD31Z/ID2Kn5r0v2H1LIS16NVeAFY+cppSiNhrpK\nvHn+9te/Mb0vVOT/Z+7NmiO5smy9b5/Jh4gAEkyyil3Vg66k//+L+l51S+pWFZkDgIjw4Yx62McD\n7MFMepCMHWVpVVYEAWSE+/E9rPUt9hipqCXYmMC2Z0QSzumIwlkhRdWBxj3qDTSM1Nhdej3lvvWq\nDw5liekHh/7zClTzb5eJuv9QnXstndnQJUOHLZ7HG63zu3IsDqE7Kfu8q8+++noRU49FiVFOS1JT\niBEdMYxj44QlTI5pOuGDp9BYl53rbUfaDdD5/zh6ni4j4xSUCGila1eFmmGvQha9KOkPj+A0FLc2\nQWxlPJ+YLxPPny6MY+A0T5wvZ374/JnT+cw4DCotrE2ZMt+/K3K0qIyuNqNLoqoXccqZbduUzeG0\nSk4xQVCQPxxyS+mLsf4eVg1NXteNVgvOgXMFrOmLJw3zoEFOaj6qOWIKDF2uqtFuAzJVUolUqViv\nkkCxogyTJkzjjC2a21g3dbyK9Ng2a/Dj0K3W+ruJCWouQR2+4qCJhhWv20oRxcrer3fSulPywd0w\nTNNEqCN4EKfu29EMtLFgmrJtdHkORQf9pK2ALTQLOe28/forqWyE94G3L1+J1w0wXOYf8fOk7A6n\nc1mpasePu6Mmhcq1CiUWDVoomZoT5qrHinZg/vHZrNuuBVbO1NqIMRNjplSDlEaWxprib+6vyjiN\nKja4beqLwDBOs+qrs5rt4p6pS08KsoK3A4MzyNlTC8Q98vbtjRTVOxLZGawhNIPJDU/BGo1kLKKF\nk1irsK1W2Utl8AE76A7KBdMx0xYnhpwy1gn324qgeb3jyVPaqOad0kh7pqSCM4bgVE5KM1RrKdmT\na9axZk7E0rqKTQNG7OHuRMeLtZUH60nhk4rUVZGBfp2gggmdGvQKHro58HBwV2qL/+mZ+vs4O/eE\nc0arF2m9qm2apUjBjcLTD2eqFG7broQ+tN+p0nBB2dbBeXywtJpVY17qw1aexKoWNH+QDvXNPQ7Y\nqi00al7IpWCl4doBoZIup9Ololbm0tnive15LDZad4YWSst9TtuVJ/3f7zaiR9sovS7vk1t1klpN\nARfp1EKn9MNhHJR1Ebw6X8WSYiKlrNVF0nmrceqa9LWniRftAnICE0bCNOLPF7RNFAZnMUAWgy1w\nuUxcnmZOTzNDCJzPZ56fnvnhxx8Y5xljLWlL7Hsk1kasjT2pvsCYfhD/hjFTqjLS1QilbtVjV2F+\nA/wCfQ+P5aVWKbrwOuRgrdV+iHd7eX8iHu7UmCM5RaRUTKmY0nAiDEPANUO15bF0fnwGTWWY3val\nZK4fsYG5MviRcHG4s86AQZfnpc8uSyv4Qee0jaqSPRFIwnJbictOzVXHYiFgnEWqzrONUw2ycaEv\nshXNWnKhiODE45o+0FzR8QXWEa93GhnaibJptV2KMOVCEHmkytvBMpxCb+cN2ZSHSKAVVTGprFl/\nb90Zuq6a07Ikl9JnvDyW4yllYtSHYkFRv1VMd/8mSv5g/rgQ8NPAMAa9Nr3uIWLOpJSpuTGOAT95\nhmnEjyPGWGI3nN0tbMuBvFDImcnqumtGl9yt+zqCt5QWkJJJre9gnMEMagJsorb6SmecOy0iS3e1\nhtHTmiMnR9mPe6ngnWJA+pAbhduowsqIR6yniqWWqg/jPgv/6Oqrjqiky3pbJbeMVU3j4+vaQ67r\nenFYkVp1MSsWau4d03+hhKActUosUkhkGoY9Na0yXWUMHgnC6+vCcl0ZxeFcoNbCtq1Y4wleZV6l\nZsqaaTHqwYtofFTTpeShoTbG4G2jSu160artGbrgSzlhxVJtJ+UdGXGPWpvH9+c3/0S6OajWTC76\np/U5sQ5QKnLIs9rhKvvQjCOK5g3DwDxPDEPAuoAxjnGwnCfL6TxwOjvGeVIbvlhyaeTeft9uV1Lc\nqN6SrF4gtinDO+dGyfB0mXl6+czTTz9CK0gtSCnUnIlmI4vwh59eOF8m3ODwLvB0PvN8uWCtZd02\n1j2y3jfNzNx2YtVl4iFvebhRu3JGH3I8uDjeacC0dZ4DHXu4Zel1iLb2WomE4HmcK60QOsa3tsa+\nb2zbRkwR4xylVNb7QryvmFbxVVi3iHeO+XmmBRjnASP24V+QDKkoJU+VTVrJtqo2/TkEni4XLk9P\nlB7kTG1M04nSKst+Z7lfub9fWZeFUlEWeGzENbIvXQaJoZI63a7gi9NMWVE+jguqiU678rdrrggR\nKWAyDFimccZPz3x5/YqJmbPzmHnCpMbttpOWjX0MOjK8L9iUca7zu7NQSi9M0J1Rk4odbXf6euJ9\nI68bw+wYRq8PPRf6h1gwKvcg5cTyuhCC76lBgp9majPsMbHd7zggOMc4B4IBYwvhNCDe4HNQ1cxt\no+yZJIYQggoBJosfAicZuTwF3r688fZ6ZS+ZJWZiKj2OrY8bStHRh9V0IkPFpoYtBUrWvUsw2lFE\nVfDUnPpY0+KdQXKhxB0Z+ix1L8hWmYzDzyM2CHva2feNmDWNSMNkLG4YOQfPNGtnmONO3TdKUa58\n7js2TO/cinpoSo3YSjfUCYXaixL9LIzTh1NNsUt9rWaJmowx/4UO8m+v73qBt4qpBW+Vt30eRnLN\n5Fvh6/YOCLN/YhoHPbxyxVbHEALOegSVK7bacGhrZbt7UkSUkFaPNuUAtZt/s4DQ+TFkRMFVom10\nezxJoVo1DnSZqTrMDn066JO1SwYRrRor2rrqAlUPpxCCfr+ehDP4wDyOjyrTeYsflL/dmhBjYmkF\nZ4Vt3TDGEGpTlYsI1lYwGdsSuRXMsXZrltKEVDWTM/d09NYSVnQ2agXGweHngFwmqJVhGLDGQbPE\n3Hi7LWy5Yt7076Ndkybx5FIRq6kziHYC0rsecRapuuA0weFH1ZVbzCN0OrjOJ+9zwJih9hYedLyS\nUnzIEKtUSt1UUpgb+7aR0k6tiZwT+7pxf1+5vd5xTgMFJuNVf+wsYjK+QbCOeZq5XHSctS0bNe0Y\nWxkng3Mj1gbEeJ3nG0trmeAUVlVzZQgavPBkZ/btieV54Xa98f12JSUtEk7PT4QhkmJPiunXewiq\nSjIiyvjIgNHiwyAM3nE+D7wmXT5Pl0llsLbijLI8sMpieTqdsFXYt43t9kYjQTzTFlVrVboqa+hw\nrQx0RZUPDhfUTVhX1OnbDKUZ7utOKtrB1IKmbeWMPY0EEWIVgnU6Ay8F59V5m0EplMEzzAPOOmxQ\nMJuqRTQ8egiB8OSgoc5n09jjhllFE4bGQLaN9vmMnzz7nrjdF/YtYnG0qghqa51y37uz+I8//4gN\nlnvcuO+RgkYv5i7FTEtkHAaskQ4MU2phNrCuiVZUXUTp1v1WGMQj1WClByY7FQQYtFK2wOANxXiq\nN7TTyL6trNtG2ROgztxwHhW7HCNpW2jVPPThTdQpLQKlRSQ3MB30h7L4S8sPEN1/9vpdDvLlttL7\naLyx1FoQY7mcT8ScWfeoEVdjYPBjDxdwGAvGBsagF4u1lvW+UHLBiao8rLcY0Wo0xagHKUflTK8a\nP6iCKuqQxwFielV5jFP4jW75wzwEB4lMj/MO2Okadm2XGlhhPp36PLcxX84ainC9UUpmmkdePn3C\nmA50OsIyAESjrqTA5vRQ9z5iUDmkMYKlYlpGWoaaaT2QWOWx0pevapdet5VpXSgpqvbVdNyAUV2t\n6WOmXLQyLlVThpYYaX2EAUZn7k0r8YON3jr75KFC6Tp16ViEEAJDUFCTs4ot8N5z4AfECLaqhLLk\n3J1wymgR0QgxMJS0U1oi5aYJQln/pH0jbpsqm2LGm6DW8nnWBbfVZbJzTq+THtTRmjC5gVo2xCRc\nqMqotgEfTh9BFq0oSqI7K0UKzglhHHSPcDpxOp1ov1jut4UiBfM8Us+KAV7viyIPRPndDTWhHNmi\nJRdqCmpzd1a19kYw3jDOI0jBB8Ufp1Y047HAOAYYK3MYuG4L+zFSquYxCjDe47wWJ3UvlCZgwcyW\n5tWsU9bOvRaBYtjWzLZFvBOVkvbP1/jAeHLUJLgOjCpVRwqg83g/BIbTxPg0Ix3FIa4XTx0+FUJA\nBtV1eO9ISQ1Til+OFK8OWhcsk4z9wFakcU615wWgB3HSUWdOGR8cwzlQ90qRypYqtSeOHaA1ax0W\noZSk54nR86JEnY2ruqxD1fr1KKiBzhsPto9AmumYDVVlmdbAOuzgEKPjxtxHQiaoJh+j+6NqtOTS\ntDBlj+vZZKhVD39qgaYzdoz0Q/w3MKd/9/pdDnJJCdtBONP5rBpigTCP2nIMCRcjxqokaI+RYXSd\nd3LCO8s8DTw/Xbi+39i3nVor1nvom+x9jdh9xzt7RC92y8gBuPpI6BHTpWX9INbgU/mQI/YFX1HY\ng1bm9QOa1bqr01idx+WcVGI3BP7w88+M80iMkU+fP7PdFn7ZE8tWcEPg9HLBNHp48U7d9e+hVYxm\nFaV+oJRcKDZTi1betelWX6WOmVoTtXbdNKr+KB1Ydbte8WHg+eUHTuczxlpizdzXHYrOuJ0PGO8x\nVnXI4lR+pSGznSmO7SEOoCMrJRceIc9HZN/x73jrGMLAOE39gaGLTedcPyQVRkTpEtRl7WS51lnU\nuUPLHHlPXTqph4KUTEuRvN5J20orWR8a48A4j1y86zS7xmlUrXusjRj1ehmGkc+fPyGSyHll299Z\n1g0kA1b5Pcaw75viH1ojR2W5s21ghMvTpcfjeZDGawis91UZGdbSSubrl1+1+zSqR1/uC/u2Qq2k\nrF2hs4H5csIax7bu7HEnl4SYgWHynE4jz89PGOfZ4k6VimTwBD6Nz2y3qAvoIlwuZ6xVSWoYB5zV\n2fxS7zoD9wYmR22JkjTYolExzULS5WHMkRRXnl5eCKeBncxYDBMO/xzYl5WYFureQVBG04ncGPCn\nkXCZICYoOu92ouHo4gxuGKFVWs4dRhceLtB1XYlp/1guqpiD4AM5FV7fvhJGjzWOfVn04ZAzMW68\nvr0RkmfLkVLoW2OYxonBOKKxTMNAjZm662jVeaecl17AOON6kAnQCiluSNVix1urDb1onrDQSYrS\nhRQGvB8YBksullSsmgltV3MVHdkNdtIxbinEvKPif3VLq68i6wFvgvoDXMCUgpSkULv/5PW7HOSX\nzy8P/akZHKZanFWgv3MOXwd8TJ2r0ZGp3YW1rytby8TNUvKqxDMxjOOEWPqCrXC731jud3WTHRxg\n0Ygs6kEw/I3cBIDWl1i1aw+1QrddcXHwWz5s74Ao9ayqW5eHYbypdrvk3El0TiFdXfOcc2FZF759\n/87gHHGLrOtKSaUjPj0tKTGwZPvQzh7UNtN/9qMKpksjW+m40Z6PeSxYRUcftahRpDTVwOaksj9v\nekCudarDNl3gKzqD1AO6UmsEUZ72geRVdot7/B62abahkUYzOhpptVJQ9CnQ1TIf4LFWInnbiPc7\nce8Gj1qZxwnnLTkm3r59oeSEIGxbZF02tm0n58i2F5y3XIYLl/PE+TKpnbsHRnuDLqP6iDOXwrYt\nyiqJGylttJJ0Zhz8g5lurWWeT70jqbjB471X5UuKpLiTRY0ul9OEt5ZlGrm+36AVrBM+//QD46Bc\nb289v/zyK9+/ftXR4J7Z98waC9OnE5cNyseJAAAgAElEQVSXZ/ULOMd2X7UyR7n1SlRUn0KpjbRF\nKIYxTLy8fOaeN2LLGvyRDpBZ5XyaGYZAGR21v7eSjc5xY6a0hA198W667LeBWKejkrN+BvtfvrO8\n3fQzdAJWsE+jzvhFMKmn1O+R+KapXg9tP/1+6gVT2hNp23BidC80joiAUsIrtoOi9j2y3KJKF1Pq\ncYv+UWCVmCl7JO+F919uGqNmIAwD3jqgIXvBGsM8T8zjSAuZTY7kMYOxDjuNj5FpjB177Ywar/p4\n0lB7sac7mkNt5axX/n9u1NgY/Ix/npjPSUcmzsDgyHthfVt4//rGEAZKa7Td6WiFo0g87lcd1Vrn\nCdOkITs1P5jm//71uxzk4/ncZ9DghoA3Bm+Vrfz4i/RWzxqjDGF0E66J6Ts10YXZlTAManJA+kHb\nH+O1dflUdzVK7aqQivo0zUOz2Wp7aGvrIRzv2vxa9H+UUh+ZkQ/cpBbu3d4PXtxDHdNKZbvekFzV\nSBIj27KSUqLkrCxnKoNzysDYFPZlk8FHJdsF78hjD4DlGO9X/WnH7378nl1RY/oFcagPVKf68bse\niTOtNXJVLXe1hmY1+1Gs1YfB0e6Vj2BsjQxDdeGiB7d+7/5E7IIeuqzzqGJrVnlaThFa0wpX30H9\nWWj15k3j/f6u0jcRRq/Y1JwS769v6tpzjuW2cr+tbNuODeomnM8j0+nE6TIxTQPOW7Z1Y71vxHWl\noeCs4LwmOImGQ5Ra+kNK1TXeqla+Vh1PhCHoPF+qmrrch5Y9p6Sz7pSZholpCFArcXcPQ9oYRk6n\nifNpZh4HWksYCiVn4pbY9oQvmT/8+Ud++tMfmYaBL/PE25fvbOuKccJ8VimoWMOwBv17vW862jCW\ny+WEzZ57XGkWZRhVjTArMYPzil5tGl9XctSCJOv1bL1mysaa1RgmgrWB6g3VgvUGPwWIAfYdE5z+\ncU45/7kS16jfLyaNYAsOvMd4ZcocTseas/oXlh1TBPMkag60qLy1XxnKeREETdmSphr4YHsAuVFJ\nKtYqsjipRM97p4VIB4iWlhBr8aMGnRO0WKwlH5KRB9SuGJUSHoq0A7hVRe9na3Xn1ej3kOk8/DFA\nN/x4H3CDx3RjHxYiBYewX2a+Bw/Gs6fCbVkfo6VaE05QMQaVWntZKAaMU+zGfyVnp+tzQvobMAc9\nzFPaWbeNfVcOhTOW0Wtoa+wKC+csTRw1q0loPge8dx0e1S9A0zhNA63jQte8quHEVH1KSwXdffd5\ncKHQyEUo1eiTktbTeCq16kzu0JuXUroBQK8XaoVasRiCGRFJpBZJJXH9+p3VvCvP2ttH25qrLmD2\nZfkQ4Um35Ead1TkMp+nE6TTxuLJ65f2YhXcgv1YT3YxTdQ3QtwMdiSvaAXnVxIqYPhNEx0KuV+F9\nOWsPdWVrbKsSByuN+TzjvAL/q4Ku1d5sdAZ5YABi3Nm3FamVfYtaPeVE3FaMCPM8/yZIw2Kl4G1j\nOg18/cvG8vadVAqDNOZ5oraiHUuphAFiTH3pCsM4MYwT0zTx8uMPDFPQBWFPHLqvO6/XG9ZoatB5\nngjjhB9G3DCw74lt3TVBJ294ZzmfZt6vN3LJDGbSgAMrGPLDwGWtZdt2tmUn7QmP62VIYZw80DT0\nIBdyVfkZpnK6jJTyxP16x9AIg+FluvDf/tc/8/f/7e95OV/453ngXwfh9dt3hnHkdDlzfnnmh5+e\nWfeN1+/v/DX/yi0tlFIZTxMOh9vMo+uhwn6/k7dExHY8cSPdd673O846gvfgGgyWOjjWGPFGcKLd\ncXIJyp0pBZ5//oT74wv725VWtDixYyCXwnrbSGvkyMjVAlN1i9aoacwaJRDu29bZ5JG366ZsdSua\nmuQs1gq5FHU4+wmDZV8TUQxSDcGraCDagplG2uDJMam70lq8D3pPJ+Wy55zBFmpFxz/eYoLDNUVX\n75sy9qvKydTgJR3FsemMXcRSisbKqbZ8oLWC0WcV06RjuLxnjDcM08Dl5cLTZaKS+Pr+lc+fnnAY\n7j//wPuWeLtufH9beft+Z11U7eLdSEPHoWnbaaWS96h7Aet5GBr+/Zn6//kp/f/idTmdHhZuEaUS\nWqsWXu/sMUUhpUTOGWHAD2pXD+OgPOF970hZ2LdILFdS15gaUclRa6oXBU0zzynpsoTuznRacRqE\nGgS60vtY2+kh32fj6KLrOES1Sv/gmEjT7+R7rFPBYs2H9rxVNc4UesV/SBDR8/MwAVjzMZfXBavO\nzI65M/RquM9UDkMNh/69NnJpusk/9O79QtTw4o+FbKmqXW5NK96cCy3o72ClpxuJAsi07W19Ntjz\nSkVn9EKl4851BNQP8m1bqSk9wEElF+K2Yyz40UHW8VNLFak7s7c8zSO2RiYvDC5w+/ad7XbDjU6J\nmXuk3RY1a1jPp6dnfvrzHzV93SrcS/fVCjjSRe/G+/VOSYlpVAnik7EY72gJRSY8DVwuE8vtSowb\n67pwvd4wxhJ86BF1iZw2Bh8Yx5FhmDQT1QYalbf3OyntpJLww9DfNyHGzO26kGJG5EJF8MPAVHv4\ngxVefvzEj88TkwfbEs+XgP+HP/A//08/U0UYppnLDy8s+53b/cblNFCXnbpFrq836qadw3g6gVM9\neEqZt5xVDGu0Gp3DSJsqrnZshDMU0zTbdAxsWOW2507ly/o7ijEYLxjvsXWkLLtSNKUfcmJwtZEF\nCCoHpOoyL6Ud7zUI3AXHQOsRglBjZV03eNflsY5iNAQm7T3ecVdRgeuVfa26ZHVeHc0ijVL0nx0h\n3fu2dyu95vOWmEk1E0YLzUGr+GHADkq13EWvaTGipMSm7JWadOkoIkSnKp1SCsE4RWTXRtoMp8kz\njx43j8RSSHnl618Wbt88xkFqkd2PjJcTf/zjC+eYCOM7MW8ozdfT6kAhq/zQGpa3dwWI5UJwniYj\n9fBr/LvX73KQB2f65KNiOsvhSLZHlLnQ8J0lXsA2rBNsMIizBDNgrBCXboVPmizU6FtoY3G+KXpS\ndKZdUmFfEvs90aQDkoLtaizBnAZKMP0ABTWhtF5hdEF/n1GrIECr4/6l6GFptN1rHCMvhf9UJT1U\nPlyeh13XiFp3Ww9clcfQ/ph+V46nsMAD3KSHeVfidFaE8s3NR9hF+42OXgzKgewHflNdfO1SS5N0\n/i9NNcDGA+jB74JTnkbpo6qUEVMxtaoevcfZlY4BaNAXmfVhpNERDWAMzahuP26RtGn6zHmwPA0X\nLucTf/7Tz1yvd+63lb/866+UFGkyEvfItu6UDgPzs2WaJy7PT3jvaVUfQrnobHXbdl7f3lnuK855\n0p7ZtsS6RsY543OmtoofPMEHJeLJzLoa1lVlrTFH1mXRTrAVSt7V1IO+z7n/na1zpF3t/PdlwY2J\nYQg9XMQSo1r11y3hjDy6oPE0cZoCP31+4ceXJz5dTorN/eOP1M+fGIegubXDxOnlhev1O9f3Vy6j\nBiXMYeSXf/nCEneFoZnK4DxiHTkUWlYWSa0Zg0oG5zAwOo+g18mWd8XrVg3baEU1z8Z0A47RgJW4\nr7qDqkm5PgUFjxVdjiMKm2peIBjqHilZF9S70c8sdChcmIaO4VARwh53CsrzMUAYA9YKiA7vjgAU\n0xol9sVkJ4yKBVuPhHnRDtGJKpAmVYvs606MmeW26G4HgVnDSLCWMB1jDHkAuEQLdH0Q1IZznpZ1\n0a4jVq34231ncapEGYeBUhJpTyxr5E5Xh3mB9MZ+izxdZqppSCmMzjAHi2TIWY1VD7s4guupQCKG\ngqX8V5IfUhI179QU8WFW6H6r7PuuGY7eMc2B1jK5CH4wGN+oUog5KrMYh+xqLNL8hcY4dqmiqI44\nxU3lPE3pZ2nPbNe9A5IgzI4cG6SG1Mo86dshrVfIffHZTdaUowpGulyv9EpaFyBN6C7P2jku7aPG\nb2ocgiNlRMFURpTNoE5S5bF/hED38ZjVXYHtrBFnHWJU1y0d7nOETBijaTXlSATq1b9g1VHJccH3\nby5dilkaKSpmNIegwQ3OKMbWfkiqDljVkQLuum2+IUqK63Pl1tBwCtdB+90Z60IAyaz7yu3tzn5b\nqSny8rc/8/LDD/z85z/z53/4O95eX/k//rd/5u2LYgCsCK1k7YSMttDGKWfbGn0Q5pyQ5kh74na9\n8euvX7heb5TS+PTyA8ZcSXvsYRg687fVgLcIFVphHAeM9VgbeH+/s9wXlvu9y12VfqcW7khKhWXb\nCMPI6XTGWcOyrKzrznZfOJ9nPl0unIIGSOTaiLFovmXTB/w8j5yfz5wvM58/PfPj58+4MCIvL5iq\nQeQVixtnxssTlynw7i2zEX749CN/+vlP/Mvnv/Df/8c/8evX7yz3FesGhtkzjgFjLry/Vd7frhh0\nDBaCY5qmrqLJvL0XasmUtUDKSGnYJrhmsEUwsZFS5N7eHgqlmrQIS7VSKIrIteAGxcVm28i7LpDp\nTBvNl62M54nhNOrBbzdKSbRWSWWjJhUH5DoxjEHDKrylViHTaJJVZSam56jKg1lfu7yxVS1w/OgV\n7DYGbm+G9O3K9fUGDZx48gbhPOBPQVEfHeTWdpUtG1Fd+75u7DGpoqsZWlQJce17oG1N5FhZb4n5\nFBBz+CAaKWrx5oPl9dcviMA0BKZTwA9O8RrGqVwxRmrKZKlU0UX05ALzMJKj5gFry/MfX7+PRX/b\nkKY353a7KSUuKqx9GDXD0TqjywWBVjLeT/hhojblWMRtpWadV1pncUY0livvfZF45369a3tf0Zgx\nP9AkkWrSVOxhosaouvZWOU2B56cTuaKKC4D+lK7tWLbqEvFQhdDasS958EUeNudDw06vLFr7zRLX\nqhqB+pslJugBq4f9Y+3bxyetLzdzKT1OrRudjsPefiwcHyMXPhaRxqjhKOaqCpxUukJC2+6cE9RK\nTZk2dt181neh1koumVgUNgSCqeqEPebjrRsWmkCqOsPel5Wya/r8+/crPhigsK8L1293gnf8/Kcf\n+fGPf+Dnf/h7Xv78J5xzDJcnAKZxYFs2UqmM48wvr1e+326kkhknzzgNrH2R2WqlOk9KhZIbe6rE\nXHtn1Bjmkek08vx8YRgCUtVoITIDhm3bQbRbFGmE4KglYIwl50JMyrkYfWMI4L0oW94ItWw8nZ/x\n4Q/4aeCvv35h3yJf43firNAxZy3b/Yr3CjaLewQDwxgoWZBscChX58i+9E7hV1iP4Bn8iTns5LDz\n+dMzf/z5T/zd3/8dn3/6xP/47//M//4vfyG3hkglDJ5cIAyOeR6Ju3K0jVUZoHWuEzVfWLaVZduU\nUe483qiW2nWjmli9nnNMpG3VUY0fCN52RKxQBtVLV6AuG+leKLuad8zQqK72/MuK9xZ/mXE41vud\nGFe87R6EokWbBMHPDiuOVi0uN5KB6g71Vu0MlaaL+D6erH2Bm0V1/NZa5tOMNOH9bdG81KYy5ZxL\nRxVnvO+4ZWt7R1sfzmRaZb/dsVY7mULBeM0TFas6/JgrbFkDT4wg1jGdB6wzeCcs95VtzVzfC9u2\nY8yuv3suiifJUaXAaKEyhAknBrIuSp1zKkj4T16/y0G+rWtf7DW2fWfddnKuOBdwtpGNKhxMT1ip\nuVFSwRjVSa+3G/vapVnG4KrDiLZAOSedcXYnWOtbcGMs1jk1NxTlq7TeWuZc2deoT89OMtRlIR0J\nK48xSztcoYdahK4YQQ/7VHRxejj8H6S+Pj5BegV9gHL6KOexmGxHbLJWsMdL9wba0rmq3Bk5zDTW\nYGtX/Rzf76iK+4Pg+FpjLS3rBb6vG9Np1Bgzq2x4eyhd+Jjt64K1kHMkl6TxaF01cyzWStEdQIxJ\nrfO5EFNi31aW9xv31yu313cuTzPj6KFVluud5Czr08T9dmPbNhpgvWe+PPHTzz9zOc/sy8ay7pye\nP3P68pXp6xfu+9aNRp6078RSyLV0sl8jZSU5juP0yCOdTyPjMHCeZwTdI7SmjJ+KwqGkv3G1VnWI\nDuoOdb71+T8MXufaHCytVjAVPZzGgHjLnhOv39/Y11W16a2pEqYkpFaqNeRc2G93UkrYrGk+pQp/\nO0xM08wQ9KBMKVOxGBfww8gwKDlw8g4bPPNgqf/L3+BdY5gc395uWr06oVQLjDhj2dfcuzzzUSiI\nmnJcsjhjGLuu2qDOaNN5NM7oYe6N0h1b7WM+mgaX20rqi28pFRMLJjZq0gdplUyWyC5CGLxq9L0h\nh4LZdOSXu0FKEwcb1jmGMUCzUAytY5VrX2Du+/5xXxZl8RhrMf4RyU3JGWc1BNteTpQCm4v69UGw\nThf6OWUdpXRgnTzuX4N/yFF7sn3TRC66Gsx7VTEZq5hjeoeAQDVNcwSsFpzGaLfeoiru2mFYMqhJ\nzlbt/I3RWLymDz59oBw47f/4+l0O8iOrr7TKtu26pOyaSc2806rIOq32Ysqs66YwnSJs94W477r8\naI3qHbaDllJfsuWYOv3NEnv2pVi1z5MMNak2VDfVfS5Y+9wYTc7RYrznzzxkfgePvGj1Wg9zUT/I\nc6GYPlJ5fJ7qYqsdESqPNeejXv+QCupX/uZBIb85xGtfevrH4fwIY2gN29GlXW7zeL+PQxyRB30t\npcT9etPRRGeY+I6nFXskhPfwXwz1MBwVtTK3Zmii4Ch9eO7s+87tdufrr18pSW3OqWZev3zj/vZO\nXiPtTz8S/vCZ5+cLcdXN/PdfvvPP//hPTMPA5enC88sLQ3A8fXrm+emiC8s1cvlhZXo6M51H3u83\nDVwojet9Y91X1hz1HW0Gg2We9TDMJVFK4TSPnE8nnHWa6/q4HvfOJum5jj2E4wjTtsYRhhFjVZXi\nbCWXxBZ3ljViGphBddnDEDhfZn74/IlaK2+1L1zXlZqTVthFqNXSSuO23Pkev/H+yze+/PKVt/cr\nw3nm5z//LdN8QpyjJp3lW2mKoRg6t3y9U7ZKapWni+cf/v4nptnzf/3yhS9v77yvK2ICwVlSGEmj\nylYbjWVZiTFSq7qijYHgHeMwQNVIuFy1Om5FFWbBB7xXB/Z93Ugp03JVm700thKhJk1DKo3Qr/Xa\nKuRKqjulFObzCE07mWYqxhmM9SzbStkSVCGcBENnLOWmmmprCfNIyZV920kxdTJjBiq2jx1xel8c\nHXSTinUO7wMNCKvVBWzogcsCKbae/qMRe0ZEg5lFsCaoI9k7csxK8PSeFJWl473HeoP1eh+BFoO5\nas6sEppVqWecwQXtuluXO9MOvXjAV3WEltoI1qsHRrTbeFSO/8nrdznIK4qbLBlKMQ8taqyFvGd8\nEU6ngB8HrDGkEqFWWsu0LEoE6+2PQ3S7HDPWVLwYmh1oArGpPrj1CvWYNbtmqSWTt9TT4tUy3fpS\nkt+8X7VB7lbkUvKHxpz+vtbfVM4iatWnfIxR6MHL0J2RPa3IHBCjfug2dKTSDpNPP+qPxUdPGtGK\nXTWmoAe8MSqHMz3OzWiZ+PjPcegjPEwiOSX2bWNYvUKH4BG3lltTgqABkarVJ5BFdBa4bqz3nX1N\nXN/fub1fud9uOkpZI+/vV422C4HmjG7djaeRGceJT58/8Td/+0dOz898++U7X//6K//nv/wV5w3D\nYPi7f/hbPn36xBBGTpcXhtliLon5U2V6OnF6PvH121fe325c3xdSqrzvC0vcVddbDbYKpsA8T1zO\nFxCtiJ1BHbOlUKpyQoyx3YZOv7EaYrRDaf0B6ZzK46y36nzdE9ueueeIrRah8PXrdy4pMZ5n5cWP\ngbv3xC2SG3jbNJfSawJ7TVn52M2wbJH89TvVCn7yLNc3/viHzwzB8vr9O7U2TudnLp9eoLf837/+\nyvv7K9d1xfuJXBr3daOUDZGCmIoT5dHn9BtYnLGczjM+OqUrejWglVy64ksPIO8HHS8I5KILcYzg\nB4/1gVYNNRa2mjRYutIPVXCDxUhgqIGGIXcvgogqlXJOiO8gM2MwDuptJhVL2QplTdy/3zrCoOL8\nQAgDDYfm4wrjOJJi6gHQCe9UUZUf0mCtjn03WBlj8MFh7ITI2NVglZJVjXU4jsdpBA5Zcu8oDCq0\ncI5hnnTnFlMvdERVeF2Xf0xIG6Js8oZ2/TFSa+vvjVBLZ9xnofZdxBAswdgHkVXoaUWC0kD/KxmC\njFVgTm2FJvpE9x3+XjsDIQSvN49B53C9ioxbJPdF1RYTDsNgLS0o/aw2DXegGR5JIEUF9o9lI5rm\nkTdVVshRkdNHGF1uIoBI6b+rVusHVxg+DnsdRx/NXFeX9Ar7QLdKXyo+lp3S/8XWQ4fp0kHqx7JV\n+k/oLlPpGu+jM+CQPj5+nrYAYvUgbrX/DJ0RQVOmTOsOtloyh9iyNrXUgy6N0rZhTEVaYU+FddlY\n7gvrsvL2/cbb9yv3+8L9dme539m3HSPKsd63nTA6plNlfj4xTifM+cQ2T5zPk0bdDQPDHwaCdzpu\naIk9R5Z1YVnunE4nTqcn7DAiPgAFCYVzq9SaaHmHpKHcArRUKVENIiUmSPpQv5xHxsGRi6bbGDT3\n8LEktg5rnTLWt11VUv3tyrnqw9UoLVAoSK04D+MgXC6WfdW4riGoWmFd1r7+bgRnCMGxGMgls+/a\n9rmnwDB4dpPUwdfkMd75/nblH//xn1huVz49zXgjvL6+UkphGs/8/Kc/M58mat759stfeX195bZu\nnM/P+M7zCM7ydBoJgyPXxn2JtLxTI30ZrQtCa033SChC2YjBe8dAX+huSYmeuSOTi2bk1m6ea0X/\nGytgLC6g1SrqirTOddmjI+UeZCHo79k0yk0688cPnrmdaG6gpUqm4YfQZ9mVvO3sMROTxTuPs4pA\nKKZoRyzd/1EqMWY1Y4kajTSgQW8kjeNTs1uMXTlnASldGaNoCwXd6cP20JMp5K0HrDcdAdbeJZM1\nUu9Qkx0GQ5FuHjMGjNUIxmPMKYYgVgUXTUc8ZC0mpTSkVeWpe5VL5lp6juh/fP0+4cshqHjfVPaY\nsdZp+ECwxKSSvGEYNFi4VgbvWLfCviXu9ytiLTEXrrcV24TROyhOre+lUZrKnDR+Lf2mKu+z6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QRoHTx2viYcc9bsnwALtFTLPDcgXWDDzxfRuPdNNY9LGjgH33vA1LonVU/xbr4/ZgIzMmpcj9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\n77ryEsbvRQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd65950d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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RUhR5HkR/N1fDvdeyzMZoKG9Y6eFXIzU2a2zgwvU+0lf4lgm8cS2uHLk5clpT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sKGrNP7NRNngryQnFUskJBgBmP45HY+8EfH700hDkNjgCee04PIamev1NkpPjFv+DTNf4Ne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3D8/iD/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\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ykiV5fQ0B0VZy3cEVyfWOV9tKJvxvE1nzQi9BKZPiN+d58DgPBVqbsXDuKcdyFQvhmwf0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0MjzjTa98IED2+/DLYg+hCwZXJmnfS2Pvj3MXQBOKSSlAgBwPhoqKL0O0fh8oHLO95fNOq5xkDE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\nROkMnut9BfMAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabd63b3790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ovFuqR0uKWI4aVShFKRqzYvP3jt1+alWWGmYXN7OkYSFzsCWPZlIRafnqRYTt\nlHm2ydxszJRkCXZLq09xU2Ik05qTmRdbcyRLRnYlylaU2etVawRH9W51HtM6E+9t6Ne09apIjdBd\nKM/JWzKtDG8YGs8PvJEpwT1NztyRsHjaH8GigjHHuH1sIA2w465M/e5RzoHHttt03n/ubo8/S1Z/\nnT1rUDyPlfvJlK3Sf+ngLv0r6TW3tn2PIB5sPMC8E91HHkaPDXhT2H6qTY7r9xSsH3vKacusCYAB\nfVCA2sFQi22BFgpdrppN3HqZb+XWfLAtnP20JBqs4vzbDe07MuyTewQxjH/DzKC/VWr3APPqMEZY\nPRHW3kwJkj23d2J2M4L5jgvzlJizGpjnZIxcMLDGFzGr+tZt1i+0JvtItpmD2sRBam1ppAKQxvKK\nJDO+aKIMhEvV/cmdAStYfhP3ja9V0dISH3jdRX1GZGb/O/rhyk6uppLnJGxzQrDF0QoG6iosVVmq\nsBQ3AW09A2PMjBzskyizwCSCMNmMQoZx+eQY6CCubfZi79X7hjJYi87KWwrRH1lb+sFY7WNT6JNh\n6wAk3iHUk3QdL4o1lunTptjmybW31ekaCMennBTvTGlOoWhdhr4w6wzoM9RLZwC9jA24ZQRxY5Tx\nugPO9zKMIB72cUnDd4wR6EMZ4us1qz9+jade61x873kluVZIevQNQMvGMLTt6QMDmBXh4NzCTBTl\ncIdqIaeJaZoQ5nWRdKGWO6uttCXJVX+DE0331GA+6ldmB+hHxktHLwcnQmFLTimRsAXOlCdydmCV\nA0l2JA7Gxj1J1ZRhzsZmc6qeMjaxmZR5ginXNgOI0lQV34dTHWxtb6GKs+okUIubIBaPi4rEW8nq\npa7NJiCWHtdNINU9zcy+DbUuvmBZqBJ2dW/f4LTagXqcuao29tYUZPNmKYIWgWr25yyKpEpOiYel\ncLevPBxgvxSSwDxfscnS2kScAGhVEpkcKQWkezCNyqk14VnilWjzV6GZlnAT3+uw4K0Cebj5Nd16\nxhwwvu+aAa/OijMGcApGGNd6hVjVn8K9DmXCyySrM3q4P8Iq0CQ06BnIGlnDeCQ6Vut0w/XHLoSr\n846uO62ncQEstcEibhZopoH2fs646blWuhJwAB8DiOJdzlHypofWwLRaEzmuh+HitvB9BvPGNYn1\n7dbtHKi+AvNH7gl47h21hbTy0tpFbSebutwZWOcJzZu+j6Vm0ILWHfXwytooK0wzSJj4hr7S+vt6\nQJ8o3qFqYOnxAAAgAElEQVR27HsZ7KT9JVpODz8k4o6lYqH1CbEFSEnAgtYdyj2SFr9RIcnkC5mW\ng3zKC/OkbOfEdoJ5UqYsiN/H3iQZCFZlKYWlVA7F3B1rSrbGslgSrjDfpJSQOmFEysyUY2PY4qya\nCUO9ncNH0Hul7UxkppSkqdWtxmkxoo/c85pLbuDM0IfUUwVkgWepcpOtHjQJh6rcLYWX9wsPxQjV\nZs6+6A8S/vmK40YEHQk2K6utLOGa2ss0AvnRyGi2mNS7uSuFtrCdlHPy1kwrvROPLOwciMtw1Skw\n2nkjuMj6eDuUnOi4ff1oZK+rNNB/uN1qFLZ5bju20rjnX5phZeNxZHmNvM70NIJD/N4A2BMgaUtx\nQAPpHiYeHXPIiBigfrIucR7Mm6yY8Gd930E5v2bKNlZpW0TVkzPO3D0i9hStC8vhDrR61sAMejAW\nWCtaHygsVC2ktLE6LQu17IwNi4F7N6/0ntrAmHWzt8EcVTx0y/F8IaIyfX7ZTIZd/YZbXJJMJtlG\nDMk2OKYeUHZAccP0gSQTObktPClzVraz+Ac2kzDPQpomJCXCw4RqmQUPRTgsprIW9ZB9UbSGH7r7\nsnt2wVQtarJIfyfBQLuBsUprj7HNZNTMKh7jE4vRulZq0bIj4TkzZhTzCdda2YjyzlTYZgPeT/eQ\nqrKokZ+UsJQF/sxW/ma2aRolWvxs3zvPqo/KFtkqW5kt3YAd60z/WN6a18rac0W65jw6LySSrq9f\n8s2e5o+gg/ka8PuzPsN9o/GaxtWzeDMG9bS/dVRew/scUcfXmtfeUEamHb2w7bAYHil0UO/BF6PL\nYStVL+tJJzwCXdH1NeOpZ15s/SSJGbGff/xOHJ/t9SpH5+jqrzg35mUWAW6JlpblAXRhkgTTdcPj\nqkopO+pyz1J2zPM1WWZQQauZ6sw1zvbAVLWF8eZx8kgbju3bZlEBEiKr68MFUcTT0wbo4YxNxN3p\njJVnMfdCrZYXRHUBiQVH8zSZkoXjzwm2k7CdE5sNXG0c0DcT8zwh2cwpWoVaKmUR5iLsk4BWKJWC\nKY0qiy+cCqkmtyEnJrHZz+g+aWO++ozIvFBUY/efox2gOv1ufSPqLUazJfjqvuO1VlMmEgx8Dar7\nohzKglblOhduZ+sPi8IsG+ZpwyQTWZTtVLEtLsznPSJk8Zwwx33sKXkKv2SY/fazbCZTtUApZ697\ni14rvTECXE9fMAbzaN96s8oa7kCL4BPLTfx0uOybyBk73BGLOLn7aA4ZyOzqnVcgflqez14HwXtG\nVu7sGvoCZmOGA+DTv2ug2dYzHqunsQ5OleWblXddJ32Goevzml/78dVvWjc+K5HO8LI/JoCz+LQ5\nMYFms+2WguTiod+YiQUMKPWAaALJrRgWYdiDQ5qZRXo5fCrhL3CsyKMHa7RIY/lp1WeNpYubdhKF\nzILqAfM3mS24XRebdaiSqUzJvFQ2k5lUtjNcbRI3V5mbrbi1yMN0KiwL7PeVZXEFpYIcbCOIpVaq\nLJ4OwMqd3FRFXZC6uH3c3juFMm19Zfx5vh3HISSu6Y/rFv8upeR5VUbyMd6r8PEe/vorQaaZn8zK\nu7lys03clsxtlbYWNCWIbSMiz7tSPdPjMduWgSwOs3fWY/ccoPerHLV8gbeUxUxTUk+ugbdqWjk3\nWI/OepKOHg3s1c/1cYdxYqGv+wcPd1sBZ+cNvZP1565SAbSynGfkx++jA8U8B0Nx3hvf5wnRseiN\n/g2RmWIDtLkdRju0EGj6uStFIGfftbmGDkc+G5jHvWW47JRR93OPr/SFThkt8OtBLEMb4wvEBoqY\nWUb9ZwynaOta/LOYr3bzfjCGpHqg1AcUSMk2CG5vEA1xZv9ZZexjYRMfler4Fuq4H6pZGqpHG1rR\nK0kKiQPIgUQlp3A5NLaegElgm4SrGa43wvVW2GwTN1cTt9eZ2+vMNGcP+EmUKhyWwm4H+z1N8SGC\nLEApLIcFqIgaC8+iJK0k9dlAvIPXa19C197nQreNHAdZkSVTnsP3Ip606yiCM/TtmfGiWrlfhPJg\nawIJQa8nS+qbE/PUk+H6hnOkIWtmtIGZeaLMo/k3lEjHhhi7r3Ox7it5BVXzlUeUlM9f9/a2emNc\nhLAjT0W59WmkcHraU2DRwbyP6eMBNWhMhg5yDiy1/9I7x9Ogerx4+VlMK1aMc7lWzhfvXGF7znef\nZroHSuQTX/neN0BJjXwf5w5h9Xd/lh1JnLhtngn+6a+zBivRcTALj3kjnRmW4LvdqA+g1eLtyXP8\nmtbgNq3WGmkdQGtESnoObbXc3XU5IKl6342twxaW5Y6UK5ktOW2J5VsbkB5g1gJ6RtY21mw3qTRQ\nb5RPBnww90fx3CXN91zAfMYPJPYk2ZPSYl4pIg7oNvuYxELqrzeJmyvh+jqx2Uw8u5l5fpN5fp2Y\nNxPTlEkpU6qwWwr3D5mHuz0Zc70k2R6eRSuO6EBmEmEWOGAe2kYbaAEz9s6hTPvcublhJqzvKC2d\nrY07aF4qdI+OMRx/9MUfZ8+970kz49wvyi9/Uih1wwMbZqncV/AIoFbWrnT8X+M1FrAlnlpSXjMw\nnyRqLdNmmJ0KqJl/Uk4tfuBY3gqQ7/cvzXaWrIOIZMZNCvq4D7PFAMdq4bArIPGGiY7P2LgrwOjA\nYtPdY4DXFrAQmCrR7ZpJobNviYQ/jSjoKQl1QBFvAAOZ2AM8nksHLuhMK/5K6w4ZSv64P4hGB4t3\nNTu3vat5oNg+jsMCpwx1LnEs2uC4wx0rmXU50WDE3WtDVj/b23aA9nptxgPpTwovgfN6MoxFguXe\nPgB74MHNdBOablAsqVILsMAy7hlQJOtJkfdD3asCqNUWOrUWkELR5OxbbRedWLMJwFVFlz2lKJoL\naRYkT5aHZLV2MLjedUjoADbaxVFEC7EdWtjIFWWpC8tyT0qJ7ebGF+OAanVQeaDwgLBHUrHw/KRM\nqTInWjKsFG6Ic2K7SVxthWdXidubmdvnme31NfN2S54mlprY7wrblw+85A7KA6XuWbSyL5XEwXqc\nKFkKhdTzuyTLjVIFUtHGnqOtmw6K69Xnz/Flg/lEiYmfxglDf5DU+1pgh9+rLVa4AiCUh1qQ03fv\nFl4dYJNhs5mZNkYkxJPIWdkaD7dxLG7OkkzCImpLG+OCRWpGnz03K1grnQ4mo8+9tXkphS9VZOd+\nf2cvnjI5ZSRNpDSR0sZCUdsC3Oi+0weuKenxhYJvjazxeHo9sshw6RkOraYz4xUdSeKu0qGgXfs4\n4OAAGSkDamuiFeaLuNONtEt6144yD77lxyBOB/JW+k4ZBi7RrHwrxupDqM0YHpc+k+KoDXolrAvX\n4zvjmhiJUTfNotpbrWH6eTC3p4UyrlAfSPUVW7lHqVSZWTRR5RqkJ21qCDAAKbIQW55pRHXqvp27\nNqVFLzDvp8YeiR7rJpggAKGYBm+DPmg7jKfWh12l+UK69QknObG5pbvw1boYWNUMwdB1B9yj7FAO\nkCqiFpgziWc5zMLGc6rkrOScmKfEdp643kxcX83cPNvy7L1rtu+8y/zsHWRzTV2U7f2OzUefkvgI\nrRY0sy8HplTIop4KwDcrtq0/V/lKshpfH/tEEjte8bmyYHb5SnNJjCRfmgKf1QjzmN5GrHfXIBTe\n22E9KdTh//hRER4WZVcK20m4zXAbgB2gv1rbiz2Sep82s1Ebrf4z5iDnB9XahBQkcRgD7YRYxP0S\nAfmymMuWSGLxPfBSnpkmZZq3lglNgi2ywgZjarC2xbbRYqfrOODie//o2DjDtT7IT1hufK8RRKON\nQsci6trOsfa+adXebrzOx9FxuYNvgPhYlNjF5DH3o2Y8kADFAbzbtHsNiqE4tKVMWLPiozcYHzbo\nMD066xEtoPHA8XN8U6/VUD5HbTGeGQMegaQLk96x0U+4zTuqVh7qhru6QfOE5snzcoc3RBnv4jvk\nHAgTi3qqVo0MfzJWWuKkkxAs2mdfiT7VHpTeSbsOSkwQZ46uPIRBsY9Ro0IodcuZsqDLHVoFSxuw\nB90hYuYf31en+YtvcjIPlUnYTJD9M02JzTxxs91wc73h+vk1Nx98wPy1nyC98wFsniPLwvzqU+bt\nr4PaFm27/YGHXTGWnyz51JyFQ64s1fKm5CQG1GqZEyPysYfrW0KwVOx4kkRSqwmKUrQ2dj5F7SXz\nGFIZgtyjiby9ooWDNiTpDLjN2MG8jAIVVCl0wJY6jOfGnKOfBJRHuxQ8wzqrQTSaTsd+P3ar8Oip\nHV/W7r/hK89ZeTteKx61ZFGWlpeh+iKS1oV5c02erkA8IizRWFEVaIEuZ8TAdmCx9sQ2he+Md9SR\n0RiPJD1qoO1lb6aLwLHHwGl86RjCY8OPX3ewDfub3T/KG0V5/FlhDlGk5xoX3A0DEPWgJj/ZbeVW\nNRFAMiihwbYYdsxelqjhYBHBGliXdwAfc09ycGyLgfF9d3WMNlupsuNfmwIpSHnJi3zHh1d7PryG\nfU18f1/5Gw+vuGdD1QlJ2S8JbwsLr9d6QMs99XBPZSEUca1jqtiTqu7ftZJafaeUSGki5+7xswoA\nkX7t8Xwm5krGMSQSZDfw0GpBNaIG1pMeSHogl50x1lSN/UphTsqcE5spsZlgM2kD7+0EV1u42voC\n5yTmtbLJ3N5OvPPeNbcffo3tN/5W9N2fptx8g5JvyOWevP0em3nDjRYOhx13n77kfoJtFvZT5pCF\nlI2B5wxTtfzk5msOxfOimKQWlWoZE4VaLDS/mfeSEpkTkhO0ybtcNT3YdikKCE0+uw3FmQayMebt\nZ/i79cRQJCJ9dkAMJW2/I739bRQsxFqIxNJocMWRZY+inW6Z3d/t+42CYflcwkHhCdv7W8y1ov5+\nPtGSyPpllbtRRaYrJM1I8kQ1Gpr2MRDvdqVjxjiOxm6LDjgagOa4mMMEanzOCoyfwO+4zfB0Ikve\n+rLUQHyFHMM03H5EOR97qDPsk0+2zxB6r+1cL+FR3H1fKzh61ur56sCjR2z+GIjjmqCax7XTlZi9\n9tjGujrb3xL0QNYdN7zk/fmBb14vvL9VDmoBHK8OD2i940EnRK6pEl46CdUDtewph1doeUDrviv9\nVvVy8vx1uf3/YVBLu04euTJsql1pxTUiodBD2aopHDlgMFUQPSAsJBaymEkjPtlTzibB8or7lm3Z\n2XKE5U+T5dveboTtJpn/+Jy42iSunyWu37tl+7WfIL34bZSb34xuPiCxQdIdyC2ShPlhx9WrV9x8\n/yPu7x942AkPh8ic6FvFSU+DOwnUpOQ6ALCAVkuOVXwXn0o3p6ha+HubtYqQfA0no5QAew9hD+Ad\nDR6tBXQkInLi3TJ2tcZTdOyd3cPJPoEcAfZuIdAyjFld/VgNruGBLR8M3aQWD0o+Ptd29FN5S4zc\n7Eax4iyuklQLS32g1gJamVTJG0iywQBqYKjnbqy9Efts+BicA0zW94kMY2cK229OcHwllswUWeH/\n41W9BifVI3U0pIoVcE8T/P5rVtjti+Ocwn0CmkkqwvNjgdOm5xqzHB16ZbMkyml1tbINz9KYrB6f\n6/dqnS6AWdeflYvWqKhO2e+54C2r8ELSB7b6Ke/mV3yw3fO1q8o1B2oSdJP4eFPZ7+851BllA8yu\nnDxqsyyU/R1ad5jLXi/PCOKn4z1Al5Xy6cXs025l3e6yGpRhY+/HQjGg7uqoBY1FS4qBuHT/7zlV\nB/HI5W1RpmbagfBPj8VuEWPKOTsLn42JX20S26vE1XVi8+IF03t/E3rzW9Hpp0i8Syahcg/TFk2K\nvPMp84vvc/PODfcvX3F/v+Nur74ZgymN5JGnaVAwOVkEZ5h6l1pZqifJYljJqH2PTa+4Vn/Js5pm\nsWjSqN7UWLajiw5mF07d/s5lFg3fdBo7Zo0L0hXtyMzjHlorZF9v01iRHcew9GesxrB2njP2keHv\ndb9cy1sLCDIgM9PKqPUUi7I7qFpe41TZZECmlkb0FG+114kcAeRYUSswGjU0REOf3jk6UoD9KpQL\nxZhFi+d47buPZw2dKu7T5hWu4cKzZLjMJibRSUJ8SjewcG2gamCuZCzdaDx6zYYDise356jjNxDX\nvpgXQGSMfPCKaQAXC4xHbdHSAqzr4o2mOFRyfeBaX/LhdeHdrW1VJphr3TXw/nXiZV14uXvgoDcg\nE8hE0gRSQBKFOnjodIV1POhPB9KohNaiGNO0NIBr0B5fYhWp6QChFLTuqWUHdU9iQeSAUEiibLIB\n+JT6Tj+hbKzXWFuqxjeWSnapQipCyQ6ianU15cTVduLmeubZzZar6+dMV1+Dq2+i0zfI6R2Qa7e0\nT3Zh2iHzrzI9e8H1i+fcfPKSu7sHNvuF/BBeKm73TgHgkKv2hU4V222IRAGKiCfgkkZIIQiKj0Nn\nrn2orxczA8irKnXxRevkboaqbcyfA8VVbEYMee/+nSCviUZDFLWAy2WpHA6FJImcrY+uLSKpPd+u\nq20d5rj/xOui6ptEPz0m3s7my9qahhgMOn4nlaoHLEOoTTem+ZqUtn56mCEGxeYWstHzg3b/oWXi\nqMRTO6M+DrQ55v7RANrichsnd7YUL0G0/HB9637EoO729VNIkJEdN8Qcz0qr843BT5gJZQD1GNjH\nO/zocN/2GB1yrzj7q4WIXhTB2WzMTGh4HMxy9YyjmlzXqbb6Cdt1bxNg6OARPBIzA9EFKfdc6x3v\nTw98MBeeJd/0wGtmTsq7s/LJtPDysOOjeg9pRphMVTYmFf3odJbTnv+ohh7Y4sjGsbWIsQskf4/k\n54//aEw0fIf3JL0nsSdHbkG3LU8STHd41gBQtpOQgWir6+bC69eppaK1GCdrizwlNpsNm+tb8vX7\nsPkA8guQK+tLCIkNKs+Ar8H26+SbX+HqnS1X15bTfE4w58o8VeZcOWSYslKqZTHMWZgsONZrOuzg\nRljCwhA5wRzHfCOJwQShpy0lKLYsIRbunxPUyC5+BNDgPt/QsybSKNRaVwv9m3XPtqRZncGZaaii\nnvM8rrf+FSoqOWadhIMOdx6KGyyftRI6lrcE5Gs2OfK0cUpb6wHdV6gVqZBmi5rTbJFzMUWJBldq\nA6ewXQVoNLt2GwTn5aSiwm48tqC7e4W7UWp+7R15GyttuNlZ6LjwtTbLKAEscWSoGf/RwdkL7Aw7\nGcg2W7ibUyT82OM+SuSWXr+nup+t2D6LWpG6R+oDuuzdWpNIskXZoExtwEFngkH3u3rWKCahwFbv\ntHrPqMboH7GBiEUH2k2sXLm85J3pjq9vDrw3KVe2WhZZU8kCt1Pl/Vl5dYBP9y+hbtE0ucof+p8z\nnj4R72y8edCsFn5HciC+gBwrCl6X0t85VGhYOmImMuJ/89BRJWlhkgOZgy2bSTBbQXwG2wZ5K4ct\n0OUcQC6dDYu4a6Apg+SRoeGdA8UWJzcT0801sn0Xmd4DuUZkaq0pmhC2qLyDzu+Rrp4zXWem2ezx\nCXF/9cKU1X3V/XnJJig5NEyY14YKCKXkmd6bcitazT//DDFtplnvY7YVp2D4oGixTSlEw1+7562x\nsaFtI4hag2D09hMH+zgczNEdHf3B7n0jmBeNFkqFKefWV1qMqPr9HnFJXM34QmE1s88ZfHJ5S4ud\nT08TgM5ItVD2O/ZV0XJg3l4jYougtdrCVeSYaPceAaZ5SfQdiN68SD6CnDnYYLPRkYKVJtwLRxuJ\njKErzS96PWhXvw2zgPMclsZ6g732thaQibWPcWpAHsd70iZ3wTvJ/hgdxQN5qpJkQcsr9ruPkLKz\nXpwzeXqG5OdIeoakyfNQR2df24v7hOTc8vR4JGL9fLFHS/8IhAmuUkAPzNzxjrzkg83Ch88y27xr\n+VKSCuGLI1J4d5vY14WPDi95pTP7ChKD3Mshw16cbaFMtbXNMRNa7/7ird1IQ3xS6wm5hnnLFbpa\nuHXVCaG6l0XMMtNwh0igoN4HXOHoGAEw2NcdoHIsNg4LnPOkbGb/bGC7NZv49iqxmYV5hrwB2eIJ\nw24RspmhnADFuKoAWtByQJcDtdqWbotmKhnF9sk01q+UoixFKSXZrj+eHKvt4dnYbqSDNbKnVdvH\nOVObxXYGHX1XmwJN4mZKscVCM0tJS6AVIG4grRR3KU5idxw52xHt9/7YwVabMWhYPC3FEoxpYsqW\nPdI872obKxLK4mRc9P5n62Xxt1/75cq18oaiMawKZdmB+wBnKnm6IqWNg+cIYqM3yNGimoN9m6qv\nH3a+CEgLcR/5NhLfep5lGZlaa3G6Q//4+SzSr4t3UYIxxKKm5cE2htNBZP3ckfofeZSMx9WBsx5I\ndUfmgXlTKXWx3NOqiGZknt3/Q6gtKpdOmUYz1uoRx2sRTnXU6jJphCX7tlsKwdGqFHLdsamv+HB7\n4GvbwrNc3J0t7I5jrSk3ufLeBj7YwrK/Z18nJN34kx1ItA2fVTmfkqZQT/WhKbTmH+7l15gR+kWx\n1uA+1YnTRc/Rg6elT/WuFx6l2cEqDe7tYUJJDIw8CTkr06QG6lu4ukrcXE/c3m54drvh+vaKfPsO\nsn0P0ju9Tb0sFi5VqLqH5RVl94rl7oH7h4X7XeFhrzwcYLcI+yIsCywlUdwzZalwWGCpbm6pfXu2\nqmHHTo0BR5/oniEGvrXS2PvQ8gNpStiuSK7Msp1rGxh3EEfMxNQnkl0pj66FZ8dsU9we6eRtHAxa\n1FJqFRH3Is2Ee2FoxO6H7jU8kIGw//fyWp95rFt+uYEcfy0BdKEslglsdle2NEdHD0aqbTD3aXLs\ntJGIREYqanmaT+QUzHtVj9GRMZ0OzwS3iSUPRTt9A0Y/6bPv+SRuDAw/KqSZTLoppT+vl3dkD+sy\nxU/tPz1YKklFlh2ZHZup8mybORwKr8rC/vBAZWNuoaIgG1LY5vFZipwfAk+Doy+g6tKAXDxDB1HH\npTDrPc/TPd+4rry/rWxSscGt3Y/GgMBmHxtRbif48Drxad3zan+HyrbZKW06jE/t7Vmvc/WC3l7B\nnNoLD3Xelul0MXbtfTAm572/paF3OYiESS8qMUHko45d7Q2oDaR7nuxxGq7NZh5uiFPyHeLdBfFq\na+H4z243XN1ek2/ehc0LtCm7YL3hE7+gegfLJ9Tdp+xf7bm/O/BqV7jfO5jvld1e2S/CUmyhdanK\noailjq220BkeX+GdYiYOXLe76WIImArwjKR3ESCjOAtXM9+MVZtSspGhETUa2q4TotamUferkS7t\nu7OdYPWFtn5UfXZZtIAK2TdPtrLHe0cQYmrdR0IRhWoXNwURzhDn5UsN5EliyhK2b/No2T+8opYK\nFeaNTfFtZp+tDYfoyxU7bZGiFgzcpS8CrcFcIbZcQkFzKNOB+U5e4Q5AEvkQRtZ8ysZPgeLcjKBr\n57CNi7iZJ0wozTsl01bDhQbizdWw+blGMY57hb1YAICWezZy4Pk28WwzcUiQCnxSld3hjlIW6nxF\nzltS3pLyxsqYJlQ2mKvjAEZwxMTji85MVyYVigN7mMQUWe55MT3wravKh1eVZ5N5bYS3Q1WhVFsQ\ntP0gC0Jlm5SvXyU+3i+8PDzw6XIHaTJ7s7MvFWl9Io6NG/WeSnuptgiXRBCP6BSpbXsy1Qduc+E2\nCZNM7HXLTrfUNFG9rmIhV90kEV4awTpxPpz80aJq+beJBdJercHcu23dAd0ZakowZfH0tZhpZZOZ\n5pk0XZn7piyo51YXIGkGURIHhE9g+Yhl9ymHO4vs3O0r+6Wy29nnYV95WIR9FfYqHIraZhQFlvBO\nkQ5sEmshAWwCpXaXxGDgq34kyXJ0R53UyqKKluL+6GaZzilm4rVfO6ytjd1xnO2IMEwspdVrjJu4\nPtb8pJUxwL+a3V08staw3Hzni80IVSspxY19PIRyk4qkbj56Sr7UQA4M01UAaUyqsGPv07BpA7K5\n7qAGzf5lIo1ZNy46Dk49+aX9nRonCZs3HZM1eerTTLOsKWh0rsYgxMt+jh23wp59fYmIS7eBN5bd\nXApHRZFahxgmbP78vjizDtw5KpFW0AXRA/NUuNkom7QwTYpcJXZlYTnsWJYd6D2aZiRtSHlG8haZ\nrkjpBtIGmGDIZjgavU4DfgzMwzu/UTUxc1rSPTdyzwebPT9xXbjJZmc+qLmtFTVGVmpCk62b2Ka4\nlmPk3brwzY2wK4XD3Uv2eu1zNfcw0T5K29T6EVY+LlaLagdwwsShiFSKb1ghuuO9q4Vv3cLNJHx8\n2PG9wxWf1Gfs2VKZvV/VbvYLdi824MeeE0BjSbVoyjl+NaAPwqhNsWvFN3AQC+lHXH8Wlv2ew+4V\ncvfrsP02Mr2PTFdeJxsP2DtAfUVavsfy8F2W+0/YPSwsSwTkgPi9LZqzsq/CoSYWTe4rHoo3urwX\n3s0dkxgAFsS8VdrbdyAYg6+SM92Uou9GZ7YaC5dZU4BDFCXaXBJHMF9TjYGRSztlRckiF1RKmSkL\nWQpZhKs5cb3J7EphKQdKUSI5oCaoxRU12pa3mn5y3LGms/Zryboe6ZNfaiA/ZnA6HK9lodZ7wrgx\n5eTpKGbWewz2ThA20VMNF+p4BFSv5JYkP2HOTN0bJbxYYwpEMm0r47MGO3XztW5Aqr3nSO9FYzDN\naNPTIUNk81IZd25vC75RW/GcaPxhpf3o7Vud14LUHVkWNrmyzTDpAllI28T9AvtS2C+WcVB5AMmU\nPFkkbr1hypWUr5F8BTJhcXhtSej4ibQAJ9UzLePuePUVLzY7Ptha9GaSyq7AfUkcigN5tX0d55TY\nVJtWb7NynSq3FD7cZnZa+Gj3wMc1s2hCIrdJa4YRNM6LtCbsoB8+4fa37RRks4o9GxY+vFr4LS8q\nt9vK9x72bO8Kh7uZqpZwtqUuCJCWDsYBFiOYC/SMjprWVUpvepGoQ2n251oStSZqEUqB3W5hd79n\n8/Il6fpXyZv/lzQ/R9ILS1LlJkvRT+HwHbj/NuXld9i//JjdvYGUgU0P/BGJWFTbUq1oNgB3hVs1\n3NmHLy8AACAASURBVAr72o+lGXDy1N14BhrcE86JK6FR6Zr5OcwlNpZHH+xEALtjSLg1xvgk2jCt\nTCwdxaOkJz2CnIU5CxMLmwzP5sTz62wzk0Nhdzjgk49WV2E5iPoa7AfIUZ9MPts2xXUqbwXIU0qP\nfreKtmq/yXDMvQlQtB447CtaF2o9MG9vyfM1ksw9ziA4AML3L1xx1dDOGFj6Ua3V/EGlNs1oi2/V\nbNIpgTqAynh99t4imH03vA7szn3flHVXUAIUAmTDVyHcBINVBQgaiKtHaraFMj2CoOjQvpsJPkD6\n0Dmqe6mgOyiv2KQDG/F0ojFgknCzzdwflPtD94NXCroopRY47KnpgXl+xjw/I0/PQMztT1PYRkHV\nF4Gi3Ir38gQUt7+7OaHs2JZXfONKef9Kybmy08z39vBrd8KrxWywYF4rs+9BKQLX28x720zemlJ6\nX5X3r+DhYeFhEbIWii5e1xbwIrDKa23Ne4YJuStrMGTrKsWZYTbbuCy82Fa+eQvfegGbTeHZFqZU\n+Gi347AsFCzntPjGyZMI2WcU1RfOwkolA22MBE22fVuYdOynLfLh+3iaSYVk/aZqZimZ3QKvdjDf\nVeZNYbM5sN2+ZLr5VdL1O+j8HJU9Vd4BLaTl28jDL6Af/1/Uj/4/Dh9/n/u7Ow6HA6UuqCxIqqRc\nbVG1CvsKFF/YdIatmmz7PLSZTqzeC7ELjxTbKi57/y0B4qKIW+IkgDumIi3FROtQBuYVN52Jr0lq\nX0chADIhKbX1hJSlAXfXJYOJyhepa/vSSIFtgm2Eqy7KlQjbOVMzvCrKTgsluZuoCLVIH1+CmwXt\njyVyu3nLW5t+iYD8TeV48BzvIB87bi+L8/JamcvCtLGpvQxsVdp8s91sBeCWr6HNbWiA2QJTbACF\nC1jfsqpfot7pYuGx7y8SQzBmCr2TjEYQXZ3noNZX1VontWsqpDMulSsk95V0BdtsoNKCe6K87QPi\n7n2p3nO9VbbZp+/SV9GvNxPPrmy/w1e7Qg2KgUK14aalWvj78kDOL0nTlX3yliwbVLJTkK5cwkMi\nxUwnBlrdc5MOfLhVvnGtvLOxwI9UhEmUq6SQ1fQqgtZMpKutCPuD8korL4F3tvDOBr51U7hfDtzv\nE/f0+MgWaRcErLHscxyMPsX3fSlrNZ9nA609QuX5VPipF8I3n8PzK0WybbG2Oyy8Nx94We+5LzEM\nZ1C7VzCznLTbu70NTHHY3pzhN51iB6DUFeTYq1Dbd7PUylKVpRQOS2K/ZB72Ynbuhx3lYaK8/HWY\nbU9S2X6fPL0ACvrwbconv0j9/i9y+PS7LPf3LPvCciiURdA6UbX47Mjtw93I7wq8mzuMn3hfV9pM\nugYFasw4Fn9tRmleIWbn7zMxizNIY7uokKQ6pvsuoOoM1z1YYlPpWIcJDxpbd+ojo40UiSHpKiC6\nvu8RKmLlOZTKw963iLNk8tSlm5wmJtvcI4VG6qRARFo+GmPq4skClZO9TF2+dEB+zICO97g7DqM3\nMC+Uw84AxPO05OnKIoikM+cB0vsULYIjGmMFiKmqT3Y61sZ/HIfWapRZEyLjood/YjGtYcKZ653l\nq3QWH0it/SSMkriiSDZfa181hdR/2qykOJhHrFsojUikBaIF0R2T7rh2s0rY14P0zEl4tk0cSuJh\nKRTfUSfVWHSqVN1Ti298kDNT3pDnK6bplpRvSPkKyVOUrLGlJLl7a9SK6IFJD7zYLPzkrfDBVeHZ\nZI0xpcrtBPkqwr6t3YL5FYXFB9UGO5ZEuJ2Un7gpfH934OP9zP0h2+xLLW9381F6wkY+Npq5p4Z9\nvlJqMaZZF66myvO58tPvwNefKVdzpYrFlr7YFj642vPd5Z5PSqboBnMldSXt0ZzRb1cgPh7znCrh\nI93GzdiXo8dVmzXZT1jqxGER9ofE7mApaQ8Pe9KnH6Oi5LpHrr9Hmp8BSrn7DvXj73D4+Nc4vLxj\n2e0opVCWQlkqy5I4LOYzflgsgrMWWfmCR9nw3tdoxNDPbO6bUEk2y6gYiIcvdbW9QFPKaDU3v6o+\nW4739aGTRVpm2FAi+H3tOydog6lFwE0+2oieNBSP3+MNfBnan5FcGR0We+cpW8KylMTnGkYEk8As\nlg++SJ9UxAyhejSspW+IMRJnncpbDNE/ladzWxyZXcZ7uE9uLXsOu0pdDsyba6bNNTJtiU2Dm0FB\nBPPyGGA9GkYyKjNRtS0GckjepG1eN5RdRw5k7CJcH7tdXNb3aiybEamjArxRtd3fppGd5Zs51xeD\nwk7qiK5tZ5KBwWtBZKHvT5o72IuAHsh1x5YdWw8mMaDyAI5aEQpXSXlxJdwdMmVvnggiQ7pQUWCx\n35cDh2XHsr9jn18yzzdmdtk8M0+XNFNT9sAiYfEBnbQwKdzkhQ+uKj95C8/TgVmgpkxKyvXcF7F6\n8EzxaAMrgdZY4YDtbLl73psq39grHy/KR3vhUGyqn8ONdOhf58hDq2fMs0VLpVBscdNNAJKUm1z4\n+nbhp57D+1eFTTa/YhHldlP4+s2Bv/Hwil97gEVuSdmCmGp9icieJJZwIOzm0vppJyXQUUA9stVY\noTE6cth8oerS2LCilLKwPwj7w8R+gf2h8urVQpV7trUi5YF093103hhkPbxEX75k+fSO/f0D+/0O\nZY/WHcth4f5BeNgLu4OwlMQSgUCLBQXVQlMuLTDHTWLmZWIx0rXabKovhmp7v1It0juFC7JASsbQ\nY9G5QlurmnKmiu0ApIh5tfiOFcmjYO1PD2JStQRhPmNQ71/KsAgeo3wY1jZrSkyTsJTqvvIWKGW7\nIykpz1zPVo5cC5PPqg6illuqeqRMVU8kpuDukxkz2eQvk438dfKkXZL1IBuusu9qoVCRgzW0VeBE\n7C5uZgYBsW28lmq50CUJKV+R07XvKjuyo3HhwfXwsLDopWo4bCxXgew9N1p7ZOQ6fOido33Xn2oT\nTY/4E+twGmaAqrabSlt3CGDuV/eZQW4KwsTdFgOZ1NKiXiergqqWMPWwqG2qWwpXs7ms3UyJF1dC\nrYWXZTA/dY3Z390aBtUdS13Q5YFyuHOTy5Y0X5HyjEaUKkrWPVd6z9evF75xvfDuZmFL9ag9sXBz\nMQCPqbCIkCl9gKHN/lhRJIcXiPDBtfLpofC9B+W7e+V+CdA/39fOSQtmIQC/giRSTkwsfHhV+U0v\n4GvXlau5ksVZY1Y2G+XF9YEXc+I2T1S55tnz58wT7F/dkQ5WZz3Sc7STD7NLz6rXzI52mU3CRRvj\nBCHJBM5el6WwZCizKb1DhYe92cthMSeiWtDtQpps/Oh+T3l1YH+nPNwV9oeFlMwenrMph6UkczGs\n4ptzsOrVjRV7PwkyJU5G3FpqO+E48osqUgVxJj5lLNd6tvfPEZAT7x+sXTwIyMfd4jEeVZVaals4\njJ13jFfJkbly6MPI8H3vK005xuJqmCIlMc2+3Z0WN6WYHz+L5Rk3k1xl7372VHONttU8G0+iNNCf\nvmpAfi4z2fgTjgG9H9NaWZa9XwyiG1LyzljDz0Upy4Gl7Kn1gKSJaZOQzZXxNxk8GpqPV2hmTw2w\nGvZ1YGrjyxwDeafg4Su8PjeiAGs7qysIb+i6NyBHUNkgszM1mVbTrwCtAHGJeXoruSss1AJx6oFJ\nCtucKLVyqJUHhf1SqUsh1cqUhO2UmJPwYpNYlsqyKLvF64bB5/f/J+/NuiRJkiu9T0TVzHyJJSO3\nyqru6g0YYMj//yP4zgcezuEQHAC91ZZbRLi7maoKH0TU3DMrsxtoguxq0M7JLTLC3VxNVZYrV65c\nNnN4SEVrlVYXajmhaUTzhlQ2pDwiacR0QDE29shdeuCr7cLLTWWfK7lzjYMd0bf0sQqHqpxqRD/q\nmtiTGPsBpgQmLXBHFxR7ujGOpfL9wZjNDdliCbl4Jp814hfGqXcGxpHzBpSk7HThy73x6yfC7QRj\n8ixO8eEoaTBuNsbd1LgdKydr3FxfsdlsecAoDxWbo+jLpfFeQ4p16zSzYGT0RLEbFmIvR8aGdXVc\nqhglO2ZbW2NelMeTD6Xo9SZrjTxWUo6ZuktlPlSOD5WHx8Lx5DBNrz018wESPZrs8MRa4he/N294\n6hos4nCPrdyl2DuuRZIdOKHDnUlgTMqQfLCzod6Hp6wU1HMk7++btEfYXRIgaI0x0NkaYRdkxajX\no/w5P35+IPTgvO8ZW6PnbstwtlAwaIZeHbd+5v3/a7MLKRC5qMHYmg187vpJQSvw6Wj80nB//GFW\nI74aeXB7Vylldk87VsgTOWdojVoWluVEjQG7RNpD3sVm64kskVZVx1DFOEvwXkbkPWbqCx5fM2Ed\n9Svy0RCHaM9eX+Gyg67j9R2q8M1ZW8HqiVYfaN2Y60SSEm3q+/Xef2SwVyEtvYgvOkxTQ+NkQaWi\nKjyeFg5L41j9tYaU2eTBo1qc+Hg9KHVKlGIstZ7Zzm3djRAZBN2wS5j6NlPqAssBjkpOAymPpDwy\nJuFmXPjZ5sDPd42nU2XQenYKBj7o1otdh5L457eNP9xXLGeGBNsMd6Py85vE8z0kmVceU1JhnHxN\n3xfhfWncL8KpqtcJ4rmfTeen9uulab2kHrqM7pMJfn4t/PKJsBl8AvrKnIrC5H6XeLrLPD0q75bG\n1e6O6+tX7HjCu9o4Vqd3nlvWV/fvpq13ODqYusYSTbvKok8r0mg2KsVz92TemOS8cjjNC6oNNweF\nLndcW0NzWllmUhvLqfLwUHn/MHOaCyowz46JL00cTmlGac2HRfTtF780xdbHsf2chdNizKdwIKsl\nEwYVsipCcS56cy6Y67CDthZMGHwk2zqWrQdZ5nTaMAotzlyHQby+dqZDAlwIE4Vn1IvMoR/f81nt\nZ80jcFaGUT+/Zanx6NT5+hmnShfxjEf6tNiAMDvFWMQbvtSftkNPlbpmvx9efyU98k97lsuo++PI\n+1NYuXxkvNfvuPg5s8IyG60ulJJo1QtSrfbRTLZWsNe3EFnbttdUtntJAxd5miOVW79j/fe50Hn+\n0wwvYl7yY6PAtu6S+MazM7H1IHgwUVFmplRp0lhq49RO2HLEdIMOmwtFwp45RApostpWNzydGwzS\nGmozaifEinfjVf/5q0l9FFhKEQl504ZG08t+EOZN4rBUDotRzL/eP2J/HJdgUXd4tMDetSDFG5Fg\nZkzK09H4+Q3cTJUp+/SbOEMgoCGE1JoyN/j2KPwf75WaHHLZZ/hqL9ztGl9II/fIVkKCIMHVJPz8\n2nh9NB4W4dh81FhrLYyCMypa32CcE2p3SHqezxHPKYtxNcDf3wlfP2lc79y9eTdjQrUhqgySSMDN\ntvJ0Z7w+DfzDf/k1z7/4O3773/+J5eFfOd7nMKpB2+vJdryZEayingH1gqEs0ekaw0RMqNYorSE0\nj5YblALzLGd4w4xEQpszj0px+MHZJK7xvcyV46Hy/qFwPBVKNQ7HxrtH11ipzSGmhkFwwpNBa0oK\n3mDKYchNsCKk6oFBdZWGyBKUnIxBK9vsHZun4gOSa2RfGShrNhWGG6cxegH7DE90vZdOX04agsbG\n6jySCENSBm2OrsrlWbVYaS9E+3GPgM8c8hsUtoMyGyEaVtxAh2NJ6rWTGvAQ4vCToAwCJGNee5qC\nvRR1sa4dkz5D3f6rQiufYqZ8Kur++Ps/vj4o2H3w/fimar6otcqKa57hh4scySyYC57KuVFMnDG8\n80HuwxLOrGyldzH2SN5f/xxz9+5BN6ydCtjZ5Wdjd5GLnAuTKm5wpbLJYM0f8um0YOWI6SOmroGC\nOL981WxoPbtgFaM4+xMvENJODCyB4wopZUY1pqxshxjfFdDAWeujMmbhahQeRgnWhv9/w1ZnCB2b\njnUXx2VTfNKGR2/SKoNV7sbEFzvliz3sOrbMBRXSP8HK6S0G90X47qRU9aOzT7AbhKX5z+uFIe+t\n+ZtBeSHGl3t4czRez8ahudHtGGvPhi4eyeoYP8wa/d6mZDydGn//TPjy1gus74/C48k7HIcRn8Yz\nCEM2bjeNZ7vGdwgvn+949sWWH/5gaIrBCKYRj9mF1oavp9rFXvvgfHeutrpsQQQk1UCtUxBhWfD6\nSmxHmnfB0pRalWVpeGOTB0y1+uCE+di4f6wcTtGWPxuHWai1R+Bxf5GA6oWjUQxNTqtUM2zObAbY\nSuP9QTjMcDQ/t0kag1Z22fdHZ5k8Lm68C0Ixf/6V8744u70zTNPngIIXRV12N/oq2nk/DeoNZSpy\ncQ4vr+Zn/xI5aP6aQxK2A0hxWNLdWadLOAc8Ja9fSZxpzIOtPqavhtNxx9ojfA98krpGzqeuv2pE\n/kl+7p/AgS6vD7jkn3iNDw8ZgH2QlvyYjeARobUF2oIk7xD1XNCjongZoGHSS/Bdlzw89fo9H0Zw\nbkSrP7ie1vUk2fRs5KJYEneEWQKyV+bFD8A4pMggjPtTwdqBugiIocOWlCZMJ7zYqjQbaFSQdo6W\nwz0lxRug2omkTjncZkGHMYSWQv+iVaxWv48YLNyab9MpC092ibk25mrUS4cWG/LDZxfuyrrWCVQc\n99znyi9u4OsbuJlsNZyrMw1HcOEfIxqW0DmJN47Q/YwzXuyPcJaD+CjBl3vh9RH+cD+zkJi7Tsw5\nQ1+zi4tPQQ8eekSeUK5S44ut8esXiedXboxfPza+fa08HJTtDu6ewLObxt0Et5vKs2XhqiyU+Xe8\nu6+8ff/fOJXvacwrwtO3lCcHtoJiHdbxD2Yhc+rc+GpCgWgdYy0k1iaU4uvYaNQmtOqysUqjVvWi\n5ZgxK47t4pK0pRjL3Hg8OU96LkarHc6zVZirtUzrVFj1yH6uRqkwSWJMC0NeGHcDmyuwqfL7Pypv\nH4z3R+NwaowCY/L6Qi9E1jFxqMZSGiaZ6HSPhWn0JinPPQIyCaaXaGDXSdDkBtwieBPzAMabbpyP\nL5wf/5pyXYZZfW+KN1zlrExZoc1kAxsSx1opJqCTZ7PhxFrQdSWkBVYWTvP6RevMGumv77WXnH9C\nxc7PFTP/vdfnZu59DpY5t/Kev1ejm0uCMmc2I+3k+7IzKSQHEiJxqBpoiai6rXrk9MzgHA+cI1Iz\nByT79B6/O7+PC6Peq/yinWHiokoqCyJOkRvUvbOYssnKsSyU5Z5ajqATkjZo3pLGvXO2dUS7QmLr\nQlsWgpHOHdd2YBoam2Rs1PvtaoNTgVPxzzkqXE8am8YjBcWYVLiZhLkkj45PRu1NPbT1c17sAP8/\nibqDOSTxZFJ+9WTkl1eN5+NCbs1vN3D1bszXwyMdYLA1ClytldqKW/rz/fDtIUalSeHZpPz8Svjh\nEcp7WCqcxKe99P6vH4cXthacFS/CbbXx1bXwjy+VF7uF/eBO+ulWaUeQBR4Pxh+LcP+olOewUWM/\nNK6454//5//G/Lvf8u0fv+d4vKdR+AC+i2W8FEa0KDTautKV1ECkcHY0zp6yoLiVWEMPPWR1Bikp\npQrz4pHvca7rZ0WCJ1+M+WQclua6O3UhpyHotzUKruIVDIsGKXPV0iSQB3gyDVxtJ7bbxDAYmipL\nNR7HRi3+ftvBHeMA4VzMWSzNz5IZlLog4oO2s3rzXrXGEjS+LgPQQ3PpDB/8KGZRUrJQkPTakQUf\n3RvgerbdQa2+d2S1JeE/HKq0wnyao7fCg5kU329WyGbkYlBDtK9P/oiCeW+Hap1FE/u8269mvv6f\nun4Shvzjjs2Pv/7vuT73Wh+//+XAAOmu1RrSFqizfy1SH7qmiYSJFuiNRp6g9SNxEZWvW6Abalgn\nT1jcA71ZpxvyHkX6ezqnPSa0NB/5laWtE9JJym5IlFpYyky1BWQGOSJ6ZKhH0rBBhhHVGMjBxiM3\nDbzPXNQp2ZFJK5vUGKKhaWlwWODdsSDAfhB2k5IxxCpd+QUxNAvXk3KqcFzK2ih0tjofPo8zg8YP\n2H40vtjBb54kXm4L+xwF2JV2eY7CBdZD1Fka69d6gc16/NSLkP7Fy/qFp7iN6xFe7pSvb5Q3s3G/\nOOxwfoqf20hnx5IwrnLjq5vEb54JTzYlIknh6c7IBptsfH8w3p+Uw0l4f4RpD7uxcZsO/P7b3/J9\ne837h8I8L3xQZA0sLICx+HsPF7oIVdDzxBikMaXKIIPzta37OAmGv9ekm+BFtSrkKpTiq7aU3n4e\nqb1CrWHIZzhVmAuU6jhvrTHCLWCBrI519+ESqsZmUrZjZivCdhCm7JliNiFZQl1VCxXYjgQNpfPL\nz8OQkwpJHL5ISRgUxqw0g7lK4MyX0Ipd1KYIp+Yn1o24Q4d9nNyH635JPb6gHwpYhMuCJ+5JGrSF\nrvfUzMiqDGIoha02cjyIE9mLwR23j8CInt1bwJMXndNOePgJFTs/d33KaH/OmP8lBv5Pa2d0CKQh\n7eRKqt3WMAKdddIXG1YcXVIYHWclrAURvCtjPW6xqYzerWEgLQoe3Th1I55XnNu99EKmkLWRRN3T\nJ2E3jRxm47g6sIa1E22ZaeWelDNpHMn5hpyvSakhOtJio0mbSXZilJlR3QAkGtWUUuFxMd4eijuP\nlHFGe3UNFpU1m0g0rgZl2STuT422gFWnbn7sVIXATWMLJxGeb+AXV/Cba+M6N08/RdB2/qkVQSA6\nGmGNsATWIQrNzkZeepFUzqjVSoWL15uSrSyTPz403p5cjKun7T8y5nJ+Xr4LXCPjdtP46kb4+k7Z\nDmcs/mZXuNnBi2fw8qR889Z4+9Boc0N2ynYUnm4Xvnt/pByE+STU0hkcdr7RNTPoDCmLA78iSYCR\nc2U/VK42RtIJM3XcPz5LCb68Px/Q5r+W5lKzLRpkSkyEd0wbn/RTjGUW5qLMZaDWxKl64XQpiWoF\nFSNlYykOgdQKu2ni2e3E3fXAu+8PHI+V40l4e195fpO43g48vFt4f2ycxLjaKMsyU5fGNO4xVSwZ\nSmLMwqZWljIzqBfhxywUy95DUquXsKLdUqKeoRJd124Q3FFZOJ6cEHOuufPKm5MTNDbKh9jcag9a\n/P+Qhc0ojCOIuICyLZUpCVMWNqmyjXNTmvCmGI/NociuUKFKCKFFcHlRy+uG/NMMqp+AIf9zLfif\nuv69RvxzP9MfGAiaHOcTHG6gLawaW70tvpPxxb/zkjzoEbpFSuVG3Dp+zlmjvB/E830REpzBtl11\nR5Tz6KsK9USSyqDqU2Hih6c8MSSX8Sy9SNJ7G6vQrEBdaFoo+khKb0nDDh02nue2Eyla8segrYkB\n6oXW1qqvUURB2Yo3WfVoWDoP3fmx+0G43fqBqmbUFuyb4AX38rAYNFGGLFyPia+fKF/fGE/SQlL/\nP5Ngx/S1WyMiWdeuRzG2Wuuzhf4g/YULR8DK7lFxAzcm424rvLqCN4vwriYO0W6O2JpVha1ci8Uq\nSlbYT8Yvnme+ujNuNovXElz4gyaJ0wynRcjJ+OKJ8eLaEFN2k1GpPL0StvcnbDbm2alqEfatjqOD\ndr2E1pX7nAHSoS7YZeP5lfD0KnMqiccFSo2S6YWcQ2enaGnr2mp1GVYXhAJTQ9QC93Yd7bIIxbxO\nlLTGDldaS1j1Iu5uaxxOytIUqXA6wvF+ZpGZ42PlWISTKfcHZdqM5CnxUBcONXOShB1mkiQ0JZaI\nsBeDU2RKOWeGwbHuJnBaHJ4pDZI5c6klIuqNvpAwjr1/ozoO5NTd6Ia25o7HUg+q4oHHn767oiag\nThds5oY0J9gNPlrQTNAJBiqDGlkb18nhmrkJBzOWmJlqKBlhEOfGFywKpZE5rFH7h7bj8vqrG/I/\nd/1bovFLmOTPFUE/HeGH515DcC98nuuCBlqRlsPq9kn1rB79HOKd6QPOdKnnUy9nv9BTMj+lF0wV\n46ztZcTPz9COpKEypHMrvOC45jgkhlmZew90/4UFVzicipyoek9atuiwQYYNWQopnxyTlKAlmrBW\nWeTcb+qf8Ey4/HBxHYCaEtxMyrEYp+IHnNbo+jNn/q2RJHE1KF9dJX52Dc+3jVFLyKZewh9nF7iu\nrHvcDzb3mj2v9vzMZFhD9/481+X2vydtXI2NV3t4NwvfHwnGB4FhXryTnJ2ESmKTjWe7xt89h69u\nK9uheHt93EczeP2gfPcO2gB3u8bdDnaDa8bM1XiyS1wPC5NBLck7UnuwEMbXDbHfQ4sP3KNN8IEa\nk8L11LjbGk+2ifdHFzizjr32DKNZzCrySHsRh0Xa0qKmIAEdeOOO9yk4jbCW4HNn43pvLtFwNE4L\nbLfGkzvhxbOBf/29cayQizOUtgPsR3gLPJyMt0vjMAvpXjg04d2SeQgDfyKxG8RlGFKMYjZjKYJp\nir3vnapm0SAWzA9XLvTmJJ9GdAlGEWsZsEY8o2pnTnrfE3KB53kPT3RKWN+R3dg71i3quH625hTY\n4ZyxVnNWTxOhIuQkbIEhuRZMh8s3KYS1qmcEF6bj4vT9+PqrG/LPaalc/t+fMuafK2Z+itr48Wv+\naGLNRcjmXZQ12q8NKKhmrGVMB0wG704znzNpYqFJYmfjE0Z5xWXXkU6y0ps6p7vjeP69FgVU3KHY\nCezIII0hXTgr8Zx4HIVNEQ5LTEBp+OH/YG0rZpVaZ8p8QOaM5hHNQt4ru8GZMcFvcEOhniY6lzmi\nOemgSF+u+FdEO1ngdhQOs3BY4LSk8AAhStXXQIUxK083yt/fws/2jauxUq1FPvIhptHpn91hGuJr\nFGtr66Hzw9nW59tZRdC16bsTAE9lLfbDlApf7ISHGf7wqBxMOZlrX6zZgLGm3KqKWuJ6aHx11fiH\nF4VX14WsBa+ABcgmwh/eCv/rvwhvG/zDS/ifvzT2zyopNSaFJ1t4NjVucmUdKG5geNNIw+mE9P0h\nTjH0tnZFkrFL+GvtKjcbY6PCSUOqyVyZsppDz71bWcRZLJSIbM1IodQsGrKsaq7ShzjPuypNjP0O\nvnw5cJiN798oD4/Ks+eFX/0m8ctfbXj3v8y8OVTGBtdj4tXzkS+fZF6/e8/8vvD6YCwNHl8XiR+z\niwAAIABJREFU5LXxWEbul8KpVJSBZpUhFW52wUaJZ1iqUqM+UYvDcPtdXoMgNaVJZW7wOBtzszP+\nLWc6oMU+aOINhGbdebmoFsn3hytN4lBZKPoQ+zSLujZTEqoWlqrQPMO7SX6/xyY8LIlDCUbN4IZ8\nHx3SS7VVX+VqUKo1DrWtKqESxsR376evv7oh/4+6PtUJ+qe+92PGS7MaDzOHQ7AASjxxxPAaRMd8\nzXD2h1BF42C0CMidIy5yVlumT/mJxhy/w3Z+Mqux7/8wfw0WYEGkeJeXZrqes1e2C6MY2yTcq3fV\n9cJXd96X/mql45lBK4x5ZModrunQzCXVMqLiDlNcvMZFiLv+4apuvfDZmOvCHCwCF8XwdTARnmyE\nL6+En98K+8GjmD7y6+LhrA727JwdDmsqFE0UyRGhulSvmNO4LAS0Gr0P95yt9bxgXZf4nLvReLaH\nXxyNQ0ssVbgvPQfxg34e32dMOvPFlfKPLzNf3lSuNp7ZXK65iTGr8K5l/vhQef6QOZwM0eJCWWps\nc+PuqvFs29i+rxxlYEEQG5wtYp2B4XujCx33T5VMGHPjdtfYbTzNrzHkGCyEpc6Oz+Euzzg0pgZp\nf+UWin2xH02Elj0qdpGwwm4jbLOQcmU3jOSN8vSZ8exF5vnLxG6THJufGzY3njw1bq4NHVxgbEjG\nzSbxeHKuuqQGE8wPxnExjkAqwq4NDKNxOsy0UtlfwbtD5TQbiyUwByFLi+zEGqUVqglzc80SUYdb\nVl0ikWC0hGM3l501xBugYm27ys356Z9zUQnCQVahhK0oNA6tsjRhBtKxsskOLz0uilmi0ihLZWqQ\npYazFhYzTrVRUY7V1v1z8Y4fvP/H19+sIf8cdPLx//1boRmsUWvxdFlTyIKmwDnOrbJdDlYwx8wv\nx61JRN1mgWsfsFri8CTQIYYWh7Rux3Pt43s0j8QgIJ6FLH6wanMPXmrM+xOnTo3Ziz6tuoxquPGI\nRD/6zOJGK9HYZmGTvbnozELpwe1FVCyd3fNxXNCpm90YOq69H4WlCofZuG/G7OaGrjFtCHcb4eXO\neLFpbFLzOULSD8zqRdZn9qEDNkoTTlVopozisrbVHLPciLM3vFNQkHTGRnuazfpedBSJKcPdZPzi\n2ngbjS7LoiwtimN9dipOmbzbVH71DP6nr5TnV8Z28iismdPhOkSCJhZJPFbjWDSGBniXp0Zz2ZO9\n8eLKuP6+cqyJU/UCXI3iYxXWYtiq9hN7TmjuDPY+nk+1+RSg5ntkVN/jnULbYgmsgdRO5XQ8uYlH\n58ni6xoQXdQgpHnvwIDy+B4swWar3N0p0pTHN8LxwXj72jg8glriyfXIOCr3Bx/QnASuJ4d3tltj\nt4NDqVDh4VE4mnFqwv0svH4QlqNL7sroGcVSu5hAyMZWF0hr5uJT1nyfDYk1422AuO2nNiP17K6t\nBJkV1hQ5G9EPLI30PS9Yil9iJFGGlEgpczI4WePYCkmERYTH1jyzDZGsTUo0M05Lo2DMrXEKHajS\nzvGLn0MH2da3/8T1N2nIV2H6z1wfc8U/dalqFDojIm8V716rwS3PpJTj78kNeLSqCQlpDRgQzaBB\nEUQxBkc2W6PMD0g5uHE1IY170rAj5w3eIEzgn9L3Bx3B67vHakFaZcoDajAvxuNcWEoBMzZjYjMq\nKSubMbGUxlIsIBHXiLELR9GNqOAtxftR2WRBL2a1rKwOOG+fTwQDcmnU5RwzGI1NUq4n4bB4d2VZ\nDEnJnWS80N1WeLap3OjCSnNbvciHxvxjymoD5gqHuUFr3GR4tREWS1iBAWOn/n9z9WSgV/0vMw5v\ntu3xlrd934yCXis/PML9ER6XzPt5Zra+Ik69nAS+uoF/eNX4rz9fuJ0cX/bCHz4fs0Gu6kXqFM0o\nfTkjUJDIZJ7shZfXcDc2vj01DgtIc0jB5WtaDJ04t327Jrk6a2isvLiqbGIo9dLc2Q/qei9iFWvq\nvRGKT62PT3SOOn0sn2P7tmqi1wZo8g5GMaZBoArf/E7QVLl7Cs+2E//yzzP3x0rRwm//1Xj/KFzv\nMldXe5ot/OG7B44HJVG5ngpTUr56pbx6obx5XWgnePNOOC4+9endyTj9QUlpIOfMyMxS1T+HgWSX\n6C3F60OLCYfqTmabYJ8aKTWnJoqCemer103OA8tNHFPHeuE3MtEP0tmzkTeBmgTJTmvYpMRuyOyH\nSmnOMmsKlqG0xoECjPgEKNglH/N3z8zDUjhVv3eNduI+POPyzX3Pftru/U0Z8ku5yI8N9b+Xg/7x\n/0tE1KtMbC20FgJKEa55MObGKOno8rg6+JAEGUBGJBmEbkniSMqNIoXDXH2sUzo3C/SdsZZiehQs\nEG1nYAvSZhRjnhdKLZRmpKQMQ2YzZsfNm7GbRk6zMZfl3PH20XO3cPWq3oW2yTD2tLsjJJf31Lya\nT6Sxn9pIZ4N+NgkiXsjZT5n7gkfkmqPxwrwdPyWGpKw6QXTmToCd/WYunv168BCWYpxOhbHCq1G5\nyUoRXzoFhgSTGGVp1KQXzknW3y9WxnFIUYakXE/GVzfwbjF+OLmutDQiUlWyNK7Hxj9+NfD3X1Tu\nrheHVIgpqs1V+fyjKOMAI0a26jzoLGh2TWzMndt+J9zdwPOd8T/uK1aNqhqCZr4m7mRdAvWc8Dd2\nWnkyGs/3yqR9pJ6AQZYWrCQ4BQyhOH00mdBqNMugIUtwjv7V4xMsjKePOCscTwN1gfcPjV//cuDp\nTeZ43/j+deObt4VDWXhzgGM12rHy3/7lEZaFNz8cudlPZBndiW4bX75SvvpC+fJFZndt3F43/un3\nCz88Vh5n3zdDNnK20CR3R57COUmDlARrzQvuOZNoJPX6lVY3zmPy8m5SieEVoV+SYDdm5mrM9dw1\nq2uC3Weycg4q8EJ3kkwShzuX2jjMJ2o1hlTZDo1tKqgo11PmYfHC84Jxv3imfqrRPKfiYiDWoRs+\nyJJFez/FfzJo5VPXX9IdCqzGoTcGSXhmPz/xWFckxB+gaQkIJrtBTyOa+pScE9pOjKmSkzDPytEW\nWjlRAit38aRQI7zQNreAQ/yXY+SKq8wZ3so7jZ07q4zZ84CmMA2JYVDSot6C7Z/ux58XP8Tj4NS5\ntE5vl4v7kICIgr/a/+z3eF48zgb8IoKP33PyrrvOCxchCkfEHEmnGq7NEKsL6ffqX2v9lS9goqTC\nZoBJjStzHfomnO9bYJsbK+WswynrYeivGU7dnPcu0hgSPNsJL4/wu/eVo+HTbhAGUTYKz3aNX79Q\nXj1tbCY/kGbO5ZcmZwKRuOHOChrGUhPkwdBkocHk0fr1Hp5fKfsfHHaonNN8Vujp8rn6164n425r\nXE/ezLQU4VidJphVSGpsEjxICFtBwCQ90nOj3/VROhyhSrC3nAapycjJXDArJCxyct776zeN795W\nvntbeJgL9zVTzPn4/+P3J6cF1szXryZ2k2BamZ4bm03jUBpjVu6uhf/ypfLsSvnmbeO7e+Ph0Bg2\nvgN+eBO9EmIMqkjIFmr8loCcHTJKgtOK1w3VN7A7RJ9n6lTFKWdXW5TG3KK4yXr0V5ijr70gZElk\nyZTQNipmHKsXuTfZuJmU/WAMpbFLicMc8r7Afelianhg12GrbougL/zFvz88G5fXfypD/vH15+CV\nD64ejSJrOok5dW9lSlhghVRMYliu+tSOPG7WJ93qgSxHNoM30bhmgrDUQmlHWoOUCykPJM1oGjjP\n8zQ8BDJoC2rFZWpF0FEQHRiGgSyQaSQqYk5pqlkZciKl6NCAcxS6BriB7ypMg4Yg1WWzTb+8m67F\nFw1W3rLf6OXfJd4gLLQoFlMFYuWCHWLrrhTV8+sGxqs9+kZWqqLbl15ghq7o6N1/yjgKycCat4E3\nqTE4GQqOnScPkc8fTqDXJT4w6eI0vf76Vxvl+c54vqm8qYkZF/efJHEzCl9cG18/Ne6uiMG9GntE\nPIGJqLgiaI5aL9mNR2rkwULrhjXi227h+Y1yu4HNPZzOcQTnnCWiihATywh3e+HJDqZUeTzAwwwP\ni8/mTCkxJac7vpvdIfWEpydZ/ay4vPhqsXyYg7gDStoYtHGzgflYaJq4uhqYT5Xjg/HHb5Xv31fe\nHh0WOgaMU5vw++9nrkbliyc79vuJm+sF3VS2P2t8913hX/61sp2EPQPPrzO/+cXA63vlj99Xvv2+\nkjbwMAuP7xOiSxRkh5XlZQKjuCOy3FiWEFXL7lxbc9383uxkZuTAt80qaolJDM24xEDy/XCuqfT1\n70ZVSZIYJDOjNBKLGLPAKMJ2UJ5shK0WHwgeGR3NVTIemkUxOTKrNePUizJU7xvoCdkZ8vz4+knJ\n2H7u+hyF8C/9+U9/j7vcJMaUlJQcKzwe66pVfOZqGJ0h0RkkzvfuU2D6NJDEqIZlZTuNlMPM0haW\nU2VZjo7DayalkZwySb0YsmLx9USmsRkcB9foIisNFnOpzkl70cuLb1P2wQ+nxSVeHW9vsQG8RJYE\npqRsxxQblv7busmLJaoppp4BTClkOFf9lEZXlWrt/PNO9StrFIM4S8A0GAFxElwy1VBzMS6RzpPu\nzjde0aK42KGiCzghqzdbePTkBcNupG2tPaQ12l9fuwUrSaCtjJxuJuNPkaAFCl/fZf5QEkcTRlGu\nB+XlNfz6ufLitrKZKk29EUtEIGn4CcMCBskDaLbImaO1fMgMua1OysTY7uCrF40vvhH+9T28e+wi\nUH5f5/qz49dZYZuMV0/g7sqZTMdZeH8Pb45OF02i7AYX6HozN+xUAT0P9L2IWC8zKp8Xaauey5Od\n8uJGeXk78s13C48H4/iw8IdZOM6Fb99W3hwbhwJzS86qEkNQ5ibcLxV7+0j97wtPnylXT5XyA/zu\nd8Yf/2Bst42/+5ny6y8HOC4IyvMr5S4NzMA37xtXU2H36KMBBxmZKVSruAZYRZuLeJXiHcOLFkwb\nc208zqDi6i1G41TckKYI5ydp0XQXY+i6B+1BxMUu9z+8Icgkul4XoxRhSD7WriyNQ4aHBqcYaTeq\nsMkG7SwvLNbO54V6Ab1GQ1OzNWv8nOX7Tx2Rf+76HKtFcQGezeSFldpgWRYf6twjz4gqPxj2w0cN\nLNY3hKd3QxI2Y+LxpCy1+OzEVmiyUCWhMlNTckOuOZgMkCmkwaVkh+RV+NLgNDtVchBjmHLsNzdo\nm0HZTMkLouAshf5ZYzfm5LDKZkz4lPHoEIzotzXhVBulugbF1ZSZkrAZhC6H3AuH3biuVfZ4JyAk\nRkM0P7rmWtDakrihaCYsxfUuuqFyg+idkWtCxNmAgfOCk/mRFOHM4KDDJH4bPjHGdcbX5911ZuL+\nRXuRtR9Wf5oJN5K3G2OjMCKMYmx04XaCF9dwtWmMQ4OQZV0zErE1k1PJHumlXthUNAl5aIxjrGMT\nTI2dwqsXmVd3jbsfjN8/9r0l6/peRoVTNp5tF756YjzZA7UxDrDbJArKw2zU4hopu01jd4LhUWhV\nqcIKWfXfLbKes7GygCyMbTa2WaA1KslNToPjQ+HhVHlYjLm6tGw1iQAohmHjfOrlpDz+YHx3amzf\n+fN9/Vp5/y6zX4ybm8wwCe3YmMS4GoWnG2NKRts1fvWssBky3z4kXj8GQcEENcGVRUOyNlLquTrf\n3jVYmg91EHf0tUKjec0CVy8cckZbJWkiJYXVGa2UhPO571XqSEhjnCiLwKHA21mhKI9VmAM2GdU1\nxRteBK19Y4YFsn641j88i9D+hZ9SRP7/5fW5aPzHo+O8YjwMic1mJOdMKY3DUam1hZSnRHofKf4l\nm0L6BnG9lUQNbrYEjJHJaUZKf0Bh2Kg0W6jFi01oQlR9eEOGNCaGYfBmHRNqNQ6nSq2uBb7bOIjp\nbTtuyHc18T756Kgiwod+vDHkzDQkpiEj5lKpPb52SMU4zYVmypQTY8INeRKy1hUu6Bjwiul3Y4wX\n2+ZOD2yhItgWsIzEvMWeXTyWFjMJeyGpRnOS49XS2/QjgvScIMyt6cobk56e9rhSJUSb8Ck68fpq\n0JSAJsyxyVBLTD0yD7hBgTFVNiQmM7IZAwu7rNxsM2NAJqas+L3R9wVu1Mlrg02ioZLQJKRhIbuM\nj8N4auiojHnky7uZ5/vK+NrZDCbBUMEdjeK0vt1Q+PJm5stb5XYH94/GzZUxbRL7Y+L3rz1DWkrj\n+qZxdTJ2WbgvgzNR5KPzETRFpQ/U6Cm9Cz5ZMV6/qzycRhYbGAbldFo4lUZDowktFMHj57u4bCMz\nW+b9Cb57rMi3lWmI5qeckCHz+jHx8FvjzR8qV2Pjqztj/JkP6Lgdjb9/2Xh6m/m/foAf/ml2mqcD\njL4fm1Alip8E28YyxYwmjRbntFljMWcWNRNOArvRjbeGUuOQEmZO5+0Rs8Qe7F+QtQOaNXM6NXhX\nBD3501qasDQvqkbCFjBlZaHQR4cTL933OPF4VmT8TyARP2lD/pcWL/+SS6QFG6Uf5iiIaOc9e0ME\nAl3cSgKS+ECQrBUfDBydfxYOIGvg10ul1NI/4EVKBZ12aLXQxAs1WdTF/qOZwyLSOcuPBgfcvGCZ\nJUX0rOHxP/TuIj7z0JuAbKUCrjMFBUyUnI0tigS+6kVRW4s1rqF+Zk44lu3t0NWcp7tUV8ebRBmy\nkaxh6iJMOWnQy0CLhh5F8SaJaAnPQdXTWG9Vzwg04KRkfghdOzqocpQw+p4ZmUlvdA0qJj79Ryzm\ne1pE7nEgQz/Dh3GcBytvRNklJQ0elU7qUmopHImRLpx6GMCAwpbZyJa5SolrOTFQAHE65iAxk9vh\np9QU08zT68qLPdwMyvtiLJw/d+98HbTybFf5zUvj2VVAPMDx5Ls1Z2WcIKux3whXm8rtybiZ4PHQ\nKP0ZXmSWvqM9i1r3Op5Z1mY8HCvvDwtvjr5+22GDaWahcX8szCbxurK+YkMoncDdKlq8+tLUvBib\nvOB//1i4Pxg0oc5QpHFj0IaRtw+V+bFRl+zKjCfXohFNiKk77OZ5VMMbgyz2dT8zntHWqGN4Rjep\nD4QwimdtDTbqmbXVmEkKaxDR16ifJRGNkWwa3b6+/48VXh/tLO7WC8eCs6o0Go4sOSrbc8DQJNLV\nwUbXgHTE9dPG/CdtyP/fvs50NodLXFMCiIKXKOSUEa1QwlpL/+3cvScrjlacN5whZa/Wt3rGZsck\njElZlv7gzlDNukEixFX1SeFZFcVnSbbQv45knB7/OvIdDSjSaX+JuVRKdaGjDvmpCuOYGLO68Y8P\n5W7L1sr8JruyhEjzZiTpkTBYGIB+SCxuu0dePRFVhe0gPNsM7PPAVY5Oy2DuXA2w18YknUzXYr5n\ncN+bnDd2HJJuJ9U4j1eMZ6LBsxZxQ95xTl/Si2YicyPkz1uC1eGvr7EPeqR6qp6CZ4FRQRJsx4H9\n5BOIEv21Zb0P6WuJO4jDIwzS+PIWxqa8fGJsNu6oNamPPhM3nmpeKH16l3j1FJ7/zlgOjsH2WohD\nT42rAV7dNH7zSrjdV4/0WmJehIej8O1rn7l6uxP2G2OXjdvReDIZ32qlFWgXeGxn7qzQTUBXnfZ4\nKrBU4e1ROC7GmCtLWXzkYBUOzeGWtjKdWDuhDY9FpHVjGBlZ7Ro8RKt6WyEZZvjuQfnu/cjxYeHx\noZJl5GGZeffohrtDYT1g6meiF5rX+4gsyUKPSHFn7Nr+PpUo9NHYDA47zbXPBzgPePCFOmd83Yiv\nG7NDjQiLGIM5b3xQQ9TPb21et8kq7LJQ1AXvSi2gsg6Q7tt1fUJ2rmJ8fP3/2pCvVyyQqJCSGw/w\nmXkpp5VN4ZdEZPzRS7SKVXFFuDH7RJRSqNVhBVULuqByiAHOZ4lX34S+F/3AJhXGnBk0kzo4sj7d\n3rzjwJyGUlsMBfdGnylzmAtzcUOEuDEcsjINzj2XGEbrtKbeMeNfm4aYp9k8sXR5Aj/4Pn3GaV3W\ngs1jZygkpUQSN5Bjhi+uBr7YJ15sHaLqww2SOa4sll3TOn614k31WROtzv55e7OQuQofxmo0ltb1\nXrzxZuVbpwuWwccPvAU8oRoSvTENSas7lDDEc/VhC4N616clYztkribYTy2cT4/8WLPu/sbWhHkW\n9kPlVy8bX995y/l+i7vg5NRECZpfM7Bs3D1VvnxuvNgW3szGsXrhMegLqDaebo2f3cEvvkjsRydX\nNxPePChv74XfftPIG+V26/reWzWuB+PJpjGmykMRqvWc7BPdg5G5+D5tPBYfHXc/p2iUg1pnjjGk\ne5YMa8Dhi3HJiFljlpC2MHPDWpcI1sVXvolH0/dL4tu3yj9/o7x7EB5OymZS5hPcHxu1ZTojytfl\nXC+6bHY6A34eBEk48EldW9+ApokSGWfW7gD6LugwmbGObuzQSlI+sPIWbBl1RVUltOHFYdTSlNZy\nGHJjkz04KtWYpWI6sDThtOBBTcdsuo/4DErxk2GtfKqh53PXp2iFH+uN/3vvobe655QuOu/wxo1e\nIEHOTYedRxDP0LFub76geTo9zwtQyUmZNDHkxDgYOS0sNdCSj+55dSg5MU6ZnIPBsnKtvUjXDVTE\n9P1DRBrshcxpXMhL5VBDHS4JY04M2WGKZmf83NZ0zn+5YqLjpIYbCMcTI42N7sYsMe08CqhDVnLO\nUZxpWCsM0fySUiO3cB4IucMl9PmojUqhFc+MhiGBZSQKTwl1IbNa8clJSjVjrnXNBJKlUPmzi3Vq\nTk3ERaNasyAZCYhSaD78oDaUidZgrvW8piI82TrDZzFhsuLONQmHUtC5kZJiIbvqw518entO8PQp\nTFvh6l55//bI0iq1CqfTju1e0ehOJHB2U+XqFu6ewvPrxu8fjftZKE0xq4jCVpVXz4yvvoC7Z5At\nU+bCYpXNJpEHpZIYcDbTKM3574Ow23g7/7tFODVds7A1qMUDj4azuBBvTHo3V9cXL40nO+Vqm9hN\niW8eHjgsRrMxWE1Kx8m7SSeyIp8WT0yut3V2Sx8JJ4CaUC1RG7w/wf/++wOHpTE3Y5oatEotvjc9\ni2osS4lRbv7vIg6xxGmPrKmRNTOKxfCUSsGLpU1GjqXxus0+TzYn+tBWD5jg47CgKyf2oqdEHcPE\n6z3e4OOwmOAQ7GI+fSohTLi9SOp7qQ2ZQ4GH1jhdnOtVAVH+BqCVf6sW+aeu/wgsXcRx25TcYPbu\nraS9o5MLi7m+M+cH6/QzxVY6kWPurrk9ZG8aGLMx5ES1GrTFy5s4G/KchZw65fDcjNPTNv/bRSFz\n5edJsG9wQfvs016GnHyQ8pjYJMe7uYg4TJxZ4LrqvqZrpImumsiIY9yjKlPSGFbrHas5WxR0nDLV\naqO2xsOpOs3wWBjw7jvRXtz0Sr1HeIZKxZor7i0W0VZSxxU7hmNG1rrKlubkDBdVH2Jrcd/OB4+W\ndks+QxJnFlgA557JFIzqXYA6AMnTbHGsFIWbK+VUPHLaDMbLO2E3Bh7dZnIuDENjGBrZIGdBzffS\nNHkRfbcTbq5cEVKyMG0GDxRCLrYXWRFlu4XbW3jxtHLz1vjh4A5LJaSCR+PrVwNfvYLdVUNLYklC\nFeHqqEyTR/sqriOf4vVzhs0GthOkk1HrZVDQoz+LDMz1bwrGwcT1YUwY00hOijXh4dQ4Lg65fCAF\nEcdjjYpXiCOeQfwtSKOcewziNYLvfSzGUqprkosLVIX+IN3WtlAt7EGVdAGwswdZ7ymJw5vb5F2W\nx2oxGq6xiPHYFIqxVVeS9Ij/zBhaj/0aAAUCeOG4PHhv4bjiDIlTYavBbI0NvblLVkWOajCXxlKD\ndkiPKIHL9//E9ZMx5P9PjfFfMqDi/P0xPkr77D7HwwWPYrs+89q7CyvdbpWUDQ5xLwpm9cg0xXSV\nrG4QHdpw2MWLcp1yRziPwG2zkrvgz/lOI3INKdmomov0Y3GudiuubXw1JrQZ4ziwGZVtFrbZBbNW\n7JJwQ9ZZB5HhNOg8bmiINnL2sVqbrOyyNyZpT7+j3lBro9ZKDQpjOfjk9RMzkxqDEnCQBNRi5JTJ\n6joegjsGTd7GXMVVDq16pKdqDLkwJi+kJpxS6c7PVgEolwoecFkFPTd6ZU95k3hBukMBgpGyusZO\nGkjaGTA+kNh5+T4F5nqv3OyFVgfmcmCpByreXTpYc3cl+FCGBNNG2Y/K7VNlKS7ulcdEHqDPF+3G\nT4BxUm6uhZfPM3ffFL55V3molUET+9F4cdX45VeZV18o07bADBKF2qudsJ0s5lFGJ2kMT0gZpknY\nT54t1GNv+Y+9Jg6jdZ59wZ3eKdL8MSWmYQCMw9w4zIXDrIFX945RW89Ih0z8o9mKm9Nx9AuD2LtL\n/QstggkfKNG7HmtAkll8WINI8iKnpHDk0SAWkE41o0+hR7x4nlQYkg+nphK4/ExTX0NpRm7CGEac\ngGU+jMclQreLz2q9cuVrZU0wSa5BbsYk0dPQfFJQckyHhjFXlzN4LMaxCcWcf79SHjkvzaeuv4oh\n/49mo/w5hcM/937dU/eCV5cCB4cpcgxoru1cuBG7THmEVBspK9txYDv6azWEajWiLdfMGAW2Y+Zw\nmqlR4DqDh17Jzikx5RRSooCFZrL5r4obznPqupYX4xB5ZrBNwrgduJ0Gx+tiUolK8/RWHfOMebBY\nx73Nf77LdG5yZhiUYTTS4AUw34iNVitLDaGg1jfzOc7y9fGp5ENOjKkxxCQiM29Bb9J5tb6+DnM3\nKD5I97Eo93NjKU5XbBiWZTX8ahHdRM0pi0M+WWHQmZyMlLwYmFS80xFlo87u8UyggRQ0V3JeyCkz\nDR7NevNXz64S203m7unEyxdbrO1ZlspxPjKfDpzmwvFYybmy2SxMm8IwztjQEMloEsZB3HgnN0TE\nMG7BIu1oZFGu9sqXX2Re/MvCN68bp4BHnu/gVy/h61fCs7vEkH1vJiuMlthsE+NYvQNSG5KMPHbt\nfNegudmObJJL1hYSqh59Kxaa3Z5V0Vydr7XGZvTaxzLPLG3gVIX7R1ja2ZB32uFKv7QkJZr8AAAg\nAElEQVTL7mjOxc9u5X8Ej354hlWCgWLddEZ1yIBaWRAKXnDcpFC9VOMxpHAboVnS4YvWmFtFqqsk\nNrfePgvTPAAYQyOHaBLsARRE5uAf6kOIN345vBOweWd44dlvTsoggpixl8ZoIA2W3jBVhRNeJK00\nXMBDo7ZqfzIk/09hyD91dUPePuAGfvabHVaJbpeewfcH0pkNC70Lby3lrJQzqxUxF4FKURxaauFU\nimuNDEMYR2eEjElZigVOzfqQkqpHPTmTLpqO+iEpq9fnYgza+XsswM5mhjp5mSaJGhG2SrBp8Pd2\nPNk3YcaxaRUJQSuPXMYEKeALasOssoRGtgV80qJwmaStYkMS0Eeia09UrkfX/Ej0dmmhIMFW6d2R\n0JPwufrnPsydjunTXMApgkpjnZIasHaSzhSIXtbIGkR7IQpGaUzJ2OTGGKPymjmzQKSisjAm/zyd\nxeSGXNndZx7nhcPcuNoPbDaZzXbLZreltkYphVpOVI4c5iOnslDMD+c4KXlwiusaufZMT891EFGY\nNsLTp8rTfePlvrG5SkgTvnyq/N3XiRd3wnZjwXQRjEQChjGT8pmZkwdh3CSGqC8MpXG9UXZDI5NY\nLJ9hEYvsKiC6FM1potWFq1KFZjyUhUMRjhGxegzh4/3WvQgr9a+bQC6MuAdEZx7G5dcj3b3Y2+es\nReJ7ZguqoRiTGFt1KeN9SryRhi7GMbILTUpKBPGz0aySU/NipSXqMX0wo/Wc0f3YVq1Z7CU0arYG\nhEPy5+uCc7E8YgwSPaXi8tFjfMbupGozStR9OnngfNrtstnjR9dPBlr5j77OHO4/fznedmanrCQ/\nCxEe9ZFqErM3fzRpG+iAR/ITSm2V41I4nApDymyyy9uqCGP26TjHxRkXDhj4dTbkqQuK0iOCPqpL\n12jzstXnnNp6J6UgkqhNOJRGqS6ehKSLkVayOqysErCHO7WcXAwpqzNRzLwFuRY/BEIhi6wNCx4B\nR8t81BVUCGzWByfcbJxpsR9gMFYnYiKUHuGID0rtTJlj9QpgCVEliUaq/nyaubLigvl0+OZQi7+3\n/9szDfEZpkAFLHlDhsrsz64RxURvvGoNUjQmdXir74UhJV6/m/n2+5nnzzPPnm54erfj6mbPZhBM\nGmUeWeaJskwsywGO7uyWpTFOjTx0ZkML+M6d7KqhIjCOjpM/vTJ+dgtf7hJ1hlfPlV//fOD2Whya\nWUByiLkhaE5oqmuWOQzCuIHcoM3GtBi328LNKGyTp/Ot0yc7AwSjiaEhimUGY27BJ4djqRxidJnG\nz9VYW4i+otWY9w5S+7D/yAKOW6GYMFpr9PvR16SLQvjXilc/XN1RGyOVSYXrKVGbZ1lji7OpkJJS\nwmFDIWejSgWrnJbRRw1EBN6zgfV0fSYa9v3SLYa/x2XPRTVfhYxn4+DZZk4DUxKvHTU4NIeLtLmu\n+lkB1Na1+JvAyP+jrh4hj+MYOG39N/2MRx6OBXvDW++16qOeOgUpfugjO64aVEV1/Lp1IZ1miCpN\nNAYneCv9OCTyYsFP71GMO5QhJ7IqHqv6G/ZRUGbCZsgxFchnIRJGEeujxRJNE5hwbJWHeaY1c156\nNNnkoN0ldd75kDUwYb+aNY7FtTuKseLeAJNWdkNjSsIoXc3QLorC7ZyamgY2mRhyY0qFTapB8Yp4\nJ1TePqjKm1Kb8v5oPCrsxLjdNDaDb/hCC9YP0RLum19bb5wi1qVrlbjRqKYsVRhGZb9Rnm4HsjqF\nqFSjtuIQliiuUeM0Rqve7TgoDjcsM9/+sfDdt43NNnFzO/Ls5Y7nLzY8fbbh5nbP/skeMWU+nDgd\nHjgd7nn/9h6VwjAK0zYxbSrTRpimRNer7lFXSnC1g5d3A1phewuHx8bd08QXLwbGydeupdg/oZ0v\n2eGjnDKjJnIy8mCMvlHZG1CNZ9dw+67x5n1jibCB1bC2vmoeMZpFmi8c58pSNfTWfXxfM2VpEtOv\nzhBJx47bxR7vQWWvkfj3nbPbvt+t+U+tPG0CAo3goBoMNAZpZDVOTWGBIVWGwXg++kzUfj6aNWdj\niasnzij3BY4ziM1e1GwOdUnUFloHp6XXsM5U4A4Rdd68l18UpSIxtLwHXoMqo2jMBo3pRGKMyX+2\nCBQRdPY5p9XOJrxDNyrSp0X+6PrJGvI/NXfz8nv6dcmCzSkxjZlaYJk7wT4iAjjz9vF/dMYKAqXC\n0rFBM6YoyuWgiMkFDnL5/im5fgZ0ARxnRPQ+vMAlAH+w45AZMqjOntaaG8Ih+1STpI77dvU/zHHf\nTfJUcohUzSQodcFjbvSIw/HA1ho5mUsPpMQuO40upYA/iEYT88ahGlH/3HxsnHeGphijJcG1Dpgh\nwRCFJwvaFbHK/tf47BGWqWkUj3rnYMRscm6P74XKakqtmXlutOLdqNejcr01xmxr+tlz4ZV9c7E3\nzg7VRZIMN+IPJ8CEXVae7GCb3UD4YffJTC0KyCLRUUcKB+hsibk0Ho+V+0cfE/bubeVwLHz/3YHd\n1cD++j23T/bc3OzYTjCOE7vNyLTcUOZCWRbu72fuH44Mw8L1vrHdwTAl0hi6LVbJqbLdNu5uhSfP\nM601bu4ST54Jw2C+f0P4TFqHJWAzwJONSzokcW3uIYf2e0SIL2/gxVv442ONZ6/0ZiYDUhOW6hSf\nAYeAirmq4hJUQhV1/r9BbQpKL8UDbnQ7W8gu4AeJbeH1mXOG2/dw7bCEeJAkVDLGlNKae04Kk7kR\nM0nM8VqnuZKzMI3wZBJG8SDkELx5TSDaeDcbsyWyKVNqHhFrZZONQTOQkT4nVTq80w0rEbA4f9+M\nlYpYqnuA0sDU6M2zCz50pJjyGHNqkJCyNcgYt5NyZcJiwrHC0lw/no69/y0Y8n/LcIg/OcbNAkfN\nmc2QKBhH9dFo/cyLtDDeMb1aQBNodiNQaotRUS0wwoRElTvJ/83cmzVJkiRnYp+qmXtE5FFZ1dfM\nYLAAZBeAkMuFgG8UPvHnU4R8oVAowyWECwwW6Onu6e668ogIdzNVPqiqmXlk1gBYoUiNt2RXZhx+\n2KH66adXpMbH9e1fdgdaYnM2RlgRtWSLxnI1IT9NGfMkyMmy2wQWdjhNlhnKLAChbQmQUSmZLZsw\nIlzWQKQW5zTcm91nYsLtbsIuZ+xSsizKpACbmJMo1F+0Iduq1rA2hPrEiokIE1lUSPLU80glNuQS\nQjyeN2Lf2bjC8d1Ime5id/AF2HchVjf7vJpzaz8nXM2C6xmYZys+Fp4KVrSQxuokalhPFkljlgEB\nWAowH4Hjk0UNTGTJMvsJrbokoGj7l4KmYS8toLi6ts4+x7Pg3XvGhwfg4ag4ns5493DGj98TkBi3\nbx7x5qtrfPFmwhdfXOHu7oD5cI08K3BeUY4nrOdHlHK0ElQCzEWQVkKeqt2KCFKq4GSjc3eX8PrL\nZPHnDgyICWqBzmAliFTsJ8XXrxjvHtAASM4WDQQGZlJ8cUv4+pZweCs4VudmwaiOIFjJsg3V5ngt\nttaOxdqXRUxeRB5VBUg6NZKC3qIuoAGPFnKQUqtu1nicK/aZIXinJli9y7wp7zkJDlBMrqwTmdLa\n+XkIBoquE6GyRVMVtrrvma2+ihVCS0AGOFVMk2BO4rShWymNJOr7l7zxizmEPQhBzSls9+euWQ+g\nEBCOYi3qzspANbS/+hZYXRlcTwYqKyV8XICHxRG/X/wTcvyPS5C/dPzrE308TpbICz0RSCxRpQxh\ngy4dEeF7IEPITIBKQSnVTEZ4bQunXJJEkf0RzvtGZzaPdOPZwy4y/gxqJiigbqJZuv5+sponUq3r\nOpOh3OylWeGosjU+CO6CCKJWyL7UTqsQpIXUJU/vn5ixY3bBrSjqJWNrwVpWz6ZkVDGIHj4Ai5ww\n5+c+WUJJdjRSq+BcBAusPGtOvivRqMyYPGgr0WnRMiFgWy/zIWY/5ke8UJFAMWXFgQgpC/azgBNQ\nhHB/tmJE5lBVj1SJbE2YY5MJicXrtpsAmRlAsh6ip4Xw7qEgZUGatFWrZJArADiC9IxVMkV792rG\nbm98+hd3wOOT4OkkOB0rHh4UH+6Bd4+KD79/wo8/HAGdcH2b8ebLCb/802t8/csrfPnVFX75J19D\nyy9Qjivq0z2W5RHH90eoLtjvFfNsc3k6EX58W/DPPy74q7+ccP2GgcoA1RZW16LBibCWFYcd41ff\nXGOpBZxWAy2pgkOcCuPmZsLdq4xdVuTVU8dbyFa0Hs5QESxVUaoVp1prciNTW5kE8z0YBy4CF6o9\nxT9F8lnjnq3v7FLEECkRkucrmLXGsCIRAqordklxxYyZCWdRFCkoq0JzsoS7zCAsuEqCNzvFxwVY\noXhcgZQmKBSLVpxrQRLFzlH3PhEOvGDHBbevCG9eWZTI8ah4eCpYdQBgXvOkr3E1+k0BqRVlXbGs\nq/mdmJEmS+hjtr6xD2vB2ROblmrPcSSLUIrCfajVMsDnZEETUrH4XtOxzMTF8UcvyIEhLRa28cfE\ng35EVqMh18QMzQnzNGEpS7Qy9jMqImHXHIf2AxjyppysaS3UMz4tdT+nhFVc/fo12ePP58xe/AdN\nUdj9os98CHexmICJrRaJVovfnidzck7JNlHQJWaWBiZQq/Am6hyceeQttMnQRopwGxXfuNaftKqg\naPVu9SZYwcmuA3VcY68nj/eeGNgl8Qgaq2+xKpkDkbjxhQQA9NwF3OcQ200QA4XtXAZFw2So8e7G\na2AwcJgUAOPjmfGbHxXvz8YrJg5BDmRYfWn2+P/MigmKpBV3B8LrPeNmx7gpimW1TkAczlmuZkoj\nchMJPdFKXQ8zUrYoEE5A3gHXr4BSBGUteHwA3r0jpB8EH0+CUwGWpWI9Cn76ruD+Y8F3/3zE3RcP\n+PKbB7x+vcftzYyrmwkHugVkj7osKOcz7h9WLKcV9w+MDw+Mn+8Fv/gVoxTjdBFRVI7mCOYXmeaM\neSeY8oJaKk7HgqdTxasrc9rnJKCJcNgJbvcrXu8Eywo8FfPDiHq8NgCIkW+rKIokVI+qyCpgVU/S\nspkUGP0SK7VAvDMttYgTOFAoYjXC12oFJ9gbc0T0UghJhmDHwCEx9syQUsxZ6HtWfQ++PhjYOmTB\n64NlZj6siqda8f6ptoACZndgw8JOD7N1aUpZcXuwvqcqDJwJRzAK2d7YAEH/XUUcoDlY8fBUVXh2\nNjfKtoJxrhUnb+0WVRpXZWSYn8pa0VnuRFLFniquWHBK6o1Twmp8fvyRC/JhAP9gBIqjQTI0lhO1\nYvHznMHn1ehp2uzJxtdZcSoK0hhECaWo10jx1H22anJcbMmSh3swR4ieO8LgghfUbrljVW0alUgs\nFHFiQDPQBHkGg1tonlEd2yCoGBImC7OzZBiLUY2QRYWgaoGqogq7Z916GEZGJHGPMlBRJKqeaKGY\nXYhPLuSULJbd4txNUSQiNEclomb7xfw1DeY/o1Lu2m0Yo0BtijkBcwqBaj6C08p4XBP+/r3id4/A\nSanFQCcCEpJHgFgkSGLvpKSEf3cH/OWXjC9uE+6ooFSjjPbZWnxFo91OgfXniHUTt0mJwDvCbiLs\nyISoKuHqgZFnwtN5wVXNUDLn4MOD4OGx4uGnio8/nfH7bx/x3Rcf8dUv9/jml1f46qsDbm8mHHYz\n8rSDYIasJyz1hGmXMO1XlPuTdf+5rzg9FWSvAxSJPCArNrbfE65vgevHgpSq1TOp5nRPyYumKuEw\nC14fBH/6WjET4f0R+LgS6ppRhDxPwaytooKzWvniFCtcx7lDowAMhMDBU6+RyQjnoPmilmrWlRJF\n2XOnL43WM0pHMSdrbcgATmtx+tT+FphlcLtTsDAyW1VFo3WM0nhabS/PLrATGdAitj6m06TYTcBV\nVuxEraepwDM6GVG1pZet9lWhRsEGsEtM2M2TVxHtNVcA7xEqQK2AMqEg9ndCUTF/A1vlyFUIqQjU\new5cZQDFcj/oEyT5H7UgNzTrTj/dJgvgEpWTFZW3psQeKZHMqZg8fTfQrZOn5sT0cL/EoTnJQuxE\n3UPuyT3MmFJG8noQYc4SMXaTUSLJA1wF1DrthNc+mh530WWhbIc5I08z4IlHmS3SYimWMl+UW3fv\nsB7mRNil5E5R28gKBUQhtRid4RlkIIUkE/h7uPVBAMiKEy3STcY5qYVGJke1MC77rLaBigquJ6uc\nODM51WJTEZXznk/iIMQRfG3YQ/G3CW9yGsuCPMUbdNhGijo3oowiE05V8VgYT0JQWNSJ+Z3IFXXA\nVAaRFR5TFnxzI7jKBa92q/fntNhrVgCFoO4ENuzfhaRWT8hyJ5zFLyfAs28pAcQZEAYdCbubFa+v\nJ9y9mbGezvj4vuL9O8GHD4KHJ8XjSfH+d4q33z3g7/cfcXtH+OaXO/ziV7f45a++xN3tLb549Qqv\ny4JXv1zw6vsHTP9FsDyt+O6fKqZccXuzx9VhwrzLzqFXqC7YHRhf7xL2hxm/+9Ecpjc3gt1sdJPR\n2IqrHfDNHeFv/2LCD28V379b8Y8fJpzvLSSxkEA8dLOSwAivIL2kZxk3Xa2+rwCwo3o1AU4wipE0\nimMZSjX/hYDEyzCr+24cwzEnpDRBACxScSrmnGZm1Koti2CXFJpnnOqEnx6OWM4rlDLy/srK5a4r\njusZN1MGsfnChKxuTSJz7OqSsBYLeT0vCUW80QtZr9wx2IFg+1qkekVFa9Y4T1Z+Q936ZgcBrOa/\nYGUvt0ue2h/JTQpoQZ5srZ9FIKWAmXEzZ1xP7p/7BMX8Ry3I4Zs8BHlHRi89jLrnEgDDnGHO9Qbd\noN5uyccYgPPbORv3C4sGPJ6tzM1+Cu7KJnCeMnbVtKcUW2yZCbspDzXM2eObIynHzMZA4+InC4XC\nrBbfrMYZWnlsQyPqymViQiJDJUEbhfBefCEFWWRJOYZeOSWLWEjqZnAvhRqbMCIOwJbdp2QNIWqx\nTMuliqUNF0/YuVUz2zm4Y58O6tPwbF7aXAbS7ebpiMUBV3oRYxUImWzDdmRkbeImVuyIAZqstgsp\n1pZu76EcKlBRFCWswhZrTsUjBhRKBVGznBz5d07UXmuqSNstmd+iVlMUzXfCWB8Fjx9WaCUc9gmv\nv5jABLz5quCbp4qHe8H9x4r37wVvPxR8fFCcV6A8En7+5xOO71a8/d0TXr8+4PbVjOurjGnHuDkw\n/uTrA1IBDntz3H14r7j/qEh5Rc7qtV4Uu72l4mdXyLuZcDhEdyfL5BVReElv3F3beCYmvFsI/GCC\nTlNFSxNXqzRoSW22LmVISCMXVi3qx6tiEqynaLgNgainH+MpTodGbRND0tXRfQVZp3lPa16IcHBN\ney7AfiZMk3X1+u6D4O3RHLeluAVZqqN7IGlGORt1yQxoKUYjesz36vz9UyEcK1BJYETIEHboNJFZ\n99IsXak2JyKlrVFz+PvaIXNuVop6LD2m3mSE+R0qKR5FsRYBCeMqM26SIsGs8/WPiyO/vBka/t2a\n3hZV4iVV9VMx4S7GfICrCtSLATGsrveaGEUq2sj6dRJ5so8LbFFgWatz7D487rWfp4SDTgARzihe\nI4QxZ0tdjqiRhsChliGZ2TU4Gr3jGMXMTG8W7Dkr3WHJFimR2KwD6/JNTptoq2GsboJOzhcHt21d\ndxhM0efMJZGbnOKbSmGcHtg6mZyL4rgC5yKW+FGsSURmq22t8AxOhPD7tCSn4cdeoEavbOgLsu/2\n6BdP/rFZQROv3hPV0vPhFfASNNk4etsbRAcniMXAS1FHjzygmm4pjXdJw709B0CxM9VYJTehSU2Y\nswgyKq6vGfsrRpqBPDEONxl3XxDWM/B0X/DxY8H794off1R8+EA4L4y6rDiezji+e8TH64yrmxk3\ntwfc3s1ImTGpYn+VcLiakOeMdS22ppcKeVo90aTicEUu7JPx6XtFzkaFiFr1wmUFTitZTH1WvLoB\nFgH2P9keXKpbRRz17i15xebcXNXVlWYVeGlh3wPqpRcUHvvsNYSgnXfRPrbRQ3afLCs4i2IRC72r\nasWtlrYG2PpzCnCuwOzRIWdh/PS44vePAmFzylZVyOItERMhc8bp5KGzk3HTDO+zKxWZCIkTVmGs\nAMDiVtlQ84QCCvqzeiJbFXfergWUjO7kgQmAl5Zwsd8UQ7LNBzBDiLCgYi1WLCyqgqZiUTsF1KJc\nLo/Pk6IfhDUAleCcPNVdt3xUSow8ZQ+TCzelnyciWkBu/iSoKs7LCgawy+Y8PE8J5+KxPwPRyeAu\nMAmOhu29oERMiHsw/kRgzmBW1GLJOpkJMxtfZ0Vy1BJnxLIcdzm7E9TNqQFpVLWY9XMxjjd7Jbzd\nlLDPGTvOzk0LxFPjlyIo1Rs0IDrkmOP0JlVMXFuaPEdKk/Zntg44gd+N77bwNfvM0yJ496S4X4Bz\nragqqG4KHibycUMX4mEaDgt9nGkafhDZg6HV/LNE6M3ESbzGuZnejWuEAMK+KyqEpsatpmg+Q3AF\nTk0IK/MgNGxXdQHtzrp2UK90F6Y0Rue6F0pSS2ayFnFdKRBXvHpN2F9PqFAsqHg6L5ATcL0HrncJ\nV7uM6z3wxZfAskz4/XeC339f8O7dCq1AXRnHI+F8rnj/9IgfvzuCMyPPjPlA+OqbA74+3OLu9gZ3\nVztwnlBrwttv3+Ld79/i4f0Jux1a78uno2LeeUZuWoFaQKtgPWecF8ZpBZgqMgumySi26s5I46iq\nj3+CV2i2olculIkISxXU6lnFiiFvw5R2BbwjDhBNO4iA6N06ZcI+A3uuYLb6+rMktwpDjXskGRTH\nau3UziAcVvM9/CNW/HAU3Fcg0wTRigLBAsFXRDj4ev/pbHsuc8Wr2cKGKxhLMYvkMDOup+xZluKW\nQSwSd4GHLvd+nkJGgy4VuF8EuxneztDkC4mFGMOppqgxoL6HK2DZympZypEwRCDcr1ahkQOVYrPB\n2vEZqZUwYC8PQwRWPMm62Vv/TKumVwXPvhfCPbFx4vt58lR3D0ecgdMqYFotFdlPYZEo7HHRjhIp\nNnIPl+oEe5hMMAHP5JoXhnJ9E5Cf0+hVE6bFk4zGaomA1eS+3s1WZnYy9Jxd3pVSUMQiTTSUG2C7\nwvkMk6ERhjeE8WlXWYFAKT4LbigSTMhwB1P1ZKDBERXIPUBrJGPQMAdBSQxX2cxmD+Sj4HTa90bU\nG705pXHog7B3K6YjYrfY2BKfOCVz/q1dCXe0b+dqc9fWH3rY1/BE5K+zgwlDmeSvjd/B5mwKgLOF\nlqpm/Pgj8HffVvz2hzOur4FffpHwzd2EN3eK2ys2R9tecbgBHh4n3N0w5lxxfCw4nhmnk/84zVUf\nBW+/PeHhreC7356xv9vj+s0B12+uMB0Ir//kBtevJ6yPK07nFT+/XfHTWxNSV1eCLw5WXkCUcX9i\n/PgeeHxSvHnNuJ4ElCsqikWmgMCagZpQiby7TUgfF8ZuzdTw9zHBqmVKA2SCoaSsZw5Hy72IBmFO\nSJNFXHGyBB1eudU62tBtENSq1jhaAUoThGDAQwiLAqVaFyB1D2rKCZzsmYSsCBgUWMRKHBe1wmGo\nguVk9OIuGZ252UjN0amIGHKBZ3B6JFmpABdgzvFZwbA5LMKIg44iCDmlGmtxcLgDsHrp1SEEfzoM\n+zM1lsBmfIZ3sKFWyNBm/JiD4zm9Eo+Wk4X/zDk3tMAETDljyqsviq3gz6n3fBxP1rfqOHA9LReA\nN6IwjlQdLFZY6VByQW5dUCLk0FBImFzWlsybLXu1Q7hjs4ot2LVW58W0xbRHg18Zo1m0C5OgbUbB\n5B9pIosiYcFpDiGFetWg4DBbg+Ph+V+atX4NunjfBeggJOMMTakQ0CNZTNhyfDIstefhMHYOH4sq\nArD4Zr+8B7+P8RrPzra947jfptQRFAHa33GV8V+hUC4AScLDI/AP3yr+1/8syJPgF18Ifv2l4Jdf\nJvziDeHrm4KZAZ4YlICbO8bru4T1zFjOgtMT8PhAuH8oOJ7NIj2fVzy9XfHhxyP4MGF/d8D1Vwe8\nuptwtU+YaAdNCQXAqQpOK4GOwM/vFesjY5cLlAWPC+HHd4rHJ2C+Isw7o/XEn8P6Xobd1QMNjNsO\nIW6LrrqTmdt6tNGNaTPWJRSnn0el8eDqYKESOdUIzyj2mPOm2O3/VaRhAoGFw1pTDXOUF3Ffk8uA\nqtbFflXvreUoehGzWoUYwpZIVbTgqQIgxi4BUdJ6c/M+HtY8nZogj76htWprYYdmgbgH3ZVArGFL\njjMKkUeAQCanYtzNaTzcwsXxeTsEXYTVjbQH0CkNRhfoheqLsoTJ65R4duV5WVGZwFP2IlAeEUFh\nqFmWXmZLu46r+hJs5pS6qQP4InehnIgxTQnTlKwWh0+5Dp+rVZogJ1XkbHVUdnP2mtx2T2UtqHVF\nLdbJppPpDGFLSY6YcYmQJ/ImwcwQsTrYRQO1GyUR7MWzuR90Vqu2N6BsQ/9eiW9E9hfjPtbGeEmA\nhjC0P54L40vR3CiYZrYDlpFpwl26sdEEAMCo1SJqeiGKjrLHzRBvjVZAo8jHN8fPMXlj+R529ikt\nEOXP2OdINOFcZ7x9qvj4VPDbHyqucsHrK8GfvCb8x18r/vrPD7i7ThCcwbuMw92M1zsAVXB+qvj4\ndsH1R8GyVDATjkfB45Pi8aHi/ljx/tszvv3tR4AZ+8Med3cHfPGLhFdvMl7fZhzerWBRlKJ4/1ih\npaDqioUyfn7HOJ4Jt3fAPDGWAgCzxfIz3PfiSUfceeIejeUCORpVR3niNptbdGmzBZdl7vRUa3xy\nPCtQDa0vVXA8A6AJnJJTrn2ORAlQgTJwLhVaFSoFC5LVfWmzQSABHpeKI6ohb7WSwQLBuXpkWkrg\nbL1xVTMWKchicoG1qxC6mHuJuj4uE9TLMJciKMV2VfLwXDhg6QX3+vnCTzPukMvEH/oXFt9nEeTJ\nSU2rXy3DJA1bdHD4QU1wJk5gKvaJ4TnjszkQqyjWtUKTFZiKWPE5J5SlVyDcTYBG3ioAACAASURB\nVAk5d88y3CQXba0bTHgSHBVaGn2eM644FIR1jYF/tjqqNWuArfXXZCZcpAYn1/5SV5swr+Y3uVCN\ndLmiXjJAQhsbV8bZY+TYuPckFQnhfJSGPnsSxvboQT+OTgNJ9gH1z1AHxuN4YxTgHbnGu8OVsHmV\nuvCP62L8XlMwHde1TRRp941VYkdvdRMBYfK8+1u26wSdLmk0FDn9Ndx3bC64hTRQPM/+3YyK0wpQ\ngCumWXF7AL66MUW5iqKS4OMqyA+EL98Bf/GnFa9eJ7x6nXB9Q0g7MgmSzDm4r4S1ZFwdJtze7XBe\nVxyfKh7vK54egeMZOK6E06IosqBWwQ/fKr7/jlE14ccfCV+8mbG/2uPua0XGAWWt+PgAVFlx/7jg\n8eEEqoK1MtazWAMPUVhcejVMrtlWi1trASyjxIO65cTuUB/7dJpfKKzQ4JhblR+sRfEk4YsxRK6I\nyCzpy2j0sfjcLGJInjShRvVIXw9WXUc9xDaKtIUVyJYcy4JE1bl7f1+47X1WtJZuYY3Fmm/OTnHn\ngYTi6qHJxA7KN4u9q7agJTc03uXSiq+8QOnF8VkEec7mvDS+qyM5qz9vv7MPmqqiFqNTElsURonN\n5Yf1RnQKhgwTiIhVsIOdc0qW9noqFrmymzMO+9lbsGlbmBoalkOldGeXOdttIeYcNaWtXkQ404Lr\nIpgHnDk1KqVhRBForRaFo1HTxQtlOYwWslKqcBONE3mNClMS6tI1wawKi1jxeNe2lDqK2h6DYGro\nQJtA1oae9QWcjU8i0pdQ+ebdGAMaS4RewGX1fxswpsZt0yCEVaMuB5piD2uIfePooPHH/R8md+QS\nbK5P3QRp/tigZZoiivV3YWHE9/waUxJcz4IvDgKpFp+sDNRVsRbFw9nqy+8PwM1NskYTXL0xg4AS\nwBOZs5MZt69nXCtjWQpub43PXRZgXQnHY8XTifF4Bn7/fsHjA3BcEuSUsRwTPt4bMjzMjEwZ+z3h\nqy8Srq8YD0+KUirOi0WP7LxMRHdiaxMwipBXpiitHo/HF7kgp8GMCyd3jBZ15IBApQovAlrgZW3D\nnQ5AZRhlV8Ad9pvvgAhEBg7VQQncoa+Ad9uBtYBTAByZyepjrV5KgNraMzpJX5hlOAgYaNa2znyt\nMXnvVtv7ql42olmwA5Bpf/0rjhdvxo7PhsitrjRgta4BNBPYBYqn65ZSoFWQcjaEywxqAfd2MFn6\nfHLzz8y3NkpgeLz3PCGdKzgx9vsdDocJU7JrRinKjUSHb3gKpB7cdYVW8VTyBKbkZUMZeUoe8mfa\ntcJqk9daABUIA+JZalZsy2LQMwSJihfad08/vD45mQc8yBtx5SZOnB0my06M+uQmlHuM7yUlos7b\nhSlCiqGrSzz3J8TyCFz/BWrl8ouB5DkQfGwCdDRkf/dsOozWQuPUe5y+1V7HBjyrc60NxVwOQDyG\nvxVKuNsC3gZPxBX6sKGbIrg8o4K0Nl0kYCRVHAh4NQF1Z1Xv8qQ4HsVQCxe7FyGwMJhXWFfoamhU\n1Cp4lgrNAqEVaQYOu4T5kCHVKkPqSlhPwLoknE4Jrw6CD/cF90+KnCs+PJ7wm98sWNeC25uEb76e\n8We/2uPLLxg5H/D+/gof71fQw4pvVPHmoeD7R8VpdWcfhrXkfxmYIBfiIcvCFxROuVGgU1uTRlnZ\n560khDdiUBu3Dry7Aolid3DrG+pJWmTZpgS2DkgwQRrOUYLXBFcLx4WiJ47BnFvW19WCLG2NFU9V\n8tWgvRTBdu0MHHbbE0CeEubd5DLL8hgi27hBigHejw0qXqJwQiQBHSxcHp9HkBMB3mzXTOaRz6XW\nFMAsIEJKE1KytPWcrS2TmXcEQFrXebIcbaNsKDqz27ZK7NXfuKN9G6v4zfG3xVc1oRGB+xopu2re\n7Kj3nFzzBnKzhVNahbaRnjGe35vGwszMKCxEqIiEIWt2zKgON83rb2GK52IFjM7VzPW72cqxctAV\n6OV2RxEbv7RFCEekGJABjQavecnNsRUCmBGFTnv9GxNxFiWi7Rp926P1PWxlZ2PUhxuzcZauSOIt\njXBNwASs1fwzEUCQRiWR12mJy3ZkqLA1AW3R+whro4WCtTY9oeTQ3FzhHg1qi9rac6QZyh7mSLNS\n0z00zQqnmTDKbnkZMIxt66JQCKQW3UwkIPG15w7oWrXRbhKhuMn+LqcKycD+VcKvryZ8+cj48EFB\nCbjeK5a14v7Jaq6/+6ni8eMT9ruEaceQZGnwE09482qHq52NrvhkckxW063UlDBFMaythneLyMcz\nIhkFEDZ6kgRo7QS9rn5dveE3oSFZGzeO2YKIInt1QmFFUSdQtKeuh1EVYIDjnvu0bZSyerBCNxQM\nSJDviQ4Kw3FvDxNNvLu/QKEkIE5mTWXj2AFsun3FxdqfbalrvznyfeNoP777AiYB8BmdnUzRRsod\nUoF51CiBSNSJYjrGg1s3+LV45fUB5VmHHwCwzTOqNYIphtljO0FBi1hBqWDEJciwtgTCvLLXEjEo\nGUpJMCeJOQVDYdj1DTVL07QhOqJUQBfeAEGGq7nQoEi7sHR6S7pQHJeCh3PFuRrfmphwSLZhmiAn\n8m8OAnFQ44E+KYQQ+h4dY6cbb9zWFTWBhQshjs2zoy/ScdHp+HeP9W735aPe7pti7vq4kHZ0LMMp\ng3MNKd5nrEdZ9Hr0svk2tSvEybbXbzSC380nahYhFEl8um9S72OJWK9deGxN6uqaw5UC9WSTQH6i\nisjtEjc5iExgUIZl9eaKSoT9dQYx4cNTxc2VgJPi/hF4eCI8HoHjacH90cqk1ay4u9nh+pAhXLGs\n1lEqiLVn7EHHPdv5vRiPAOUUoYrjl0IJtDwBU8LkPDNHBJc4kvVcBxHbe7O7iRZVkFjp6cbGQFuI\n5CAXR/Q20HxosqUJcu8/uo3Bwebe4f4sdWGrEnvegIV1fzIrATBB3spVRG9cDOGVsPtoeTJtqBpy\ncLDw8vHZ4sjZI9yZ2bPCRgOWWuo8J7ZkGNiD5hycc48ssXhuQ4sxQDEFhARS807vmHDI7ELVtGpU\n7RSR5gxR51ctjMgGMhEh59yzQFU93KiilNoWRDja7Lki0gQ9LTkgDo2VzYGmdCieP2Ga7HmqWAW9\n47ng4bhaijVZ6U54BiOFNG5jGP//1NTHQh5FznbxxjOphkD/pBT7xAVo2MBxL4GE/9DXnr8ZAjv+\nMsHiFsPIdQwb1wQIBgnkAn6Dji6u7dcfqYHRT4JQaH79QE1bVBqCoJ2wXV1VMI5IdxT3SBD7TtA7\nIw/bH8Uilzy6iAn7Q0KpimVd8cPbCk4Trq922N8suJoFr2/FuPSnjMcHxtv3gncfgHcfCD9/YJyE\nUdeKnx9P+P27iqczrJabGI8sRB0xtpEalhw6Bg4XSzybxd6L164nSFFINcXrvn5DvpxAYqV2p0Sw\nEiZWbgBOl6mol6pm7LJ1nuc1kgUtl4MHy6/PzKU/xKlLWAhy9po5XnUa44xul0jw+gPQCGUr2hJ7\nyGUYe80f4u5bUThdM5y7FdNzhaVi5ckwrkVgeK7t8VkEeSkF1ByRHZmRCwujU7gJJxEBmFuoUCD1\ncDImjwTpnTt6wwOoaXomq6T36mpveaWJkVgc6Vg5zaUI1ur9IEVQSsVpreZMTIwEMcerSt+I8OL5\nbIu41tonFmjKASCvBwJ0U20cFXc0htBUsd7qZCZ6N7vc7GvUjx26EZjPz9v+ajxdH3O/TEcpCMFI\n41n6hrjYFD3EcBT+F8t/yOiznxHtdKj3/F4DyW5x0Rj9Mrzax2ArV/szBJ1BRpPBlXazVAanVKvv\nMw6WX6IZfYquLJp3NO7IiSr/nlW/s9IKdrSI+Ua/dNglXqDL1078CMGKSmn7HOAUSyUcnwj/5R+A\nh9MZu/0ZVBK+vAOmCXhzl3G1A673BW/eTDg+FXx4qPj+QwFPE451wj/fC06Vsaogqaeoax+Fjh7t\nzlursxjbYQJ730mxRtAxtmoIHERen0SbIx/sHpKgMsnAhWi1WkkaPidFTcCc7b5KqShe0YU3gvji\nfjZT6cEJ8UAOxADuuQuNBKe2vuPfEMSRuGTRcckj09SyVAEgEVh6qd5EXRWMeSl2Y2rVSWEJRkES\nmL8s6jY9Pz5Pir4GN26Zm1UEVH2jw0wpHuufiLQoB0IIc8v0jMGJyJBALZOH+sW34MJvnpJ1wgG8\nCpn9XkW95rI0YSKiWJYVkghAwqxmKpFqWwiBwpWoOUKBAdV5uzcCQBqFn8K0vzz8bh3W2ELpuj8W\nmsnELqn+YJNpeuGlzWu+oGh4c5MBOXy+S/rhvVHYX7zePDTD16mfb+vQjL+fn3c8/8aGaYI8hNpW\nmfVNfIGtQgGGUHYF2qyqURgh1utL9xMUkptcpoE9RNJzCxqYjr9rUxwmFLRZdzygWjSE5zu5zXXc\nk92D5caoOw8JpTA+fEj4/XsB5YJ9AqQC+x3w+lXC7qDYzQrihOUsuL2t2N1am7efHjIUq9VQCcqA\nNljb7+HiPsM7eXFY+Qynutqa8oQ5IkCsHgoQPiTxcbXwYQNoCRF7bR+1RjFLNZppR9bycE+CMyz5\np895n3JXQ4M1Ncwvom2d9+rU6q/Y2IcSaxh9WM9Mjq7J/Hf7ecJ+zthNyXhxVxIhozgiZqBexC/O\nRW1N7OcZ1znh/rhgEc8BJ2vsnnN6PtD4TII8hC/AyNkb+3JEsfTUaEKUg21GMVTVnIaJoShOY5jg\nj02nIphS8vK0TsNQxLOG0BavAWFFlIpWb9agoBQa2TS9qnFoVSfjxpms5+Uwo9V3l4i4orGMTQk2\nXNQiU9gF+afoAwxIb6A5NBAD0dYki39HqzeOFzbX5fHCbWzeG6mVf/MR3wkZBwR8vUDiFwL4BeXQ\nnZiftC47BTEIalO2emlgfOJ+/3UPGVX9WhSc2coIpRTI3uR79IwNVeWOTPLrqaFxS0O3MTG07c60\npqNcoEdSyQD+Q+ia58XI47UA6xk4EYFRsZ8rvvmSsHvN2B0YxMnCXVlB+4p1zVhqxpQi6guIsEEh\nxSg+1IHGi2BEQ2SioXUmU3TK9jAcfiXV1jxFqFcFtHLMFfvdDjkniBanMe0ZiwCnCpTVkP1Egn1S\naFXvi5maXtduPlzMv4cnkvuwqpXLyCkbULvoBNbXyAVg8XMxWQPzq6sJV9cTDvtsOTBO1QQFZoL8\nItKqnYxAxLg9XOGbVzeoP/yMuiwN1DEDKf8RIfKUxmXRCcvuUBs5ykFkEVqESqqW8ZW8rGuk8lox\n94ykPfVe3BQVEaxFcC6GvkkBzhkgxvG8ojgtYtmeGZktpFBh/J5WBbx5MUfGlrryqJYYwD4ZVYC6\nFqsi5xbI3SFZxAoNKHs83LSOZ6dQYC6EzIkjg7yhloQwct3jQQhhHHSMn3tDjQDhGd+g4ud32JVs\ng7RxjpevvxHUG2pluMjmfjdwfwvqx0tYaJK/3okfb4XQ7j7MZ7OKjCqwYPN+391C7Pe2tXI6uguu\nXMdnbv4d/6wjdJGIlnGhJWg0WVATQBfMvVSz/dRq6d7iRcTUhWGn7pzmcmkkKlA6I8+CeVboaog3\nzYzrK8YuW3SUiNUVL2oOOoZgN1Vc7+1nl41OtHrrPja+NhUwHnhjKUQYnjxDFERGqaTEpjgg0Cot\nrb2pdTXQ1rwLzFirQBcviqZRATWmn1ALWxvCxLieGVkEp0o4CnnKfEfioD5fsWY42WSvonh4eARz\nxmG3w+1sTWIaCyD+bINVFzRuj5l3YT4l7ObkBcgIxKlTd82aCuuir0FyuZE54eb6Bl9/+RV+eneP\nx3X1RhWx1F5GMZ8taiX6TFpI3uhmw7Ch3EFA2+8SGYc+5ahRwhtc1+KUYQg7zF3rrem5d1Vbur3A\nhOOUGZmCc89WHhUEqMWVti7vzud1Pts5LAIoEVYVnFfFaalYizml5kyggysdexJsoxvQeGq0jR6m\nu2EtBMIZxBb8LyuCFf0xn0/2i9RAjNNm7w0jqQC3mFvaJtY9O/TFN9rMEg31skbpvLmh8d3tZ9SF\nAiUkSq21m5WmVaCtIGrniq3HTmjywH22b2x8C9I3e2iOdqvx5LwR5v3xB80ksIqVwl5QStzaswUU\nY0Lh6RNYTLSfVVS8i45aw+NiV1rWYg0kyHhl8nBQDYXm85ayIGegqCkCygDPQJRtsKhWQVXfC6pI\nLJiyYJ7s+1Y/KNaf+wyAjrgVW8Hqo23ZnR6NphHCN+xnNLJlqwzj9ofPiiqWWjAhWgqifQ8KsIoh\negdxmYEZVpO+kjkKo0tRrIseFWUIlxNDKeG8KEQXqALX0+yWr81X9NsNoBOUioXBal93ZD1uUw4f\nHzf5YMEZ0flL+xbb6j2IKo7LgrcPjzhVzxUBHI50RXJ5fB6OnEyrQ73ymafi12q8VIQCWaGiCFHs\nAxAe4d2cMXkiUJSQlYjcpyjk4wlDZO2hMANTFUxFsBbFuhqS3k0ZU4K3bDMTuAjhVHrh+5m9mQLi\nBwiLIpMVtRJSHE8Vj+eC+ydrejslyzgljYiNEOYAYln7c0E7I2dve2uuSAYCzFnki6nVigY6hQBg\nmznQV0rne7fOKdt0svmu07e+iUOo9dC+QBdAbEjdXCM+18MW2yOhcf9BtcR7NDpKIyYbTRkQGBkT\nrifgZrKCSVVDOAAFVvxJ/d/saGaFtQFLIEsWGWLbG0L2pJPI+gzBDQmOlwDvrC6IZwjkj2ZBGHKu\nkEKo1dqkFamWgq4mlhIpWBSQavHVdTumRRRlBZZVcV7EG54AeYI3iNDuF4qRb/H+ACcBe99ZJEEl\nQRFGEctlgHj0DJkviF1OZLZmJ5rE0zR8DDI3Id5ghPPjVa3bFMiiQPp8GoiKPBFjh7g5R5m9Lspw\nXrKmn9b2TAy5x3lDWQVdxiQAJeshW4tVIiUgU7Ud4wIeztVDua9t3y+JGUgTgIxSFpzXBYqphUQC\n1uzBlLwaLQbAWvv1Okz2oq0MJoA42bhRRK3YYKs33AjrLspwKwASa47+w9u3+PnDB6zr2vZSp1bx\n4vH5ytgOGz84bhRxHsm1Gdoct0N9kWRmUHYeCtZVm7zZwZSAacqYpwlzmmDISiC1oorVCie1tP0I\nP5zmCYBglYrzKljrirVY7ZSbfcYhZ+SELt0usCmHsEJHuk2YDQLz3zhIbYz+/zpGp4odbnbCs88Y\n5rxlq0UBJY+esDRzVe+8Q/T81l4A5Z9C7xGjHzYm88tjFGKSyAR2qUCWiv/+TcJf3FhDaYFVnaxK\nKLASDlXtNa3WUPhIwJ9fV7w+FFjbAHl2pfE/E8ouPLzMsf0n7b6ePVXz59i41aqoq1ipUxaIJKxV\nUTXASY0v+nctDllgzR9Kgf+Y5bicjZ5hF1gpV1sh1FFhS+YKgacODhSevOK2CJFnFqtTDH0UmBIs\n97qJZQNZvo5z5qaM4S3ayDl8bksjlLo5J08FTluqKzR6PnZojFfjxDdUFrZ7STThVGycWa0hBcHa\nDrTeBUGBGPy3Z2z5Er4X2FU2e/PylAAISi2uaAWc+ucbAIj7GrcCEThlpJz6vbtyin3TdrU35ghh\nbs8EK57n/jofHQ+9lGfyMI4/ilZv7M7L8OoSj8tRW0IQwVG5G3ORpZkcMCROyJkxZ8KUrRZ5t2Gk\nhfQRkwfik6VHO+JbSsV5WXEuhp7EN0HUWqYGUYHtVnZh2EKUXBkx27lbaGRHqXbE/fmCCjN+PLUR\ntsCgKP5bjk4ZdCXTz6ddLxEsK9H7EBLU27pZr8Qc/P5LQvzylJ+8X+ofQgiXl504hEhmJFRxgaIF\nX+4VdRcO7MiItYgAK8zvOWNiRfpXVnw5A68zkNSaijSh/Mkb1b5L2y1vH7whJoQED7QK35UwJKhm\n5YXzs6kDRfPfEDw3Qa1yZqmKWghlVQgD50VQK0yokDUN1knB8FThJiBDyFBslc18qCiiowYRWqhv\nzF8CIcFS130LOGqPeGgbB1Z7LSoEggzth+g1VtOuU8SiyIzL93mTLgzjXpoj0IfPLJS+r/rMmKJd\nqz02UUKOCJ54f1xtPo8hwAEvQBf8tN+J0UIMC/+UPt9tfEYvzrgW+tXYa600B7cSiLypTZyvWd7D\nc7lssYxR3ezRZo/8sQry2AhWYMo6ZDMnQLlp9eSCHGS1ewFxPlgbVz5n60A/TQlTIqhW1FqxrKtT\nE0ACMHk2psKQWlAaRQqW84LH04JjqeA0W0ISqGXsWW2IGNRRGPVd0mQuRVJQRxGB9EKgdwmh7VdD\nHdsxGmkLi8P9b8PpcR9hRj+bC/8/Q5HU6oDkRNh7F/OrGZhZPyFyYyzjWmj6QS+v1TZl+/SFlTB+\nVBEF372yCjIJ8lxbxmSjXxTIw+ZRJZAmkDKQBAdWvGJGlgnEpQkz9Q3UQtxiPFwokmvZ0YppkSMX\nVBBCJLgQYyUkyUjqyYZMDXkDIdDgQq0LciuDbHWtSzEBuiywin0+0GUVyGyLYc5s5WQdxfou8VT+\nviYDDHGEGMKqckbH9+B8s5vz0SzFfE3b+QpLOnnpBE7Jo7K0JfmJqkna0D6udqNukaAj7OCUTXFI\n44cFQdlsV0Z0arI8kQRR67SVqO/ZAcsP+6jFwDWKMkT/uP9UPTKprdVYaBg2YF8w7bchKi9CUCmC\nI3yvB73ykrXedUf3kdlvXkvgheOzxZF3G9SFXoroE6/mEaYbEHaIaTEy4ZI5Y8oZOWXj9WBOieVc\ncW6IxKDInAmTc99MxRezNYSdvRPIcVX3UjNyJq81nkGwnos2ohxLAsONIWowAD7Hqg1lmQHRHWR/\n8Gi8dgiQ7aS2FUablfQvnLNz4f0HHVnBeM7IQJ04424PXM3UkqhmBiZWTAm4mSqmVpryX/FMf/DW\nhg38SXPDBoEgmJPg7ppxs0/Gu9rdu0PLP61myooYrVK9/CqRZQvyTFh3hAwTsqMJEU7yUUj3CJ2m\ng0N9tM+gnwLqaduULCFk4orrVHBOwElNIJVG63htbDUHJ3zNhCCPOPIqAESNZmlKxOqUlNnC8OoO\nUGUsK3kJB2BV9GJoA5/exnUAHL3+fAWrkSqiEf6rvRWfAuKJeEpmSUMLoGi+reY5ILNiM1kD8YbI\n/SdATxu+gWJoA/7CmhnpwXAak6fyK6z8c9fIYS0P8iT2lSuj5Fnh5HKpgS42n4zHPTYxFHsqpH4j\nS9o5GTxlkDIU1R3TpvClird268DsWfKc/9++h7ZG/tC+/2yIfHPvbuKkRE07BkoCfLH5oLEX2cnJ\nJ8AkZ48McM5UHTUkgpdUiYJH4lEmbnqx/Y+9jRIzY85sSiK7HmxVBdH+PzzJH3xADe27MSKff582\nMLuHB45RO5vrU/92T9q4OMYXRkHekhPsDKIeWkmCOTNe7U3rZ1bMSZBJPZJHMSMcvhF18PLjB+q5\nvI3L537p2K7X2HwWg59ZQDlUnaPYWCtEVjUnzHZxnhwAU0UiK3swVrZ7PlhdmBuXSoOCBXr8tKNv\n7XMFoFMDYlE1t3vGr7/KuD0qHhdr7P2weKQIkW12oZaKb9ywtEYF1cPoBMC6uqJw/riQ8allLVgX\nW/WPp4zHk+LxDCyVwGKlXrtliPY8JqwAlnCSGjUyZWDOPvIUY9ynw/jhXovH5ky9ngi13Cj28F/2\niBJGi5Ztczeu6uZ6css1BGQb3WGcya/Z9qUOc9KWjba1ZNRXSHS34lyeJOamqMgFMXl6Vkfw1GS1\n3Qo1BD8eqvCOX9msBGFIBVKyqJ4ATI2i1c1i32xkHS9onudP7prPh8iblmx70CkUhF0KANY/AR6Z\nwQRy8y2FAG+NBdwhB5968s5CsA46Ddk2D3kzuBATTm4q5nkyQUf2fqdT4gHwaQC5fdD2cb14rdlM\n8KX3BwA2+X3HOMU6UPXwsfjQKG8GVIQRPbDVcUmJLZQNavU0oMi5YAejqRiCzJY1R6Tg5FEydUgY\niU2swyXGex6Gq92Xn68rK2qvj58OtNXmBl7ejNRotaCyEMubPATUNy+zm91ecA3cNm5ulQ4xXDse\noq+HuB/yD433FAraEFXMC7fvRILJ3QHY7xKeCvBwEjw8Kd4/CpYCXM9e3kHQHcoeqSFVW6haVRPm\npdhci0e5VAJqUaxrwflstMjTAtwfFfcnwiq2wU8rsJaO6shNPfL1Xd0aSUyYJsZ+NotMAFCxeS+1\nZ0B3QWnCRuDlMEgRXZrIY3JJnTMGuuBzi+5yT9htDVx0CHVswUzzJTUl6sK5fYD7UmqyhNqpWhis\nAzejc+1r5DHv7PTUpYwNp0EUqRvXiqqVYFA1YDrPE0olSDXrDNRWT7+RC4DXq40GAIw3CEPP+mfH\n50kIYjbTgmzxQiNjk5rDM1uJQBA56vY6x+wxnRpSjYL3ZdRiYXxM5PHlyTRsaGsFQFZEa2OpRFgR\nRY0WQ0dVYA1YRS2guqEFXx+xQEYBekljqD2DcosjGAT3ZShjF+1GJ/jGg8cc+0qNqbfGCgMHenEP\ncapwto5CPOWENBuvyKpIYvHDUrz2BXwhiSEM8vRpl/4tUmLr/GnDAx4W6Kis2eviAF14R4QS/DlH\nFqm9jp4f0J1TdkFu44q+y5twkW6C+zw3A54AbnNJ2PDgDERNnYhe0UAfcQVFX0cgX6vGGdTKSCiY\nZYUW6yF5mAlvJsIvXs0QWCbgzcGyGOFWhFSxHIdKqNUEcPGm2LXYPZRqjRiYPBZaCLxaL8vHc8Wx\nZBwr47QCWQsez4SnM+HpuBrNOLOXifZ5cBU0MeMqAzeT4tVs1sCUGOdKOBW2uGYSj6gz2rCqRyiS\ncens4yIue9mzoOEKL7Krg5xuvj+EYPYQZPdjxT7QSOIidvAd50HLiG3rA4rIr6Awj7hjncDYYugR\nOQ+lnxNb27fa8yZinuHZntw02RBbDkIpgofTCbt3D8gT44s3E4gSOAE5extqBSyIwO4mAGjsWzuz\n7YMW3tr2Qy/yd3l8noSgoR5JK7/qIShh9gfyDM0V9RYAQQrJFecb0BlgvdRpdQAAIABJREFUgku8\nBCiRtXVL4SSyTwxCL744CCam/hptrxXm4CcU4/Nn/ST30AXC8090uBvvtagMRyqBPntHlhCOo/No\n2KwD+kgpIc87TPsrgJJZNeUETkDlBbUUzxY0iJKouHfA8cTlDQ/yTTV8DZ8ajxiTLQoZ3/uUIG8U\nWxsZ7UlU6OupX2vc2AOv2gRvt/rG++vCRRtQH7KZnitrv6Zo7TNFjOtr4OuvCUsxRLsWYCnA2f9d\ni0AWxbpYc+agB7VGVie1IluGyK0URKkWYtdq61NHzU9nxWlVnArhWBlUFQ8L8HhSHM+Kw+TPlAmc\nOvXgog0TBDMJJkcGSSPD0SWzQ+IoOxuUhclSQiv+5dy6RgSaxu6NYeQByXaFHwK6RYiN89I/1UHV\n8IER4Y/upp430R61nbOVmwW1n8RkiJwI0ZsgkAj5ODTg5eyCZeIqzqeK02nBskSTeJdbDWn1tR9C\nWi/plVh7m0cckefz4/MIchE3t6x9WdQgGAe98YUa2r8LESL2DewCPExcX2hKlgWnYg6h62ly/t0R\nJmxMQrk14ULkqJwbZWHFj8YwKdvp/+a48A1P/ukJ8SXn9xUKqvnTvQKkMXhzAnbJXWbNVEb/ro/H\nKHCYGSlPmHe32F19hWl/jVoXnI9vsZ4ZoCconVFXBXkW5UTABHOCFZYWhR37Oh6luR+pKzodoh3a\ns1HH8WNCjpnTzwV5zJcFNTiWcUK1KVbqWbdtpnw+N/TZC4rkuUx4zn3HEwIe4UHczkXkSFNLXysK\nXN0odgdCLRZxcjoDx6Pi4UmBJ8VaBNXjoKuYII/4aamKWuEhl0a9rFWbEF+LheUGKieYID8uwHlV\nnIviXBhSJ2s+fC44n4FzVsuj2DGm2TIbCZGkIl4ULqzk1BpaVHf4hSK1IBRCAlArXJgzLLvJLJDq\nc2sRR/CYIwvBVHikSeSzbs2bi00Re5PCKHWAFfNH/TVFkxWEAWmH8u672EOXXXBTULFjsl3MOPU1\nOMj1UFaxCUQFaynNgiL28aBYFX0dtX3gykRfEAfj+ms69BNy57MI8t08O4XCbfQjMH6QCt1pNTyk\nqPhA87DZ7Hs5WdZYqRVPpzMgisOUAE1olEaIEDITyRaUa2afJdOuAqmeug9C9WzPf6P4RjzMGLXy\nB8/RhJ4tXIs5tgHYTQwlxi6ZhbFLlt24y1tlNF5nFOxNkE+vsL/6NV59+R/w+qtfQPWMjx/+GW9/\n+i3o9BGpFDx8fEI5reAquD4kXGfFBMW5nPFUCs619nuNORxQkJnKGyC72ZB9w7Uhapv0Yig2G7aF\nZAEbYWuCfut82nYxGm4O/fXuTNY2hqrm8Lpcj+2emrKhJvTj5jWck54GD1rBSbDbW1bmbq+4uiUs\nxTo+TaSYklud1bKbOy9OHhNvv5sQV6wrcC7qyqsLnlUYS0km+KsltFRJltFZCOez4siW2BW1U6bZ\nrGATkoQVhFUZixJWylgEWMUsv4kZSa13UM8qtcYZ4oEGzEYjVlGgWrJQq/VPHs4ow74elsA4twBa\nyVbLlrS1G6h9AyQo7uVlcNTO35aSSYPEPXR5ToRCCtLa6NWAcAS05+33eAnM+lWIE1KekPIMoIIr\nQFT6HiRuX4v1Fs+/zZTurw8fevHZPk/1wxxx4Wg3OHLKjRsLa44iLlQB9Jjy8TANa9+vcBqBCFPO\naCFKbvLa57XV+X3J+xzlJnm8Fm0F8r/tCEE3SqftwnspnjRC0RITrmbGYQJ2LBbSlQj7bMlQ5om/\npHwu/iLjyKf5Fl988xf4D3/zP+GbX/8JiAs+vv8Wv/3ta3x8/xZlrahrwXo6QpcjXu8FeyqQ5Qk/\n//A9+OEBJHV7jUDZFNwibe9guJWe0XkhzC/G69nGpC2u2aBv1hfWeFAH47m6f6R/fnRYBcIfhbR2\nYdGAR/98+5GekRjx4LbC2C0mRUoVO7KSpHPOiAbcDYFXUwLBg8drNV4rZBTNSghLJNZvEcV5UWRm\nXM22uE+rtEieUoC1ErgApyXiuSumyWYrikxFuzqolZKdPImqO9Y7Qo3uPaJWiM6e3fa2cuSCeBgl\nAWsUzFITka2TFgJUDdVP23qOXerNnr15A5isUumztTNCmGE5wR2J6AowJcJhn/HN169xPJ/AWbHf\nTcBpRUGs447ibQ3os9OHcgh2gVPCNM9uLWfUXVdMnTXpyrCJmIvzdqCypfEuj88TfsiOwTUQtkUV\ncJCVwW8jHoA9jKw77UaRFU6NyDRmAHOyZs3zlMEU3GVMsU1ObIBGkfqmbQsU7sCK+0WgvH+9MA+d\nvpFXvjY2Z2lIHJtnC1Q4pYQpMxIpdrBntWxW+EaNb/dJj/W8QeOJMc1X+OKbX+Gv/9Pf4Bd/9ivk\nHfD48ddIrxi//+EHnI8Vb+5eYT3e4+H99+B6DyqPWB7eQz/8DH2KuPrgRv1P5mGR6+ZJ7N/euuwl\nW7IjnRGAaIP3gY76yKk/XwzfpeD3BJcN3xpeMz8PbRVCu268HeeIcUUsk74OmsLViDzxOHA1xU9u\nPVpzBENX7FSEwCiIqtWRtAntOgj2UqkL8komkFermN2AECx56FQEmROuZ8aUCVDBxCbAajVUzwxg\nEahUj+e29oUmyF3heH0ReFPx6mGMqgTOyRG97aGUzNZdFVgW+9elpWXXAl48zMpHqxcno6Zo7T0y\nU+jZogj/TBWjeKqXBWAkKF8KbY8Dv7D2xjXmugdEtn/2+4xffPMa53WB6IrDfsKyRs5I6ICt5dYy\n0IbTN1rQI1+meQYogTGh7izzvEuEEXG/uB0uhPZz8Doen0WQi3u8VcxB1OI4ddhg8ahi1cyKVxXa\nzVPTZL3hsAfhk4VCQbXxXR1+cRcKuIi2ULRkALizplM9hLGWNICGguyPMeqE0JvRwmPaudUtM8Ey\nXCueskm7C+UE4/DAwd0pMhRZPSY+nF2DRh8X9Qu4BCAz8ecdY3+VkHeE3XUG717h9ftv8Fgr6GHF\nf/e3/yMe3/2Iv/s/n/AP//n/wfL0DlxPeDouqJE+HEhlFOqgjfC1wQy4G8bqJQUyzkVAE/+Uajt/\njLqq9Nnb0CvcSziE06zNdjy+OfXIwxjMQotwwr4gpDltbU0o2DMw4esk1kX7ivt1Imuzj41SafcU\nn4VWqC4DmpeBlqGG7KsnBJlQVxf0hFqtLndEh4AIpRKWFZBFMakh82MCstfGP1VgLgXg2oGUWG0X\nhaDUatQCzOJLEOyzQimhVMKpKIp4vDkxpCrOy4I0MXJiS7hSc8KvIpDKWKt6qntX0ymnFlMuomaN\nON1CPudValv/lCwrdAQBAVQstDSqkYbBNAQAGPrqSoNC+dtuzkyYJwaqIFm/N8xEqBGL2JzXdt5Q\n4pET1zaV/yF+XylZExvQBChjnqvFpytsEW1qSXWL0W7Zdn+GQIhQiCzWtAiUC146Po8gFzExS935\nYE6DrVlro2MOhLVWcCLs/NUxR0OH74f5q1Ktg7moK4itkOzDpcPrJsAlWrmptgWv3jCVePhOC2iN\n/418l02mtX8yHi5SkLsQ/5exPRGQzaj0SBV0E79Jp0t13pHi9m317Nd7vP3pn/D3/+//BToI3kxv\nkHLBm9sr8PUN1qdH3D2ccTXf4PSnf4nv/8tv8PjwHc73b7Gez5aF6DKuPTpCSfb7bpLUX1A0o2d4\nuW/w7VgETPEN+2yk9OLTrvwJXmxou4kHbNWvN54iikvR+Dijkvbn8+JQ7Wvaf9rptd+RhZINPHoM\nggtPK4ZkAlo8k9PZlkGYe0nbqiiVLcPTnZ5Vx3BU++yUTJArmVCuRXE+Cu6nionF63vD4+gUWhRc\nrOGySEKpjKWaIJw4ITMjeRSuCU7L1hQQJPc6SJYJ7CUeBDhL34+tFDI66OagV2DN0G2b2iAWr20e\nSV3RhSsqpRKiiXkLS+9yoU2IoIOEUPCxddyKZ1jtcGTkyoBWsKizA04N+YIQMYUjZEEUMZca6wdo\nFljKyQrxUYYqYZomC4f22PpYQU05+ZFAuOKEm5TAVfAExSNCAZJlmb9wfB5qRQGwe/5de3IT3HYw\nefjPYE6NcWKjlRFx2V2oODpyMxcYBHnbz6Ok6doQiMJc8BoO43UC+fVzNC/4hYDOTNhPFu43p4SD\n1013SNeC/vu5t8clSGe1yo7BDXaQOyqiT6uFljAlFefjB/z8+3/E3//d/4Hbr29A8xnTVLF/esDV\n4yPS27eYzgX7b77Gn919hX/66k/x+MPvcP/wLURqE24trGq46XEWw2RsH7m4vWc+gRjCS7TTAnrb\nTF2MD3XF5sJmpD62JwxUHFRZlINoGLp9bry7TVjn80fZ3usgxNtaU5/7oeJdAJBmCIbwrhHJgh6C\n6DVX1qJYPHxxqYSiXnME3Xk4ZSCRKYlMChaCesnmqOgJAbxkN3RVpGRNV0plnFbLDBUl7IQwT4TJ\nY8ETW0hi8kzprMlVko0pw+rxJIrek3DfDaMn+wTdZcqAGb2eCVl/gEjlh1sr0WQ8Jau6YntBN1Z5\nzE6j2WI99thYF+z+H5kvbJqsvpNWsabQawVtZtkUg3H7vqYDd27AgI0ZUUJKGdM0QZEsOSxNnolu\nzEFYC+PXmQjXKeHf3d3iz1+9wvn9I75/OuJ35YwzE6Zdwv7qZZH9WQT5nKdWBY7ITLJEBAhvnEvj\nprYJsQ0b6bShiZns+wyJOkhNE4+ywrKm+gaLwjqdT6a2oEIT99AkX4DNFOpIr2t8ACLIJLiaGLtp\nj4ktmmZO3ApOKWgj3JqjA9hkSQ5n/fTRoOWIEgdLQU04EIfJXlHXI9bzexwfv8e7d/8Vi77Fev8O\n33z7e3z5j9/j+tufkGiC/tlfAH/91/jLv/qPuH98i5+/+0fw+QneKNIUbTzG5vetsLMN7OMUfD29\njCxeROawaBC0XIAIDOvXs+sM33K+Ui/WQ1ykJ3tsEVFfa9vzANwccX+IqxwF/sYR+uwHz37EkXmt\nYsK7WHW/Ej8FWFf1WHTFWRhLJVRhRM/PiQX7nWI3mVLZnRivrghfv0642SkmVpRVgWQd3osV2QWn\nivOScF4Fj2fF+yfgXAVgq1V0s7Oqortk9coRfDkbxVNUoWvFzOq0DCGpIIs3QkkZouz8tk9WVGoM\npO8JgEoEdvRrb1p0Wwjwth9jn2jMG7lc8F1GcPqxoR5bR34mJmvJmL0ctmaFVkbRxae4U2QKtOib\n7fz331t9JTCIZqS0MyuiEFJae/PlWIPalRlgpbl/eb3H//w//BX+l7/9T/jhf/8N/re//y0+/nRC\nJcJuP+H21eHFdfd54sjVBqWKWAlNRLbfdqEHT6w0JB/EOUKIY4O3O7JyE+YC3rXPjCBx5Msj9NCE\nvQBkPTjVza9N84Z+gvZrYsKcPDOVktcpcQQDfXa9URn01OPt6f9QoEzolSjeb2ND/c1ByFlFvQrG\ngrIccTre4+/+798AnMD3D9h/POL1uw+Q9x+R4Bl0orj7m3+PV9d7zHdXOL47Q1cZFNqFEG9Wy2gl\n9YiPEJKjsNscAa4J46wgNnw7d2xU6utmGJH2zPZK3NMw77S9j+2IPxthRJhicKSjAui/b5/lpc+0\nHwzC3OWFaqdHghuXIZyxqpWDXSvhXIFjIZwrY62WKT0TwDkKbvn+0OSVPysoetqoxaYnAJUI5Gi/\nVlvvxASyes+oqljUQhvnkGFeOdFKBgtOq2IRH7sMiyaBCaZd8hBEUiwQVHiMdTOfYl7EDXV28GTI\ntSq84Xh3rCfyxLBIth7mn+CO0/iwDn4EwChSXxxRkoOT51eH3Gmbse/17TIZkVe3qoKKTclqrZBX\nfCQeOk/hYs1dgIgrML4Q4Ou14L4uyFpApBAWUCakPyZqhRTGC6ppbyavOZGASARqNNelENZB8KEL\n8oFes89RT+R5GdbaecbtR2oLRqvREOrFQK3WTo/XRdzWwPHHa4kIlL0fuiMTph450ZBDWyS0ucFL\nHfEpRL4VcugL8JOHIpxxFQXL+YiHj+/xX396D60Zb7DDenUL2t1BX8/WhGOpqD/8BP73v7BmHTc7\nPD4wpBJS3SLXdscNsAQiHhzBpANi7UrguTA3VceOioZgv2Hjx7likcQmfHnEus/FzhMbagwzVB2U\n9IVltBlJVVzesr3eFdP4TJdCHJ9A5CbIveqgaO876dZrFW2CfCmWNn8sjKWy9ZVMliRWpcLAtAlb\nFUGV6lQjLM5bFZW88gSF0jCwkZPRDSKERToAsVaHtm8Jnc45rxXHCuu8BEASkN162SdrOl6oQip5\nZdI0WD7jOra8DWuHGE5omwgmc6Sq0zeMIZrIlehG7fv88WB9xSSF8A9qhZnQeqs2iTAK8kHpNCE+\nrqe4oPnF9lcz9lc7q0lOBGZTjiOojPGLG250WxUsbz/g/rf/hPfv3+FpOaOSJVmr16956fg8ceT/\nH3Pv9iPJkpz5/cw8IjKrqqtvZ85wLjszXBJLQIRWu4IgvQoQBAF61D+qV0Er6EEP+yItIOwuRC5J\nzYjD4Zlz7WtdMiPC3U0PZu4R2d3kw0JCTwxquk5WZmREuLv5Z2affSbimXRJjIM3JE7qxq4NQq0V\nK82d9c/1pRGLuRnvvaCUhgHpD6d/7nKRXg4CWOsyglIiYSHiyGQY3AXbhHL88xdoOk6p7CYKFZHU\n3TQPzcjOAG33wvafFz8f2Ys9ivwHLP1HRQWyna1WqFI4nR54/O73fPV64emTP+KXf/zHPPsX/5Kb\nJ7dM55ny/h3l1RvWx0ceFyMvzr9pMfmLe9h9V4s99gUmTVagofF2/5fXbmabfg6hShcP1TnH0Wqr\nFWZweb6OiMVLRdoCtL1XQCS4bTPs/bN9bD+4sLjWFlvt19oC65+w9P94SMV6gjOk9T826HWTeq1F\n++85ioKWwmbIV2UuymDu+RWpZJrKoD+7ApSY16VWtNReTVgDvNSI0Q8Io8BBJBqjFE9iqjGYd9Yq\nJhH79hxQscJ5rSzVpQWuBuGQfHM5ClwnWCisBo8CNao7W5+AVhfohrVVqrrMBtX7crYc2kViswEp\n2+Zam+pNhbKaV7+6NhD04LZai9jQDeyeOnvh4e0AyD8AlgwQFY5XI89f3vL85S3jNFLMvSTVtDPm\nOwNuHW5SzHizzvzNV18x/vAdv1/OfDtnSlUsCUiDrB8fnweRq3l7RATVwqCJKY2M4zUmlVJXcn4E\nEXLxIp7Qr/OBt+1ZiFxWt7XxqIHc6860NojvxUC2G7+KinEcBST1tLqIm2PXUvbr7kiy3cuu/Fbj\nvy9Q+i4jYvsJEydp39FCER/ahiZC1M8XZ+r/yHb27lmIbAa3fZ1BV1srRj4vzMs9d3cLh+klOo3o\nL36C/JNfgI6wLMj9Pen+DuEd9vB3SDGmUIWTZnj7RTVKmG1qoXu4LnU3cHv3oZEDN0OaK+SiKImU\nSoQ1YmO0lmewCwMrPfz2IUMpjP5+YXYjDi3x1p5hocZbdNOXMS6ebTuPh98amq+7V1vLC8FImLls\n3TZL4r67CqNF15yIs7Iz6u1vNboFFf9Zi7AU4Vw9vDJJZare+s69XR+EGpRcP0ecJyZO1WbwNJJ4\nPjabCFnTHzFfAxjJildoIjSG9zAkpuqKgSJR+ON3jogxxSROOGVztV1orrE41MGT4TRL7cbYn3eT\nKm5Azde9bXa3tvDFNrb7bkk+dhsAaHIOTlNugM/nWANL7lG2TcNDRSoOyJC0zT2L7xBvg3c8Thyv\nppDSUOpgwVrRWO9ttu1AF4SsQeXduvD3tfCqZu7MWEIYzCUF+OTxmZovO7Ly6kRj1IHrw3O++OJX\npKOylDte//Bb5DSz5HV7sObu1cVSjV2y8aql+Apo/e6a3kNvF8X+w23ienHNYVSGgV5wZEjoOPuk\n+TBNfbk5b4ZC9ufvXOoPPtCQA20pb0iwv3Ufutnbvk9+7/6VMI4dMV+iTDPIS2YuMM+ZZV7Jy0o+\njOQXT0jX1yzzTJ2vKacn8HqF5HHYoQblrRk/2byh7Zd2Dzuj155fqxrc/gT9Yz5A5wXWLFATw4D3\nlRQQqYh6013tuQftXHrbPee2KfglucFtl9eMgRJSpWHQk0innjb03p5n9+LCcmzGW3doe2PAtHnQ\nSt93JMaPQjPttc2YxwIPZoOX7IO2YqEKa4UlfuZAyMVagtDHOAKDO6RvXdtcddssqNLXh2MY6fME\nNqA0iHmrP/Win9IMv0hwyJu2SYTDxLrxGStbki8arnRjHc8JiY3GfIz7vbDJVIi291unHgY22aZ6\nOz4AR/t1tV8LlG1+9BV4aShoebwkDZzR7U4/V9zHNA4cpsHzfqLUITEOQzTwiGcgDYxIv1TFPaKK\n8GDwUIUTsKZtHMb9F+6Oz2TInZ43psTxAAe54dntL/lP/5P/lpc/veVh+Zp/82/+J7779hsezist\n2h3BiT4g3YX3s9KHq1Zqzb440kCt2ifqNkDWZ6qIM0tEYkLEszKzQPS75MnOLeoTwj68lv0R0lzb\nW7k09p+YaPGM2u97M2zEJUhDG1uc2Lb5tX1C9mf212qgplIqVmB+nHn33RsevnnF+fkT7Fq4+93v\nWB9mqgmZGTs/euFIWzE7KmX/Urn4mg++1egsAIGtYXC7sZjUKjzOxsNDYl2VYSykVL03pMomxasW\nPH2NmKqHtVLIoDoLQtz702gsHS3NnMkQ5V0GRmVQb6ZQM9TqwQijxDRxBNaRlHmVpnQ6fUi6FoFI\nLjY+YQeG1T/jrIaG/VpLNpeVuIyZRuLTmqEnYuXxQ1R7xk9RiypRnJlR2RB3zPWL8+zCN31qS/Tv\nTH73Sy1YbJgS3OnjkLBBeVhd2KtVtNba4EhhEOMgbuA1CaYh9ibmgnn4OHbBvKivcGkCZ+z0auqY\nFx1CtRxDmz3esMCfc18kO8DVEHB7tA6JO0gr2aInaljL1l3Kao+hNxnjlIQhKUl3tAuRy8Q/MAwD\n4zAyJI0uSnUz4hHv90iQdKVVbwsIT2TkqQzcmPAmOgw1y5dwjftPHZ8ptOIUnEEqU1IGHbm6ecLP\nfvEz/uhXL3hYBv7umy+5e3wL799D7PAm5trHbaEKHfU0q+ec9Mr1OCAqHEZl7DspPgl2RrQhtySA\nRoKIZnZkCw9sYAy2YfyPPuwT5+nFUchGk2zv321g8YLf9x4B/iPf1TTY/YtaabVX763rypvHE6eS\nef/tN5QfvqL8/hW2VMow8P4w8/D999h5js/Hou1jYdvLPRbJppVDe6EPAht03S048ZjmUoylisu3\n5opGY+L+7h0q6pW0sTi3Yr7mQru2ukoz5vSKWNf08Njxk+vEL388AI8u6bBjAXVs3iZGXLu73hY5\nHTidvWG1JiXJEGjRsOh/iiXMho6OrZbezg2TXb4nzmlEhyC7+J7GXinmJfBepBLa4C0xGvtm70a/\nO6eFsW9diLbdVkCqN6u2qOLUbU155yD3xs5YsM+UZa3MGVQT16PHxJ+oc9xVKhnjmJQxedepqq3Z\nR9uwHFAYrfCpolE9rNpnT58vVr3i0eeLj061Ruzd1vm2rugTsVP+6sbeeTwVXr2/AzGOk5JqjgYR\n+80vGDUJmub89gWy8wKd8DAEbVmSUJMypBTjZ1GoSG+j1+LkFW9T9xgJ5vdWOdtuM64QBvCj4/MY\ncmnoKXjaWsn1zNu7b9HXjzzk73hczsyluHsl0cG7U4Jki40D2wrzhzcl77mZ1DnqrdqsoacPcbOH\ndpvGsuFSm9CNTzNIzVWzj07xH3F8ejPwmFxsNtYKf+Sjz7S5epE0/NQ1fYCO25co3ofzmIxcFr6/\ne8urt6+4Or2m/vY33I7PkPHAzMoP3/+e96++o5zOsaG288Ui26HxbsR339cToLKxRDq9a3MxKOZx\n37VAoZLGlcNYSOp4tYs6mSN6ayC/MzuEjO5mQ7is1uYNzoHuc0gRFR7PwrwqP/1yYmRGZO1ob8uv\nbH5Ox162Pfxa4eGhsmQ/r4d93B2PkrcwphI/bkS97N4cPoeGSI/GtbnW0fienhhIvIYxZ8d4CYOB\nbhonPWQTXkDPvfRK1XZ/3hC5WgrUXBnV+6WOCcZAygcVim5x/VzdSCcxjmpcJ5gLVDFKhNS6PKw0\nG9BCn21NNS/FEWvaMBQer7+c5tWEXZoBX/8fF3I1VHGxigxc0iFxnuGrr9+Sa+bpk4kvbsZOQW7G\nfIMjuuXFYmxawTYBwDZs0kJLodxoUEqhSO2aO9abTBvZhLuaUSu8NeMHMe7NN0QzY5kzj48Lnzo+\niyEfKL5IeszokVevfsO/+l//R+qhci53vH79NflcWBcfgLFNAnwTaHG7hmLMDIkGzleH0TPuEvG5\nQGWOxtuwNBpcG9gWJnBjXkPQqCWcNtPwDyPf/68Oo38xe1egG+/dFTT0ufvkp8622+sErRGPE+N2\nEl7PJ37/5vf8+m/+Apkmnt2f+Nl//98gP33B6f3v+fZ//ve8efs9+XSixCpsWQTaJe74n82A+qX7\nhN0STRtrZUNPfnc5Cw9nl3jVlLk+Vl4+hasxujbV7bGYNpTcDJ8Xe5SyhZjAGRbZ9rmS0EKxwTds\nEe7OcFo9Qeel19Gei8FpcLKFhPz+Yt64qwMItSp3d4U37wqnuSDJVQWnSRnHgWGopKGgQw5jo0hN\n3g0oWrv59W1l4QKNfkL75hY+ydaMuOCF+DUotxIGm47A+w87Si4bw6NvSrZ9j4pyHEdGVq5S4Wp0\n/RWNTfB6SugwIOvgoYPiiFVj5SSrJNHY/5yj7ptKpRQ8pCBtLrVZsxXM+DqvndmyN+bdqIvs8gnW\nPaQ+DztoaKO2zU/vXJQQHViL8PX373h4fODZ04njz78guci6nzfO5Utoh5raHh/rqm0atVRKLtjk\nKMNpt04DzbmwWt62JXPBtHbO79eZHwKfz+oBlWSu0XL3cGLN8yfW+Ocy5FLIBnMWptUYOVPzyv37\nBxaFxTLLnKmhCjSlEU3uskxDMEiwbgz2R+vx2TBQCvS5sUs+htORSWsQAAAgAElEQVQCkeDcoxYL\n1ONtuLyBbPqHgPT/L8eHX9Vc/I2ZHRtPpNY/5Shs6GP7e3NLVYzbUVlL4c3ywF/+5V9Qnj3jz549\n5Yfv/pb17v/h99//mh++/Tvuzw+YbgyTHey4QN97F1P698Zi3TOFmjHfuTe5GA+PmWVxnehDgsmM\nZFvyuX3exLbQGhuK7eziQDnFrDcQ6QhUBKpSDM4U3k4jpcAPrxa+uBEPy0nxgHkwMzwOHCGJjiJt\nd95AYHjrvHUunB69XZrzN/z+VROavP1ZGiwULNXj/anpIfoM9MYrbQOkP/cIgfdGDYQOibW50W1y\n8wCsb4CtqUNPrsd91Fp7b9DGqdZambRypRaUQhjVaYmCYgWWuqsFUTfmIhaKBOIAqbJdnzqdssXJ\nm3ntYW0DsQhNAJtKyxZObSDOAvE2MONiapdGvKlXfnzsXpeE6IjpiDEgMiBk6OO7uUh7kNAxUszh\n5nXWWik5U0tG1OPwpWT3ksK7ggjtqVNCLebp0ua6SZ8zlYKIkquxfloz6zOV6GshZ+fDzkskM4rw\ncDpxqsraXJTq4ZfjNDCIs1yGXaLqsvajLfJY8jGojWYEbJZRLtGu4cmmtRhLNdZSWKvrW5TibIbD\noEzDliX387UdPs63y2a377vEU7vDLv5ht7Q+OmKa9ElacfYOO6T+wZPoS3//zZsJDkQp3pzidoAl\nL3z/7d9znO+50ZnzX/1b5vrA969/x3fffc/5NLvXE2XKKvBkaoZmQykNYV9wzDHQneHV+Olj4A9K\n1Q3b9QEOo3A9CaO6ZOvdrNyvnuDz2Hbxzd1zXdH2LtTydui/3a1ooEUxRBKDJAaFoxSOk3E+F+7e\nFW5VOapX5jqVNDreGj3G2WUkzMtSSsEbRSyOvm6ujLUq81KY58JSzKVoC1jVbmyJir8W4pkOcHUQ\nbq8KQkvgstuw2EbW3HgXhLUKZ4RRXAZijA1TJTj45jFkqxVLAWdkc9KoW8KyurX181NJwCSuCJgi\ngSmqJJMoEIpetx0tR+RbInxlGz3PdmPiFdCOSkuN0I/Q3QjRrWhI29puHlH83hKhfd3Ydk8dcOwA\ndPvsfmW1xLNruWhvstFNRk+U9EW/e3C71Woba6nWSime2JYw4LXk3XdJBxR7xtV27UF/Dq32FkWo\nJpSuEHt5fBZDfhRjrpm6ZlbU5R5RVqvekcQUqZVEZRyE45QYTEkYSXe98CyMdHP1O280dlKhu5su\n3GMQPOFWIQaOUOcq3uX8XDgvlbVWcvUuQU+mEbmauJrw8mPZXC5oEyosa0yCDjAudvAdoa0b8s3a\n14Y24xPt3nyoa5/Y1bQXOHh4M6KwDYWwN2SbEYi39tnc4pDXE2gy3p8fefOQ+Ytv7qnf/JaSV0qe\nOZfKuVTm4u3JzOBqVP7kiwPX6lrvPVS1XyltYUaOoxd76O5tQluVHA/w45c+yZPi0qhSeP1O+A/f\nKv/hdeVxUQaEIcGQPGE7Jk9oTwmmFKqTKiSUQaq/d0wcKIxqaBp4MglPpszNceEwLs7RPlXKEw93\noCBJNoOx2/StmQpzyl1ejbvHyg/vC1cj/PiLxHAckLpSloVTqcwLLLNxPgvns3I+CaczPJ6Fhxnu\nFyMdlJcvhX/2y0ISDfbLjmYXm5ZaW+rKasLDWjmZcE7J+4MeCkuFcRCy5UDaA1Em6sm2huqrIVL7\nhPUkqIIpJeXAOuqZByOa/0pP2JYwRGriEagYVIFgZ2jwHdvzgsHgoML16HN0yZXFKlWNnAu5Ohe+\nhWW2him2uXkNQIh2A1q21RVPZ7eYYt6bNqqydUxeI1bdwqu1V59YoOPa0fF2GduJG3nCalx/NM9o\nDaqdppr98xG6a9MeVaTKhSJiAx7OKKoNigCKSOJTx2cx5LcjTJp4dvCmDYNCLoVrNfKKt6JCGKVG\nZVih1+Ls0G5tg0qY7/2uCYEqgoNq9Cz5h0etsCyVh3Ph/jGzVk+2eYI4UUMmThHESp9AF5lrtt37\ng1dj0GRD7hcZ9UvXr99is77W7sDaXEStkMzTQhY0wAsqX9tJPvkabKzmlggzsMJxSAzJWKzyzfvM\n3ePCvMzkUrw60IRsxqTCi+tG1dyEpLbE0v7efePzUmi6bvyHcTFBkOg/Wq1EYlJAhQXhXVa+m+Fu\nTgwkhEhIWeXqMIJVSsnkSKwl8a7wg7oRKmYc1asWixV+8Qz+7Evjn/905I+eC3YLoyVujjAMRkqJ\nC72NnfHYezuORgsDmS9vE1ejcD0I395XahVGmVBVjpNxNVaeXlXyaqwLnFfnzD8ulfvFuDt7EUkx\nQyxjErS13VJuj01pnqDzrddo2Nx0WN6tzjBZsnI8G+8eMqPtwhLmFlWT9Hh1RcnmWuAekjKyCFmh\nqGu1VDOkFoxEcWGVbZ6J+OKtpedI2sZXcTXTDlJiHdTIYKcmKNXK2dsNs/f3uAAK0jYUa5vt7n22\nswe7h6YReh3YoW7pfk5PbrqNjSdv+xHfzVe7/G9wO/bu3TvevnnD9XHELFGyI/K2Tvchorbhobq7\n/hp2ZjucBlpJqfKp47MY8qdTaPuGMa5UFjWeHTXikL7wrpLwZDRuhuy6xFXC/bq0h9any6UrZYEg\n6mUFEewMGX0ALPooBttZdzzVbqhaXd6nDPbuWvr+skuwWJuS8snP7Udtv1819L8Jh8V7zL2NWuOK\npPYsfz+Btclv+x3CCyzbWwUQby6Qa/uCxNvTwquHynmhs0Vaq8FjgicRQvRQybbwAlxvN9VcbLHg\ngBOx0V1PzIjHiIAkD9/4vXvC2lGXUiyxmrKap8GWQD/XOmJWWDOsUd0oOOqboj5gXitXyUMw59X7\nJ3556w07rg7FWSbsWRUfxFbbLfYNaxstlcohFV48TSRNzCb85deZ+7My4n0xD6lyGPB+qzR5V4OD\ncD3BFUL+wVgLzAug4YgLlz+0ROCOsWVeCJQNliosFe6za4NXPKd0VGXOwtOsPDkatwdjAA7aDGKw\nfqp2hFqKsYiyVCMTFbCxqAwX1Gp0Rh8vn5g9N2IEcYAAAtvK69WqpV5Uibbk57aMd+a5baTN6+1W\nvM3nD9Zoy7/EDtgKdhIhvAWXMCoWWPM6eqJ0vyAvRt56bLvlX2o1zucz5/MZ51SKM1Nq3ua4NYCz\n/VwwcuyyzqR7OGxyJR8en8WQP5s8iVlLZZXKKsYyCmkYu6tzWio3h4FnV3CdMisrGSXLsO3CNRaD\nXaLhPRultow27ErGZQuF0L3KeF0dCQZ3vTa/KRITDUnsjfFF2fYebe+QqUVncunv3xDApXbKHq1v\nJzJR9mzqGiGjpSaEzCROJ+x3dDH5Ptg6bDNKFkHrapW3j4WjKLc3V8z1zFKVLBM1KiqThlRw8iz8\ntnwuEXZTYpRY/B8qRvYwTDyv7pVIK4/f7j8hHE24JvFsGKjFcxlzNEjAFBmNEeFgiVq9a71VmBSu\nJmcyzatyHLSPnXjGHK0LAxXVEjYh0Tj7faNq3sxFjCrGwipJKseDI/nHKnzzLvG//brwu2+diZOm\nzNVYuR6N22PiyTHx5CDcHoynU+XFEX58O6Als55XHh8LehUlxbSYfxOL2jYblRBpEzZkbi4pey6+\naaFKtoHHojw/F56fjB89gV88rUxp5Wila754S7kBcE0Vy8ZqxpxgrRJeSqDWXpjUJpTPI78+X0ed\nuqe+wWbTKHwKVcXc+gVIg+hR9Rn/aiv2aXkB+jPZQZoOkFq82Y38J4rVdgYxbSswms/EfwWQvPTu\nP2XJ/T2l1iBGaCS225o2pjGh6sV3LazVf9ocV0Eigb73aDt3PA4ncLQr//j4LIb8eoiKuQEyxtmE\nWhK5FNCBYVImdVQ8V2OKarBBwcsLtIXdOkp1OukFlqWR7KVPuIao/H0xtgTo8+RZ1BH3R5g8gWbi\nnFxpDzQ+/JFyXz8ay2IXD7Md0ugoRrpb2BBJfx33FOZcObV2WOGOWhSGzNWTgs+PLlYk8c1t5+5z\nYwMm8e/mlTTUezJBZOLZOLn+TF3RgFxqEQcNNkWPh+/c2v3G1e53T1FsroB/tLnk+wXjD6ZV+glQ\nxFjVWBVOVjiZUJNAhVQjXrxE/D1akIk6glytMoaODiqsFtW+CirFk3dD6JFYE2ZqBkU64tKokPwI\nJ0r1MnUVxtELgJZFkDVhNZEFzqpkhPeLoWcY7ryqeUiQUuXHh8qfvKhcjZWrozFNcHs1Mgwr6+qe\nUhsjlQgNSDPofcuJa3WX3EyoES5RvOvPWpXTWhkojJZ5gnEtlVFgGCuJwjkL6wrHlHhxBQtg3rqI\n+WykyesyfLQSlUTTmkd8A6lJKQoZYSmZYh42KTgNNBvkqGpy0kJsT4Fc/V71ArF2xM8lA8df31C4\niD+Ttvb6mu8/G9AxPNtezRtQt4CK4Y08agdlxkUMZbe+vWCpyRL423zzV9c5T8I4JKQq81Sjs1Gs\np2gOvz+2CMP+atuLwpoLdv4DCq0chpaJNQYTKIlTTjycjdNirFHkcC5Gys6sOCbnPZeo6morvQYa\n2OdyLx/Ilg1uzLOYEvFunwhDEqZBOU6xO+zeO46eyc7mCcXU0KhYn1x9V4hztolmSOz4vuOm+Omj\nTkPon94Q1goPq/FuLuRWEl2CrhQcYiFxO0mfyD051i6pTbQeJdqjmdjn5XLyN9fUEUwHTDtEFOas\nn3+XELTddyMdkTf2RZ/5n7hl+XCMJJ6h0ZX8Nt9ru1QJFkUKv95TsM3Paq27aKSIuMiCadNW2Wir\n29zx31qnGh+mtkP6+ypEBhc0xKCkOGWyxVhNvPmCZe+92Y2DKLYkXh68sfHNjXO1D5MDkLVdibTO\n8tvzcWPeCt3afQJWqbhnoiIkM0qK0IsoaxXmFe4eC+9Hb0BxwBgpLFnItTIN8OzKC1FKbHAez5U+\nxrUzNDbmSLXCWitzcfrgXPosIZuv61yNXD2YMii0zj9Cmw+xNmSbZQ0RbwZ8W9O9Q1CbX/GJ2ke+\niYBtxtyAKi5TbRbdx2C7vx326JdzaXP7vOuTp12TRR5IpReFjSqMw8AQFZ41ZAn24SG//jbO0o18\nDfooZoyjMIyfupDPxSMfzGlQCVIV6jKg55H7h8LdUjmbk+TX5IvrehKOGtKmtssmB+2ptlhB7Oz9\niImxK1xrs337jxioITk7xh/i0GlIHp92itVarXcM6iZPtm3Bqx612cAopQ79iJwZNHEYE8chEliy\nIfpuFHehFkNYK5wyvDtXcruR0AJXAR2GLubjiG2n/7cLBXTe9j5BbI1rL4wIkxipFurqVEPTiCLW\nDduYpEAx8Yq0ZNnOXdxtTD0GqGx6KX1XsH4dZg1V7RkKYZxx9D0aDOax4CLmlYU1kqKR4JwCZS3m\nbAwB1Ly0R8Q9QO8e7yoWWVwbZEAYTTDxkMbWlLmFxSxYUR2P9/HuxkUNxDVYJoxJhFU95GUm2CAs\napuSnw5MQ2JMhSGtPL2qTMl1w5diW1HV3ngHzVL37dTaJl2dJVKrh8xqAIZSXJ+8DAnTAVN4mFfe\nn+F4iPdirCVjtTAl4+bgmja+SSWs+H0kcRBWahjyEtemRrHMUtxrMksejhEv7GkG3EMQlSRCia5P\nKcCUV9IWeqtFtk3dp8oWk97m9F75dAMebW03O9unZ4RjK7tEf2msFEFaQ9F44Foba+bSgDbPuIaq\nntM3w9MX6fO8jd2gjs6ncfTNJD7bqjqTiNdOKBwGFyETTeRSmddCxri6Gbi6nvjU8Xl45KM1HpVP\nfgq318LPXii3i3DKwkNOHJNxMxrXozKpx7xUtCPmQR25m9FDKIAbX21IocXXNhfJB9ZRlCdivMWW\nxsPsyDJc51oNsZCvEQuktzuXfym5Co9FKHNmXY3ZIvkW1KbDCM9MPFZLTC6TzhNtfNluELG+q2tS\n916AilfNaRCyHVl4teBmIPebmr/m69oNUucvC0ioCQrCIMKUhkDWFp1m2rO0UFlq3OwQs9oFtS9T\nufEdzXtWDxU01TzCgGMb39/EtjL1/SHeN3KKPaKgm9dbcYlhCakrCd10M/q2L07bLPGaJ70kGn8Q\nc8FRsk8X67ulWIK6c6FjXjWedjMyEnEPS5vhTfH8Tcy78UjIobolR2UFso/BUElDhSL+DDraDM8k\nfiTYF0nM9YoUznS1Ar+WoIRWM87Z5+ySK2LKQRS1xDkb50UY1Cl8XvJfvWK0QK05QgQwpNKrKp1d\nb8HfF26PyjhWKoVUY04jrJmoVZCeDFRzvosPv6d9TaJIzVrzjOqde6yitXUP2xD1HpQIbWOVoO82\nKu6G2WokOzYa4+aZFWufyCDZ15O4UBW2raY9PgS67G7LpTW6ququkUzMe1Xv0OTdwoyqnlyWIlAr\nKnAclGeHgZ88GXj+ZOL6+oDoxDdvHvj7H96zCDx/OvLy5R9Qq7dxigWhEjGlwlODn78YeFjgfhbe\nzsaTybga3ECZVXLZh/rbjhuuUrxqxlbsYNswu4ZK4293zMdePtR5mk2rw5FdaQUw/b2tiMAuwH2A\naZZsiKlTt2rlNBcX84/u39WkTzZfpDuPcufQt9BLF3tq6Bqw2tywTUag1QNemNI+mR2Z7cuXPUao\nG6qMhgz+MGsUkkSCtt+oxLc05FH7d/RFst8sRaL4RzxB2pXLdog9vKkW+umn6N4D/afFhbXKtmlZ\nX2p9TBtn2WMinlzroRnrrPcIc+0Q9rYf4Y0s2M2fMCb9PWE+dgksC352bBVhyH0zNoQMwf7wDcSr\naysiHjDS4NtH2P0ilCK6oTvCkLkhh6E19G0I9GIMnDGyFEfT3ZPSRKmFeYVkrvdSIAx4PLpYWQrI\nYB05m0RYRyqjJI7JQylVBI2k3yl7WNBUepjjkOD2AMVaBy3pyooxszdDWysFQlxvYJv+LdSzN+Tt\n0zGGLWez8xr7HGN7ph4aqkgtvpFqRcVj5tXqZYhlm4YBiNoFbSf10Gk0w2nRVxyYpJqZ1DgObujN\nnPeeSaDeqP32WvnJ85HnNyPDqKzVOA4wJqFK4uo48uz2DwmRT/RdleoVY4MWriZ4mIV3J08GPTv6\njcyz9wUEIVva4ri7hddaP8V+3GN4BqG+I4EUGp6O5drpUR5XzYRCWRhyq15l93Gpr11s017BBtTK\nzdXRMfGyMmcvyEity5C6bkIvONgF3/qktC2J1ZgKEvCgGdVm/Nt8MxOs+mKS3aR1AygbupDtm9p2\nUrFgrziHeFlmr0xru+LFfX94tL/t3+cX6glkTyimEdKAS8nGBo5ujpIjnNrvq3tXHY1KR0n9DoII\nsFXqtiRWhHtqf3LhxkYJemzwG9UwnmX7vX3v3ky0/0QC6dv2tzhqeDCNiePSr85nR7093mLFw1nh\n2UmE7XbD4l+/N9q9EYv1SkmfO0Qsdtc5Z9vvu/fgC8TZO4M6LfGYfCNal8ope4LPtAlxhehWGHIR\nIRXZumSJG/EEjKJUcWOPKDo4hdcMVqtYVRZLIJXrUbg9QJYxKqjp88vMKObnTIQufJuDwTH3BjPW\n75eWeN9WRKB6ukfTEvPtWTRRLW3ryApYZhoVqyFR2/V4IvzZn6Ns86MtqNghhNBjt4TqQOs5KlZ8\no6grRzVuBhhwLz+bskRx32E0rg5wezMwDZXT+ZGHs3E+ZcDDLMMwMk2fNtmfxZBfHQMBmWER0mgD\nsqoyNuGcnNFaIcfiEZDB6TqYbGAMwow390wio25kcwGfMYEOQYmSC5Pj7yvG41KZs5FzdVSnQCkM\nOnj1qUpsGpEYCzTXgKNrwST++Bc/QYfE7779nkri/rR6ia5rV6Kx7cCWkGsXY0TmPRar7ryBnsQ1\nMAmVuv4Ighpo9IntetrbBGybRFs4TYzaaWyB3krmfF4oue5EqnbFFrKxCD46uiHc4uIpgQ6uK9I8\ni6bBjElwxpshi1BYR/c7tGWNwxwvNJqa2e6NzuMt1YWZiLCYBTJuHy3NEAu7KlPbbX7BjLCWzPZz\nWfCEvfflnlVhfXxqxenDO2cixbktUHRD61B7bmP3COk5hmacae76tkk7It7x3onXd8Dgcmgk9Nud\n2ZIkFCVNKDWxZjcoORdK8fyOatM9F9a4BpOmXaQMQwJN2Gre9AI3oFZd47uYVyUuVdFaGLRwIATs\naJn3mPdxXhsTkgafc+YbhKbN++koXNgBngBRbKE614PZDG/4ZR0UNULAqMLxMPCzP3rJvJy92E7x\nPJRudmoPZvoTbki/fb/3UQx05Vo9VjN5zszzglWLzQ9MFTXDbKWSnEZqEhW05udoHZPMN9Nxqlxd\nf3rxfZ4S/SmogVGs0+JdpoWhCrpsK9jMua3ZnHhYGqZuCzgOCUhVqjGvOUIgAE3HUKNPqHRk2gbZ\n8NBKLoXzXFjWErEtIRnI5Emk7aI+NuIt6ZeS8OzpE4ZB+fbVDxwPoxd5zI1v0Z1yGj2yGcYOyGxL\n2PhOT7jw0q/XQXaNpI3L7uYYf3fb2vNphsORzDiOoWJHJDtDtD7QRzVXbmsNsPfHBef9HwPpzUD2\nak56X8a9Ml1D7pcJ34jFyw6V9gvYjfXueVl4FCY+kNHTYTOk4uX8Up0PvT9lM9w+hhttsjkijVpp\nNI/s0nBvz6a9f7/w4zr7/V0enUK4+4Ptb/SDedHi7tqulZ1B70/lcmC6vECcqAGXfVejgrq3aniy\ntN0P29ooVcgl6IHBjbbYrAeNJxTccgudovadnpzcONbtmfhUCL680Y3roK31cjy/qN+Qjww3/Z4t\nBqE9p578R9jJi9GEiZsHnVQ4HhIvnl2zLImyLrAunqfoY/6PT/c2O1TEK1OtgBWsrJQCOWfyuiK1\nMGC0yuY1Pp3xCuQxCdMoTArruoErCc9tHBy1f+r4PIh8sjCcwtKMCuZ6xqUgCR948aTWXEfOtTKH\nMaeyW0RtYH2651I5zQtmyYWSEohVkrogUNqvmg9gX63uYi5LxnDNjlGVadwblEtDdrHI8LDBkGIi\nloVWYOSl1q4TIyJYVWdexOpvCTkB1KLKMCyK9O/bUOU2waIRQHF2CzTVRtkU5wK2T6NyrRPHQRml\nohRUMkIOkTFxFBub5L6walP6+ziksB3GVsm5oeTNqPnfESLMtXuQHzxU6W7rB4+8PSPZxahlfx7p\nBrUZvkHhEIs7WwunbChLIGLQ1sMZe1tqrX5gdy0Xd71HbLZdZ89HhCfRRKncIbGIz7fHtKOsNpzS\nz7rFgvcbTv+dtuDbxW3PdC/65Bt8k8Dd+NgF0Ij5dgUise36W+OKQOSCOvDEA3OqniQXhDUHyi9G\nKFv4ZqIeEl3j3pr8rPV7khj3iClrbFMxn/okbg9n9+zcVO/CTXgzd/f028O8NOQac0fFBdo4Doyp\nsiZjKW5i96yy7ZEGdNgPlPmGPCTvMjWox91ryQ7Mi/8+kDlqQTQqbsVzPbMlZ6xMwpOrkUkKec2c\nl+bNeZnaqMZx+vSW8nkKgsbEUj1TncvWb2/CGEmM5iJZlcJDrnz9bmZmoIjHmA/Js79hd2KxtXJh\nN2DjOHA4TEzTSMkrKZgdtYJuZV2B/GLnlzbECcJNk2CHaPjgDZ8hO2QXk9Jdbrh7eEQx1iVzeiws\na5tUzRVvLb6ka1o4CN2FUaieEGuXapsRd70LX8S1GKsY94vy7WM0HChuyD2TpCG9WlA1DufMVYIn\nyXgyVp5ewWFwmplL/UpH/i2cYFyixk8f2xbTPCy/VqCEBEdl9/zYYOzF8RFeBnYa2w212+UGuo2M\ndQRn1ToiH9W/R2Nh9NPH+KtE/F7aeMqFQW3zw+9th3936Hz72RkA/2CMuUTqMDyuRiWUbbNuiN72\nv8d9NdZIj/0Gc0Jb39Kmm95t97bp1tBjWasjQI/UezK4Fs8V7Ts9NT5z3x77OT3Uks1BhCtKhsVG\nQozLUb03mnAgZShZEqXuJGgjCdwqGlW93ZnHltmKctr40DbxAE2y28h2HomKOeMFozXQ9s061lij\nbGIQBjdJoUqlBLccbEt4Bohq823b0dscqKRBOF4lbg8T18eBMYGVQiIxqXEzVF4eKuXohvy8Vu6X\nxDl7GGkY4PaZ8vLLA2ktzKeFu5oRKwzq3s+YXIX1U8dnMeSqiYdH+Pqd8P29U8qeHuBHN5VJXa4W\nfOHPBd4tQkmKppGhKofkdC1HPm54rK0EwY395PzzwygxgQhotTMUbbcH2CXaEIky3aA3Nh8/Fsan\n9kQj0EQtvH334Ea2FhecKkbatb9uUeEm9u/0RL+OjZLtjIoKWFxbNyxm0OQszYWPiirv5rrrBBPG\nQgXMGyeLQRG4GhLXR+XLG3h+ZagW7pdMEo8t5x1a29S8tw2P9pz2z3G/Mza+tRG8XYmiKP97R5O6\n3xh3p+1j0pbmlshuUKypQfY5hTDEOGQ8gZ4x79dandaYayWbeZisG3DpPOwmqNQu6eMqwmYYtw3I\n+qTxkfWYcxsz35r3n7BuHHf3GLflOR7ZZF2Nrj3UUGx7vmbQFPPcmG1MqG1+Rs4hwLVVIYcWixfn\nhLZRbEZhijtyTVjzIePOI9hQG2UgmDctbGZ+P1V882leUBtFv/8YL4Fk9PMgkFCKWhh+Y+8FWmws\nLcacZIuXE+ukeSXJ2pzYh8yke2j+WvXvsUK1YA01mu+g1NyuuHl+0jeSbTxbrgmmw8jN1QRM3N5M\nXB8GjhqFjCbYrXL/o8StJYoV7s7Cq3uj3BdSNW6SA9yUlHwuPJyNdzM8ZiHHWpdBGKY/IENuqrx+\nEH79tfC3r+DFtfDHX1T+6JkxjkZKPrzeQko524DoyJgSgqJaUPFJ2XwpidUmUXQyjXAYKodwZcSk\n057cvsRE7NVCtZ1ku07bmsvu/9fWoMk2pI1NkKrx5r0b8tbGyZNArslQ8XxIa79V8PvwkvAtCSYI\nQwgnOaukUcIaOgoUI8Ywjeg48Hjy7iHNODWDpyLeUUdAUK4PIy9vR376VHl6LOSyUKgM4tz30m23\nf39DZ/vnYi1h2H76gu1Wqb8m+xXc3huLDmmM5zjas21IbfklwOQAACAASURBVLdBt7FSAbVdGIQt\nHAUOBNYIj1Rz7ySrstTKUq0bxEYDVN0t9LgWi33bqmHqyoL9+rtxN6RzJh0nNOqeSUVIwdlvy749\nIWG/8fktWvcEnFkTOaQIx/h1BNo18bLy6ol/tT2dNTa95nSE99LOXWr0AdAw6EV6o2PYmrGkCHHQ\nzixbvoYaz17BZRGiEjHWoOGhmIN4/Nxbx9U+j1u4MIkXd1XzHFZneiiBitv02nb5FEBvkG3e7fdS\nP28857bBdU8q0D7hfYRYXA3kLppIg2ukLAtkKhqqLNqfxOadNkMuljjcTByurjikA8+eHrk9jtwM\nyu0IV6NXdtpPJ15OI0s23j4mro/GpJn7VTmOcG0D6+PK3f3K67vCDyfh3aLMxdAES4Y5fwpGfiZD\nXlOlaiKTOFXhRpQ6QjqeSbVAMmqKQg5VNKXoiuLUoC7qKvFwG0JhvxgVEyFTyXhsrpiQJDlH1TyD\n7OL8CgxuDMxVJNpOTyyKLtDVUdkHR8Aes8rD4yOIN5cupTEoHPVlM/+plbW6VkrOHrPEguUh7h6W\nWEy51NBLbnHU6PkXeg3jMDKOA/a4OjqxpmdR2ZTVmttceXVn1HXhzXvhi2vlZnIjU4ov/JTSZmg+\nyDhedMdpcPXDJyIfvvDBn8QTZv4oXcq4WZ72/w1tpc7sKIgM7i6b5wGcvbTJf1ZCm8Wcr9066RSg\nkLoH1jw5bQHV3Te3C1Ri41R64nR75+WG3+8tnpVZ22zjhMHhr7WECW+aG+wM+PZcPwzTUL34qVav\n7s2E9nYz0DWsqvgGUtsFSHSXAd9wdsVOucJswgiMZi4cFrrbxZzZo7V5sOJp9dCx2frfGkSQRvG1\nozX1sF9SZZToVi+N0uvx+BZSUjSYQBYa4UE/bJmzmM8NACEQ2f9tTMwHywIcNPTeUqMXRrhPzW1T\nRaIS02ELNUgRnwz9tU0VvyCxhGjieBw5Pjsixxt+dHvg2TFxOyaejMbRjGSZZzeVIfszePks8fMf\nDZx+Lry9W1lyBU7M383c3VcezpWHk3CefbNNZeSrv114fPPqo3kHn8mQp6SMo3I8KjdPrqhUTucV\nEWVMlUOqjER8TZybkYsbsCQ+8BeNMnossLXH8kEqsTBcQtPI2auqihiphVJ009WW8L1MjBqTRfFi\npGp1+7r4ysau2JwtYjH5BFqLbboekdEv5mX3azXmAufiGeoakyPVaJoQhRQaG9D2zfv79sW0LCu1\nVtacuzEo1fuiShQo7IO9loWHM3w/CK8flB9dC8+vfMGouuDPptAYFmWHyt0R2VC50df7HltvaDqW\nz87uB5L58J6kI574hC9uD2CDDqjCqB7PrEF6Pg4Dk0ZDNfGQ1BhocBRPhC+lgiQGVbB1993hUTXD\nunMNLvICPcEo7G+xs1da4s6iW45oaOhbcz/otQFScWGo3SNqXe27J+D/1hZmqQEUXPsZEGrre6vC\nNA3YIIzVOM2rb/7hzyGGSWVQLyDy0AQuiFa84jSrIJIoVqmq3owi1oiHnoyIkzk7QzbqrLXNTzwX\n0jy6Jq+gBqq+sZqpF7S1cW85AIvcj22Mrc4c6tu7G/QcnXNUNgpy7LmI+D0S4Z1S27h53LxzxsTl\nhJMUBgpDjJEiyDiQh0SOSS1m+yHfHdo34avjyMsXN9w8fcbL24HbAY5kJjNGMoNWbq8Sh3rAaQ8j\n1RK1CPfP4XSu3tBmrhzU5ZChgFVenR0gvn+fOT98utfb5zHkw8A4uM7D09sj57uF+4eVWgcGrRwH\n4xCTR8XRaLZKEWd/5MF1N5pcajMfQrAUUqS/LJBLBGtdbErxtmMuBbshC+txAGt6GGwqaHuk1CCX\nEfHHMGyEIZE0eJyzNLfPp4HhMfe1wlJhLl48sZbW7spISZg65glD3ji3YWyaO2841e58nhER8pop\nJTQtrCKa3KBwmX1fknGnjrxPZ4OiHFLybbBbUdl/5RYIkN0Tsy2O2UahGbs2Hn0/ABqBv8ubNgNo\nW1KqJZGb++tGXEHdS/A4JoxaKdWlZw9DYqSipWAyehwVR/1jhBaWXDiMI5MqwhwGtG5j3yHwFirZ\nQvWbe442nNfuu40tfcNWa9WW0qdVb6xRC0qKpGpLurn1tooXqO1CKxbzooSRsww1R8IRparHTV/c\nXJGmK9DE7795zf3DzFLMtX+kIFoZknFI4noe4ro1VCMjrClBFQoZG4/I4YCqUtcZkZWrY0XmgkW+\np4VvasTbq1PNOOfKmjdqZMLReNIaneRbkrVVQDTM3PTpm7+y2wBdyDxK6umGu0c22+yTRods0rK7\njbZtAy2UKu36apTP495hEmwYGNLAEt17Glhp89h2E8TzGpXDlHh+e+Dly2tuhspVqqS6ILlCcPeP\n08jBjkwazWoiKf7siXJeKqezcXo0rk9wmOBxWbhbVt4tlYXinZTWDVDuj89iyIsKa6nMp8J8Xjgt\nmZPCYkcmhWFYmAZBtTIVGBKci5P3FjOyNRpXa3+mYMIwqMe5NLkxD1U6sxUn1A9oSs6pjvBJR1E1\no5bxgI+7p86n3YzVB2a/7+wduQWcOM8za/FYnbd8IhQbo8tOjQo6c/qhT7H4PSpdayvnF4GIQ5ag\nUNYoPfcMvDQrEhVxTg8RcE41dbtT201CFEVJozJoYkgjUMi5gHnxQkPkFoqTqpsb1BC7dEjJ7m9s\nnkqn4PlGtjeQ3WjvkHztse94PTqnaFlJeYHVk3QZodhAqzNYLGOlRvNuX/CjNA5+8TioeUJ0kK0g\nRHbaGBpIrz2fDZG3Tb4h8u2e+r+2PRfVFLoajV0Sc0xbWGHjEndlw7YhtI2ubr+3sMha4bEeuCvK\nu6XymI2a4OnNyJ/+6c/5xa9+xZPnL/lX/8u/5q9/8zWv38/BVVYGEQ6qHJOriaokMpEEFyVFHKog\nXD3/EU9e/ISrm1vevvoWLe/55U+U11+94uHto6+42FhKVR5PXhFtJF7PlXMxBlFSxKSqiLPMwiSq\ntpVUSBKyABYiXXgopqlx9UrkKPwIn7sNUV+VYkASbxcni68vcF2T8MIFL9jxBLBASSxVWeLc2sMx\njV64rX/Hbj7227Tw9SWCF//kM3V5pIrrvmSEPAhSM3k+U9bCIAPTYcSS0zFrNcpqCIWSM6+WE3/7\nuvDr74y/fSv8cEo8mgCjR8bkcq2147MY8n/3m8p3r4zv3xtvzxXRa/Io/N2bB26nxGITORkHLWhy\n9FuR0Cag7+cOamL39/pvH2SUUo2yFMwchagZa6nkEgUwhNiT+kMfVbkaB8QUlcS8upJbDaMozUWm\nAa+9f91+8UmQSyEX91+r+eIehqG/p5psdLzmWrb/mUWBj7NmPFG0md8t0UJHtIIypcThIDyehcdl\ndTTWLjY+sQPPJDGuRnh6nbg6pCjHrwgaxUNEJ3MNY7cZ8mkgNtqN9ncZloirlQ9e3rmoIp+ek5sR\ndyOXs3Npnx/gp7fwsLaQcSV7oSyjZlKUU65qIb8KWmsXGpsRDuL00HKTeHo0xhT9EGUrce/c5g8u\nbr83tQ1u+7cPBl2orWS0eNm2tRwJ0XEej/O7QWyf9sT7ZXw8zl4hZ+HeEl89wDcPxrsF1pqQJEyl\nMH77jun6FdOovLhOPLsaePf+RI1k3YAxif8MUllNOEWJ/PUUypdqmCR+8k//hJ//+X/Fj3/yS373\n27/m/u1v+PLpa+b7M6f3Z8xcU6QiUJS7ufCweBOK05pR4GZIPXEpIuiYuH36gidPv2Q5PzKkFdGF\nb75+Tz1DteR1I8Urc5eSnRKrG7++Vq+6dgop7h11lVJBiwUd0ptsGIJJcrfWQv45OA1JlGuplCuD\naxib5o1VZDbWNTyF7obLzi3drcE+/ytWM7Uu1KJkgdVCnwZjCDBg5iDuccm8ejR+/8548z5z91i4\nPxe+v1v55l3h23fw5hEec2XF7zkN6Q9LxvZf/1+V8wwPZ+P9Wnjy7MgDE3/x9XueXRmDKm9n43qA\nx7V6N/vq1LlhT1mjrSsBSX0BV5TTsrDkTC3Gy5sJqnCeV05riVi7x+o1fkQ9EXoYEljyJgrm4vrb\n+G1xgo2zYfEXBRx9u4Kb0cycamIcR2rxTcGZCMLW79UnS4vbWqtO1C3Rg0bS0gh32a9D8Uq462ng\n6fVI0pbszH0D6M8qbiQpXI3w8hqe3wiHg7FW7wozJpfRPA7eed0Tf8GDj5Ll4wA3o+cimgv9gRX3\nb429r3s0u+KV/Y60/3TLrTbEf54LVPMY/gvhXELl7oLVkNEWM1WjBKqzXLzwCmEWQUqhVnhO4ssb\n1/HJpTAlHO3sd7oP72nvJfQXPn6/BXi/Go3byZhwJO6xXbiqLQ7s2uXHwQs9epigG/HNsNcKSxbe\nZ+F398ZX93CfDUh+PQ8rrx6/4+3dibevfmA+nxmS9M0w8AqjGJNWBqk8lsRjcTN0EEjJmAYHRP/k\nFz/nn//n/5Jf/erPefnFLd/8PjHWv+ab66PraReiXF/QklhK5m6unLInV29G5dD0UNRIAxyur/jZ\nr/4pf/pn/xnv3/1A0jtKfsf9+7/hvC5kU0/m1so5F9bsyotpSBEa9HVVWkOKVi3cnjtQs2sjmYJX\nXyskwSrktTLPheoEeAZRnshAmQplnBmoHMbKNFbSAmQnFHS5ausBlm1jsQ2511rJuZJLJpdW7Oey\nvVXx4ih173bJxpuHyl99Xfjff5P56tXM+8fKkuGhVB7XyrJCWbNTZnGK8IERGf6AOgT9H7/RaOlU\nMTnzYPe8PR8431cSq7u+VTkOnmR8e4ZVxTUXpIKlDY2Kq62d18q785nVFB0mTnNmXr0b95RGzIx3\n58y7UyWXJpFloQfiGinaXLgwTaqJMUrNN9DdKia3RJ/Z5q7VUqkle2I1PpJCUL6E8SmR9KwWxr9G\ngY81173FA30RmkTc34nhMAajB08oXY/Ck4NycxDWPHBaDM1uATYjupmfq1H58ibxy+eJacjUajys\nkNLINCqHwXh2JVxPQQkjeL/i8WlXsTMGC+U43SZ4+65et1mh8d96WXlksXriUHdsAnGaiIdMlLuH\nlfNcmFLmy5soAY9FKtCEHfsGobI1qqa2uGur+/NvzQhjFGmcT8ZBbWtysJun7dmpujHt+6c02ur+\nfQbVPa2U4M9+eeBnOZPJkMGqU//mxfuMlgrFBiYqL44WwCEG27YaAxMlA2crvF/gLo+cTDHJCGtw\nuuHhXPj11+/5+vUdVwpzBqaJUlqTbkOTMQ3C1eDrZ1DXSnkyGcfRm6roOHElj8jpax6/P8L9Nxzm\nNwzrA9dkniSP2Wdc16ckeDIl5iqUZaDUhVHxghhxg39QePH8C/78X/yX/Nf/3f/Aw/33vH/7a776\nu3/Hr//9V7x9P6MlowiPtXLOlZLDezAhpS13Y2JcXw1cX00cjkeWyJ8JwnyaPck/DAyaQv4ZzDKn\nx9UL5byzB6aJXJT7dyfm04lBjH/ys1t+/OMnLO8fOK8r85yD6ABRI+o5s1oppp3dk6pxniv3p8J4\nrhzEw2kpeUW5IBEO9Xi5GSx54NvXmX/7f9/x7amwmjKOI4bL/85roeSmd058lgg/fXx8FkP+mGEI\nneNaK/ePDzzMM7a4uFWS6Ahu7hbO64qF8homwUzxJgJFEuc6cLfAXD3upBXWXLs2RG8mG1rHa9mS\nLWqBLGvs61Y3mpgKpFBdCwPeVBX3K97YSeGKMKTESKsqdP0VsUJqnG7bDKxbnKbpEMbPvEhICh1R\nDYM38tVI3u0DyUm988rdaeX+vHJec9AZ6y4uTijICUkTST2hg7jCXBVIgxdQ3UyQc3RyEY9KKi45\nqiqMybgacAGmjlM+LFSQ/s++YrCj7QuwK/05SL9eQ6QyjsL10RiHfbxSYhOXi2/z/4qGt+zizT18\nFcgNQa1y1Lo1ekY7bW/b8rZDGw//QyC+Owyfz5ZXjgiaKqgbUUdwwpKiyYOFGJK599OMe+t1iVlP\n7pUwIqNUhpohe45Je+EMIIVclHVVhuuRAeGohlQ3JldjgmTMVnisBRFnr2jErhdTtCjDYPz+t7+m\nWOFHP/pLhvNbjo+vKPN7dK3IOLJEIwZRmEy4GV229lTcM5YorGpdckaF43Tk9vYlz7/8GVfXB1Tv\nefP9M9I0oFpIpaA2McT6JmSfm2aKYOjgSfkXz28Yh5F3dzNzXklJuL058PLFDQXj/bJiqhyPE89v\nr8jzmbf6wOnB+wRUE99tDUpeMSsUhPv3J15/D7rO1NW73vv//DoM57yLhTBb9QYbRZW7xwJvV2So\npLnClFmHM/lq4GoQJjM0LySrSCR9c4XTUnlcff3p0EKzLl5Wa+szwEeA7MPj81R2JmM6JKZBOZ1n\n5vWELcLtOHAzOdp6e4KiToOrdY2EkfQkXBI4KpxIrJZ4yIam0QsZYwFLPDSnCe5obW3Vm0T8VxEd\nMKp3UynFv4NNaKt9qJ+7303E4mzLpE9jom2c0mu+SxQumfdBbIbbA2x+pl0owl3J2mPTY0re61Hi\nvsTj53P1Mv91dX2G+3nlvLroVbvN/eCreq4+18Q5p55UPjgNiOvRuBkrdmieyyYvsLWvcsQ1BBSu\nAVXjCdEC4BvKDmPeKmk3G7/7fTPmEGEYMa6vYByd494qHC3YIds5pE/2rpAY4YlqwV5gs79mbsgP\ngzEONVr3xdnsgwuLMb5Iav+Dh6PfBEzVKxcFd7EdaAvDEJs+juisuqErOE02bREnulY+7hXcJONW\nC1cm3K9GFQ8T+r6uiAwIA6MmEv53xPWsrwalWOGxuL8yAUN4g3M2MvAQOs5v/ua3fP/9d/zpL5/x\nyyvlxox3d8bDg/F6Sbw7e2giIZ5YN3UUrl5aM0TRzZiUIVUGEUZNaBoQHSBNSLsCbyFEqwcRGuCA\npCH7HDFxUWMcHCiVYrx+c8+aM8dJuUnw5PoWGYRsmbMYx+uBL798xnqfqPPK6/TIo2xrNdeCrRUp\n3rHoh9cn5tPKkwmukjEFBbjpzNMYMC0ZXVu7xcTjDPXeGA/GcF5hOLOme+pNwo6KDcJka3DltYfx\nLJIzTcu81NK7B22Vre3tES76xPF5CoKsIJJCl7pQayGpcn0QXtw6arxfFmSYfIIOrmUt6jGopUA1\nZUoDp+qc12xwMw5A5TwvMcFb8iM42kqPrxFsDw2ute+SYHgZd0NealupdEPcm+0NtYqwwKIwJuVG\nNUqbN364/44XCNVWWuwjJApRvodFYUfDmCredHpMEosFhrA3c64s88pavMCjlMpSPMHjTX7qllCM\nDSGlxGLC/SK8O8EX18qTSbiZPDEzaWaQgozGmnwCOe852napN9NNSUiDQOKi6cIF2N6cBloUpW+o\n7SFabDSt44Ftn1U1ro7GlVl4MWxx7D4WwQqBnRF3a2y+yzjrjN1nxTcOVdfmVs0haxAXFkqX23X2\nnf8DnL4/DCgcJ3h+6xvfulrQSn2zLdVDErk6GlurL9ykBR0MhupFZ41MXn1eiFUmIE3Gz54OnC1x\nV3Mwn2L+4eGhwyRYXbwIDji7yhDJVvJ5IU3KYCOn7HUGmpSxZGzOzNV4uyrXI/yzOvFf/Inx4qho\ngW9q4a++zfyfX83cLZmbceKgQqqZ4zQh6nHxwSoHywylcBycwogYkgyRQrWVtczM+ZE5P7LWQrGR\nyshZlTkKmlQS2uizVhB1NHz3OPPd+zOn1ZhXz+ucFljOK/Pjws3t9P8y926/kiRHmt/P3CMi89yq\n+sImm6Q0O5zZwWoBPa0gQM/Sg/5evelN0AWCIEG7WGAlLGZ3uMOZYd+ruk6dS2ZGuLvpwczc41Q3\nocdiktXdVZUnM8LD3S6fffYZcm33TDZjvtSJ45xMAaY1yzZrZY0ahHoBWpQ5NW6vJ379ycSvXk0k\nvSBUzxSnF8FRBEhFYZ6uIN9wuiQurVDaGZ1O1HMzmuhxQSajpGpTWqmuMmpBXT83u2xvL9pl5zAz\nTX9GgyW0maC91o2yaW9PrzVxuhhH9bKKcV8nJWfryms0NCfOxcTwuTlSnit1K0wIizj/tgzSfGCk\nKQWXNyhhyVP0KCg1NzpGYQQ6FtocC6vNCpGkgDfwIsh4uMukTJHKCyZIFM5AjJIkmFFPDZcPCGMX\nebsd5ICQzHgb4yEnYcqTzUzEYKKiNlBCJZGScWUFrCvWYaFajcHTWnPcPSLnTE6JwwxXS2YSo1Gd\nayFmoUbLf+/GS3bdGosgo9uvR9S8xCAGrMLOQDJ+wnexEuGHPzcxiEW8VZ7+907Z2zkO+3dgM45j\nZxmGsaen9o/IEgbXOCLy4U3UHfYO8PmT+1qAeYK7a+tI3Yo7b22dCx6sjFaFUlpvWJkPloFoGxme\n9tt1PniaDS9O0Vge12yfcXM88NtffMbvvrhhvVz45od73j5WkjSukreuq/KwNU5bshqQJhbfD5vv\n77/+1RX/9e/u+KtPD9zmzP1D5f7xzPsTPK2J52IXt2bLAp5W0zfPAjcTXEtiwYctq7CpIveP/If/\n8LfM/8v/xLc/fMPb737P93/8W77/7ontqVE34aFtPBcT9xKHjNoEk4iZUFFEK3VrbJfCWkysuSC0\n0jgcbFL78Wpyf+66NWJ7dTRvGbezNqtTVU3WOYyRJQ66ekfsRIr2pD2MF4GLam8W+vSzz7n+5FOe\n3z/0oRyzCIdJOEz4+th5VE0eGFnWL9g+2GqhtkJrdWfAPcsUYds2zufzz+69jxORN2HdqonPV0+B\nEc5bIoYlFJ24bHaDIJ2ql1PmXBuXJjAdSfnEcVJeCdwsJud6zkYwNN5u4zCZAdoOmUsxw2eQjdGd\nWn8gZmEmGSJB4DagmRffxA7ElJ061qEBJbSlzRjZZ9Tk38PgklsjkXOXCeEh6VoqvVsS19KWyCai\ne80w06JGqYyGoW5qwmDhw6RTopTiRlyQ5Bo2JfN+tSKqiqDJ5qRmMmjtOiF7bLsr9cko9kY0Ey8V\n+VnD/fOGfFf09E/80FRayh1NQ/SOvz1F8IUxd7sdnbcqOF/aqaT+jz3cEhOJdgHRC7aNqmUjL4gt\nL95rPztlQQ5iFLhKDwJibGBTo0i3qi5vmmia0GwGbI3UvTdPmVbMuWXOMvFYlHO1MWiWkaQOTRyX\nmc8/ecW/+Jv/nHJ55mrJ5K/vWdeNWWBJmUtrTmlbmLA5sLZ/Essi/OJO+K9+d8d/89d3/OZ2Zr3A\n91vj3VPlXILHHcwo0Em4aCO3xtIaNzm7BG3mYVVO6s1vbx+R//ff892Pz3z1/Xfc//gdp3dvKPdn\njqtyXeD7Vrg063wWtYao2owafEA4zJm7q4kpQ5KV8/sHFyizPotNjQI5NXWhNtg2GyhddTShJYSc\nzcBXJxmYNo5lmvPUmFMjayXpNLLwvssi83NjjnJ9fc317S0P7x9601ImM6fEJCBtN+CZZPCkGLc/\no2ytsZaCEgOZP9xgdi/Pz39OhjyJa4dE8c+ilefSWMlIzkw3C3U7UzYbdWQBs9CKkg4CeWY6Hvls\nnrlrjc3TsfNayVk4F6WUjawbn14b1n1TEtdLMpEqFbZSWLdqhdE2BLLCEIAizQyjqrBVpZaVsgnT\nrRWUsrgSm0Y3G0OBUAVRi4hNOWLQwWxcljkD9S7TFAL9sVB7wxcxrlokdynaGRAlZmzup+YEZLEf\nJoynaylxX1dO50JOmSUbNv7ZEb68TfzyLiOugSJa8ByGQMG7bjeBgxussLfQ+yyF3f3YIkWoGffm\nkTHDEJvTyF3HY5BOG0ODfHzsh6/4bitph2DrB23xANHNShj/nbH24ueLb/ngjL3obg3cPsNy9Giw\nWeBSqpBqss5bvx7FmlXsrppHcnQj0RA2STzUzPenzJtN+PZ94e0ZNE0mEqbW6CKAJmGTxvXnn/Dq\n6nOONwfW9h/5/od3rGtjysIkiWNK3MlszXfSTJJWlE9eLfyrf37Hv/rLW3776oCuiXXdeN4a7+vE\nRY21YW33xvxSbRyujmhNnB/PPF6AnDiK8O1l46lZy/+pPvN4/gN//0/f8lwKp9OZej7zWVW+IHGX\nEt+VxJOfQ1pDvBciKMB3x5m/+OUntHzFd/dn7h/PBsuKMM+GxycqWiopJ6gb63oi+ci4pnYeljlx\nfSW0WqxY2aD4dx1y5vV8xU2yGoIxuL0DWCOxk37WYkO8f3zilGcuWmyMXUlsaWJbGxljLwkLsiRm\nsTrCMcGNwEEba7OgL4aVW9Aw9p4Fnur28Kevj6NHvsy2KK2xbZt32M0s8w2/+vKXXF0f+PbN1zyV\nlbVBj9rA6YQT14fMzZXhRq0lam1QoOTMp8sVq2NvSuVudonMNvHJTTZKXINaTJBmqzacQZulVsWl\nXEtVpwk6dpmEWqIDzMCE5gyUHFh8pOjs4Npw4F68iseTsOheJRltzg2YOCKQY1ZhCgcwDGhKMqRx\ndfehu++zK1EGoySuz7D42pTqkUgh+fAA412H8UxuZCV2l6fhxigcwkTD5kk30AGOd2eUrNAdnX0R\n0dCXLAyqdrggdEKGzKrfXwDvfk+Rj+wjc+vWpHuV3j+5s9YvhmB4x2x80Qs77p9q6IRh9nGtAVk1\n2qCm7jIOxT27jA8MB2jDfgnP3yGVUCoszWoZ3zwoX18a78/Kc7Fo03p2hwN8fL7wj3/8nv/5f/+3\n3B4m6nbh/cOJpraXppw4HGxQsiSDAZck5JL48vaKv/jFNX/15ZFfHCyNrxelbI2syt2ceT0nXmU4\neXYDdmZodhbqdOBJQZsJT6X5wJFGo1JL5lIUnSt5yizLFaoTn6cLX+bGTVXm+8l1xKsXeA3Dr6Vy\n++rIb377Cf/8L7/k2x9Xvnt89kw2+ehA5Xo58PnNDbd3R+7PZ/RceHx7Qlrl9FyHplKSHijg/910\nsZwvedeoF+tVwuX6PkKGMWdkA/O8cHV7x3w1cbUJbVt5f1pBCq0JV8vMWqDpxqltPFyO5OMrfvOb\nW3785g3npxMX7wJXd+gvJTDsv2t9sSH766MY8l99ERTlEQAAIABJREFU8pqUJ1Th8enE0+lsuJhk\nrg5Hbq8OvMG63ySKhSmiNzguE7dXEzdX4iJI2KzBi2FfKXtzEAZpTLo6T9ca80NuVsioj0kLiVQL\nBoStNNZqOOelFrbaaJJYNwyaydrToB5dgesZ+X/7msfYudYPqUeYYkpveHMFLqcaeHQOTF9CRD8a\nFNQP5hiEYCiCDAOiPSZ+GXH6f8fnkSBlYZoyy2KjpnIWVIu3+MeMxojwfZMBA7cYhlXCcDovOjjl\nwUPPXmyWwEfcYvZo3I1zCogoeTwtbbwjntWLe91DSjIgFPAuyvj9LmPojtOvJb0M88V/qANtsmOu\nKAw8Py5bDItl+NZ+7MKIRwYSV6JeI+mg+HCMPqWBdRMeL/D22YTWSgWjxtXhuMQa3r5/85537x44\nzomrJXG1zMxJmcXYRSkJx8Wmti8TzCgThX82C/9iTvyyNWQtnFFqaZRqtacvbxN/89nEgZnH1XjP\nW0usFWpKrArPi+mbF8lseeGLT17T2sbD8z2PF4PzjO2SyEnIM7w+Ng6yUtdKkQmTo1BoFckWyBRp\nSE5ITpxK5f75mfdPJzO2YYiruqxvYkFgrZxPK9vJ8JmH5wtrcYilCduWev3LpIMzilBEKbV49L7f\nX/4s9/0D7kbNEdjv87KQ0wItcykbp7UwTzNXhyu2WtjKRtka376feHeeSctMniar2dXqUf9LYx3w\n6J+iHsJHMuT/4i/+GcerK9I08+7pxN/9wx/56ptvOZ2f+Ic//C2HGVqtpK0yufJgbdBSYp4nrpeJ\n2+PC9SGz5ImmiVOBlk6obiSpTI55Z01Iw/WqFWhMmCjX9ZxZJtNmMdW6MCDViiDVMPet4e39wloS\nz6vy49OZx7VyqsJZZ8OtI/tWL7o6j9vJBz06t84+7RsBbH9Ew0nC2SG7IqLQpYXMmIt0hb8Vx/l/\n5kEHRKSq3WjtDZpIdP1ZR+dhEQ4LbGsFqWbwew3BIkjiGj7UI1G8AUh2zmVEn3s1vI7p9wKULYIN\nDzaoILlXsFqlG1xlp3uku2vxhVV1gxoNNn5txD3rDluR3ZrFurixlYj0B0wTgl3xhyGJa9eaMXqM\n9nrOSwx/0DMjINlfGS9+79PigYNaA88xC+IqbJGZ1ToitpwzKpmSch/kUTelUDlk5Zgak1YrsJM4\nHG9oOnG6VJ7frdy8e+Dm7Ynl7ZGb371GfnnF6k1r14vyN79s/PruyNMl87xd83jOvD/D/XPlzdOF\nt88XfuTCc0usAnnJ/Jd/+Tu28yP//j+957k1a3I5b3ApaKlciZKuZ74typtT4V01ynGWTG0lFh4h\n8cPbM+/efc3/9W/+YDN8m8tQY233m8D3Pz5xOV+4v594XqvRKusz21ZZW7FRkc0y1MuadmcyOrNd\n12mCrUyom8fO1f/gXFmnaaVl4e3bt/y4NdIizLeFmyTMsyBZIE/Icku5vKNsha3A77965N/98Z6/\n+2HjzVq5FKOCZgm4bZxlEWFZjK3SopD2weujGPL/9r//7/j97/8jf/f731v3JRtpsgM5L8JhSQgz\ncswmiFRNn7dUgJnWZpBrluNn/OLTT7m+fYUsNzw8vOHp8UcuT+99Mk+hlNXSkdpILhWaRJEpsRwz\n1Mp62ZiYyD437/p6Yc529GrFWQZKqSHu3ni8CA/nwruz8vYkvDvbNI8W0Tgvy3Y2TCLs24gc42VU\nTNwS2msfSasGT33AD5bppWGAfvIa793/WzzaiCtMWJv4zWHis7uFL24Tb95trKsQ8Ij9vPFbJTWP\nIpvdWDgwv54+tMMj9qTdbPpVjfh7xDr2OeGQxGmYAogvnOzXbOdApEcxAU91G9CdS+/N7T8ejiwo\nXsrOpvvve1XAM6TBfPqQVy72ENHWuqGNprzkdxj/jmzA7i+iOjqTQbN1G0/JRtRdZeGYQwBsd9+7\np26FbEPfbfiKN7ppfZEaqO+DqoVtLWxPhbIq96Xx/alw97xyns4sJNJtNt2gYjWs7bxSLhutCXcH\n4fYgfHEDv319xfN64HmtPNXG+yI8lkR++IaynvnsSnlcN1ppbGfIVF7dHfjk5kBt8HZt/PF04blu\nVK3kFBltyDEray1Oty0dekOqB022p89WJaUAa1GKU/uq+mDy6JTWkNq1bM+eh4GJAS82D44642k8\nOT8L40xWbZwfn7lcFM3wSU28utmYAdWJx7Py/vwjqWzMDWZJXE/wxZ3wvAqXd80kr5NJLhiPfDBX\nAO9t+dOvj2LI/+pf/jXfvvmK0/mBtTTmuXF7O7GuFxuW7NHOlBNZBS2JtFaLrNPEeVOe1sRF77j+\n5Ld8+eWvuX79C969+5b7t9/y/u33pGmilgvn53sent7x+PDI4+lsMqaTGqQwSWcQkK3AJxlTBJyS\n07XoqVWrMdi48VnL3D+tHB9ss9yfCqdLM26qp9CKUacIyKZHazaVPDZhtGObIROiAT/KWPXFAQYQ\n586rh/G6M2w/ff3/dYUJ6lNXjCEw54mmmUutNIamioh1f2ZJtnEcJ8zQKZQiRJe5IxVhsC3l6NHz\nLiYNSMhodo7Hg1HuGIdIPvjJfaRtfxdTTsOQx33bH7yQ3H3hoF6+74Xl2927yMsOz5+s509S4l32\nED8v5vtifmtcu/2dZS2abBq7CZepyTrnwRiSF4Ylon8nEEhDdCKTjbbn8gPZm7mmTK+ttFLQrXCV\nM8fDNSlPXEpB32/Mb09c50PvZhbN1FVZz5VzS1xfNw6zcMzKzZzYjpm1TKy18lSU95twfv+GpI3f\nvDIxmx+elftLpUhlyopMmR8fK28uwo8lsbWCajGIJInLTu+acNR0dLrjVZ/epaCYOqRWNTZcsfrB\nnCPDEmh2780NeXf0tL6p1DPrxksK6NhUOpQp4ymocjqdeP9Y2FrlF2nmdWscp8ZFjSn34+MDi2Su\nUuY6W6Z+e4DPbye+eyw8ro3i0Fdr+pPIu9a6gwF/+voohvzHp/cUNg4HoW4XPrtbOF695pvvfuBy\n3jg/b0hKzNm2+VptOjcIKSv35zPfvDvzq3fK76bXHD/5klef/4p0dWC+uuH67jM+/cWv0Lbx8OZr\n/v6Pf8/3D3/PH3544BevD3yaKldsmPRtZro6cDxc24g5KTytlaeLNQFMM0xZSamZ8E3OJM0cmFku\njTlZwWhdV949rjxs2p0RAofFdB9wQx4Rom0AGbALTplSHF5InWMRVfKGRRtgeiFrEzTZ8FbUWSMv\nXrZTo0D34d/YSzut8XxZuX9QJp15+yw8rJPpkgTOnZQFa/w4iAyoxH9llKwMbog3WlVpNlQgZVQq\ndGNuV9LtvfjfSAe5UCngmpeCdGExHJgK9o/BKInUkn9+7aew+4NdimyHYsfoEWDnOFT3ncC2jpE9\nvMgoxgf2d/T76oZbiU5VK6JFsa2PVzDjLkp2znOKwrBUDlPlMFmRMrVkVDYtO18zspSoZyQPhOYM\nyywcsjFUDnPm+jAxp9nw6mnii9uJ3/3Fb/nykzvyN9/yVO85n57QZ+Xq5sD1YeE6HWgXC1bKWjk9\nr6xWnqI2G9xxWSuTNK6midfHhfeqaEpM8zW/eT3x7f3KP75Z+epx492Phe9/fCY1o9G2ZBIctVWa\nGPbfau2PMbuccfDsbX/58AXPnhom/laqIq2xiEW/FStOi8Ls52XV4lrju5mq4fAZoN0eEx857A4q\n9T9fLxceTyuP58q3ItwVuHlte/bhpHzzZuVwPHKYCosWzio81wmR2ZufEpNYR3ir9SfBV611N/Dl\np6+Pw1pJJ24yvL6645evv6CmxLvnZx6fzrxbny19EuWyGb3EJm8bj/R4nKhFeTyd+E9//Iov//Ca\nuj0yT4ltfeZyeuZyOnH1x6+Yp2zFlifl3ZPy1Ztn1rWy3sJ2rZzOJoXTSgM2U06jIVNm26wDLGEH\nwoSTon1fmSajMZ5W4d0JnmpC8sTByR2RtokGriV92ktE57ERWkTr6loOGLNjS51n0dX+JAqn2CT0\n1rnerousg4s9GmYiTXQj4kYPMeF8USv8nNfG87lxyPD1feHN08apvIwm50WYJ+syjZkP4v89efv+\nlMWFwjwKnOD2mPjhR+X11DjmRupHpRHFzqpWkwgIx5ppFBtG4I6t0U+QGfEYjwc5KZNYFLZMcJht\n6vhhFpZsAscWoVpXqpNwEHWNcHX3KdLVFJv/Iu5PbZ1zj6alWwHV1KPFAJjcLdgaepE31jM7ENJh\nF0lUj9g1JcNKszBNwpKbGXnTTezR+UsHHdCeZ3Oa2Jpai2fDGBrYHjvWwnVKvPr0wK9/fceXf/0Z\nn9zdcp+f0cdKq89c6srt1Ssu85Hff/PIm/szl0vhkD2LLWM0XHPt+6qZUoC6+V8ItMpdVvLdzHGe\nubov/OP9ha+fNkqaLAbRqHPYfVjE49RkrK06iuVdw1/ZMTuqzerNyu1cuRFl1gQqnKeZZ2nUuvo6\n2x5tau0+U87YyY/qhLLP0kY9O561B2RYBg3YEJdsbdcGgVqhOUmmlsIiG7UoT0V40sRzSZyrcKpK\nbZapNlW0VO98H3vHWQoeG/05GfJ54tXtJ/zyl/8Zr16/4u37d7x7fnaP401Bzo1uXmSb5sQ0wZLh\n0pS1XPjh/ke+/vZrUn1g0gtNre11Wyv88IY8TUzzxMMmPJ42Tqvy/tki8csGyxOgVjQqpRoWjiB5\nYi2NbVOyGgc9Z9PtaNWiw2kCSRNFM8+rDb5I02SRqhtydJcaIr2L0+A56fu2azaoumHuz62DcXVn\n+OnwjbLkTJoTNVvpJwpgg6FihyI4yiKWZocFnhyXFTHK5aUYbvf+XHn7VHm6REelbWSjD7oSYgSC\nEt2fOlr4Q5clwzQJV0vin46NY1JmGdzwiH8aFtmVFvxrbwhRnOoVjBgZayfWxm9du9q/c8nCYU4c\nl8pxFo5L5mpOpsedTE99mswwB5yRxfS4k0+PmsSV7cOJZVhmuDrA7VVimcEoeGacafSBJwZFQJjU\nEPmyZiBrgqu96xOnuJpQXKmwVmVryZhXKSE5kydhygXZfG/tLcyLl+z+beJr/fe6b+ZqLIsgiyBH\n2DjxsDbe1AuNZo0xYjNl3z01/s9/eOD904VjVv7yk9nICN54U5uPUxRrGKrNHJ7rDKKtcSXJuq8n\n4fXNgfu1cX8uPEvybJPuOBVx2YrRWdvvyvWK9hFx/zuBmyz8Zha+PGSkCj9cbCjKRZx26Vlcdgpm\nEhs7meeZtVTWzUS0XsBr7L5gB+0E9KIKN9e3tOsr8qVymE5kVQ5y5Oq4kCk83DSeNyvAGsJgvQWq\nBn2lBFoik2w9AAsIx/O/n16Tvz6KIV+On/OLXyVa/ozj7cQ//t//B3/44x9YzyfQQhInDroxSzjO\nJwYhWNIt1Lby/vGeV1eNT26cE+op2vPTme35iabK4waXy4XrmyNK4/1Fub80VFdaswHJFvvHRlKX\nrlSWVK3TSyfmeSbnGaFxaZVtU9bSuFzMAeSc3CiYImESLMr3KLlpcnqiRcDdzEZbdmzJzhfHKICN\nLpQfwGtEeTklWrOUtBLC/PHoXXqzjWaT5PgrYsyOOFwpK+RMkWTNCf73kVloQEKufIcmxgR5ddaO\ndpjHIAm7jJSMJrpkG3NtfsFgjH1TReDoEBs3ONkWg4lHWPsiseHgDSTRNNN0MaMslSyrHVaXEZ6l\nMUvjkMWFnMyYT45FmyFv5Ix35AmmAlJYZuH6mLi7TnxylzkcKnnaXNTJDLc6S9Jmdpq655yExZ0C\nfpe1KVWbC2XpiGiboNUKfKUlLsUofltaTHRpgbSa9dceBeyjcluXcKoxRaGpU2bVqgiTCjPCRZX7\ny5k/fv3Ew5vvyQrv3lfujpnPX8/c3Cy8uV/522+f+N/+nx85lcKvXk28XuBKTWK2Of23iXUum+6N\nZXm9vqANivLmeeOrx4Ieb5gdfmkXN64pcUlWD1LU6IZJfMyb0W2TJNMmae7EPDuKvZwlcZcm/mqZ\n+S9e33BZC//22x95t1qANCXXYNHGJIpOZsSvrhY+/fRz3j888/2btx8Yzd1eI2ox0XQXBl344osv\n+OWrL3l/Lszf/xPT+sC0Lrz65DNurqHUiXcPTzw8X3g6V8gGy24IDwd4KMr92rwpcBRl97Courzz\nz70+iiH/6m9/4Ntvf+C7H76nTU+8/eqP6OXCJMLN1cxhyYSat8YGzBblSTbpmhh79M0Pb3h8fMch\ntWE5WjItX9/IpwrP541Sq+0BoKrBEtp8/iBhTsK8OiQhHmnVxtpWOxzNJERjmLLh1qk/ewsY1VJj\nP2MvogcLtX2T04suEDCG0Kl0loPbdYuTEXfYnEhFs+mVN4SW91SpTlbsbb9JbOyWeIE0I8yikJRN\nhKfaaOvKNGfubhPHo1CorjwYK+WRCbvUVq1BRT2riLW0uajmgJp4qhz+6IN9Edo34QSsCOpxbf9u\n/+aAKgJPb2BcobMpPCZhU++pr5XkbJIkkErAIntKp9M3UxraMhpGcXL5XmGZ4GYxuh/ODhF/VkE4\nigPfdWl8H0YGor5JRPOL3oIOsUk4YJuQpLrxtMHJcCcf6pDwqSS7IphFPgFzLZM1zMwpcRBhTiaZ\nezVn45nPcJiVZTZp4gX49CYzpwZto7QD37555pvvVpZl5qkJD5vw3aPyeW4cklcjaqWqDUCIYMT2\ndSInw/1PeeK7h8of3lXafEJyRqaZtKrVkVCe6tk4/4IrfE6kKZOa143ceJvyoO8PgiXme7Ip5/PG\nY3qiVO1PF886rw6zZ9P2cXnKHA4zV8uE3lyzrRu0B3LKzNPsBIc+fNf/bzlk82sREb766huevrqH\ntPDl5YGtnvin+0fePhTkeuZpu7AWIM1cXx24cg68SuLcTrxbL+SzQSpWaBWHbxxiIm7/zygi/zf/\n+l9zf//Au4d3bDzww7s3lK2ijlVOEz3yDA0McbZA06gwG4fz3cMTDw/NkcOgrXnkJookZfMUNgxa\nsETUuWpxiHvji44/64Fxc4/YpQXszxW6+l43xgGg6eBAh6MwMzgs90tN7TiUw9CFQdtjn3tcVLFN\n3mmJez2Q8GsMjWu8SBnvzxKS+ZWKGlOlWYHwarF25uKGUhULOXE/E/fpF6IacFhgtOOABcauTttJ\ncZ8YRGS+wDoF9/UcdePfusVzNsyOJtNUTBipmYOasmUBpWZUjaJWO8MnDkXQPXXHCHL+eTz3eJ7J\nrk187Y7ZsPSmO6frWQv+2S+ebTQ/SXSE7moYcR3N5mU2zEjbABWcW2wZjjE2YgLrz0RmHiWYE6FL\nOkfTmrE1TGoiFRsMkVU5ZiU1tRF6IhSUU6noU+H7dyun58qXdxMkg4B+fFZurk0rxOaLDidv2yz2\nt3VEa1LOpfG0CWudODdlPmB1rbbZtYkFJYfZZ93WRpAps2RERu1nbLpgdo3734B3rfH1eaU1eFTL\nPKpa45vBb2YbNBnNUwROpxNbUQuINKStpXf1vTCf4VN0xOzv37/n7fmBaTnyWaqsWvh+XSHfw81C\nyzaMHXxYS2ocko1onFyfJrZ1Dwj1xUEw29a7tF++Pooh/x//1/8B9S7LmmykkjbrJmzafDM3N6iW\nwifX7Q2qFShU01iJdtuYlwjiUpg2lT6cKSkKjz5812lYZsTFtVPEhg24oSokejkbUI9mo+ig8b+O\ni0asihlyUodrIh3ujUASjSv+3i5vy0s+NNKNjYK1RRM3ZdeSkw0PjnTPb2xct8Zhw7sIpTuv/llq\nEr4N7RCBwSbZsgEVSI2k1Yp+qLVRh8piLwIlE953pynJI1MxjFgazK4AF3Q+cziu3R336RmPzTdV\nxB2oPWUvtOGNGQqteaHS8f9NTWXQOMGg4vNc+zPaeSE3tIRzCsco/i0Sh8iYDgEb9DyhP+NwtpFy\nJJJaodYckhWzVBqawvjH3rYCnDpv3gIH+/yUkhXvQ/fDn+lPXm4NVG3c4Nn5xzmJC7AJSxKWpByB\n60l4PAmvD4mbSZhS4zDDVKCenvnxsbGI8C8/nbg9CG9OjadzpV5no+iqonmyyFwUfCC2ZWZjPz5e\nGpJmPr2beb8WigiXBg/r2XBpqWhKvF4mbqbE6VR4XxpnNVXCJPYsXjRcMgypBUGNc0p8lTPPTh5/\n15THamyvGWFbDZtGGzLBrJbxvHlzT9NMnmYOruvQWldZ8e2gw/GiI+DyZ1Vbo63KehDOGdbDzOO6\ncmkFcqJqRluCZkPebxfh1dXC89bYrJ7rjDW/I427c1kLSYj8GU0IqhKFPRumWqu19oqOhUNyN15m\nt3eCUGlEynbLQ4+EMIAe8SUn/ochUZdxTSJG87G/tmGyknpnYYRX9vdhgH3DKGS14mVElmGkw/im\nfm3xKTJMlBebIv3u9wkENW+YBMNgA64YUa7fY6LHJMJwMBER2V/s47fhgOzfrTsPCKdoOXMNASNv\nj5f4rMHTsnsUNRPs7emJSsrW/t29qLgt9AB21ngO9FFxYIc1RuEVhUmVUPdLXiQ1Mofpxxe1Qls4\n0oRHsFJtrRo9QzBnYYqaDQfvNLIB71xlTw2MKMhlqfx+5+SslIh08eYVDRaKa4+oYjTBwW5RaSMr\nFJOR6GJtvv7DUMsY9wcUDaaSUTj3WhzR2KRqzrWJsFbl8VKpze4rtH0yRqnNkpgyLM+Nm1m4nhNL\nNjLCIRt/Y7sk7paJ19cTF8/G3nkX4lZMEx0NjNyj8Dg3WO2ktcRpU6bU+OWVjUK735R3TvGdYxpQ\nEr44Lnx6zDylTDvZCDV1RoyNT0sGk3mGFSqiS4Iv7o7cTBP1srHWzNaUszZksuBsK9UYSNWZQhUu\nW0EorJtBJbk2lisdTrf13JmgqYZ9CdshXhFXrKHofTORrrUKK4mECZWVZmMpW4NZMmVTnsrGD08b\nT6tJgOBZ7UsfHYb9TzhvPtaEoDRhT19Njzd5pVrEsWuIoheevraY7OCRjkGiYdI0uix6pNTb0dFu\nMPaKYmFwX/4+GnGCFPYykcP/JAldJEuc+bCzV924WwznY+NkGM/gE4f5ILIMN3Iv6IP+M15TcoNm\nxsWyCiJ8JXDAfiGMzCLWpN+E/1lAVdH+HtK6ktzQqJslqSQSHetV/wTfzWbKpV9jH7Tsa9zXwD8v\nx5q7IbczYvdqwZSQI/IJI97X1ahoYQB7Buq+y8bTecFYx96KIlXdBwzdGTkXXuJZ+J1o36p2H2rT\nkaQ/P7rDqL49vZpr2WXT3fras+7qmIizVhjFdsWL1QZnlerQl0cH22yzTIOW25QgOVhmNgm3S2JK\nFiWuqbF6ZBdBZcNkJ1aAJqSt8bTCkhuzd5EespKpXM8zd5OxgF61xro21lyh+VxQLzwq0FzXOfRm\n8Ge/NZtodH1ovF4UrcLjWrmsxTFma7475MTdnPj8mLiWmftWuS/YzvLgS9JErsUzPghtoizKMpnk\nxnqpNJloqSFauJ5d3rqGyqmtf45CeXfm9l2tJVetDPsQRjuMKUg0n/m+jO0nktBZqMlE91qyfbcV\nVziMZ5ab55WJ583mlAaVsdukD+yOZe0vG4Xi9VEM+THPaGrOPR1mUrIpEZZmuC4ERm74UsuWstp5\naDboeAcZdHoVdMw1/kST0JpZjigqpd3fN209S4io3cDRHTfYFaqGtOrOGEXUGQ7EYYPkmGYotIkY\nu6UbGtlBPTJ4zNYFad8nWBeqvd8NtkfFHXtW57T6IImIntVnkMaaEN/jji1FxOEOIGkja0EkUZOA\nGsc1ozvtF7d6RArt8dfwjn5d/oVRwIx0GwyKceNkcyqjS8qLhNCZQ4nR+BPRnsEs2lvng2m0ifQo\nahLThgnBrogSu8yvr/uck43RS2LYtAMYZlgtogyDLmo9BMZDD+NMVyM0PZadmBlGc5NknOWBKY+9\naEiXOU1T3RQvxgm1JDeU4nvUMFzjdfmsz83YDOITf47ZjH9pjdsMFzUtEvVNEDh2dWNlbCCDvS4N\ntlI5sTFJ4fogHA8zKTeuJuVurpznFTRxqUJwve2+8IK8x+V+P6cGD7VxIHM9C5eLQIPnYswd20ri\nPHC4mWwm7Kst8WZVNgl4JZGz6axsTWnVKaduSLdS2Tz3SNOEauMoG9fHic20NnhqlulV13AKOFCS\nQ2caBtxsg/SAZX+vAmTfk82553YPsyRurjN3B2xQRIFtg/NlM2qtnzVNjWmeuT4e0PcXio8yjKy2\nw7O+Z+kBwc/b1I8TkbOCG5IwhFHhL0nY/LqjiJQkDKzQfMArQGuTVY7Dm/biUqScPSaM/9vDC8Pr\ngbEd7GZqi/6H5iBMfbkXNnQYgSQpbF9YFsISDUNKN97htQ0vNu5yV4ZNbtjjO1rDuKwjIpRsHaMp\nmDBq95ClJ/52kEN5S3UkNT7j0y7JudIpeOCxZsFgcb0HTZyL8rwpazODOWczekmikYeBSe9ewmgJ\nN/bHqB+oG3CLMBqpqRni3hBi5jUTHsovzvHWWNtec8CuOxzpJEBWMiHwFa5wHMI+fsyhmpwCUjOj\n3SK9lcC7h9OOtaJhE+HcYOH3o1VM4/pFgGJr3rMldVNXx89Ksnk0U7LINidb2zaPKK1pRVK2/RJO\np4nXBpIV8yZlkrVnQk2SNzp5QOBYa9s5kZzyC8djyZ05t1dX8Ol15ZO7haoLt+fE7aewroVaY5Sg\n9J/bnP5aa+2O6kaFNFc+vUrc3DRaUj7XlQdWiju2pGqsIGakLVwvE58tjYdZufjnWLbdmKfMQUz3\nqNYCak1el9PKxmp9JC6RJ6lydTX5vNsGrdq69wzSDmL2AMoSU+eRSgOpHcax0xNOXomuUiXRqrKt\npu/0w6PytEZ+b6J9IjY9avF+hS8+e8Wr22umeeHNIzxvj2xPZw90BM2zM1QCrox+i72BH6+PYsin\nNJoU9rKt1kwiTJ5uqhtVo3d5ChtFRhXrftPhu0Kwioj0w5uCnzfTSqkSgwZ2hca+PvFdlro19cKV\nvc0absJn/ox3HPzSneH3qCxeIs2i8uQDjfM5qed4AAAgAElEQVQw9FqbccFrRE5mwySpRYE5+Ne4\nQ+nHyAyshAPQUUhEvVBCj5YDk+xa3GrFoMWdqWriaW28vzTOxQpJUzamQRa67GrKqV9/bPWcxBqB\nmhVhJ6RnMJ3R4ZHYJLEmZoyD7UL/vTmVeI5GCdTuRIbHdLZGcuPskXzrbi6+h86AcgYrhjcHVON+\nMhw8AyoLjZNRnJYhP6u7bCWkkePSvBhLZG19n41ajKh45iEkbCC0iHiWEwX14cynZC338fytpZ8u\nltX3iIwpU3bGLGAKPaCUHC/3upP2uog1ch2mYoO5MyTJHJLyOlW2zRxRvxd/BmtRavHGNn9+FeV2\nTRwXuDpsyFT5da7M1/68W+t0wrvcuFsKx0X4UpQ8eQNNiy5fz7xaYl1t/mnXmlFzIusivTFORLia\nbe3LIdFSY44Zt3FUfV0QLwhnHIqTHthJOGyHxyL46yxh35vaGudVRsen/y+pdEVTgHVTHk+Fempc\nSiNkOmy/wYRH556N2QCesSc/fH0kQ+432WEIj8pxvEvHsNqOnYpHdeJzJ8O47iqi0SdAHlhWP/CY\nUU9Ne5tukuTFLQbjJKhwjqUWf3hGH/dUNh5kfK5vGEvLRqppfVo7g9wNu0M7Hlm14CMzpr4XN+S1\nRoQEuZmMaqTZGmtI5+rQO0kjy4m17dG5HT5T1wydY0CtYHRJeJQvPBXh4dI4F5sEk8SaZayr0z7Q\nDIg1/GTxFvgpMak32lRseIE3ipRmz5YGU8qmla02Hd1snnUjKoKk3FkbkVdmaUxR7AI6BKXBBTfT\nHeqTVoAKvLx/TC8yS8gBBLSTBuAWTrjDXZ4KhSPRLvJv32RZgK2nFWzxgGEYINV4ZmOvAabwKK4q\nSTNWC8bmIkUB1OAEqgUjOVlymrMYF6cmaBmRCdSKdVWhZiiTf69Cana9Ux6DvGNdumaPgGYoYrN1\nL+VCKG1Kalxf5U5djbNgIwX9DBDBiYCKYdaqNL0wTYXDdeJXMvV1bY4fSy0k3ZhzZblJfP6pZTiX\nYjizqWNa9+j5Yo1gSKJ6NlCaUHRiu2AyG7VxXGxfLWnmusK5NNatsLoIHv4sbDsJ11M28kOD1NxG\nqUGzCYdb29a7dxuWTVzNCRXry5DGjvXisgrqYyIrfP3midoeff5w5lwUlezwZUVaNQZXw0KelI1m\n++fEI2/e52Z0u55nd/eofeMPTBzf1LVCQC7jZZ8xiFn2MkP+QdjsRlE8shM8g/KDEulMUOOyI+km\nPg9tGla/Izn49+zxQnUmQwt2BB2uiQJY72osxTs+w+A3N0oCWXujibbqHZ7+mftoVMQ5sYHPW+Te\nMX+/r+aGPIkiVV0+19b+nIUoUk7zYiPkpJIonvLZtbQ0uUqceJWudWW9GdeUwWY6JlVqNr0aETit\nFmFVld6tO4m1hAuWiWwtnFF1/DTEpUwH5mqyLs2mjaKF5FCbqJJTY0mQUmZrQlXrCp3yLmhAmTBl\nzeDnhqiVDfNIzmqyha+SBxyCQTnBM7Zs0hxICwgrnKuqa7aY08v+GTjE0zTvcgm7fsFlcCPWVasP\nKSDhZNyn1Oa6Hv5sJVVECvMexvF1t6YaRgOaPU1/TpWWvOzhMIJml8lINp92SrNritkZNfkgi1Zb\n80zCpQSkhXrfCIpUFcnJMg0SqQpzsxK5JTHCVoXAG5s2lmyaKGVTlqaUbM45YzZhnZMbyWQ6NVM0\nAW7Ug1ALlKKIrJQG69woaiqJRSc2Z5A0rze1ZjIVCEylcH6055FzouXE47Z1RdRarViZk1Lrxqtj\n5mpZvN5iz3irMX/TBmAfl8ycBPFgcK1wKrA55l+8xqWazem17mUc8vkzK3ZeotLdGyfADLXDKAQu\nGam1dGOqPQXZGWjf2J262//aDtULH+YQAJ4BvKT+xVv80KdkzImItqX1N3+oRx284dgUcX9VI1WP\ngmzyaF07bOLmvFf7tQkt0rueWainvX5A/L3R2CQirhAXD93XTJoVef3Cu+GIw+xfruocbz9LikV8\ny4xHI2lkIcEj1JdLHb/CmCme0VTYmgGFpw0uNYrNeL1APDp1fNWj8IDddoQ8rn2CUc7WFVk8P3Zz\nbFG/AComzVCE0jz72NVQsvoYOLwwGzBXwn8lV8eDGN9m+qZh7O36sjicI2J8cbGGk6hTxFzanGJC\nEt1QR8Qal9XTcHGjHeuoEVz4sBHZQWqx7rHtPaDJjOcQzibFu/y+1ZuQpPmtxT1mHZlsUmiCd9yP\n/U8EBCPLyNE843ra+P5tNfD9hiTPKpq/T6vXGBPbFhxtg8Oi8FyKG7VwQq1BM+XNpgM4S34ukGpZ\nzCyWpKhQmjIlOz8hNtcIQ25LWKt2Vov2wzmou2Vr3oMw1jqLtdJdTYlblDmbhIgV3j0jc1z8ODev\n/VjmsCQ4Zgvu+mL6A1WU1gZpQpzbHk/xw9dHMeTnEtbDft8LWLozqDqKoapO8RGQLI6Z7l47o6Rx\nMOKXY1rxxuRSpfZx8UjCGNmCRXHSNo1djIr29uH4PtkZeeO97SAXP8hBZAx8xFJJlwXoUbz0Zodu\n9EMvJZzUfrAyYEWX4CAPZxfSqLVUq8RLovVnH44zoj26cRtFXIdKvONN88Q5iR2m7qhisUcEO03S\nMdickjdiWeq61oCalLVmEwtqzTIPscas5txbc4Y400PIHeG2PVNdTzpNlitpsijKCr/iQlj2nadN\nebgIlxq6MHbNTftmQYNp4thySMmakmKyTM/nqdoKGpzQRcJ4SY/MDpdF+1AWKw4vWZgzHKZEJkS6\nCBSjWwbxfoYceyUnoCLqpTmJ7xr7NEWKiUffVoUlupztHNl6m60242QYv9ByGrh/6MYm73pUQWrw\neLw+lGI/QjTsDcpm9SCmmaa4O1sjTimSGqoJaQZRNOeHqyjbKtFGQUvWldu0dviDkKgohs93yJK2\nYzTtmHCKPYXomvTMO9PIySicYR6SJFpyhyE+u9czw9TpprUXd41CbM9SgKyVSSu5VWPcaWLC0hyj\nhQZHHNBMw6DIJVdvXIRpSiPD8zNpLKjUVUZfRJy710cx5DkaWLqBGakksaEsFPKIKDmmbSYw7dM1\ncC+WelvM+KeCY422+G4V+2J0tn+PhAau7M7GuemG8+FY+dgsRn+LWGe836LpF3H+KFS4E+lpp39a\nbyBV6Q1Odn9WWMMdlZ3+bNGHV7njl9HKbJqROPwR+KwtVeq0vYim4gk037gqrkvimydJomSlKGyK\nHUSE+NCAKxS8EGWRuKpSS/RRGsbYnE6q1I5LqwTlK56ZrVuk+gH3xICLqsplqzYkweTw+hpXjDN8\nKSZoVpoNMO6BTCxZ3LdT74xZoL4SkKUyxeFJkQOmbsCjJtI7ONU2Q2jYGAvJ+OxTwBOOZ0vcF9Lp\nczG4O4pbOYXBbkxZmSUxi3DMjTmrC5/Ze2bXM4nn33F4CQKBTzciAj/pzzbkj6O+EOfS/t7rNE36\nCD8VEI1h2OJZ4agBxCkEq0c1tfXXEuwy817alLYb56ZA8WEQScSygAa1+mB1h75UoLlyJAwoq/i6\njWJ67GqjLk5ixVuN+HHHaFKNeM/F7FR3kEscLUUz3Yk6LxE0WcdwbH6NBjGH2jx16no+HpFPGkBw\n/BqQpG0P9b3frA4lO3vwM6+PYsg7/hvmW+J5inNs7c9UjQIm7gFNJtULj4pDMePB7ZOOESEMjDm+\nJ8KgfQU4MpuRtvrP9pDJjfmOAzHebw82NrbE1Qiwe//u63Y4pRk2B0wIpxY1f9/naHajs79gwYov\n/ooORhtEIZ0pIxHJix/cWAKHDHqM78yA4Oab4xRKSpQMW3NqaIv7Fy862mc2z1yyGzMlvtPuvfmf\nNWlU/2V6Oo4bQ7+WRBT+7HumnDjkzDJZNBubPp6VMYxM02NrwqUIJ52ct2sX3TOj3QPoZUR9kbjZ\nnxo5nJ7mesaSM04nHA05TbEb9H2XMAeYUSqJDSt2iUemkY0lcZ1zN2bm7Ixy20fLORyY1SYFLZMw\nO3spi8kCz9k+c2s2/caKxQHpxOek3q8wOQ3W7schIvGCdRamyZ1O6w+uSy1M2UgCAfPEPmpeEBcv\nQGaxzKgWWz8NfrZzHVV3DgLxCUdhXHVH6nGz7JG+Pb5oXLODEASEMNR9jxOEiXHm9s+8HyRt7IDH\nERX3t3jgtmeC7c6R0VWtrpdi32r0iuB7WfpnpX7afdf34DH161V56SglbuxnXh/JkNsmjpXoRSiN\nqHQ8DX/Eo2HGnLU1jETKxP7+nGscVoBYiJ2X9oPRYY8es8eD2dGN/FrDTA/GgTM28PdLbJTEKLBK\nv34x4Ll/HxJRk31zePBuyGXQ4ywhcHVDFaIYaul7ArFoxwqg5uxypGmYUqMZcIuyfaaKR/f4RVn0\nFAYx6GCiiYJQmg3S2Jp1rYGlg1FnULVW+Qaj5V6DEurOpTpGmVpvRjEHE1SroZDIbl1zThymias5\ns0zKJNWMZ8zAS1YE2ppyKXAqwqVlNmZCG4Zm2nyBfHWYKwIrv17dPdseAappiEcUrYRUsT9uMOhA\n7SBG1DVOtHhsiDu+UfvI/h/N39M8Ak5u+BPJm4SUViy6nyfrghQ1mmUY8qpwabDWYP2MLDO/cDAY\ny4jIKOlj5ZakLJOwzOYwxPsr8DVZpsRxEZbUmKSZFLAkYih3lG8V0+BptXEuhVYT6k0v02ROQ/Aa\nh/c+lBbnW0a2hqBpECCkSzs4ESGlDq/0sW8jSnK9skGCGJKwA5K0x5YIazNgJjtPakYEQRy3BjQm\neHlGiX3WC1JJwMF+nl8M43Y71xiOqVv8gFuJorWTJGKf/szr47BW1P2RelSXHIvDsbidZQ6jqs7F\njQp+RI2DfhfdloPpEpNz7KR62iUNqpiuQf+a4WW16Y7oEjGxf8SuOCtpLHI3zzq+P/5njsOq3/TU\nSPt3x/VJwpsSdk6lR6XieiLNU93u8ZyfKv07ElYEihFsZrwbczINZkmusueRt0TTje7ig9hIzj6o\nCFUS1fUiAh7JqbmzsJ+psaEl7j1gqFh/3+ySUKaOiYsG1i+UYP741VjUnZgTLKlyEDMgMfIu1ipl\nN0bR2VowTr73Wvd3yqhZ9EPtzsyMgDEr0pTIUzA+DOJoVb2Q16CoQ0xOw3R4T/o3Bexna9AxdJGu\nlqlamZJR11IySGirgGRSym7fBMXuo4gZarnYvpEKodW/5JDuFbZaUaLzNLIWQXeYUg4cX3CDNBz9\nnGHJ5hzSvoM2m4G/OSTmbCJzghn+OWVj8+TEVk0jJSVlbfBcjUbqZSSWyfoRUsqsa+lRdKlRYxFQ\nZwWl1J+X1Sawa8JpsMkCLXvMTjqVXSH2heWJwMW8eXRkhu6+7TjpBjiyjWEA/Ol6FqbQM77IZNE2\nPk/xOkYEc7JDCiAGyoxUUIlGwB5hyc7+/Ekz/rFEs3qhyQpJElzNEdYSUes+Yo4AZ3hS7c8mHlrn\nqAS2mqK4FX8ckeyI2P3tfZkM6xtGOzZSf8UJ1d01BhTRfz+uu6d7u++Lj5Q2CqR9P0n8rPbvFTTY\nXkSzQkThPcoK+IlR25YP/m1Rt/bopQ/0jdpAv1ChJaitkf39VU34yLBESFI7foemHpGDev1jsAIC\n84vN2CTcrj1X1DVWmh2scAj4uzLKIo0lmYEp4p8r4ax8XbKxAWZRZmnW/DW58BhDnsHG+u0PkPbW\nfhFjtVCjGKadM6z+szYFyNkEEs08Vq+wy+7SZv1cJo9qU2SMqixZmbJF7yY3LojYsOIIWGzohLMq\n6k4SoTmeD/4ZhgjXGlh3snPg+7n1vkALaMJQdS442mtRNo917CfAYZXG1ex0TuxaluzF3GQzQGtV\nLquJ8m6qXFp0IAe805hng3DWC84Usf2YHZ+PdQw2kxlum9w0OWyT09i/A6oKWusOJnXDKS8OoHYn\nPH4fhy+eXJAiIqqWF2Ygms3iHJqbTN2QxKcMNH5nonfB5f4zLcCIex+f9adjcXt9nIg8cHDoUTUy\nmlO6E4yD71GpGayRlsaUnUEQgvFA7CUaeGT8gTg7ZF9gHeh0h1Di+/wze7RtH8I+Td9TwfZ6IPbR\nkX2Mq9T+iO2NXigfEJNziZXBivAtwiS2WQ2bNXeTkzJni1TTzvj3tmLHMIuasuDWBs5t6bVFsZNH\n9KJYJBQ0OV9w0WoGwbVChNYPEy3gKneuzhLpaSfqzVtuyIlokO5AVP1Qixlx3RV8U1OyNosUJ6GI\ndsEp0D4tPomyCCwCV6lRo27g0FRp5pzUKbDihlrdYPYOO1cpjA7APsszKbSA+8TlAfB79s+LB5uk\nP9coVkXBPlT7sjSjsHmzh7XbZ88QTC+lFpsK34rx7OOew6Db+sSGGnQ660oZBsSCDuk7ihSGXHqG\nUuNkeNFvv5sVW98paa+/1GYY/5yEZcpMk7rBm5BWqFpZ1cfpJbu31jZnaTTK6m39anz7DmkFD8Gl\nfIPlM6XEnGzCU0rRvBbMIR82nYYRN4MeBjmCRVun3gzoZy5Ci7AqBlPubMEu+7c9PNZyzBxQX8HA\n0EdEL+wDJXgBPYTDj0/cwa7DYvyZReQ93cbTIbVbT8n0IqLxIATxAydOriUy5KR6+OoGxH4vvmtF\nLAIx7Hh050WJL/VoKXWMS+3DPLL2dFQ/WED1i/LoGJGOC4vLqwI9VVd4OdljXPbuj8bhQQTRarok\nocvim9Le6QMukm0xSVFkGvrWKk67Unox0W7NcVhvI9+iQAfMEYWhpGyYd2mKtLGNRMLQ0TVbLPkf\nnFt7DB6dRjoaX+OOuGkbkZ+YQ1GFgrqEwoiPEuL1CDPWE8G0sF/RYZg8qpxTQqbEkmSsF8WiVccx\nE9WKdWk3si72lViDybo1njflsUBtowhlU8+1H+w4vuJBQmM064R4VJiIHVEKIRNQTyOKqRZ01Gra\n8FtplKqUAqU486c/jRE1xtf443cjKOaMu0EeOLyi1hBGFzK2H9KKTyu1ndA1cNRX0qiovRINrGrG\nMgtM08YyJ47LzNXBPkM8Qt82A8uaYt+9NYxgasOPURyyUhu/2LA5BSj0WbGeIURwFwZSjFLYzwp0\ndk5kqGbQU3esEASKMYM2EQYD8DmwAS1FA1tE+Rnj60cGG3vcMjjfH80zRVJEn+Yg3GlFbUHdYAij\nwG1dyxFijmf+c6+PYsjVvbmqFS4j9bbhAfTco+qg49gB8g7CHQwx0iZAYlM7ZqzDeETEPHa5dMzb\n2Cix+e27B3tZ+vqNVn/14scuflc7vIhX4hkV9Hh+4wvCZHhk3o3v8BFxwIdvD5zNmz2SfXhEuvaV\nYzZ9WJTAQBupf++SFE12MItHeC+yGpFOWxyx54g4rcDbjPngBTRSffkZY/UYWYG9DN7wKMjT5fjJ\nlMyYF6AyCsTBYu6GMJgzRLTl73LII2OwWvT+xY9UwpA79ptah/W6WRZb04URSbrOkrNJRnNOj8Nk\nGErFecga8zl95UV6xmPX7tRDrFt08eJ51TB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5dNPxE08+yrwZEsjzcOoR55z+78Pw/+Vi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jhWge/XVZv3I4163ajnDrCeQNF2AqwBeJiVjp2eGXVqWcA+gcNDnz7dCsSPdP41h+PwgWN4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fabdc913990>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test_net = solver.test_nets[0]\n",
+    "for image_index in range(5):\n",
+    "    plt.figure()\n",
+    "    plt.imshow(transformer.deprocess(copy(test_net.blobs['data'].data[image_index, ...])))\n",
+    "    gtlist = test_net.blobs['label'].data[image_index, ...].astype(np.int)\n",
+    "    estlist = test_net.blobs['score'].data[image_index, ...] > 0\n",
+    "    plt.title('GT: {} \\n EST: {}'.format(classes[np.where(gtlist)], classes[np.where(estlist)]))\n",
+    "    plt.axis('off')"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": "Multilabel classification on PASCAL VOC using a Python data layer.",
+  "example_name": "Multilabel Classification with Python Data Layer",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.11"
+  },
+  "priority": 5
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/pycaffe/caffenet.py b/examples/pycaffe/caffenet.py
new file mode 100644
index 00000000000..82af2294435
--- /dev/null
+++ b/examples/pycaffe/caffenet.py
@@ -0,0 +1,55 @@
+from __future__ import print_function
+from caffe import layers as L, params as P, to_proto
+from caffe.proto import caffe_pb2
+
+# helper function for common structures
+
+def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
+    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
+                                num_output=nout, pad=pad, group=group)
+    return conv, L.ReLU(conv, in_place=True)
+
+def fc_relu(bottom, nout):
+    fc = L.InnerProduct(bottom, num_output=nout)
+    return fc, L.ReLU(fc, in_place=True)
+
+def max_pool(bottom, ks, stride=1):
+    return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
+
+def caffenet(lmdb, batch_size=256, include_acc=False):
+    data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
+        transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
+
+    # the net itself
+    conv1, relu1 = conv_relu(data, 11, 96, stride=4)
+    pool1 = max_pool(relu1, 3, stride=2)
+    norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
+    conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
+    pool2 = max_pool(relu2, 3, stride=2)
+    norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
+    conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
+    conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
+    conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
+    pool5 = max_pool(relu5, 3, stride=2)
+    fc6, relu6 = fc_relu(pool5, 4096)
+    drop6 = L.Dropout(relu6, in_place=True)
+    fc7, relu7 = fc_relu(drop6, 4096)
+    drop7 = L.Dropout(relu7, in_place=True)
+    fc8 = L.InnerProduct(drop7, num_output=1000)
+    loss = L.SoftmaxWithLoss(fc8, label)
+
+    if include_acc:
+        acc = L.Accuracy(fc8, label)
+        return to_proto(loss, acc)
+    else:
+        return to_proto(loss)
+
+def make_net():
+    with open('train.prototxt', 'w') as f:
+        print(caffenet('/path/to/caffe-train-lmdb'), file=f)
+
+    with open('test.prototxt', 'w') as f:
+        print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
+
+if __name__ == '__main__':
+    make_net()
diff --git a/examples/pycaffe/layers/pascal_multilabel_datalayers.py b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
new file mode 100644
index 00000000000..68e4fa7960a
--- /dev/null
+++ b/examples/pycaffe/layers/pascal_multilabel_datalayers.py
@@ -0,0 +1,216 @@
+# imports
+import json
+import time
+import pickle
+import scipy.misc
+import skimage.io
+import caffe
+
+import numpy as np
+import os.path as osp
+
+from xml.dom import minidom
+from random import shuffle
+from threading import Thread
+from PIL import Image
+
+from tools import SimpleTransformer
+
+
+class PascalMultilabelDataLayerSync(caffe.Layer):
+
+    """
+    This is a simple syncronous datalayer for training a multilabel model on
+    PASCAL.
+    """
+
+    def setup(self, bottom, top):
+
+        self.top_names = ['data', 'label']
+
+        # === Read input parameters ===
+
+        # params is a python dictionary with layer parameters.
+        params = eval(self.param_str)
+
+        # Check the paramameters for validity.
+        check_params(params)
+
+        # store input as class variables
+        self.batch_size = params['batch_size']
+
+        # Create a batch loader to load the images.
+        self.batch_loader = BatchLoader(params, None)
+
+        # === reshape tops ===
+        # since we use a fixed input image size, we can shape the data layer
+        # once. Else, we'd have to do it in the reshape call.
+        top[0].reshape(
+            self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
+        # Note the 20 channels (because PASCAL has 20 classes.)
+        top[1].reshape(self.batch_size, 20)
+
+        print_info("PascalMultilabelDataLayerSync", params)
+
+    def forward(self, bottom, top):
+        """
+        Load data.
+        """
+        for itt in range(self.batch_size):
+            # Use the batch loader to load the next image.
+            im, multilabel = self.batch_loader.load_next_image()
+
+            # Add directly to the caffe data layer
+            top[0].data[itt, ...] = im
+            top[1].data[itt, ...] = multilabel
+
+    def reshape(self, bottom, top):
+        """
+        There is no need to reshape the data, since the input is of fixed size
+        (rows and columns)
+        """
+        pass
+
+    def backward(self, top, propagate_down, bottom):
+        """
+        These layers does not back propagate
+        """
+        pass
+
+
+class BatchLoader(object):
+
+    """
+    This class abstracts away the loading of images.
+    Images can either be loaded singly, or in a batch. The latter is used for
+    the asyncronous data layer to preload batches while other processing is
+    performed.
+    """
+
+    def __init__(self, params, result):
+        self.result = result
+        self.batch_size = params['batch_size']
+        self.pascal_root = params['pascal_root']
+        self.im_shape = params['im_shape']
+        # get list of image indexes.
+        list_file = params['split'] + '.txt'
+        self.indexlist = [line.rstrip('\n') for line in open(
+            osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
+        self._cur = 0  # current image
+        # this class does some simple data-manipulations
+        self.transformer = SimpleTransformer()
+
+        print "BatchLoader initialized with {} images".format(
+            len(self.indexlist))
+
+    def load_next_image(self):
+        """
+        Load the next image in a batch.
+        """
+        # Did we finish an epoch?
+        if self._cur == len(self.indexlist):
+            self._cur = 0
+            shuffle(self.indexlist)
+
+        # Load an image
+        index = self.indexlist[self._cur]  # Get the image index
+        image_file_name = index + '.jpg'
+        im = np.asarray(Image.open(
+            osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
+        im = scipy.misc.imresize(im, self.im_shape)  # resize
+
+        # do a simple horizontal flip as data augmentation
+        flip = np.random.choice(2)*2-1
+        im = im[:, ::flip, :]
+
+        # Load and prepare ground truth
+        multilabel = np.zeros(20).astype(np.float32)
+        anns = load_pascal_annotation(index, self.pascal_root)
+        for label in anns['gt_classes']:
+            # in the multilabel problem we don't care how MANY instances
+            # there are of each class. Only if they are present.
+            # The "-1" is b/c we are not interested in the background
+            # class.
+            multilabel[label - 1] = 1
+
+        self._cur += 1
+        return self.transformer.preprocess(im), multilabel
+
+
+def load_pascal_annotation(index, pascal_root):
+    """
+    This code is borrowed from Ross Girshick's FAST-RCNN code
+    (https://github.com/rbgirshick/fast-rcnn).
+    It parses the PASCAL .xml metadata files.
+    See publication for further details: (http://arxiv.org/abs/1504.08083).
+
+    Thanks Ross!
+
+    """
+    classes = ('__background__',  # always index 0
+               'aeroplane', 'bicycle', 'bird', 'boat',
+               'bottle', 'bus', 'car', 'cat', 'chair',
+                         'cow', 'diningtable', 'dog', 'horse',
+                         'motorbike', 'person', 'pottedplant',
+                         'sheep', 'sofa', 'train', 'tvmonitor')
+    class_to_ind = dict(zip(classes, xrange(21)))
+
+    filename = osp.join(pascal_root, 'Annotations', index + '.xml')
+    # print 'Loading: {}'.format(filename)
+
+    def get_data_from_tag(node, tag):
+        return node.getElementsByTagName(tag)[0].childNodes[0].data
+
+    with open(filename) as f:
+        data = minidom.parseString(f.read())
+
+    objs = data.getElementsByTagName('object')
+    num_objs = len(objs)
+
+    boxes = np.zeros((num_objs, 4), dtype=np.uint16)
+    gt_classes = np.zeros((num_objs), dtype=np.int32)
+    overlaps = np.zeros((num_objs, 21), dtype=np.float32)
+
+    # Load object bounding boxes into a data frame.
+    for ix, obj in enumerate(objs):
+        # Make pixel indexes 0-based
+        x1 = float(get_data_from_tag(obj, 'xmin')) - 1
+        y1 = float(get_data_from_tag(obj, 'ymin')) - 1
+        x2 = float(get_data_from_tag(obj, 'xmax')) - 1
+        y2 = float(get_data_from_tag(obj, 'ymax')) - 1
+        cls = class_to_ind[
+            str(get_data_from_tag(obj, "name")).lower().strip()]
+        boxes[ix, :] = [x1, y1, x2, y2]
+        gt_classes[ix] = cls
+        overlaps[ix, cls] = 1.0
+
+    overlaps = scipy.sparse.csr_matrix(overlaps)
+
+    return {'boxes': boxes,
+            'gt_classes': gt_classes,
+            'gt_overlaps': overlaps,
+            'flipped': False,
+            'index': index}
+
+
+def check_params(params):
+    """
+    A utility function to check the parameters for the data layers.
+    """
+    assert 'split' in params.keys(
+    ), 'Params must include split (train, val, or test).'
+
+    required = ['batch_size', 'pascal_root', 'im_shape']
+    for r in required:
+        assert r in params.keys(), 'Params must include {}'.format(r)
+
+
+def print_info(name, params):
+    """
+    Ouput some info regarding the class
+    """
+    print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
+        name,
+        params['split'],
+        params['batch_size'],
+        params['im_shape'])
diff --git a/examples/pycaffe/layers/pyloss.py b/examples/pycaffe/layers/pyloss.py
new file mode 100644
index 00000000000..6200e6bbc55
--- /dev/null
+++ b/examples/pycaffe/layers/pyloss.py
@@ -0,0 +1,37 @@
+import caffe
+import numpy as np
+
+
+class EuclideanLossLayer(caffe.Layer):
+    """
+    Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer
+    to demonstrate the class interface for developing layers in Python.
+    """
+
+    def setup(self, bottom, top):
+        # check input pair
+        if len(bottom) != 2:
+            raise Exception("Need two inputs to compute distance.")
+
+    def reshape(self, bottom, top):
+        # check input dimensions match
+        if bottom[0].count != bottom[1].count:
+            raise Exception("Inputs must have the same dimension.")
+        # difference is shape of inputs
+        self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
+        # loss output is scalar
+        top[0].reshape(1)
+
+    def forward(self, bottom, top):
+        self.diff[...] = bottom[0].data - bottom[1].data
+        top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
+
+    def backward(self, top, propagate_down, bottom):
+        for i in range(2):
+            if not propagate_down[i]:
+                continue
+            if i == 0:
+                sign = 1
+            else:
+                sign = -1
+            bottom[i].diff[...] = sign * self.diff / bottom[i].num
diff --git a/examples/pycaffe/linreg.prototxt b/examples/pycaffe/linreg.prototxt
new file mode 100644
index 00000000000..c0fb0776d0a
--- /dev/null
+++ b/examples/pycaffe/linreg.prototxt
@@ -0,0 +1,60 @@
+name: 'LinearRegressionExample'
+# define a simple network for linear regression on dummy data
+# that computes the loss by a PythonLayer.
+layer {
+  type: 'DummyData'
+  name: 'x'
+  top: 'x'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+layer {
+  type: 'DummyData'
+  name: 'y'
+  top: 'y'
+  dummy_data_param {
+    shape: { dim: 10 dim: 3 dim: 2 }
+    data_filler: { type: 'gaussian' }
+  }
+}
+# include InnerProduct layers for parameters
+# so the net will need backward
+layer {
+  type: 'InnerProduct'
+  name: 'ipx'
+  top: 'ipx'
+  bottom: 'x'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'InnerProduct'
+  name: 'ipy'
+  top: 'ipy'
+  bottom: 'y'
+  inner_product_param {
+    num_output: 10
+    weight_filler { type: 'xavier' }
+  }
+}
+layer {
+  type: 'Python'
+  name: 'loss'
+  top: 'loss'
+  bottom: 'ipx'
+  bottom: 'ipy'
+  python_param {
+    # the module name -- usually the filename -- that needs to be in $PYTHONPATH
+    module: 'pyloss'
+    # the layer name -- the class name in the module
+    layer: 'EuclideanLossLayer'
+  }
+  # set loss weight so Caffe knows this is a loss layer.
+  # since PythonLayer inherits directly from Layer, this isn't automatically
+  # known to Caffe
+  loss_weight: 1
+}
diff --git a/examples/pycaffe/tools.py b/examples/pycaffe/tools.py
new file mode 100644
index 00000000000..88b1834af1e
--- /dev/null
+++ b/examples/pycaffe/tools.py
@@ -0,0 +1,121 @@
+import numpy as np
+
+
+class SimpleTransformer:
+
+    """
+    SimpleTransformer is a simple class for preprocessing and deprocessing
+    images for caffe.
+    """
+
+    def __init__(self, mean=[128, 128, 128]):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.scale = 1.0
+
+    def set_mean(self, mean):
+        """
+        Set the mean to subtract for centering the data.
+        """
+        self.mean = mean
+
+    def set_scale(self, scale):
+        """
+        Set the data scaling.
+        """
+        self.scale = scale
+
+    def preprocess(self, im):
+        """
+        preprocess() emulate the pre-processing occuring in the vgg16 caffe
+        prototxt.
+        """
+
+        im = np.float32(im)
+        im = im[:, :, ::-1]  # change to BGR
+        im -= self.mean
+        im *= self.scale
+        im = im.transpose((2, 0, 1))
+
+        return im
+
+    def deprocess(self, im):
+        """
+        inverse of preprocess()
+        """
+        im = im.transpose(1, 2, 0)
+        im /= self.scale
+        im += self.mean
+        im = im[:, :, ::-1]  # change to RGB
+
+        return np.uint8(im)
+
+
+class CaffeSolver:
+
+    """
+    Caffesolver is a class for creating a solver.prototxt file. It sets default
+    values and can export a solver parameter file.
+    Note that all parameters are stored as strings. Strings variables are
+    stored as strings in strings.
+    """
+
+    def __init__(self, testnet_prototxt_path="testnet.prototxt",
+                 trainnet_prototxt_path="trainnet.prototxt", debug=False):
+
+        self.sp = {}
+
+        # critical:
+        self.sp['base_lr'] = '0.001'
+        self.sp['momentum'] = '0.9'
+
+        # speed:
+        self.sp['test_iter'] = '100'
+        self.sp['test_interval'] = '250'
+
+        # looks:
+        self.sp['display'] = '25'
+        self.sp['snapshot'] = '2500'
+        self.sp['snapshot_prefix'] = '"snapshot"'  # string withing a string!
+
+        # learning rate policy
+        self.sp['lr_policy'] = '"fixed"'
+
+        # important, but rare:
+        self.sp['gamma'] = '0.1'
+        self.sp['weight_decay'] = '0.0005'
+        self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
+        self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
+
+        # pretty much never change these.
+        self.sp['max_iter'] = '100000'
+        self.sp['test_initialization'] = 'false'
+        self.sp['average_loss'] = '25'  # this has to do with the display.
+        self.sp['iter_size'] = '1'  # this is for accumulating gradients
+
+        if (debug):
+            self.sp['max_iter'] = '12'
+            self.sp['test_iter'] = '1'
+            self.sp['test_interval'] = '4'
+            self.sp['display'] = '1'
+
+    def add_from_file(self, filepath):
+        """
+        Reads a caffe solver prototxt file and updates the Caffesolver
+        instance parameters.
+        """
+        with open(filepath, 'r') as f:
+            for line in f:
+                if line[0] == '#':
+                    continue
+                splitLine = line.split(':')
+                self.sp[splitLine[0].strip()] = splitLine[1].strip()
+
+    def write(self, filepath):
+        """
+        Export solver parameters to INPUT "filepath". Sorted alphabetically.
+        """
+        f = open(filepath, 'w')
+        for key, value in sorted(self.sp.items()):
+            if not(type(value) is str):
+                raise TypeError('All solver parameters must be strings')
+            f.write('%s: %s\n' % (key, value))
diff --git a/examples/siamese/convert_mnist_siamese_data.cpp b/examples/siamese/convert_mnist_siamese_data.cpp
index 71c56a0ae61..928b3fbf4d5 100644
--- a/examples/siamese/convert_mnist_siamese_data.cpp
+++ b/examples/siamese/convert_mnist_siamese_data.cpp
@@ -10,12 +10,15 @@
 
 #include "glog/logging.h"
 #include "google/protobuf/text_format.h"
-#include "leveldb/db.h"
 #include "stdint.h"
 
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
 #include "caffe/util/math_functions.hpp"
 
+#ifdef USE_LEVELDB
+#include "leveldb/db.h"
+
 uint32_t swap_endian(uint32_t val) {
     val = ((val << 8) & 0xFF00FF00) | ((val >> 8) & 0xFF00FF);
     return (val << 16) | (val >> 16);
@@ -73,8 +76,6 @@ void convert_dataset(const char* image_filename, const char* label_filename,
   char label_i;
   char label_j;
   char* pixels = new char[2 * rows * cols];
-  const int kMaxKeyLength = 10;
-  char key[kMaxKeyLength];
   std::string value;
 
   caffe::Datum datum;
@@ -97,12 +98,12 @@ void convert_dataset(const char* image_filename, const char* label_filename,
       datum.set_label(0);
     }
     datum.SerializeToString(&value);
-    snprintf(key, kMaxKeyLength, "%08d", itemid);
-    db->Put(leveldb::WriteOptions(), std::string(key), value);
+    std::string key_str = caffe::format_int(itemid, 8);
+    db->Put(leveldb::WriteOptions(), key_str, value);
   }
 
   delete db;
-  delete pixels;
+  delete [] pixels;
 }
 
 int main(int argc, char** argv) {
@@ -121,3 +122,8 @@ int main(int argc, char** argv) {
   }
   return 0;
 }
+#else
+int main(int argc, char** argv) {
+  LOG(FATAL) << "This example requires LevelDB; compile with USE_LEVELDB.";
+}
+#endif  // USE_LEVELDB
diff --git a/examples/siamese/mnist_siamese.ipynb b/examples/siamese/mnist_siamese.ipynb
index 8e076663ca6..1a4e30eda43 100644
--- a/examples/siamese/mnist_siamese.ipynb
+++ b/examples/siamese/mnist_siamese.ipynb
@@ -1,154 +1,1909 @@
 {
- "metadata": {
-  "description": "Extracting features and plotting the Siamese network embedding.",
-  "example_name": "Siamese network embedding",
-  "include_in_docs": true,
-  "priority": 6,
-  "signature": "sha256:845bb18929f96543ba2611eb5eca744fd98939cbef876df6bc319c29f616fc64"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Setup\n",
-      "\n",
-      "Import Caffe and the usual modules."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import matplotlib.pyplot as plt\n",
-      "%matplotlib inline\n",
-      "\n",
-      "# Make sure that caffe is on the python path:\n",
-      "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
-      "import sys\n",
-      "sys.path.insert(0, caffe_root + 'python')\n",
-      "\n",
-      "import caffe"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load the trained net\n",
-      "\n",
-      "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "MODEL_FILE = 'mnist_siamese.prototxt'\n",
-      "# decrease if you want to preview during training\n",
-      "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
-      "caffe.set_mode_cpu()\n",
-      "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Load some MNIST test data"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
-      "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
-      "n = 10000\n",
-      "\n",
-      "with open(TEST_DATA_FILE, 'rb') as f:\n",
-      "    f.read(16) # skip the header\n",
-      "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
-      "\n",
-      "with open(TEST_LABEL_FILE, 'rb') as f:\n",
-      "    f.read(8) # skip the header\n",
-      "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Generate the Siamese features"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# reshape and preprocess\n",
-      "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
-      "out = net.forward_all(data=caffe_in)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Visualize the learned Siamese embedding"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup\n",
+    "\n",
+    "Import Caffe and the usual modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Make sure that caffe is on the python path:\n",
+    "caffe_root = '../../'  # this file is expected to be in {caffe_root}/examples/siamese\n",
+    "import sys\n",
+    "sys.path.insert(0, caffe_root + 'python')\n",
+    "\n",
+    "import caffe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the trained net\n",
+    "\n",
+    "Load the model definition and weights and set to CPU mode TEST phase computation with input scaling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_FILE = 'mnist_siamese.prototxt'\n",
+    "# decrease if you want to preview during training\n",
+    "PRETRAINED_FILE = 'mnist_siamese_iter_50000.caffemodel' \n",
+    "caffe.set_mode_cpu()\n",
+    "net = caffe.Net(MODEL_FILE, PRETRAINED_FILE, caffe.TEST)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load some MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "TEST_DATA_FILE = '../../data/mnist/t10k-images-idx3-ubyte'\n",
+    "TEST_LABEL_FILE = '../../data/mnist/t10k-labels-idx1-ubyte'\n",
+    "n = 10000\n",
+    "\n",
+    "with open(TEST_DATA_FILE, 'rb') as f:\n",
+    "    f.read(16) # skip the header\n",
+    "    raw_data = np.fromstring(f.read(n * 28*28), dtype=np.uint8)\n",
+    "\n",
+    "with open(TEST_LABEL_FILE, 'rb') as f:\n",
+    "    f.read(8) # skip the header\n",
+    "    labels = np.fromstring(f.read(n), dtype=np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generate the Siamese features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# reshape and preprocess\n",
+    "caffe_in = raw_data.reshape(n, 1, 28, 28) * 0.00390625 # manually scale data instead of using `caffe.io.Transformer`\n",
+    "out = net.forward_all(data=caffe_in)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualize the learned Siamese embedding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "feat = out['feat']\n",
-      "f = plt.figure(figsize=(16,9))\n",
-      "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
-      "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
-      "for i in range(10):\n",
-      "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
-      "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
+     "data": {
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+       "jDGLJO2W9EcNpgf8T8uyfpe7USFfGWPmS/qF7MXMAknPWpb1o7THT0TgCQAAAAC4ck1YVVsAAAAA\n",
+       "wJWJwBMAAAAAkFUEngAAAACArCLwBAAAAABkFYEnAAAAACCrCDwBAAAAAFlF4AkAAAAAyCoCTwAA\n",
+       "AABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n"
+      ],
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fadf4552a90>"
+      ]
+     },
      "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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4SNg1HI5SrNZ89u2riXkfGzY08/zzt7FhwzEWLrw1rflJRm1t44x7awVBEIS5RyKeQs7R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\nmQpmVsCdLcyKeM1mXGC6H+dTJdHH1XTny+FQP2S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DVVVKG7x6aYNEwD6xZw/6T5/Gjbt3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w6+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\nAABAVv1/lzHCzGUnjVoAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x7fadf4552a90>"
-       ]
-      }
-     ],
-     "prompt_number": 5
+     "output_type": "display_data"
     }
    ],
-   "metadata": {}
+   "source": [
+    "feat = out['feat']\n",
+    "f = plt.figure(figsize=(16,9))\n",
+    "c = ['#ff0000', '#ffff00', '#00ff00', '#00ffff', '#0000ff', \n",
+    "     '#ff00ff', '#990000', '#999900', '#009900', '#009999']\n",
+    "for i in range(10):\n",
+    "    plt.plot(feat[labels==i,0].flatten(), feat[labels==i,1].flatten(), '.', c=c[i])\n",
+    "plt.legend(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])\n",
+    "plt.grid()\n",
+    "plt.show()"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "description": "Extracting features and plotting the Siamese network embedding.",
+  "example_name": "Siamese network embedding",
+  "include_in_docs": true,
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "python2"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.9"
+  },
+  "priority": 7
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/siamese/mnist_siamese.prototxt b/examples/siamese/mnist_siamese.prototxt
index 0e903f85909..5d783ba02ca 100644
--- a/examples/siamese/mnist_siamese.prototxt
+++ b/examples/siamese/mnist_siamese.prototxt
@@ -1,9 +1,12 @@
 name: "mnist_siamese"
-input: "data"
-input_dim: 10000
-input_dim: 1
-input_dim: 28
-input_dim: 28
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param {
+    shape: { dim: 10000 dim: 1 dim: 28 dim: 28 }
+  }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/examples/siamese/readme.md b/examples/siamese/readme.md
index ce98ec10819..83db8c94395 100644
--- a/examples/siamese/readme.md
+++ b/examples/siamese/readme.md
@@ -39,13 +39,19 @@ exactly the same as the [LeNet model](mnist.html), the only difference is that
 we have replaced the top layers that produced probabilities over the 10 digit
 classes with a linear "feature" layer that produces a 2 dimensional vector.
 
-    layers {
+    layer {
       name: "feat"
-      type: INNER_PRODUCT
+      type: "InnerProduct"
       bottom: "ip2"
       top: "feat"
-      blobs_lr: 1
-      blobs_lr: 2
+      param {
+        name: "feat_w"
+        lr_mult: 1
+      }
+      param {
+        name: "feat_b"
+        lr_mult: 2
+      }
       inner_product_param {
         num_output: 2
       }
@@ -64,17 +70,19 @@ earlier. Each entry in this database contains the image data for a pair of
 images (`pair_data`) and a binary label saying if they belong to the same class
 or different classes (`sim`).
 
-    layers {
+    layer {
       name: "pair_data"
-      type: DATA
+      type: "Data"
       top: "pair_data"
       top: "sim"
-      data_param {
-        source: "examples/siamese/mnist-siamese-train-leveldb"
+      include { phase: TRAIN }
+      transform_param {
         scale: 0.00390625
+      }
+      data_param {
+        source: "examples/siamese/mnist_siamese_train_leveldb"
         batch_size: 64
       }
-      include: { phase: TRAIN }
     }
 
 In order to pack a pair of images into the same blob in the database we pack one
@@ -83,16 +91,16 @@ so we add a slice layer after the data layer. This takes the `pair_data` and
 slices it along the channel dimension so that we have a single image in `data`
 and its paired image in `data_p.`
 
-    layers {
-        name: "slice_pair"
-        type: SLICE
-        bottom: "pair_data"
-        top: "data"
-        top: "data_p"
-        slice_param {
-            slice_dim: 1
-            slice_point: 1
-        }
+    layer {
+      name: "slice_pair"
+      type: "Slice"
+      bottom: "pair_data"
+      top: "data"
+      top: "data_p"
+      slice_param {
+        slice_dim: 1
+        slice_point: 1
+      }
     }
 
 ### Building the First Side of the Siamese Net
@@ -105,17 +113,17 @@ parameters allows Caffe to share the parameters between layers on both sides of
 the siamese net. In the definition this looks like:
 
     ...
-    param: "conv1_w"
-    param: "conv1_b"
+    param { name: "conv1_w" ...  }
+    param { name: "conv1_b" ...  }
     ...
-    param: "conv2_w"
-    param: "conv2_b"
+    param { name: "conv2_w" ...  }
+    param { name: "conv2_b" ...  }
     ...
-    param: "ip1_w"
-    param: "ip1_b"
+    param { name: "ip1_w" ...  }
+    param { name: "ip1_b" ...  }
     ...
-    param: "ip2_w"
-    param: "ip2_b"
+    param { name: "ip2_w" ...  }
+    param { name: "ip2_b" ...  }
     ...
 
 ### Building the Second Side of the Siamese Net
@@ -133,9 +141,9 @@ an Invariant Mapping". This loss function encourages matching pairs to be close
 together in feature space while pushing non-matching pairs apart. This cost
 function is implemented with the `CONTRASTIVE_LOSS` layer:
 
-    layers {
+    layer {
         name: "loss"
-        type: CONTRASTIVE_LOSS
+        type: "ContrastiveLoss"
         contrastive_loss_param {
             margin: 1.0
         }
diff --git a/examples/web_demo/app.py b/examples/web_demo/app.py
index bbeff5eb362..09411f33f10 100644
--- a/examples/web_demo/app.py
+++ b/examples/web_demo/app.py
@@ -10,14 +10,14 @@
 import tornado.httpserver
 import numpy as np
 import pandas as pd
-import Image
+from PIL import Image
 import cStringIO as StringIO
 import urllib
 import exifutil
 
 import caffe
 
-REPO_DIRNAME = os.path.abspath(os.path.dirname(__file__) + '/../..')
+REPO_DIRNAME = os.path.abspath(os.path.dirname(os.path.abspath(__file__)) + '/../..')
 UPLOAD_FOLDER = '/tmp/caffe_demos_uploads'
 ALLOWED_IMAGE_EXTENSIONS = set(['png', 'bmp', 'jpg', 'jpe', 'jpeg', 'gif'])
 
diff --git a/examples/web_demo/requirements.txt b/examples/web_demo/requirements.txt
index 8fb1d2ccbb2..43e1b98cc34 100644
--- a/examples/web_demo/requirements.txt
+++ b/examples/web_demo/requirements.txt
@@ -4,3 +4,4 @@ tornado
 numpy
 pandas
 pillow
+pyyaml
diff --git a/include/caffe/blob.hpp b/include/caffe/blob.hpp
index 472cc1841f7..af360ac24bd 100644
--- a/include/caffe/blob.hpp
+++ b/include/caffe/blob.hpp
@@ -8,9 +8,8 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
-#include "caffe/util/math_functions.hpp"
 
-const int kMaxBlobAxes = INT_MAX;
+const int kMaxBlobAxes = 32;
 
 namespace caffe {
 
@@ -109,7 +108,7 @@ class Blob {
    * @brief Returns the 'canonical' version of a (usually) user-specified axis,
    *        allowing for negative indexing (e.g., -1 for the last axis).
    *
-   * @param index the axis index.
+   * @param axis_index the axis index.
    *        If 0 <= index < num_axes(), return index.
    *        If -num_axes <= index <= -1, return (num_axes() - (-index)),
    *        e.g., the last axis index (num_axes() - 1) if index == -1,
@@ -219,6 +218,7 @@ class Blob {
 
   const Dtype* cpu_data() const;
   void set_cpu_data(Dtype* data);
+  const int* gpu_shape() const;
   const Dtype* gpu_data() const;
   const Dtype* cpu_diff() const;
   const Dtype* gpu_diff() const;
@@ -268,6 +268,7 @@ class Blob {
  protected:
   shared_ptr<SyncedMemory> data_;
   shared_ptr<SyncedMemory> diff_;
+  shared_ptr<SyncedMemory> shape_data_;
   vector<int> shape_;
   int count_;
   int capacity_;
diff --git a/include/caffe/caffe.hpp b/include/caffe/caffe.hpp
index 3c829f2f9b0..06882096c55 100644
--- a/include/caffe/caffe.hpp
+++ b/include/caffe/caffe.hpp
@@ -10,10 +10,12 @@
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/util/upgrade_proto.hpp"
 
 #endif  // CAFFE_CAFFE_HPP_
diff --git a/include/caffe/common.hpp b/include/caffe/common.hpp
index 6cf80a37bc1..3c6a076ec2f 100644
--- a/include/caffe/common.hpp
+++ b/include/caffe/common.hpp
@@ -18,8 +18,12 @@
 
 #include "caffe/util/device_alternate.hpp"
 
+// Convert macro to string
+#define STRINGIFY(m) #m
+#define AS_STRING(m) STRINGIFY(m)
+
 // gflags 2.1 issue: namespace google was changed to gflags without warning.
-// Luckily we will be able to use GFLAGS_GFAGS_H_ to detect if it is version
+// Luckily we will be able to use GFLAGS_GFLAGS_H_ to detect if it is version
 // 2.1. If yes, we will add a temporary solution to redirect the namespace.
 // TODO(Yangqing): Once gflags solves the problem in a more elegant way, let's
 // remove the following hack.
@@ -98,12 +102,12 @@ void GlobalInit(int* pargc, char*** pargv);
 class Caffe {
  public:
   ~Caffe();
-  inline static Caffe& Get() {
-    if (!singleton_.get()) {
-      singleton_.reset(new Caffe());
-    }
-    return *singleton_;
-  }
+
+  // Thread local context for Caffe. Moved to common.cpp instead of
+  // including boost/thread.hpp to avoid a boost/NVCC issues (#1009, #1010)
+  // on OSX. Also fails on Linux with CUDA 7.0.18.
+  static Caffe& Get();
+
   enum Brew { CPU, GPU };
 
   // This random number generator facade hides boost and CUDA rng
@@ -149,6 +153,16 @@ class Caffe {
   static void SetDevice(const int device_id);
   // Prints the current GPU status.
   static void DeviceQuery();
+  // Check if specified device is available
+  static bool CheckDevice(const int device_id);
+  // Search from start_id to the highest possible device ordinal,
+  // return the ordinal of the first available device.
+  static int FindDevice(const int start_id = 0);
+  // Parallel training info
+  inline static int solver_count() { return Get().solver_count_; }
+  inline static void set_solver_count(int val) { Get().solver_count_ = val; }
+  inline static bool root_solver() { return Get().root_solver_; }
+  inline static void set_root_solver(bool val) { Get().root_solver_ = val; }
 
  protected:
 #ifndef CPU_ONLY
@@ -158,7 +172,8 @@ class Caffe {
   shared_ptr<RNG> random_generator_;
 
   Brew mode_;
-  static shared_ptr<Caffe> singleton_;
+  int solver_count_;
+  bool root_solver_;
 
  private:
   // The private constructor to avoid duplicate instantiation.
diff --git a/include/caffe/common_layers.hpp b/include/caffe/common_layers.hpp
deleted file mode 100644
index cae1c3e4ee6..00000000000
--- a/include/caffe/common_layers.hpp
+++ /dev/null
@@ -1,464 +0,0 @@
-#ifndef CAFFE_COMMON_LAYERS_HPP_
-#define CAFFE_COMMON_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Compute the index of the @f$ K @f$ max values for each datum across
- *        all dimensions @f$ (C \times H \times W) @f$.
- *
- * Intended for use after a classification layer to produce a prediction.
- * If parameter out_max_val is set to true, output is a vector of pairs
- * (max_ind, max_val) for each image.
- *
- * NOTE: does not implement Backwards operation.
- */
-template <typename Dtype>
-class ArgMaxLayer : public Layer<Dtype> {
- public:
-  /**
-   * @param param provides ArgMaxParameter argmax_param,
-   *     with ArgMaxLayer options:
-   *   - top_k (\b optional uint, default 1).
-   *     the number @f$ K @f$ of maximal items to output.
-   *   - out_max_val (\b optional bool, default false).
-   *     if set, output a vector of pairs (max_ind, max_val) for each image.
-   */
-  explicit ArgMaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "ArgMax"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times 1 \times K \times 1) @f$ or, if out_max_val
-   *      @f$ (N \times 2 \times K \times 1) @f$
-   *      the computed outputs @f$
-   *       y_n = \arg\max\limits_i x_{ni}
-   *      @f$ (for @f$ K = 1 @f$).
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /// @brief Not implemented (non-differentiable function)
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    NOT_IMPLEMENTED;
-  }
-  bool out_max_val_;
-  size_t top_k_;
-};
-
-/**
- * @brief Takes at least two Blob%s and concatenates them along either the num
- *        or channel dimension, outputting the result.
- */
-template <typename Dtype>
-class ConcatLayer : public Layer<Dtype> {
- public:
-  explicit ConcatLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Concat"; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_1 @f$
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_2 @f$
-   *   -# ...
-   *   - K @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x_K @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
-   *      the concatenated output @f$
-   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the concatenate inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *        respect to the outputs
-   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
-   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to concatenated outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length K), into which the top gradient
-   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
-   *        inputs @f$
-   *        \left[ \begin{array}{cccc}
-   *          \frac{\partial E}{\partial x_1} &
-   *          \frac{\partial E}{\partial x_2} &
-   *          ... &
-   *          \frac{\partial E}{\partial x_K}
-   *        \end{array} \right] =
-   *        \frac{\partial E}{\partial y}
-   *        @f$
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-  int num_concats_;
-  int concat_input_size_;
-  int concat_axis_;
-};
-
-/**
- * @brief Compute elementwise operations, such as product and sum,
- *        along multiple input Blobs.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class EltwiseLayer : public Layer<Dtype> {
- public:
-  explicit EltwiseLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Eltwise"; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  EltwiseParameter_EltwiseOp op_;
-  vector<Dtype> coeffs_;
-  Blob<int> max_idx_;
-
-  bool stable_prod_grad_;
-};
-
-/**
- * @brief Reshapes the input Blob into flat vectors.
- *
- * Note: because this layer does not change the input values -- merely the
- * dimensions -- it can simply copy the input. The copy happens "virtually"
- * (thus taking effectively 0 real time) by setting, in Forward, the data
- * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
- * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
- * (see Blob::ShareDiff).
- */
-template <typename Dtype>
-class FlattenLayer : public Layer<Dtype> {
- public:
-  explicit FlattenLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Flatten"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2+)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times CHW \times 1 \times 1) @f$
-   *      the outputs -- i.e., the (virtually) copied, flattened inputs
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the concatenate inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *        respect to the outputs
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length K), into which the top error
-   *        gradient is (virtually) copied
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Also known as a "fully-connected" layer, computes an inner product
- *        with a set of learned weights, and (optionally) adds biases.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class InnerProductLayer : public Layer<Dtype> {
- public:
-  explicit InnerProductLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "InnerProduct"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int M_;
-  int K_;
-  int N_;
-  bool bias_term_;
-  Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class MVNLayer : public Layer<Dtype> {
- public:
-  explicit MVNLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MVN"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> mean_, variance_, temp_;
-
-  /// sum_multiplier is used to carry out sum using BLAS
-  Blob<Dtype> sum_multiplier_;
-};
-
-/**
- * @brief Ignores bottom blobs while producing no top blobs. (This is useful
- *        to suppress outputs during testing.)
- */
-template <typename Dtype>
-class SilenceLayer : public Layer<Dtype> {
- public:
-  explicit SilenceLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "Silence"; }
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  // We can't define Forward_gpu here, since STUB_GPU will provide
-  // its own definition for CPU_ONLY mode.
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the softmax function.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SoftmaxLayer : public Layer<Dtype> {
- public:
-  explicit SoftmaxLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Softmax"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int outer_num_;
-  int inner_num_;
-  int softmax_axis_;
-  /// sum_multiplier is used to carry out sum using BLAS
-  Blob<Dtype> sum_multiplier_;
-  /// scale is an intermediate Blob to hold temporary results.
-  Blob<Dtype> scale_;
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief cuDNN implementation of SoftmaxLayer.
- *        Fallback to SoftmaxLayer for CPU mode.
- */
-template <typename Dtype>
-class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
- public:
-  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
-      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNSoftmaxLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Creates a "split" path in the network by copying the bottom Blob
- *        into multiple top Blob%s to be used by multiple consuming layers.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SplitLayer : public Layer<Dtype> {
- public:
-  explicit SplitLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Split"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-};
-
-/**
- * @brief Takes a Blob and slices it along either the num or channel dimension,
- *        outputting multiple sliced Blob results.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class SliceLayer : public Layer<Dtype> {
- public:
-  explicit SliceLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Slice"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 2; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int count_;
-  int num_slices_;
-  int slice_size_;
-  int slice_axis_;
-  vector<int> slice_point_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_COMMON_LAYERS_HPP_
diff --git a/include/caffe/data_layers.hpp b/include/caffe/data_layers.hpp
deleted file mode 100644
index 2bb9d948169..00000000000
--- a/include/caffe/data_layers.hpp
+++ /dev/null
@@ -1,330 +0,0 @@
-#ifndef CAFFE_DATA_LAYERS_HPP_
-#define CAFFE_DATA_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "boost/scoped_ptr.hpp"
-#include "hdf5.h"
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/data_transformer.hpp"
-#include "caffe/filler.hpp"
-#include "caffe/internal_thread.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-#include "caffe/util/db.hpp"
-
-namespace caffe {
-
-/**
- * @brief Provides base for data layers that feed blobs to the Net.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class BaseDataLayer : public Layer<Dtype> {
- public:
-  explicit BaseDataLayer(const LayerParameter& param);
-  virtual ~BaseDataLayer() {}
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden except by the BasePrefetchingDataLayer.
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
- protected:
-  TransformationParameter transform_param_;
-  shared_ptr<DataTransformer<Dtype> > data_transformer_;
-  bool output_labels_;
-};
-
-template <typename Dtype>
-class BasePrefetchingDataLayer :
-    public BaseDataLayer<Dtype>, public InternalThread {
- public:
-  explicit BasePrefetchingDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param) {}
-  virtual ~BasePrefetchingDataLayer() {}
-  // LayerSetUp: implements common data layer setup functionality, and calls
-  // DataLayerSetUp to do special data layer setup for individual layer types.
-  // This method may not be overridden.
-  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void CreatePrefetchThread();
-  virtual void JoinPrefetchThread();
-  // The thread's function
-  virtual void InternalThreadEntry() {}
-
- protected:
-  Blob<Dtype> prefetch_data_;
-  Blob<Dtype> prefetch_label_;
-  Blob<Dtype> transformed_data_;
-};
-
-template <typename Dtype>
-class DataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit DataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~DataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Data"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  virtual void InternalThreadEntry();
-
-  shared_ptr<db::DB> db_;
-  shared_ptr<db::Cursor> cursor_;
-};
-
-/**
- * @brief Provides data to the Net generated by a Filler.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class DummyDataLayer : public Layer<Dtype> {
- public:
-  explicit DummyDataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "DummyData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-
-  vector<shared_ptr<Filler<Dtype> > > fillers_;
-  vector<bool> refill_;
-};
-
-/**
- * @brief Provides data to the Net from HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5DataLayer : public Layer<Dtype> {
- public:
-  explicit HDF5DataLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual ~HDF5DataLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "HDF5Data"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int MinTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
-  virtual void LoadHDF5FileData(const char* filename);
-
-  std::vector<std::string> hdf_filenames_;
-  unsigned int num_files_;
-  unsigned int current_file_;
-  hsize_t current_row_;
-  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
-  std::vector<unsigned int> data_permutation_;
-  std::vector<unsigned int> file_permutation_;
-};
-
-/**
- * @brief Write blobs to disk as HDF5 files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class HDF5OutputLayer : public Layer<Dtype> {
- public:
-  explicit HDF5OutputLayer(const LayerParameter& param)
-      : Layer<Dtype>(param), file_opened_(false) {}
-  virtual ~HDF5OutputLayer();
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  // Data layers have no bottoms, so reshaping is trivial.
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top) {}
-
-  virtual inline const char* type() const { return "HDF5Output"; }
-  // TODO: no limit on the number of blobs
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 0; }
-
-  inline std::string file_name() const { return file_name_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void SaveBlobs();
-
-  bool file_opened_;
-  std::string file_name_;
-  hid_t file_id_;
-  Blob<Dtype> data_blob_;
-  Blob<Dtype> label_blob_;
-};
-
-/**
- * @brief Provides data to the Net from image files.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit ImageDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~ImageDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "ImageData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  virtual void ShuffleImages();
-  virtual void InternalThreadEntry();
-
-  vector<std::pair<std::string, int> > lines_;
-  int lines_id_;
-};
-
-/**
- * @brief Provides data to the Net from memory.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class MemoryDataLayer : public BaseDataLayer<Dtype> {
- public:
-  explicit MemoryDataLayer(const LayerParameter& param)
-      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MemoryData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
-  virtual void AddDatumVector(const vector<Datum>& datum_vector);
-  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
-      const vector<int>& labels);
-
-  // Reset should accept const pointers, but can't, because the memory
-  //  will be given to Blob, which is mutable
-  void Reset(Dtype* data, Dtype* label, int n);
-  void set_batch_size(int new_size);
-
-  int batch_size() { return batch_size_; }
-  int channels() { return channels_; }
-  int height() { return height_; }
-  int width() { return width_; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  int batch_size_, channels_, height_, width_, size_;
-  Dtype* data_;
-  Dtype* labels_;
-  int n_;
-  size_t pos_;
-  Blob<Dtype> added_data_;
-  Blob<Dtype> added_label_;
-  bool has_new_data_;
-};
-
-/**
- * @brief Provides data to the Net from windows of images files, specified
- *        by a window data file.
- *
- * TODO(dox): thorough documentation for Forward and proto params.
- */
-template <typename Dtype>
-class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
- public:
-  explicit WindowDataLayer(const LayerParameter& param)
-      : BasePrefetchingDataLayer<Dtype>(param) {}
-  virtual ~WindowDataLayer();
-  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "WindowData"; }
-  virtual inline int ExactNumBottomBlobs() const { return 0; }
-  virtual inline int ExactNumTopBlobs() const { return 2; }
-
- protected:
-  virtual unsigned int PrefetchRand();
-  virtual void InternalThreadEntry();
-
-  shared_ptr<Caffe::RNG> prefetch_rng_;
-  vector<std::pair<std::string, vector<int> > > image_database_;
-  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
-  vector<vector<float> > fg_windows_;
-  vector<vector<float> > bg_windows_;
-  Blob<Dtype> data_mean_;
-  vector<Dtype> mean_values_;
-  bool has_mean_file_;
-  bool has_mean_values_;
-  bool cache_images_;
-  vector<std::pair<std::string, Datum > > image_database_cache_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_DATA_LAYERS_HPP_
diff --git a/include/caffe/data_reader.hpp b/include/caffe/data_reader.hpp
new file mode 100644
index 00000000000..8ed5542cb8d
--- /dev/null
+++ b/include/caffe/data_reader.hpp
@@ -0,0 +1,82 @@
+#ifndef CAFFE_DATA_READER_HPP_
+#define CAFFE_DATA_READER_HPP_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/util/blocking_queue.hpp"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Reads data from a source to queues available to data layers.
+ * A single reading thread is created per source, even if multiple solvers
+ * are running in parallel, e.g. for multi-GPU training. This makes sure
+ * databases are read sequentially, and that each solver accesses a different
+ * subset of the database. Data is distributed to solvers in a round-robin
+ * way to keep parallel training deterministic.
+ */
+class DataReader {
+ public:
+  explicit DataReader(const LayerParameter& param);
+  ~DataReader();
+
+  inline BlockingQueue<Datum*>& free() const {
+    return queue_pair_->free_;
+  }
+  inline BlockingQueue<Datum*>& full() const {
+    return queue_pair_->full_;
+  }
+
+ protected:
+  // Queue pairs are shared between a body and its readers
+  class QueuePair {
+   public:
+    explicit QueuePair(int size);
+    ~QueuePair();
+
+    BlockingQueue<Datum*> free_;
+    BlockingQueue<Datum*> full_;
+
+  DISABLE_COPY_AND_ASSIGN(QueuePair);
+  };
+
+  // A single body is created per source
+  class Body : public InternalThread {
+   public:
+    explicit Body(const LayerParameter& param);
+    virtual ~Body();
+
+   protected:
+    void InternalThreadEntry();
+    void read_one(db::Cursor* cursor, QueuePair* qp);
+
+    const LayerParameter param_;
+    BlockingQueue<shared_ptr<QueuePair> > new_queue_pairs_;
+
+    friend class DataReader;
+
+  DISABLE_COPY_AND_ASSIGN(Body);
+  };
+
+  // A source is uniquely identified by its layer name + path, in case
+  // the same database is read from two different locations in the net.
+  static inline string source_key(const LayerParameter& param) {
+    return param.name() + ":" + param.data_param().source();
+  }
+
+  const shared_ptr<QueuePair> queue_pair_;
+  shared_ptr<Body> body_;
+
+  static map<const string, boost::weak_ptr<DataReader::Body> > bodies_;
+
+DISABLE_COPY_AND_ASSIGN(DataReader);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_READER_HPP_
diff --git a/include/caffe/data_transformer.hpp b/include/caffe/data_transformer.hpp
index 880356601a4..97b4ee6a8c4 100644
--- a/include/caffe/data_transformer.hpp
+++ b/include/caffe/data_transformer.hpp
@@ -50,6 +50,7 @@ class DataTransformer {
   void Transform(const vector<Datum> & datum_vector,
                 Blob<Dtype>* transformed_blob);
 
+#ifdef USE_OPENCV
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a vector of Mat.
@@ -62,6 +63,7 @@ class DataTransformer {
    */
   void Transform(const vector<cv::Mat> & mat_vector,
                 Blob<Dtype>* transformed_blob);
+
   /**
    * @brief Applies the transformation defined in the data layer's
    * transform_param block to a cv::Mat
@@ -73,6 +75,7 @@ class DataTransformer {
    *    set_cpu_data() is used. See image_data_layer.cpp for an example.
    */
   void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob);
+#endif  // USE_OPENCV
 
   /**
    * @brief Applies the same transformation defined in the data layer's
@@ -87,6 +90,43 @@ class DataTransformer {
    */
   void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);
 
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param datum
+   *    Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const Datum& datum);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param datum_vector
+   *    A vector of Datum containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const vector<Datum> & datum_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *    It uses the first element to infer the shape of the blob.
+   *
+   * @param mat_vector
+   *    A vector of Mat containing the data to be transformed.
+   */
+#ifdef USE_OPENCV
+  vector<int> InferBlobShape(const vector<cv::Mat> & mat_vector);
+  /**
+   * @brief Infers the shape of transformed_blob will have when
+   *    the transformation is applied to the data.
+   *
+   * @param cv_img
+   *    cv::Mat containing the data to be transformed.
+   */
+  vector<int> InferBlobShape(const cv::Mat& cv_img);
+#endif  // USE_OPENCV
+
  protected:
    /**
    * @brief Generates a random integer from Uniform({0, 1, ..., n-1}).
@@ -112,4 +152,3 @@ class DataTransformer {
 }  // namespace caffe
 
 #endif  // CAFFE_DATA_TRANSFORMER_HPP_
-
diff --git a/include/caffe/filler.hpp b/include/caffe/filler.hpp
index bb18e8e1e28..dad9ad46b3b 100644
--- a/include/caffe/filler.hpp
+++ b/include/caffe/filler.hpp
@@ -8,7 +8,6 @@
 #include <string>
 
 #include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -126,17 +125,18 @@ class PositiveUnitballFiller : public Filler<Dtype> {
 };
 
 /**
- * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$
- *        is set inversely proportional to the number of incoming nodes.
+ * @brief Fills a Blob with values @f$ x \sim U(-a, +a) @f$ where @f$ a @f$ is
+ *        set inversely proportional to number of incoming nodes, outgoing
+ *        nodes, or their average.
  *
  * A Filler based on the paper [Bengio and Glorot 2010]: Understanding
- * the difficulty of training deep feedforward neuralnetworks, but does not
- * use the fan_out value.
+ * the difficulty of training deep feedforward neuralnetworks.
  *
- * It fills the incoming matrix by randomly sampling uniform data from
- * [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
- * of input nodes. You should make sure the input blob has shape (num, a, b, c)
- * where a * b * c = fan_in.
+ * It fills the incoming matrix by randomly sampling uniform data from [-scale,
+ * scale] where scale = sqrt(3 / n) where n is the fan_in, fan_out, or their
+ * average, depending on the variance_norm option. You should make sure the
+ * input blob has shape (num, a, b, c) where a * b * c = fan_in and num * b * c
+ * = fan_out. Note that this is currently not the case for inner product layers.
  *
  * TODO(dox): make notation in above comment consistent with rest & use LaTeX.
  */
@@ -148,7 +148,16 @@ class XavierFiller : public Filler<Dtype> {
   virtual void Fill(Blob<Dtype>* blob) {
     CHECK(blob->count());
     int fan_in = blob->count() / blob->num();
-    Dtype scale = sqrt(Dtype(3) / fan_in);
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype scale = sqrt(Dtype(3) / n);
     caffe_rng_uniform<Dtype>(blob->count(), -scale, scale,
         blob->mutable_cpu_data());
     CHECK_EQ(this->filler_param_.sparse(), -1)
@@ -156,6 +165,101 @@ class XavierFiller : public Filler<Dtype> {
   }
 };
 
+/**
+ * @brief Fills a Blob with values @f$ x \sim N(0, \sigma^2) @f$ where
+ *        @f$ \sigma^2 @f$ is set inversely proportional to number of incoming
+ *        nodes, outgoing nodes, or their average.
+ *
+ * A Filler based on the paper [He, Zhang, Ren and Sun 2015]: Specifically
+ * accounts for ReLU nonlinearities.
+ *
+ * Aside: for another perspective on the scaling factor, see the derivation of
+ * [Saxe, McClelland, and Ganguli 2013 (v3)].
+ *
+ * It fills the incoming matrix by randomly sampling Gaussian data with std =
+ * sqrt(2 / n) where n is the fan_in, fan_out, or their average, depending on
+ * the variance_norm option. You should make sure the input blob has shape (num,
+ * a, b, c) where a * b * c = fan_in and num * b * c = fan_out. Note that this
+ * is currently not the case for inner product layers.
+ */
+template <typename Dtype>
+class MSRAFiller : public Filler<Dtype> {
+ public:
+  explicit MSRAFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK(blob->count());
+    int fan_in = blob->count() / blob->num();
+    int fan_out = blob->count() / blob->channels();
+    Dtype n = fan_in;  // default to fan_in
+    if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_AVERAGE) {
+      n = (fan_in + fan_out) / Dtype(2);
+    } else if (this->filler_param_.variance_norm() ==
+        FillerParameter_VarianceNorm_FAN_OUT) {
+      n = fan_out;
+    }
+    Dtype std = sqrt(Dtype(2) / n);
+    caffe_rng_gaussian<Dtype>(blob->count(), Dtype(0), std,
+        blob->mutable_cpu_data());
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
+
+/*!
+@brief Fills a Blob with coefficients for bilinear interpolation.
+
+A common use case is with the DeconvolutionLayer acting as upsampling.
+You can upsample a feature map with shape of (B, C, H, W) by any integer factor
+using the following proto.
+\code
+layer {
+  name: "upsample", type: "Deconvolution"
+  bottom: "{{bottom_name}}" top: "{{top_name}}"
+  convolution_param {
+    kernel_size: {{2 * factor - factor % 2}} stride: {{factor}}
+    num_output: {{C}} group: {{C}}
+    pad: {{ceil((factor - 1) / 2.)}}
+    weight_filler: { type: "bilinear" } bias_term: false
+  }
+  param { lr_mult: 0 decay_mult: 0 }
+}
+\endcode
+Please use this by replacing `{{}}` with your values. By specifying
+`num_output: {{C}} group: {{C}}`, it behaves as
+channel-wise convolution. The filter shape of this deconvolution layer will be
+(C, 1, K, K) where K is `kernel_size`, and this filler will set a (K, K)
+interpolation kernel for every channel of the filter identically. The resulting
+shape of the top feature map will be (B, C, factor * H, factor * W).
+Note that the learning rate and the
+weight decay are set to 0 in order to keep coefficient values of bilinear
+interpolation unchanged during training. If you apply this to an image, this
+operation is equivalent to the following call in Python with Scikit.Image.
+\code{.py}
+out = skimage.transform.rescale(img, factor, mode='constant', cval=0)
+\endcode
+ */
+template <typename Dtype>
+class BilinearFiller : public Filler<Dtype> {
+ public:
+  explicit BilinearFiller(const FillerParameter& param)
+      : Filler<Dtype>(param) {}
+  virtual void Fill(Blob<Dtype>* blob) {
+    CHECK_EQ(blob->num_axes(), 4) << "Blob must be 4 dim.";
+    CHECK_EQ(blob->width(), blob->height()) << "Filter must be square";
+    Dtype* data = blob->mutable_cpu_data();
+    int f = ceil(blob->width() / 2.);
+    float c = (2 * f - 1 - f % 2) / (2. * f);
+    for (int i = 0; i < blob->count(); ++i) {
+      float x = i % blob->width();
+      float y = (i / blob->width()) % blob->height();
+      data[i] = (1 - fabs(x / f - c)) * (1 - fabs(y / f - c));
+    }
+    CHECK_EQ(this->filler_param_.sparse(), -1)
+         << "Sparsity not supported by this Filler.";
+  }
+};
 
 /**
  * @brief Get a specific filler from the specification given in FillerParameter.
@@ -176,6 +280,10 @@ Filler<Dtype>* GetFiller(const FillerParameter& param) {
     return new UniformFiller<Dtype>(param);
   } else if (type == "xavier") {
     return new XavierFiller<Dtype>(param);
+  } else if (type == "msra") {
+    return new MSRAFiller<Dtype>(param);
+  } else if (type == "bilinear") {
+    return new BilinearFiller<Dtype>(param);
   } else {
     CHECK(false) << "Unknown filler name: " << param.type();
   }
diff --git a/include/caffe/internal_thread.hpp b/include/caffe/internal_thread.hpp
index 815ca54605e..6a8c5a02892 100644
--- a/include/caffe/internal_thread.hpp
+++ b/include/caffe/internal_thread.hpp
@@ -14,18 +14,22 @@ namespace caffe {
 /**
  * Virtual class encapsulate boost::thread for use in base class
  * The child class will acquire the ability to run a single thread,
- * by reimplementing the virutal function InternalThreadEntry.
+ * by reimplementing the virtual function InternalThreadEntry.
  */
 class InternalThread {
  public:
   InternalThread() : thread_() {}
   virtual ~InternalThread();
 
-  /** Returns true if the thread was successfully started. **/
-  bool StartInternalThread();
+  /**
+   * Caffe's thread local state will be initialized using the current
+   * thread values, e.g. device id, solver index etc. The random seed
+   * is initialized using caffe_rng_rand.
+   */
+  void StartInternalThread();
 
   /** Will not return until the internal thread has exited. */
-  bool WaitForInternalThreadToExit();
+  void StopInternalThread();
 
   bool is_started() const;
 
@@ -34,6 +38,13 @@ class InternalThread {
       with the code you want your thread to run. */
   virtual void InternalThreadEntry() {}
 
+  /* Should be tested when running loops to exit when requested. */
+  bool must_stop();
+
+ private:
+  void entry(int device, Caffe::Brew mode, int rand_seed, int solver_count,
+      bool root_solver);
+
   shared_ptr<boost::thread> thread_;
 };
 
diff --git a/include/caffe/layer.hpp b/include/caffe/layer.hpp
index 2d13ef97c05..10f353f94f9 100644
--- a/include/caffe/layer.hpp
+++ b/include/caffe/layer.hpp
@@ -9,7 +9,13 @@
 #include "caffe/common.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/util/device_alternate.hpp"
+#include "caffe/util/math_functions.hpp"
+
+/**
+ Forward declare boost::thread instead of including boost/thread.hpp
+ to avoid a boost/NVCC issues (#1009, #1010) on OSX.
+ */
+namespace boost { class mutex; }
 
 namespace caffe {
 
@@ -32,7 +38,7 @@ class Layer {
    * layer.
    */
   explicit Layer(const LayerParameter& param)
-    : layer_param_(param) {
+    : layer_param_(param), is_shared_(false) {
       // Set phase and copy blobs (if there are any).
       phase_ = param.phase();
       if (layer_param_.blobs_size() > 0) {
@@ -60,6 +66,7 @@ class Layer {
    */
   void SetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+    InitMutex();
     CheckBlobCounts(bottom, top);
     LayerSetUp(bottom, top);
     Reshape(bottom, top);
@@ -86,7 +93,31 @@ class Layer {
       const vector<Blob<Dtype>*>& top) {}
 
   /**
-   * @brief Adjust the shapes of top blobs and internal buffers to accomodate
+   * @brief Whether a layer should be shared by multiple nets during data
+   *        parallelism. By default, all layers except for data layers should
+   *        not be shared. data layers should be shared to ensure each worker
+   *        solver access data sequentially during data parallelism.
+   */
+  virtual inline bool ShareInParallel() const { return false; }
+
+  /** @brief Return whether this layer is actually shared by other nets.
+   *         If ShareInParallel() is true and using more than one GPU and the
+   *         net has TRAIN phase, then this function is expected return true.
+   */
+  inline bool IsShared() const { return is_shared_; }
+
+  /** @brief Set whether this layer is actually shared by other nets
+   *         If ShareInParallel() is true and using more than one GPU and the
+   *         net has TRAIN phase, then is_shared should be set true.
+   */
+  inline void SetShared(bool is_shared) {
+    CHECK(ShareInParallel() || !is_shared)
+        << type() << "Layer does not support sharing.";
+    is_shared_ = is_shared;
+  }
+
+  /**
+   * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
    *
    * @param bottom the input blobs, with the requested input shapes
@@ -95,7 +126,7 @@ class Layer {
    * This method should reshape top blobs as needed according to the shapes
    * of the bottom (input) blobs, as well as reshaping any internal buffers
    * and making any other necessary adjustments so that the layer can
-   * accomodate the bottom blobs.
+   * accommodate the bottom blobs.
    */
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) = 0;
@@ -139,7 +170,7 @@ class Layer {
    * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
    * top blob diffs.
    *
-   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
+   * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
    */
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
@@ -396,6 +427,20 @@ class Layer {
     }
   }
 
+ private:
+  /** Whether this layer is actually shared by other nets*/
+  bool is_shared_;
+
+  /** The mutex for sequential forward if this layer is shared */
+  shared_ptr<boost::mutex> forward_mutex_;
+
+  /** Initialize forward_mutex_ */
+  void InitMutex();
+  /** Lock forward_mutex_ if this layer is shared */
+  void Lock();
+  /** Unlock forward_mutex_ if this layer is shared */
+  void Unlock();
+
   DISABLE_COPY_AND_ASSIGN(Layer);
 };  // class Layer
 
@@ -405,7 +450,10 @@ class Layer {
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
+  // Lock during forward to ensure sequential forward
+  Lock();
   Dtype loss = 0;
+  Reshape(bottom, top);
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Forward_cpu(bottom, top);
@@ -434,6 +482,7 @@ inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
+  Unlock();
   return loss;
 }
 
diff --git a/include/caffe/layer_factory.hpp b/include/caffe/layer_factory.hpp
index 2fcd93869a0..f385afccfee 100644
--- a/include/caffe/layer_factory.hpp
+++ b/include/caffe/layer_factory.hpp
@@ -41,8 +41,10 @@
 
 #include <map>
 #include <string>
+#include <vector>
 
 #include "caffe/common.hpp"
+#include "caffe/layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 
 namespace caffe {
@@ -71,30 +73,42 @@ class LayerRegistry {
 
   // Get a layer using a LayerParameter.
   static shared_ptr<Layer<Dtype> > CreateLayer(const LayerParameter& param) {
-    LOG(INFO) << "Creating layer " << param.name();
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Creating layer " << param.name();
+    }
     const string& type = param.type();
     CreatorRegistry& registry = Registry();
     CHECK_EQ(registry.count(type), 1) << "Unknown layer type: " << type
-        << " (known types: " << LayerTypeList() << ")";
+        << " (known types: " << LayerTypeListString() << ")";
     return registry[type](param);
   }
 
+  static vector<string> LayerTypeList() {
+    CreatorRegistry& registry = Registry();
+    vector<string> layer_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      layer_types.push_back(iter->first);
+    }
+    return layer_types;
+  }
+
  private:
   // Layer registry should never be instantiated - everything is done with its
   // static variables.
   LayerRegistry() {}
 
-  static string LayerTypeList() {
-    CreatorRegistry& registry = Registry();
-    string layer_types;
-    for (typename CreatorRegistry::iterator iter = registry.begin();
-         iter != registry.end(); ++iter) {
-      if (iter != registry.begin()) {
-        layer_types += ", ";
+  static string LayerTypeListString() {
+    vector<string> layer_types = LayerTypeList();
+    string layer_types_str;
+    for (vector<string>::iterator iter = layer_types.begin();
+         iter != layer_types.end(); ++iter) {
+      if (iter != layer_types.begin()) {
+        layer_types_str += ", ";
       }
-      layer_types += iter->first;
+      layer_types_str += *iter;
     }
-    return layer_types;
+    return layer_types_str;
   }
 };
 
diff --git a/include/caffe/layers/absval_layer.hpp b/include/caffe/layers/absval_layer.hpp
new file mode 100644
index 00000000000..9b5305dceb4
--- /dev/null
+++ b/include/caffe/layers/absval_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_ABSVAL_LAYER_HPP_
+#define CAFFE_ABSVAL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = |x| @f$
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class AbsValLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit AbsValLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "AbsVal"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /// @copydoc AbsValLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the absolute value inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \mathrm{sign}(x) \frac{\partial E}{\partial y}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ABSVAL_LAYER_HPP_
diff --git a/include/caffe/layers/accuracy_layer.hpp b/include/caffe/layers/accuracy_layer.hpp
new file mode 100644
index 00000000000..fe2adb939e4
--- /dev/null
+++ b/include/caffe/layers/accuracy_layer.hpp
@@ -0,0 +1,95 @@
+#ifndef CAFFE_ACCURACY_LAYER_HPP_
+#define CAFFE_ACCURACY_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the classification accuracy for a one-of-many
+ *        classification task.
+ */
+template <typename Dtype>
+class AccuracyLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides AccuracyParameter accuracy_param,
+   *     with AccuracyLayer options:
+   *   - top_k (\b optional, default 1).
+   *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
+   *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
+   *     correct if the correct label is among the top 5 predicted labels.
+   */
+  explicit AccuracyLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Accuracy"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+  // If there are two top blobs, then the second blob will contain
+  // accuracies per class.
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlos() const { return 2; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$, a Blob with values in
+   *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+   *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
+   *      label @f$ \hat{l}_n @f$ given by its maximal index:
+   *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels @f$ l @f$, an integer-valued Blob with values
+   *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+   *      indicating the correct class label among the @f$ K @f$ classes
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      the computed accuracy: @f$
+   *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
+   *      @f$, where @f$
+   *      \delta\{\mathrm{condition}\} = \left\{
+   *         \begin{array}{lr}
+   *            1 & \mbox{if condition} \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+
+  /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    for (int i = 0; i < propagate_down.size(); ++i) {
+      if (propagate_down[i]) { NOT_IMPLEMENTED; }
+    }
+  }
+
+  int label_axis_, outer_num_, inner_num_;
+
+  int top_k_;
+
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// Keeps counts of the number of samples per class.
+  Blob<Dtype> nums_buffer_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ACCURACY_LAYER_HPP_
diff --git a/include/caffe/layers/argmax_layer.hpp b/include/caffe/layers/argmax_layer.hpp
new file mode 100644
index 00000000000..4fef363e850
--- /dev/null
+++ b/include/caffe/layers/argmax_layer.hpp
@@ -0,0 +1,77 @@
+#ifndef CAFFE_ARGMAX_LAYER_HPP_
+#define CAFFE_ARGMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute the index of the @f$ K @f$ max values for each datum across
+ *        all dimensions @f$ (C \times H \times W) @f$.
+ *
+ * Intended for use after a classification layer to produce a prediction.
+ * If parameter out_max_val is set to true, output is a vector of pairs
+ * (max_ind, max_val) for each image. The axis parameter specifies an axis
+ * along which to maximise.
+ *
+ * NOTE: does not implement Backwards operation.
+ */
+template <typename Dtype>
+class ArgMaxLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides ArgMaxParameter argmax_param,
+   *     with ArgMaxLayer options:
+   *   - top_k (\b optional uint, default 1).
+   *     the number @f$ K @f$ of maximal items to output.
+   *   - out_max_val (\b optional bool, default false).
+   *     if set, output a vector of pairs (max_ind, max_val) unless axis is set then
+   *     output max_val along the specified axis.
+   *   - axis (\b optional int).
+   *     if set, maximise along the specified axis else maximise the flattened
+   *     trailing dimensions for each index of the first / num dimension.
+   */
+  explicit ArgMaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ArgMax"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times 1 \times K) @f$ or, if out_max_val
+   *      @f$ (N \times 2 \times K) @f$ unless axis set than e.g.
+   *      @f$ (N \times K \times H \times W) @f$ if axis == 1
+   *      the computed outputs @f$
+   *       y_n = \arg\max\limits_i x_{ni}
+   *      @f$ (for @f$ K = 1 @f$).
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+  bool out_max_val_;
+  size_t top_k_;
+  bool has_axis_;
+  int axis_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ARGMAX_LAYER_HPP_
diff --git a/include/caffe/layers/base_conv_layer.hpp b/include/caffe/layers/base_conv_layer.hpp
new file mode 100644
index 00000000000..0160a833dd2
--- /dev/null
+++ b/include/caffe/layers/base_conv_layer.hpp
@@ -0,0 +1,174 @@
+#ifndef CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+#define CAFFE_BASE_CONVOLUTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/im2col.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Abstract base class that factors out the BLAS code common to
+ *        ConvolutionLayer and DeconvolutionLayer.
+ */
+template <typename Dtype>
+class BaseConvolutionLayer : public Layer<Dtype> {
+ public:
+  explicit BaseConvolutionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
+
+ protected:
+  // Helper functions that abstract away the column buffer and gemm arguments.
+  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
+  // we just called weight_cpu_gemm with the same input.
+  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_cpu_bias(Dtype* output, const Dtype* bias);
+  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* output);
+  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
+      weights);
+  void backward_cpu_bias(Dtype* bias, const Dtype* input);
+
+#ifndef CPU_ONLY
+  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
+      Dtype* output, bool skip_im2col = false);
+  void forward_gpu_bias(Dtype* output, const Dtype* bias);
+  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
+      Dtype* col_output);
+  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
+      weights);
+  void backward_gpu_bias(Dtype* bias, const Dtype* input);
+#endif
+
+  /// @brief The spatial dimensions of the input.
+  inline int input_shape(int i) {
+    return (*bottom_shape_)[channel_axis_ + i];
+  }
+  // reverse_dimensions should return true iff we are implementing deconv, so
+  // that conv helpers know which dimensions are which.
+  virtual bool reverse_dimensions() = 0;
+  // Compute height_out_ and width_out_ from other parameters.
+  virtual void compute_output_shape() = 0;
+
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+  /// @brief The spatial dimensions of the dilation.
+  Blob<int> dilation_;
+  /// @brief The spatial dimensions of the convolution input.
+  Blob<int> conv_input_shape_;
+  /// @brief The spatial dimensions of the col_buffer.
+  vector<int> col_buffer_shape_;
+  /// @brief The spatial dimensions of the output.
+  vector<int> output_shape_;
+  const vector<int>* bottom_shape_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
+  int num_;
+  int channels_;
+  int group_;
+  int out_spatial_dim_;
+  int weight_offset_;
+  int num_output_;
+  bool bias_term_;
+  bool is_1x1_;
+  bool force_nd_im2col_;
+
+ private:
+  // wrap im2col/col2im so we don't have to remember the (long) argument lists
+  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_cpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_cpu(data, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), col_buff);
+    }
+  }
+  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_cpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
+    } else {
+      col2im_nd_cpu(col_buff, num_spatial_axes_, conv_input_shape_.cpu_data(),
+          col_buffer_shape_.data(), kernel_shape_.cpu_data(),
+          pad_.cpu_data(), stride_.cpu_data(), dilation_.cpu_data(), data);
+    }
+  }
+#ifndef CPU_ONLY
+  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_gpu(data, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], col_buff);
+    } else {
+      im2col_nd_gpu(data, num_spatial_axes_, num_kernels_im2col_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(),
+          stride_.gpu_data(), dilation_.gpu_data(), col_buff);
+    }
+  }
+  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_gpu(col_buff, conv_in_channels_,
+          conv_input_shape_.cpu_data()[1], conv_input_shape_.cpu_data()[2],
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1], data);
+    } else {
+      col2im_nd_gpu(col_buff, num_spatial_axes_, num_kernels_col2im_,
+          conv_input_shape_.gpu_data(), col_buffer_.gpu_shape(),
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          dilation_.gpu_data(), data);
+    }
+  }
+#endif
+
+  int num_kernels_im2col_;
+  int num_kernels_col2im_;
+  int conv_out_channels_;
+  int conv_in_channels_;
+  int conv_out_spatial_dim_;
+  int kernel_dim_;
+  int col_offset_;
+  int output_offset_;
+
+  Blob<Dtype> col_buffer_;
+  Blob<Dtype> bias_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BASE_CONVOLUTION_LAYER_HPP_
diff --git a/include/caffe/layers/base_data_layer.hpp b/include/caffe/layers/base_data_layer.hpp
new file mode 100644
index 00000000000..2c49b73184b
--- /dev/null
+++ b/include/caffe/layers/base_data_layer.hpp
@@ -0,0 +1,86 @@
+#ifndef CAFFE_DATA_LAYERS_HPP_
+#define CAFFE_DATA_LAYERS_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides base for data layers that feed blobs to the Net.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class BaseDataLayer : public Layer<Dtype> {
+ public:
+  explicit BaseDataLayer(const LayerParameter& param);
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden except by the BasePrefetchingDataLayer.
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+ protected:
+  TransformationParameter transform_param_;
+  shared_ptr<DataTransformer<Dtype> > data_transformer_;
+  bool output_labels_;
+};
+
+template <typename Dtype>
+class Batch {
+ public:
+  Blob<Dtype> data_, label_;
+};
+
+template <typename Dtype>
+class BasePrefetchingDataLayer :
+    public BaseDataLayer<Dtype>, public InternalThread {
+ public:
+  explicit BasePrefetchingDataLayer(const LayerParameter& param);
+  // LayerSetUp: implements common data layer setup functionality, and calls
+  // DataLayerSetUp to do special data layer setup for individual layer types.
+  // This method may not be overridden.
+  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  // Prefetches batches (asynchronously if to GPU memory)
+  static const int PREFETCH_COUNT = 3;
+
+ protected:
+  virtual void InternalThreadEntry();
+  virtual void load_batch(Batch<Dtype>* batch) = 0;
+
+  Batch<Dtype> prefetch_[PREFETCH_COUNT];
+  BlockingQueue<Batch<Dtype>*> prefetch_free_;
+  BlockingQueue<Batch<Dtype>*> prefetch_full_;
+
+  Blob<Dtype> transformed_data_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_LAYERS_HPP_
diff --git a/include/caffe/layers/batch_norm_layer.hpp b/include/caffe/layers/batch_norm_layer.hpp
new file mode 100644
index 00000000000..9b2d5126efb
--- /dev/null
+++ b/include/caffe/layers/batch_norm_layer.hpp
@@ -0,0 +1,81 @@
+#ifndef CAFFE_BATCHNORM_LAYER_HPP_
+#define CAFFE_BATCHNORM_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance across
+ *        the batch.
+ *
+ * This layer computes Batch Normalization described in [1].  For
+ * each channel in the data (i.e. axis 1), it subtracts the mean and divides
+ * by the variance, where both statistics are computed across both spatial
+ * dimensions and across the different examples in the batch.
+ *
+ * By default, during training time, the network is computing global mean/
+ * variance statistics via a running average, which is then used at test
+ * time to allow deterministic outputs for each input.  You can manually
+ * toggle whether the network is accumulating or using the statistics via the
+ * use_global_stats option.  IMPORTANT: for this feature to work, you MUST
+ * set the learning rate to zero for all three parameter blobs, i.e.,
+ * param {lr_mult: 0} three times in the layer definition.
+ *
+ * Note that the original paper also included a per-channel learned bias and
+ * scaling factor.  It is possible (though a bit cumbersome) to implement
+ * this in caffe using a single-channel DummyDataLayer filled with zeros,
+ * followed by a Convolution layer with output the same size as the current.
+ * This produces a channel-specific value that can be added or multiplied by
+ * the BatchNorm layer's output.
+ *
+ * [1] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network
+ *     Training by Reducing Internal Covariate Shift." arXiv preprint
+ *     arXiv:1502.03167 (2015).
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class BatchNormLayer : public Layer<Dtype> {
+ public:
+  explicit BatchNormLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BatchNorm"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> mean_, variance_, temp_, x_norm_;
+  bool use_global_stats_;
+  Dtype moving_average_fraction_;
+  int channels_;
+  Dtype eps_;
+
+  // extra temporarary variables is used to carry out sums/broadcasting
+  // using BLAS
+  Blob<Dtype> batch_sum_multiplier_;
+  Blob<Dtype> num_by_chans_;
+  Blob<Dtype> spatial_sum_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BATCHNORM_LAYER_HPP_
diff --git a/include/caffe/layers/batch_reindex_layer.hpp b/include/caffe/layers/batch_reindex_layer.hpp
new file mode 100644
index 00000000000..ebb3a567bc4
--- /dev/null
+++ b/include/caffe/layers/batch_reindex_layer.hpp
@@ -0,0 +1,83 @@
+#ifndef CAFFE_BATCHREINDEX_LAYER_HPP_
+#define CAFFE_BATCHREINDEX_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Index into the input blob along its first axis.
+ *
+ * This layer can be used to select, reorder, and even replicate examples in a
+ * batch.  The second blob is cast to int and treated as an index into the
+ * first axis of the first blob.
+ */
+template <typename Dtype>
+class BatchReindexLayer : public Layer<Dtype> {
+ public:
+  explicit BatchReindexLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "BatchReindex"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times ...) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (M) @f$
+   *      the inputs @f$ x_2 @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (M \times ...) @f$:
+   *      the reindexed array @f$
+   *        y = x_1[x_2]
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the reordered input.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *        with respect to the outputs
+   *   -# @f$ (M \times ...) @f$:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2):
+   *   - @f$ \frac{\partial E}{\partial y} @f$ is de-indexed (summing where
+   *     required) back to the input x_1
+   *   - This layer cannot backprop to x_2, i.e. propagate_down[1] must be
+   *     false.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+  struct pair_sort_first {
+    bool operator()(const std::pair<int, int> &left,
+                    const std::pair<int, int> &right) {
+      return left.first < right.first;
+    }
+  };
+  void check_batch_reindex(int initial_num, int final_num,
+                           const Dtype* ridx_data);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BATCHREINDEX_LAYER_HPP_
diff --git a/include/caffe/layers/bias_layer.hpp b/include/caffe/layers/bias_layer.hpp
new file mode 100644
index 00000000000..eedc3aaa351
--- /dev/null
+++ b/include/caffe/layers/bias_layer.hpp
@@ -0,0 +1,54 @@
+#ifndef CAFFE_BIAS_LAYER_HPP_
+#define CAFFE_BIAS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Computes a sum of two input Blobs, with the shape of the
+ *        latter Blob "broadcast" to match the shape of the former.
+ *        Equivalent to tiling the latter Blob, then computing the elementwise
+ *        sum.
+ *
+ * The second input may be omitted, in which case it's learned as a parameter
+ * of the layer.
+ */
+template <typename Dtype>
+class BiasLayer : public Layer<Dtype> {
+ public:
+  explicit BiasLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Bias"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MaxBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+ private:
+  Blob<Dtype> bias_multiplier_;
+  int outer_dim_, bias_dim_, inner_dim_, dim_;
+};
+
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_BIAS_LAYER_HPP_
diff --git a/include/caffe/layers/bnll_layer.hpp b/include/caffe/layers/bnll_layer.hpp
new file mode 100644
index 00000000000..be07c748364
--- /dev/null
+++ b/include/caffe/layers/bnll_layer.hpp
@@ -0,0 +1,70 @@
+#ifndef CAFFE_BNLL_LAYER_HPP_
+#define CAFFE_BNLL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
+ *        @f$ y = \log(1 + \exp(x)) @f$ otherwise.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$
+ *      y = \left\{
+ *         \begin{array}{ll}
+ *            x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
+ *            \log(1 + \exp(x)) & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ */
+template <typename Dtype>
+class BNLLLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit BNLLLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "BNLL"; }
+
+ protected:
+  /// @copydoc BNLLLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the BNLL inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_BNLL_LAYER_HPP_
diff --git a/include/caffe/layers/concat_layer.hpp b/include/caffe/layers/concat_layer.hpp
new file mode 100644
index 00000000000..a1570249197
--- /dev/null
+++ b/include/caffe/layers/concat_layer.hpp
@@ -0,0 +1,87 @@
+#ifndef CAFFE_CONCAT_LAYER_HPP_
+#define CAFFE_CONCAT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes at least two Blob%s and concatenates them along either the num
+ *        or channel dimension, outputting the result.
+ */
+template <typename Dtype>
+class ConcatLayer : public Layer<Dtype> {
+ public:
+  explicit ConcatLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Concat"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_1 @f$
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_2 @f$
+   *   -# ...
+   *   - K @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x_K @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+   *      the concatenated output @f$
+   *        y = [\begin{array}{cccc} x_1 & x_2 & ... & x_K \end{array}]
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   *   -# @f$ (KN \times C \times H \times W) @f$ if axis == 0, or
+   *      @f$ (N \times KC \times H \times W) @f$ if axis == 1:
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to concatenated outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top gradient
+   *        @f$ \frac{\partial E}{\partial y} @f$ is deconcatenated back to the
+   *        inputs @f$
+   *        \left[ \begin{array}{cccc}
+   *          \frac{\partial E}{\partial x_1} &
+   *          \frac{\partial E}{\partial x_2} &
+   *          ... &
+   *          \frac{\partial E}{\partial x_K}
+   *        \end{array} \right] =
+   *        \frac{\partial E}{\partial y}
+   *        @f$
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+  int num_concats_;
+  int concat_input_size_;
+  int concat_axis_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONCAT_LAYER_HPP_
diff --git a/include/caffe/layers/contrastive_loss_layer.hpp b/include/caffe/layers/contrastive_loss_layer.hpp
new file mode 100644
index 00000000000..e890afb8207
--- /dev/null
+++ b/include/caffe/layers/contrastive_loss_layer.hpp
@@ -0,0 +1,101 @@
+#ifndef CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+#define CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the contrastive loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ *              \left(1-y\right) \max \left(margin-d, 0\right)^2
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$. This can be
+ *          used to train siamese networks.
+ *
+ * @param bottom input Blob vector (length 3)
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ a \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times 1 \times 1) @f$
+ *      the features @f$ b \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the binary similarity @f$ s \in [0, 1]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed contrastive loss: @f$ E =
+ *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d^2 +
+ *          \left(1-y\right) \max \left(margin-d, 0\right)^2
+ *          @f$ where @f$
+ *          d = \left| \left| a_n - b_n \right| \right|_2 @f$.
+ * This can be used to train siamese networks.
+ */
+template <typename Dtype>
+class ContrastiveLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit ContrastiveLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 3; }
+  virtual inline const char* type() const { return "ContrastiveLoss"; }
+  /**
+   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
+   * to the first two inputs.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 2;
+  }
+
+ protected:
+  /// @copydoc ContrastiveLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
+   *
+   * Computes the gradients with respect to the two input vectors (bottom[0] and
+   * bottom[1]), but not the similarity label (bottom[2]).
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$a@f$; Backward fills their diff with
+   *      gradients if propagate_down[0]
+   *   -# @f$ (N \times C \times 1 \times 1) @f$
+   *      the features @f$b@f$; Backward fills their diff with gradients if
+   *      propagate_down[1]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> diff_;  // cached for backward pass
+  Blob<Dtype> dist_sq_;  // cached for backward pass
+  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
+  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONTRASTIVE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/conv_layer.hpp b/include/caffe/layers/conv_layer.hpp
new file mode 100644
index 00000000000..93a618ddd72
--- /dev/null
+++ b/include/caffe/layers/conv_layer.hpp
@@ -0,0 +1,84 @@
+#ifndef CAFFE_CONV_LAYER_HPP_
+#define CAFFE_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolves the input image with a bank of learned filters,
+ *        and (optionally) adds biases.
+ *
+ *   Caffe convolves by reduction to matrix multiplication. This achieves
+ *   high-throughput and generality of input and filter dimensions but comes at
+ *   the cost of memory for matrices. This makes use of efficiency in BLAS.
+ *
+ *   The input is "im2col" transformed to a channel K' x H x W data matrix
+ *   for multiplication with the N x K' x H x W filter matrix to yield a
+ *   N' x H x W output matrix that is then "col2im" restored. K' is the
+ *   input channel * kernel height * kernel width dimension of the unrolled
+ *   inputs so that the im2col matrix has a column for each input region to
+ *   be filtered. col2im restores the output spatial structure by rolling up
+ *   the output channel N' columns of the output matrix.
+ */
+template <typename Dtype>
+class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ConvolutionParameter convolution_param,
+   *    with ConvolutionLayer options:
+   *  - num_output. The number of filters.
+   *  - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
+   *  kernel_size for square filters or kernel_h and kernel_w for rectangular
+   *  filters.
+   *  - stride / stride_h / stride_w (\b optional, default 1). The filter
+   *  stride, given by stride_size for equal dimensions or stride_h and stride_w
+   *  for different strides. By default the convolution is dense with stride 1.
+   *  - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
+   *  convolution, given by pad for equal dimensions or pad_h and pad_w for
+   *  different padding. Input padding is computed implicitly instead of
+   *  actually padding.
+   *  - dilation (\b optional, default 1). The filter
+   *  dilation, given by dilation_size for equal dimensions for different
+   *  dilation. By default the convolution has dilation 1.
+   *  - group (\b optional, default 1). The number of filter groups. Group
+   *  convolution is a method for reducing parameterization by selectively
+   *  connecting input and output channels. The input and output channel dimensions must be divisible
+   *  by the number of groups. For group @f$ \geq 1 @f$, the
+   *  convolutional filters' input and output channels are separated s.t. each
+   *  group takes 1 / group of the input channels and makes 1 / group of the
+   *  output channels. Concretely 4 input channels, 8 output channels, and
+   *  2 groups separate input channels 1-2 and output channels 1-4 into the
+   *  first group and input channels 3-4 and output channels 5-8 into the second
+   *  group.
+   *  - bias_term (\b optional, default true). Whether to have a bias.
+   *  - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
+   *    kernels + stream parallelism) engines.
+   */
+  explicit ConvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Convolution"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return false; }
+  virtual void compute_output_shape();
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_CONV_LAYER_HPP_
diff --git a/include/caffe/layers/crop_layer.hpp b/include/caffe/layers/crop_layer.hpp
new file mode 100644
index 00000000000..5c605b2ae9e
--- /dev/null
+++ b/include/caffe/layers/crop_layer.hpp
@@ -0,0 +1,67 @@
+#ifndef CAFFE_CROP_LAYER_HPP_
+#define CAFFE_CROP_LAYER_HPP_
+
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and crop it, to the shape specified by the second input
+ *  Blob, across all dimensions after the specified axis.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+
+template <typename Dtype>
+class CropLayer : public Layer<Dtype> {
+ public:
+  explicit CropLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Crop"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  vector<int> offsets;
+
+ private:
+  void crop_copy(const vector<Blob<Dtype>*>& bottom,
+               const vector<Blob<Dtype>*>& top,
+               const vector<int>& offsets,
+               vector<int> indices,
+               int cur_dim,
+               const Dtype* src_data,
+               Dtype* dest_data,
+               bool is_forward);
+
+  void crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+                const vector<Blob<Dtype>*>& top,
+                const vector<int>& offsets,
+                vector<int> indices,
+                int cur_dim,
+                const Dtype* src_data,
+                Dtype* dest_data,
+                bool is_forward);
+};
+}  // namespace caffe
+
+#endif  // CAFFE_CROP_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_conv_layer.hpp b/include/caffe/layers/cudnn_conv_layer.hpp
new file mode 100644
index 00000000000..31fe49a71fa
--- /dev/null
+++ b/include/caffe/layers/cudnn_conv_layer.hpp
@@ -0,0 +1,72 @@
+#ifndef CAFFE_CUDNN_CONV_LAYER_HPP_
+#define CAFFE_CUDNN_CONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/conv_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of ConvolutionLayer.
+ *        Fallback to ConvolutionLayer for CPU mode.
+ *
+ * cuDNN accelerates convolution through forward kernels for filtering and bias
+ * plus backward kernels for the gradient w.r.t. the filters, biases, and
+ * inputs. Caffe + cuDNN further speeds up the computation through forward
+ * parallelism across groups and backward parallelism across gradients.
+ *
+ * The CUDNN engine does not have memory overhead for matrix buffers. For many
+ * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
+ * but for fully-convolutional models and large inputs the CAFFE engine can be
+ * faster as long as it fits in memory.
+*/
+template <typename Dtype>
+class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
+ public:
+  explicit CuDNNConvolutionLayer(const LayerParameter& param)
+      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNConvolutionLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t* handle_;
+  cudaStream_t*  stream_;
+
+  // algorithms for forward and backwards convolutions
+  cudnnConvolutionFwdAlgo_t *fwd_algo_;
+  cudnnConvolutionBwdFilterAlgo_t *bwd_filter_algo_;
+  cudnnConvolutionBwdDataAlgo_t *bwd_data_algo_;
+
+  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
+  cudnnTensorDescriptor_t    bias_desc_;
+  cudnnFilterDescriptor_t      filter_desc_;
+  vector<cudnnConvolutionDescriptor_t> conv_descs_;
+  int bottom_offset_, top_offset_, bias_offset_;
+
+  size_t *workspace_fwd_sizes_;
+  size_t *workspace_bwd_data_sizes_;
+  size_t *workspace_bwd_filter_sizes_;
+  size_t workspaceSizeInBytes;  // size of underlying storage
+  void *workspaceData;  // underlying storage
+  void **workspace;  // aliases into workspaceData
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_CONV_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_lcn_layer.hpp b/include/caffe/layers/cudnn_lcn_layer.hpp
new file mode 100644
index 00000000000..74cf4775e51
--- /dev/null
+++ b/include/caffe/layers/cudnn_lcn_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_CUDNN_LCN_LAYER_HPP_
+#define CAFFE_CUDNN_LCN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLCNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLCNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false), tempDataSize(0),
+        tempData1(NULL), tempData2(NULL) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLCNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_, pre_pad_;
+  Dtype alpha_, beta_, k_;
+
+  size_t tempDataSize;
+  void *tempData1, *tempData2;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_LCN_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_lrn_layer.hpp b/include/caffe/layers/cudnn_lrn_layer.hpp
new file mode 100644
index 00000000000..000ccc36507
--- /dev/null
+++ b/include/caffe/layers/cudnn_lrn_layer.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_CUDNN_LRN_LAYER_HPP_
+#define CAFFE_CUDNN_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/lrn_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayer : public LRNLayer<Dtype> {
+ public:
+  explicit CuDNNLRNLayer(const LayerParameter& param)
+      : LRNLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNLRNLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnLRNDescriptor_t norm_desc_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+
+  int size_;
+  Dtype alpha_, beta_, k_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_LRN_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_pooling_layer.hpp b/include/caffe/layers/cudnn_pooling_layer.hpp
new file mode 100644
index 00000000000..6d0db47d660
--- /dev/null
+++ b/include/caffe/layers/cudnn_pooling_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_CUDNN_POOLING_LAYER_HPP_
+#define CAFFE_CUDNN_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/pooling_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/*
+ * @brief cuDNN implementation of PoolingLayer.
+ *        Fallback to PoolingLayer for CPU mode.
+*/
+template <typename Dtype>
+class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
+ public:
+  explicit CuDNNPoolingLayer(const LayerParameter& param)
+      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNPoolingLayer();
+  // Currently, cuDNN does not support the extra top blob.
+  virtual inline int MinTopBlobs() const { return -1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
+  cudnnPoolingDescriptor_t  pooling_desc_;
+  cudnnPoolingMode_t        mode_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_POOLING_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_relu_layer.hpp b/include/caffe/layers/cudnn_relu_layer.hpp
new file mode 100644
index 00000000000..e01f568abc9
--- /dev/null
+++ b/include/caffe/layers/cudnn_relu_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_RELU_LAYER_HPP_
+#define CAFFE_CUDNN_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of ReLULayer.
+ */
+template <typename Dtype>
+class CuDNNReLULayer : public ReLULayer<Dtype> {
+ public:
+  explicit CuDNNReLULayer(const LayerParameter& param)
+      : ReLULayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNReLULayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_RELU_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_sigmoid_layer.hpp b/include/caffe/layers/cudnn_sigmoid_layer.hpp
new file mode 100644
index 00000000000..9c597958b0b
--- /dev/null
+++ b/include/caffe/layers/cudnn_sigmoid_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+#define CAFFE_CUDNN_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of SigmoidLayer.
+ */
+template <typename Dtype>
+class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
+ public:
+  explicit CuDNNSigmoidLayer(const LayerParameter& param)
+      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNSigmoidLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_SIGMOID_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_softmax_layer.hpp b/include/caffe/layers/cudnn_softmax_layer.hpp
new file mode 100644
index 00000000000..174368e413d
--- /dev/null
+++ b/include/caffe/layers/cudnn_softmax_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+#define CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief cuDNN implementation of SoftmaxLayer.
+ *        Fallback to SoftmaxLayer for CPU mode.
+ */
+template <typename Dtype>
+class CuDNNSoftmaxLayer : public SoftmaxLayer<Dtype> {
+ public:
+  explicit CuDNNSoftmaxLayer(const LayerParameter& param)
+      : SoftmaxLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNSoftmaxLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_SOFTMAX_LAYER_HPP_
diff --git a/include/caffe/layers/cudnn_tanh_layer.hpp b/include/caffe/layers/cudnn_tanh_layer.hpp
new file mode 100644
index 00000000000..c0f0053f71e
--- /dev/null
+++ b/include/caffe/layers/cudnn_tanh_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_CUDNN_TANH_LAYER_HPP_
+#define CAFFE_CUDNN_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+
+namespace caffe {
+
+#ifdef USE_CUDNN
+/**
+ * @brief CuDNN acceleration of TanHLayer.
+ */
+template <typename Dtype>
+class CuDNNTanHLayer : public TanHLayer<Dtype> {
+ public:
+  explicit CuDNNTanHLayer(const LayerParameter& param)
+      : TanHLayer<Dtype>(param), handles_setup_(false) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual ~CuDNNTanHLayer();
+
+ protected:
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool handles_setup_;
+  cudnnHandle_t             handle_;
+  cudnnTensorDescriptor_t bottom_desc_;
+  cudnnTensorDescriptor_t top_desc_;
+};
+#endif
+
+}  // namespace caffe
+
+#endif  // CAFFE_CUDNN_TANH_LAYER_HPP_
diff --git a/include/caffe/layers/data_layer.hpp b/include/caffe/layers/data_layer.hpp
new file mode 100644
index 00000000000..6c361791a0c
--- /dev/null
+++ b/include/caffe/layers/data_layer.hpp
@@ -0,0 +1,39 @@
+#ifndef CAFFE_DATA_LAYER_HPP_
+#define CAFFE_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/db.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+class DataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit DataLayer(const LayerParameter& param);
+  virtual ~DataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // DataLayer uses DataReader instead for sharing for parallelism
+  virtual inline bool ShareInParallel() const { return false; }
+  virtual inline const char* type() const { return "Data"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  DataReader reader_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/deconv_layer.hpp b/include/caffe/layers/deconv_layer.hpp
new file mode 100644
index 00000000000..23ae887e61e
--- /dev/null
+++ b/include/caffe/layers/deconv_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_DECONV_LAYER_HPP_
+#define CAFFE_DECONV_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_conv_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Convolve the input with a bank of learned filters, and (optionally)
+ *        add biases, treating filters and convolution parameters in the
+ *        opposite sense as ConvolutionLayer.
+ *
+ *   ConvolutionLayer computes each output value by dotting an input window with
+ *   a filter; DeconvolutionLayer multiplies each input value by a filter
+ *   elementwise, and sums over the resulting output windows. In other words,
+ *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
+ *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
+ *   parameters, but they take the opposite sense as in ConvolutionLayer (so
+ *   padding is removed from the output rather than added to the input, and
+ *   stride results in upsampling rather than downsampling).
+ */
+template <typename Dtype>
+class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
+ public:
+  explicit DeconvolutionLayer(const LayerParameter& param)
+      : BaseConvolutionLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Deconvolution"; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual inline bool reverse_dimensions() { return true; }
+  virtual void compute_output_shape();
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DECONV_LAYER_HPP_
diff --git a/include/caffe/layers/dropout_layer.hpp b/include/caffe/layers/dropout_layer.hpp
new file mode 100644
index 00000000000..e83143bc3cc
--- /dev/null
+++ b/include/caffe/layers/dropout_layer.hpp
@@ -0,0 +1,80 @@
+#ifndef CAFFE_DROPOUT_LAYER_HPP_
+#define CAFFE_DROPOUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
+ *        the rest of the vector magnitude accordingly.
+ *
+ * @param bottom input Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the inputs @f$ x @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the computed outputs @f$ y = |x| @f$
+ */
+template <typename Dtype>
+class DropoutLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides DropoutParameter dropout_param,
+   *     with DropoutLayer options:
+   *   - dropout_ratio (\b optional, default 0.5).
+   *     Sets the probability @f$ p @f$ that any given unit is dropped.
+   */
+  explicit DropoutLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Dropout"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs. At training time, we have @f$
+   *      y_{\mbox{train}} = \left\{
+   *         \begin{array}{ll}
+   *            \frac{x}{1 - p} & \mbox{if } u > p \\
+   *            0 & \mbox{otherwise}
+   *         \end{array} \right.
+   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
+   *      input at each iteration. At test time, we simply have
+   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
+  Blob<unsigned int> rand_vec_;
+  /// the probability @f$ p @f$ of dropping any input
+  Dtype threshold_;
+  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
+  Dtype scale_;
+  unsigned int uint_thres_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DROPOUT_LAYER_HPP_
diff --git a/include/caffe/layers/dummy_data_layer.hpp b/include/caffe/layers/dummy_data_layer.hpp
new file mode 100644
index 00000000000..4180f1d01e4
--- /dev/null
+++ b/include/caffe/layers/dummy_data_layer.hpp
@@ -0,0 +1,49 @@
+#ifndef CAFFE_DUMMY_DATA_LAYER_HPP_
+#define CAFFE_DUMMY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net generated by a Filler.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class DummyDataLayer : public Layer<Dtype> {
+ public:
+  explicit DummyDataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "DummyData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  vector<shared_ptr<Filler<Dtype> > > fillers_;
+  vector<bool> refill_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_DUMMY_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/eltwise_layer.hpp b/include/caffe/layers/eltwise_layer.hpp
new file mode 100644
index 00000000000..091de834362
--- /dev/null
+++ b/include/caffe/layers/eltwise_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_ELTWISE_LAYER_HPP_
+#define CAFFE_ELTWISE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute elementwise operations, such as product and sum,
+ *        along multiple input Blobs.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EltwiseLayer : public Layer<Dtype> {
+ public:
+  explicit EltwiseLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Eltwise"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  EltwiseParameter_EltwiseOp op_;
+  vector<Dtype> coeffs_;
+  Blob<int> max_idx_;
+
+  bool stable_prod_grad_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_ELTWISE_LAYER_HPP_
diff --git a/include/caffe/layers/elu_layer.hpp b/include/caffe/layers/elu_layer.hpp
new file mode 100644
index 00000000000..0796e898007
--- /dev/null
+++ b/include/caffe/layers/elu_layer.hpp
@@ -0,0 +1,86 @@
+#ifndef CAFFE_ELU_LAYER_HPP_
+#define CAFFE_ELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Exponential Linear Unit non-linearity @f$
+ *        y = \left\{
+ *        \begin{array}{lr}
+ *            x                  & \mathrm{if} \; x > 0 \\
+ *            \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+ *        \end{array} \right.
+ *      @f$.  
+ */
+template <typename Dtype>
+class ELULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ELUParameter elu_param,
+   *     with ELULayer options:
+   *   - alpha (\b optional, default 1).
+   *     the value @f$ \alpha @f$ by which controls saturation for negative inputs.
+   */
+  explicit ELULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "ELU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \left\{
+   *        \begin{array}{lr}
+   *            x                  & \mathrm{if} \; x > 0 \\
+   *            \alpha (\exp(x)-1) & \mathrm{if} \; x \le 0
+   *        \end{array} \right.
+   *      @f$.  
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the ELU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            1           & \mathrm{if} \; x > 0 \\
+   *            y + \alpha  & \mathrm{if} \; x \le 0
+   *        \end{array} \right.
+   *      @f$ if propagate_down[0].
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_ELU_LAYER_HPP_
diff --git a/include/caffe/layers/embed_layer.hpp b/include/caffe/layers/embed_layer.hpp
new file mode 100644
index 00000000000..36137a625b6
--- /dev/null
+++ b/include/caffe/layers/embed_layer.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_EMBED_LAYER_HPP_
+#define CAFFE_EMBED_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A layer for learning "embeddings" of one-hot vector input.
+ *        Equivalent to an InnerProductLayer with one-hot vectors as input, but
+ *        for efficiency the input is the "hot" index of each column itself.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class EmbedLayer : public Layer<Dtype> {
+ public:
+  explicit EmbedLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Embed"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EMBED_LAYER_HPP_
diff --git a/include/caffe/layers/euclidean_loss_layer.hpp b/include/caffe/layers/euclidean_loss_layer.hpp
new file mode 100644
index 00000000000..f564569e27a
--- /dev/null
+++ b/include/caffe/layers/euclidean_loss_layer.hpp
@@ -0,0 +1,107 @@
+#ifndef CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+#define CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the Euclidean (L2) loss @f$
+ *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$ for real-valued regression tasks.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [-\infty, +\infty]@f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed Euclidean loss: @f$ E =
+ *          \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
+ *        \right| \right|_2^2 @f$
+ *
+ * This can be used for least-squares regression tasks.  An InnerProductLayer
+ * input to a EuclideanLossLayer exactly formulates a linear least squares
+ * regression problem. With non-zero weight decay the problem becomes one of
+ * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
+ * example wherein we check that the gradients computed for a Net with exactly
+ * this structure match hand-computed gradient formulas for ridge regression.
+ *
+ * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
+ * linear least squares problems! We use it only as an instructive example.)
+ */
+template <typename Dtype>
+class EuclideanLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit EuclideanLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), diff_() {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "EuclideanLoss"; }
+  /**
+   * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
+   * to both inputs -- override to return true and always allow force_backward.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return true;
+  }
+
+ protected:
+  /// @copydoc EuclideanLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the Euclidean error gradient w.r.t. the inputs.
+   *
+   * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
+   * gradients with respect to the label inputs bottom[1] (but still only will
+   * if propagate_down[1] is set, due to being produced by learnable parameters
+   * or if force_backward is set). In fact, this layer is "commutative" -- the
+   * result is the same regardless of the order of the two bottoms.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$\hat{y}@f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial \hat{y}} =
+   *            \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
+   *      @f$ if propagate_down[0]
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the targets @f$y@f$; Backward fills their diff with gradients
+   *      @f$ \frac{\partial E}{\partial y} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
+   *      @f$ if propagate_down[1]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> diff_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EUCLIDEAN_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/exp_layer.hpp b/include/caffe/layers/exp_layer.hpp
new file mode 100644
index 00000000000..9fc8c396a74
--- /dev/null
+++ b/include/caffe/layers/exp_layer.hpp
@@ -0,0 +1,80 @@
+#ifndef CAFFE_EXP_LAYER_HPP_
+#define CAFFE_EXP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class ExpLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ExpParameter exp_param,
+   *     with ExpLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit ExpLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Exp"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \gamma ^ {\alpha x + \beta}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype inner_scale_, outer_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_EXP_LAYER_HPP_
diff --git a/include/caffe/layers/filter_layer.hpp b/include/caffe/layers/filter_layer.hpp
new file mode 100644
index 00000000000..e040e66612b
--- /dev/null
+++ b/include/caffe/layers/filter_layer.hpp
@@ -0,0 +1,77 @@
+#ifndef CAFFE_FILTER_LAYER_HPP_
+#define CAFFE_FILTER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes two+ Blobs, interprets last Blob as a selector and
+ *  filter remaining Blobs accordingly with selector data (0 means that
+ * the corresponding item has to be filtered, non-zero means that corresponding
+ * item needs to stay).
+ */
+template <typename Dtype>
+class FilterLayer : public Layer<Dtype> {
+ public:
+  explicit FilterLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Filter"; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_1 @f$
+   *   -# ...
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs to be filtered @f$ x_K @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the selector blob
+   * @param top output Blob vector (length 1+)
+   *   -# @f$ (S \times C \times H \times W) @f$ ()
+   *        the filtered output @f$ x_1 @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   *      @f$ (S \times C \times H \times W) @f$
+   *        the filtered output @f$ x_K @f$
+   *        where S is the number of items
+   *        that haven't been filtered
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the forwarded inputs.
+   *
+   * @param top output Blob vector (length 1+), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 2+), into which the top error
+   *        gradient is copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool first_reshape_;
+  vector<int> indices_to_forward_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_FILTER_LAYER_HPP_
diff --git a/include/caffe/layers/flatten_layer.hpp b/include/caffe/layers/flatten_layer.hpp
new file mode 100644
index 00000000000..e494bbb588f
--- /dev/null
+++ b/include/caffe/layers/flatten_layer.hpp
@@ -0,0 +1,61 @@
+#ifndef CAFFE_FLATTEN_LAYER_HPP_
+#define CAFFE_FLATTEN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Reshapes the input Blob into flat vectors.
+ *
+ * Note: because this layer does not change the input values -- merely the
+ * dimensions -- it can simply copy the input. The copy happens "virtually"
+ * (thus taking effectively 0 real time) by setting, in Forward, the data
+ * pointer of the top Blob to that of the bottom Blob (see Blob::ShareData),
+ * and in Backward, the diff pointer of the bottom Blob to that of the top Blob
+ * (see Blob::ShareDiff).
+ */
+template <typename Dtype>
+class FlattenLayer : public Layer<Dtype> {
+ public:
+  explicit FlattenLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Flatten"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2+)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times CHW \times 1 \times 1) @f$
+   *      the outputs -- i.e., the (virtually) copied, flattened inputs
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the concatenate inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *        respect to the outputs
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length K), into which the top error
+   *        gradient is (virtually) copied
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_FLATTEN_LAYER_HPP_
diff --git a/include/caffe/layers/hdf5_data_layer.hpp b/include/caffe/layers/hdf5_data_layer.hpp
new file mode 100644
index 00000000000..b04cf8e1940
--- /dev/null
+++ b/include/caffe/layers/hdf5_data_layer.hpp
@@ -0,0 +1,62 @@
+#ifndef CAFFE_HDF5_DATA_LAYER_HPP_
+#define CAFFE_HDF5_DATA_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5DataLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5DataLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual ~HDF5DataLayer();
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "HDF5Data"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void LoadHDF5FileData(const char* filename);
+
+  std::vector<std::string> hdf_filenames_;
+  unsigned int num_files_;
+  unsigned int current_file_;
+  hsize_t current_row_;
+  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;
+  std::vector<unsigned int> data_permutation_;
+  std::vector<unsigned int> file_permutation_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_HDF5_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/hdf5_output_layer.hpp b/include/caffe/layers/hdf5_output_layer.hpp
new file mode 100644
index 00000000000..487d08fc06c
--- /dev/null
+++ b/include/caffe/layers/hdf5_output_layer.hpp
@@ -0,0 +1,64 @@
+#ifndef CAFFE_HDF5_OUTPUT_LAYER_HPP_
+#define CAFFE_HDF5_OUTPUT_LAYER_HPP_
+
+#include "hdf5.h"
+
+#include <string>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+#define HDF5_DATA_DATASET_NAME "data"
+#define HDF5_DATA_LABEL_NAME "label"
+
+/**
+ * @brief Write blobs to disk as HDF5 files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class HDF5OutputLayer : public Layer<Dtype> {
+ public:
+  explicit HDF5OutputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param), file_opened_(false) {}
+  virtual ~HDF5OutputLayer();
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "HDF5Output"; }
+  // TODO: no limit on the number of blobs
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+  inline std::string file_name() const { return file_name_; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void SaveBlobs();
+
+  bool file_opened_;
+  std::string file_name_;
+  hid_t file_id_;
+  Blob<Dtype> data_blob_;
+  Blob<Dtype> label_blob_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_HDF5_OUTPUT_LAYER_HPP_
diff --git a/include/caffe/layers/hinge_loss_layer.hpp b/include/caffe/layers/hinge_loss_layer.hpp
new file mode 100644
index 00000000000..54e42bd44da
--- /dev/null
+++ b/include/caffe/layers/hinge_loss_layer.hpp
@@ -0,0 +1,104 @@
+#ifndef CAFFE_HINGE_LOSS_LAYER_HPP_
+#define CAFFE_HINGE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the hinge loss for a one-of-many classification task.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ t @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
+ *      taking the inner product @f$ X^T W @f$ of the D-dimensional features
+ *      @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
+ *      parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
+ *      an InnerProductLayer (with num_output = D) providing predictions to a
+ *      HingeLossLayer and no other learnable parameters or losses is
+ *      equivalent to an SVM.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed hinge loss: @f$ E =
+ *        \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
+ *        [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
+ *      @f$, for the @f$ L^p @f$ norm
+ *      (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
+ *      is also available), and @f$
+ *      \delta\{\mathrm{condition}\} = \left\{
+ *         \begin{array}{lr}
+ *            1 & \mbox{if condition} \\
+ *           -1 & \mbox{otherwise}
+ *         \end{array} \right.
+ *      @f$
+ *
+ * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
+ * the inner product @f$ X^T W @f$ of the features
+ * @f$ X \in \mathcal{R}^{D \times N} @f$
+ * and the learned hyperplane parameters
+ * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
+ * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
+ * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
+ * outside the InnerProductLayer and no other losses outside the
+ * HingeLossLayer).
+ */
+template <typename Dtype>
+class HingeLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit HingeLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "HingeLoss"; }
+
+ protected:
+  /// @copydoc HingeLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the hinge loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$t@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial t} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_HINGE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/im2col_layer.hpp b/include/caffe/layers/im2col_layer.hpp
new file mode 100644
index 00000000000..71e32f7427f
--- /dev/null
+++ b/include/caffe/layers/im2col_layer.hpp
@@ -0,0 +1,65 @@
+#ifndef CAFFE_IM2COL_LAYER_HPP_
+#define CAFFE_IM2COL_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief A helper for image operations that rearranges image regions into
+ *        column vectors.  Used by ConvolutionLayer to perform convolution
+ *        by matrix multiplication.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class Im2colLayer : public Layer<Dtype> {
+ public:
+  explicit Im2colLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Im2col"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief The spatial dimensions of a filter kernel.
+  Blob<int> kernel_shape_;
+  /// @brief The spatial dimensions of the stride.
+  Blob<int> stride_;
+  /// @brief The spatial dimensions of the padding.
+  Blob<int> pad_;
+  /// @brief The spatial dimensions of the dilation.
+  Blob<int> dilation_;
+
+  int num_spatial_axes_;
+  int bottom_dim_;
+  int top_dim_;
+
+  int channel_axis_;
+  int num_;
+  int channels_;
+
+  bool force_nd_im2col_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_IM2COL_LAYER_HPP_
diff --git a/include/caffe/layers/image_data_layer.hpp b/include/caffe/layers/image_data_layer.hpp
new file mode 100644
index 00000000000..a0d3384e4c9
--- /dev/null
+++ b/include/caffe/layers/image_data_layer.hpp
@@ -0,0 +1,47 @@
+#ifndef CAFFE_IMAGE_DATA_LAYER_HPP_
+#define CAFFE_IMAGE_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from image files.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit ImageDataLayer(const LayerParameter& param)
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~ImageDataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ImageData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+  shared_ptr<Caffe::RNG> prefetch_rng_;
+  virtual void ShuffleImages();
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  vector<std::pair<std::string, int> > lines_;
+  int lines_id_;
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_IMAGE_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/infogain_loss_layer.hpp b/include/caffe/layers/infogain_loss_layer.hpp
new file mode 100644
index 00000000000..633f339a28e
--- /dev/null
+++ b/include/caffe/layers/infogain_loss_layer.hpp
@@ -0,0 +1,110 @@
+#ifndef CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+#define CAFFE_INFOGAIN_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief A generalization of MultinomialLogisticLossLayer that takes an
+ *        "information gain" (infogain) matrix specifying the "value" of all label
+ *        pairs.
+ *
+ * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
+ * identity.
+ *
+ * @param bottom input Blob vector (length 2-3)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ *   -# @f$ (1 \times 1 \times K \times K) @f$
+ *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
+ *      the third bottom blob input if not provided as the infogain_mat in the
+ *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
+ *      MultinomialLogisticLossLayer.
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed infogain multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
+ *        \log(\hat{p}_{n,k})
+ *      @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
+ */
+template <typename Dtype>
+class InfogainLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit InfogainLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param), infogain_() {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
+  // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
+  // file specified by LayerParameter.)
+  virtual inline int ExactNumBottomBlobs() const { return -1; }
+  virtual inline int MinBottomBlobs() const { return 2; }
+  virtual inline int MaxBottomBlobs() const { return 3; }
+
+  virtual inline const char* type() const { return "InfogainLoss"; }
+
+ protected:
+  /// @copydoc InfogainLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the infogain loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set. (The same applies to the infogain matrix, if
+   * provided as bottom[2] rather than in the layer_param.)
+   *
+   * @param top output Blob vector (length 1), providing the error gradient
+   *      with respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels (similarly for propagate_down[2] and the
+   *      infogain matrix, if provided as bottom[2])
+   * @param bottom input Blob vector (length 2-3)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   *   -# @f$ (1 \times 1 \times K \times K) @f$
+   *      (\b optional) the information gain matrix -- ignored as its error
+   *      gradient computation is not implemented.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> infogain_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INFOGAIN_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/inner_product_layer.hpp b/include/caffe/layers/inner_product_layer.hpp
new file mode 100644
index 00000000000..18d0d6192eb
--- /dev/null
+++ b/include/caffe/layers/inner_product_layer.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_
+#define CAFFE_INNER_PRODUCT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Also known as a "fully-connected" layer, computes an inner product
+ *        with a set of learned weights, and (optionally) adds biases.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class InnerProductLayer : public Layer<Dtype> {
+ public:
+  explicit InnerProductLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "InnerProduct"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int M_;
+  int K_;
+  int N_;
+  bool bias_term_;
+  Blob<Dtype> bias_multiplier_;
+  bool transpose_;  ///< if true, assume transposed weights
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INNER_PRODUCT_LAYER_HPP_
diff --git a/include/caffe/layers/input_layer.hpp b/include/caffe/layers/input_layer.hpp
new file mode 100644
index 00000000000..f4472678c69
--- /dev/null
+++ b/include/caffe/layers/input_layer.hpp
@@ -0,0 +1,44 @@
+#ifndef CAFFE_INPUT_LAYER_HPP_
+#define CAFFE_INPUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net by assigning tops directly.
+ *
+ * This data layer is a container that merely holds the data assigned to it;
+ * forward, backward, and reshape are all no-ops.
+ */
+template <typename Dtype>
+class InputLayer : public Layer<Dtype> {
+ public:
+  explicit InputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  // Data layers should be shared by multiple solvers in parallel
+  virtual inline bool ShareInParallel() const { return true; }
+  // Data layers have no bottoms, so reshaping is trivial.
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "Input"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_INPUT_LAYER_HPP_
diff --git a/include/caffe/layers/log_layer.hpp b/include/caffe/layers/log_layer.hpp
new file mode 100644
index 00000000000..7d037d2bdca
--- /dev/null
+++ b/include/caffe/layers/log_layer.hpp
@@ -0,0 +1,82 @@
+#ifndef CAFFE_LOG_LAYER_HPP_
+#define CAFFE_LOG_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = log_{\gamma}(\alpha x + \beta) @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and base @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class LogLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides LogParameter log_param,
+   *     with LogLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
+   *         the base @f$ \gamma @f$
+   */
+  explicit LogLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Log"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = log_{\gamma}(\alpha x + \beta)
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the exp inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Dtype base_scale_;
+  Dtype input_scale_, input_shift_;
+  Dtype backward_num_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LOG_LAYER_HPP_
diff --git a/include/caffe/layers/loss_layer.hpp b/include/caffe/layers/loss_layer.hpp
new file mode 100644
index 00000000000..dbdf612c062
--- /dev/null
+++ b/include/caffe/layers/loss_layer.hpp
@@ -0,0 +1,53 @@
+#ifndef CAFFE_LOSS_LAYER_HPP_
+#define CAFFE_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+const float kLOG_THRESHOLD = 1e-20;
+
+/**
+ * @brief An interface for Layer%s that take two Blob%s as input -- usually
+ *        (1) predictions and (2) ground-truth labels -- and output a
+ *        singleton Blob representing the loss.
+ *
+ * LossLayers are typically only capable of backpropagating to their first input
+ * -- the predictions.
+ */
+template <typename Dtype>
+class LossLayer : public Layer<Dtype> {
+ public:
+  explicit LossLayer(const LayerParameter& param)
+     : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(
+      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+
+  /**
+   * @brief For convenience and backwards compatibility, instruct the Net to
+   *        automatically allocate a single top Blob for LossLayers, into which
+   *        they output their singleton loss, (even if the user didn't specify
+   *        one in the prototxt, etc.).
+   */
+  virtual inline bool AutoTopBlobs() const { return true; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+  /**
+   * We usually cannot backpropagate to the labels; ignore force_backward for
+   * these inputs.
+   */
+  virtual inline bool AllowForceBackward(const int bottom_index) const {
+    return bottom_index != 1;
+  }
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/lrn_layer.hpp b/include/caffe/layers/lrn_layer.hpp
new file mode 100644
index 00000000000..06cf71a94cb
--- /dev/null
+++ b/include/caffe/layers/lrn_layer.hpp
@@ -0,0 +1,94 @@
+#ifndef CAFFE_LRN_LAYER_HPP_
+#define CAFFE_LRN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/eltwise_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Normalize the input in a local region across or within feature maps.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class LRNLayer : public Layer<Dtype> {
+ public:
+  explicit LRNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "LRN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int size_;
+  int pre_pad_;
+  Dtype alpha_;
+  Dtype beta_;
+  Dtype k_;
+  int num_;
+  int channels_;
+  int height_;
+  int width_;
+
+  // Fields used for normalization ACROSS_CHANNELS
+  // scale_ stores the intermediate summing results
+  Blob<Dtype> scale_;
+
+  // Fields used for normalization WITHIN_CHANNEL
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  vector<Blob<Dtype>*> split_top_vec_;
+  shared_ptr<PowerLayer<Dtype> > square_layer_;
+  Blob<Dtype> square_input_;
+  Blob<Dtype> square_output_;
+  vector<Blob<Dtype>*> square_bottom_vec_;
+  vector<Blob<Dtype>*> square_top_vec_;
+  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
+  Blob<Dtype> pool_output_;
+  vector<Blob<Dtype>*> pool_top_vec_;
+  shared_ptr<PowerLayer<Dtype> > power_layer_;
+  Blob<Dtype> power_output_;
+  vector<Blob<Dtype>*> power_top_vec_;
+  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
+  Blob<Dtype> product_input_;
+  vector<Blob<Dtype>*> product_bottom_vec_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_LRN_LAYER_HPP_
diff --git a/include/caffe/layers/memory_data_layer.hpp b/include/caffe/layers/memory_data_layer.hpp
new file mode 100644
index 00000000000..8abcc8c1b68
--- /dev/null
+++ b/include/caffe/layers/memory_data_layer.hpp
@@ -0,0 +1,63 @@
+#ifndef CAFFE_MEMORY_DATA_LAYER_HPP_
+#define CAFFE_MEMORY_DATA_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/base_data_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from memory.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class MemoryDataLayer : public BaseDataLayer<Dtype> {
+ public:
+  explicit MemoryDataLayer(const LayerParameter& param)
+      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MemoryData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+  virtual void AddDatumVector(const vector<Datum>& datum_vector);
+#ifdef USE_OPENCV
+  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,
+      const vector<int>& labels);
+#endif  // USE_OPENCV
+
+  // Reset should accept const pointers, but can't, because the memory
+  //  will be given to Blob, which is mutable
+  void Reset(Dtype* data, Dtype* label, int n);
+  void set_batch_size(int new_size);
+
+  int batch_size() { return batch_size_; }
+  int channels() { return channels_; }
+  int height() { return height_; }
+  int width() { return width_; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  int batch_size_, channels_, height_, width_, size_;
+  Dtype* data_;
+  Dtype* labels_;
+  int n_;
+  size_t pos_;
+  Blob<Dtype> added_data_;
+  Blob<Dtype> added_label_;
+  bool has_new_data_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MEMORY_DATA_LAYER_HPP_
diff --git a/include/caffe/layers/multinomial_logistic_loss_layer.hpp b/include/caffe/layers/multinomial_logistic_loss_layer.hpp
new file mode 100644
index 00000000000..3977cf9ea57
--- /dev/null
+++ b/include/caffe/layers/multinomial_logistic_loss_layer.hpp
@@ -0,0 +1,92 @@
+#ifndef CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+#define CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, directly taking a predicted probability
+ *        distribution as input.
+ *
+ * When predictions are not already a probability distribution, you should
+ * instead use the SoftmaxWithLossLayer, which maps predictions to a
+ * distribution using the SoftmaxLayer, before computing the multinomial
+ * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ \hat{p} @f$, a Blob with values in
+ *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
+ *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
+ *      should sum to 1 as in a probability distribution: @f$
+ *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed multinomial logistic loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$
+ */
+template <typename Dtype>
+class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
+
+ protected:
+  /// @copydoc MultinomialLogisticLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ \hat{p} @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MULTINOMIAL_LOGISTIC_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/mvn_layer.hpp b/include/caffe/layers/mvn_layer.hpp
new file mode 100644
index 00000000000..3a235ceca64
--- /dev/null
+++ b/include/caffe/layers/mvn_layer.hpp
@@ -0,0 +1,48 @@
+#ifndef CAFFE_MVN_LAYER_HPP_
+#define CAFFE_MVN_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes the input to have 0-mean and/or unit (1) variance.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class MVNLayer : public Layer<Dtype> {
+ public:
+  explicit MVNLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "MVN"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> mean_, variance_, temp_;
+
+  /// sum_multiplier is used to carry out sum using BLAS
+  Blob<Dtype> sum_multiplier_;
+  Dtype eps_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_MVN_LAYER_HPP_
diff --git a/include/caffe/layers/neuron_layer.hpp b/include/caffe/layers/neuron_layer.hpp
new file mode 100644
index 00000000000..10c108ce682
--- /dev/null
+++ b/include/caffe/layers/neuron_layer.hpp
@@ -0,0 +1,32 @@
+#ifndef CAFFE_NEURON_LAYER_HPP_
+#define CAFFE_NEURON_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief An interface for layers that take one blob as input (@f$ x @f$)
+ *        and produce one equally-sized blob as output (@f$ y @f$), where
+ *        each element of the output depends only on the corresponding input
+ *        element.
+ */
+template <typename Dtype>
+class NeuronLayer : public Layer<Dtype> {
+ public:
+  explicit NeuronLayer(const LayerParameter& param)
+     : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_NEURON_LAYER_HPP_
diff --git a/include/caffe/layers/normalize_layer.hpp b/include/caffe/layers/normalize_layer.hpp
new file mode 100644
index 00000000000..c211fc475ca
--- /dev/null
+++ b/include/caffe/layers/normalize_layer.hpp
@@ -0,0 +1,50 @@
+#ifndef CAFFE_NORMALIZE_LAYER_HPP_
+#define CAFFE_NORMALIZE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Normalizes input.
+ * https://github.com/kuprel/caffe
+ */
+template <typename Dtype>
+class NormalizeLayer : public Layer<Dtype> {
+ public:
+  explicit NormalizeLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Normalize"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  Blob<Dtype> norm_;
+  Blob<Dtype> sum_channel_multiplier_, sum_spatial_multiplier_;
+  Blob<Dtype> buffer_, buffer_channel_, buffer_spatial_;
+  bool across_spatial_;
+  bool channel_shared_;
+  Dtype eps_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_NORMALIZE_LAYER_HPP_
diff --git a/include/caffe/layers/parse_evaluate_layer.hpp b/include/caffe/layers/parse_evaluate_layer.hpp
new file mode 100644
index 00000000000..eddff06c249
--- /dev/null
+++ b/include/caffe/layers/parse_evaluate_layer.hpp
@@ -0,0 +1,68 @@
+#ifndef CAFFE_PARSE_EVALUATE_LAYER_HPP_
+#define CAFFE_PARSE_EVALUATE_LAYER_HPP_
+
+#include <set>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Count the prediction and ground truth statistics for each datum.
+ *
+ * NOTE: This does not implement Backwards operation.
+ */
+template <typename Dtype>
+class ParseEvaluateLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides ParseEvaluateParameter parse_evaluate_param,
+   *     with ParseEvaluateLayer options:
+   *   - num_labels (\b optional int32.).
+   *     number of labels. must provide!!
+   *   - ignore_label (\b repeated int32).
+   *     If any, ignore evaluating the corresponding label for each
+   *     image.
+   */
+  explicit ParseEvaluateLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ParseEvaluate"; }
+  virtual inline int ExactNumBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times 1 \times H \times W) @f$
+   *      the prediction label @f$ x @f$
+   *   -# @f$ (N \times 1 \times H \times W) @f$
+   *      the ground truth label @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times 1 \times 3) @f$
+   *      the counts for different class @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  // number of total labels
+  int num_labels_;
+  // store ignored labels
+  std::set<Dtype> ignore_labels_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_PARSE_EVALUATE_LAYER_HPP_
diff --git a/include/caffe/layers/parse_output_layer.hpp b/include/caffe/layers/parse_output_layer.hpp
new file mode 100644
index 00000000000..3d87d174cfb
--- /dev/null
+++ b/include/caffe/layers/parse_output_layer.hpp
@@ -0,0 +1,71 @@
+#ifndef CAFFE_PARSE_OUTPUT_LAYER_HPP_
+#define CAFFE_PARSE_OUTPUT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute the segmentation label of the @f$ H \times W @f$ for each datum across
+ *        all channels @f$ C @f$.
+ *
+ * Intended for use after a classification layer to produce a prediction of
+ * segmentation label.
+ * If parameter out_max_val is set to true, also output the predicted value for
+ * the corresponding label for each image.
+ *
+ * NOTE: does not implement Backwards operation.
+ */
+template <typename Dtype>
+class ParseOutputLayer : public Layer<Dtype> {
+ public:
+  /**
+   * @param param provides ParseOutputParameter parse_output_param,
+   *     with ParseOutputLayer options:
+   *   - out_max_val (\b optional bool, default false).
+   *     if set, output the predicted value for the corresponding label for each
+   *     image.
+   */
+  explicit ParseOutputLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "ParseOutput"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times 1 \times H \times W) @f$ or, if out_max_val
+   *      @f$ (N \times 2 \times H \times W) @f$
+   *      the computed outputs @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  bool out_max_val_;
+
+  // max_prob_ is used to store the maximum probability value
+  Blob<Dtype> max_prob_;
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_PARSE_OUTPUT_LAYER_HPP_
diff --git a/include/caffe/layers/pooling_layer.hpp b/include/caffe/layers/pooling_layer.hpp
new file mode 100644
index 00000000000..f4d6803ba8e
--- /dev/null
+++ b/include/caffe/layers/pooling_layer.hpp
@@ -0,0 +1,60 @@
+#ifndef CAFFE_POOLING_LAYER_HPP_
+#define CAFFE_POOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Pools the input image by taking the max, average, etc. within regions.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class PoolingLayer : public Layer<Dtype> {
+ public:
+  explicit PoolingLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Pooling"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  // MAX POOL layers can output an extra top blob for the mask;
+  // others can only output the pooled inputs.
+  virtual inline int MaxTopBlobs() const {
+    return (this->layer_param_.pooling_param().pool() ==
+            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
+  }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int kernel_h_, kernel_w_;
+  int stride_h_, stride_w_;
+  int pad_h_, pad_w_;
+  int channels_;
+  int height_, width_;
+  int pooled_height_, pooled_width_;
+  bool global_pooling_;
+  Blob<Dtype> rand_idx_;
+  Blob<int> max_idx_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_POOLING_LAYER_HPP_
diff --git a/include/caffe/layers/power_layer.hpp b/include/caffe/layers/power_layer.hpp
new file mode 100644
index 00000000000..6ecbafcaca8
--- /dev/null
+++ b/include/caffe/layers/power_layer.hpp
@@ -0,0 +1,89 @@
+#ifndef CAFFE_POWER_LAYER_HPP_
+#define CAFFE_POWER_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
+ *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
+ *        and power @f$ \gamma @f$.
+ */
+template <typename Dtype>
+class PowerLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PowerParameter power_param,
+   *     with PowerLayer options:
+   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
+   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
+   *   - power (\b optional, default 1) the power @f$ \gamma @f$
+   */
+  explicit PowerLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Power"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (\alpha x + \beta) ^ \gamma
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the power inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} =
+   *            \frac{\partial E}{\partial y}
+   *            \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
+   *            \frac{\partial E}{\partial y}
+   *            \frac{\alpha \gamma y}{\alpha x + \beta}
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief @f$ \gamma @f$ from layer_param_.power_param()
+  Dtype power_;
+  /// @brief @f$ \alpha @f$ from layer_param_.power_param()
+  Dtype scale_;
+  /// @brief @f$ \beta @f$ from layer_param_.power_param()
+  Dtype shift_;
+  /// @brief Result of @f$ \alpha \gamma @f$
+  Dtype diff_scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_POWER_LAYER_HPP_
diff --git a/include/caffe/layers/prelu_layer.hpp b/include/caffe/layers/prelu_layer.hpp
new file mode 100644
index 00000000000..3ddfb484b66
--- /dev/null
+++ b/include/caffe/layers/prelu_layer.hpp
@@ -0,0 +1,101 @@
+#ifndef CAFFE_PRELU_LAYER_HPP_
+#define CAFFE_PRELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Parameterized Rectified Linear Unit non-linearity @f$
+ *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+ *        @f$. The differences from ReLULayer are 1) negative slopes are
+ *        learnable though backprop and 2) negative slopes can vary across
+ *        channels. The number of axes of input blob should be greater than or
+ *        equal to 2. The 1st axis (0-based) is seen as channels.
+ */
+template <typename Dtype>
+class PReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides PReLUParameter prelu_param,
+   *     with PReLULayer options:
+   *   - filler (\b optional, FillerParameter,
+   *     default {'type': constant 'value':0.25}).
+   *   - channel_shared (\b optional, default false).
+   *     negative slopes are shared across channels.
+   */
+  explicit PReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "PReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the computed outputs for each channel @f$i@f$ @f$
+   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
+   *      @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the PReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times ...) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times ...) @f$
+   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
+   *      diff with gradients @f$
+   *        \frac{\partial E}{\partial x_i} = \left\{
+   *        \begin{array}{lr}
+   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   *      If param_propagate_down_[0] is true, it fills the diff with gradients
+   *      @f$
+   *        \frac{\partial E}{\partial a_i} = \left\{
+   *        \begin{array}{lr}
+   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
+   *            0 & \mathrm{if} \; x_i > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  bool channel_shared_;
+  Blob<Dtype> multiplier_;  // dot multiplier for backward computation of params
+  Blob<Dtype> backward_buff_;  // temporary buffer for backward computation
+  Blob<Dtype> bottom_memory_;  // memory for in-place computation
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_PRELU_LAYER_HPP_
diff --git a/include/caffe/python_layer.hpp b/include/caffe/layers/python_layer.hpp
similarity index 54%
rename from include/caffe/python_layer.hpp
rename to include/caffe/layers/python_layer.hpp
index 816ef453720..b839d52684e 100644
--- a/include/caffe/python_layer.hpp
+++ b/include/caffe/layers/python_layer.hpp
@@ -14,26 +14,27 @@ template <typename Dtype>
 class PythonLayer : public Layer<Dtype> {
  public:
   PythonLayer(PyObject* self, const LayerParameter& param)
-      : Layer<Dtype>(param), self_(self) { }
+      : Layer<Dtype>(param), self_(bp::handle<>(bp::borrowed(self))) { }
 
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      bp::call_method<bp::object>(self_, "setup", bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
+    // Disallow PythonLayer in MultiGPU training stage, due to GIL issues
+    // Details: https://github.com/BVLC/caffe/issues/2936
+    if (this->phase_ == TRAIN && Caffe::solver_count() > 1
+        && !ShareInParallel()) {
+      LOG(FATAL) << "PythonLayer is not implemented in Multi-GPU training";
     }
+    self_.attr("param_str") = bp::str(
+        this->layer_param_.python_param().param_str());
+    self_.attr("setup")(bottom, top);
   }
-
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      bp::call_method<bp::object>(self_, "reshape", bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("reshape")(bottom, top);
+  }
+
+  virtual inline bool ShareInParallel() const {
+    return this->layer_param_.python_param().share_in_parallel();
   }
 
   virtual inline const char* type() const { return "Python"; }
@@ -41,26 +42,15 @@ class PythonLayer : public Layer<Dtype> {
  protected:
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-    try {
-      bp::call_method<bp::object>(self_, "forward", bottom, top);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("forward")(bottom, top);
   }
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    try {
-      bp::call_method<bp::object>(self_, "backward", top, propagate_down,
-          bottom);
-    } catch (bp::error_already_set) {
-      PyErr_Print();
-      throw;
-    }
+    self_.attr("backward")(top, propagate_down, bottom);
   }
 
  private:
-  PyObject* self_;
+  bp::object self_;
 };
 
 }  // namespace caffe
diff --git a/include/caffe/layers/reduction_layer.hpp b/include/caffe/layers/reduction_layer.hpp
new file mode 100644
index 00000000000..804a495b11c
--- /dev/null
+++ b/include/caffe/layers/reduction_layer.hpp
@@ -0,0 +1,59 @@
+#ifndef CAFFE_REDUCTION_LAYER_HPP_
+#define CAFFE_REDUCTION_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Compute "reductions" -- operations that return a scalar output Blob
+ *        for an input Blob of arbitrary size, such as the sum, absolute sum,
+ *        and sum of squares.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class ReductionLayer : public Layer<Dtype> {
+ public:
+  explicit ReductionLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reduction"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// @brief the reduction operation performed by the layer
+  ReductionParameter_ReductionOp op_;
+  /// @brief a scalar coefficient applied to all outputs
+  Dtype coeff_;
+  /// @brief the index of the first input axis to reduce
+  int axis_;
+  /// @brief the number of reductions performed
+  int num_;
+  /// @brief the input size of each reduction
+  int dim_;
+  /// @brief a helper Blob used for summation (op_ == SUM)
+  Blob<Dtype> sum_multiplier_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_REDUCTION_LAYER_HPP_
diff --git a/include/caffe/layers/relu_layer.hpp b/include/caffe/layers/relu_layer.hpp
new file mode 100644
index 00000000000..d7a73f7a8d1
--- /dev/null
+++ b/include/caffe/layers/relu_layer.hpp
@@ -0,0 +1,85 @@
+#ifndef CAFFE_RELU_LAYER_HPP_
+#define CAFFE_RELU_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
+ *        The simple max is fast to compute, and the function does not saturate.
+ */
+template <typename Dtype>
+class ReLULayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ReLUParameter relu_param,
+   *     with ReLULayer options:
+   *   - negative_slope (\b optional, default 0).
+   *     the value @f$ \nu @f$ by which negative values are multiplied.
+   */
+  explicit ReLULayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "ReLU"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \max(0, x)
+   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the ReLU inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            0 & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$ if propagate_down[0], by default.
+   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
+   *      the computed gradients are @f$
+   *        \frac{\partial E}{\partial x} = \left\{
+   *        \begin{array}{lr}
+   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
+   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
+   *        \end{array} \right.
+   *      @f$.
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_RELU_LAYER_HPP_
diff --git a/include/caffe/layers/reshape_layer.hpp b/include/caffe/layers/reshape_layer.hpp
new file mode 100644
index 00000000000..d11e06384ce
--- /dev/null
+++ b/include/caffe/layers/reshape_layer.hpp
@@ -0,0 +1,52 @@
+#ifndef CAFFE_XXX_LAYER_HPP_
+#define CAFFE_XXX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/*
+ * @brief Reshapes the input Blob into an arbitrary-sized output Blob.
+ *
+ * Note: similarly to FlattenLayer, this layer does not change the input values
+ * (see FlattenLayer, Blob::ShareData and Blob::ShareDiff).
+ */
+template <typename Dtype>
+class ReshapeLayer : public Layer<Dtype> {
+ public:
+  explicit ReshapeLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Reshape"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
+
+  /// @brief vector of axes indices whose dimensions we'll copy from the bottom
+  vector<int> copy_axes_;
+  /// @brief the index of the axis whose dimension we infer, or -1 if none
+  int inferred_axis_;
+  /// @brief the product of the "constant" output dimensions
+  int constant_count_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_XXX_LAYER_HPP_
diff --git a/include/caffe/layers/scale_layer.hpp b/include/caffe/layers/scale_layer.hpp
new file mode 100644
index 00000000000..924df2e51ab
--- /dev/null
+++ b/include/caffe/layers/scale_layer.hpp
@@ -0,0 +1,83 @@
+#ifndef CAFFE_SCALE_LAYER_HPP_
+#define CAFFE_SCALE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/bias_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes a product of two input Blobs, with the shape of the
+ *        latter Blob "broadcast" to match the shape of the former.
+ *        Equivalent to tiling the latter Blob, then computing the elementwise
+ *        product.
+ *
+ * The second input may be omitted, in which case it's learned as a parameter
+ * of the layer.
+ */
+template <typename Dtype>
+class ScaleLayer: public Layer<Dtype> {
+ public:
+  explicit ScaleLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Scale"; }
+  // Scale
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MaxBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  /**
+   * In the below shape specifications, @f$ i @f$ denotes the value of the
+   * `axis` field given by `this->layer_param_.scale_param().axis()`, after
+   * canonicalization (i.e., conversion from negative to positive index,
+   * if applicable).
+   *
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (d_0 \times ... \times
+   *           d_i \times ... \times d_j \times ... \times d_n) @f$
+   *      the first factor @f$ x @f$
+   *   -# @f$ (d_i \times ... \times d_j) @f$
+   *      the second factor @f$ y @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (d_0 \times ... \times
+   *           d_i \times ... \times d_j \times ... \times d_n) @f$
+   *      the product @f$ z = x y @f$ computed after "broadcasting" y.
+   *      Equivalent to tiling @f$ y @f$ to have the same shape as @f$ x @f$,
+   *      then computing the elementwise product.
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  shared_ptr<Layer<Dtype> > bias_layer_;
+  vector<Blob<Dtype>*> bias_bottom_vec_;
+  vector<bool> bias_propagate_down_;
+  int bias_param_id_;
+
+  Blob<Dtype> sum_multiplier_;
+  Blob<Dtype> sum_result_;
+  Blob<Dtype> temp_;
+  int axis_;
+  int outer_dim_, scale_dim_, inner_dim_;
+};
+
+
+}  // namespace caffe
+
+#endif  // CAFFE_SCALE_LAYER_HPP_
diff --git a/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
new file mode 100644
index 00000000000..598dca5ff2c
--- /dev/null
+++ b/include/caffe/layers/sigmoid_cross_entropy_loss_layer.hpp
@@ -0,0 +1,110 @@
+#ifndef CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+#define CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the cross-entropy (logistic) loss @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n +
+ *                  (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *        @f$, often used for predicting targets interpreted as probabilities.
+ *
+ * This layer is implemented rather than separate
+ * SigmoidLayer + CrossEntropyLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SigmoidLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the scores @f$ x \in [-\infty, +\infty]@f$,
+ *      which this layer maps to probability predictions
+ *      @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
+ *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the targets @f$ y \in [0, 1] @f$
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy loss: @f$
+ *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
+ *                  p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
+ *              \right]
+ *      @f$
+ */
+template <typename Dtype>
+class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
+ public:
+  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param),
+          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
+          sigmoid_output_(new Blob<Dtype>()) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
+
+ protected:
+  /// @copydoc SigmoidCrossEntropyLossLayer
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
+   *        predictions.
+   *
+   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as gradient computation with respect
+   *      to the targets is not implemented.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$x@f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} =
+   *          \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
+   *      @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// The internal SigmoidLayer used to map predictions to probabilities.
+  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
+  /// sigmoid_output stores the output of the SigmoidLayer.
+  shared_ptr<Blob<Dtype> > sigmoid_output_;
+  /// bottom vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
+  /// top vector holder to call the underlying SigmoidLayer::Forward
+  vector<Blob<Dtype>*> sigmoid_top_vec_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SIGMOID_CROSS_ENTROPY_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/sigmoid_layer.hpp b/include/caffe/layers/sigmoid_layer.hpp
new file mode 100644
index 00000000000..ac0f6927feb
--- /dev/null
+++ b/include/caffe/layers/sigmoid_layer.hpp
@@ -0,0 +1,71 @@
+#ifndef CAFFE_SIGMOID_LAYER_HPP_
+#define CAFFE_SIGMOID_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Sigmoid function non-linearity @f$
+ *         y = (1 + \exp(-x))^{-1}
+ *     @f$, a classic choice in neural networks.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class SigmoidLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit SigmoidLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "Sigmoid"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = (1 + \exp(-x))^{-1}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y} y (1 - y)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SIGMOID_LAYER_HPP_
diff --git a/include/caffe/layers/silence_layer.hpp b/include/caffe/layers/silence_layer.hpp
new file mode 100644
index 00000000000..fba087fcef0
--- /dev/null
+++ b/include/caffe/layers/silence_layer.hpp
@@ -0,0 +1,43 @@
+#ifndef CAFFE_SILENCE_LAYER_HPP_
+#define CAFFE_SILENCE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Ignores bottom blobs while producing no top blobs. (This is useful
+ *        to suppress outputs during testing.)
+ */
+template <typename Dtype>
+class SilenceLayer : public Layer<Dtype> {
+ public:
+  explicit SilenceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+
+  virtual inline const char* type() const { return "Silence"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 0; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {}
+  // We can't define Forward_gpu here, since STUB_GPU will provide
+  // its own definition for CPU_ONLY mode.
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SILENCE_LAYER_HPP_
diff --git a/include/caffe/layers/slice_layer.hpp b/include/caffe/layers/slice_layer.hpp
new file mode 100644
index 00000000000..10a0abb6eeb
--- /dev/null
+++ b/include/caffe/layers/slice_layer.hpp
@@ -0,0 +1,51 @@
+#ifndef CAFFE_SLICE_LAYER_HPP_
+#define CAFFE_SLICE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Takes a Blob and slices it along either the num or channel dimension,
+ *        outputting multiple sliced Blob results.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SliceLayer : public Layer<Dtype> {
+ public:
+  explicit SliceLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Slice"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+  int num_slices_;
+  int slice_size_;
+  int slice_axis_;
+  vector<int> slice_point_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SLICE_LAYER_HPP_
diff --git a/include/caffe/layers/softmax_layer.hpp b/include/caffe/layers/softmax_layer.hpp
new file mode 100644
index 00000000000..c65b8703e43
--- /dev/null
+++ b/include/caffe/layers/softmax_layer.hpp
@@ -0,0 +1,50 @@
+#ifndef CAFFE_SOFTMAX_LAYER_HPP_
+#define CAFFE_SOFTMAX_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Computes the softmax function.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SoftmaxLayer : public Layer<Dtype> {
+ public:
+  explicit SoftmaxLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Softmax"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int outer_num_;
+  int inner_num_;
+  int softmax_axis_;
+  /// sum_multiplier is used to carry out sum using BLAS
+  Blob<Dtype> sum_multiplier_;
+  /// scale is an intermediate Blob to hold temporary results.
+  Blob<Dtype> scale_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOFTMAX_LAYER_HPP_
diff --git a/include/caffe/layers/softmax_loss_layer.hpp b/include/caffe/layers/softmax_loss_layer.hpp
new file mode 100644
index 00000000000..f07e8a02cf1
--- /dev/null
+++ b/include/caffe/layers/softmax_loss_layer.hpp
@@ -0,0 +1,130 @@
+#ifndef CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+#define CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/loss_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Computes the multinomial logistic loss for a one-of-many
+ *        classification task, passing real-valued predictions through a
+ *        softmax to get a probability distribution over classes.
+ *
+ * This layer should be preferred over separate
+ * SoftmaxLayer + MultinomialLogisticLossLayer
+ * as its gradient computation is more numerically stable.
+ * At test time, this layer can be replaced simply by a SoftmaxLayer.
+ *
+ * @param bottom input Blob vector (length 2)
+ *   -# @f$ (N \times C \times H \times W) @f$
+ *      the predictions @f$ x @f$, a Blob with values in
+ *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
+ *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
+ *      probability distribution over classes using the softmax function
+ *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
+ *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
+ *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+ *      the labels @f$ l @f$, an integer-valued Blob with values
+ *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
+ *      indicating the correct class label among the @f$ K @f$ classes
+ * @param top output Blob vector (length 1)
+ *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+ *      the computed cross-entropy classification loss: @f$ E =
+ *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
+ *      @f$, for softmax output class probabilites @f$ \hat{p} @f$
+ */
+template <typename Dtype>
+class SoftmaxWithLossLayer : public LossLayer<Dtype> {
+ public:
+   /**
+    * @param param provides LossParameter loss_param, with options:
+    *  - ignore_label (optional)
+    *    Specify a label value that should be ignored when computing the loss.
+    *  - normalize (optional, default true)
+    *    If true, the loss is normalized by the number of (nonignored) labels
+    *    present; otherwise the loss is simply summed over spatial locations.
+    */
+  explicit SoftmaxWithLossLayer(const LayerParameter& param)
+      : LossLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
+  virtual inline int ExactNumTopBlobs() const { return -1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+  virtual inline int MaxTopBlobs() const { return 2; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /**
+   * @brief Computes the softmax loss error gradient w.r.t. the predictions.
+   *
+   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
+   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
+   * if propagate_down[1] is set.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
+   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
+   *      as @f$ \lambda @f$ is the coefficient of this layer's output
+   *      @f$\ell_i@f$ in the overall Net loss
+   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
+   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
+   *      (*Assuming that this top Blob is not used as a bottom (input) by any
+   *      other layer of the Net.)
+   * @param propagate_down see Layer::Backward.
+   *      propagate_down[1] must be false as we can't compute gradients with
+   *      respect to the labels.
+   * @param bottom input Blob vector (length 2)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the predictions @f$ x @f$; Backward computes diff
+   *      @f$ \frac{\partial E}{\partial x} @f$
+   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
+   *      the labels -- ignored as we can't compute their error gradients
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  /// Read the normalization mode parameter and compute the normalizer based
+  /// on the blob size.  If normalization_mode is VALID, the count of valid
+  /// outputs will be read from valid_count, unless it is -1 in which case
+  /// all outputs are assumed to be valid.
+  virtual Dtype get_normalizer(
+      LossParameter_NormalizationMode normalization_mode, int valid_count);
+
+  /// The internal SoftmaxLayer used to map predictions to a distribution.
+  shared_ptr<Layer<Dtype> > softmax_layer_;
+  /// prob stores the output probability predictions from the SoftmaxLayer.
+  Blob<Dtype> prob_;
+  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_bottom_vec_;
+  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
+  vector<Blob<Dtype>*> softmax_top_vec_;
+  /// Whether to ignore instances with a certain label.
+  bool has_ignore_label_;
+  /// The label indicating that an instance should be ignored.
+  int ignore_label_;
+  /// How to normalize the output loss.
+  LossParameter_NormalizationMode normalization_;
+
+  int softmax_axis_, outer_num_, inner_num_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOFTMAX_WITH_LOSS_LAYER_HPP_
diff --git a/include/caffe/layers/split_layer.hpp b/include/caffe/layers/split_layer.hpp
new file mode 100644
index 00000000000..8140dfc7c40
--- /dev/null
+++ b/include/caffe/layers/split_layer.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_SPLIT_LAYER_HPP_
+#define CAFFE_SPLIT_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Creates a "split" path in the network by copying the bottom Blob
+ *        into multiple top Blob%s to be used by multiple consuming layers.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class SplitLayer : public Layer<Dtype> {
+ public:
+  explicit SplitLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Split"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int MinTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  int count_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SPLIT_LAYER_HPP_
diff --git a/include/caffe/layers/spp_layer.hpp b/include/caffe/layers/spp_layer.hpp
new file mode 100644
index 00000000000..9f145cc77e3
--- /dev/null
+++ b/include/caffe/layers/spp_layer.hpp
@@ -0,0 +1,76 @@
+#ifndef CAFFE_SPP_LAYER_HPP_
+#define CAFFE_SPP_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Does spatial pyramid pooling on the input image
+ *        by taking the max, average, etc. within regions
+ *        so that the result vector of different sized
+ *        images are of the same size.
+ */
+template <typename Dtype>
+class SPPLayer : public Layer<Dtype> {
+ public:
+  explicit SPPLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "SPP"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  // calculates the kernel and stride dimensions for the pooling layer,
+  // returns a correctly configured LayerParameter for a PoolingLayer
+  virtual LayerParameter GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param);
+
+  int pyramid_height_;
+  int bottom_h_, bottom_w_;
+  int num_;
+  int channels_;
+  int kernel_h_, kernel_w_;
+  int pad_h_, pad_w_;
+  bool reshaped_first_time_;
+
+  /// the internal Split layer that feeds the pooling layers
+  shared_ptr<SplitLayer<Dtype> > split_layer_;
+  /// top vector holder used in call to the underlying SplitLayer::Forward
+  vector<Blob<Dtype>*> split_top_vec_;
+  /// bottom vector holder used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_bottom_vecs_;
+  /// the internal Pooling layers of different kernel sizes
+  vector<shared_ptr<PoolingLayer<Dtype> > > pooling_layers_;
+  /// top vector holders used in call to the underlying PoolingLayer::Forward
+  vector<vector<Blob<Dtype>*>*> pooling_top_vecs_;
+  /// pooling_outputs stores the outputs of the PoolingLayers
+  vector<Blob<Dtype>*> pooling_outputs_;
+  /// the internal Flatten layers that the Pooling layers feed into
+  vector<FlattenLayer<Dtype>*> flatten_layers_;
+  /// top vector holders used in call to the underlying FlattenLayer::Forward
+  vector<vector<Blob<Dtype>*>*> flatten_top_vecs_;
+  /// flatten_outputs stores the outputs of the FlattenLayers
+  vector<Blob<Dtype>*> flatten_outputs_;
+  /// bottom vector holder used in call to the underlying ConcatLayer::Forward
+  vector<Blob<Dtype>*> concat_bottom_vec_;
+  /// the internal Concat layers that the Flatten layers feed into
+  shared_ptr<ConcatLayer<Dtype> > concat_layer_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SPP_LAYER_HPP_
diff --git a/include/caffe/layers/tanh_layer.hpp b/include/caffe/layers/tanh_layer.hpp
new file mode 100644
index 00000000000..8f95e9322d9
--- /dev/null
+++ b/include/caffe/layers/tanh_layer.hpp
@@ -0,0 +1,73 @@
+#ifndef CAFFE_TANH_LAYER_HPP_
+#define CAFFE_TANH_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief TanH hyperbolic tangent non-linearity @f$
+ *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+ *     @f$, popular in auto-encoders.
+ *
+ * Note that the gradient vanishes as the values move away from 0.
+ * The ReLULayer is often a better choice for this reason.
+ */
+template <typename Dtype>
+class TanHLayer : public NeuronLayer<Dtype> {
+ public:
+  explicit TanHLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+
+  virtual inline const char* type() const { return "TanH"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *        y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  /**
+   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
+   *
+   * @param top output Blob vector (length 1), providing the error gradient with
+   *      respect to the outputs
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
+   *      with respect to computed outputs @f$ y @f$
+   * @param propagate_down see Layer::Backward.
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$; Backward fills their diff with
+   *      gradients @f$
+   *        \frac{\partial E}{\partial x}
+   *            = \frac{\partial E}{\partial y}
+   *              \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
+   *            = \frac{\partial E}{\partial y} (1 - y^2)
+   *      @f$ if propagate_down[0]
+   */
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_TANH_LAYER_HPP_
diff --git a/include/caffe/layers/threshold_layer.hpp b/include/caffe/layers/threshold_layer.hpp
new file mode 100644
index 00000000000..3bf4db63e5c
--- /dev/null
+++ b/include/caffe/layers/threshold_layer.hpp
@@ -0,0 +1,64 @@
+#ifndef CAFFE_THRESHOLD_LAYER_HPP_
+#define CAFFE_THRESHOLD_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+#include "caffe/layers/neuron_layer.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
+ *        above threshold; 0 otherwise.
+ */
+template <typename Dtype>
+class ThresholdLayer : public NeuronLayer<Dtype> {
+ public:
+  /**
+   * @param param provides ThresholdParameter threshold_param,
+   *     with ThresholdLayer options:
+   *   - threshold (\b optional, default 0).
+   *     the threshold value @f$ t @f$ to which the input values are compared.
+   */
+  explicit ThresholdLayer(const LayerParameter& param)
+      : NeuronLayer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Threshold"; }
+
+ protected:
+  /**
+   * @param bottom input Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the inputs @f$ x @f$
+   * @param top output Blob vector (length 1)
+   *   -# @f$ (N \times C \times H \times W) @f$
+   *      the computed outputs @f$
+   *       y = \left\{
+   *       \begin{array}{lr}
+   *         0 & \mathrm{if} \; x \le t \\
+   *         1 & \mathrm{if} \; x > t
+   *       \end{array} \right.
+   *      @f$
+   */
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  /// @brief Not implemented (non-differentiable function)
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+    NOT_IMPLEMENTED;
+  }
+
+  Dtype threshold_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_THRESHOLD_LAYER_HPP_
diff --git a/include/caffe/layers/tile_layer.hpp b/include/caffe/layers/tile_layer.hpp
new file mode 100644
index 00000000000..fbdbe2f0c53
--- /dev/null
+++ b/include/caffe/layers/tile_layer.hpp
@@ -0,0 +1,43 @@
+#ifndef CAFFE_TILE_LAYER_HPP_
+#define CAFFE_TILE_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Copy a Blob along specified dimensions.
+ */
+template <typename Dtype>
+class TileLayer : public Layer<Dtype> {
+ public:
+  explicit TileLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "Tile"; }
+  virtual inline int ExactNumBottomBlobs() const { return 1; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  unsigned int axis_, tiles_, outer_dim_, inner_dim_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_TILE_LAYER_HPP_
diff --git a/include/caffe/layers/unpooling_layer.hpp b/include/caffe/layers/unpooling_layer.hpp
new file mode 100644
index 00000000000..b69c49f5d39
--- /dev/null
+++ b/include/caffe/layers/unpooling_layer.hpp
@@ -0,0 +1,57 @@
+#ifndef CAFFE_UNPOOLING_LAYER_HPP_
+#define CAFFE_UNPOOLING_LAYER_HPP_
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief UnPools the input image by assigning fixed, bilinear interpolation,
+ * etc. within regions.
+ *
+ * TODO(dox): thorough documentation for Forward, Backward, and proto params.
+ */
+template <typename Dtype>
+class UnPoolingLayer : public Layer<Dtype> {
+ public:
+  explicit UnPoolingLayer(const LayerParameter& param)
+      : Layer<Dtype>(param) {}
+  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "UnPooling"; }
+  virtual inline int MinBottomBlobs() const { return 1; }
+  virtual inline int MaxBottomBlobs() const { return 2; }
+  virtual inline int ExactNumTopBlobs() const { return 1; }
+
+ protected:
+  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
+
+  // fill mask for different unpool type
+  void FillMask();
+
+  int out_kernel_h_, out_kernel_w_;
+  int out_stride_h_, out_stride_w_;
+  int out_pad_h_, out_pad_w_;
+  int num_, channels_;
+  int height_, width_;
+  int unpooled_height_, unpooled_width_;
+  Blob<int> mask_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_UNPOOLING_LAYER_HPP_
diff --git a/include/caffe/layers/window_data_layer.hpp b/include/caffe/layers/window_data_layer.hpp
new file mode 100644
index 00000000000..35f41b80e63
--- /dev/null
+++ b/include/caffe/layers/window_data_layer.hpp
@@ -0,0 +1,55 @@
+#ifndef CAFFE_WINDOW_DATA_LAYER_HPP_
+#define CAFFE_WINDOW_DATA_LAYER_HPP_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+/**
+ * @brief Provides data to the Net from windows of images files, specified
+ *        by a window data file.
+ *
+ * TODO(dox): thorough documentation for Forward and proto params.
+ */
+template <typename Dtype>
+class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> {
+ public:
+  explicit WindowDataLayer(const LayerParameter& param)
+      : BasePrefetchingDataLayer<Dtype>(param) {}
+  virtual ~WindowDataLayer();
+  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top);
+
+  virtual inline const char* type() const { return "WindowData"; }
+  virtual inline int ExactNumBottomBlobs() const { return 0; }
+  virtual inline int ExactNumTopBlobs() const { return 2; }
+
+ protected:
+  virtual unsigned int PrefetchRand();
+  virtual void load_batch(Batch<Dtype>* batch);
+
+  shared_ptr<Caffe::RNG> prefetch_rng_;
+  vector<std::pair<std::string, vector<int> > > image_database_;
+  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };
+  vector<vector<float> > fg_windows_;
+  vector<vector<float> > bg_windows_;
+  Blob<Dtype> data_mean_;
+  vector<Dtype> mean_values_;
+  bool has_mean_file_;
+  bool has_mean_values_;
+  bool cache_images_;
+  vector<std::pair<std::string, Datum > > image_database_cache_;
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_WINDOW_DATA_LAYER_HPP_
diff --git a/include/caffe/loss_layers.hpp b/include/caffe/loss_layers.hpp
deleted file mode 100644
index d3eecd2e510..00000000000
--- a/include/caffe/loss_layers.hpp
+++ /dev/null
@@ -1,770 +0,0 @@
-#ifndef CAFFE_LOSS_LAYERS_HPP_
-#define CAFFE_LOSS_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-const float kLOG_THRESHOLD = 1e-20;
-
-/**
- * @brief Computes the classification accuracy for a one-of-many
- *        classification task.
- */
-template <typename Dtype>
-class AccuracyLayer : public Layer<Dtype> {
- public:
-  /**
-   * @param param provides AccuracyParameter accuracy_param,
-   *     with AccuracyLayer options:
-   *   - top_k (\b optional, default 1).
-   *     Sets the maximum rank @f$ k @f$ at which a prediction is considered
-   *     correct.  For example, if @f$ k = 5 @f$, a prediction is counted
-   *     correct if the correct label is among the top 5 predicted labels.
-   */
-  explicit AccuracyLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Accuracy"; }
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ x @f$, a Blob with values in
-   *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
-   *      the @f$ K = CHW @f$ classes. Each @f$ x_n @f$ is mapped to a predicted
-   *      label @f$ \hat{l}_n @f$ given by its maximal index:
-   *      @f$ \hat{l}_n = \arg\max\limits_k x_{nk} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels @f$ l @f$, an integer-valued Blob with values
-   *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
-   *      indicating the correct class label among the @f$ K @f$ classes
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      the computed accuracy: @f$
-   *        \frac{1}{N} \sum\limits_{n=1}^N \delta\{ \hat{l}_n = l_n \}
-   *      @f$, where @f$
-   *      \delta\{\mathrm{condition}\} = \left\{
-   *         \begin{array}{lr}
-   *            1 & \mbox{if condition} \\
-   *            0 & \mbox{otherwise}
-   *         \end{array} \right.
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-
-  /// @brief Not implemented -- AccuracyLayer cannot be used as a loss.
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    for (int i = 0; i < propagate_down.size(); ++i) {
-      if (propagate_down[i]) { NOT_IMPLEMENTED; }
-    }
-  }
-
-  int label_axis_, outer_num_, inner_num_;
-
-  int top_k_;
-
-  /// Whether to ignore instances with a certain label.
-  bool has_ignore_label_;
-  /// The label indicating that an instance should be ignored.
-  int ignore_label_;
-};
-
-/**
- * @brief An interface for Layer%s that take two Blob%s as input -- usually
- *        (1) predictions and (2) ground-truth labels -- and output a
- *        singleton Blob representing the loss.
- *
- * LossLayers are typically only capable of backpropagating to their first input
- * -- the predictions.
- */
-template <typename Dtype>
-class LossLayer : public Layer<Dtype> {
- public:
-  explicit LossLayer(const LayerParameter& param)
-     : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(
-      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(
-      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 2; }
-
-  /**
-   * @brief For convenience and backwards compatibility, instruct the Net to
-   *        automatically allocate a single top Blob for LossLayers, into which
-   *        they output their singleton loss, (even if the user didn't specify
-   *        one in the prototxt, etc.).
-   */
-  virtual inline bool AutoTopBlobs() const { return true; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-  /**
-   * We usually cannot backpropagate to the labels; ignore force_backward for
-   * these inputs.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 1;
-  }
-};
-
-/**
- * @brief Computes the contrastive loss @f$
- *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- *              \left(1-y\right) \max \left(margin-d, 0\right)
- *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$. This can be
- *          used to train siamese networks.
- *
- * @param bottom input Blob vector (length 3)
- *   -# @f$ (N \times C \times 1 \times 1) @f$
- *      the features @f$ a \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times C \times 1 \times 1) @f$
- *      the features @f$ b \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the binary similarity @f$ s \in [0, 1]@f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed contrastive loss: @f$ E =
- *          \frac{1}{2N} \sum\limits_{n=1}^N \left(y\right) d +
- *          \left(1-y\right) \max \left(margin-d, 0\right)
- *          @f$ where @f$
- *          d = \left| \left| a_n - b_n \right| \right|_2^2 @f$.
- * This can be used to train siamese networks.
- */
-template <typename Dtype>
-class ContrastiveLossLayer : public LossLayer<Dtype> {
- public:
-  explicit ContrastiveLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 3; }
-  virtual inline const char* type() const { return "ContrastiveLoss"; }
-  /**
-   * Unlike most loss layers, in the ContrastiveLossLayer we can backpropagate
-   * to the first two inputs.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return bottom_index != 2;
-  }
-
- protected:
-  /// @copydoc ContrastiveLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the Contrastive error gradient w.r.t. the inputs.
-   *
-   * Computes the gradients with respect to the two input vectors (bottom[0] and
-   * bottom[1]), but not the similarity label (bottom[2]).
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$a@f$; Backward fills their diff with
-   *      gradients if propagate_down[0]
-   *   -# @f$ (N \times C \times 1 \times 1) @f$
-   *      the features @f$b@f$; Backward fills their diff with gradients if
-   *      propagate_down[1]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> diff_;  // cached for backward pass
-  Blob<Dtype> dist_sq_;  // cached for backward pass
-  Blob<Dtype> diff_sq_;  // tmp storage for gpu forward pass
-  Blob<Dtype> summer_vec_;  // tmp storage for gpu forward pass
-};
-
-/**
- * @brief Computes the Euclidean (L2) loss @f$
- *          E = \frac{1}{2N} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- *        \right| \right|_2^2 @f$ for real-valued regression tasks.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{y} \in [-\infty, +\infty]@f$
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the targets @f$ y \in [-\infty, +\infty]@f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed Euclidean loss: @f$ E =
- *          \frac{1}{2n} \sum\limits_{n=1}^N \left| \left| \hat{y}_n - y_n
- *        \right| \right|_2^2 @f$
- *
- * This can be used for least-squares regression tasks.  An InnerProductLayer
- * input to a EuclideanLossLayer exactly formulates a linear least squares
- * regression problem. With non-zero weight decay the problem becomes one of
- * ridge regression -- see src/caffe/test/test_sgd_solver.cpp for a concrete
- * example wherein we check that the gradients computed for a Net with exactly
- * this structure match hand-computed gradient formulas for ridge regression.
- *
- * (Note: Caffe, and SGD in general, is certainly \b not the best way to solve
- * linear least squares problems! We use it only as an instructive example.)
- */
-template <typename Dtype>
-class EuclideanLossLayer : public LossLayer<Dtype> {
- public:
-  explicit EuclideanLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), diff_() {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "EuclideanLoss"; }
-  /**
-   * Unlike most loss layers, in the EuclideanLossLayer we can backpropagate
-   * to both inputs -- override to return true and always allow force_backward.
-   */
-  virtual inline bool AllowForceBackward(const int bottom_index) const {
-    return true;
-  }
-
- protected:
-  /// @copydoc EuclideanLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the Euclidean error gradient w.r.t. the inputs.
-   *
-   * Unlike other children of LossLayer, EuclideanLossLayer \b can compute
-   * gradients with respect to the label inputs bottom[1] (but still only will
-   * if propagate_down[1] is set, due to being produced by learnable parameters
-   * or if force_backward is set). In fact, this layer is "commutative" -- the
-   * result is the same regardless of the order of the two bottoms.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$\hat{y}@f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial \hat{y}} =
-   *            \frac{1}{n} \sum\limits_{n=1}^N (\hat{y}_n - y_n)
-   *      @f$ if propagate_down[0]
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the targets @f$y@f$; Backward fills their diff with gradients
-   *      @f$ \frac{\partial E}{\partial y} =
-   *          \frac{1}{n} \sum\limits_{n=1}^N (y_n - \hat{y}_n)
-   *      @f$ if propagate_down[1]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> diff_;
-};
-
-/**
- * @brief Computes the hinge loss for a one-of-many classification task.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ t @f$, a Blob with values in
- *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- *      the @f$ K = CHW @f$ classes. In an SVM, @f$ t @f$ is the result of
- *      taking the inner product @f$ X^T W @f$ of the D-dimensional features
- *      @f$ X \in \mathcal{R}^{D \times N} @f$ and the learned hyperplane
- *      parameters @f$ W \in \mathcal{R}^{D \times K} @f$, so a Net with just
- *      an InnerProductLayer (with num_output = D) providing predictions to a
- *      HingeLossLayer and no other learnable parameters or losses is
- *      equivalent to an SVM.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed hinge loss: @f$ E =
- *        \frac{1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^K
- *        [\max(0, 1 - \delta\{l_n = k\} t_{nk})] ^ p
- *      @f$, for the @f$ L^p @f$ norm
- *      (defaults to @f$ p = 1 @f$, the L1 norm; L2 norm, as in L2-SVM,
- *      is also available), and @f$
- *      \delta\{\mathrm{condition}\} = \left\{
- *         \begin{array}{lr}
- *            1 & \mbox{if condition} \\
- *           -1 & \mbox{otherwise}
- *         \end{array} \right.
- *      @f$
- *
- * In an SVM, @f$ t \in \mathcal{R}^{N \times K} @f$ is the result of taking
- * the inner product @f$ X^T W @f$ of the features
- * @f$ X \in \mathcal{R}^{D \times N} @f$
- * and the learned hyperplane parameters
- * @f$ W \in \mathcal{R}^{D \times K} @f$. So, a Net with just an
- * InnerProductLayer (with num_output = @f$k@f$) providing predictions to a
- * HingeLossLayer is equivalent to an SVM (assuming it has no other learned
- * outside the InnerProductLayer and no other losses outside the
- * HingeLossLayer).
- */
-template <typename Dtype>
-class HingeLossLayer : public LossLayer<Dtype> {
- public:
-  explicit HingeLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "HingeLoss"; }
-
- protected:
-  /// @copydoc HingeLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the hinge loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$t@f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial t} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief A generalization of MultinomialLogisticLossLayer that takes an
- *        "information gain" (infogain) matrix specifying the "value" of all label
- *        pairs.
- *
- * Equivalent to the MultinomialLogisticLossLayer if the infogain matrix is the
- * identity.
- *
- * @param bottom input Blob vector (length 2-3)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- *   -# @f$ (1 \times 1 \times K \times K) @f$
- *      (\b optional) the infogain matrix @f$ H @f$.  This must be provided as
- *      the third bottom blob input if not provided as the infogain_mat in the
- *      InfogainLossParameter. If @f$ H = I @f$, this layer is equivalent to the
- *      MultinomialLogisticLossLayer.
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed infogain multinomial logistic loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N H_{l_n} \log(\hat{p}_n) =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \sum\limits_{k=1}^{K} H_{l_n,k}
- *        \log(\hat{p}_{n,k})
- *      @f$, where @f$ H_{l_n} @f$ denotes row @f$l_n@f$ of @f$H@f$.
- */
-template <typename Dtype>
-class InfogainLossLayer : public LossLayer<Dtype> {
- public:
-  explicit InfogainLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param), infogain_() {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  // InfogainLossLayer takes 2-3 bottom Blobs; if there are 3 the third should
-  // be the infogain matrix.  (Otherwise the infogain matrix is loaded from a
-  // file specified by LayerParameter.)
-  virtual inline int ExactNumBottomBlobs() const { return -1; }
-  virtual inline int MinBottomBlobs() const { return 2; }
-  virtual inline int MaxBottomBlobs() const { return 3; }
-
-  virtual inline const char* type() const { return "InfogainLoss"; }
-
- protected:
-  /// @copydoc InfogainLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the infogain loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set. (The same applies to the infogain matrix, if
-   * provided as bottom[2] rather than in the layer_param.)
-   *
-   * @param top output Blob vector (length 1), providing the error gradient
-   *      with respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels (similarly for propagate_down[2] and the
-   *      infogain matrix, if provided as bottom[2])
-   * @param bottom input Blob vector (length 2-3)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   *   -# @f$ (1 \times 1 \times K \times K) @f$
-   *      (\b optional) the information gain matrix -- ignored as its error
-   *      gradient computation is not implemented.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Blob<Dtype> infogain_;
-};
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- *        classification task, directly taking a predicted probability
- *        distribution as input.
- *
- * When predictions are not already a probability distribution, you should
- * instead use the SoftmaxWithLossLayer, which maps predictions to a
- * distribution using the SoftmaxLayer, before computing the multinomial
- * logistic loss. The SoftmaxWithLossLayer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ \hat{p} @f$, a Blob with values in
- *      @f$ [0, 1] @f$ indicating the predicted probability of each of the
- *      @f$ K = CHW @f$ classes.  Each prediction vector @f$ \hat{p}_n @f$
- *      should sum to 1 as in a probability distribution: @f$
- *      \forall n \sum\limits_{k=1}^K \hat{p}_{nk} = 1 @f$.
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed multinomial logistic loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- *      @f$
- */
-template <typename Dtype>
-class MultinomialLogisticLossLayer : public LossLayer<Dtype> {
- public:
-  explicit MultinomialLogisticLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "MultinomialLogisticLoss"; }
-
- protected:
-  /// @copydoc MultinomialLogisticLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the multinomial logistic loss error gradient w.r.t. the
-   *        predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ \hat{p} @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial \hat{p}} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes the cross-entropy (logistic) loss @f$
- *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- *                  p_n \log \hat{p}_n +
- *                  (1 - p_n) \log(1 - \hat{p}_n)
- *              \right]
- *        @f$, often used for predicting targets interpreted as probabilities.
- *
- * This layer is implemented rather than separate
- * SigmoidLayer + CrossEntropyLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SigmoidLayer.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the scores @f$ x \in [-\infty, +\infty]@f$,
- *      which this layer maps to probability predictions
- *      @f$ \hat{p}_n = \sigma(x_n) \in [0, 1] @f$
- *      using the sigmoid function @f$ \sigma(.) @f$ (see SigmoidLayer).
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the targets @f$ y \in [0, 1] @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed cross-entropy loss: @f$
- *          E = \frac{-1}{n} \sum\limits_{n=1}^N \left[
- *                  p_n \log \hat{p}_n + (1 - p_n) \log(1 - \hat{p}_n)
- *              \right]
- *      @f$
- */
-template <typename Dtype>
-class SigmoidCrossEntropyLossLayer : public LossLayer<Dtype> {
- public:
-  explicit SigmoidCrossEntropyLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param),
-          sigmoid_layer_(new SigmoidLayer<Dtype>(param)),
-          sigmoid_output_(new Blob<Dtype>()) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "SigmoidCrossEntropyLoss"; }
-
- protected:
-  /// @copydoc SigmoidCrossEntropyLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the sigmoid cross-entropy loss error gradient w.r.t. the
-   *        predictions.
-   *
-   * Gradients cannot be computed with respect to the target inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as gradient computation with respect
-   *      to the targets is not implemented.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$x@f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial x} =
-   *          \frac{1}{n} \sum\limits_{n=1}^N (\hat{p}_n - p_n)
-   *      @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// The internal SigmoidLayer used to map predictions to probabilities.
-  shared_ptr<SigmoidLayer<Dtype> > sigmoid_layer_;
-  /// sigmoid_output stores the output of the SigmoidLayer.
-  shared_ptr<Blob<Dtype> > sigmoid_output_;
-  /// bottom vector holder to call the underlying SigmoidLayer::Forward
-  vector<Blob<Dtype>*> sigmoid_bottom_vec_;
-  /// top vector holder to call the underlying SigmoidLayer::Forward
-  vector<Blob<Dtype>*> sigmoid_top_vec_;
-};
-
-// Forward declare SoftmaxLayer for use in SoftmaxWithLossLayer.
-template <typename Dtype> class SoftmaxLayer;
-
-/**
- * @brief Computes the multinomial logistic loss for a one-of-many
- *        classification task, passing real-valued predictions through a
- *        softmax to get a probability distribution over classes.
- *
- * This layer should be preferred over separate
- * SoftmaxLayer + MultinomialLogisticLossLayer
- * as its gradient computation is more numerically stable.
- * At test time, this layer can be replaced simply by a SoftmaxLayer.
- *
- * @param bottom input Blob vector (length 2)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the predictions @f$ x @f$, a Blob with values in
- *      @f$ [-\infty, +\infty] @f$ indicating the predicted score for each of
- *      the @f$ K = CHW @f$ classes. This layer maps these scores to a
- *      probability distribution over classes using the softmax function
- *      @f$ \hat{p}_{nk} = \exp(x_{nk}) /
- *      \left[\sum_{k'} \exp(x_{nk'})\right] @f$ (see SoftmaxLayer).
- *   -# @f$ (N \times 1 \times 1 \times 1) @f$
- *      the labels @f$ l @f$, an integer-valued Blob with values
- *      @f$ l_n \in [0, 1, 2, ..., K - 1] @f$
- *      indicating the correct class label among the @f$ K @f$ classes
- * @param top output Blob vector (length 1)
- *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
- *      the computed cross-entropy classification loss: @f$ E =
- *        \frac{-1}{N} \sum\limits_{n=1}^N \log(\hat{p}_{n,l_n})
- *      @f$, for softmax output class probabilites @f$ \hat{p} @f$
- */
-template <typename Dtype>
-class SoftmaxWithLossLayer : public LossLayer<Dtype> {
- public:
-   /**
-    * @param param provides LossParameter loss_param, with options:
-    *  - ignore_label (optional)
-    *    Specify a label value that should be ignored when computing the loss.
-    *  - normalize (optional, default true)
-    *    If true, the loss is normalized by the number of (nonignored) labels
-    *    present; otherwise the loss is simply summed over spatial locations.
-    */
-  explicit SoftmaxWithLossLayer(const LayerParameter& param)
-      : LossLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "SoftmaxWithLoss"; }
-  virtual inline int ExactNumTopBlobs() const { return -1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline int MaxTopBlobs() const { return 2; }
-
- protected:
-  /// @copydoc SoftmaxWithLossLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /**
-   * @brief Computes the softmax loss error gradient w.r.t. the predictions.
-   *
-   * Gradients cannot be computed with respect to the label inputs (bottom[1]),
-   * so this method ignores bottom[1] and requires !propagate_down[1], crashing
-   * if propagate_down[1] is set.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (1 \times 1 \times 1 \times 1) @f$
-   *      This Blob's diff will simply contain the loss_weight* @f$ \lambda @f$,
-   *      as @f$ \lambda @f$ is the coefficient of this layer's output
-   *      @f$\ell_i@f$ in the overall Net loss
-   *      @f$ E = \lambda_i \ell_i + \mbox{other loss terms}@f$; hence
-   *      @f$ \frac{\partial E}{\partial \ell_i} = \lambda_i @f$.
-   *      (*Assuming that this top Blob is not used as a bottom (input) by any
-   *      other layer of the Net.)
-   * @param propagate_down see Layer::Backward.
-   *      propagate_down[1] must be false as we can't compute gradients with
-   *      respect to the labels.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the predictions @f$ x @f$; Backward computes diff
-   *      @f$ \frac{\partial E}{\partial x} @f$
-   *   -# @f$ (N \times 1 \times 1 \times 1) @f$
-   *      the labels -- ignored as we can't compute their error gradients
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-
-  /// The internal SoftmaxLayer used to map predictions to a distribution.
-  shared_ptr<Layer<Dtype> > softmax_layer_;
-  /// prob stores the output probability predictions from the SoftmaxLayer.
-  Blob<Dtype> prob_;
-  /// bottom vector holder used in call to the underlying SoftmaxLayer::Forward
-  vector<Blob<Dtype>*> softmax_bottom_vec_;
-  /// top vector holder used in call to the underlying SoftmaxLayer::Forward
-  vector<Blob<Dtype>*> softmax_top_vec_;
-  /// Whether to ignore instances with a certain label.
-  bool has_ignore_label_;
-  /// The label indicating that an instance should be ignored.
-  int ignore_label_;
-  /// Whether to normalize the loss by the total number of values present
-  /// (otherwise just by the batch size).
-  bool normalize_;
-
-  int softmax_axis_, outer_num_, inner_num_;
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_LOSS_LAYERS_HPP_
diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index 075afebc9b0..0addb3c2a6d 100644
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -23,19 +23,25 @@ namespace caffe {
 template <typename Dtype>
 class Net {
  public:
-  explicit Net(const NetParameter& param);
-  explicit Net(const string& param_file, Phase phase);
+  explicit Net(const NetParameter& param, const Net* root_net = NULL);
+  explicit Net(const string& param_file, Phase phase,
+      const Net* root_net = NULL);
   virtual ~Net() {}
 
   /// @brief Initialize a network with a NetParameter.
   void Init(const NetParameter& param);
 
   /**
-   * @brief Run Forward with the input Blob%s already fed separately.
+   * @brief Run Forward and return the result.
    *
-   * You can get the input blobs using input_blobs().
    */
-  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
+  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
+  /// @brief DEPRECATED; use Forward() instead.
+  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
+    LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
+        << "will be removed in a future version. Use Forward().";
+    return Forward(loss);
+  }
 
   /**
    * The From and To variants of Forward and Backward operate on the
@@ -48,14 +54,15 @@ class Net {
   Dtype ForwardFromTo(int start, int end);
   Dtype ForwardFrom(int start);
   Dtype ForwardTo(int end);
-  /// @brief Run forward using a set of bottom blobs, and return the result.
+  /// @brief DEPRECATED; set input blobs then use Forward() instead.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
       Dtype* loss = NULL);
+
   /**
-   * @brief Run forward using a serialized BlobProtoVector and return the
-   *        result as a serialized BlobProtoVector
+   * @brief Zeroes out the diffs of all net parameters.
+   *        Should be run before Backward.
    */
-  string Forward(const string& input_blob_protos, Dtype* loss = NULL);
+  void ClearParamDiffs();
 
   /**
    * The network backward should take no input and output, since it solely
@@ -75,15 +82,22 @@ class Net {
    */
   void Reshape();
 
-  Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
+  Dtype ForwardBackward() {
     Dtype loss;
-    Forward(bottom, &loss);
+    Forward(&loss);
     Backward();
     return loss;
   }
 
   /// @brief Updates the network weights based on the diff values computed.
   void Update();
+  /**
+   * @brief Shares weight data of owner blobs with shared blobs.
+   *
+   * Note: this is called by Net::Init, and thus should normally not be
+   * called manually.
+   */
+  void ShareWeights();
 
   /**
    * @brief For an already initialized net, implicitly copies (i.e., using no
@@ -98,8 +112,12 @@ class Net {
    */
   void CopyTrainedLayersFrom(const NetParameter& param);
   void CopyTrainedLayersFrom(const string trained_filename);
+  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
+  void CopyTrainedLayersFromHDF5(const string trained_filename);
   /// @brief Writes the net to a proto.
   void ToProto(NetParameter* param, bool write_diff = false) const;
+  /// @brief Writes the net to an HDF5 file.
+  void ToHDF5(const string& filename, bool write_diff = false) const;
 
   /// @brief returns the network name.
   inline const string& name() const { return name_; }
@@ -131,25 +149,51 @@ class Net {
   inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
     return top_vecs_;
   }
+  /// @brief returns the ids of the top blobs of layer i
+  inline const vector<int> & top_ids(int i) const {
+    CHECK_GE(i, 0) << "Invalid layer id";
+    CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
+    return top_id_vecs_[i];
+  }
+  /// @brief returns the ids of the bottom blobs of layer i
+  inline const vector<int> & bottom_ids(int i) const {
+    CHECK_GE(i, 0) << "Invalid layer id";
+    CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
+    return bottom_id_vecs_[i];
+  }
   inline const vector<vector<bool> >& bottom_need_backward() const {
     return bottom_need_backward_;
   }
   inline const vector<Dtype>& blob_loss_weights() const {
     return blob_loss_weights_;
   }
+  inline const vector<bool>& layer_need_backward() const {
+    return layer_need_backward_;
+  }
   /// @brief returns the parameters
   inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
     return params_;
   }
-  /// @brief returns the parameter learning rate multipliers
+  inline const vector<Blob<Dtype>*>& learnable_params() const {
+    return learnable_params_;
+  }
+  /// @brief returns the learnable parameter learning rate multipliers
   inline const vector<float>& params_lr() const { return params_lr_; }
+  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
+  /// @brief returns the learnable parameter decay multipliers
   inline const vector<float>& params_weight_decay() const {
     return params_weight_decay_;
   }
+  inline const vector<bool>& has_params_decay() const {
+    return has_params_decay_;
+  }
   const map<string, int>& param_names_index() const {
     return param_names_index_;
   }
   inline const vector<int>& param_owners() const { return param_owners_; }
+  inline const vector<string>& param_display_names() const {
+    return param_display_names_;
+  }
   /// @brief Input and output blob numbers
   inline int num_inputs() const { return net_input_blobs_.size(); }
   inline int num_outputs() const { return net_output_blobs_.size(); }
@@ -185,7 +229,7 @@ class Net {
 
  protected:
   // Helpers for Init.
-  /// @brief Append a new input or top blob to the net.
+  /// @brief Append a new top blob to the net.
   void AppendTop(const NetParameter& param, const int layer_id,
                  const int top_id, set<string>* available_blobs,
                  map<string, int>* blob_name_to_idx);
@@ -197,8 +241,6 @@ class Net {
   void AppendParam(const NetParameter& param, const int layer_id,
                    const int param_id);
 
-  /// @brief Helper for displaying debug info in Forward about input Blobs.
-  void InputDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Forward.
   void ForwardDebugInfo(const int layer_id);
   /// @brief Helper for displaying debug info in Backward.
@@ -206,9 +248,6 @@ class Net {
   /// @brief Helper for displaying debug info in Update.
   void UpdateDebugInfo(const int param_id);
 
-  /// @brief Get misc parameters, e.g. the LR multiplier and weight decay.
-  void GetLearningRateAndWeightDecay();
-
   /// @brief The network name
   string name_;
   /// @brief The phase: TRAIN or TEST
@@ -247,15 +286,27 @@ class Net {
   vector<Blob<Dtype>*> net_output_blobs_;
   /// The parameters in the network.
   vector<shared_ptr<Blob<Dtype> > > params_;
-  /// the learning rate multipliers
+  vector<Blob<Dtype>*> learnable_params_;
+  /**
+   * The mapping from params_ -> learnable_params_: we have
+   * learnable_param_ids_.size() == params_.size(),
+   * and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
+   * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
+   * and learnable_params_[learnable_param_ids_[i]] gives its owner.
+   */
+  vector<int> learnable_param_ids_;
+  /// the learning rate multipliers for learnable_params_
   vector<float> params_lr_;
-  /// the weight decay multipliers
+  vector<bool> has_params_lr_;
+  /// the weight decay multipliers for learnable_params_
   vector<float> params_weight_decay_;
+  vector<bool> has_params_decay_;
   /// The bytes of memory used by this net
   size_t memory_used_;
   /// Whether to compute and display debug info for the net.
   bool debug_info_;
-
+  /// The root net that actually holds the shared layers in data parallelism
+  const Net* const root_net_;
   DISABLE_COPY_AND_ASSIGN(Net);
 };
 
diff --git a/include/caffe/neuron_layers.hpp b/include/caffe/neuron_layers.hpp
deleted file mode 100644
index 323215134c7..00000000000
--- a/include/caffe/neuron_layers.hpp
+++ /dev/null
@@ -1,743 +0,0 @@
-#ifndef CAFFE_NEURON_LAYERS_HPP_
-#define CAFFE_NEURON_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-#define HDF5_DATA_DATASET_NAME "data"
-#define HDF5_DATA_LABEL_NAME "label"
-
-namespace caffe {
-
-/**
- * @brief An interface for layers that take one blob as input (@f$ x @f$)
- *        and produce one equally-sized blob as output (@f$ y @f$), where
- *        each element of the output depends only on the corresponding input
- *        element.
- */
-template <typename Dtype>
-class NeuronLayer : public Layer<Dtype> {
- public:
-  explicit NeuronLayer(const LayerParameter& param)
-     : Layer<Dtype>(param) {}
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-};
-
-/**
- * @brief Computes @f$ y = |x| @f$
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class AbsValLayer : public NeuronLayer<Dtype> {
- public:
-  explicit AbsValLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "AbsVal"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  /// @copydoc AbsValLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the absolute value inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \mathrm{sign}(x) \frac{\partial E}{\partial y}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief Computes @f$ y = x + \log(1 + \exp(-x)) @f$ if @f$ x > 0 @f$;
- *        @f$ y = \log(1 + \exp(x)) @f$ otherwise.
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$
- *      y = \left\{
- *         \begin{array}{ll}
- *            x + \log(1 + \exp(-x)) & \mbox{if } x > 0 \\
- *            \log(1 + \exp(x)) & \mbox{otherwise}
- *         \end{array} \right.
- *      @f$
- */
-template <typename Dtype>
-class BNLLLayer : public NeuronLayer<Dtype> {
- public:
-  explicit BNLLLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "BNLL"; }
-
- protected:
-  /// @copydoc BNLLLayer
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the BNLL inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 2)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-/**
- * @brief During training only, sets a random portion of @f$x@f$ to 0, adjusting
- *        the rest of the vector magnitude accordingly.
- *
- * @param bottom input Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the inputs @f$ x @f$
- * @param top output Blob vector (length 1)
- *   -# @f$ (N \times C \times H \times W) @f$
- *      the computed outputs @f$ y = |x| @f$
- */
-template <typename Dtype>
-class DropoutLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides DropoutParameter dropout_param,
-   *     with DropoutLayer options:
-   *   - dropout_ratio (\b optional, default 0.5).
-   *     Sets the probability @f$ p @f$ that any given unit is dropped.
-   */
-  explicit DropoutLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Dropout"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs. At training time, we have @f$
-   *      y_{\mbox{train}} = \left\{
-   *         \begin{array}{ll}
-   *            \frac{x}{1 - p} & \mbox{if } u > p \\
-   *            0 & \mbox{otherwise}
-   *         \end{array} \right.
-   *      @f$, where @f$ u \sim U(0, 1)@f$ is generated independently for each
-   *      input at each iteration. At test time, we simply have
-   *      @f$ y_{\mbox{test}} = \mathbb{E}[y_{\mbox{train}}] = x @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// when divided by UINT_MAX, the randomly generated values @f$u\sim U(0,1)@f$
-  Blob<unsigned int> rand_vec_;
-  /// the probability @f$ p @f$ of dropping any input
-  Dtype threshold_;
-  /// the scale for undropped inputs at train time @f$ 1 / (1 - p) @f$
-  Dtype scale_;
-  unsigned int uint_thres_;
-};
-
-/**
- * @brief Computes @f$ y = \gamma ^ {\alpha x + \beta} @f$,
- *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- *        and base @f$ \gamma @f$.
- */
-template <typename Dtype>
-class ExpLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ExpParameter exp_param,
-   *     with ExpLayer options:
-   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
-   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
-   *   - base (\b optional, default -1 for a value of @f$ e \approx 2.718 @f$)
-   *         the base @f$ \gamma @f$
-   */
-  explicit ExpLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Exp"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \gamma ^ {\alpha x + \beta}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the exp inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \frac{\partial E}{\partial y} y \alpha \log_e(gamma)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  Dtype inner_scale_, outer_scale_;
-};
-
-/**
- * @brief Computes @f$ y = (\alpha x + \beta) ^ \gamma @f$,
- *        as specified by the scale @f$ \alpha @f$, shift @f$ \beta @f$,
- *        and power @f$ \gamma @f$.
- */
-template <typename Dtype>
-class PowerLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides PowerParameter power_param,
-   *     with PowerLayer options:
-   *   - scale (\b optional, default 1) the scale @f$ \alpha @f$
-   *   - shift (\b optional, default 0) the shift @f$ \beta @f$
-   *   - power (\b optional, default 1) the power @f$ \gamma @f$
-   */
-  explicit PowerLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Power"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = (\alpha x + \beta) ^ \gamma
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the power inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} =
-   *            \frac{\partial E}{\partial y}
-   *            \alpha \gamma (\alpha x + \beta) ^ {\gamma - 1} =
-   *            \frac{\partial E}{\partial y}
-   *            \frac{\alpha \gamma y}{\alpha x + \beta}
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  /// @brief @f$ \gamma @f$ from layer_param_.power_param()
-  Dtype power_;
-  /// @brief @f$ \alpha @f$ from layer_param_.power_param()
-  Dtype scale_;
-  /// @brief @f$ \beta @f$ from layer_param_.power_param()
-  Dtype shift_;
-  /// @brief Result of @f$ \alpha \gamma @f$
-  Dtype diff_scale_;
-};
-
-/**
- * @brief Rectified Linear Unit non-linearity @f$ y = \max(0, x) @f$.
- *        The simple max is fast to compute, and the function does not saturate.
- */
-template <typename Dtype>
-class ReLULayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ReLUParameter relu_param,
-   *     with ReLULayer options:
-   *   - negative_slope (\b optional, default 0).
-   *     the value @f$ \nu @f$ by which negative values are multiplied.
-   */
-  explicit ReLULayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "ReLU"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \max(0, x)
-   *      @f$ by default.  If a non-zero negative_slope @f$ \nu @f$ is provided,
-   *      the computed outputs are @f$ y = \max(0, x) + \nu \min(0, x) @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the ReLU inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x} = \left\{
-   *        \begin{array}{lr}
-   *            0 & \mathrm{if} \; x \le 0 \\
-   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
-   *        \end{array} \right.
-   *      @f$ if propagate_down[0], by default.
-   *      If a non-zero negative_slope @f$ \nu @f$ is provided,
-   *      the computed gradients are @f$
-   *        \frac{\partial E}{\partial x} = \left\{
-   *        \begin{array}{lr}
-   *            \nu \frac{\partial E}{\partial y} & \mathrm{if} \; x \le 0 \\
-   *            \frac{\partial E}{\partial y} & \mathrm{if} \; x > 0
-   *        \end{array} \right.
-   *      @f$.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of ReLULayer.
- */
-template <typename Dtype>
-class CuDNNReLULayer : public ReLULayer<Dtype> {
- public:
-  explicit CuDNNReLULayer(const LayerParameter& param)
-      : ReLULayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNReLULayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Sigmoid function non-linearity @f$
- *         y = (1 + \exp(-x))^{-1}
- *     @f$, a classic choice in neural networks.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class SigmoidLayer : public NeuronLayer<Dtype> {
- public:
-  explicit SigmoidLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Sigmoid"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = (1 + \exp(-x))^{-1}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *            = \frac{\partial E}{\partial y} y (1 - y)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of SigmoidLayer.
- */
-template <typename Dtype>
-class CuDNNSigmoidLayer : public SigmoidLayer<Dtype> {
- public:
-  explicit CuDNNSigmoidLayer(const LayerParameter& param)
-      : SigmoidLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNSigmoidLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief TanH hyperbolic tangent non-linearity @f$
- *         y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
- *     @f$, popular in auto-encoders.
- *
- * Note that the gradient vanishes as the values move away from 0.
- * The ReLULayer is often a better choice for this reason.
- */
-template <typename Dtype>
-class TanHLayer : public NeuronLayer<Dtype> {
- public:
-  explicit TanHLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "TanH"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *        y = \frac{\exp(2x) - 1}{\exp(2x) + 1}
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the sigmoid inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$; Backward fills their diff with
-   *      gradients @f$
-   *        \frac{\partial E}{\partial x}
-   *            = \frac{\partial E}{\partial y}
-   *              \left(1 - \left[\frac{\exp(2x) - 1}{exp(2x) + 1} \right]^2 \right)
-   *            = \frac{\partial E}{\partial y} (1 - y^2)
-   *      @f$ if propagate_down[0]
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-};
-
-#ifdef USE_CUDNN
-/**
- * @brief CuDNN acceleration of TanHLayer.
- */
-template <typename Dtype>
-class CuDNNTanHLayer : public TanHLayer<Dtype> {
- public:
-  explicit CuDNNTanHLayer(const LayerParameter& param)
-      : TanHLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNTanHLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_;
-  cudnnTensorDescriptor_t top_desc_;
-};
-#endif
-
-/**
- * @brief Tests whether the input exceeds a threshold: outputs 1 for inputs
- *        above threshold; 0 otherwise.
- */
-template <typename Dtype>
-class ThresholdLayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides ThresholdParameter threshold_param,
-   *     with ThresholdLayer options:
-   *   - threshold (\b optional, default 0).
-   *     the threshold value @f$ t @f$ to which the input values are compared.
-   */
-  explicit ThresholdLayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Threshold"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times H \times W) @f$
-   *      the computed outputs @f$
-   *       y = \left\{
-   *       \begin{array}{lr}
-   *         0 & \mathrm{if} \; x \le t \\
-   *         1 & \mathrm{if} \; x > t
-   *       \end{array} \right.
-   *      @f$
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  /// @brief Not implemented (non-differentiable function)
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-    NOT_IMPLEMENTED;
-  }
-
-  Dtype threshold_;
-};
-
-/**
- * @brief Parameterized Rectified Linear Unit non-linearity @f$
- *        y_i = \max(0, x_i) + a_i \min(0, x_i)
- *        @f$. The differences from ReLULayer are 1) negative slopes are
- *        learnable though backprop and 2) negative slopes can vary across
- *        channels. The number of axes of input blob should be greater than or
- *        equal to 2. The 1st axis (0-based) is seen as channels.
- */
-template <typename Dtype>
-class PReLULayer : public NeuronLayer<Dtype> {
- public:
-  /**
-   * @param param provides PReLUParameter prelu_param,
-   *     with PReLULayer options:
-   *   - filler (\b optional, FillerParameter,
-   *     default {'type': constant 'value':0.25}).
-   *   - channel_shared (\b optional, default false).
-   *     negative slopes are shared across channels.
-   */
-  explicit PReLULayer(const LayerParameter& param)
-      : NeuronLayer<Dtype>(param) {}
-
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "PReLU"; }
-
- protected:
-  /**
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the inputs @f$ x @f$
-   * @param top output Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the computed outputs for each channel @f$i@f$ @f$
-   *        y_i = \max(0, x_i) + a_i \min(0, x_i)
-   *      @f$.
-   */
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  /**
-   * @brief Computes the error gradient w.r.t. the PReLU inputs.
-   *
-   * @param top output Blob vector (length 1), providing the error gradient with
-   *      respect to the outputs
-   *   -# @f$ (N \times C \times ...) @f$
-   *      containing error gradients @f$ \frac{\partial E}{\partial y} @f$
-   *      with respect to computed outputs @f$ y @f$
-   * @param propagate_down see Layer::Backward.
-   * @param bottom input Blob vector (length 1)
-   *   -# @f$ (N \times C \times ...) @f$
-   *      the inputs @f$ x @f$; For each channel @f$i@f$, backward fills their
-   *      diff with gradients @f$
-   *        \frac{\partial E}{\partial x_i} = \left\{
-   *        \begin{array}{lr}
-   *            a_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
-   *            \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i > 0
-   *        \end{array} \right.
-   *      @f$.
-   *      If param_propagate_down_[0] is true, it fills the diff with gradients
-   *      @f$
-   *        \frac{\partial E}{\partial a_i} = \left\{
-   *        \begin{array}{lr}
-   *            \sum_{x_i} x_i \frac{\partial E}{\partial y_i} & \mathrm{if} \; x_i \le 0 \\
-   *            0 & \mathrm{if} \; x_i > 0
-   *        \end{array} \right.
-   *      @f$.
-   */
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool channel_shared_;
-  Blob<Dtype> multiplier_;  // dot multipler for backward computation of params
-  Blob<Dtype> bottom_memory_;  // memory for in-place computation
-};
-
-}  // namespace caffe
-
-#endif  // CAFFE_NEURON_LAYERS_HPP_
diff --git a/include/caffe/parallel.hpp b/include/caffe/parallel.hpp
new file mode 100644
index 00000000000..6c496c884e3
--- /dev/null
+++ b/include/caffe/parallel.hpp
@@ -0,0 +1,121 @@
+#ifndef CAFFE_PARALLEL_HPP_
+#define CAFFE_PARALLEL_HPP_
+
+#include <boost/date_time/posix_time/posix_time.hpp>
+
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+// Represents a net parameters. Once a net is created, its parameter buffers can
+// be replaced by ones from Params, to allow parallelization. Params ensures
+// parameters are allocated in one consecutive array.
+template<typename Dtype>
+class Params {
+ public:
+  explicit Params(shared_ptr<Solver<Dtype> > root_solver);
+  virtual ~Params() {
+  }
+
+  inline size_t size() const {
+    return size_;
+  }
+  inline Dtype* data() const {
+    return data_;
+  }
+  inline Dtype* diff() const {
+    return diff_;
+  }
+
+ protected:
+  const size_t size_;           // Size of buffers
+  Dtype* data_;                 // Network parameters
+  Dtype* diff_;                 // Gradient
+
+DISABLE_COPY_AND_ASSIGN(Params);
+};
+
+// Params stored in GPU memory.
+template<typename Dtype>
+class GPUParams : public Params<Dtype> {
+ public:
+  GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device);
+  virtual ~GPUParams();
+
+  void configure(Solver<Dtype>* solver) const;
+
+ protected:
+  using Params<Dtype>::size_;
+  using Params<Dtype>::data_;
+  using Params<Dtype>::diff_;
+};
+
+class DevicePair {
+ public:
+  DevicePair(int parent, int device)
+      : parent_(parent),
+        device_(device) {
+  }
+  inline int parent() {
+    return parent_;
+  }
+  inline int device() {
+    return device_;
+  }
+
+  // Group GPUs in pairs, by proximity depending on machine's topology
+  static void compute(const vector<int> devices, vector<DevicePair>* pairs);
+
+ protected:
+  int parent_;
+  int device_;
+};
+
+// Synchronous data parallelism using map-reduce between local GPUs.
+template<typename Dtype>
+class P2PSync : public GPUParams<Dtype>, public Solver<Dtype>::Callback,
+    public InternalThread {
+ public:
+  explicit P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+                   P2PSync<Dtype>* parent, const SolverParameter& param);
+  virtual ~P2PSync();
+
+  inline const shared_ptr<Solver<Dtype> >& solver() const {
+    return solver_;
+  }
+
+  void Run(const vector<int>& gpus);
+  void Prepare(const vector<int>& gpus,
+               vector<shared_ptr<P2PSync<Dtype> > >* syncs);
+  inline const int initial_iter() const { return initial_iter_; }
+
+ protected:
+  void on_start();
+  void on_gradients_ready();
+
+  void InternalThreadEntry();
+
+  P2PSync<Dtype>* parent_;
+  vector<P2PSync<Dtype>*> children_;
+  BlockingQueue<P2PSync<Dtype>*> queue_;
+  const int initial_iter_;
+  Dtype* parent_grads_;
+  shared_ptr<Solver<Dtype> > solver_;
+
+  using Params<Dtype>::size_;
+  using Params<Dtype>::data_;
+  using Params<Dtype>::diff_;
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/sgd_solvers.hpp b/include/caffe/sgd_solvers.hpp
new file mode 100644
index 00000000000..1fc52d87137
--- /dev/null
+++ b/include/caffe/sgd_solvers.hpp
@@ -0,0 +1,148 @@
+#ifndef CAFFE_SGD_SOLVERS_HPP_
+#define CAFFE_SGD_SOLVERS_HPP_
+
+#include <string>
+#include <vector>
+
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+/**
+ * @brief Optimizes the parameters of a Net using
+ *        stochastic gradient descent (SGD) with momentum.
+ */
+template <typename Dtype>
+class SGDSolver : public Solver<Dtype> {
+ public:
+  explicit SGDSolver(const SolverParameter& param)
+      : Solver<Dtype>(param) { PreSolve(); }
+  explicit SGDSolver(const string& param_file)
+      : Solver<Dtype>(param_file) { PreSolve(); }
+  virtual inline const char* type() const { return "SGD"; }
+
+  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
+
+ protected:
+  void PreSolve();
+  Dtype GetLearningRate();
+  virtual void ApplyUpdate();
+  virtual void Normalize(int param_id);
+  virtual void Regularize(int param_id);
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  virtual void ClipGradients();
+  virtual void SnapshotSolverState(const string& model_filename);
+  virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);
+  virtual void SnapshotSolverStateToHDF5(const string& model_filename);
+  virtual void RestoreSolverStateFromHDF5(const string& state_file);
+  virtual void RestoreSolverStateFromBinaryProto(const string& state_file);
+  // history maintains the historical momentum data.
+  // update maintains update related data and is not needed in snapshots.
+  // temp maintains other information that might be needed in computation
+  //   of gradients/updates and is not needed in snapshots
+  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
+
+  DISABLE_COPY_AND_ASSIGN(SGDSolver);
+};
+
+template <typename Dtype>
+class NesterovSolver : public SGDSolver<Dtype> {
+ public:
+  explicit NesterovSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) {}
+  explicit NesterovSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) {}
+  virtual inline const char* type() const { return "Nesterov"; }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+};
+
+template <typename Dtype>
+class AdaGradSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaGradSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit AdaGradSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+  virtual inline const char* type() const { return "AdaGrad"; }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with AdaGrad.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
+};
+
+
+template <typename Dtype>
+class RMSPropSolver : public SGDSolver<Dtype> {
+ public:
+  explicit RMSPropSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
+  explicit RMSPropSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+  virtual inline const char* type() const { return "RMSProp"; }
+
+ protected:
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+  void constructor_sanity_check() {
+    CHECK_EQ(0, this->param_.momentum())
+        << "Momentum cannot be used with RMSProp.";
+    CHECK_GE(this->param_.rms_decay(), 0)
+        << "rms_decay should lie between 0 and 1.";
+    CHECK_LT(this->param_.rms_decay(), 1)
+        << "rms_decay should lie between 0 and 1.";
+  }
+
+  DISABLE_COPY_AND_ASSIGN(RMSPropSolver);
+};
+
+template <typename Dtype>
+class AdaDeltaSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdaDeltaSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { AdaDeltaPreSolve(); }
+  explicit AdaDeltaSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); }
+  virtual inline const char* type() const { return "AdaDelta"; }
+
+ protected:
+  void AdaDeltaPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);
+};
+
+/**
+ * @brief AdamSolver, an algorithm for first-order gradient-based optimization
+ *        of stochastic objective functions, based on adaptive estimates of
+ *        lower-order moments. Described in [1].
+ *
+ * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization."
+ *     arXiv preprint arXiv:1412.6980v8 (2014).
+ */
+template <typename Dtype>
+class AdamSolver : public SGDSolver<Dtype> {
+ public:
+  explicit AdamSolver(const SolverParameter& param)
+      : SGDSolver<Dtype>(param) { AdamPreSolve();}
+  explicit AdamSolver(const string& param_file)
+      : SGDSolver<Dtype>(param_file) { AdamPreSolve(); }
+  virtual inline const char* type() const { return "Adam"; }
+
+ protected:
+  void AdamPreSolve();
+  virtual void ComputeUpdateValue(int param_id, Dtype rate);
+
+  DISABLE_COPY_AND_ASSIGN(AdamSolver);
+};
+
+}  // namespace caffe
+
+#endif  // CAFFE_SGD_SOLVERS_HPP_
diff --git a/include/caffe/solver.hpp b/include/caffe/solver.hpp
index 4dcdc3dc20b..9acba0abc24 100644
--- a/include/caffe/solver.hpp
+++ b/include/caffe/solver.hpp
@@ -1,147 +1,162 @@
-#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_
-#define CAFFE_OPTIMIZATION_SOLVER_HPP_
-
+#ifndef CAFFE_SOLVER_HPP_
+#define CAFFE_SOLVER_HPP_
+#include <boost/function.hpp>
 #include <string>
 #include <vector>
 
 #include "caffe/net.hpp"
+#include "caffe/solver_factory.hpp"
 
 namespace caffe {
 
+/**
+  * @brief Enumeration of actions that a client of the Solver may request by
+  * implementing the Solver's action request function, which a
+  * a client may optionally provide in order to request early termination
+  * or saving a snapshot without exiting. In the executable caffe, this
+  * mechanism is used to allow the snapshot to be saved when stopping
+  * execution with a SIGINT (Ctrl-C).
+  */
+  namespace SolverAction {
+    enum Enum {
+      NONE = 0,  // Take no special action.
+      STOP = 1,  // Stop training. snapshot_after_train controls whether a
+                 // snapshot is created.
+      SNAPSHOT = 2  // Take a snapshot, and keep training.
+    };
+  }
+
+/**
+ * @brief Type of a function that returns a Solver Action enumeration.
+ */
+typedef boost::function<SolverAction::Enum()> ActionCallback;
+
 /**
  * @brief An interface for classes that perform optimization on Net%s.
  *
- * Requires implementation of ComputeUpdateValue to compute a parameter update
+ * Requires implementation of ApplyUpdate to compute a parameter update
  * given the current state of the Net parameters.
  */
 template <typename Dtype>
 class Solver {
  public:
-  explicit Solver(const SolverParameter& param);
-  explicit Solver(const string& param_file);
+  explicit Solver(const SolverParameter& param,
+      const Solver* root_solver = NULL);
+  explicit Solver(const string& param_file, const Solver* root_solver = NULL);
   void Init(const SolverParameter& param);
   void InitTrainNet();
   void InitTestNets();
+
+  // Client of the Solver optionally may call this in order to set the function
+  // that the solver uses to see what action it should take (e.g. snapshot or
+  // exit training early).
+  void SetActionFunction(ActionCallback func);
+  SolverAction::Enum GetRequestedAction();
   // The main entry of the solver function. In default, iter will be zero. Pass
   // in a non-zero iter number to resume training for a pre-trained net.
   virtual void Solve(const char* resume_file = NULL);
   inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }
   void Step(int iters);
-  // The Restore function implements how one should restore the solver to a
-  // previously snapshotted state. You should implement the RestoreSolverState()
-  // function that restores the state from a SolverState protocol buffer.
+  // The Restore method simply dispatches to one of the
+  // RestoreSolverStateFrom___ protected methods. You should implement these
+  // methods to restore the state from the appropriate snapshot type.
   void Restore(const char* resume_file);
+  // The Solver::Snapshot function implements the basic snapshotting utility
+  // that stores the learned net. You should implement the SnapshotSolverState()
+  // function that produces a SolverState protocol buffer that needs to be
+  // written to disk together with the learned net.
+  void Snapshot();
   virtual ~Solver() {}
+  inline const SolverParameter& param() const { return param_; }
   inline shared_ptr<Net<Dtype> > net() { return net_; }
   inline const vector<shared_ptr<Net<Dtype> > >& test_nets() {
     return test_nets_;
   }
   int iter() { return iter_; }
 
+  // Invoked at specific points during an iteration
+  class Callback {
+   protected:
+    virtual void on_start() = 0;
+    virtual void on_gradients_ready() = 0;
+
+    template <typename T>
+    friend class Solver;
+  };
+  const vector<Callback*>& callbacks() const { return callbacks_; }
+  void add_callback(Callback* value) {
+    callbacks_.push_back(value);
+  }
+
+  void CheckSnapshotWritePermissions();
+  /**
+   * @brief Returns the solver type.
+   */
+  virtual inline const char* type() const { return ""; }
+
  protected:
-  // Get the update value for the current iteration.
-  virtual void ComputeUpdateValue() = 0;
-  // The Solver::Snapshot function implements the basic snapshotting utility
-  // that stores the learned net. You should implement the SnapshotSolverState()
-  // function that produces a SolverState protocol buffer that needs to be
-  // written to disk together with the learned net.
-  void Snapshot();
+  // Make and apply the update value for the current iteration.
+  virtual void ApplyUpdate() = 0;
+  string SnapshotFilename(const string extension);
+  string SnapshotToBinaryProto();
+  string SnapshotToHDF5();
   // The test routine
   void TestAll();
   void Test(const int test_net_id = 0);
-  virtual void SnapshotSolverState(SolverState* state) = 0;
-  virtual void RestoreSolverState(const SolverState& state) = 0;
+  void TestSegmentation(const int test_net_id = 0);
+  virtual void SnapshotSolverState(const string& model_filename) = 0;
+  virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;
+  virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;
   void DisplayOutputBlobs(const int net_id);
+  void UpdateSmoothedLoss(Dtype loss, int start_iter, int average_loss);
 
   SolverParameter param_;
   int iter_;
   int current_step_;
   shared_ptr<Net<Dtype> > net_;
   vector<shared_ptr<Net<Dtype> > > test_nets_;
+  vector<Callback*> callbacks_;
+  vector<Dtype> losses_;
+  Dtype smoothed_loss_;
 
-  DISABLE_COPY_AND_ASSIGN(Solver);
-};
+  // The root solver that holds root nets (actually containing shared layers)
+  // in data parallelism
+  const Solver* const root_solver_;
 
+  // A function that can be set by a client of the Solver to provide indication
+  // that it wants a snapshot saved and/or to exit early.
+  ActionCallback action_request_function_;
 
-/**
- * @brief Optimizes the parameters of a Net using
- *        stochastic gradient descent (SGD) with momentum.
- */
-template <typename Dtype>
-class SGDSolver : public Solver<Dtype> {
- public:
-  explicit SGDSolver(const SolverParameter& param)
-      : Solver<Dtype>(param) { PreSolve(); }
-  explicit SGDSolver(const string& param_file)
-      : Solver<Dtype>(param_file) { PreSolve(); }
-
-  const vector<shared_ptr<Blob<Dtype> > >& history() { return history_; }
-
- protected:
-  void PreSolve();
-  Dtype GetLearningRate();
-  virtual void ComputeUpdateValue();
-  virtual void ClipGradients();
-  virtual void SnapshotSolverState(SolverState * state);
-  virtual void RestoreSolverState(const SolverState& state);
-  // history maintains the historical momentum data.
-  // update maintains update related data and is not needed in snapshots.
-  // temp maintains other information that might be needed in computation
-  //   of gradients/updates and is not needed in snapshots
-  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;
-
-  DISABLE_COPY_AND_ASSIGN(SGDSolver);
-};
-
-template <typename Dtype>
-class NesterovSolver : public SGDSolver<Dtype> {
- public:
-  explicit NesterovSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) {}
-  explicit NesterovSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) {}
+  // True iff a request to stop early was received.
+  bool requested_early_exit_;
 
- protected:
-  virtual void ComputeUpdateValue();
-
-  DISABLE_COPY_AND_ASSIGN(NesterovSolver);
+  DISABLE_COPY_AND_ASSIGN(Solver);
 };
 
+/**
+ * @brief Solver that only computes gradients, used as worker
+ *        for multi-GPU training.
+ */
 template <typename Dtype>
-class AdaGradSolver : public SGDSolver<Dtype> {
+class WorkerSolver : public Solver<Dtype> {
  public:
-  explicit AdaGradSolver(const SolverParameter& param)
-      : SGDSolver<Dtype>(param) { constructor_sanity_check(); }
-  explicit AdaGradSolver(const string& param_file)
-      : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); }
+  explicit WorkerSolver(const SolverParameter& param,
+      const Solver<Dtype>* root_solver = NULL)
+      : Solver<Dtype>(param, root_solver) {}
 
  protected:
-  virtual void ComputeUpdateValue();
-  void constructor_sanity_check() {
-    CHECK_EQ(0, this->param_.momentum())
-        << "Momentum cannot be used with AdaGrad.";
+  void ApplyUpdate() {}
+  void SnapshotSolverState(const string& model_filename) {
+    LOG(FATAL) << "Should not be called on worker solver.";
   }
-
-  DISABLE_COPY_AND_ASSIGN(AdaGradSolver);
-};
-
-template <typename Dtype>
-Solver<Dtype>* GetSolver(const SolverParameter& param) {
-  SolverParameter_SolverType type = param.solver_type();
-
-  switch (type) {
-  case SolverParameter_SolverType_SGD:
-      return new SGDSolver<Dtype>(param);
-  case SolverParameter_SolverType_NESTEROV:
-      return new NesterovSolver<Dtype>(param);
-  case SolverParameter_SolverType_ADAGRAD:
-      return new AdaGradSolver<Dtype>(param);
-  default:
-      LOG(FATAL) << "Unknown SolverType: " << type;
+  void RestoreSolverStateFromBinaryProto(const string& state_file) {
+    LOG(FATAL) << "Should not be called on worker solver.";
   }
-  return (Solver<Dtype>*) NULL;
-}
+  void RestoreSolverStateFromHDF5(const string& state_file) {
+    LOG(FATAL) << "Should not be called on worker solver.";
+  }
+};
 
 }  // namespace caffe
 
-#endif  // CAFFE_OPTIMIZATION_SOLVER_HPP_
+#endif  // CAFFE_SOLVER_HPP_
diff --git a/include/caffe/solver_factory.hpp b/include/caffe/solver_factory.hpp
new file mode 100644
index 00000000000..cfff721af40
--- /dev/null
+++ b/include/caffe/solver_factory.hpp
@@ -0,0 +1,137 @@
+/**
+ * @brief A solver factory that allows one to register solvers, similar to
+ * layer factory. During runtime, registered solvers could be called by passing
+ * a SolverParameter protobuffer to the CreateSolver function:
+ *
+ *     SolverRegistry<Dtype>::CreateSolver(param);
+ *
+ * There are two ways to register a solver. Assuming that we have a solver like:
+ *
+ *   template <typename Dtype>
+ *   class MyAwesomeSolver : public Solver<Dtype> {
+ *     // your implementations
+ *   };
+ *
+ * and its type is its C++ class name, but without the "Solver" at the end
+ * ("MyAwesomeSolver" -> "MyAwesome").
+ *
+ * If the solver is going to be created simply by its constructor, in your c++
+ * file, add the following line:
+ *
+ *    REGISTER_SOLVER_CLASS(MyAwesome);
+ *
+ * Or, if the solver is going to be created by another creator function, in the
+ * format of:
+ *
+ *    template <typename Dtype>
+ *    Solver<Dtype*> GetMyAwesomeSolver(const SolverParameter& param) {
+ *      // your implementation
+ *    }
+ *
+ * then you can register the creator function instead, like
+ *
+ * REGISTER_SOLVER_CREATOR(MyAwesome, GetMyAwesomeSolver)
+ *
+ * Note that each solver type should only be registered once.
+ */
+
+#ifndef CAFFE_SOLVER_FACTORY_H_
+#define CAFFE_SOLVER_FACTORY_H_
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+template <typename Dtype>
+class Solver;
+
+template <typename Dtype>
+class SolverRegistry {
+ public:
+  typedef Solver<Dtype>* (*Creator)(const SolverParameter&);
+  typedef std::map<string, Creator> CreatorRegistry;
+
+  static CreatorRegistry& Registry() {
+    static CreatorRegistry* g_registry_ = new CreatorRegistry();
+    return *g_registry_;
+  }
+
+  // Adds a creator.
+  static void AddCreator(const string& type, Creator creator) {
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 0)
+        << "Solver type " << type << " already registered.";
+    registry[type] = creator;
+  }
+
+  // Get a solver using a SolverParameter.
+  static Solver<Dtype>* CreateSolver(const SolverParameter& param) {
+    const string& type = param.type();
+    CreatorRegistry& registry = Registry();
+    CHECK_EQ(registry.count(type), 1) << "Unknown solver type: " << type
+        << " (known types: " << SolverTypeListString() << ")";
+    return registry[type](param);
+  }
+
+  static vector<string> SolverTypeList() {
+    CreatorRegistry& registry = Registry();
+    vector<string> solver_types;
+    for (typename CreatorRegistry::iterator iter = registry.begin();
+         iter != registry.end(); ++iter) {
+      solver_types.push_back(iter->first);
+    }
+    return solver_types;
+  }
+
+ private:
+  // Solver registry should never be instantiated - everything is done with its
+  // static variables.
+  SolverRegistry() {}
+
+  static string SolverTypeListString() {
+    vector<string> solver_types = SolverTypeList();
+    string solver_types_str;
+    for (vector<string>::iterator iter = solver_types.begin();
+         iter != solver_types.end(); ++iter) {
+      if (iter != solver_types.begin()) {
+        solver_types_str += ", ";
+      }
+      solver_types_str += *iter;
+    }
+    return solver_types_str;
+  }
+};
+
+
+template <typename Dtype>
+class SolverRegisterer {
+ public:
+  SolverRegisterer(const string& type,
+      Solver<Dtype>* (*creator)(const SolverParameter&)) {
+    // LOG(INFO) << "Registering solver type: " << type;
+    SolverRegistry<Dtype>::AddCreator(type, creator);
+  }
+};
+
+
+#define REGISTER_SOLVER_CREATOR(type, creator)                                 \
+  static SolverRegisterer<float> g_creator_f_##type(#type, creator<float>);    \
+  static SolverRegisterer<double> g_creator_d_##type(#type, creator<double>)   \
+
+#define REGISTER_SOLVER_CLASS(type)                                            \
+  template <typename Dtype>                                                    \
+  Solver<Dtype>* Creator_##type##Solver(                                       \
+      const SolverParameter& param)                                            \
+  {                                                                            \
+    return new type##Solver<Dtype>(param);                                     \
+  }                                                                            \
+  REGISTER_SOLVER_CREATOR(type, Creator_##type##Solver)
+
+}  // namespace caffe
+
+#endif  // CAFFE_SOLVER_FACTORY_H_
diff --git a/include/caffe/syncedmem.hpp b/include/caffe/syncedmem.hpp
index 1b726de9564..38ee4664028 100644
--- a/include/caffe/syncedmem.hpp
+++ b/include/caffe/syncedmem.hpp
@@ -4,30 +4,34 @@
 #include <cstdlib>
 
 #include "caffe/common.hpp"
-#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
-// Theoretically, CaffeMallocHost and CaffeFreeHost should simply call the
-// cudaMallocHost and cudaFree functions in order to create pinned memory.
-// However, those codes rely on the existence of a cuda GPU (I don't know
-// why that is a must since allocating memory should not be accessing the
-// GPU resource, but it just creates an error as of Cuda 5.0) and will cause
-// problem when running on a machine without GPU. Thus, we simply define
-// these two functions for safety and possible future change if the problem
-// of calling cuda functions disappears in a future version.
-//
-// In practice, although we are creating unpinned memory here, as long as we
-// are constantly accessing them the memory pages almost always stays in
-// the physical memory (assuming we have large enough memory installed), and
-// does not seem to create a memory bottleneck here.
-
-inline void CaffeMallocHost(void** ptr, size_t size) {
+// If CUDA is available and in GPU mode, host memory will be allocated pinned,
+// using cudaMallocHost. It avoids dynamic pinning for transfers (DMA).
+// The improvement in performance seems negligible in the single GPU case,
+// but might be more significant for parallel training. Most importantly,
+// it improved stability for large models on many GPUs.
+inline void CaffeMallocHost(void** ptr, size_t size, bool* use_cuda) {
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaMallocHost(ptr, size));
+    *use_cuda = true;
+    return;
+  }
+#endif
   *ptr = malloc(size);
+  *use_cuda = false;
   CHECK(*ptr) << "host allocation of size " << size << " failed";
 }
 
-inline void CaffeFreeHost(void* ptr) {
+inline void CaffeFreeHost(void* ptr, bool use_cuda) {
+#ifndef CPU_ONLY
+  if (use_cuda) {
+    CUDA_CHECK(cudaFreeHost(ptr));
+    return;
+  }
+#endif
   free(ptr);
 }
 
@@ -42,20 +46,27 @@ class SyncedMemory {
  public:
   SyncedMemory()
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
-        own_cpu_data_(false) {}
+        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+        gpu_device_(-1) {}
   explicit SyncedMemory(size_t size)
       : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
-        own_cpu_data_(false) {}
+        own_cpu_data_(false), cpu_malloc_use_cuda_(false), own_gpu_data_(false),
+        gpu_device_(-1) {}
   ~SyncedMemory();
   const void* cpu_data();
   void set_cpu_data(void* data);
   const void* gpu_data();
+  void set_gpu_data(void* data);
   void* mutable_cpu_data();
   void* mutable_gpu_data();
   enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };
   SyncedHead head() { return head_; }
   size_t size() { return size_; }
 
+#ifndef CPU_ONLY
+  void async_gpu_push(const cudaStream_t& stream);
+#endif
+
  private:
   void to_cpu();
   void to_gpu();
@@ -64,6 +75,9 @@ class SyncedMemory {
   size_t size_;
   SyncedHead head_;
   bool own_cpu_data_;
+  bool cpu_malloc_use_cuda_;
+  bool own_gpu_data_;
+  int gpu_device_;
 
   DISABLE_COPY_AND_ASSIGN(SyncedMemory);
 };  // class SyncedMemory
diff --git a/include/caffe/test/test_caffe_main.hpp b/include/caffe/test/test_caffe_main.hpp
index bd5f31e063f..fc156091476 100644
--- a/include/caffe/test/test_caffe_main.hpp
+++ b/include/caffe/test/test_caffe_main.hpp
@@ -40,34 +40,36 @@ class MultiDeviceTest : public ::testing::Test {
 
 typedef ::testing::Types<float, double> TestDtypes;
 
-struct FloatCPU {
-  typedef float Dtype;
+template <typename TypeParam>
+struct CPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::CPU;
 };
 
-struct DoubleCPU {
-  typedef double Dtype;
-  static const Caffe::Brew device = Caffe::CPU;
+template <typename Dtype>
+class CPUDeviceTest : public MultiDeviceTest<CPUDevice<Dtype> > {
 };
 
 #ifdef CPU_ONLY
 
-typedef ::testing::Types<FloatCPU, DoubleCPU> TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>,
+                         CPUDevice<double> > TestDtypesAndDevices;
 
 #else
 
-struct FloatGPU {
-  typedef float Dtype;
+template <typename TypeParam>
+struct GPUDevice {
+  typedef TypeParam Dtype;
   static const Caffe::Brew device = Caffe::GPU;
 };
 
-struct DoubleGPU {
-  typedef double Dtype;
-  static const Caffe::Brew device = Caffe::GPU;
+template <typename Dtype>
+class GPUDeviceTest : public MultiDeviceTest<GPUDevice<Dtype> > {
 };
 
-typedef ::testing::Types<FloatCPU, DoubleCPU, FloatGPU, DoubleGPU>
-    TestDtypesAndDevices;
+typedef ::testing::Types<CPUDevice<float>, CPUDevice<double>,
+                         GPUDevice<float>, GPUDevice<double> >
+                         TestDtypesAndDevices;
 
 #endif
 
diff --git a/include/caffe/test/test_gradient_check_util.hpp b/include/caffe/test/test_gradient_check_util.hpp
index 22937711b58..b25a84875ef 100644
--- a/include/caffe/test/test_gradient_check_util.hpp
+++ b/include/caffe/test/test_gradient_check_util.hpp
@@ -45,6 +45,10 @@ class GradientChecker {
   void CheckGradientEltwise(Layer<Dtype>* layer,
       const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top);
 
+  // Checks the gradient of a single output with respect to particular input
+  // blob(s).  If check_bottom = i >= 0, check only the ith bottom Blob.
+  // If check_bottom == -1, check everything -- all bottom Blobs and all
+  // param Blobs.  Otherwise (if check_bottom < -1), check only param Blobs.
   void CheckGradientSingle(Layer<Dtype>* layer,
       const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top,
       int check_bottom, int top_id, int top_data_id, bool element_wise = false);
@@ -80,21 +84,25 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
       CHECK_EQ(top_count, bottom[blob_id]->count());
     }
   }
-  // First, figure out what blobs we need to check against.
+  // First, figure out what blobs we need to check against, and zero init
+  // parameter blobs.
   vector<Blob<Dtype>*> blobs_to_check;
-  vector<bool> propagate_down(bottom.size(), check_bottom < 0);
+  vector<bool> propagate_down(bottom.size(), check_bottom == -1);
   for (int i = 0; i < layer->blobs().size(); ++i) {
-    blobs_to_check.push_back(layer->blobs()[i].get());
+    Blob<Dtype>* blob = layer->blobs()[i].get();
+    caffe_set(blob->count(), static_cast<Dtype>(0), blob->mutable_cpu_diff());
+    blobs_to_check.push_back(blob);
   }
-  if (check_bottom < 0) {
+  if (check_bottom == -1) {
     for (int i = 0; i < bottom.size(); ++i) {
       blobs_to_check.push_back(bottom[i]);
     }
-  } else {
+  } else if (check_bottom >= 0) {
     CHECK_LT(check_bottom, bottom.size());
     blobs_to_check.push_back(bottom[check_bottom]);
     propagate_down[check_bottom] = true;
   }
+  CHECK_GT(blobs_to_check.size(), 0) << "No blobs to check.";
   // Compute the gradient analytically using Backward
   Caffe::set_random_seed(seed_);
   // Ignore the loss from the layer (it's just the weighted sum of the losses
@@ -161,8 +169,9 @@ void GradientChecker<Dtype>::CheckGradientSingle(Layer<Dtype>* layer,
           || fabs(feature) > kink_ + kink_range_) {
         // We check relative accuracy, but for too small values, we threshold
         // the scale factor by 1.
-        Dtype scale = std::max(
-            std::max(fabs(computed_gradient), fabs(estimated_gradient)), 1.);
+        Dtype scale = std::max<Dtype>(
+            std::max(fabs(computed_gradient), fabs(estimated_gradient)),
+            Dtype(1.));
         EXPECT_NEAR(computed_gradient, estimated_gradient, threshold_ * scale)
           << "debug: (top_id, top_data_id, blob_id, feat_id)="
           << top_id << "," << top_data_id << "," << blob_id << "," << feat_id
diff --git a/include/caffe/util/blocking_queue.hpp b/include/caffe/util/blocking_queue.hpp
new file mode 100644
index 00000000000..d3de2e59b80
--- /dev/null
+++ b/include/caffe/util/blocking_queue.hpp
@@ -0,0 +1,45 @@
+#ifndef CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+#define CAFFE_UTIL_BLOCKING_QUEUE_HPP_
+
+#include <queue>
+#include <string>
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue {
+ public:
+  explicit BlockingQueue();
+
+  void push(const T& t);
+
+  bool try_pop(T* t);
+
+  // This logs a message if the threads needs to be blocked
+  // useful for detecting e.g. when data feeding is too slow
+  T pop(const string& log_on_wait = "");
+
+  bool try_peek(T* t);
+
+  // Return element without removing it
+  T peek();
+
+  size_t size() const;
+
+ protected:
+  /**
+   Move synchronization fields out instead of including boost/thread.hpp
+   to avoid a boost/NVCC issues (#1009, #1010) on OSX. Also fails on
+   Linux CUDA 7.0.18.
+   */
+  class sync;
+
+  std::queue<T> queue_;
+  shared_ptr<sync> sync_;
+
+DISABLE_COPY_AND_ASSIGN(BlockingQueue);
+};
+
+}  // namespace caffe
+
+#endif
diff --git a/include/caffe/util/cudnn.hpp b/include/caffe/util/cudnn.hpp
index b531dd5fa7a..8a7e17c6cd4 100644
--- a/include/caffe/util/cudnn.hpp
+++ b/include/caffe/util/cudnn.hpp
@@ -7,6 +7,9 @@
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
+#define CUDNN_VERSION_MIN(major, minor, patch) \
+    (CUDNN_VERSION >= (major * 1000 + minor * 100 + patch))
+
 #define CUDNN_CHECK(condition) \
   do { \
     cudnnStatus_t status = condition; \
diff --git a/include/caffe/util/db.hpp b/include/caffe/util/db.hpp
index afdb8d2c4f8..59ec3d390ba 100644
--- a/include/caffe/util/db.hpp
+++ b/include/caffe/util/db.hpp
@@ -3,10 +3,6 @@
 
 #include <string>
 
-#include "leveldb/db.h"
-#include "leveldb/write_batch.h"
-#include "lmdb.h"
-
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
 
@@ -49,138 +45,6 @@ class DB {
   DISABLE_COPY_AND_ASSIGN(DB);
 };
 
-class LevelDBCursor : public Cursor {
- public:
-  explicit LevelDBCursor(leveldb::Iterator* iter)
-    : iter_(iter) { SeekToFirst(); }
-  ~LevelDBCursor() { delete iter_; }
-  virtual void SeekToFirst() { iter_->SeekToFirst(); }
-  virtual void Next() { iter_->Next(); }
-  virtual string key() { return iter_->key().ToString(); }
-  virtual string value() { return iter_->value().ToString(); }
-  virtual bool valid() { return iter_->Valid(); }
-
- private:
-  leveldb::Iterator* iter_;
-};
-
-class LevelDBTransaction : public Transaction {
- public:
-  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
-  virtual void Put(const string& key, const string& value) {
-    batch_.Put(key, value);
-  }
-  virtual void Commit() {
-    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
-    CHECK(status.ok()) << "Failed to write batch to leveldb "
-                       << std::endl << status.ToString();
-  }
-
- private:
-  leveldb::DB* db_;
-  leveldb::WriteBatch batch_;
-
-  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
-};
-
-class LevelDB : public DB {
- public:
-  LevelDB() : db_(NULL) { }
-  virtual ~LevelDB() { Close(); }
-  virtual void Open(const string& source, Mode mode);
-  virtual void Close() {
-    if (db_ != NULL) {
-      delete db_;
-      db_ = NULL;
-    }
-  }
-  virtual LevelDBCursor* NewCursor() {
-    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
-  }
-  virtual LevelDBTransaction* NewTransaction() {
-    return new LevelDBTransaction(db_);
-  }
-
- private:
-  leveldb::DB* db_;
-};
-
-inline void MDB_CHECK(int mdb_status) {
-  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
-}
-
-class LMDBCursor : public Cursor {
- public:
-  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
-    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
-    SeekToFirst();
-  }
-  virtual ~LMDBCursor() {
-    mdb_cursor_close(mdb_cursor_);
-    mdb_txn_abort(mdb_txn_);
-  }
-  virtual void SeekToFirst() { Seek(MDB_FIRST); }
-  virtual void Next() { Seek(MDB_NEXT); }
-  virtual string key() {
-    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
-  }
-  virtual string value() {
-    return string(static_cast<const char*>(mdb_value_.mv_data),
-        mdb_value_.mv_size);
-  }
-  virtual bool valid() { return valid_; }
-
- private:
-  void Seek(MDB_cursor_op op) {
-    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
-    if (mdb_status == MDB_NOTFOUND) {
-      valid_ = false;
-    } else {
-      MDB_CHECK(mdb_status);
-      valid_ = true;
-    }
-  }
-
-  MDB_txn* mdb_txn_;
-  MDB_cursor* mdb_cursor_;
-  MDB_val mdb_key_, mdb_value_;
-  bool valid_;
-};
-
-class LMDBTransaction : public Transaction {
- public:
-  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
-    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
-  virtual void Put(const string& key, const string& value);
-  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
-
- private:
-  MDB_dbi* mdb_dbi_;
-  MDB_txn* mdb_txn_;
-
-  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
-};
-
-class LMDB : public DB {
- public:
-  LMDB() : mdb_env_(NULL) { }
-  virtual ~LMDB() { Close(); }
-  virtual void Open(const string& source, Mode mode);
-  virtual void Close() {
-    if (mdb_env_ != NULL) {
-      mdb_dbi_close(mdb_env_, mdb_dbi_);
-      mdb_env_close(mdb_env_);
-      mdb_env_ = NULL;
-    }
-  }
-  virtual LMDBCursor* NewCursor();
-  virtual LMDBTransaction* NewTransaction();
-
- private:
-  MDB_env* mdb_env_;
-  MDB_dbi mdb_dbi_;
-};
-
 DB* GetDB(DataParameter::DB backend);
 DB* GetDB(const string& backend);
 
diff --git a/include/caffe/util/db_leveldb.hpp b/include/caffe/util/db_leveldb.hpp
new file mode 100644
index 00000000000..e9fa0d32b66
--- /dev/null
+++ b/include/caffe/util/db_leveldb.hpp
@@ -0,0 +1,75 @@
+#ifdef USE_LEVELDB
+#ifndef CAFFE_UTIL_DB_LEVELDB_HPP
+#define CAFFE_UTIL_DB_LEVELDB_HPP
+
+#include <string>
+
+#include "leveldb/db.h"
+#include "leveldb/write_batch.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+class LevelDBCursor : public Cursor {
+ public:
+  explicit LevelDBCursor(leveldb::Iterator* iter)
+    : iter_(iter) { SeekToFirst(); }
+  ~LevelDBCursor() { delete iter_; }
+  virtual void SeekToFirst() { iter_->SeekToFirst(); }
+  virtual void Next() { iter_->Next(); }
+  virtual string key() { return iter_->key().ToString(); }
+  virtual string value() { return iter_->value().ToString(); }
+  virtual bool valid() { return iter_->Valid(); }
+
+ private:
+  leveldb::Iterator* iter_;
+};
+
+class LevelDBTransaction : public Transaction {
+ public:
+  explicit LevelDBTransaction(leveldb::DB* db) : db_(db) { CHECK_NOTNULL(db_); }
+  virtual void Put(const string& key, const string& value) {
+    batch_.Put(key, value);
+  }
+  virtual void Commit() {
+    leveldb::Status status = db_->Write(leveldb::WriteOptions(), &batch_);
+    CHECK(status.ok()) << "Failed to write batch to leveldb "
+                       << std::endl << status.ToString();
+  }
+
+ private:
+  leveldb::DB* db_;
+  leveldb::WriteBatch batch_;
+
+  DISABLE_COPY_AND_ASSIGN(LevelDBTransaction);
+};
+
+class LevelDB : public DB {
+ public:
+  LevelDB() : db_(NULL) { }
+  virtual ~LevelDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (db_ != NULL) {
+      delete db_;
+      db_ = NULL;
+    }
+  }
+  virtual LevelDBCursor* NewCursor() {
+    return new LevelDBCursor(db_->NewIterator(leveldb::ReadOptions()));
+  }
+  virtual LevelDBTransaction* NewTransaction() {
+    return new LevelDBTransaction(db_);
+  }
+
+ private:
+  leveldb::DB* db_;
+};
+
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LEVELDB_HPP
+#endif  // USE_LEVELDB
diff --git a/include/caffe/util/db_lmdb.hpp b/include/caffe/util/db_lmdb.hpp
new file mode 100644
index 00000000000..4e1568ace50
--- /dev/null
+++ b/include/caffe/util/db_lmdb.hpp
@@ -0,0 +1,93 @@
+#ifdef USE_LMDB
+#ifndef CAFFE_UTIL_DB_LMDB_HPP
+#define CAFFE_UTIL_DB_LMDB_HPP
+
+#include <string>
+
+#include "lmdb.h"
+
+#include "caffe/util/db.hpp"
+
+namespace caffe { namespace db {
+
+inline void MDB_CHECK(int mdb_status) {
+  CHECK_EQ(mdb_status, MDB_SUCCESS) << mdb_strerror(mdb_status);
+}
+
+class LMDBCursor : public Cursor {
+ public:
+  explicit LMDBCursor(MDB_txn* mdb_txn, MDB_cursor* mdb_cursor)
+    : mdb_txn_(mdb_txn), mdb_cursor_(mdb_cursor), valid_(false) {
+    SeekToFirst();
+  }
+  virtual ~LMDBCursor() {
+    mdb_cursor_close(mdb_cursor_);
+    mdb_txn_abort(mdb_txn_);
+  }
+  virtual void SeekToFirst() { Seek(MDB_FIRST); }
+  virtual void Next() { Seek(MDB_NEXT); }
+  virtual string key() {
+    return string(static_cast<const char*>(mdb_key_.mv_data), mdb_key_.mv_size);
+  }
+  virtual string value() {
+    return string(static_cast<const char*>(mdb_value_.mv_data),
+        mdb_value_.mv_size);
+  }
+  virtual bool valid() { return valid_; }
+
+ private:
+  void Seek(MDB_cursor_op op) {
+    int mdb_status = mdb_cursor_get(mdb_cursor_, &mdb_key_, &mdb_value_, op);
+    if (mdb_status == MDB_NOTFOUND) {
+      valid_ = false;
+    } else {
+      MDB_CHECK(mdb_status);
+      valid_ = true;
+    }
+  }
+
+  MDB_txn* mdb_txn_;
+  MDB_cursor* mdb_cursor_;
+  MDB_val mdb_key_, mdb_value_;
+  bool valid_;
+};
+
+class LMDBTransaction : public Transaction {
+ public:
+  explicit LMDBTransaction(MDB_dbi* mdb_dbi, MDB_txn* mdb_txn)
+    : mdb_dbi_(mdb_dbi), mdb_txn_(mdb_txn) { }
+  virtual void Put(const string& key, const string& value);
+  virtual void Commit() { MDB_CHECK(mdb_txn_commit(mdb_txn_)); }
+
+ private:
+  MDB_dbi* mdb_dbi_;
+  MDB_txn* mdb_txn_;
+
+  DISABLE_COPY_AND_ASSIGN(LMDBTransaction);
+};
+
+class LMDB : public DB {
+ public:
+  LMDB() : mdb_env_(NULL) { }
+  virtual ~LMDB() { Close(); }
+  virtual void Open(const string& source, Mode mode);
+  virtual void Close() {
+    if (mdb_env_ != NULL) {
+      mdb_dbi_close(mdb_env_, mdb_dbi_);
+      mdb_env_close(mdb_env_);
+      mdb_env_ = NULL;
+    }
+  }
+  virtual LMDBCursor* NewCursor();
+  virtual LMDBTransaction* NewTransaction();
+
+ private:
+  MDB_env* mdb_env_;
+  MDB_dbi mdb_dbi_;
+};
+
+}  // namespace db
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_DB_LMDB_HPP
+#endif  // USE_LMDB
diff --git a/include/caffe/util/device_alternate.hpp b/include/caffe/util/device_alternate.hpp
index 6ea595dba2d..e3fe4fe29fd 100644
--- a/include/caffe/util/device_alternate.hpp
+++ b/include/caffe/util/device_alternate.hpp
@@ -81,14 +81,8 @@ namespace caffe {
 const char* cublasGetErrorString(cublasStatus_t error);
 const char* curandGetErrorString(curandStatus_t error);
 
-// CUDA: thread number configuration.
-// Use 1024 threads per block, which requires cuda sm_2x or above,
-// or fall back to attempt compatibility (best of luck to you).
-#if __CUDA_ARCH__ >= 200
-    const int CAFFE_CUDA_NUM_THREADS = 1024;
-#else
-    const int CAFFE_CUDA_NUM_THREADS = 512;
-#endif
+// CUDA: use 512 threads per block
+const int CAFFE_CUDA_NUM_THREADS = 512;
 
 // CUDA: number of blocks for threads.
 inline int CAFFE_GET_BLOCKS(const int N) {
diff --git a/include/caffe/util/format.hpp b/include/caffe/util/format.hpp
new file mode 100644
index 00000000000..925ad2e0479
--- /dev/null
+++ b/include/caffe/util/format.hpp
@@ -0,0 +1,18 @@
+#ifndef CAFFE_UTIL_FORMAT_H_
+#define CAFFE_UTIL_FORMAT_H_
+
+#include <iomanip>  // NOLINT(readability/streams)
+#include <sstream>  // NOLINT(readability/streams)
+#include <string>
+
+namespace caffe {
+
+inline std::string format_int(int n, int numberOfLeadingZeros = 0 ) {
+  std::ostringstream s;
+  s << std::setw(numberOfLeadingZeros) << std::setfill('0') << n;
+  return s.str();
+}
+
+}
+
+#endif   // CAFFE_UTIL_FORMAT_H_
diff --git a/include/caffe/util/gpu_util.cuh b/include/caffe/util/gpu_util.cuh
new file mode 100644
index 00000000000..994202f2a1a
--- /dev/null
+++ b/include/caffe/util/gpu_util.cuh
@@ -0,0 +1,35 @@
+#ifndef CAFFE_UTIL_GPU_UTIL_H_
+#define CAFFE_UTIL_GPU_UTIL_H_
+
+namespace caffe {
+
+template <typename Dtype>
+inline __device__ Dtype caffe_gpu_atomic_add(const Dtype val, Dtype* address);
+
+template <>
+inline __device__
+float caffe_gpu_atomic_add(const float val, float* address) {
+  return atomicAdd(address, val);
+}
+
+// double atomicAdd implementation taken from:
+// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#axzz3PVCpVsEG
+template <>
+inline __device__
+double caffe_gpu_atomic_add(const double val, double* address) {
+  unsigned long long int* address_as_ull =  // NOLINT(runtime/int)
+      // NOLINT_NEXT_LINE(runtime/int)
+      reinterpret_cast<unsigned long long int*>(address);
+  unsigned long long int old = *address_as_ull;  // NOLINT(runtime/int)
+  unsigned long long int assumed;  // NOLINT(runtime/int)
+  do {
+    assumed = old;
+    old = atomicCAS(address_as_ull, assumed,
+        __double_as_longlong(val + __longlong_as_double(assumed)));
+  } while (assumed != old);
+  return __longlong_as_double(old);
+}
+
+}  // namespace caffe
+
+#endif  // CAFFE_UTIL_GPU_UTIL_H_
diff --git a/include/caffe/util/hdf5.hpp b/include/caffe/util/hdf5.hpp
new file mode 100644
index 00000000000..ce568c5eb0d
--- /dev/null
+++ b/include/caffe/util/hdf5.hpp
@@ -0,0 +1,39 @@
+#ifndef CAFFE_UTIL_HDF5_H_
+#define CAFFE_UTIL_HDF5_H_
+
+#include <string>
+
+#include "hdf5.h"
+#include "hdf5_hl.h"
+
+#include "caffe/blob.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_load_nd_dataset(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob);
+
+template <typename Dtype>
+void hdf5_save_nd_dataset(
+    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,
+    bool write_diff = false);
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name);
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);
+string hdf5_load_string(hid_t loc_id, const string& dataset_name);
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+                      const string& s);
+
+int hdf5_get_num_links(hid_t loc_id);
+string hdf5_get_name_by_idx(hid_t loc_id, int idx);
+
+}  // namespace caffe
+
+#endif   // CAFFE_UTIL_HDF5_H_
diff --git a/include/caffe/util/im2col.hpp b/include/caffe/util/im2col.hpp
index 0051e2fa067..a35bc6e0b1c 100644
--- a/include/caffe/util/im2col.hpp
+++ b/include/caffe/util/im2col.hpp
@@ -3,29 +3,57 @@
 
 namespace caffe {
 
+template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col);
+
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_col);
+
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_im);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im);
+
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+    const int col_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col);
 
 template <typename Dtype>
 void im2col_gpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_col);
+
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+    const int im_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_im);
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im);
 
 }  // namespace caffe
 
diff --git a/include/caffe/util/io.hpp b/include/caffe/util/io.hpp
index 3a62c3c9fa9..1a599883ca3 100644
--- a/include/caffe/util/io.hpp
+++ b/include/caffe/util/io.hpp
@@ -1,47 +1,52 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
 
-#include <unistd.h>
+#include <boost/filesystem.hpp>
+#include <iomanip>
+#include <iostream>  // NOLINT(readability/streams)
 #include <string>
 
 #include "google/protobuf/message.h"
-#include "hdf5.h"
-#include "hdf5_hl.h"
 
-#include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/format.hpp"
 
-#define HDF5_NUM_DIMS 4
+#ifndef CAFFE_TMP_DIR_RETRIES
+#define CAFFE_TMP_DIR_RETRIES 100
+#endif
 
 namespace caffe {
 
 using ::google::protobuf::Message;
-
-inline void MakeTempFilename(string* temp_filename) {
-  temp_filename->clear();
-  *temp_filename = "/tmp/caffe_test.XXXXXX";
-  char* temp_filename_cstr = new char[temp_filename->size() + 1];
-  // NOLINT_NEXT_LINE(runtime/printf)
-  strcpy(temp_filename_cstr, temp_filename->c_str());
-  int fd = mkstemp(temp_filename_cstr);
-  CHECK_GE(fd, 0) << "Failed to open a temporary file at: " << *temp_filename;
-  close(fd);
-  *temp_filename = temp_filename_cstr;
-  delete[] temp_filename_cstr;
-}
+using ::boost::filesystem::path;
 
 inline void MakeTempDir(string* temp_dirname) {
   temp_dirname->clear();
-  *temp_dirname = "/tmp/caffe_test.XXXXXX";
-  char* temp_dirname_cstr = new char[temp_dirname->size() + 1];
-  // NOLINT_NEXT_LINE(runtime/printf)
-  strcpy(temp_dirname_cstr, temp_dirname->c_str());
-  char* mkdtemp_result = mkdtemp(temp_dirname_cstr);
-  CHECK(mkdtemp_result != NULL)
-      << "Failed to create a temporary directory at: " << *temp_dirname;
-  *temp_dirname = temp_dirname_cstr;
-  delete[] temp_dirname_cstr;
+  const path& model =
+    boost::filesystem::temp_directory_path()/"caffe_test.%%%%-%%%%";
+  for ( int i = 0; i < CAFFE_TMP_DIR_RETRIES; i++ ) {
+    const path& dir = boost::filesystem::unique_path(model).string();
+    bool done = boost::filesystem::create_directory(dir);
+    if ( done ) {
+      *temp_dirname = dir.string();
+      return;
+    }
+  }
+  LOG(FATAL) << "Failed to create a temporary directory.";
+}
+
+inline void MakeTempFilename(string* temp_filename) {
+  static path temp_files_subpath;
+  static uint64_t next_temp_file = 0;
+  temp_filename->clear();
+  if ( temp_files_subpath.empty() ) {
+    string path_string="";
+    MakeTempDir(&path_string);
+    temp_files_subpath = path_string;
+  }
+  *temp_filename =
+    (temp_files_subpath/caffe::format_int(next_temp_file++, 9)).string();
 }
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto);
@@ -124,6 +129,7 @@ inline bool ReadImageToDatum(const string& filename, const int label,
 bool DecodeDatumNative(Datum* datum);
 bool DecodeDatum(Datum* datum, bool is_color);
 
+#ifdef USE_OPENCV
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color);
 
@@ -139,20 +145,7 @@ cv::Mat DecodeDatumToCVMatNative(const Datum& datum);
 cv::Mat DecodeDatumToCVMat(const Datum& datum, bool is_color);
 
 void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
-
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_load_nd_dataset(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob);
-
-template <typename Dtype>
-void hdf5_save_nd_dataset(
-    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob);
+#endif  // USE_OPENCV
 
 }  // namespace caffe
 
diff --git a/include/caffe/util/math_functions.hpp b/include/caffe/util/math_functions.hpp
index f43036fcebc..6f6d3feeae2 100644
--- a/include/caffe/util/math_functions.hpp
+++ b/include/caffe/util/math_functions.hpp
@@ -88,6 +88,9 @@ void caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r);
 template <typename Dtype>
 void caffe_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_abs(const int n, const Dtype* a, Dtype* y);
 
@@ -98,9 +101,6 @@ template <typename Dtype>
 Dtype caffe_cpu_strided_dot(const int n, const Dtype* x, const int incx,
     const Dtype* y, const int incy);
 
-template <typename Dtype>
-int caffe_cpu_hamming_distance(const int n, const Dtype* x, const Dtype* y);
-
 // Returns the sum of the absolute values of the elements of vector x
 template <typename Dtype>
 Dtype caffe_cpu_asum(const int n, const Dtype* x);
@@ -203,6 +203,9 @@ void caffe_gpu_abs(const int n, const Dtype* a, Dtype* y);
 template <typename Dtype>
 void caffe_gpu_exp(const int n, const Dtype* a, Dtype* y);
 
+template <typename Dtype>
+void caffe_gpu_log(const int n, const Dtype* a, Dtype* y);
+
 template <typename Dtype>
 void caffe_gpu_powx(const int n, const Dtype* a, const Dtype b, Dtype* y);
 
@@ -228,10 +231,6 @@ void caffe_gpu_rng_bernoulli(const int n, const Dtype p, int* r);
 template <typename Dtype>
 void caffe_gpu_dot(const int n, const Dtype* x, const Dtype* y, Dtype* out);
 
-template <typename Dtype>
-uint32_t caffe_gpu_hamming_distance(const int n, const Dtype* x,
-                                    const Dtype* y);
-
 template <typename Dtype>
 void caffe_gpu_asum(const int n, const Dtype* x, Dtype* y);
 
diff --git a/include/caffe/util/mkl_alternate.hpp b/include/caffe/util/mkl_alternate.hpp
index 32fdbf79932..3355b6658a3 100644
--- a/include/caffe/util/mkl_alternate.hpp
+++ b/include/caffe/util/mkl_alternate.hpp
@@ -33,6 +33,7 @@ extern "C" {
 
 DEFINE_VSL_UNARY_FUNC(Sqr, y[i] = a[i] * a[i]);
 DEFINE_VSL_UNARY_FUNC(Exp, y[i] = exp(a[i]));
+DEFINE_VSL_UNARY_FUNC(Ln, y[i] = log(a[i]));
 DEFINE_VSL_UNARY_FUNC(Abs, y[i] = fabs(a[i]));
 
 // A simple way to define the vsl unary functions with singular parameter b.
diff --git a/include/caffe/util/signal_handler.h b/include/caffe/util/signal_handler.h
new file mode 100644
index 00000000000..fb84c65bd2e
--- /dev/null
+++ b/include/caffe/util/signal_handler.h
@@ -0,0 +1,24 @@
+#ifndef INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+#define INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
+
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/solver.hpp"
+
+namespace caffe {
+
+class SignalHandler {
+ public:
+  // Contructor. Specify what action to take when a signal is received.
+  SignalHandler(SolverAction::Enum SIGINT_action,
+                SolverAction::Enum SIGHUP_action);
+  ~SignalHandler();
+  ActionCallback GetActionFunction();
+ private:
+  SolverAction::Enum CheckForSignals() const;
+  SolverAction::Enum SIGINT_action_;
+  SolverAction::Enum SIGHUP_action_;
+};
+
+}  // namespace caffe
+
+#endif  // INCLUDE_CAFFE_UTIL_SIGNAL_HANDLER_H_
diff --git a/include/caffe/util/upgrade_proto.hpp b/include/caffe/util/upgrade_proto.hpp
index c1f21a0d4d8..14e1936a8c2 100644
--- a/include/caffe/util/upgrade_proto.hpp
+++ b/include/caffe/util/upgrade_proto.hpp
@@ -10,6 +10,15 @@ namespace caffe {
 // Return true iff the net is not the current version.
 bool NetNeedsUpgrade(const NetParameter& net_param);
 
+// Check for deprecations and upgrade the NetParameter as needed.
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
+
+// Read parameters from a file into a NetParameter proto message.
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+                                    NetParameter* param);
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+                                      NetParameter* param);
+
 // Return true iff any layer contains parameters specified using
 // deprecated V0LayerParameter.
 bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param);
@@ -50,14 +59,23 @@ bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
 
 const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type);
 
-// Check for deprecations and upgrade the NetParameter as needed.
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param);
+// Return true iff the Net contains input fields.
+bool NetNeedsInputUpgrade(const NetParameter& net_param);
 
-// Read parameters from a file into a NetParameter proto message.
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
-                                    NetParameter* param);
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
-                                      NetParameter* param);
+// Perform all necessary transformations to upgrade input fields into layers.
+void UpgradeNetInput(NetParameter* net_param);
+
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param);
+
+bool UpgradeSolverType(SolverParameter* solver_param);
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param);
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+                                       SolverParameter* param);
 
 }  // namespace caffe
 
diff --git a/include/caffe/vision_layers.hpp b/include/caffe/vision_layers.hpp
deleted file mode 100644
index cd0ab8babb0..00000000000
--- a/include/caffe/vision_layers.hpp
+++ /dev/null
@@ -1,458 +0,0 @@
-#ifndef CAFFE_VISION_LAYERS_HPP_
-#define CAFFE_VISION_LAYERS_HPP_
-
-#include <string>
-#include <utility>
-#include <vector>
-
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/neuron_layers.hpp"
-#include "caffe/proto/caffe.pb.h"
-
-namespace caffe {
-
-/**
- * @brief Abstract base class that factors out the BLAS code common to
- *        ConvolutionLayer and DeconvolutionLayer.
- */
-template <typename Dtype>
-class BaseConvolutionLayer : public Layer<Dtype> {
- public:
-  explicit BaseConvolutionLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline int MinBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  virtual inline bool EqualNumBottomTopBlobs() const { return true; }
-
- protected:
-  // Helper functions that abstract away the column buffer and gemm arguments.
-  // The last argument in forward_cpu_gemm is so that we can skip the im2col if
-  // we just called weight_cpu_gemm with the same input.
-  void forward_cpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* output, bool skip_im2col = false);
-  void forward_cpu_bias(Dtype* output, const Dtype* bias);
-  void backward_cpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* output);
-  void weight_cpu_gemm(const Dtype* input, const Dtype* output, Dtype*
-      weights);
-  void backward_cpu_bias(Dtype* bias, const Dtype* input);
-
-#ifndef CPU_ONLY
-  void forward_gpu_gemm(const Dtype* col_input, const Dtype* weights,
-      Dtype* output, bool skip_im2col = false);
-  void forward_gpu_bias(Dtype* output, const Dtype* bias);
-  void backward_gpu_gemm(const Dtype* input, const Dtype* weights,
-      Dtype* col_output);
-  void weight_gpu_gemm(const Dtype* col_input, const Dtype* output, Dtype*
-      weights);
-  void backward_gpu_bias(Dtype* bias, const Dtype* input);
-#endif
-
-  // reverse_dimensions should return true iff we are implementing deconv, so
-  // that conv helpers know which dimensions are which.
-  virtual bool reverse_dimensions() = 0;
-  // Compute height_out_ and width_out_ from other parameters.
-  virtual void compute_output_shape() = 0;
-
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int num_;
-  int channels_;
-  int pad_h_, pad_w_;
-  int height_, width_;
-  int group_;
-  int num_output_;
-  int height_out_, width_out_;
-  bool bias_term_;
-  bool is_1x1_;
-
- private:
-  // wrap im2col/col2im so we don't have to remember the (long) argument lists
-  inline void conv_im2col_cpu(const Dtype* data, Dtype* col_buff) {
-    im2col_cpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
-  }
-  inline void conv_col2im_cpu(const Dtype* col_buff, Dtype* data) {
-    col2im_cpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
-  }
-#ifndef CPU_ONLY
-  inline void conv_im2col_gpu(const Dtype* data, Dtype* col_buff) {
-    im2col_gpu(data, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, col_buff);
-  }
-  inline void conv_col2im_gpu(const Dtype* col_buff, Dtype* data) {
-    col2im_gpu(col_buff, conv_in_channels_, conv_in_height_, conv_in_width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_, stride_h_, stride_w_, data);
-  }
-#endif
-
-  int conv_out_channels_;
-  int conv_in_channels_;
-  int conv_out_spatial_dim_;
-  int conv_in_height_;
-  int conv_in_width_;
-  int kernel_dim_;
-  int weight_offset_;
-  int col_offset_;
-  int output_offset_;
-
-  Blob<Dtype> col_buffer_;
-  Blob<Dtype> bias_multiplier_;
-};
-
-/**
- * @brief Convolves the input image with a bank of learned filters,
- *        and (optionally) adds biases.
- *
- *   Caffe convolves by reduction to matrix multiplication. This achieves
- *   high-throughput and generality of input and filter dimensions but comes at
- *   the cost of memory for matrices. This makes use of efficiency in BLAS.
- *
- *   The input is "im2col" transformed to a channel K' x H x W data matrix
- *   for multiplication with the N x K' x H x W filter matrix to yield a
- *   N' x H x W output matrix that is then "col2im" restored. K' is the
- *   input channel * kernel height * kernel width dimension of the unrolled
- *   inputs so that the im2col matrix has a column for each input region to
- *   be filtered. col2im restores the output spatial structure by rolling up
- *   the output channel N' columns of the output matrix.
- */
-template <typename Dtype>
-class ConvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
-  /**
-   * @param param provides ConvolutionParameter convolution_param,
-   *    with ConvolutionLayer options:
-   *  - num_output. The number of filters.
-   *  - kernel_size / kernel_h / kernel_w. The filter dimensions, given by
-   *  kernel_size for square filters or kernel_h and kernel_w for rectangular
-   *  filters.
-   *  - stride / stride_h / stride_w (\b optional, default 1). The filter
-   *  stride, given by stride_size for equal dimensions or stride_h and stride_w
-   *  for different strides. By default the convolution is dense with stride 1.
-   *  - pad / pad_h / pad_w (\b optional, default 0). The zero-padding for
-   *  convolution, given by pad for equal dimensions or pad_h and pad_w for
-   *  different padding. Input padding is computed implicitly instead of
-   *  actually padding.
-   *  - group (\b optional, default 1). The number of filter groups. Group
-   *  convolution is a method for reducing parameterization by selectively
-   *  connecting input and output channels. The input and output channel dimensions must be divisible
-   *  by the number of groups. For group @f$ \geq 1 @f$, the
-   *  convolutional filters' input and output channels are separated s.t. each
-   *  group takes 1 / group of the input channels and makes 1 / group of the
-   *  output channels. Concretely 4 input channels, 8 output channels, and
-   *  2 groups separate input channels 1-2 and output channels 1-4 into the
-   *  first group and input channels 3-4 and output channels 5-8 into the second
-   *  group.
-   *  - bias_term (\b optional, default true). Whether to have a bias.
-   *  - engine: convolution has CAFFE (matrix multiplication) and CUDNN (library
-   *    kernels + stream parallelism) engines.
-   */
-  explicit ConvolutionLayer(const LayerParameter& param)
-      : BaseConvolutionLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Convolution"; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual inline bool reverse_dimensions() { return false; }
-  virtual void compute_output_shape();
-};
-
-/**
- * @brief Convolve the input with a bank of learned filters, and (optionally)
- *        add biases, treating filters and convolution parameters in the
- *        opposite sense as ConvolutionLayer.
- *
- *   ConvolutionLayer computes each output value by dotting an input window with
- *   a filter; DeconvolutionLayer multiplies each input value by a filter
- *   elementwise, and sums over the resulting output windows. In other words,
- *   DeconvolutionLayer is ConvolutionLayer with the forward and backward passes
- *   reversed. DeconvolutionLayer reuses ConvolutionParameter for its
- *   parameters, but they take the opposite sense as in ConvolutionLayer (so
- *   padding is removed from the output rather than added to the input, and
- *   stride results in upsampling rather than downsampling).
- */
-template <typename Dtype>
-class DeconvolutionLayer : public BaseConvolutionLayer<Dtype> {
- public:
-  explicit DeconvolutionLayer(const LayerParameter& param)
-      : BaseConvolutionLayer<Dtype>(param) {}
-
-  virtual inline const char* type() const { return "Deconvolution"; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual inline bool reverse_dimensions() { return true; }
-  virtual void compute_output_shape();
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of ConvolutionLayer.
- *        Fallback to ConvolutionLayer for CPU mode.
- *
- * cuDNN accelerates convolution through forward kernels for filtering and bias
- * plus backward kernels for the gradient w.r.t. the filters, biases, and
- * inputs. Caffe + cuDNN further speeds up the computation through forward
- * parallelism across groups and backward parallelism across gradients.
- *
- * The CUDNN engine does not have memory overhead for matrix buffers. For many
- * input and filter regimes the CUDNN engine is faster than the CAFFE engine,
- * but for fully-convolutional models and large inputs the CAFFE engine can be
- * faster as long as it fits in memory.
-*/
-template <typename Dtype>
-class CuDNNConvolutionLayer : public ConvolutionLayer<Dtype> {
- public:
-  explicit CuDNNConvolutionLayer(const LayerParameter& param)
-      : ConvolutionLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNConvolutionLayer();
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t* handle_;
-  cudaStream_t*  stream_;
-  vector<cudnnTensorDescriptor_t> bottom_descs_, top_descs_;
-  cudnnTensorDescriptor_t    bias_desc_;
-  cudnnFilterDescriptor_t      filter_desc_;
-  vector<cudnnConvolutionDescriptor_t> conv_descs_;
-  int bottom_offset_, top_offset_, weight_offset_, bias_offset_;
-  size_t workspaceSizeInBytes;
-  void *workspace;
-};
-#endif
-
-/**
- * @brief A helper for image operations that rearranges image regions into
- *        column vectors.  Used by ConvolutionLayer to perform convolution
- *        by matrix multiplication.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class Im2colLayer : public Layer<Dtype> {
- public:
-  explicit Im2colLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Im2col"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int channels_;
-  int height_, width_;
-  int pad_h_, pad_w_;
-};
-
-// Forward declare PoolingLayer and SplitLayer for use in LRNLayer.
-template <typename Dtype> class PoolingLayer;
-template <typename Dtype> class SplitLayer;
-
-/**
- * @brief Normalize the input in a local region across or within feature maps.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class LRNLayer : public Layer<Dtype> {
- public:
-  explicit LRNLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "LRN"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  virtual void CrossChannelForward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void CrossChannelForward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void WithinChannelForward(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void CrossChannelBackward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void CrossChannelBackward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void WithinChannelBackward(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int size_;
-  int pre_pad_;
-  Dtype alpha_;
-  Dtype beta_;
-  Dtype k_;
-  int num_;
-  int channels_;
-  int height_;
-  int width_;
-
-  // Fields used for normalization ACROSS_CHANNELS
-  // scale_ stores the intermediate summing results
-  Blob<Dtype> scale_;
-
-  // Fields used for normalization WITHIN_CHANNEL
-  shared_ptr<SplitLayer<Dtype> > split_layer_;
-  vector<Blob<Dtype>*> split_top_vec_;
-  shared_ptr<PowerLayer<Dtype> > square_layer_;
-  Blob<Dtype> square_input_;
-  Blob<Dtype> square_output_;
-  vector<Blob<Dtype>*> square_bottom_vec_;
-  vector<Blob<Dtype>*> square_top_vec_;
-  shared_ptr<PoolingLayer<Dtype> > pool_layer_;
-  Blob<Dtype> pool_output_;
-  vector<Blob<Dtype>*> pool_top_vec_;
-  shared_ptr<PowerLayer<Dtype> > power_layer_;
-  Blob<Dtype> power_output_;
-  vector<Blob<Dtype>*> power_top_vec_;
-  shared_ptr<EltwiseLayer<Dtype> > product_layer_;
-  Blob<Dtype> product_input_;
-  vector<Blob<Dtype>*> product_bottom_vec_;
-};
-
-
-/**
- * @brief Pools the input image by taking the max, average, etc. within regions.
- *
- * TODO(dox): thorough documentation for Forward, Backward, and proto params.
- */
-template <typename Dtype>
-class PoolingLayer : public Layer<Dtype> {
- public:
-  explicit PoolingLayer(const LayerParameter& param)
-      : Layer<Dtype>(param) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-
-  virtual inline const char* type() const { return "Pooling"; }
-  virtual inline int ExactNumBottomBlobs() const { return 1; }
-  virtual inline int MinTopBlobs() const { return 1; }
-  // MAX POOL layers can output an extra top blob for the mask;
-  // others can only output the pooled inputs.
-  virtual inline int MaxTopBlobs() const {
-    return (this->layer_param_.pooling_param().pool() ==
-            PoolingParameter_PoolMethod_MAX) ? 2 : 1;
-  }
-
- protected:
-  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  int kernel_h_, kernel_w_;
-  int stride_h_, stride_w_;
-  int pad_h_, pad_w_;
-  int channels_;
-  int height_, width_;
-  int pooled_height_, pooled_width_;
-  bool global_pooling_;
-  Blob<Dtype> rand_idx_;
-  Blob<int> max_idx_;
-};
-
-#ifdef USE_CUDNN
-/*
- * @brief cuDNN implementation of PoolingLayer.
- *        Fallback to PoolingLayer for CPU mode.
-*/
-template <typename Dtype>
-class CuDNNPoolingLayer : public PoolingLayer<Dtype> {
- public:
-  explicit CuDNNPoolingLayer(const LayerParameter& param)
-      : PoolingLayer<Dtype>(param), handles_setup_(false) {}
-  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual ~CuDNNPoolingLayer();
-  // Currently, cuDNN does not support the extra top blob.
-  virtual inline int MinTopBlobs() const { return -1; }
-  virtual inline int ExactNumTopBlobs() const { return 1; }
-
- protected:
-  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
-      const vector<Blob<Dtype>*>& top);
-  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
-      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
-
-  bool handles_setup_;
-  cudnnHandle_t             handle_;
-  cudnnTensorDescriptor_t bottom_desc_, top_desc_;
-  cudnnPoolingDescriptor_t  pooling_desc_;
-  cudnnPoolingMode_t        mode_;
-};
-#endif
-
-}  // namespace caffe
-
-#endif  // CAFFE_VISION_LAYERS_HPP_
diff --git a/matlab/+caffe/+test/test_io.m b/matlab/+caffe/+test/test_io.m
new file mode 100644
index 00000000000..2c34bd1e938
--- /dev/null
+++ b/matlab/+caffe/+test/test_io.m
@@ -0,0 +1,18 @@
+classdef test_io < matlab.unittest.TestCase
+  methods (Test)
+    function test_read_write_mean(self)
+      % randomly generate mean data
+      width = 200;
+      height = 300;
+      channels = 3;
+      mean_data_write = 255 * rand(width, height, channels, 'single');
+      % write mean data to binary proto
+      mean_proto_file = tempname();
+      caffe.io.write_mean(mean_data_write, mean_proto_file);
+      % read mean data from saved binary proto and test whether they are equal
+      mean_data_read = caffe.io.read_mean(mean_proto_file);
+      self.verifyEqual(mean_data_write, mean_data_read)
+      delete(mean_proto_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
new file mode 100644
index 00000000000..3dabe84d111
--- /dev/null
+++ b/matlab/+caffe/+test/test_net.m
@@ -0,0 +1,96 @@
+classdef test_net < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    model_file
+    net
+  end
+  
+  methods (Static)
+    function model_file = simple_net_file(num_output)
+      model_file = tempname();
+      fid = fopen(model_file, 'w');
+      fprintf(fid, [ ...
+        'name: "testnet" force_backward: true\n' ...
+        'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...
+        'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...
+        '    num: 5 channels: 1 height: 1 width: 1\n' ...
+        '    data_filler { type: "gaussian" std: 1 }\n' ...
+        '    data_filler { type: "constant" } } }\n' ...
+        'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...
+        '  convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...
+        '    weight_filler { type: "gaussian" std: 1 }\n' ...
+        '    bias_filler { type: "constant" value: 2 } }\n' ...
+        '    param { decay_mult: 1 } param { decay_mult: 0 }\n' ...
+        '    }\n' ...
+        'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...
+        '  inner_product_param { num_output: ' num2str(num_output) ...
+        '    weight_filler { type: "gaussian" std: 2.5 }\n' ...
+        '    bias_filler { type: "constant" value: -3 } } }\n' ...
+        'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...
+        '  top: "loss" }' ]);
+      fclose(fid);
+    end
+  end
+  methods
+    function self = test_net()
+      self.num_output = 13;
+      self.model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      self.net = caffe.Net(self.model_file, 'train');
+      % also make sure get_solver runs
+      caffe.get_net(self.model_file, 'train');
+      
+      % fill in valid labels
+      self.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.net.blobs('label').shape));
+      
+      delete(self.model_file);
+    end
+  end
+  methods (Test)
+    function self = test_blob(self)
+      self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_data(), ...
+        10 * ones(self.net.blobs('data').shape, 'single'));
+      self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));
+      self.verifyEqual(self.net.blobs('data').get_diff(), ...
+        -2 * ones(self.net.blobs('data').shape, 'single'));
+      original_shape = self.net.blobs('data').shape;
+      self.net.blobs('data').reshape([6 5 4 3 2 1]);
+      self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);
+      self.net.blobs('data').reshape(original_shape);
+      self.net.reshape();
+    end
+    function self = test_layer(self)
+      self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);
+      self.verifyEqual(self.net.layers('conv').params(2).shape, 11);
+      self.verifyEqual(self.net.layers('conv').type(), 'Convolution');
+    end
+    function test_forward_backward(self)
+      self.net.forward_prefilled();
+      self.net.backward_prefilled();
+    end
+    function test_inputs_outputs(self)
+      self.verifyEqual(self.net.inputs, cell(0, 1))
+      self.verifyEqual(self.net.outputs, {'loss'});
+    end
+    function test_save_and_read(self)
+      weights_file = tempname();
+      self.net.save(weights_file);
+      model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
+      net2 = caffe.Net(model_file2, 'train');
+      net2.copy_from(weights_file);
+      net3 = caffe.Net(model_file2, weights_file, 'train');
+      delete(model_file2);
+      delete(weights_file);
+      for l = 1:length(self.net.layer_vec)
+        for i = 1:length(self.net.layer_vec(l).params)
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net2.layer_vec(l).params(i).get_data());
+          self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
+            net3.layer_vec(l).params(i).get_data());
+        end
+      end
+    end
+  end
+end
diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
new file mode 100644
index 00000000000..739258b0e85
--- /dev/null
+++ b/matlab/+caffe/+test/test_solver.m
@@ -0,0 +1,45 @@
+classdef test_solver < matlab.unittest.TestCase
+  
+  properties
+    num_output
+    solver
+  end
+  
+  methods
+    function self = test_solver()
+      self.num_output = 13;
+      model_file = caffe.test.test_net.simple_net_file(self.num_output);
+      solver_file = tempname();
+      
+      fid = fopen(solver_file, 'w');
+      fprintf(fid, [ ...
+        'net: "'  model_file  '"\n' ...
+        'test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n' ...
+        'weight_decay: 0.0005 lr_policy: "inv" gamma: 0.0001 power: 0.75\n' ...
+        'display: 100 max_iter: 100 snapshot_after_train: false\n' ]);
+      fclose(fid);
+      
+      self.solver = caffe.Solver(solver_file);
+      % also make sure get_solver runs
+      caffe.get_solver(solver_file);
+      caffe.set_mode_cpu();
+      % fill in valid labels
+      self.solver.net.blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.net.blobs('label').shape));
+      self.solver.test_nets(1).blobs('label').set_data(randi( ...
+        self.num_output - 1, self.solver.test_nets(1).blobs('label').shape));
+      
+      delete(solver_file);
+      delete(model_file);
+    end
+  end
+  methods (Test)
+    function test_solve(self)
+      self.verifyEqual(self.solver.iter(), 0)
+      self.solver.step(30);
+      self.verifyEqual(self.solver.iter(), 30)
+      self.solver.solve()
+      self.verifyEqual(self.solver.iter(), 100)
+    end
+  end
+end
diff --git a/matlab/+caffe/Blob.m b/matlab/+caffe/Blob.m
new file mode 100644
index 00000000000..e39f7ee3f20
--- /dev/null
+++ b/matlab/+caffe/Blob.m
@@ -0,0 +1,78 @@
+classdef Blob < handle
+  % Wrapper class of caffe::Blob in matlab
+  
+  properties (Access = private)
+    hBlob_self
+  end
+  
+  methods
+    function self = Blob(hBlob_blob)
+      CHECK(is_valid_handle(hBlob_blob), 'invalid Blob handle');
+      
+      % setup self handle
+      self.hBlob_self = hBlob_blob;
+    end
+    function shape = shape(self)
+      shape = caffe_('blob_get_shape', self.hBlob_self);
+    end
+    function reshape(self, shape)
+      shape = self.check_and_preprocess_shape(shape);
+      caffe_('blob_reshape', self.hBlob_self, shape);
+    end
+    function data = get_data(self)
+      data = caffe_('blob_get_data', self.hBlob_self);
+    end
+    function set_data(self, data)
+      data = self.check_and_preprocess_data(data);
+      caffe_('blob_set_data', self.hBlob_self, data);
+    end
+    function diff = get_diff(self)
+      diff = caffe_('blob_get_diff', self.hBlob_self);
+    end
+    function set_diff(self, diff)
+      diff = self.check_and_preprocess_data(diff);
+      caffe_('blob_set_diff', self.hBlob_self, diff);
+    end
+  end
+  
+  methods (Access = private)
+    function shape = check_and_preprocess_shape(~, shape)
+      CHECK(isempty(shape) || (isnumeric(shape) && isrow(shape)), ...
+        'shape must be a integer row vector');
+      shape = double(shape);
+    end
+    function data = check_and_preprocess_data(self, data)
+      CHECK(isnumeric(data), 'data or diff must be numeric types');
+      self.check_data_size_matches(data);
+      if ~isa(data, 'single')
+        data = single(data);
+      end
+    end
+    function check_data_size_matches(self, data)
+      % check whether size of data matches shape of this blob
+      % note: matlab arrays always have at least 2 dimensions. To compare
+      % shape between size of data and shape of this blob, extend shape of
+      % this blob to have at least 2 dimensions
+      self_shape_extended = self.shape;
+      if isempty(self_shape_extended)
+        % target blob is a scalar (0 dim)
+        self_shape_extended = [1, 1];
+      elseif isscalar(self_shape_extended)
+        % target blob is a vector (1 dim)
+        self_shape_extended = [self_shape_extended, 1];
+      end
+      % Also, matlab cannot have tailing dimension 1 for ndim > 2, so you
+      % cannot create 20 x 10 x 1 x 1 array in matlab as it becomes 20 x 10
+      % Extend matlab arrays to have tailing dimension 1 during shape match
+      data_size_extended = ...
+        [size(data), ones(1, length(self_shape_extended) - ndims(data))];
+      is_matched = ...
+        (length(self_shape_extended) == length(data_size_extended)) ...
+        && all(self_shape_extended == data_size_extended);
+      CHECK(is_matched, ...
+        sprintf('%s, input data/diff size: [ %s] vs target blob shape: [ %s]', ...
+        'input data/diff size does not match target blob shape', ...
+        sprintf('%d ', data_size_extended), sprintf('%d ', self_shape_extended)));
+    end
+  end
+end
diff --git a/matlab/+caffe/Layer.m b/matlab/+caffe/Layer.m
new file mode 100644
index 00000000000..4c2023101a5
--- /dev/null
+++ b/matlab/+caffe/Layer.m
@@ -0,0 +1,32 @@
+classdef Layer < handle
+  % Wrapper class of caffe::Layer in matlab
+  
+  properties (Access = private)
+    hLayer_self
+    attributes
+    % attributes fields:
+    %     hBlob_blobs
+  end
+  properties (SetAccess = private)
+    params
+  end
+  
+  methods
+    function self = Layer(hLayer_layer)
+      CHECK(is_valid_handle(hLayer_layer), 'invalid Layer handle');
+      
+      % setup self handle and attributes
+      self.hLayer_self = hLayer_layer;
+      self.attributes = caffe_('layer_get_attr', self.hLayer_self);
+      
+      % setup weights
+      self.params = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs)
+        self.params(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+    end
+    function layer_type = type(self)
+      layer_type = caffe_('layer_get_type', self.hLayer_self);
+    end
+  end
+end
diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
new file mode 100644
index 00000000000..e6295bba1a4
--- /dev/null
+++ b/matlab/+caffe/Net.m
@@ -0,0 +1,133 @@
+classdef Net < handle
+  % Wrapper class of caffe::Net in matlab
+  
+  properties (Access = private)
+    hNet_self
+    attributes
+    % attribute fields
+    %     hLayer_layers
+    %     hBlob_blobs
+    %     input_blob_indices
+    %     output_blob_indices
+    %     layer_names
+    %     blob_names
+  end
+  properties (SetAccess = private)
+    layer_vec
+    blob_vec
+    inputs
+    outputs
+    name2layer_index
+    name2blob_index
+    layer_names
+    blob_names
+  end
+  
+  methods
+    function self = Net(varargin)
+      % decide whether to construct a net from model_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a net from model_file
+        self = caffe.get_net(varargin{:});
+        return
+      end
+      % construct a net from handle
+      hNet_net = varargin{1};
+      CHECK(is_valid_handle(hNet_net), 'invalid Net handle');
+      
+      % setup self handle and attributes
+      self.hNet_self = hNet_net;
+      self.attributes = caffe_('net_get_attr', self.hNet_self);
+      
+      % setup layer_vec
+      self.layer_vec = caffe.Layer.empty();
+      for n = 1:length(self.attributes.hLayer_layers)
+        self.layer_vec(n) = caffe.Layer(self.attributes.hLayer_layers(n));
+      end
+      
+      % setup blob_vec
+      self.blob_vec = caffe.Blob.empty();
+      for n = 1:length(self.attributes.hBlob_blobs);
+        self.blob_vec(n) = caffe.Blob(self.attributes.hBlob_blobs(n));
+      end
+      
+      % setup input and output blob and their names
+      % note: add 1 to indices as matlab is 1-indexed while C++ is 0-indexed
+      self.inputs = ...
+        self.attributes.blob_names(self.attributes.input_blob_indices + 1);
+      self.outputs = ...
+        self.attributes.blob_names(self.attributes.output_blob_indices + 1);
+      
+      % create map objects to map from name to layers and blobs
+      self.name2layer_index = containers.Map(self.attributes.layer_names, ...
+        1:length(self.attributes.layer_names));
+      self.name2blob_index = containers.Map(self.attributes.blob_names, ...
+        1:length(self.attributes.blob_names));
+      
+      % expose layer_names and blob_names for public read access
+      self.layer_names = self.attributes.layer_names;
+      self.blob_names = self.attributes.blob_names;
+    end
+    function layer = layers(self, layer_name)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      layer = self.layer_vec(self.name2layer_index(layer_name));
+    end
+    function blob = blobs(self, blob_name)
+      CHECK(ischar(blob_name), 'blob_name must be a string');
+      blob = self.blob_vec(self.name2blob_index(blob_name));
+    end
+    function blob = params(self, layer_name, blob_index)
+      CHECK(ischar(layer_name), 'layer_name must be a string');
+      CHECK(isscalar(blob_index), 'blob_index must be a scalar');
+      blob = self.layer_vec(self.name2layer_index(layer_name)).params(blob_index);
+    end
+    function forward_prefilled(self)
+      caffe_('net_forward', self.hNet_self);
+    end
+    function backward_prefilled(self)
+      caffe_('net_backward', self.hNet_self);
+    end
+    function res = forward(self, input_data)
+      CHECK(iscell(input_data), 'input_data must be a cell array');
+      CHECK(length(input_data) == length(self.inputs), ...
+        'input data cell length must match input blob number');
+      % copy data to input blobs
+      for n = 1:length(self.inputs)
+        self.blobs(self.inputs{n}).set_data(input_data{n});
+      end
+      self.forward_prefilled();
+      % retrieve data from output blobs
+      res = cell(length(self.outputs), 1);
+      for n = 1:length(self.outputs)
+        res{n} = self.blobs(self.outputs{n}).get_data();
+      end
+    end
+    function res = backward(self, output_diff)
+      CHECK(iscell(output_diff), 'output_diff must be a cell array');
+      CHECK(length(output_diff) == length(self.outputs), ...
+        'output diff cell length must match output blob number');
+      % copy diff to output blobs
+      for n = 1:length(self.outputs)
+        self.blobs(self.outputs{n}).set_diff(output_diff{n});
+      end
+      self.backward_prefilled();
+      % retrieve diff from input blobs
+      res = cell(length(self.inputs), 1);
+      for n = 1:length(self.inputs)
+        res{n} = self.blobs(self.inputs{n}).get_diff();
+      end
+    end
+    function copy_from(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      CHECK_FILE_EXIST(weights_file);
+      caffe_('net_copy_from', self.hNet_self, weights_file);
+    end
+    function reshape(self)
+      caffe_('net_reshape', self.hNet_self);
+    end
+    function save(self, weights_file)
+      CHECK(ischar(weights_file), 'weights_file must be a string');
+      caffe_('net_save', self.hNet_self, weights_file);
+    end
+  end
+end
diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
new file mode 100644
index 00000000000..f8bdc4e22b2
--- /dev/null
+++ b/matlab/+caffe/Solver.m
@@ -0,0 +1,56 @@
+classdef Solver < handle
+  % Wrapper class of caffe::SGDSolver in matlab
+  
+  properties (Access = private)
+    hSolver_self
+    attributes
+    % attribute fields
+    %     hNet_net
+    %     hNet_test_nets
+  end
+  properties (SetAccess = private)
+    net
+    test_nets
+  end
+  
+  methods
+    function self = Solver(varargin)
+      % decide whether to construct a solver from solver_file or handle
+      if ~(nargin == 1 && isstruct(varargin{1}))
+        % construct a solver from solver_file
+        self = caffe.get_solver(varargin{:});
+        return
+      end
+      % construct a solver from handle
+      hSolver_solver = varargin{1};
+      CHECK(is_valid_handle(hSolver_solver), 'invalid Solver handle');
+      
+      % setup self handle and attributes
+      self.hSolver_self = hSolver_solver;
+      self.attributes = caffe_('solver_get_attr', self.hSolver_self);
+      
+      % setup net and test_nets
+      self.net = caffe.Net(self.attributes.hNet_net);
+      self.test_nets = caffe.Net.empty();
+      for n = 1:length(self.attributes.hNet_test_nets)
+        self.test_nets(n) = caffe.Net(self.attributes.hNet_test_nets(n));
+      end
+    end
+    function iter = iter(self)
+      iter = caffe_('solver_get_iter', self.hSolver_self);
+    end
+    function restore(self, snapshot_filename)
+      CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
+      CHECK_FILE_EXIST(snapshot_filename);
+      caffe_('solver_restore', self.hSolver_self, snapshot_filename);
+    end
+    function solve(self)
+      caffe_('solver_solve', self.hSolver_self);
+    end
+    function step(self, iters)
+      CHECK(isscalar(iters) && iters > 0, 'iters must be positive integer');
+      iters = double(iters);
+      caffe_('solver_step', self.hSolver_self, iters);
+    end
+  end
+end
diff --git a/matlab/+caffe/get_net.m b/matlab/+caffe/get_net.m
new file mode 100644
index 00000000000..4b5683eb82e
--- /dev/null
+++ b/matlab/+caffe/get_net.m
@@ -0,0 +1,37 @@
+function net = get_net(varargin)
+% net = get_net(model_file, phase_name) or
+% net = get_net(model_file, weights_file, phase_name)
+%   Construct a net from model_file, and load weights from weights_file
+%   phase_name can only be 'train' or 'test'
+
+CHECK(nargin == 2 || nargin == 3, ['usage: ' ...
+  'net = get_net(model_file, phase_name) or ' ...
+  'net = get_net(model_file, weights_file, phase_name)']);
+if nargin == 3
+  model_file = varargin{1};
+  weights_file = varargin{2};
+  phase_name = varargin{3};
+elseif nargin == 2
+  model_file = varargin{1};
+  phase_name = varargin{2};
+end
+
+CHECK(ischar(model_file), 'model_file must be a string');
+CHECK(ischar(phase_name), 'phase_name must be a string');
+CHECK_FILE_EXIST(model_file);
+CHECK(strcmp(phase_name, 'train') || strcmp(phase_name, 'test'), ...
+  sprintf('phase_name can only be %strain%s or %stest%s', ...
+  char(39), char(39), char(39), char(39)));
+
+% construct caffe net from model_file
+hNet = caffe_('get_net', model_file, phase_name);
+net = caffe.Net(hNet);
+
+% load weights from weights_file
+if nargin == 3
+  CHECK(ischar(weights_file), 'weights_file must be a string');
+  CHECK_FILE_EXIST(weights_file);
+  net.copy_from(weights_file);
+end
+
+end
diff --git a/matlab/+caffe/get_solver.m b/matlab/+caffe/get_solver.m
new file mode 100644
index 00000000000..74d576eb31b
--- /dev/null
+++ b/matlab/+caffe/get_solver.m
@@ -0,0 +1,10 @@
+function solver = get_solver(solver_file)
+% solver = get_solver(solver_file)
+%   Construct a Solver object from solver_file
+
+CHECK(ischar(solver_file), 'solver_file must be a string');
+CHECK_FILE_EXIST(solver_file);
+pSolver = caffe_('get_solver', solver_file);
+solver = caffe.Solver(pSolver);
+
+end
diff --git a/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat
new file mode 100644
index 00000000000..21df3d39aaa
Binary files /dev/null and b/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat differ
diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
new file mode 100644
index 00000000000..4b072fecdab
--- /dev/null
+++ b/matlab/+caffe/io.m
@@ -0,0 +1,41 @@
+classdef io
+  % a class for input and output functions
+  
+  methods (Static)
+    function im_data = load_image(im_file)
+      % im_data = load_image(im_file)
+      %   load an image from disk into Caffe-supported data format
+      %   switch channels from RGB to BGR, make width the fastest dimension
+      %   and convert to single
+      %   returns im_data in W x H x C. For colored images, C = 3 in BGR
+      %   channels, and for grayscale images, C = 1
+      CHECK(ischar(im_file), 'im_file must be a string');
+      CHECK_FILE_EXIST(im_file);
+      im_data = imread(im_file);
+      % permute channels from RGB to BGR for colored images
+      if size(im_data, 3) == 3
+        im_data = im_data(:, :, [3, 2, 1]);
+      end
+      % flip width and height to make width the fastest dimension
+      im_data = permute(im_data, [2, 1, 3]);
+      % convert from uint8 to single
+      im_data = single(im_data);
+    end
+    function mean_data = read_mean(mean_proto_file)
+      % mean_data = read_mean(mean_proto_file)
+      %   read image mean data from binaryproto file
+      %   returns mean_data in W x H x C with BGR channels
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+      CHECK_FILE_EXIST(mean_proto_file);
+      mean_data = caffe_('read_mean', mean_proto_file);
+    end
+    function write_mean(mean_data, mean_proto_file)
+      % write_mean(mean_data, mean_proto_file)
+      %   write image mean data to binaryproto file
+      %   mean_data should be W x H x C with BGR channels
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
+      CHECK(isa(mean_data, 'single'), 'mean_data must be a SINGLE matrix');
+      caffe_('write_mean', mean_data, mean_proto_file);
+    end   
+  end
+end
diff --git a/matlab/+caffe/private/CHECK.m b/matlab/+caffe/private/CHECK.m
new file mode 100644
index 00000000000..21706549cfa
--- /dev/null
+++ b/matlab/+caffe/private/CHECK.m
@@ -0,0 +1,7 @@
+function CHECK(expr, error_msg)
+
+if ~expr
+  error(error_msg);
+end
+
+end
diff --git a/matlab/+caffe/private/CHECK_FILE_EXIST.m b/matlab/+caffe/private/CHECK_FILE_EXIST.m
new file mode 100644
index 00000000000..8c80fb8094f
--- /dev/null
+++ b/matlab/+caffe/private/CHECK_FILE_EXIST.m
@@ -0,0 +1,7 @@
+function CHECK_FILE_EXIST(filename)
+
+if exist(filename, 'file') == 0
+  error('%s does not exist', filename);
+end
+
+end
diff --git a/matlab/+caffe/private/caffe_.cpp b/matlab/+caffe/private/caffe_.cpp
new file mode 100644
index 00000000000..1b1b2bff861
--- /dev/null
+++ b/matlab/+caffe/private/caffe_.cpp
@@ -0,0 +1,581 @@
+//
+// caffe_.cpp provides wrappers of the caffe::Solver class, caffe::Net class,
+// caffe::Layer class and caffe::Blob class and some caffe::Caffe functions,
+// so that one could easily use Caffe from matlab.
+// Note that for matlab, we will simply use float as the data type.
+
+// Internally, data is stored with dimensions reversed from Caffe's:
+// e.g., if the Caffe blob axes are (num, channels, height, width),
+// the matcaffe data is stored as (width, height, channels, num)
+// where width is the fastest dimension.
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "mex.h"
+
+#include "caffe/caffe.hpp"
+
+#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+// Do CHECK and throw a Mex error if check fails
+inline void mxCHECK(bool expr, const char* msg) {
+  if (!expr) {
+    mexErrMsgTxt(msg);
+  }
+}
+inline void mxERROR(const char* msg) { mexErrMsgTxt(msg); }
+
+// Check if a file exists and can be opened
+void mxCHECK_FILE_EXIST(const char* file) {
+  std::ifstream f(file);
+  if (!f.good()) {
+    f.close();
+    std::string msg("Could not open file ");
+    msg += file;
+    mxERROR(msg.c_str());
+  }
+  f.close();
+}
+
+// The pointers to caffe::Solver and caffe::Net instances
+static vector<shared_ptr<Solver<float> > > solvers_;
+static vector<shared_ptr<Net<float> > > nets_;
+// init_key is generated at the beginning and everytime you call reset
+static double init_key = static_cast<double>(caffe_rng_rand());
+
+/** -----------------------------------------------------------------
+ ** data conversion functions
+ **/
+// Enum indicates which blob memory to use
+enum WhichMemory { DATA, DIFF };
+
+// Copy matlab array to Blob data or diff
+static void mx_mat_to_blob(const mxArray* mx_mat, Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  mxCHECK(blob->count() == mxGetNumberOfElements(mx_mat),
+      "number of elements in target blob doesn't match that in input mxArray");
+  const float* mat_mem_ptr = reinterpret_cast<const float*>(mxGetData(mx_mat));
+  float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_cpu_data() : blob->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ?
+        blob->mutable_gpu_data() : blob->mutable_gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), mat_mem_ptr, blob_mem_ptr);
+}
+
+// Copy Blob data or diff to matlab array
+static mxArray* blob_to_mx_mat(const Blob<float>* blob,
+    WhichMemory data_or_diff) {
+  const int num_axes = blob->num_axes();
+  vector<mwSize> dims(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    dims[mat_axis] = static_cast<mwSize>(blob->shape(blob_axis));
+  }
+  // matlab array needs to have at least one dimension, convert scalar to 1-dim
+  if (num_axes == 0) {
+    dims.push_back(1);
+  }
+  mxArray* mx_mat =
+      mxCreateNumericArray(dims.size(), dims.data(), mxSINGLE_CLASS, mxREAL);
+  float* mat_mem_ptr = reinterpret_cast<float*>(mxGetData(mx_mat));
+  const float* blob_mem_ptr = NULL;
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->cpu_data() : blob->cpu_diff());
+    break;
+  case Caffe::GPU:
+    blob_mem_ptr = (data_or_diff == DATA ? blob->gpu_data() : blob->gpu_diff());
+    break;
+  default:
+    mxERROR("Unknown Caffe mode");
+  }
+  caffe_copy(blob->count(), blob_mem_ptr, mat_mem_ptr);
+  return mx_mat;
+}
+
+// Convert vector<int> to matlab row vector
+static mxArray* int_vec_to_mx_vec(const vector<int>& int_vec) {
+  mxArray* mx_vec = mxCreateDoubleMatrix(int_vec.size(), 1, mxREAL);
+  double* vec_mem_ptr = mxGetPr(mx_vec);
+  for (int i = 0; i < int_vec.size(); i++) {
+    vec_mem_ptr[i] = static_cast<double>(int_vec[i]);
+  }
+  return mx_vec;
+}
+
+// Convert vector<string> to matlab cell vector of strings
+static mxArray* str_vec_to_mx_strcell(const vector<std::string>& str_vec) {
+  mxArray* mx_strcell = mxCreateCellMatrix(str_vec.size(), 1);
+  for (int i = 0; i < str_vec.size(); i++) {
+    mxSetCell(mx_strcell, i, mxCreateString(str_vec[i].c_str()));
+  }
+  return mx_strcell;
+}
+
+/** -----------------------------------------------------------------
+ ** handle and pointer conversion functions
+ ** a handle is a struct array with the following fields
+ **   (uint64) ptr      : the pointer to the C++ object
+ **   (double) init_key : caffe initialization key
+ **/
+// Convert a handle in matlab to a pointer in C++. Check if init_key matches
+template <typename T>
+static T* handle_to_ptr(const mxArray* mx_handle) {
+  mxArray* mx_ptr = mxGetField(mx_handle, 0, "ptr");
+  mxArray* mx_init_key = mxGetField(mx_handle, 0, "init_key");
+  mxCHECK(mxIsUint64(mx_ptr), "pointer type must be uint64");
+  mxCHECK(mxGetScalar(mx_init_key) == init_key,
+      "Could not convert handle to pointer due to invalid init_key. "
+      "The object might have been cleared.");
+  return reinterpret_cast<T*>(*reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)));
+}
+
+// Create a handle struct vector, without setting up each handle in it
+template <typename T>
+static mxArray* create_handle_vec(int ptr_num) {
+  const int handle_field_num = 2;
+  const char* handle_fields[handle_field_num] = { "ptr", "init_key" };
+  return mxCreateStructMatrix(ptr_num, 1, handle_field_num, handle_fields);
+}
+
+// Set up a handle in a handle struct vector by its index
+template <typename T>
+static void setup_handle(const T* ptr, int index, mxArray* mx_handle_vec) {
+  mxArray* mx_ptr = mxCreateNumericMatrix(1, 1, mxUINT64_CLASS, mxREAL);
+  *reinterpret_cast<uint64_t*>(mxGetData(mx_ptr)) =
+      reinterpret_cast<uint64_t>(ptr);
+  mxSetField(mx_handle_vec, index, "ptr", mx_ptr);
+  mxSetField(mx_handle_vec, index, "init_key", mxCreateDoubleScalar(init_key));
+}
+
+// Convert a pointer in C++ to a handle in matlab
+template <typename T>
+static mxArray* ptr_to_handle(const T* ptr) {
+  mxArray* mx_handle = create_handle_vec<T>(1);
+  setup_handle(ptr, 0, mx_handle);
+  return mx_handle;
+}
+
+// Convert a vector of shared_ptr in C++ to handle struct vector
+template <typename T>
+static mxArray* ptr_vec_to_handle_vec(const vector<shared_ptr<T> >& ptr_vec) {
+  mxArray* mx_handle_vec = create_handle_vec<T>(ptr_vec.size());
+  for (int i = 0; i < ptr_vec.size(); i++) {
+    setup_handle(ptr_vec[i].get(), i, mx_handle_vec);
+  }
+  return mx_handle_vec;
+}
+
+/** -----------------------------------------------------------------
+ ** matlab command functions: caffe_(api_command, arg1, arg2, ...)
+ **/
+// Usage: caffe_('get_solver', solver_file);
+static void get_solver(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('get_solver', solver_file)");
+  char* solver_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(solver_file);
+  SolverParameter solver_param;
+  ReadSolverParamsFromTextFileOrDie(solver_file, &solver_param);
+  shared_ptr<Solver<float> > solver(
+      SolverRegistry<float>::CreateSolver(solver_param));
+  solvers_.push_back(solver);
+  plhs[0] = ptr_to_handle<Solver<float> >(solver.get());
+  mxFree(solver_file);
+}
+
+// Usage: caffe_('solver_get_attr', hSolver)
+static void solver_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_attr', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  const int solver_attr_num = 2;
+  const char* solver_attrs[solver_attr_num] = { "hNet_net", "hNet_test_nets" };
+  mxArray* mx_solver_attr = mxCreateStructMatrix(1, 1, solver_attr_num,
+      solver_attrs);
+  mxSetField(mx_solver_attr, 0, "hNet_net",
+      ptr_to_handle<Net<float> >(solver->net().get()));
+  mxSetField(mx_solver_attr, 0, "hNet_test_nets",
+      ptr_vec_to_handle_vec<Net<float> >(solver->test_nets()));
+  plhs[0] = mx_solver_attr;
+}
+
+// Usage: caffe_('solver_get_iter', hSolver)
+static void solver_get_iter(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_get_iter', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  plhs[0] = mxCreateDoubleScalar(solver->iter());
+}
+
+// Usage: caffe_('solver_restore', hSolver, snapshot_file)
+static void solver_restore(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('solver_restore', hSolver, snapshot_file)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  char* snapshot_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(snapshot_file);
+  solver->Restore(snapshot_file);
+  mxFree(snapshot_file);
+}
+
+// Usage: caffe_('solver_solve', hSolver)
+static void solver_solve(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('solver_solve', hSolver)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  solver->Solve();
+}
+
+// Usage: caffe_('solver_step', hSolver, iters)
+static void solver_step(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('solver_step', hSolver, iters)");
+  Solver<float>* solver = handle_to_ptr<Solver<float> >(prhs[0]);
+  int iters = mxGetScalar(prhs[1]);
+  solver->Step(iters);
+}
+
+// Usage: caffe_('get_net', model_file, phase_name)
+static void get_net(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsChar(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('get_net', model_file, phase_name)");
+  char* model_file = mxArrayToString(prhs[0]);
+  char* phase_name = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(model_file);
+  Phase phase;
+  if (strcmp(phase_name, "train") == 0) {
+      phase = TRAIN;
+  } else if (strcmp(phase_name, "test") == 0) {
+      phase = TEST;
+  } else {
+    mxERROR("Unknown phase");
+  }
+  shared_ptr<Net<float> > net(new caffe::Net<float>(model_file, phase));
+  nets_.push_back(net);
+  plhs[0] = ptr_to_handle<Net<float> >(net.get());
+  mxFree(model_file);
+  mxFree(phase_name);
+}
+
+// Usage: caffe_('net_get_attr', hNet)
+static void net_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_get_attr', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  const int net_attr_num = 6;
+  const char* net_attrs[net_attr_num] = { "hLayer_layers", "hBlob_blobs",
+      "input_blob_indices", "output_blob_indices", "layer_names", "blob_names"};
+  mxArray* mx_net_attr = mxCreateStructMatrix(1, 1, net_attr_num,
+      net_attrs);
+  mxSetField(mx_net_attr, 0, "hLayer_layers",
+      ptr_vec_to_handle_vec<Layer<float> >(net->layers()));
+  mxSetField(mx_net_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handle_vec<Blob<float> >(net->blobs()));
+  mxSetField(mx_net_attr, 0, "input_blob_indices",
+      int_vec_to_mx_vec(net->input_blob_indices()));
+  mxSetField(mx_net_attr, 0, "output_blob_indices",
+      int_vec_to_mx_vec(net->output_blob_indices()));
+  mxSetField(mx_net_attr, 0, "layer_names",
+      str_vec_to_mx_strcell(net->layer_names()));
+  mxSetField(mx_net_attr, 0, "blob_names",
+      str_vec_to_mx_strcell(net->blob_names()));
+  plhs[0] = mx_net_attr;
+}
+
+// Usage: caffe_('net_forward', hNet)
+static void net_forward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_forward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->ForwardPrefilled();
+}
+
+// Usage: caffe_('net_backward', hNet)
+static void net_backward(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_backward', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Backward();
+}
+
+// Usage: caffe_('net_copy_from', hNet, weights_file)
+static void net_copy_from(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_copy_from', hNet, weights_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  char* weights_file = mxArrayToString(prhs[1]);
+  mxCHECK_FILE_EXIST(weights_file);
+  net->CopyTrainedLayersFrom(weights_file);
+  mxFree(weights_file);
+}
+
+// Usage: caffe_('net_reshape', hNet)
+static void net_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('net_reshape', hNet)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  net->Reshape();
+}
+
+// Usage: caffe_('net_save', hNet, save_file)
+static void net_save(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('net_save', hNet, save_file)");
+  Net<float>* net = handle_to_ptr<Net<float> >(prhs[0]);
+  char* weights_file = mxArrayToString(prhs[1]);
+  NetParameter net_param;
+  net->ToProto(&net_param, false);
+  WriteProtoToBinaryFile(net_param, weights_file);
+  mxFree(weights_file);
+}
+
+// Usage: caffe_('layer_get_attr', hLayer)
+static void layer_get_attr(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_attr', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  const int layer_attr_num = 1;
+  const char* layer_attrs[layer_attr_num] = { "hBlob_blobs" };
+  mxArray* mx_layer_attr = mxCreateStructMatrix(1, 1, layer_attr_num,
+      layer_attrs);
+  mxSetField(mx_layer_attr, 0, "hBlob_blobs",
+      ptr_vec_to_handle_vec<Blob<float> >(layer->blobs()));
+  plhs[0] = mx_layer_attr;
+}
+
+// Usage: caffe_('layer_get_type', hLayer)
+static void layer_get_type(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('layer_get_type', hLayer)");
+  Layer<float>* layer = handle_to_ptr<Layer<float> >(prhs[0]);
+  plhs[0] = mxCreateString(layer->type());
+}
+
+// Usage: caffe_('blob_get_shape', hBlob)
+static void blob_get_shape(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_shape', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const int num_axes = blob->num_axes();
+  mxArray* mx_shape = mxCreateDoubleMatrix(1, num_axes, mxREAL);
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    shape_mem_mtr[mat_axis] = static_cast<double>(blob->shape(blob_axis));
+  }
+  plhs[0] = mx_shape;
+}
+
+// Usage: caffe_('blob_reshape', hBlob, new_shape)
+static void blob_reshape(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsDouble(prhs[1]),
+      "Usage: caffe_('blob_reshape', hBlob, new_shape)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  const mxArray* mx_shape = prhs[1];
+  double* shape_mem_mtr = mxGetPr(mx_shape);
+  const int num_axes = mxGetNumberOfElements(mx_shape);
+  vector<int> blob_shape(num_axes);
+  for (int blob_axis = 0, mat_axis = num_axes - 1; blob_axis < num_axes;
+       ++blob_axis, --mat_axis) {
+    blob_shape[blob_axis] = static_cast<int>(shape_mem_mtr[mat_axis]);
+  }
+  blob->Reshape(blob_shape);
+}
+
+// Usage: caffe_('blob_get_data', hBlob)
+static void blob_get_data(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_data', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DATA);
+}
+
+// Usage: caffe_('blob_set_data', hBlob, new_data)
+static void blob_set_data(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_data', hBlob, new_data)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DATA);
+}
+
+// Usage: caffe_('blob_get_diff', hBlob)
+static void blob_get_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsStruct(prhs[0]),
+      "Usage: caffe_('blob_get_diff', hBlob)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  plhs[0] = blob_to_mx_mat(blob, DIFF);
+}
+
+// Usage: caffe_('blob_set_diff', hBlob, new_diff)
+static void blob_set_diff(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsStruct(prhs[0]) && mxIsSingle(prhs[1]),
+      "Usage: caffe_('blob_set_diff', hBlob, new_diff)");
+  Blob<float>* blob = handle_to_ptr<Blob<float> >(prhs[0]);
+  mx_mat_to_blob(prhs[1], blob, DIFF);
+}
+
+// Usage: caffe_('set_mode_cpu')
+static void set_mode_cpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_cpu')");
+  Caffe::set_mode(Caffe::CPU);
+}
+
+// Usage: caffe_('set_mode_gpu')
+static void set_mode_gpu(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('set_mode_gpu')");
+  Caffe::set_mode(Caffe::GPU);
+}
+
+// Usage: caffe_('set_device', device_id)
+static void set_device(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsDouble(prhs[0]),
+      "Usage: caffe_('set_device', device_id)");
+  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
+  Caffe::SetDevice(device_id);
+}
+
+// Usage: caffe_('get_init_key')
+static void get_init_key(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('get_init_key')");
+  plhs[0] = mxCreateDoubleScalar(init_key);
+}
+
+// Usage: caffe_('reset')
+static void reset(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('reset')");
+  // Clear solvers and stand-alone nets
+  mexPrintf("Cleared %d solvers and %d stand-alone nets\n",
+      solvers_.size(), nets_.size());
+  solvers_.clear();
+  nets_.clear();
+  // Generate new init_key, so that handles created before becomes invalid
+  init_key = static_cast<double>(caffe_rng_rand());
+}
+
+// Usage: caffe_('read_mean', mean_proto_file)
+static void read_mean(MEX_ARGS) {
+  mxCHECK(nrhs == 1 && mxIsChar(prhs[0]),
+      "Usage: caffe_('read_mean', mean_proto_file)");
+  char* mean_proto_file = mxArrayToString(prhs[0]);
+  mxCHECK_FILE_EXIST(mean_proto_file);
+  Blob<float> data_mean;
+  BlobProto blob_proto;
+  bool result = ReadProtoFromBinaryFile(mean_proto_file, &blob_proto);
+  mxCHECK(result, "Could not read your mean file");
+  data_mean.FromProto(blob_proto);
+  plhs[0] = blob_to_mx_mat(&data_mean, DATA);
+  mxFree(mean_proto_file);
+}
+
+// Usage: caffe_('write_mean', mean_data, mean_proto_file)
+static void write_mean(MEX_ARGS) {
+  mxCHECK(nrhs == 2 && mxIsSingle(prhs[0]) && mxIsChar(prhs[1]),
+      "Usage: caffe_('write_mean', mean_data, mean_proto_file)");
+  char* mean_proto_file = mxArrayToString(prhs[1]);
+  int ndims = mxGetNumberOfDimensions(prhs[0]);
+  mxCHECK(ndims >= 2 && ndims <= 3, "mean_data must have at 2 or 3 dimensions");
+  const mwSize *dims = mxGetDimensions(prhs[0]);
+  int width = dims[0];
+  int height = dims[1];
+  int channels;
+  if (ndims == 3)
+    channels = dims[2];
+  else
+    channels = 1;
+  Blob<float> data_mean(1, channels, height, width);
+  mx_mat_to_blob(prhs[0], &data_mean, DATA);
+  BlobProto blob_proto;
+  data_mean.ToProto(&blob_proto, false);
+  WriteProtoToBinaryFile(blob_proto, mean_proto_file);
+  mxFree(mean_proto_file);
+}
+
+// Usage: caffe_('version')
+static void version(MEX_ARGS) {
+  mxCHECK(nrhs == 0, "Usage: caffe_('version')");
+  // Return version string
+  plhs[0] = mxCreateString(AS_STRING(CAFFE_VERSION));
+}
+
+/** -----------------------------------------------------------------
+ ** Available commands.
+ **/
+struct handler_registry {
+  string cmd;
+  void (*func)(MEX_ARGS);
+};
+
+static handler_registry handlers[] = {
+  // Public API functions
+  { "get_solver",         get_solver      },
+  { "solver_get_attr",    solver_get_attr },
+  { "solver_get_iter",    solver_get_iter },
+  { "solver_restore",     solver_restore  },
+  { "solver_solve",       solver_solve    },
+  { "solver_step",        solver_step     },
+  { "get_net",            get_net         },
+  { "net_get_attr",       net_get_attr    },
+  { "net_forward",        net_forward     },
+  { "net_backward",       net_backward    },
+  { "net_copy_from",      net_copy_from   },
+  { "net_reshape",        net_reshape     },
+  { "net_save",           net_save        },
+  { "layer_get_attr",     layer_get_attr  },
+  { "layer_get_type",     layer_get_type  },
+  { "blob_get_shape",     blob_get_shape  },
+  { "blob_reshape",       blob_reshape    },
+  { "blob_get_data",      blob_get_data   },
+  { "blob_set_data",      blob_set_data   },
+  { "blob_get_diff",      blob_get_diff   },
+  { "blob_set_diff",      blob_set_diff   },
+  { "set_mode_cpu",       set_mode_cpu    },
+  { "set_mode_gpu",       set_mode_gpu    },
+  { "set_device",         set_device      },
+  { "get_init_key",       get_init_key    },
+  { "reset",              reset           },
+  { "read_mean",          read_mean       },
+  { "write_mean",         write_mean      },
+  { "version",            version         },
+  // The end.
+  { "END",                NULL            },
+};
+
+/** -----------------------------------------------------------------
+ ** matlab entry point.
+ **/
+// Usage: caffe_(api_command, arg1, arg2, ...)
+void mexFunction(MEX_ARGS) {
+  mexLock();  // Avoid clearing the mex file.
+  mxCHECK(nrhs > 0, "Usage: caffe_(api_command, arg1, arg2, ...)");
+  // Handle input command
+  char* cmd = mxArrayToString(prhs[0]);
+  bool dispatched = false;
+  // Dispatch to cmd handler
+  for (int i = 0; handlers[i].func != NULL; i++) {
+    if (handlers[i].cmd.compare(cmd) == 0) {
+      handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
+      dispatched = true;
+      break;
+    }
+  }
+  if (!dispatched) {
+    ostringstream error_msg;
+    error_msg << "Unknown command '" << cmd << "'";
+    mxERROR(error_msg.str().c_str());
+  }
+  mxFree(cmd);
+}
diff --git a/matlab/+caffe/private/is_valid_handle.m b/matlab/+caffe/private/is_valid_handle.m
new file mode 100644
index 00000000000..a0648ecdf61
--- /dev/null
+++ b/matlab/+caffe/private/is_valid_handle.m
@@ -0,0 +1,27 @@
+function valid = is_valid_handle(hObj)
+% valid = is_valid_handle(hObj) or is_valid_handle('get_new_init_key')
+%   Check if a handle is valid (has the right data type and init_key matches)
+%   Use is_valid_handle('get_new_init_key') to get new init_key from C++;
+
+% a handle is a struct array with the following fields
+%   (uint64) ptr      : the pointer to the C++ object
+%   (double) init_key : caffe initialization key
+
+persistent init_key;
+if isempty(init_key)
+  init_key = caffe_('get_init_key');
+end
+
+% is_valid_handle('get_new_init_key') to get new init_key from C++;
+if ischar(hObj) && strcmp(hObj, 'get_new_init_key')
+  init_key = caffe_('get_init_key');
+  return
+else
+  % check whether data types are correct and init_key matches
+  valid = isstruct(hObj) ...
+    && isscalar(hObj.ptr) && isa(hObj.ptr, 'uint64') ...
+    && isscalar(hObj.init_key) && isa(hObj.init_key, 'double') ...
+    && hObj.init_key == init_key;
+end
+
+end
diff --git a/matlab/+caffe/reset_all.m b/matlab/+caffe/reset_all.m
new file mode 100644
index 00000000000..a8b33dee8d5
--- /dev/null
+++ b/matlab/+caffe/reset_all.m
@@ -0,0 +1,8 @@
+function reset_all()
+% reset_all()
+%   clear all solvers and stand-alone nets and reset Caffe to initial status
+
+caffe_('reset');
+is_valid_handle('get_new_init_key');
+
+end
diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
new file mode 100644
index 00000000000..6dbf6b23151
--- /dev/null
+++ b/matlab/+caffe/run_tests.m
@@ -0,0 +1,20 @@
+function results = run_tests()
+% results = run_tests()
+%   run all tests in this caffe matlab wrapper package
+
+% use CPU for testing
+caffe.set_mode_cpu();
+
+% reset caffe before testing
+caffe.reset_all();
+
+% put all test cases here
+results = [...
+  run(caffe.test.test_net) ...
+  run(caffe.test.test_solver) ...
+  run(caffe.test.test_io) ];
+
+% reset caffe after testing
+caffe.reset_all();
+
+end
diff --git a/matlab/+caffe/set_device.m b/matlab/+caffe/set_device.m
new file mode 100644
index 00000000000..f94068cbe98
--- /dev/null
+++ b/matlab/+caffe/set_device.m
@@ -0,0 +1,11 @@
+function set_device(device_id)
+% set_device(device_id)
+%   set Caffe's GPU device ID
+
+CHECK(isscalar(device_id) && device_id >= 0, ...
+  'device_id must be non-negative integer');
+device_id = double(device_id);
+
+caffe_('set_device', device_id);
+
+end
diff --git a/matlab/+caffe/set_mode_cpu.m b/matlab/+caffe/set_mode_cpu.m
new file mode 100644
index 00000000000..a87e0e2852b
--- /dev/null
+++ b/matlab/+caffe/set_mode_cpu.m
@@ -0,0 +1,7 @@
+function set_mode_cpu()
+% set_mode_cpu()
+%   set Caffe to CPU mode
+
+caffe_('set_mode_cpu');
+
+end
diff --git a/matlab/+caffe/set_mode_gpu.m b/matlab/+caffe/set_mode_gpu.m
new file mode 100644
index 00000000000..78e5f6773a1
--- /dev/null
+++ b/matlab/+caffe/set_mode_gpu.m
@@ -0,0 +1,7 @@
+function set_mode_gpu()
+% set_mode_gpu()
+%   set Caffe to GPU mode
+
+caffe_('set_mode_gpu');
+
+end
diff --git a/matlab/+caffe/version.m b/matlab/+caffe/version.m
new file mode 100644
index 00000000000..61cae4f76dc
--- /dev/null
+++ b/matlab/+caffe/version.m
@@ -0,0 +1,7 @@
+function version_str = version()
+% version()
+%   show Caffe's version.
+
+version_str = caffe_('version');
+
+end
diff --git a/matlab/CMakeLists.txt b/matlab/CMakeLists.txt
index 791a4e70f43..f420df8d412 100644
--- a/matlab/CMakeLists.txt
+++ b/matlab/CMakeLists.txt
@@ -31,8 +31,8 @@ function(caffe_fetch_and_set_proper_mexext mexfile_variable)
 endfunction()
 
 # global settings
-file(GLOB Matlab_srcs caffe/matcaffe.cpp)
-set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/caffe/caffe.mex)
+file(GLOB Matlab_srcs +caffe/private/caffe_.cpp)
+set(Matlab_caffe_mex ${PROJECT_SOURCE_DIR}/matlab/+caffe/private/caffe_.mex)
 
 caffe_get_current_cflags(cflags)
 caffe_parse_linker_libs(Caffe_LINKER_LIBS folders libflags macos_frameworks)
@@ -43,7 +43,7 @@ string(REPLACE ";" ";-L" link_folders "-L${folders}")
 string(REPLACE ";" ":"  rpath_folders   "${folders}")
 
 if(build_using MATCHES "Matlab")
-  set(libflags -lcaffe${CAffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
+  set(libflags -lcaffe${Caffe_POSTFIX} ${libflags}) # Matlab R2014a complans for -Wl,--whole-archive
 
   caffe_fetch_and_set_proper_mexext(Matlab_caffe_mex)
   add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Matlab_mex}
@@ -56,7 +56,7 @@ elseif(build_using MATCHES "Octave")
   if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
     set(libflags -Wl,-force_load,$<TARGET_LINKER_FILE:caffe> ${libflags})
   elseif("${CMAKE_CXX_COMPILER_ID}" STREQUAL "GNU")
-    set(libflags -Wl,--whole-archive -lcaffe${CAffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
+    set(libflags -Wl,--whole-archive -lcaffe${Caffe_POSTFIX} -Wl,--no-whole-archive ${libflags})
   endif()
 
   add_custom_command(OUTPUT ${Matlab_caffe_mex} COMMAND ${Octave_compiler}
diff --git a/matlab/caffe/ilsvrc_2012_mean.mat b/matlab/caffe/ilsvrc_2012_mean.mat
deleted file mode 100644
index f1da25c84a1..00000000000
Binary files a/matlab/caffe/ilsvrc_2012_mean.mat and /dev/null differ
diff --git a/matlab/caffe/matcaffe.cpp b/matlab/caffe/matcaffe.cpp
deleted file mode 100644
index da37d920b20..00000000000
--- a/matlab/caffe/matcaffe.cpp
+++ /dev/null
@@ -1,421 +0,0 @@
-//
-// matcaffe.cpp provides a wrapper of the caffe::Net class as well as some
-// caffe::Caffe functions so that one could easily call it from matlab.
-// Note that for matlab, we will simply use float as the data type.
-
-#include <sstream>
-#include <string>
-#include <vector>
-
-#include "mex.h"
-
-#include "caffe/caffe.hpp"
-
-#define MEX_ARGS int nlhs, mxArray **plhs, int nrhs, const mxArray **prhs
-
-// Log and throw a Mex error
-inline void mex_error(const std::string &msg) {
-  LOG(ERROR) << msg;
-  mexErrMsgTxt(msg.c_str());
-}
-
-using namespace caffe;  // NOLINT(build/namespaces)
-
-// The pointer to the internal caffe::Net instance
-static shared_ptr<Net<float> > net_;
-static int init_key = -2;
-
-// Five things to be aware of:
-//   caffe uses row-major order
-//   matlab uses column-major order
-//   caffe uses BGR color channel order
-//   matlab uses RGB color channel order
-//   images need to have the data mean subtracted
-//
-// Data coming in from matlab needs to be in the order
-//   [width, height, channels, images]
-// where width is the fastest dimension.
-// Here is the rough matlab for putting image data into the correct
-// format:
-//   % convert from uint8 to single
-//   im = single(im);
-//   % reshape to a fixed size (e.g., 227x227)
-//   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-//   % permute from RGB to BGR and subtract the data mean (already in BGR)
-//   im = im(:,:,[3 2 1]) - data_mean;
-//   % flip width and height to make width the fastest dimension
-//   im = permute(im, [2 1 3]);
-//
-// If you have multiple images, cat them with cat(4, ...)
-//
-// The actual forward function. It takes in a cell array of 4-D arrays as
-// input and outputs a cell array.
-
-static mxArray* do_forward(const mxArray* const bottom) {
-  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  if (static_cast<unsigned int>(mxGetDimensions(bottom)[0]) !=
-      input_blobs.size()) {
-    mex_error("Invalid input size");
-  }
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(bottom, i);
-    if (!mxIsSingle(elem)) {
-      mex_error("MatCaffe require single-precision float point data");
-    }
-    if (mxGetNumberOfElements(elem) != input_blobs[i]->count()) {
-      std::string error_msg;
-      error_msg += "MatCaffe input size does not match the input size ";
-      error_msg += "of the network";
-      mex_error(error_msg);
-    }
-
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_cpu_data());
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), data_ptr,
-          input_blobs[i]->mutable_gpu_data());
-      break;
-    default:
-      mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-  const vector<Blob<float>*>& output_blobs = net_->ForwardPrefilled();
-  mxArray* mx_out = mxCreateCellMatrix(output_blobs.size(), 1);
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {output_blobs[i]->width(), output_blobs[i]->height(),
-      output_blobs[i]->channels(), output_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->cpu_data(),
-          data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), output_blobs[i]->gpu_data(),
-          data_ptr);
-      break;
-    default:
-      mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_backward(const mxArray* const top_diff) {
-  const vector<Blob<float>*>& output_blobs = net_->output_blobs();
-  const vector<Blob<float>*>& input_blobs = net_->input_blobs();
-  if (static_cast<unsigned int>(mxGetDimensions(top_diff)[0]) !=
-      output_blobs.size()) {
-    mex_error("Invalid input size");
-  }
-  // First, copy the output diff
-  for (unsigned int i = 0; i < output_blobs.size(); ++i) {
-    const mxArray* const elem = mxGetCell(top_diff, i);
-    const float* const data_ptr =
-        reinterpret_cast<const float* const>(mxGetPr(elem));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_cpu_diff());
-      break;
-    case Caffe::GPU:
-      caffe_copy(output_blobs[i]->count(), data_ptr,
-          output_blobs[i]->mutable_gpu_diff());
-      break;
-    default:
-        mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-  // LOG(INFO) << "Start";
-  net_->Backward();
-  // LOG(INFO) << "End";
-  mxArray* mx_out = mxCreateCellMatrix(input_blobs.size(), 1);
-  for (unsigned int i = 0; i < input_blobs.size(); ++i) {
-    // internally data is stored as (width, height, channels, num)
-    // where width is the fastest dimension
-    mwSize dims[4] = {input_blobs[i]->width(), input_blobs[i]->height(),
-      input_blobs[i]->channels(), input_blobs[i]->num()};
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    mxSetCell(mx_out, i, mx_blob);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    switch (Caffe::mode()) {
-    case Caffe::CPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->cpu_diff(), data_ptr);
-      break;
-    case Caffe::GPU:
-      caffe_copy(input_blobs[i]->count(), input_blobs[i]->gpu_diff(), data_ptr);
-      break;
-    default:
-        mex_error("Unknown Caffe mode");
-    }  // switch (Caffe::mode())
-  }
-
-  return mx_out;
-}
-
-static mxArray* do_get_weights() {
-  const vector<shared_ptr<Layer<float> > >& layers = net_->layers();
-  const vector<string>& layer_names = net_->layer_names();
-
-  // Step 1: count the number of layers with weights
-  int num_layers = 0;
-  {
-    string prev_layer_name = "";
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        num_layers++;
-      }
-    }
-  }
-
-  // Step 2: prepare output array of structures
-  mxArray* mx_layers;
-  {
-    const mwSize dims[2] = {num_layers, 1};
-    const char* fnames[2] = {"weights", "layer_names"};
-    mx_layers = mxCreateStructArray(2, dims, 2, fnames);
-  }
-
-  // Step 3: copy weights into output
-  {
-    string prev_layer_name = "";
-    int mx_layer_index = 0;
-    for (unsigned int i = 0; i < layers.size(); ++i) {
-      vector<shared_ptr<Blob<float> > >& layer_blobs = layers[i]->blobs();
-      if (layer_blobs.size() == 0) {
-        continue;
-      }
-
-      mxArray* mx_layer_cells = NULL;
-      if (layer_names[i] != prev_layer_name) {
-        prev_layer_name = layer_names[i];
-        const mwSize dims[2] = {static_cast<mwSize>(layer_blobs.size()), 1};
-        mx_layer_cells = mxCreateCellArray(2, dims);
-        mxSetField(mx_layers, mx_layer_index, "weights", mx_layer_cells);
-        mxSetField(mx_layers, mx_layer_index, "layer_names",
-            mxCreateString(layer_names[i].c_str()));
-        mx_layer_index++;
-      }
-
-      for (unsigned int j = 0; j < layer_blobs.size(); ++j) {
-        // internally data is stored as (width, height, channels, num)
-        // where width is the fastest dimension
-        mwSize dims[4] = {layer_blobs[j]->width(), layer_blobs[j]->height(),
-            layer_blobs[j]->channels(), layer_blobs[j]->num()};
-
-        mxArray* mx_weights =
-          mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-        mxSetCell(mx_layer_cells, j, mx_weights);
-        float* weights_ptr = reinterpret_cast<float*>(mxGetPr(mx_weights));
-
-        switch (Caffe::mode()) {
-        case Caffe::CPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->cpu_data(),
-              weights_ptr);
-          break;
-        case Caffe::GPU:
-          caffe_copy(layer_blobs[j]->count(), layer_blobs[j]->gpu_data(),
-              weights_ptr);
-          break;
-        default:
-          mex_error("Unknown Caffe mode");
-        }
-      }
-    }
-  }
-
-  return mx_layers;
-}
-
-static void get_weights(MEX_ARGS) {
-  plhs[0] = do_get_weights();
-}
-
-static void set_mode_cpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::CPU);
-}
-
-static void set_mode_gpu(MEX_ARGS) {
-  Caffe::set_mode(Caffe::GPU);
-}
-
-static void set_device(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  int device_id = static_cast<int>(mxGetScalar(prhs[0]));
-  Caffe::SetDevice(device_id);
-}
-
-static void get_init_key(MEX_ARGS) {
-  plhs[0] = mxCreateDoubleScalar(init_key);
-}
-
-static void init(MEX_ARGS) {
-  if (nrhs != 3) {
-    ostringstream error_msg;
-    error_msg << "Expected 3 arguments, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  char* param_file = mxArrayToString(prhs[0]);
-  char* model_file = mxArrayToString(prhs[1]);
-  char* phase_name = mxArrayToString(prhs[2]);
-
-  Phase phase;
-  if (strcmp(phase_name, "train") == 0) {
-      phase = TRAIN;
-  } else if (strcmp(phase_name, "test") == 0) {
-      phase = TEST;
-  } else {
-    mex_error("Unknown phase.");
-  }
-
-  net_.reset(new Net<float>(string(param_file), phase));
-  net_->CopyTrainedLayersFrom(string(model_file));
-
-  mxFree(param_file);
-  mxFree(model_file);
-  mxFree(phase_name);
-
-  init_key = random();  // NOLINT(caffe/random_fn)
-
-  if (nlhs == 1) {
-    plhs[0] = mxCreateDoubleScalar(init_key);
-  }
-}
-
-static void reset(MEX_ARGS) {
-  if (net_) {
-    net_.reset();
-    init_key = -2;
-    LOG(INFO) << "Network reset, call init before use it again";
-  }
-}
-
-static void forward(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  plhs[0] = do_forward(prhs[0]);
-}
-
-static void backward(MEX_ARGS) {
-  if (nrhs != 1) {
-    ostringstream error_msg;
-    error_msg << "Expected 1 argument, got " << nrhs;
-    mex_error(error_msg.str());
-  }
-
-  plhs[0] = do_backward(prhs[0]);
-}
-
-static void is_initialized(MEX_ARGS) {
-  if (!net_) {
-    plhs[0] = mxCreateDoubleScalar(0);
-  } else {
-    plhs[0] = mxCreateDoubleScalar(1);
-  }
-}
-
-static void read_mean(MEX_ARGS) {
-    if (nrhs != 1) {
-        mexErrMsgTxt("Usage: caffe('read_mean', 'path_to_binary_mean_file'");
-        return;
-    }
-    const string& mean_file = mxArrayToString(prhs[0]);
-    Blob<float> data_mean;
-    LOG(INFO) << "Loading mean file from: " << mean_file;
-    BlobProto blob_proto;
-    bool result = ReadProtoFromBinaryFile(mean_file.c_str(), &blob_proto);
-    if (!result) {
-        mexErrMsgTxt("Couldn't read the file");
-        return;
-    }
-    data_mean.FromProto(blob_proto);
-    mwSize dims[4] = {data_mean.width(), data_mean.height(),
-                      data_mean.channels(), data_mean.num() };
-    mxArray* mx_blob =  mxCreateNumericArray(4, dims, mxSINGLE_CLASS, mxREAL);
-    float* data_ptr = reinterpret_cast<float*>(mxGetPr(mx_blob));
-    caffe_copy(data_mean.count(), data_mean.cpu_data(), data_ptr);
-    mexWarnMsgTxt("Remember that Caffe saves in [width, height, channels]"
-                  " format and channels are also BGR!");
-    plhs[0] = mx_blob;
-}
-
-/** -----------------------------------------------------------------
- ** Available commands.
- **/
-struct handler_registry {
-  string cmd;
-  void (*func)(MEX_ARGS);
-};
-
-static handler_registry handlers[] = {
-  // Public API functions
-  { "forward",            forward         },
-  { "backward",           backward        },
-  { "init",               init            },
-  { "is_initialized",     is_initialized  },
-  { "set_mode_cpu",       set_mode_cpu    },
-  { "set_mode_gpu",       set_mode_gpu    },
-  { "set_device",         set_device      },
-  { "get_weights",        get_weights     },
-  { "get_init_key",       get_init_key    },
-  { "reset",              reset           },
-  { "read_mean",          read_mean       },
-  // The end.
-  { "END",                NULL            },
-};
-
-
-/** -----------------------------------------------------------------
- ** matlab entry point: caffe(api_command, arg1, arg2, ...)
- **/
-void mexFunction(MEX_ARGS) {
-  mexLock();  // Avoid clearing the mex file.
-  if (nrhs == 0) {
-    mex_error("No API command given");
-    return;
-  }
-
-  { // Handle input command
-    char *cmd = mxArrayToString(prhs[0]);
-    bool dispatched = false;
-    // Dispatch to cmd handler
-    for (int i = 0; handlers[i].func != NULL; i++) {
-      if (handlers[i].cmd.compare(cmd) == 0) {
-        handlers[i].func(nlhs, plhs, nrhs-1, prhs+1);
-        dispatched = true;
-        break;
-      }
-    }
-    if (!dispatched) {
-      ostringstream error_msg;
-      error_msg << "Unknown command '" << cmd << "'";
-      mex_error(error_msg.str());
-    }
-    mxFree(cmd);
-  }
-}
diff --git a/matlab/caffe/matcaffe_batch.m b/matlab/caffe/matcaffe_batch.m
deleted file mode 100644
index f6d1aa83b84..00000000000
--- a/matlab/caffe/matcaffe_batch.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function [scores,list_im] = matcaffe_batch(list_im, use_gpu)
-% scores = matcaffe_batch(list_im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-%   list_im  list of images files
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   scores   1000 x num_images ILSVRC output vector
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-%  scores = matcaffe_batch({'peppers.png','onion.png'});
-%  scores = matcaffe_batch('list_images.txt', 1);
-if nargin < 1
-  % For test purposes
-  list_im = {'peppers.png','onions.png'};
-end
-if ischar(list_im)
-    %Assume it is a file contaning the list of images
-    filename = list_im;
-    list_im = read_cell(filename);
-end
-% Adjust the batch size and dim to match with models/bvlc_reference_caffenet/deploy.prototxt
-batch_size = 10;
-dim = 1000;
-disp(list_im)
-if mod(length(list_im),batch_size)
-    warning(['Assuming batches of ' num2str(batch_size) ' images rest will be filled with zeros'])
-end
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
-else
-  matcaffe_init();
-end
-
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-
-% prepare input
-
-num_images = length(list_im);
-scores = zeros(dim,num_images,'single');
-num_batches = ceil(length(list_im)/batch_size)
-initic=tic;
-for bb = 1 : num_batches
-    batchtic = tic;
-    range = 1+batch_size*(bb-1):min(num_images,batch_size * bb);
-    tic
-    input_data = prepare_batch(list_im(range),IMAGE_MEAN,batch_size);
-    toc, tic
-    fprintf('Batch %d out of %d %.2f%% Complete ETA %.2f seconds\n',...
-        bb,num_batches,bb/num_batches*100,toc(initic)/bb*(num_batches-bb));
-    output_data = caffe('forward', {input_data});
-    toc
-    output_data = squeeze(output_data{1});
-    scores(:,range) = output_data(:,mod(range-1,batch_size)+1);
-    toc(batchtic)
-end
-toc(initic);
-
-if exist('filename', 'var')
-    save([filename '.probs.mat'],'list_im','scores','-v7.3');
-end
-
-
-
diff --git a/matlab/caffe/matcaffe_demo.m b/matlab/caffe/matcaffe_demo.m
deleted file mode 100644
index a931f910cbf..00000000000
--- a/matlab/caffe/matcaffe_demo.m
+++ /dev/null
@@ -1,110 +0,0 @@
-function [scores, maxlabel] = matcaffe_demo(im, use_gpu)
-% scores = matcaffe_demo(im, use_gpu)
-%
-% Demo of the matlab wrapper using the ILSVRC network.
-%
-% input
-%   im       color image as uint8 HxWx3
-%   use_gpu  1 to use the GPU, 0 to use the CPU
-%
-% output
-%   scores   1000-dimensional ILSVRC score vector
-%
-% You may need to do the following before you start matlab:
-%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
-%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-% Or the equivalent based on where things are installed on your system
-%
-% Usage:
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo(im, 1);
-%  [score, class] = max(scores);
-% Five things to be aware of:
-%   caffe uses row-major order
-%   matlab uses column-major order
-%   caffe uses BGR color channel order
-%   matlab uses RGB color channel order
-%   images need to have the data mean subtracted
-
-% Data coming in from matlab needs to be in the order 
-%   [width, height, channels, images]
-% where width is the fastest dimension.
-% Here is the rough matlab for putting image data into the correct
-% format:
-%   % convert from uint8 to single
-%   im = single(im);
-%   % reshape to a fixed size (e.g., 227x227)
-%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-%   % permute from RGB to BGR and subtract the data mean (already in BGR)
-%   im = im(:,:,[3 2 1]) - data_mean;
-%   % flip width and height to make width the fastest dimension
-%   im = permute(im, [2 1 3]);
-
-% If you have multiple images, cat them with cat(4, ...)
-
-% The actual forward function. It takes in a cell array of 4-D arrays as
-% input and outputs a cell array. 
-
-
-% init caffe network (spews logging info)
-if exist('use_gpu', 'var')
-  matcaffe_init(use_gpu);
-else
-  matcaffe_init();
-end
-
-if nargin < 1
-  % For demo purposes we will use the peppers image
-  im = imread('peppers.png');
-end
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-size(scores)
-scores = squeeze(scores);
-scores = mean(scores,2);
-
-[~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im)
-% ------------------------------------------------------------------------
-d = load('ilsvrc_2012_mean');
-IMAGE_MEAN = d.image_mean;
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-
-% resize to fixed input size
-im = single(im);
-im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-% permute from RGB to BGR (IMAGE_MEAN is already BGR)
-im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-curr = 1;
-for i = indices
-  for j = indices
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-center = floor(indices(2) / 2)+1;
-images(:,:,:,5) = ...
-    permute(im(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:), ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
diff --git a/matlab/caffe/matcaffe_demo_vgg.m b/matlab/caffe/matcaffe_demo_vgg.m
deleted file mode 100644
index 4e5a98eb5f4..00000000000
--- a/matlab/caffe/matcaffe_demo_vgg.m
+++ /dev/null
@@ -1,96 +0,0 @@
-function scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
-%
-% Demo of the matlab wrapper using the networks described in the BMVC-2014 paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets"
-%
-% INPUT
-%   im - color image as uint8 HxWx3
-%   use_gpu - 1 to use the GPU, 0 to use the CPU
-%   model_def_file - network configuration (.prototxt file)
-%   model_file - network weights (.caffemodel file)
-%   mean_file - mean BGR image as uint8 HxWx3 (.mat file)
-%
-% OUTPUT
-%   scores   1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-%  model_def_file = 'zoo/VGG_CNN_F_deploy.prototxt';
-%  model_file = 'zoo/VGG_CNN_F.caffemodel';
-%  mean_file = 'zoo/VGG_mean.mat';
-%  use_gpu = true;
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file);
-% 
-% NOTES
-%  the image crops are prepared as described in the paper (the aspect ratio is preserved)
-%
-% PREREQUISITES
-%  You may need to do the following before you start matlab:
-%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-%  Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_file)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_file)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-d = load(mean_file);
-IMAGE_MEAN = d.image_mean;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
-    im = imresize(im, [IMAGE_DIM NaN]);
-else
-    im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
-  for j = indices_x
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :)-IMAGE_MEAN, [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-images(:,:,:,5) = ...
-    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:)-IMAGE_MEAN, ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
diff --git a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m b/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
deleted file mode 100644
index 5f7898a7029..00000000000
--- a/matlab/caffe/matcaffe_demo_vgg_mean_pix.m
+++ /dev/null
@@ -1,102 +0,0 @@
-function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
-% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
-%
-% Demo of the matlab wrapper based on the networks used for the "VGG" entry
-% in the ILSVRC-2014 competition and described in the tech. report 
-% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
-% http://arxiv.org/abs/1409.1556/
-%
-% INPUT
-%   im - color image as uint8 HxWx3
-%   use_gpu - 1 to use the GPU, 0 to use the CPU
-%   model_def_file - network configuration (.prototxt file)
-%   model_file - network weights (.caffemodel file)
-%
-% OUTPUT
-%   scores   1000-dimensional ILSVRC score vector
-%
-% EXAMPLE USAGE
-%  model_def_file = 'zoo/deploy.prototxt';
-%  model_file = 'zoo/model.caffemodel';
-%  use_gpu = true;
-%  im = imread('../../examples/images/cat.jpg');
-%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
-% 
-% NOTES
-%  mean pixel subtraction is used instead of the mean image subtraction
-%
-% PREREQUISITES
-%  You may need to do the following before you start matlab:
-%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64
-%   $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
-%  Or the equivalent based on where things are installed on your system
-
-% init caffe network (spews logging info)
-matcaffe_init(use_gpu, model_def_file, model_file);
-
-% mean BGR pixel
-mean_pix = [103.939, 116.779, 123.68];
-
-% prepare oversampled input
-% input_data is Height x Width x Channel x Num
-tic;
-input_data = {prepare_image(im, mean_pix)};
-toc;
-
-% do forward pass to get scores
-% scores are now Width x Height x Channels x Num
-tic;
-scores = caffe('forward', input_data);
-toc;
-
-scores = scores{1};
-% size(scores)
-scores = squeeze(scores);
-% scores = mean(scores,2);
-
-% [~,maxlabel] = max(scores);
-
-% ------------------------------------------------------------------------
-function images = prepare_image(im, mean_pix)
-% ------------------------------------------------------------------------
-IMAGE_DIM = 256;
-CROPPED_DIM = 224;
-
-% resize to fixed input size
-im = single(im);
-
-if size(im, 1) < size(im, 2)
-    im = imresize(im, [IMAGE_DIM NaN]);
-else
-    im = imresize(im, [NaN IMAGE_DIM]);
-end
-
-% RGB -> BGR
-im = im(:, :, [3 2 1]);
-
-% oversample (4 corners, center, and their x-axis flips)
-images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
-
-indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
-indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
-center_y = floor(indices_y(2) / 2)+1;
-center_x = floor(indices_x(2) / 2)+1;
-
-curr = 1;
-for i = indices_y
-  for j = indices_x
-    images(:, :, :, curr) = ...
-        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
-    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
-    curr = curr + 1;
-  end
-end
-images(:,:,:,5) = ...
-    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
-        [2 1 3]);
-images(:,:,:,10) = images(end:-1:1, :, :, curr);
-
-% mean BGR pixel subtraction
-for c = 1:3
-    images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
-end
diff --git a/matlab/caffe/matcaffe_init.m b/matlab/caffe/matcaffe_init.m
deleted file mode 100644
index 5d0a0a70bde..00000000000
--- a/matlab/caffe/matcaffe_init.m
+++ /dev/null
@@ -1,41 +0,0 @@
-function  matcaffe_init(use_gpu, model_def_file, model_file)
-% matcaffe_init(model_def_file, model_file, use_gpu)
-% Initilize matcaffe wrapper
-
-if nargin < 1
-  % By default use CPU
-  use_gpu = 0;
-end
-if nargin < 2 || isempty(model_def_file)
-  % By default use imagenet_deploy
-  model_def_file = '../../models/bvlc_reference_caffenet/deploy.prototxt';
-end
-if nargin < 3 || isempty(model_file)
-  % By default use caffe reference model
-  model_file = '../../models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel';
-end
-
-
-if caffe('is_initialized') == 0
-  if exist(model_file, 'file') == 0
-    % NOTE: you'll have to get the pre-trained ILSVRC network
-    error('You need a network model file');
-  end
-  if ~exist(model_def_file,'file')
-    % NOTE: you'll have to get network definition
-    error('You need the network prototxt definition');
-  end
-  % load network in TEST phase
-  caffe('init', model_def_file, model_file, 'test')
-end
-fprintf('Done with init\n');
-
-% set to use GPU or CPU
-if use_gpu
-  fprintf('Using GPU Mode\n');
-  caffe('set_mode_gpu');
-else
-  fprintf('Using CPU Mode\n');
-  caffe('set_mode_cpu');
-end
-fprintf('Done with set_mode\n');
diff --git a/matlab/caffe/prepare_batch.m b/matlab/caffe/prepare_batch.m
deleted file mode 100644
index 345c8eb5f0b..00000000000
--- a/matlab/caffe/prepare_batch.m
+++ /dev/null
@@ -1,41 +0,0 @@
-% ------------------------------------------------------------------------
-function images = prepare_batch(image_files,IMAGE_MEAN,batch_size)
-% ------------------------------------------------------------------------
-if nargin < 2
-    d = load('ilsvrc_2012_mean');
-    IMAGE_MEAN = d.image_mean; 
-end
-num_images = length(image_files);
-if nargin < 3
-    batch_size = num_images;
-end
-
-IMAGE_DIM = 256;
-CROPPED_DIM = 227;
-indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
-center = floor(indices(2) / 2)+1;
-
-num_images = length(image_files);
-images = zeros(CROPPED_DIM,CROPPED_DIM,3,batch_size,'single');
-
-parfor i=1:num_images
-    % read file
-    fprintf('%c Preparing %s\n',13,image_files{i});
-    try
-        im = imread(image_files{i});
-        % resize to fixed input size
-        im = single(im);
-        im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
-        % Transform GRAY to RGB
-        if size(im,3) == 1
-            im = cat(3,im,im,im);
-        end
-        % permute from RGB to BGR (IMAGE_MEAN is already BGR)
-        im = im(:,:,[3 2 1]) - IMAGE_MEAN;
-        % Crop the center of the image
-        images(:,:,:,i) = permute(im(center:center+CROPPED_DIM-1,...
-            center:center+CROPPED_DIM-1,:),[2 1 3]);
-    catch
-        warning('Problems with file',image_files{i});
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/print_cell.m b/matlab/caffe/print_cell.m
deleted file mode 100644
index 864340d4be9..00000000000
--- a/matlab/caffe/print_cell.m
+++ /dev/null
@@ -1,42 +0,0 @@
-function res=print_cell(input,file,linesep,cellsep)
-assert(iscell(input),'The input should be a cell')
-if nargin < 4
-    cellsep = '\t';
-end
-if nargin < 3
-    linesep = '\n';
-end
-if exist('file','var') && ~isempty(file)
-    %%
-    fid = fopen(file,'w');
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                fprintf(fid,['%s' cellsep],input{l}{i});
-            end
-            fprintf(fid,linesep);
-        else
-            if size(input,2) > 1
-                for i=1:size(input,2)
-                    fprintf(fid,'%s ',input{l,i});
-                end
-                fprintf(fid,linesep);
-            else
-                fprintf(fid,['%s' linesep],input{l});
-            end
-        end
-    end
-    fclose(fid);
-else
-    res = '';
-    for l=1:length(input)
-        if iscell(input{l})
-            for i=1:length(input{l})
-                res = [res sprintf([cellsep{1} '%s' cellsep{2}],input{l}{i})];
-            end
-            res = [res sprintf(linesep)];
-        else
-            res = [res sprintf(['%s' linesep],input{l}(:))];
-        end
-    end
-end
\ No newline at end of file
diff --git a/matlab/caffe/read_cell.m b/matlab/caffe/read_cell.m
deleted file mode 100644
index 19831167106..00000000000
--- a/matlab/caffe/read_cell.m
+++ /dev/null
@@ -1,21 +0,0 @@
-function res=read_cell(filename,linesep,cellsep)
-if nargin < 2, linesep='\n'; end
-if nargin < 3, cellsep = '\t'; end
-if exist(filename,'file')
-    fid = fopen(filename);
-else
-    % Assume that filename is either a file ide or a string
-    fid = filename;
-end
-
-fileLines = textscan(fid,'%s','delimiter',linesep,'BufSize',100000);
-
-fileLines = fileLines{1};
-
-if regexp(fileLines{1},cellsep,'once')
-    fileLines = regexprep(fileLines,['^' cellsep '|' cellsep '$'],'');
-    res = regexp(fileLines,cellsep,'split');
-    res = cell2matcell(res);
-else
-    res = fileLines;
-end
diff --git a/matlab/demo/classification_demo.m b/matlab/demo/classification_demo.m
new file mode 100644
index 00000000000..2b60332970b
--- /dev/null
+++ b/matlab/demo/classification_demo.m
@@ -0,0 +1,147 @@
+function [scores, maxlabel] = classification_demo(im, use_gpu)
+% [scores, maxlabel] = classification_demo(im, use_gpu)
+%
+% Image classification demo using BVLC CaffeNet.
+%
+% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
+% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
+%
+% ****************************************************************************
+% For detailed documentation and usage on Caffe's Matlab interface, please
+% refer to Caffe Interface Tutorial at
+% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab
+% ****************************************************************************
+%
+% input
+%   im       color image as uint8 HxWx3
+%   use_gpu  1 to use the GPU, 0 to use the CPU
+%
+% output
+%   scores   1000-dimensional ILSVRC score vector
+%   maxlabel the label of the highest score
+%
+% You may need to do the following before you start matlab:
+%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64
+%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6
+% Or the equivalent based on where things are installed on your system
+%
+% Usage:
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = classification_demo(im, 1);
+%  [score, class] = max(scores);
+% Five things to be aware of:
+%   caffe uses row-major order
+%   matlab uses column-major order
+%   caffe uses BGR color channel order
+%   matlab uses RGB color channel order
+%   images need to have the data mean subtracted
+
+% Data coming in from matlab needs to be in the order
+%   [width, height, channels, images]
+% where width is the fastest dimension.
+% Here is the rough matlab for putting image data into the correct
+% format in W x H x C with BGR channels:
+%   % permute channels from RGB to BGR
+%   im_data = im(:, :, [3, 2, 1]);
+%   % flip width and height to make width the fastest dimension
+%   im_data = permute(im_data, [2, 1, 3]);
+%   % convert from uint8 to single
+%   im_data = single(im_data);
+%   % reshape to a fixed size (e.g., 227x227).
+%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % subtract mean_data (already in W x H x C with BGR channels)
+%   im_data = im_data - mean_data;
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% Add caffe/matlab to you Matlab search PATH to use matcaffe
+if exist('../+caffe', 'dir')
+  addpath('..');
+else
+  error('Please run this demo from caffe/matlab/demo');
+end
+
+% Set caffe mode
+if exist('use_gpu', 'var') && use_gpu
+  caffe.set_mode_gpu();
+  gpu_id = 0;  % we will use the first gpu in this demo
+  caffe.set_device(gpu_id);
+else
+  caffe.set_mode_cpu();
+end
+
+% Initialize the network using BVLC CaffeNet for image classification
+% Weights (parameter) file needs to be downloaded from Model Zoo.
+model_dir = '../../models/bvlc_reference_caffenet/';
+net_model = [model_dir 'deploy.prototxt'];
+net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];
+phase = 'test'; % run with phase test (so that dropout isn't applied)
+if ~exist(net_weights, 'file')
+  error('Please download CaffeNet from Model Zoo before you run this demo');
+end
+
+% Initialize a network
+net = caffe.Net(net_model, net_weights, phase);
+
+if nargin < 1
+  % For demo purposes we will use the cat image
+  fprintf('using caffe/examples/images/cat.jpg as input image\n');
+  im = imread('../../examples/images/cat.jpg');
+end
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im)};
+toc;
+
+% do forward pass to get scores
+% scores are now Channels x Num, where Channels == 1000
+tic;
+% The net forward function. It takes in a cell array of N-D arrays
+% (where N == 4 here) containing data of input blob(s) and outputs a cell
+% array containing data from output blob(s)
+scores = net.forward(input_data);
+toc;
+
+scores = scores{1};
+scores = mean(scores, 2);  % take average scores over 10 crops
+
+[~, maxlabel] = max(scores);
+
+% call caffe.reset_all() to reset caffe
+caffe.reset_all();
+
+% ------------------------------------------------------------------------
+function crops_data = prepare_image(im)
+% ------------------------------------------------------------------------
+% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that
+% is already in W x H x C with BGR channels
+d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');
+mean_data = d.mean_data;
+IMAGE_DIM = 256;
+CROPPED_DIM = 227;
+
+% Convert an image returned by Matlab's imread to im_data in caffe's data
+% format: W x H x C with BGR channels
+im_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR
+im_data = permute(im_data, [2, 1, 3]);  % flip width and height
+im_data = single(im_data);  % convert from uint8 to single
+im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data
+im_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)
+
+% oversample (4 corners, center, and their x-axis flips)
+crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;
+n = 1;
+for i = indices
+  for j = indices
+    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);
+    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);
+    n = n + 1;
+  end
+end
+center = floor(indices(2) / 2) + 1;
+crops_data(:,:,:,5) = ...
+  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);
+crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);
diff --git a/matlab/caffe/hdf5creation/.gitignore b/matlab/hdf5creation/.gitignore
similarity index 100%
rename from matlab/caffe/hdf5creation/.gitignore
rename to matlab/hdf5creation/.gitignore
diff --git a/matlab/caffe/hdf5creation/demo.m b/matlab/hdf5creation/demo.m
similarity index 98%
rename from matlab/caffe/hdf5creation/demo.m
rename to matlab/hdf5creation/demo.m
index f554b87e5f6..4f9f7b5a454 100644
--- a/matlab/caffe/hdf5creation/demo.m
+++ b/matlab/hdf5creation/demo.m
@@ -52,9 +52,9 @@
 fprintf('HDF5 filename listed in %s \n', 'list.txt');
 
 % NOTE: In net definition prototxt, use list.txt as input to HDF5_DATA as: 
-% layers {
+% layer {
 %   name: "data"
-%   type: HDF5_DATA
+%   type: "HDF5Data"
 %   top: "data"
 %   top: "labelvec"
 %   hdf5_data_param {
diff --git a/matlab/caffe/hdf5creation/store2hdf5.m b/matlab/hdf5creation/store2hdf5.m
similarity index 91%
rename from matlab/caffe/hdf5creation/store2hdf5.m
rename to matlab/hdf5creation/store2hdf5.m
index 0a0016dca40..4e8c81d9de8 100644
--- a/matlab/caffe/hdf5creation/store2hdf5.m
+++ b/matlab/hdf5creation/store2hdf5.m
@@ -39,8 +39,8 @@
       info=h5info(filename);
       prev_dat_sz=info.Datasets(1).Dataspace.Size;
       prev_lab_sz=info.Datasets(2).Dataspace.Size;
-      assert(prev_dat_sz(1:end-1)==dat_dims(1:end-1), 'Data dimensions must match existing dimensions in dataset');
-      assert(prev_lab_sz(1:end-1)==lab_dims(1:end-1), 'Label dimensions must match existing dimensions in dataset');
+      assert(all(prev_dat_sz(1:end-1)==dat_dims(1:end-1)), 'Data dimensions must match existing dimensions in dataset');
+      assert(all(prev_lab_sz(1:end-1)==lab_dims(1:end-1)), 'Label dimensions must match existing dimensions in dataset');
       startloc.dat=[ones(1,length(dat_dims)-1), prev_dat_sz(end)+1];
       startloc.lab=[ones(1,length(lab_dims)-1), prev_lab_sz(end)+1];
     end
diff --git a/models/bvlc_alexnet/deploy.prototxt b/models/bvlc_alexnet/deploy.prototxt
index ced055b85d0..45b2b0e361a 100644
--- a/models/bvlc_alexnet/deploy.prototxt
+++ b/models/bvlc_alexnet/deploy.prototxt
@@ -1,9 +1,10 @@
 name: "AlexNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/bvlc_googlenet/deploy.prototxt b/models/bvlc_googlenet/deploy.prototxt
index 4648bf26efc..50b54a9f3c1 100644
--- a/models/bvlc_googlenet/deploy.prototxt
+++ b/models/bvlc_googlenet/deploy.prototxt
@@ -1,9 +1,10 @@
 name: "GoogleNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 224
-input_dim: 224
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 224 dim: 224 } }
+}
 layer {
   name: "conv1/7x7_s2"
   type: "Convolution"
diff --git a/models/bvlc_googlenet/train_val.prototxt b/models/bvlc_googlenet/train_val.prototxt
index 79ede2b9d9c..5dee3abe28f 100644
--- a/models/bvlc_googlenet/train_val.prototxt
+++ b/models/bvlc_googlenet/train_val.prototxt
@@ -61,7 +61,6 @@ layer {
     stride: 2
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -115,7 +114,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -148,7 +146,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -202,7 +199,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -234,7 +230,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -267,7 +262,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -299,7 +293,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -332,7 +325,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -376,7 +368,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -417,7 +408,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -449,7 +439,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -482,7 +471,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -514,7 +502,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -547,7 +534,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -591,7 +577,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -643,7 +628,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -675,7 +659,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -708,7 +691,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -740,7 +722,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -773,7 +754,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -817,7 +797,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -869,7 +848,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.08
     }
     bias_filler {
       type: "constant"
@@ -900,7 +878,6 @@ layer {
     num_output: 1024
     weight_filler {
       type: "xavier"
-      std: 0.02
     }
     bias_filler {
       type: "constant"
@@ -940,7 +917,6 @@ layer {
     num_output: 1000
     weight_filler {
       type: "xavier"
-      std: 0.0009765625
     }
     bias_filler {
       type: "constant"
@@ -997,7 +973,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1029,7 +1004,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1062,7 +1036,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1094,7 +1067,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1127,7 +1099,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1171,7 +1142,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1212,7 +1182,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1244,7 +1213,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1277,7 +1245,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1309,7 +1276,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1342,7 +1308,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1386,7 +1351,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1427,7 +1391,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1459,7 +1422,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1492,7 +1454,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1524,7 +1485,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1557,7 +1517,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1601,7 +1560,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -1653,7 +1611,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.08
     }
     bias_filler {
       type: "constant"
@@ -1684,7 +1641,6 @@ layer {
     num_output: 1024
     weight_filler {
       type: "xavier"
-      std: 0.02
     }
     bias_filler {
       type: "constant"
@@ -1724,7 +1680,6 @@ layer {
     num_output: 1000
     weight_filler {
       type: "xavier"
-      std: 0.0009765625
     }
     bias_filler {
       type: "constant"
@@ -1781,7 +1736,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1813,7 +1767,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -1846,7 +1799,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1878,7 +1830,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -1911,7 +1862,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -1955,7 +1905,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -2007,7 +1956,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2039,7 +1987,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -2072,7 +2019,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2104,7 +2050,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -2137,7 +2082,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2181,7 +2125,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
@@ -2222,7 +2165,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2254,7 +2196,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.09
     }
     bias_filler {
       type: "constant"
@@ -2287,7 +2228,6 @@ layer {
     kernel_size: 3
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2319,7 +2259,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.2
     }
     bias_filler {
       type: "constant"
@@ -2352,7 +2291,6 @@ layer {
     kernel_size: 5
     weight_filler {
       type: "xavier"
-      std: 0.03
     }
     bias_filler {
       type: "constant"
@@ -2396,7 +2334,6 @@ layer {
     kernel_size: 1
     weight_filler {
       type: "xavier"
-      std: 0.1
     }
     bias_filler {
       type: "constant"
diff --git a/models/bvlc_reference_caffenet/deploy.prototxt b/models/bvlc_reference_caffenet/deploy.prototxt
index 29ccf1469f7..907116ef91c 100644
--- a/models/bvlc_reference_caffenet/deploy.prototxt
+++ b/models/bvlc_reference_caffenet/deploy.prototxt
@@ -1,9 +1,10 @@
 name: "CaffeNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/bvlc_reference_caffenet/train_val.prototxt b/models/bvlc_reference_caffenet/train_val.prototxt
index c79472e09ab..e3e427968ab 100644
--- a/models/bvlc_reference_caffenet/train_val.prototxt
+++ b/models/bvlc_reference_caffenet/train_val.prototxt
@@ -45,7 +45,7 @@ layer {
 #    mean_value: 104
 #    mean_value: 117
 #    mean_value: 123
-#    mirror: true
+#    mirror: false
 #  }
   data_param {
     source: "examples/imagenet/ilsvrc12_val_lmdb"
diff --git a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
index ea9cf98a926..e330a770676 100644
--- a/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
+++ b/models/bvlc_reference_rcnn_ilsvrc13/deploy.prototxt
@@ -1,9 +1,10 @@
 name: "R-CNN-ilsvrc13"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/finetune_flickr_style/deploy.prototxt b/models/finetune_flickr_style/deploy.prototxt
index 4a924f74927..b8f99c74453 100644
--- a/models/finetune_flickr_style/deploy.prototxt
+++ b/models/finetune_flickr_style/deploy.prototxt
@@ -1,9 +1,10 @@
 name: "FlickrStyleCaffeNet"
-input: "data"
-input_dim: 10
-input_dim: 3
-input_dim: 227
-input_dim: 227
+layer {
+  name: "data"
+  type: "Input"
+  top: "data"
+  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
+}
 layer {
   name: "conv1"
   type: "Convolution"
diff --git a/models/finetune_flickr_style/train_val.prototxt b/models/finetune_flickr_style/train_val.prototxt
index aa9c73e17ce..985353be369 100644
--- a/models/finetune_flickr_style/train_val.prototxt
+++ b/models/finetune_flickr_style/train_val.prototxt
@@ -369,12 +369,6 @@ layer {
     }
   }
 }
-layer {
-  name: "loss"
-  type: "SoftmaxWithLoss"
-  bottom: "fc8_flickr"
-  bottom: "label"
-}
 layer {
   name: "accuracy"
   type: "Accuracy"
@@ -385,3 +379,10 @@ layer {
     phase: TEST
   }
 }
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "fc8_flickr"
+  bottom: "label"
+  top: "loss"
+}
diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt
index df0401daa1c..a22641401f0 100644
--- a/python/CMakeLists.txt
+++ b/python/CMakeLists.txt
@@ -1,5 +1,5 @@
 if(NOT HAVE_PYTHON)
-  message(STATUS "Python interface is disabled or not all required dependecies found. Building without it...")
+  message(STATUS "Python interface is disabled or not all required dependencies found. Building without it...")
   return()
 endif()
 
@@ -18,7 +18,7 @@ if(UNIX OR APPLE)
                        COMMAND ${CMAKE_COMMAND} -E make_directory ${PROJECT_SOURCE_DIR}/python/caffe/proto
                        COMMAND touch ${PROJECT_SOURCE_DIR}/python/caffe/proto/__init__.py
                        COMMAND cp ${proto_gen_folder}/*.py ${PROJECT_SOURCE_DIR}/python/caffe/proto/
-                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${CAffe_POSTFIX}.so")
+                       COMMENT "Creating symlink ${__linkname} -> ${PROJECT_BINARY_DIR}/lib/_caffe${Caffe_POSTFIX}.so")
 endif()
 
 # ---[ Install
diff --git a/python/caffe/__init__.py b/python/caffe/__init__.py
index 37e8956da4f..e2881b89c1b 100644
--- a/python/caffe/__init__.py
+++ b/python/caffe/__init__.py
@@ -1,6 +1,8 @@
-from .pycaffe import Net, SGDSolver
-from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver
+from .pycaffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, RMSPropSolver, AdaDeltaSolver, AdamSolver
+from ._caffe import set_mode_cpu, set_mode_gpu, set_device, Layer, get_solver, layer_type_list
+from ._caffe import __version__
 from .proto.caffe_pb2 import TRAIN, TEST
 from .classifier import Classifier
 from .detector import Detector
-import io
+from . import io
+from .net_spec import layers, params, NetSpec, to_proto
diff --git a/python/caffe/_caffe.cpp b/python/caffe/_caffe.cpp
index dff7f627016..a2c46a123aa 100644
--- a/python/caffe/_caffe.cpp
+++ b/python/caffe/_caffe.cpp
@@ -15,7 +15,9 @@
 #include <fstream>  // NOLINT
 
 #include "caffe/caffe.hpp"
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
+#include "caffe/sgd_solvers.hpp"
 
 // Temporary solution for numpy < 1.7 versions: old macro, no promises.
 // You're strongly advised to upgrade to >= 1.7.
@@ -133,8 +135,8 @@ void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
 
 Solver<Dtype>* GetSolverFromFile(const string& filename) {
   SolverParameter param;
-  ReadProtoFromTextFileOrDie(filename, &param);
-  return GetSolver<Dtype>(param);
+  ReadSolverParamsFromTextFileOrDie(filename, &param);
+  return SolverRegistry<Dtype>::CreateSolver(param);
 }
 
 struct NdarrayConverterGenerator {
@@ -190,16 +192,36 @@ bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
   return bp::object();
 }
 
+bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
+  if (bp::len(kwargs) > 0) {
+    throw std::runtime_error("BlobVec.add_blob takes no kwargs");
+  }
+  typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
+  BlobVec* self = bp::extract<BlobVec*>(args[0]);
+  vector<int> shape(bp::len(args) - 1);
+  for (int i = 1; i < bp::len(args); ++i) {
+    shape[i - 1] = bp::extract<int>(args[i]);
+  }
+  self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  // We need to explicitly return None to use bp::raw_function.
+  return bp::object();
+}
+
 BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
 
 BOOST_PYTHON_MODULE(_caffe) {
   // below, we prepend an underscore to methods that will be replaced
   // in Python
+
+  bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
+
   // Caffe utility functions
   bp::def("set_mode_cpu", &set_mode_cpu);
   bp::def("set_mode_gpu", &set_mode_gpu);
   bp::def("set_device", &Caffe::SetDevice);
 
+  bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
+
   bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
     bp::no_init)
     .def("__init__", bp::make_constructor(&Net_Init))
@@ -211,6 +233,12 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
         &Net<Dtype>::CopyTrainedLayersFrom))
     .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
+    .add_property("_blob_loss_weights", bp::make_function(
+        &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
+    .def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,
+        bp::return_value_policy<bp::copy_const_reference>()))
+    .def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,
+        bp::return_value_policy<bp::copy_const_reference>()))
     .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
         bp::return_internal_reference<>()))
     .add_property("layers", bp::make_function(&Net<Dtype>::layers,
@@ -227,9 +255,15 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("_set_input_arrays", &Net_SetInputArrays,
         bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
     .def("save", &Net_Save);
+  bp::register_ptr_to_python<shared_ptr<Net<Dtype> > >();
 
   bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
     "Blob", bp::no_init)
+    .add_property("shape",
+        bp::make_function(
+            static_cast<const vector<int>& (Blob<Dtype>::*)() const>(
+                &Blob<Dtype>::shape),
+            bp::return_value_policy<bp::copy_const_reference>()))
     .add_property("num",      &Blob<Dtype>::num)
     .add_property("channels", &Blob<Dtype>::channels)
     .add_property("height",   &Blob<Dtype>::height)
@@ -241,6 +275,7 @@ BOOST_PYTHON_MODULE(_caffe) {
           NdarrayCallPolicies()))
     .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
           NdarrayCallPolicies()));
+  bp::register_ptr_to_python<shared_ptr<Blob<Dtype> > >();
 
   bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
     boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
@@ -262,7 +297,9 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
           &Solver<Dtype>::Solve), SolveOverloads())
     .def("step", &Solver<Dtype>::Step)
-    .def("restore", &Solver<Dtype>::Restore);
+    .def("restore", &Solver<Dtype>::Restore)
+    .def("snapshot", &Solver<Dtype>::Snapshot);
+  bp::register_ptr_to_python<shared_ptr<Solver<Dtype> > >();
 
   bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
@@ -273,13 +310,23 @@ BOOST_PYTHON_MODULE(_caffe) {
   bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
     shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
         "AdaGradSolver", bp::init<string>());
+  bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
+        "RMSPropSolver", bp::init<string>());
+  bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
+        "AdaDeltaSolver", bp::init<string>());
+  bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
+    shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
+        "AdamSolver", bp::init<string>());
 
   bp::def("get_solver", &GetSolverFromFile,
       bp::return_value_policy<bp::manage_new_object>());
 
   // vector wrappers for all the vector types we use
   bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
-    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>());
+    .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
+    .def("add_blob", bp::raw_function(&BlobVec_add_blob));
   bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
     .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
   bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
@@ -288,6 +335,8 @@ BOOST_PYTHON_MODULE(_caffe) {
     .def(bp::vector_indexing_suite<vector<string> >());
   bp::class_<vector<int> >("IntVec")
     .def(bp::vector_indexing_suite<vector<int> >());
+  bp::class_<vector<Dtype> >("DtypeVec")
+    .def(bp::vector_indexing_suite<vector<Dtype> >());
   bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
     .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
   bp::class_<vector<bool> >("BoolVec")
diff --git a/python/caffe/classifier.py b/python/caffe/classifier.py
index 49f8003ce9d..537193db8f8 100644
--- a/python/caffe/classifier.py
+++ b/python/caffe/classifier.py
@@ -12,24 +12,24 @@ class Classifier(caffe.Net):
     """
     Classifier extends Net for image class prediction
     by scaling, center cropping, or oversampling.
+
+    Parameters
+    ----------
+    image_dims : dimensions to scale input for cropping/sampling.
+        Default is to scale to net input size for whole-image crop.
+    mean, input_scale, raw_scale, channel_swap: params for
+        preprocessing options.
     """
     def __init__(self, model_file, pretrained_file, image_dims=None,
                  mean=None, input_scale=None, raw_scale=None,
                  channel_swap=None):
-        """
-        Take
-        image_dims: dimensions to scale input for cropping/sampling.
-            Default is to scale to net input size for whole-image crop.
-            mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        """
         caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
         # configure pre-processing
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
             {in_: self.blobs[in_].data.shape})
-        self.transformer.set_transpose(in_, (2,0,1))
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)
         if input_scale is not None:
@@ -44,24 +44,28 @@ def __init__(self, model_file, pretrained_file, image_dims=None,
             image_dims = self.crop_dims
         self.image_dims = image_dims
 
-
     def predict(self, inputs, oversample=True):
         """
         Predict classification probabilities of inputs.
 
-        Take
-        inputs: iterable of (H x W x K) input ndarrays.
-        oversample: average predictions across center, corners, and mirrors
-                    when True (default). Center-only prediction when False.
+        Parameters
+        ----------
+        inputs : iterable of (H x W x K) input ndarrays.
+        oversample : boolean
+            average predictions across center, corners, and mirrors
+            when True (default). Center-only prediction when False.
 
-        Give
-        predictions: (N x C) ndarray of class probabilities
-                     for N images and C classes.
+        Returns
+        -------
+        predictions: (N x C) ndarray of class probabilities for N images and C
+            classes.
         """
         # Scale to standardize input dimensions.
         input_ = np.zeros((len(inputs),
-            self.image_dims[0], self.image_dims[1], inputs[0].shape[2]),
-            dtype=np.float32)
+                           self.image_dims[0],
+                           self.image_dims[1],
+                           inputs[0].shape[2]),
+                          dtype=np.float32)
         for ix, in_ in enumerate(inputs):
             input_[ix] = caffe.io.resize_image(in_, self.image_dims)
 
@@ -78,7 +82,7 @@ def predict(self, inputs, oversample=True):
             input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
 
         # Classify
-        caffe_in = np.zeros(np.array(input_.shape)[[0,3,1,2]],
+        caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
                             dtype=np.float32)
         for ix, in_ in enumerate(input_):
             caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
diff --git a/python/caffe/coord_map.py b/python/caffe/coord_map.py
new file mode 100644
index 00000000000..a3413cfa855
--- /dev/null
+++ b/python/caffe/coord_map.py
@@ -0,0 +1,185 @@
+"""
+Determine spatial relationships between layers to relate their coordinates.
+Coordinates are mapped from input-to-output (forward), but can
+be mapped output-to-input (backward) by the inverse mapping too.
+This helps crop and align feature maps among other uses.
+"""
+
+from __future__ import division
+import numpy as np
+from caffe import layers as L
+
+PASS_THROUGH_LAYERS = ['AbsVal', 'BatchNorm', 'Bias', 'BNLL', 'Dropout',
+                       'Eltwise', 'ELU', 'Log', 'LRN', 'Exp', 'MVN', 'Power',
+                       'ReLU', 'PReLU', 'Scale', 'Sigmoid', 'Split', 'TanH',
+                       'Threshold']
+
+
+def conv_params(fn):
+    """
+    Extract the spatial parameters that determine the coordinate mapping:
+    kernel size, stride, padding, and dilation.
+
+    Implementation detail: Convolution, Deconvolution, and Im2col layers
+    define these in the convolution_param message, while Pooling has its
+    own fields in pooling_param. This method deals with these details to
+    extract canonical parameters.
+    """
+    params = fn.params.get('convolution_param', fn.params)
+    axis = params.get('axis', 1)
+    ks = np.array(params['kernel_size'], ndmin=1)
+    dilation = np.array(params.get('dilation', 1), ndmin=1)
+    assert len({'pad_h', 'pad_w', 'kernel_h', 'kernel_w', 'stride_h',
+                'stride_w'} & set(fn.params)) == 0, \
+        'cropping does not support legacy _h/_w params'
+    return (axis, np.array(params.get('stride', 1), ndmin=1),
+            (ks - 1) * dilation + 1,
+            np.array(params.get('pad', 0), ndmin=1))
+
+
+def crop_params(fn):
+    """
+    Extract the crop layer parameters with defaults.
+    """
+    params = fn.params.get('crop_param', fn.params)
+    axis = params.get('axis', 2)  # default to spatial crop for N, C, H, W
+    offset = np.array(params.get('offset', 0), ndmin=1)
+    return (axis, offset)
+
+
+class UndefinedMapException(Exception):
+    """
+    Exception raised for layers that do not have a defined coordinate mapping.
+    """
+    pass
+
+
+def coord_map(fn):
+    """
+    Define the coordinate mapping by its
+    - axis
+    - scale: output coord[i * scale] <- input_coord[i]
+    - shift: output coord[i] <- output_coord[i + shift]
+    s.t. the identity mapping, as for pointwise layers like ReLu, is defined by
+    (None, 1, 0) since it is independent of axis and does not transform coords.
+    """
+    if fn.type_name in ['Convolution', 'Pooling', 'Im2col']:
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, 1 / stride, (pad - (ks - 1) / 2) / stride
+    elif fn.type_name == 'Deconvolution':
+        axis, stride, ks, pad = conv_params(fn)
+        return axis, stride, (ks - 1) / 2 - pad
+    elif fn.type_name in PASS_THROUGH_LAYERS:
+        return None, 1, 0
+    elif fn.type_name == 'Crop':
+        axis, offset = crop_params(fn)
+        axis -= 1  # -1 for last non-coordinate dim.
+        return axis, 1, - offset
+    else:
+        raise UndefinedMapException
+
+
+class AxisMismatchException(Exception):
+    """
+    Exception raised for mappings with incompatible axes.
+    """
+    pass
+
+
+def compose(base_map, next_map):
+    """
+    Compose a base coord map with scale a1, shift b1 with a further coord map
+    with scale a2, shift b2. The scales multiply and the further shift, b2,
+    is scaled by base coord scale a1.
+    """
+    ax1, a1, b1 = base_map
+    ax2, a2, b2 = next_map
+    if ax1 is None:
+        ax = ax2
+    elif ax2 is None or ax1 == ax2:
+        ax = ax1
+    else:
+        raise AxisMismatchException
+    return ax, a1 * a2, a1 * b2 + b1
+
+
+def inverse(coord_map):
+    """
+    Invert a coord map by de-scaling and un-shifting;
+    this gives the backward mapping for the gradient.
+    """
+    ax, a, b = coord_map
+    return ax, 1 / a, -b / a
+
+
+def coord_map_from_to(top_from, top_to):
+    """
+    Determine the coordinate mapping betweeen a top (from) and a top (to).
+    Walk the graph to find a common ancestor while composing the coord maps for
+    from and to until they meet. As a last step the from map is inverted.
+    """
+    # We need to find a common ancestor of top_from and top_to.
+    # We'll assume that all ancestors are equivalent here (otherwise the graph
+    # is an inconsistent state (which we could improve this to check for)).
+    # For now use a brute-force algorithm.
+
+    def collect_bottoms(top):
+        """
+        Collect the bottoms to walk for the coordinate mapping.
+        The general rule is that all the bottoms of a layer can be mapped, as
+        most layers have the same coordinate mapping for each bottom.
+        Crop layer is a notable exception. Only the first/cropped bottom is
+        mappable; the second/dimensions bottom is excluded from the walk.
+        """
+        bottoms = top.fn.inputs
+        if top.fn.type_name == 'Crop':
+            bottoms = bottoms[:1]
+        return bottoms
+
+    # walk back from top_from, keeping the coord map as we go
+    from_maps = {top_from: (None, 1, 0)}
+    frontier = {top_from}
+    while frontier:
+        top = frontier.pop()
+        try:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
+                from_maps[bottom] = compose(from_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            pass
+
+    # now walk back from top_to until we hit a common blob
+    to_maps = {top_to: (None, 1, 0)}
+    frontier = {top_to}
+    while frontier:
+        top = frontier.pop()
+        if top in from_maps:
+            return compose(to_maps[top], inverse(from_maps[top]))
+        try:
+            bottoms = collect_bottoms(top)
+            for bottom in bottoms:
+                to_maps[bottom] = compose(to_maps[top], coord_map(top.fn))
+                frontier.add(bottom)
+        except UndefinedMapException:
+            continue
+
+    # if we got here, we did not find a blob in common
+    raise RuntimeError('Could not compute map between tops; are they '
+                       'connected by spatial layers?')
+
+
+def crop(top_from, top_to):
+    """
+    Define a Crop layer to crop a top (from) to another top (to) by
+    determining the coordinate mapping between the two and net spec'ing
+    the axis and shift parameters of the crop.
+    """
+    ax, a, b = coord_map_from_to(top_from, top_to)
+    assert (a == 1).all(), 'scale mismatch on crop (a = {})'.format(a)
+    assert (b <= 0).all(), 'cannot crop negative offset (b = {})'.format(b)
+    assert (np.round(b) == b).all(), 'cannot crop noninteger offset ' \
+        '(b = {})'.format(b)
+    return L.Crop(top_from, top_to,
+                  crop_param=dict(axis=ax + 1,  # +1 for first cropping dim.
+                                  offset=list(-np.round(b).astype(int))))
diff --git a/python/caffe/detector.py b/python/caffe/detector.py
index a67b818b93f..75cd3b1202f 100644
--- a/python/caffe/detector.py
+++ b/python/caffe/detector.py
@@ -23,25 +23,25 @@ class Detector(caffe.Net):
     """
     Detector extends Net for windowed detection by a list of crops or
     selective search proposals.
+
+    Parameters
+    ----------
+    mean, input_scale, raw_scale, channel_swap : params for preprocessing
+        options.
+    context_pad : amount of surrounding context to take s.t. a `context_pad`
+        sized border of pixels in the network input image is context, as in
+        R-CNN feature extraction.
     """
     def __init__(self, model_file, pretrained_file, mean=None,
                  input_scale=None, raw_scale=None, channel_swap=None,
                  context_pad=None):
-        """
-        Take
-            mean, input_scale, raw_scale, channel_swap: params for
-            preprocessing options.
-        context_pad: amount of surrounding context to take s.t. a `context_pad`
-            sized border of pixels in the network input image is context, as in
-            R-CNN feature extraction.
-        """
         caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
 
         # configure pre-processing
         in_ = self.inputs[0]
         self.transformer = caffe.io.Transformer(
             {in_: self.blobs[in_].data.shape})
-        self.transformer.set_transpose(in_, (2,0,1))
+        self.transformer.set_transpose(in_, (2, 0, 1))
         if mean is not None:
             self.transformer.set_mean(in_, mean)
         if input_scale is not None:
@@ -53,17 +53,18 @@ def __init__(self, model_file, pretrained_file, mean=None,
 
         self.configure_crop(context_pad)
 
-
     def detect_windows(self, images_windows):
         """
         Do windowed detection over given images and windows. Windows are
         extracted then warped to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         images_windows: (image filename, window list) iterable.
         context_crop: size of context border to crop in pixels.
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -82,7 +83,7 @@ def detect_windows(self, images_windows):
         for ix, window_in in enumerate(window_inputs):
             caffe_in[ix] = self.transformer.preprocess(in_, window_in)
         out = self.forward_all(**{in_: caffe_in})
-        predictions = out[self.outputs[0]].squeeze(axis=(2,3))
+        predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
 
         # Package predictions with images and windows.
         detections = []
@@ -97,16 +98,17 @@ def detect_windows(self, images_windows):
                 ix += 1
         return detections
 
-
     def detect_selective_search(self, image_fnames):
         """
         Do windowed detection over Selective Search proposals by extracting
         the crop and warping to the input dimensions of the net.
 
-        Take
+        Parameters
+        ----------
         image_fnames: list
 
-        Give
+        Returns
+        -------
         detections: list of {filename: image filename, window: crop coordinates,
             predictions: prediction vector} dicts.
         """
@@ -120,17 +122,18 @@ def detect_selective_search(self, image_fnames):
         # Run windowed detection on the selective search list.
         return self.detect_windows(zip(image_fnames, windows_list))
 
-
     def crop(self, im, window):
         """
         Crop a window from the image for detection. Include surrounding context
         according to the `context_pad` configuration.
 
-        Take
+        Parameters
+        ----------
         im: H x W x K image ndarray to crop.
         window: bounding box coordinates as ymin, xmin, ymax, xmax.
 
-        Give
+        Returns
+        -------
         crop: cropped window.
         """
         # Crop window from the image.
@@ -175,14 +178,14 @@ def crop(self, im, window):
 
         return crop
 
-
     def configure_crop(self, context_pad):
         """
         Configure crop dimensions and amount of context for cropping.
         If context is included, make the special input mean for context padding.
 
-        Take
-        context_pad: amount of context for cropping.
+        Parameters
+        ----------
+        context_pad : amount of context for cropping.
         """
         # crop dimensions
         in_ = self.inputs[0]
@@ -204,8 +207,8 @@ def configure_crop(self, context_pad):
                 crop_mean = mean.copy().transpose(inv_transpose)
                 if channel_order is not None:
                     channel_order_inverse = [channel_order.index(i)
-                                            for i in range(crop_mean.shape[2])]
-                    crop_mean = crop_mean[:,:, channel_order_inverse]
+                                             for i in range(crop_mean.shape[2])]
+                    crop_mean = crop_mean[:, :, channel_order_inverse]
                 if raw_scale is not None:
                     crop_mean /= raw_scale
                 self.crop_mean = crop_mean
diff --git a/python/caffe/draw.py b/python/caffe/draw.py
index 6a4dbd47351..cfa3fc5b1fb 100644
--- a/python/caffe/draw.py
+++ b/python/caffe/draw.py
@@ -1,42 +1,57 @@
 """
 Caffe network visualization: draw the NetParameter protobuffer.
 
-NOTE: this requires pydot>=1.0.2, which is not included in requirements.txt
-since it requires graphviz and other prerequisites outside the scope of the
-Caffe.
+
+.. note::
+
+    This requires pydot>=1.0.2, which is not included in requirements.txt since
+    it requires graphviz and other prerequisites outside the scope of the
+    Caffe.
 """
 
 from caffe.proto import caffe_pb2
-from google.protobuf import text_format
-import pydot
+
+"""
+pydot is not supported under python 3 and pydot2 doesn't work properly.
+pydotplus works nicely (pip install pydotplus)
+"""
+try:
+    # Try to load pydotplus
+    import pydotplus as pydot
+except ImportError:
+    import pydot
 
 # Internal layer and blob styles.
-LAYER_STYLE_DEFAULT = {'shape': 'record', 'fillcolor': '#6495ED',
-         'style': 'filled'}
-NEURON_LAYER_STYLE = {'shape': 'record', 'fillcolor': '#90EE90',
-         'style': 'filled'}
-BLOB_STYLE = {'shape': 'octagon', 'fillcolor': '#E0E0E0',
-        'style': 'filled'}
+LAYER_STYLE_DEFAULT = {'shape': 'record',
+                       'fillcolor': '#6495ED',
+                       'style': 'filled'}
+NEURON_LAYER_STYLE = {'shape': 'record',
+                      'fillcolor': '#90EE90',
+                      'style': 'filled'}
+BLOB_STYLE = {'shape': 'octagon',
+              'fillcolor': '#E0E0E0',
+              'style': 'filled'}
+
 
 def get_pooling_types_dict():
     """Get dictionary mapping pooling type number to type name
     """
     desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
     d = {}
-    for k,v in desc.values_by_name.items():
+    for k, v in desc.values_by_name.items():
         d[v.number] = k
     return d
 
 
-def determine_edge_label_by_layertype(layer, layertype):
-    """Define edge label based on layer type
+def get_edge_label(layer):
+    """Define edge label based on layer type.
     """
 
-    if layertype == 'Data':
+    if layer.type == 'Data':
         edge_label = 'Batch ' + str(layer.data_param.batch_size)
-    elif layertype == 'Convolution':
+    elif layer.type == 'Convolution' or layer.type == 'Deconvolution':
         edge_label = str(layer.convolution_param.num_output)
-    elif layertype == 'InnerProduct':
+    elif layer.type == 'InnerProduct':
         edge_label = str(layer.inner_product_param.num_output)
     else:
         edge_label = '""'
@@ -44,8 +59,19 @@ def determine_edge_label_by_layertype(layer, layertype):
     return edge_label
 
 
-def determine_node_label_by_layertype(layer, layertype, rankdir):
-    """Define node label based on layer type
+def get_layer_label(layer, rankdir):
+    """Define node label based on layer type.
+
+    Parameters
+    ----------
+    layer : ?
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+
+    Returns
+    -------
+    string :
+        A label for the current layer
     """
 
     if rankdir in ('TB', 'BT'):
@@ -55,28 +81,28 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
     else:
         # If graph orientation is horizontal, vertical space is free and
         # horizontal space is not; separate words with newlines
-        separator = '\n'
+        separator = '\\n'
 
-    if layertype == 'Convolution':
+    if layer.type == 'Convolution' or layer.type == 'Deconvolution':
         # Outer double quotes needed or else colon characters don't parse
         # properly
         node_label = '"%s%s(%s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
                       separator,
-                      layertype,
+                      layer.type,
                       separator,
-                      layer.convolution_param.kernel_size,
+                      layer.convolution_param.kernel_size[0] if len(layer.convolution_param.kernel_size._values) else 1,
                       separator,
-                      layer.convolution_param.stride,
+                      layer.convolution_param.stride[0] if len(layer.convolution_param.stride._values) else 1,
                       separator,
-                      layer.convolution_param.pad)
-    elif layertype == 'Pooling':
+                      layer.convolution_param.pad[0] if len(layer.convolution_param.pad._values) else 0)
+    elif layer.type == 'Pooling':
         pooling_types_dict = get_pooling_types_dict()
         node_label = '"%s%s(%s %s)%skernel size: %d%sstride: %d%spad: %d"' %\
                      (layer.name,
                       separator,
                       pooling_types_dict[layer.pooling_param.pool],
-                      layertype,
+                      layer.type,
                       separator,
                       layer.pooling_param.kernel_size,
                       separator,
@@ -84,15 +110,15 @@ def determine_node_label_by_layertype(layer, layertype, rankdir):
                       separator,
                       layer.pooling_param.pad)
     else:
-        node_label = '"%s%s(%s)"' % (layer.name, separator, layertype)
+        node_label = '"%s%s(%s)"' % (layer.name, separator, layer.type)
     return node_label
 
 
 def choose_color_by_layertype(layertype):
-    """Define colors for nodes based on the layer type
+    """Define colors for nodes based on the layer type.
     """
     color = '#6495ED'  # Default
-    if layertype == 'Convolution':
+    if layertype == 'Convolution' or layertype == 'Deconvolution':
         color = '#FF5050'
     elif layertype == 'Pooling':
         color = '#FF9900'
@@ -102,64 +128,95 @@ def choose_color_by_layertype(layertype):
 
 
 def get_pydot_graph(caffe_net, rankdir, label_edges=True):
-  pydot_graph = pydot.Dot(caffe_net.name, graph_type='digraph', rankdir=rankdir)
-  pydot_nodes = {}
-  pydot_edges = []
-  for layer in caffe_net.layer:
-    name = layer.name
-    layertype = layer.type
-    node_label = determine_node_label_by_layertype(layer, layertype, rankdir)
-    if (len(layer.bottom) == 1 and len(layer.top) == 1 and
-        layer.bottom[0] == layer.top[0]):
-      # We have an in-place neuron layer.
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          node_label, **NEURON_LAYER_STYLE)
-    else:
-      layer_style = LAYER_STYLE_DEFAULT
-      layer_style['fillcolor'] = choose_color_by_layertype(layertype)
-      pydot_nodes[name + '_' + layertype] = pydot.Node(
-          node_label, **layer_style)
-    for bottom_blob in layer.bottom:
-      pydot_nodes[bottom_blob + '_blob'] = pydot.Node(
-        '%s' % (bottom_blob), **BLOB_STYLE)
-      edge_label = '""'
-      pydot_edges.append({'src': bottom_blob + '_blob',
-                          'dst': name + '_' + layertype,
-                          'label': edge_label})
-    for top_blob in layer.top:
-      pydot_nodes[top_blob + '_blob'] = pydot.Node(
-        '%s' % (top_blob))
-      if label_edges:
-        edge_label = determine_edge_label_by_layertype(layer, layertype)
-      else:
-        edge_label = '""'
-      pydot_edges.append({'src': name + '_' + layertype,
-                          'dst': top_blob + '_blob',
-                          'label': edge_label})
-  # Now, add the nodes and edges to the graph.
-  for node in pydot_nodes.values():
-    pydot_graph.add_node(node)
-  for edge in pydot_edges:
-    pydot_graph.add_edge(
-        pydot.Edge(pydot_nodes[edge['src']], pydot_nodes[edge['dst']],
-                   label=edge['label']))
-  return pydot_graph
+    """Create a data structure which represents the `caffe_net`.
+
+    Parameters
+    ----------
+    caffe_net : object
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+    label_edges : boolean, optional
+        Label the edges (default is True).
+
+    Returns
+    -------
+    pydot graph object
+    """
+    pydot_graph = pydot.Dot(caffe_net.name,
+                            graph_type='digraph',
+                            rankdir=rankdir)
+    pydot_nodes = {}
+    pydot_edges = []
+    for layer in caffe_net.layer:
+        node_label = get_layer_label(layer, rankdir)
+        node_name = "%s_%s" % (layer.name, layer.type)
+        if (len(layer.bottom) == 1 and len(layer.top) == 1 and
+           layer.bottom[0] == layer.top[0]):
+            # We have an in-place neuron layer.
+            pydot_nodes[node_name] = pydot.Node(node_label,
+                                                **NEURON_LAYER_STYLE)
+        else:
+            layer_style = LAYER_STYLE_DEFAULT
+            layer_style['fillcolor'] = choose_color_by_layertype(layer.type)
+            pydot_nodes[node_name] = pydot.Node(node_label, **layer_style)
+        for bottom_blob in layer.bottom:
+            pydot_nodes[bottom_blob + '_blob'] = pydot.Node('%s' % bottom_blob,
+                                                            **BLOB_STYLE)
+            edge_label = '""'
+            pydot_edges.append({'src': bottom_blob + '_blob',
+                                'dst': node_name,
+                                'label': edge_label})
+        for top_blob in layer.top:
+            pydot_nodes[top_blob + '_blob'] = pydot.Node('%s' % (top_blob))
+            if label_edges:
+                edge_label = get_edge_label(layer)
+            else:
+                edge_label = '""'
+            pydot_edges.append({'src': node_name,
+                                'dst': top_blob + '_blob',
+                                'label': edge_label})
+    # Now, add the nodes and edges to the graph.
+    for node in pydot_nodes.values():
+        pydot_graph.add_node(node)
+    for edge in pydot_edges:
+        pydot_graph.add_edge(
+            pydot.Edge(pydot_nodes[edge['src']],
+                       pydot_nodes[edge['dst']],
+                       label=edge['label']))
+    return pydot_graph
+
 
 def draw_net(caffe_net, rankdir, ext='png'):
-  """Draws a caffe net and returns the image string encoded using the given
-  extension.
+    """Draws a caffe net and returns the image string encoded using the given
+    extension.
+
+    Parameters
+    ----------
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    ext : string, optional
+        The image extension (the default is 'png').
+
+    Returns
+    -------
+    string :
+        Postscript representation of the graph.
+    """
+    return get_pydot_graph(caffe_net, rankdir).create(format=ext)
 
-  Input:
-    caffe_net: a caffe.proto.caffe_pb2.NetParameter protocol buffer.
-    ext: the image extension. Default 'png'.
-  """
-  return get_pydot_graph(caffe_net, rankdir).create(format=ext)
 
 def draw_net_to_file(caffe_net, filename, rankdir='LR'):
-  """Draws a caffe net, and saves it to file using the format given as the
-  file extension. Use '.raw' to output raw text that you can manually feed
-  to graphviz to draw graphs.
-  """
-  ext = filename[filename.rfind('.')+1:]
-  with open(filename, 'wb') as fid:
-    fid.write(draw_net(caffe_net, rankdir, ext))
+    """Draws a caffe net, and saves it to file using the format given as the
+    file extension. Use '.raw' to output raw text that you can manually feed
+    to graphviz to draw graphs.
+
+    Parameters
+    ----------
+    caffe_net : a caffe.proto.caffe_pb2.NetParameter protocol buffer.
+    filename : string
+        The path to a file where the networks visualization will be stored.
+    rankdir : {'LR', 'TB', 'BT'}
+        Direction of graph layout.
+    """
+    ext = filename[filename.rfind('.')+1:]
+    with open(filename, 'wb') as fid:
+        fid.write(draw_net(caffe_net, rankdir, ext))
diff --git a/python/caffe/io.py b/python/caffe/io.py
index 6ae2cf13cc0..75310589cec 100644
--- a/python/caffe/io.py
+++ b/python/caffe/io.py
@@ -8,34 +8,38 @@
     from caffe.proto import caffe_pb2
 except:
     import sys
-    if sys.version_info >= (3,0):
+    if sys.version_info >= (3, 0):
         print("Failed to include caffe_pb2, things might go wrong!")
     else:
         raise
 
-## proto / datum / ndarray conversion
 
+## proto / datum / ndarray conversion
 def blobproto_to_array(blob, return_diff=False):
-    """Convert a blob proto to an array. In default, we will just return the data,
+    """
+    Convert a blob proto to an array. In default, we will just return the data,
     unless return_diff is True, in which case we will return the diff.
     """
+    # Read the data into an array
     if return_diff:
-        return np.array(blob.diff).reshape(
-            blob.num, blob.channels, blob.height, blob.width)
+        data = np.array(blob.diff)
     else:
-        return np.array(blob.data).reshape(
-            blob.num, blob.channels, blob.height, blob.width)
+        data = np.array(blob.data)
 
+    # Reshape the array
+    if blob.HasField('num') or blob.HasField('channels') or blob.HasField('height') or blob.HasField('width'):
+        # Use legacy 4D shape
+        return data.reshape(blob.num, blob.channels, blob.height, blob.width)
+    else:
+        return data.reshape(blob.shape.dim)
 
 def array_to_blobproto(arr, diff=None):
-    """Converts a 4-dimensional array to blob proto. If diff is given, also
+    """Converts a N-dimensional array to blob proto. If diff is given, also
     convert the diff. You need to make sure that arr and diff have the same
     shape, and this function does not do sanity check.
     """
-    if arr.ndim != 4:
-        raise ValueError('Incorrect array shape.')
     blob = caffe_pb2.BlobProto()
-    blob.num, blob.channels, blob.height, blob.width = arr.shape;
+    blob.shape.dim.extend(arr.shape)
     blob.data.extend(arr.astype(float).flat)
     if diff is not None:
         blob.diff.extend(diff.astype(float).flat)
@@ -81,7 +85,7 @@ def datum_to_array(datum):
     as one can easily get it by calling datum.label.
     """
     if len(datum.data):
-        return np.fromstring(datum.data, dtype = np.uint8).reshape(
+        return np.fromstring(datum.data, dtype=np.uint8).reshape(
             datum.channels, datum.height, datum.width)
     else:
         return np.array(datum.float_data).astype(float).reshape(
@@ -97,8 +101,9 @@ class Transformer:
     Note: this is mostly for illustrative purposes and it is likely better
     to define your own input preprocessing routine for your needs.
 
-    Take
-    net: a Net for which the input should be prepared
+    Parameters
+    ----------
+    net : a Net for which the input should be prepared
     """
     def __init__(self, inputs):
         self.inputs = inputs
@@ -108,13 +113,11 @@ def __init__(self, inputs):
         self.mean = {}
         self.input_scale = {}
 
-
     def __check_input(self, in_):
         if in_ not in self.inputs:
             raise Exception('{} is not one of the net inputs: {}'.format(
                 in_, self.inputs))
 
-
     def preprocess(self, in_, data):
         """
         Format input for Caffe:
@@ -126,12 +129,14 @@ def preprocess(self, in_, data):
         - subtract mean
         - scale feature
 
-        Take
-        in_: name of input blob to preprocess for
-        data: (H' x W' x K) ndarray
+        Parameters
+        ----------
+        in_ : name of input blob to preprocess for
+        data : (H' x W' x K) ndarray
 
-        Give
-        caffe_in: (K x H x W) ndarray for input to a Net
+        Returns
+        -------
+        caffe_in : (K x H x W) ndarray for input to a Net
         """
         self.__check_input(in_)
         caffe_in = data.astype(np.float32, copy=False)
@@ -155,7 +160,6 @@ def preprocess(self, in_, data):
             caffe_in *= input_scale
         return caffe_in
 
-
     def deprocess(self, in_, data):
         """
         Invert Caffe formatting; see preprocess().
@@ -174,20 +178,20 @@ def deprocess(self, in_, data):
         if raw_scale is not None:
             decaf_in /= raw_scale
         if channel_swap is not None:
-            decaf_in = decaf_in[channel_swap, :, :]
+            decaf_in = decaf_in[np.argsort(channel_swap), :, :]
         if transpose is not None:
-            decaf_in = decaf_in.transpose([transpose[t] for t in transpose])
+            decaf_in = decaf_in.transpose(np.argsort(transpose))
         return decaf_in
 
-
     def set_transpose(self, in_, order):
         """
         Set the input channel order for e.g. RGB to BGR conversion
         as needed for the reference ImageNet model.
 
-        Take
-        in_: which input to assign this channel order
-        order: the order to transpose the dimensions
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to transpose the dimensions
         """
         self.__check_input(in_)
         if len(order) != len(self.inputs[in_]) - 1:
@@ -195,16 +199,16 @@ def set_transpose(self, in_, order):
                             'dimensions as the input.')
         self.transpose[in_] = order
 
-
     def set_channel_swap(self, in_, order):
         """
         Set the input channel order for e.g. RGB to BGR conversion
         as needed for the reference ImageNet model.
         N.B. this assumes the channels are the first dimension AFTER transpose.
 
-        Take
-        in_: which input to assign this channel order
-        order: the order to take the channels.
+        Parameters
+        ----------
+        in_ : which input to assign this channel order
+        order : the order to take the channels.
             (2,1,0) maps RGB to BGR for example.
         """
         self.__check_input(in_)
@@ -213,7 +217,6 @@ def set_channel_swap(self, in_, order):
                             'dimensions as the input channels.')
         self.channel_swap[in_] = order
 
-
     def set_raw_scale(self, in_, scale):
         """
         Set the scale of raw features s.t. the input blob = input * scale.
@@ -221,21 +224,22 @@ def set_raw_scale(self, in_, scale):
         like CaffeNet and AlexNet represent images in [0, 255] so the raw_scale
         of these models must be 255.
 
-        Take
-        in_: which input to assign this scale factor
-        scale: scale coefficient
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
         """
         self.__check_input(in_)
         self.raw_scale[in_] = scale
 
-
     def set_mean(self, in_, mean):
         """
         Set the mean to subtract for centering the data.
 
-        Take
-        in_: which input to assign this mean.
-        mean: mean ndarray (input dimensional or broadcastable)
+        Parameters
+        ----------
+        in_ : which input to assign this mean.
+        mean : mean ndarray (input dimensional or broadcastable)
         """
         self.__check_input(in_)
         ms = mean.shape
@@ -254,16 +258,16 @@ def set_mean(self, in_, mean):
                 raise ValueError('Mean shape incompatible with input shape.')
         self.mean[in_] = mean
 
-
     def set_input_scale(self, in_, scale):
         """
         Set the scale of preprocessed inputs s.t. the blob = blob * scale.
         N.B. input_scale is done AFTER mean subtraction and other preprocessing
         while raw_scale is done BEFORE.
 
-        Take
-        in_: which input to assign this scale factor
-        scale: scale coefficient
+        Parameters
+        ----------
+        in_ : which input to assign this scale factor
+        scale : scale coefficient
         """
         self.__check_input(in_)
         self.input_scale[in_] = scale
@@ -275,17 +279,20 @@ def load_image(filename, color=True):
     """
     Load an image converting from grayscale or alpha as needed.
 
-    Take
-    filename: string
-    color: flag for color format. True (default) loads as RGB while False
+    Parameters
+    ----------
+    filename : string
+    color : boolean
+        flag for color format. True (default) loads as RGB while False
         loads as intensity (if image is already grayscale).
 
-    Give
-    image: an image with type np.float32 in range [0, 1]
+    Returns
+    -------
+    image : an image with type np.float32 in range [0, 1]
         of size (H x W x 3) in RGB or
         of size (H x W x 1) in grayscale.
     """
-    img = skimage.img_as_float(skimage.io.imread(filename)).astype(np.float32)
+    img = skimage.img_as_float(skimage.io.imread(filename, as_grey=not color)).astype(np.float32)
     if img.ndim == 2:
         img = img[:, :, np.newaxis]
         if color:
@@ -299,29 +306,33 @@ def resize_image(im, new_dims, interp_order=1):
     """
     Resize an image array with interpolation.
 
-    Take
-    im: (H x W x K) ndarray
-    new_dims: (height, width) tuple of new dimensions.
-    interp_order: interpolation order, default is linear.
+    Parameters
+    ----------
+    im : (H x W x K) ndarray
+    new_dims : (height, width) tuple of new dimensions.
+    interp_order : interpolation order, default is linear.
 
-    Give
-    im: resized ndarray with shape (new_dims[0], new_dims[1], K)
+    Returns
+    -------
+    im : resized ndarray with shape (new_dims[0], new_dims[1], K)
     """
     if im.shape[-1] == 1 or im.shape[-1] == 3:
         im_min, im_max = im.min(), im.max()
         if im_max > im_min:
-            # skimage is fast but only understands {1,3} channel images in [0, 1].
+            # skimage is fast but only understands {1,3} channel images
+            # in [0, 1].
             im_std = (im - im_min) / (im_max - im_min)
             resized_std = resize(im_std, new_dims, order=interp_order)
             resized_im = resized_std * (im_max - im_min) + im_min
         else:
             # the image is a constant -- avoid divide by 0
-            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]), dtype=np.float32)
+            ret = np.empty((new_dims[0], new_dims[1], im.shape[-1]),
+                           dtype=np.float32)
             ret.fill(im_min)
             return ret
     else:
         # ndimage interpolates anything but more slowly.
-        scale = tuple(np.array(new_dims) / np.array(im.shape[:2]))
+        scale = tuple(np.array(new_dims, dtype=float) / np.array(im.shape[:2]))
         resized_im = zoom(im, scale + (1,), order=interp_order)
     return resized_im.astype(np.float32)
 
@@ -330,12 +341,14 @@ def oversample(images, crop_dims):
     """
     Crop images into the four corners, center, and their mirrored versions.
 
-    Take
-    image: iterable of (H x W x K) ndarrays
-    crop_dims: (height, width) tuple for the crops.
+    Parameters
+    ----------
+    image : iterable of (H x W x K) ndarrays
+    crop_dims : (height, width) tuple for the crops.
 
-    Give
-    crops: (10*N x H x W x K) ndarray of crops for number of inputs N.
+    Returns
+    -------
+    crops : (10*N x H x W x K) ndarray of crops for number of inputs N.
     """
     # Dimensions and center.
     im_shape = np.array(images[0].shape)
@@ -359,7 +372,7 @@ def oversample(images, crop_dims):
 
     # Extract crops
     crops = np.empty((10 * len(images), crop_dims[0], crop_dims[1],
-                            im_shape[-1]), dtype=np.float32)
+                      im_shape[-1]), dtype=np.float32)
     ix = 0
     for im in images:
         for crop in crops_ix:
diff --git a/python/caffe/net_spec.py b/python/caffe/net_spec.py
new file mode 100644
index 00000000000..63de4cce4b2
--- /dev/null
+++ b/python/caffe/net_spec.py
@@ -0,0 +1,226 @@
+"""Python net specification.
+
+This module provides a way to write nets directly in Python, using a natural,
+functional style. See examples/pycaffe/caffenet.py for an example.
+
+Currently this works as a thin wrapper around the Python protobuf interface,
+with layers and parameters automatically generated for the "layers" and
+"params" pseudo-modules, which are actually objects using __getattr__ magic
+to generate protobuf messages.
+
+Note that when using to_proto or Top.to_proto, names of intermediate blobs will
+be automatically generated. To explicitly specify blob names, use the NetSpec
+class -- assign to its attributes directly to name layers, and call
+NetSpec.to_proto to serialize all assigned layers.
+
+This interface is expected to continue to evolve as Caffe gains new capabilities
+for specifying nets. In particular, the automatically generated layer names
+are not guaranteed to be forward-compatible.
+"""
+
+from collections import OrderedDict, Counter
+
+from .proto import caffe_pb2
+from google import protobuf
+import six
+
+
+def param_name_dict():
+    """Find out the correspondence between layer names and parameter names."""
+
+    layer = caffe_pb2.LayerParameter()
+    # get all parameter names (typically underscore case) and corresponding
+    # type names (typically camel case), which contain the layer names
+    # (note that not all parameters correspond to layers, but we'll ignore that)
+    param_names = [s for s in dir(layer) if s.endswith('_param')]
+    param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
+    # strip the final '_param' or 'Parameter'
+    param_names = [s[:-len('_param')] for s in param_names]
+    param_type_names = [s[:-len('Parameter')] for s in param_type_names]
+    return dict(zip(param_type_names, param_names))
+
+
+def to_proto(*tops):
+    """Generate a NetParameter that contains all layers needed to compute
+    all arguments."""
+
+    layers = OrderedDict()
+    autonames = Counter()
+    for top in tops:
+        top.fn._to_proto(layers, {}, autonames)
+    net = caffe_pb2.NetParameter()
+    net.layer.extend(layers.values())
+    return net
+
+
+def assign_proto(proto, name, val):
+    """Assign a Python object to a protobuf message, based on the Python
+    type (in recursive fashion). Lists become repeated fields/messages, dicts
+    become messages, and other types are assigned directly. For convenience,
+    repeated fields whose values are not lists are converted to single-element
+    lists; e.g., `my_repeated_int_field=3` is converted to
+    `my_repeated_int_field=[3]`."""
+
+    is_repeated_field = hasattr(getattr(proto, name), 'extend')
+    if is_repeated_field and not isinstance(val, list):
+        val = [val]
+    if isinstance(val, list):
+        if isinstance(val[0], dict):
+            for item in val:
+                proto_item = getattr(proto, name).add()
+                for k, v in six.iteritems(item):
+                    assign_proto(proto_item, k, v)
+        else:
+            getattr(proto, name).extend(val)
+    elif isinstance(val, dict):
+        for k, v in six.iteritems(val):
+            assign_proto(getattr(proto, name), k, v)
+    else:
+        setattr(proto, name, val)
+
+
+class Top(object):
+    """A Top specifies a single output blob (which could be one of several
+    produced by a layer.)"""
+
+    def __init__(self, fn, n):
+        self.fn = fn
+        self.n = n
+
+    def to_proto(self):
+        """Generate a NetParameter that contains all layers needed to compute
+        this top."""
+
+        return to_proto(self)
+
+    def _to_proto(self, layers, names, autonames):
+        return self.fn._to_proto(layers, names, autonames)
+
+
+class Function(object):
+    """A Function specifies a layer, its parameters, and its inputs (which
+    are Tops from other layers)."""
+
+    def __init__(self, type_name, inputs, params):
+        self.type_name = type_name
+        self.inputs = inputs
+        self.params = params
+        self.ntop = self.params.get('ntop', 1)
+        # use del to make sure kwargs are not double-processed as layer params
+        if 'ntop' in self.params:
+            del self.params['ntop']
+        self.in_place = self.params.get('in_place', False)
+        if 'in_place' in self.params:
+            del self.params['in_place']
+        self.tops = tuple(Top(self, n) for n in range(self.ntop))
+
+    def _get_name(self, names, autonames):
+        if self not in names and self.ntop > 0:
+            names[self] = self._get_top_name(self.tops[0], names, autonames)
+        elif self not in names:
+            autonames[self.type_name] += 1
+            names[self] = self.type_name + str(autonames[self.type_name])
+        return names[self]
+
+    def _get_top_name(self, top, names, autonames):
+        if top not in names:
+            autonames[top.fn.type_name] += 1
+            names[top] = top.fn.type_name + str(autonames[top.fn.type_name])
+        return names[top]
+
+    def _to_proto(self, layers, names, autonames):
+        if self in layers:
+            return
+        bottom_names = []
+        for inp in self.inputs:
+            inp._to_proto(layers, names, autonames)
+            bottom_names.append(layers[inp.fn].top[inp.n])
+        layer = caffe_pb2.LayerParameter()
+        layer.type = self.type_name
+        layer.bottom.extend(bottom_names)
+
+        if self.in_place:
+            layer.top.extend(layer.bottom)
+        else:
+            for top in self.tops:
+                layer.top.append(self._get_top_name(top, names, autonames))
+        layer.name = self._get_name(names, autonames)
+
+        for k, v in six.iteritems(self.params):
+            # special case to handle generic *params
+            if k.endswith('param'):
+                assign_proto(layer, k, v)
+            else:
+                try:
+                    assign_proto(getattr(layer,
+                        _param_names[self.type_name] + '_param'), k, v)
+                except (AttributeError, KeyError):
+                    assign_proto(layer, k, v)
+
+        layers[self] = layer
+
+
+class NetSpec(object):
+    """A NetSpec contains a set of Tops (assigned directly as attributes).
+    Calling NetSpec.to_proto generates a NetParameter containing all of the
+    layers needed to produce all of the assigned Tops, using the assigned
+    names."""
+
+    def __init__(self):
+        super(NetSpec, self).__setattr__('tops', OrderedDict())
+
+    def __setattr__(self, name, value):
+        self.tops[name] = value
+
+    def __getattr__(self, name):
+        return self.tops[name]
+
+    def __setitem__(self, key, value):
+        self.__setattr__(key, value)
+
+    def __getitem__(self, item):
+        return self.__getattr__(item)
+
+    def to_proto(self):
+        names = {v: k for k, v in six.iteritems(self.tops)}
+        autonames = Counter()
+        layers = OrderedDict()
+        for name, top in six.iteritems(self.tops):
+            top._to_proto(layers, names, autonames)
+        net = caffe_pb2.NetParameter()
+        net.layer.extend(layers.values())
+        return net
+
+
+class Layers(object):
+    """A Layers object is a pseudo-module which generates functions that specify
+    layers; e.g., Layers().Convolution(bottom, kernel_size=3) will produce a Top
+    specifying a 3x3 convolution applied to bottom."""
+
+    def __getattr__(self, name):
+        def layer_fn(*args, **kwargs):
+            fn = Function(name, args, kwargs)
+            if fn.ntop == 0:
+                return fn
+            elif fn.ntop == 1:
+                return fn.tops[0]
+            else:
+                return fn.tops
+        return layer_fn
+
+
+class Parameters(object):
+    """A Parameters object is a pseudo-module which generates constants used
+    in layer parameters; e.g., Parameters().Pooling.MAX is the value used
+    to specify max pooling."""
+
+    def __getattr__(self, name):
+       class Param:
+            def __getattr__(self, param_name):
+                return getattr(getattr(caffe_pb2, name + 'Parameter'), param_name)
+       return Param()
+
+
+_param_names = param_name_dict()
+layers = Layers()
+params = Parameters()
diff --git a/python/caffe/pycaffe.py b/python/caffe/pycaffe.py
index 3c19261f690..c5c0b824a77 100644
--- a/python/caffe/pycaffe.py
+++ b/python/caffe/pycaffe.py
@@ -5,14 +5,17 @@
 
 from collections import OrderedDict
 try:
-	from itertools import izip_longest
+    from itertools import izip_longest
 except:
-	from itertools import zip_longest as izip_longest
+    from itertools import zip_longest as izip_longest
 import numpy as np
 
-from ._caffe import Net, SGDSolver
+from ._caffe import Net, SGDSolver, NesterovSolver, AdaGradSolver, \
+        RMSPropSolver, AdaDeltaSolver, AdamSolver
 import caffe.io
 
+import six
+
 # We directly update methods from Net here (rather than using composition or
 # inheritance) so that nets created by caffe (e.g., by SGDSolver) will
 # automatically have the improved interface.
@@ -27,6 +30,15 @@ def _Net_blobs(self):
     return OrderedDict(zip(self._blob_names, self._blobs))
 
 
+@property
+def _Net_blob_loss_weights(self):
+    """
+    An OrderedDict (bottom to top, i.e., input to output) of network
+    blob loss weights indexed by name
+    """
+    return OrderedDict(zip(self._blob_names, self._blob_loss_weights))
+
+
 @property
 def _Net_params(self):
     """
@@ -53,16 +65,19 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
     """
     Forward pass: prepare inputs and run the net forward.
 
-    Take
-    blobs: list of blobs to return in addition to output blobs.
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            For formatting inputs for Caffe, see Net.preprocess().
-            If None, input is taken from data layers.
-    start: optional name of layer at which to begin the forward pass
-    end: optional name of layer at which to finish the forward pass (inclusive)
-
-    Give
-    outs: {blob name: blob ndarray} dict.
+    Parameters
+    ----------
+    blobs : list of blobs to return in addition to output blobs.
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             For formatting inputs for Caffe, see Net.preprocess().
+             If None, input is taken from data layers.
+    start : optional name of layer at which to begin the forward pass
+    end : optional name of layer at which to finish the forward pass
+          (inclusive)
+
+    Returns
+    -------
+    outs : {blob name: blob ndarray} dict.
     """
     if blobs is None:
         blobs = []
@@ -84,8 +99,8 @@ def _Net_forward(self, blobs=None, start=None, end=None, **kwargs):
             raise Exception('Input blob arguments do not match net inputs.')
         # Set input according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for in_, blob in kwargs.iteritems():
-            if blob.shape[0] != self.blobs[in_].num:
+        for in_, blob in six.iteritems(kwargs):
+            if blob.shape[0] != self.blobs[in_].shape[0]:
                 raise Exception('Input is not batch sized')
             self.blobs[in_].data[...] = blob
 
@@ -99,14 +114,17 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
     """
     Backward pass: prepare diffs and run the net backward.
 
-    Take
-    diffs: list of diffs to return in addition to bottom diffs.
-    kwargs: Keys are output blob names and values are diff ndarrays.
+    Parameters
+    ----------
+    diffs : list of diffs to return in addition to bottom diffs.
+    kwargs : Keys are output blob names and values are diff ndarrays.
             If None, top diffs are taken from forward loss.
-    start: optional name of layer at which to begin the backward pass
-    end: optional name of layer at which to finish the backward pass (inclusive)
+    start : optional name of layer at which to begin the backward pass
+    end : optional name of layer at which to finish the backward pass
+        (inclusive)
 
-    Give
+    Returns
+    -------
     outs: {blob name: diff ndarray} dict.
     """
     if diffs is None:
@@ -129,10 +147,8 @@ def _Net_backward(self, diffs=None, start=None, end=None, **kwargs):
             raise Exception('Top diff arguments do not match net outputs.')
         # Set top diffs according to defined shapes and make arrays single and
         # C-contiguous as Caffe expects.
-        for top, diff in kwargs.iteritems():
-            if diff.ndim != 4:
-                raise Exception('{} diff is not 4-d'.format(top))
-            if diff.shape[0] != self.blobs[top].num:
+        for top, diff in six.iteritems(kwargs):
+            if diff.shape[0] != self.blobs[top].shape[0]:
                 raise Exception('Diff is not batch sized')
             self.blobs[top].diff[...] = diff
 
@@ -146,25 +162,27 @@ def _Net_forward_all(self, blobs=None, **kwargs):
     """
     Run net forward in batches.
 
-    Take
-    blobs: list of blobs to extract as in forward()
-    kwargs: Keys are input blob names and values are blob ndarrays.
-            Refer to forward().
+    Parameters
+    ----------
+    blobs : list of blobs to extract as in forward()
+    kwargs : Keys are input blob names and values are blob ndarrays.
+             Refer to forward().
 
-    Give
-    all_outs: {blob name: list of blobs} dict.
+    Returns
+    -------
+    all_outs : {blob name: list of blobs} dict.
     """
     # Collect outputs from batches
     all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
     for batch in self._batch(kwargs):
         outs = self.forward(blobs=blobs, **batch)
-        for out, out_blob in outs.iteritems():
+        for out, out_blob in six.iteritems(outs):
             all_outs[out].extend(out_blob.copy())
     # Package in ndarray.
     for out in all_outs:
         all_outs[out] = np.asarray(all_outs[out])
     # Discard padding.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out in all_outs:
             all_outs[out] = all_outs[out][:-pad]
@@ -175,14 +193,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     """
     Run net forward + backward in batches.
 
-    Take
+    Parameters
+    ----------
     blobs: list of blobs to extract as in forward()
     diffs: list of diffs to extract as in backward()
     kwargs: Keys are input (for forward) and output (for backward) blob names
             and values are ndarrays. Refer to forward() and backward().
             Prefilled variants are called for lack of input or output blobs.
 
-    Give
+    Returns
+    -------
     all_blobs: {blob name: blob ndarray} dict.
     all_diffs: {blob name: diff ndarray} dict.
     """
@@ -197,16 +217,16 @@ def _Net_forward_backward_all(self, blobs=None, diffs=None, **kwargs):
     for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
         batch_blobs = self.forward(blobs=blobs, **fb)
         batch_diffs = self.backward(diffs=diffs, **bb)
-        for out, out_blobs in batch_blobs.iteritems():
-            all_outs[out].extend(out_blobs)
-        for diff, out_diffs in batch_diffs.iteritems():
-            all_diffs[diff].extend(out_diffs)
+        for out, out_blobs in six.iteritems(batch_blobs):
+            all_outs[out].extend(out_blobs.copy())
+        for diff, out_diffs in six.iteritems(batch_diffs):
+            all_diffs[diff].extend(out_diffs.copy())
     # Package in ndarray.
     for out, diff in zip(all_outs, all_diffs):
         all_outs[out] = np.asarray(all_outs[out])
         all_diffs[diff] = np.asarray(all_diffs[diff])
     # Discard padding at the end and package in ndarray.
-    pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
+    pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
     if pad:
         for out, diff in zip(all_outs, all_diffs):
             all_outs[out] = all_outs[out][:-pad]
@@ -229,17 +249,19 @@ def _Net_batch(self, blobs):
     """
     Batch blob lists according to net's batch size.
 
-    Take
+    Parameters
+    ----------
     blobs: Keys blob names and values are lists of blobs (of any length).
            Naturally, all the lists should have the same length.
 
-    Give (yield)
+    Yields
+    ------
     batch: {blob name: list of blobs} dict for a single batch.
     """
-    num = len(blobs.itervalues().next())
-    batch_size = self.blobs.itervalues().next().num
+    num = len(six.next(six.itervalues(blobs)))
+    batch_size = six.next(six.itervalues(self.blobs)).shape[0]
     remainder = num % batch_size
-    num_batches = num / batch_size
+    num_batches = num // batch_size
 
     # Yield full batches.
     for b in range(num_batches):
@@ -256,8 +278,25 @@ def _Net_batch(self, blobs):
                                                  padding])
         yield padded_batch
 
+
+class _Net_IdNameWrapper:
+    """
+    A simple wrapper that allows the ids propery to be accessed as a dict
+    indexed by names. Used for top and bottom names
+    """
+    def __init__(self, net, func):
+        self.net, self.func = net, func
+
+    def __getitem__(self, name):
+        # Map the layer name to id
+        ids = self.func(self.net, list(self.net._layer_names).index(name))
+        # Map the blob id to name
+        id_to_name = list(self.net.blobs)
+        return [id_to_name[i] for i in ids]
+
 # Attach methods to Net.
 Net.blobs = _Net_blobs
+Net.blob_loss_weights = _Net_blob_loss_weights
 Net.params = _Net_params
 Net.forward = _Net_forward
 Net.backward = _Net_backward
@@ -267,3 +306,5 @@ def _Net_batch(self, blobs):
 Net._batch = _Net_batch
 Net.inputs = _Net_inputs
 Net.outputs = _Net_outputs
+Net.top_names = property(lambda n: _Net_IdNameWrapper(n, Net._top_ids))
+Net.bottom_names = property(lambda n: _Net_IdNameWrapper(n, Net._bottom_ids))
diff --git a/python/caffe/test/test_coord_map.py b/python/caffe/test/test_coord_map.py
new file mode 100644
index 00000000000..613260e25df
--- /dev/null
+++ b/python/caffe/test/test_coord_map.py
@@ -0,0 +1,192 @@
+import unittest
+
+import numpy as np
+import random
+
+import caffe
+from caffe import layers as L
+from caffe import params as P
+from caffe.coord_map import coord_map_from_to, crop
+
+
+def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
+    """
+    Define net spec for simple conv-pool-deconv pattern common to all
+    coordinate mapping tests.
+    """
+    n = caffe.NetSpec()
+    n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
+    n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
+    n.conv = L.Convolution(
+        n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
+    n.pool = L.Pooling(
+        n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
+    # for upsampling kernel size is 2x stride
+    try:
+        deconv_ks = [s*2 for s in dstride]
+    except:
+        deconv_ks = dstride*2
+    n.deconv = L.Deconvolution(
+        n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
+    return n
+
+
+class TestCoordMap(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_conv_pool_deconv(self):
+        """
+        Map through conv, pool, and deconv.
+        """
+        n = coord_net_spec()
+        # identity for 2x pool, 2x deconv
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, 0)
+        # shift-by-one for 4x pool, 4x deconv
+        n = coord_net_spec(pool=4, dstride=4)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertEquals(a, 1)
+        self.assertEquals(b, -1)
+
+    def test_pass(self):
+        """
+        A pass-through layer (ReLU) and conv (1x1, stride 1, pad 0)
+        both do identity mapping.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.relu = L.ReLU(n.deconv)
+        n.conv1x1 = L.Convolution(
+            n.relu, num_output=10, kernel_size=1, stride=1, pad=0)
+        for top in [n.relu, n.conv1x1]:
+            ax_pass, a_pass, b_pass = coord_map_from_to(top, n.data)
+            self.assertEquals(ax, ax_pass)
+            self.assertEquals(a, a_pass)
+            self.assertEquals(b, b_pass)
+
+    def test_padding(self):
+        """
+        Padding conv adds offset while padding deconv subtracts offset.
+        """
+        n = coord_net_spec()
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        pad = random.randint(0, 10)
+        # conv padding
+        n = coord_net_spec(pad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b - pad, b_pad)
+        # deconv padding
+        n = coord_net_spec(dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b + pad, b_pad)
+        # pad both to cancel out
+        n = coord_net_spec(pad=pad, dpad=pad)
+        _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(a, a_pad)
+        self.assertEquals(b, b_pad)
+
+    def test_multi_conv(self):
+        """
+        Multiple bottoms/tops of a layer are identically mapped.
+        """
+        n = coord_net_spec()
+        # multi bottom/top
+        n.conv_data, n.conv_aux = L.Convolution(
+            n.data, n.aux, ntop=2, num_output=10, kernel_size=5, stride=2,
+            pad=0)
+        ax1, a1, b1 = coord_map_from_to(n.conv_data, n.data)
+        ax2, a2, b2 = coord_map_from_to(n.conv_aux, n.aux)
+        self.assertEquals(ax1, ax2)
+        self.assertEquals(a1, a2)
+        self.assertEquals(b1, b2)
+
+    def test_rect(self):
+        """
+        Anisotropic mapping is equivalent to its isotropic parts.
+        """
+        n3x3 = coord_net_spec(ks=3, stride=1, pad=0)
+        n5x5 = coord_net_spec(ks=5, stride=2, pad=10)
+        n3x5 = coord_net_spec(ks=[3, 5], stride=[1, 2], pad=[0, 10])
+        ax_3x3, a_3x3, b_3x3 = coord_map_from_to(n3x3.deconv, n3x3.data)
+        ax_5x5, a_5x5, b_5x5 = coord_map_from_to(n5x5.deconv, n5x5.data)
+        ax_3x5, a_3x5, b_3x5 = coord_map_from_to(n3x5.deconv, n3x5.data)
+        self.assertTrue(ax_3x3 == ax_5x5 == ax_3x5)
+        self.assertEquals(a_3x3, a_3x5[0])
+        self.assertEquals(b_3x3, b_3x5[0])
+        self.assertEquals(a_5x5, a_3x5[1])
+        self.assertEquals(b_5x5, b_3x5[1])
+
+    def test_nd_conv(self):
+        """
+        ND conv maps the same way in more dimensions.
+        """
+        n = caffe.NetSpec()
+        # define data with 3 spatial dimensions, otherwise the same net
+        n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
+        n.conv = L.Convolution(
+            n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
+            pad=[0, 1, 2])
+        n.pool = L.Pooling(
+            n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
+        n.deconv = L.Deconvolution(
+            n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        self.assertEquals(ax, 1)
+        self.assertTrue(len(a) == len(b))
+        self.assertTrue(np.all(a == 1))
+        self.assertEquals(b[0] - 1, b[1])
+        self.assertEquals(b[1] - 1, b[2])
+
+    def test_crop_of_crop(self):
+        """
+        Map coordinates through Crop layer:
+        crop an already-cropped output to the input and check change in offset.
+        """
+        n = coord_net_spec()
+        offset = random.randint(0, 10)
+        ax, a, b = coord_map_from_to(n.deconv, n.data)
+        n.crop = L.Crop(n.deconv, n.data, axis=2, offset=offset)
+        ax_crop, a_crop, b_crop = coord_map_from_to(n.crop, n.data)
+        self.assertEquals(ax, ax_crop)
+        self.assertEquals(a, a_crop)
+        self.assertEquals(b + offset, b_crop)
+
+    def test_crop_helper(self):
+        """
+        Define Crop layer by crop().
+        """
+        n = coord_net_spec()
+        crop(n.deconv, n.data)
+
+    def test_catch_unconnected(self):
+        """
+        Catch mapping spatially unconnected tops.
+        """
+        n = coord_net_spec()
+        n.ip = L.InnerProduct(n.deconv, num_output=10)
+        with self.assertRaises(RuntimeError):
+            coord_map_from_to(n.ip, n.data)
+
+    def test_catch_scale_mismatch(self):
+        """
+        Catch incompatible scales, such as when the top to be cropped
+        is mapped to a differently strided reference top.
+        """
+        n = coord_net_spec(pool=3, dstride=2)  # pool 3x but deconv 2x
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)
+
+    def test_catch_negative_crop(self):
+        """
+        Catch impossible offsets, such as when the top to be cropped
+        is mapped to a larger reference top.
+        """
+        n = coord_net_spec(dpad=10)  # make output smaller than input
+        with self.assertRaises(AssertionError):
+            crop(n.deconv, n.data)
diff --git a/python/caffe/test/test_io.py b/python/caffe/test/test_io.py
new file mode 100644
index 00000000000..8c86ef75fb2
--- /dev/null
+++ b/python/caffe/test/test_io.py
@@ -0,0 +1,41 @@
+import numpy as np
+import unittest
+
+import caffe
+
+class TestBlobProtoToArray(unittest.TestCase):
+
+    def test_old_format(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+        shape = (1,1,10,10)
+        blob.num, blob.channels, blob.height, blob.width = shape
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr.shape, shape)
+
+    def test_new_format(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+        blob.shape.dim.extend(list(data.shape))
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr.shape, data.shape)
+
+    def test_no_shape(self):
+        data = np.zeros((10,10))
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+
+        with self.assertRaises(ValueError):
+            caffe.io.blobproto_to_array(blob)
+
+    def test_scalar(self):
+        data = np.ones((1)) * 123
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        blob.data.extend(list(data.flatten()))
+
+        arr = caffe.io.blobproto_to_array(blob)
+        self.assertEqual(arr, 123)
diff --git a/python/caffe/test/test_layer_type_list.py b/python/caffe/test/test_layer_type_list.py
new file mode 100644
index 00000000000..47f4cf6d008
--- /dev/null
+++ b/python/caffe/test/test_layer_type_list.py
@@ -0,0 +1,11 @@
+import unittest
+
+import caffe
+
+class TestLayerTypeList(unittest.TestCase):
+
+    def test_standard_types(self):
+        #removing 'Data' from list 
+        for type_name in ['Data', 'Convolution', 'InnerProduct']:
+            self.assertIn(type_name, caffe.layer_type_list(),
+                    '%s not in layer_type_list()' % type_name)
diff --git a/python/caffe/test/test_net.py b/python/caffe/test/test_net.py
index 62b407da8aa..aad828aa8aa 100644
--- a/python/caffe/test/test_net.py
+++ b/python/caffe/test/test_net.py
@@ -2,14 +2,16 @@
 import tempfile
 import os
 import numpy as np
+import six
 
 import caffe
 
+
 def simple_net_file(num_output):
     """Make a simple net prototxt, based on test_net.cpp, returning the name
     of the (temporary) file."""
 
-    f = tempfile.NamedTemporaryFile(delete=False)
+    f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
     f.write("""name: 'testnet' force_backward: true
     layer { type: 'DummyData' name: 'data' top: 'data' top: 'label'
       dummy_data_param { num: 5 channels: 2 height: 3 width: 4
@@ -31,6 +33,7 @@ def simple_net_file(num_output):
     f.close()
     return f.name
 
+
 class TestNet(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
@@ -45,7 +48,7 @@ def setUp(self):
     def test_memory(self):
         """Check that holding onto blob data beyond the life of a Net is OK"""
 
-        params = sum(map(list, self.net.params.itervalues()), [])
+        params = sum(map(list, six.itervalues(self.net.params)), [])
         blobs = self.net.blobs.values()
         del self.net
 
@@ -65,7 +68,7 @@ def test_inputs_outputs(self):
         self.assertEqual(self.net.outputs, ['loss'])
 
     def test_save_and_read(self):
-        f = tempfile.NamedTemporaryFile(delete=False)
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.close()
         self.net.save(f.name)
         net_file = simple_net_file(self.num_output)
diff --git a/python/caffe/test/test_net_spec.py b/python/caffe/test/test_net_spec.py
new file mode 100644
index 00000000000..fee3c0aaebe
--- /dev/null
+++ b/python/caffe/test/test_net_spec.py
@@ -0,0 +1,81 @@
+import unittest
+import tempfile
+import caffe
+from caffe import layers as L
+from caffe import params as P
+
+def lenet(batch_size):
+    n = caffe.NetSpec()
+    n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                         dict(dim=[batch_size, 1, 1, 1])],
+                                  transform_param=dict(scale=1./255), ntop=2)
+    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    n.ip1 = L.InnerProduct(n.pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    n.relu1 = L.ReLU(n.ip1, in_place=True)
+    n.ip2 = L.InnerProduct(n.relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
+    return n.to_proto()
+
+def anon_lenet(batch_size):
+    data, label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
+                                     dict(dim=[batch_size, 1, 1, 1])],
+                              transform_param=dict(scale=1./255), ntop=2)
+    conv1 = L.Convolution(data, kernel_size=5, num_output=20,
+        weight_filler=dict(type='xavier'))
+    pool1 = L.Pooling(conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    conv2 = L.Convolution(pool1, kernel_size=5, num_output=50,
+        weight_filler=dict(type='xavier'))
+    pool2 = L.Pooling(conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
+    ip1 = L.InnerProduct(pool2, num_output=500,
+        weight_filler=dict(type='xavier'))
+    relu1 = L.ReLU(ip1, in_place=True)
+    ip2 = L.InnerProduct(relu1, num_output=10,
+        weight_filler=dict(type='xavier'))
+    loss = L.SoftmaxWithLoss(ip2, label)
+    return loss.to_proto()
+
+def silent_net():
+    n = caffe.NetSpec()
+    n.data, n.data2 = L.DummyData(shape=dict(dim=3), ntop=2)
+    n.silence_data = L.Silence(n.data, ntop=0)
+    n.silence_data2 = L.Silence(n.data2, ntop=0)
+    return n.to_proto()
+
+class TestNetSpec(unittest.TestCase):
+    def load_net(self, net_proto):
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
+        f.write(str(net_proto))
+        f.close()
+        return caffe.Net(f.name, caffe.TEST)
+
+    def test_lenet(self):
+        """Construct and build the Caffe version of LeNet."""
+
+        net_proto = lenet(50)
+        # check that relu is in-place
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        # check that all layers are present
+        self.assertEqual(len(net.layers), 9)
+
+        # now the check the version with automatically-generated layer names
+        net_proto = anon_lenet(50)
+        self.assertEqual(net_proto.layer[6].bottom,
+                net_proto.layer[6].top)
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.layers), 9)
+
+    def test_zero_tops(self):
+        """Test net construction for top-less layers."""
+
+        net_proto = silent_net()
+        net = self.load_net(net_proto)
+        self.assertEqual(len(net.forward()), 0)
diff --git a/python/caffe/test/test_python_layer.py b/python/caffe/test/test_python_layer.py
index dd99f6f15b9..e46b7118014 100644
--- a/python/caffe/test/test_python_layer.py
+++ b/python/caffe/test/test_python_layer.py
@@ -1,9 +1,11 @@
 import unittest
 import tempfile
 import os
+import six
 
 import caffe
 
+
 class SimpleLayer(caffe.Layer):
     """A layer that just multiplies by ten"""
 
@@ -19,8 +21,31 @@ def forward(self, bottom, top):
     def backward(self, top, propagate_down, bottom):
         bottom[0].diff[...] = 10 * top[0].diff
 
+
+class ExceptionLayer(caffe.Layer):
+    """A layer for checking exceptions from Python"""
+
+    def setup(self, bottom, top):
+        raise RuntimeError
+
+class ParameterLayer(caffe.Layer):
+    """A layer that just multiplies by ten"""
+
+    def setup(self, bottom, top):
+        self.blobs.add_blob(1)
+        self.blobs[0].data[0] = 0
+
+    def reshape(self, bottom, top):
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        pass
+
+    def backward(self, top, propagate_down, bottom):
+        self.blobs[0].diff[0] = 1
+
 def python_net_file():
-    with tempfile.NamedTemporaryFile(delete=False) as f:
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
         f.write("""name: 'pythonnet' force_backward: true
         input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
         layer { type: 'Python' name: 'one' bottom: 'data' top: 'one'
@@ -31,6 +56,29 @@ def python_net_file():
           python_param { module: 'test_python_layer' layer: 'SimpleLayer' } }""")
         return f.name
 
+
+def exception_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+          python_param { module: 'test_python_layer' layer: 'ExceptionLayer' } }
+          """)
+        return f.name
+
+
+def parameter_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'layer' bottom: 'data' top: 'top'
+          python_param { module: 'test_python_layer' layer: 'ParameterLayer' } }
+          """)
+        return f.name
+
+
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+    'Caffe built without Python layer support')
 class TestPythonLayer(unittest.TestCase):
     def setUp(self):
         net_file = python_net_file()
@@ -55,6 +103,40 @@ def test_reshape(self):
         s = 4
         self.net.blobs['data'].reshape(s, s, s, s)
         self.net.forward()
-        for blob in self.net.blobs.itervalues():
+        for blob in six.itervalues(self.net.blobs):
             for d in blob.data.shape:
                 self.assertEqual(s, d)
+
+    def test_exception(self):
+        net_file = exception_net_file()
+        self.assertRaises(RuntimeError, caffe.Net, net_file, caffe.TEST)
+        os.remove(net_file)
+
+    def test_parameter(self):
+        net_file = parameter_net_file()
+        net = caffe.Net(net_file, caffe.TRAIN)
+        # Test forward and backward
+        net.forward()
+        net.backward()
+        layer = net.layers[list(net._layer_names).index('layer')]
+        self.assertEqual(layer.blobs[0].data[0], 0)
+        self.assertEqual(layer.blobs[0].diff[0], 1)
+        layer.blobs[0].data[0] += layer.blobs[0].diff[0]
+        self.assertEqual(layer.blobs[0].data[0], 1)
+
+        # Test saving and loading
+        h, caffemodel_file = tempfile.mkstemp()
+        net.save(caffemodel_file)
+        layer.blobs[0].data[0] = -1
+        self.assertEqual(layer.blobs[0].data[0], -1)
+        net.copy_from(caffemodel_file)
+        self.assertEqual(layer.blobs[0].data[0], 1)
+        os.remove(caffemodel_file)
+        
+        # Test weight sharing
+        net2 = caffe.Net(net_file, caffe.TRAIN)
+        net2.share_with(net)
+        layer = net.layers[list(net2._layer_names).index('layer')]
+        self.assertEqual(layer.blobs[0].data[0], 1)
+
+        os.remove(net_file)
diff --git a/python/caffe/test/test_python_layer_with_param_str.py b/python/caffe/test/test_python_layer_with_param_str.py
new file mode 100644
index 00000000000..c36048ae9f0
--- /dev/null
+++ b/python/caffe/test/test_python_layer_with_param_str.py
@@ -0,0 +1,61 @@
+import unittest
+import tempfile
+import os
+import six
+
+import caffe
+
+
+class SimpleParamLayer(caffe.Layer):
+    """A layer that just multiplies by the numeric value of its param string"""
+
+    def setup(self, bottom, top):
+        try:
+            self.value = float(self.param_str)
+        except ValueError:
+            raise ValueError("Parameter string must be a legible float")
+
+    def reshape(self, bottom, top):
+        top[0].reshape(*bottom[0].data.shape)
+
+    def forward(self, bottom, top):
+        top[0].data[...] = self.value * bottom[0].data
+
+    def backward(self, top, propagate_down, bottom):
+        bottom[0].diff[...] = self.value * top[0].diff
+
+
+def python_param_net_file():
+    with tempfile.NamedTemporaryFile(mode='w+', delete=False) as f:
+        f.write("""name: 'pythonnet' force_backward: true
+        input: 'data' input_shape { dim: 10 dim: 9 dim: 8 }
+        layer { type: 'Python' name: 'mul10' bottom: 'data' top: 'mul10'
+          python_param { module: 'test_python_layer_with_param_str'
+                layer: 'SimpleParamLayer' param_str: '10' } }
+        layer { type: 'Python' name: 'mul2' bottom: 'mul10' top: 'mul2'
+          python_param { module: 'test_python_layer_with_param_str'
+                layer: 'SimpleParamLayer' param_str: '2' } }""")
+        return f.name
+
+
+@unittest.skipIf('Python' not in caffe.layer_type_list(),
+    'Caffe built without Python layer support')
+class TestLayerWithParam(unittest.TestCase):
+    def setUp(self):
+        net_file = python_param_net_file()
+        self.net = caffe.Net(net_file, caffe.TRAIN)
+        os.remove(net_file)
+
+    def test_forward(self):
+        x = 8
+        self.net.blobs['data'].data[...] = x
+        self.net.forward()
+        for y in self.net.blobs['mul2'].data.flat:
+            self.assertEqual(y, 2 * 10 * x)
+
+    def test_backward(self):
+        x = 7
+        self.net.blobs['mul2'].diff[...] = x
+        self.net.backward()
+        for y in self.net.blobs['data'].diff.flat:
+            self.assertEqual(y, 2 * 10 * x)
diff --git a/python/caffe/test/test_solver.py b/python/caffe/test/test_solver.py
index d59f23d973a..f618fded8cd 100644
--- a/python/caffe/test/test_solver.py
+++ b/python/caffe/test/test_solver.py
@@ -2,19 +2,22 @@
 import tempfile
 import os
 import numpy as np
+import six
 
 import caffe
 from test_net import simple_net_file
 
+
 class TestSolver(unittest.TestCase):
     def setUp(self):
         self.num_output = 13
         net_f = simple_net_file(self.num_output)
-        f = tempfile.NamedTemporaryFile(delete=False)
+        f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
         f.write("""net: '""" + net_f + """'
         test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
         weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
-        display: 100 max_iter: 100 snapshot_after_train: false""")
+        display: 100 max_iter: 100 snapshot_after_train: false
+        snapshot_prefix: "model" """)
         f.close()
         self.solver = caffe.SGDSolver(f.name)
         # also make sure get_solver runs
@@ -44,8 +47,16 @@ def test_net_memory(self):
 
         total = 0
         for net in nets:
-            for ps in net.params.itervalues():
+            for ps in six.itervalues(net.params):
                 for p in ps:
                     total += p.data.sum() + p.diff.sum()
-            for bl in net.blobs.itervalues():
+            for bl in six.itervalues(net.blobs):
                 total += bl.data.sum() + bl.diff.sum()
+
+    def test_snapshot(self):
+        self.solver.snapshot()
+        # Check that these files exist and then remove them
+        files = ['model_iter_0.caffemodel', 'model_iter_0.solverstate']
+        for fn in files:
+            assert os.path.isfile(fn)
+            os.remove(fn)
diff --git a/python/detect.py b/python/detect.py
index 691098f5c53..1aba964a9d8 100755
--- a/python/detect.py
+++ b/python/detect.py
@@ -46,7 +46,7 @@ def main(argv):
     parser.add_argument(
         "--model_def",
         default=os.path.join(pycaffe_dir,
-                "../models/bvlc_reference_caffenet/deploy.prototxt.prototxt"),
+                "../models/bvlc_reference_caffenet/deploy.prototxt"),
         help="Model definition file."
     )
     parser.add_argument(
diff --git a/python/draw_net.py b/python/draw_net.py
index 6320f775ef7..ec76a744da3 100755
--- a/python/draw_net.py
+++ b/python/draw_net.py
@@ -2,7 +2,7 @@
 """
 Draw a graph of the net architecture.
 """
-import argparse
+from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
 from google.protobuf import text_format
 
 import caffe
@@ -14,7 +14,8 @@ def parse_args():
     """Parse input arguments
     """
 
-    parser = argparse.ArgumentParser(description='Draw a network graph')
+    parser = ArgumentParser(description=__doc__,
+                            formatter_class=ArgumentDefaultsHelpFormatter)
 
     parser.add_argument('input_net_proto_file',
                         help='Input network prototxt file')
@@ -22,10 +23,10 @@ def parse_args():
                         help='Output image file')
     parser.add_argument('--rankdir',
                         help=('One of TB (top-bottom, i.e., vertical), '
-                              'RL (right-left, i.e., horizontal), or another'
-                              'valid dot option; see'
-                              'http://www.graphviz.org/doc/info/attrs.html#k:rankdir'
-                              '(default: LR)'),
+                              'RL (right-left, i.e., horizontal), or another '
+                              'valid dot option; see '
+                              'http://www.graphviz.org/doc/info/'
+                              'attrs.html#k:rankdir'),
                         default='LR')
 
     args = parser.parse_args()
diff --git a/python/requirements.txt b/python/requirements.txt
index 7bc164a42b5..e7d89e67f48 100644
--- a/python/requirements.txt
+++ b/python/requirements.txt
@@ -3,7 +3,7 @@ numpy>=1.7.1
 scipy>=0.13.2
 scikit-image>=0.9.3
 matplotlib>=1.3.1
-ipython>=1.1.0
+ipython>=3.0.0
 h5py>=2.2.0
 leveldb>=0.191
 networkx>=1.8.1
@@ -14,3 +14,4 @@ protobuf>=2.5.0
 python-gflags>=2.0
 pyyaml>=3.10
 Pillow>=2.3.0
+six>=1.1.0
\ No newline at end of file
diff --git a/scripts/cpp_lint.py b/scripts/cpp_lint.py
index f750489f4f9..14c76ecd6bf 100755
--- a/scripts/cpp_lint.py
+++ b/scripts/cpp_lint.py
@@ -1564,7 +1564,7 @@ def CheckForMultilineCommentsAndStrings(filename, clean_lines, linenum, error):
 caffe_alt_function_list = (
     ('memset', ['caffe_set', 'caffe_memset']),
     ('cudaMemset', ['caffe_gpu_set', 'caffe_gpu_memset']),
-    ('memcpy', ['caffe_copy', 'caffe_memcpy']),
+    ('memcpy', ['caffe_copy']),
     ('cudaMemcpy', ['caffe_copy', 'caffe_gpu_memcpy']),
     )
 
diff --git a/scripts/download_model_binary.py b/scripts/download_model_binary.py
index 48e9015fd26..66f72f2477e 100755
--- a/scripts/download_model_binary.py
+++ b/scripts/download_model_binary.py
@@ -18,7 +18,7 @@ def reporthook(count, block_size, total_size):
     if count == 0:
         start_time = time.time()
         return
-    duration = time.time() - start_time
+    duration = (time.time() - start_time) or 0.01
     progress_size = int(count * block_size)
     speed = int(progress_size / (1024 * duration))
     percent = int(count * block_size * 100 / total_size)
@@ -32,7 +32,7 @@ def parse_readme_frontmatter(dirname):
     with open(readme_filename) as f:
         lines = [line.strip() for line in f.readlines()]
     top = lines.index('---')
-    bottom = lines[top + 1:].index('---')
+    bottom = lines.index('---', top + 1)
     frontmatter = yaml.load('\n'.join(lines[top + 1:bottom]))
     assert all(key in frontmatter for key in required_keys)
     return dirname, frontmatter
diff --git a/scripts/download_model_from_gist.sh b/scripts/download_model_from_gist.sh
index a1dccf78b5b..89527b7516f 100755
--- a/scripts/download_model_from_gist.sh
+++ b/scripts/download_model_from_gist.sh
@@ -18,7 +18,7 @@ fi
 
 echo "Downloading Caffe model info to $MODEL_DIR ..."
 mkdir -p $MODEL_DIR
-wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.tar.gz
-tar xzf $MODEL_DIR/gist.tar.gz --directory=$MODEL_DIR --strip-components=1
-rm $MODEL_DIR/gist.tar.gz
+wget https://gist.github.com/$GIST/download -O $MODEL_DIR/gist.zip
+unzip -j $MODEL_DIR/gist.zip -d $MODEL_DIR
+rm $MODEL_DIR/gist.zip
 echo "Done"
diff --git a/scripts/travis/travis_build_and_test.sh b/scripts/travis/travis_build_and_test.sh
index 8ff63f31fdd..174f1ee5a0a 100755
--- a/scripts/travis/travis_build_and_test.sh
+++ b/scripts/travis/travis_build_and_test.sh
@@ -1,5 +1,6 @@
 #!/bin/bash
-# Script called by Travis to do a CPU-only build of and test Caffe.
+# Script called by Travis to build and test Caffe.
+# Travis CI tests are CPU-only for lack of compatible hardware.
 
 set -e
 MAKE="make --jobs=$NUM_THREADS --keep-going"
@@ -7,8 +8,22 @@ MAKE="make --jobs=$NUM_THREADS --keep-going"
 if $WITH_CMAKE; then
   mkdir build
   cd build
-  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release -DCPU_ONLY=ON ..
+  CPU_ONLY=" -DCPU_ONLY=ON"
+  if ! $WITH_CUDA; then
+    CPU_ONLY=" -DCPU_ONLY=OFF"
+  fi
+  PYTHON_ARGS=""
+  if [ "$PYTHON_VERSION" = "3" ]; then
+    PYTHON_ARGS="$PYTHON_ARGS -Dpython_version=3 -DBOOST_LIBRARYDIR=$CONDA_DIR/lib/"
+  fi
+  if $WITH_IO; then
+    IO_ARGS="-DUSE_OPENCV=ON -DUSE_LMDB=ON -DUSE_LEVELDB=ON"
+  else
+    IO_ARGS="-DUSE_OPENCV=OFF -DUSE_LMDB=OFF -DUSE_LEVELDB=OFF"
+  fi
+  cmake -DBUILD_python=ON -DCMAKE_BUILD_TYPE=Release $CPU_ONLY $PYTHON_ARGS -DCMAKE_INCLUDE_PATH="$CONDA_DIR/include/" -DCMAKE_LIBRARY_PATH="$CONDA_DIR/lib/" $IO_ARGS ..
   $MAKE
+  $MAKE pytest
   if ! $WITH_CUDA; then
     $MAKE runtest
     $MAKE lint
@@ -19,6 +34,11 @@ else
   if ! $WITH_CUDA; then
     export CPU_ONLY=1
   fi
+  if $WITH_IO; then
+    export USE_LMDB=1
+    export USE_LEVELDB=1
+    export USE_OPENCV=1
+  fi
   $MAKE all test pycaffe warn lint || true
   if ! $WITH_CUDA; then
     $MAKE runtest
diff --git a/scripts/travis/travis_install.sh b/scripts/travis/travis_install.sh
index 0e8c37861b0..ca8c410cfbe 100755
--- a/scripts/travis/travis_install.sh
+++ b/scripts/travis/travis_install.sh
@@ -4,7 +4,6 @@
 set -e
 
 MAKE="make --jobs=$NUM_THREADS"
-
 # Install apt packages where the Ubuntu 12.04 default and ppa works for Caffe
 
 # This ppa is for gflags and glog
@@ -12,9 +11,8 @@ add-apt-repository -y ppa:tuleu/precise-backports
 apt-get -y update
 apt-get install \
     wget git curl \
-    python-dev python-numpy \
+    python-dev python-numpy python3-dev\
     libleveldb-dev libsnappy-dev libopencv-dev \
-    libboost-dev libboost-system-dev libboost-python-dev libboost-thread-dev \
     libprotobuf-dev protobuf-compiler \
     libatlas-dev libatlas-base-dev \
     libhdf5-serial-dev libgflags-dev libgoogle-glog-dev \
@@ -24,9 +22,10 @@ apt-get install \
 # if needed.  By default, Aptitude in Ubuntu 12.04 installs CMake 2.8.7, but
 # Caffe requires a minimum CMake version of 2.8.8.
 if $WITH_CMAKE; then
-  add-apt-repository -y ppa:ubuntu-sdk-team/ppa
-  apt-get -y update
-  apt-get -y install cmake
+  # cmake 3 will make sure that the python interpreter and libraries match
+  wget --no-check-certificate http://www.cmake.org/files/v3.2/cmake-3.2.3-Linux-x86_64.sh -O cmake3.sh
+  chmod +x cmake3.sh
+  ./cmake3.sh --prefix=/usr/ --skip-license --exclude-subdir
 fi
 
 # Install CUDA, if needed
@@ -47,12 +46,12 @@ if $WITH_CUDA; then
 fi
 
 # Install LMDB
-LMDB_URL=ftp://ftp.openldap.org/pub/OpenLDAP/openldap-release/openldap-2.4.39.tgz
-LMDB_FILE=/tmp/openldap.tgz
+LMDB_URL=https://github.com/LMDB/lmdb/archive/LMDB_0.9.14.tar.gz
+LMDB_FILE=/tmp/lmdb.tar.gz
 pushd .
-curl $LMDB_URL -o $LMDB_FILE
+wget $LMDB_URL -O $LMDB_FILE
 tar -C /tmp -xzvf $LMDB_FILE
-cd /tmp/openldap*/libraries/liblmdb/
+cd /tmp/lmdb*/libraries/liblmdb/
 $MAKE
 $MAKE install
 popd
@@ -60,10 +59,41 @@ rm -f $LMDB_FILE
 
 # Install the Python runtime dependencies via miniconda (this is much faster
 # than using pip for everything).
-wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
-chmod +x miniconda.sh
-./miniconda.sh -b
-export PATH=/home/travis/miniconda/bin:$PATH
-conda update --yes conda
-conda install --yes numpy scipy matplotlib scikit-image pip
-pip install protobuf
+export PATH=$CONDA_DIR/bin:$PATH
+if [ ! -d $CONDA_DIR ]; then
+  if [ "$PYTHON_VERSION" -eq "3" ]; then
+    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
+  else
+    wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
+  fi
+  chmod +x miniconda.sh
+  ./miniconda.sh -b -p $CONDA_DIR
+
+  conda update --yes conda
+  # The version of boost we're using for Python 3 depends on 3.4 for now.
+  if [ "$PYTHON_VERSION" -eq "3" ]; then
+    conda install --yes python=3.4
+  fi
+  conda install --yes numpy scipy matplotlib scikit-image pip
+  # Let conda install boost (so that boost_python matches)
+  conda install --yes -c https://conda.binstar.org/menpo boost=1.56.0
+fi
+
+# install protobuf 3 (just use the miniconda3 directory to avoid having to setup the path again)
+if [ "$PYTHON_VERSION" -eq "3" ] && [ ! -e "$CONDA_DIR/bin/protoc" ]; then
+  pushd .
+  wget https://github.com/google/protobuf/archive/v3.0.0-alpha-3.1.tar.gz -O protobuf-3.tar.gz
+  tar -C /tmp -xzvf protobuf-3.tar.gz
+  cd /tmp/protobuf-3*/
+  ./autogen.sh
+  ./configure --prefix=$CONDA_DIR
+  $MAKE
+  $MAKE install
+  popd
+fi
+
+if [ "$PYTHON_VERSION" -eq "3" ]; then
+  pip install --pre protobuf==3.0.0b2
+else
+  pip install protobuf
+fi
diff --git a/scripts/travis/travis_setup_makefile_config.sh b/scripts/travis/travis_setup_makefile_config.sh
index ba326262bf8..83aacf11fb0 100755
--- a/scripts/travis/travis_setup_makefile_config.sh
+++ b/scripts/travis/travis_setup_makefile_config.sh
@@ -11,8 +11,16 @@ if $WITH_CUDA; then
   echo "CUDA_ARCH := $GENCODE" >> Makefile.config
 fi
 
+# Remove IO library settings from Makefile.config
+# to avoid conflicts with CI configuration
+sed -i -e '/USE_LMDB/d' Makefile.config
+sed -i -e '/USE_LEVELDB/d' Makefile.config
+sed -i -e '/USE_OPENCV/d' Makefile.config
+
 cat << 'EOF' >> Makefile.config
-ANACONDA_HOME := $(HOME)/miniconda
+# Travis' nvcc doesn't like newer boost versions
+NVCCFLAGS := -Xcudafe --diag_suppress=cc_clobber_ignored -Xcudafe --diag_suppress=useless_using_declaration -Xcudafe --diag_suppress=set_but_not_used
+ANACONDA_HOME := $(CONDA_DIR)
 PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
 		$(ANACONDA_HOME)/include/python2.7 \
 		$(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include
diff --git a/src/caffe/CMakeLists.txt b/src/caffe/CMakeLists.txt
index 40e6c11f5b0..8a80c940488 100644
--- a/src/caffe/CMakeLists.txt
+++ b/src/caffe/CMakeLists.txt
@@ -20,6 +20,10 @@ endif()
 add_library(caffe ${srcs})
 target_link_libraries(caffe proto ${Caffe_LINKER_LIBS})
 caffe_default_properties(caffe)
+set_target_properties(caffe PROPERTIES
+    VERSION   ${CAFFE_TARGET_VERSION}
+    SOVERSION ${CAFFE_TARGET_SOVERSION}
+    )
 
 # ---[ Tests
  add_subdirectory(test)
diff --git a/src/caffe/blob.cpp b/src/caffe/blob.cpp
index 6d2b3f502d9..c86fd5d1d94 100644
--- a/src/caffe/blob.cpp
+++ b/src/caffe/blob.cpp
@@ -24,10 +24,16 @@ void Blob<Dtype>::Reshape(const vector<int>& shape) {
   CHECK_LE(shape.size(), kMaxBlobAxes);
   count_ = 1;
   shape_.resize(shape.size());
+  if (!shape_data_ || shape_data_->size() < shape.size() * sizeof(int)) {
+    shape_data_.reset(new SyncedMemory(shape.size() * sizeof(int)));
+  }
+  int* shape_data = static_cast<int*>(shape_data_->mutable_cpu_data());
   for (int i = 0; i < shape.size(); ++i) {
     CHECK_GE(shape[i], 0);
+    CHECK_LE(shape[i], INT_MAX / count_) << "blob size exceeds INT_MAX";
     count_ *= shape[i];
     shape_[i] = shape[i];
+    shape_data[i] = shape[i];
   }
   if (count_ > capacity_) {
     capacity_ = count_;
@@ -66,6 +72,12 @@ Blob<Dtype>::Blob(const vector<int>& shape)
   Reshape(shape);
 }
 
+template <typename Dtype>
+const int* Blob<Dtype>::gpu_shape() const {
+  CHECK(shape_data_);
+  return (const int*)shape_data_->gpu_data();
+}
+
 template <typename Dtype>
 const Dtype* Blob<Dtype>::cpu_data() const {
   CHECK(data_);
@@ -455,10 +467,25 @@ void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
   }
   // copy data
   Dtype* data_vec = mutable_cpu_data();
-  for (int i = 0; i < count_; ++i) {
-    data_vec[i] = proto.data(i);
+  if (proto.double_data_size() > 0) {
+    CHECK_EQ(count_, proto.double_data_size());
+    for (int i = 0; i < count_; ++i) {
+      data_vec[i] = proto.double_data(i);
+    }
+  } else {
+    CHECK_EQ(count_, proto.data_size());
+    for (int i = 0; i < count_; ++i) {
+      data_vec[i] = proto.data(i);
+    }
   }
-  if (proto.diff_size() > 0) {
+  if (proto.double_diff_size() > 0) {
+    CHECK_EQ(count_, proto.double_diff_size());
+    Dtype* diff_vec = mutable_cpu_diff();
+    for (int i = 0; i < count_; ++i) {
+      diff_vec[i] = proto.double_diff(i);
+    }
+  } else if (proto.diff_size() > 0) {
+    CHECK_EQ(count_, proto.diff_size());
     Dtype* diff_vec = mutable_cpu_diff();
     for (int i = 0; i < count_; ++i) {
       diff_vec[i] = proto.diff(i);
@@ -466,20 +493,40 @@ void Blob<Dtype>::FromProto(const BlobProto& proto, bool reshape) {
   }
 }
 
-template <typename Dtype>
-void Blob<Dtype>::ToProto(BlobProto* proto, bool write_diff) const {
+template <>
+void Blob<double>::ToProto(BlobProto* proto, bool write_diff) const {
+  proto->clear_shape();
+  for (int i = 0; i < shape_.size(); ++i) {
+    proto->mutable_shape()->add_dim(shape_[i]);
+  }
+  proto->clear_double_data();
+  proto->clear_double_diff();
+  const double* data_vec = cpu_data();
+  for (int i = 0; i < count_; ++i) {
+    proto->add_double_data(data_vec[i]);
+  }
+  if (write_diff) {
+    const double* diff_vec = cpu_diff();
+    for (int i = 0; i < count_; ++i) {
+      proto->add_double_diff(diff_vec[i]);
+    }
+  }
+}
+
+template <>
+void Blob<float>::ToProto(BlobProto* proto, bool write_diff) const {
   proto->clear_shape();
   for (int i = 0; i < shape_.size(); ++i) {
     proto->mutable_shape()->add_dim(shape_[i]);
   }
   proto->clear_data();
   proto->clear_diff();
-  const Dtype* data_vec = cpu_data();
+  const float* data_vec = cpu_data();
   for (int i = 0; i < count_; ++i) {
     proto->add_data(data_vec[i]);
   }
   if (write_diff) {
-    const Dtype* diff_vec = cpu_diff();
+    const float* diff_vec = cpu_diff();
     for (int i = 0; i < count_; ++i) {
       proto->add_diff(diff_vec[i]);
     }
diff --git a/src/caffe/common.cpp b/src/caffe/common.cpp
index af96cac40aa..dee681654aa 100644
--- a/src/caffe/common.cpp
+++ b/src/caffe/common.cpp
@@ -1,4 +1,6 @@
+#include <boost/thread.hpp>
 #include <glog/logging.h>
+#include <cmath>
 #include <cstdio>
 #include <ctime>
 
@@ -7,7 +9,15 @@
 
 namespace caffe {
 
-shared_ptr<Caffe> Caffe::singleton_;
+// Make sure each thread can have different values.
+static boost::thread_specific_ptr<Caffe> thread_instance_;
+
+Caffe& Caffe::Get() {
+  if (!thread_instance_.get()) {
+    thread_instance_.reset(new Caffe());
+  }
+  return *(thread_instance_.get());
+}
 
 // random seeding
 int64_t cluster_seedgen(void) {
@@ -25,7 +35,7 @@ int64_t cluster_seedgen(void) {
 
   pid = getpid();
   s = time(NULL);
-  seed = abs(((s * 181) * ((pid - 83) * 359)) % 104729);
+  seed = std::abs(((s * 181) * ((pid - 83) * 359)) % 104729);
   return seed;
 }
 
@@ -42,7 +52,8 @@ void GlobalInit(int* pargc, char*** pargv) {
 #ifdef CPU_ONLY  // CPU-only Caffe.
 
 Caffe::Caffe()
-    : random_generator_(), mode_(Caffe::CPU) { }
+    : random_generator_(), mode_(Caffe::CPU),
+      solver_count_(1), root_solver_(true) { }
 
 Caffe::~Caffe() { }
 
@@ -59,6 +70,15 @@ void Caffe::DeviceQuery() {
   NO_GPU;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  NO_GPU;
+  return false;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  NO_GPU;
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:
@@ -86,7 +106,7 @@ void* Caffe::RNG::generator() {
 
 Caffe::Caffe()
     : cublas_handle_(NULL), curand_generator_(NULL), random_generator_(),
-    mode_(Caffe::CPU) {
+    mode_(Caffe::CPU), solver_count_(1), root_solver_(true) {
   // Try to create a cublas handler, and report an error if failed (but we will
   // keep the program running as one might just want to run CPU code).
   if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
@@ -181,6 +201,39 @@ void Caffe::DeviceQuery() {
   return;
 }
 
+bool Caffe::CheckDevice(const int device_id) {
+  // This function checks the availability of GPU #device_id.
+  // It attempts to create a context on the device by calling cudaFree(0).
+  // cudaSetDevice() alone is not sufficient to check the availability.
+  // It lazily records device_id, however, does not initialize a
+  // context. So it does not know if the host thread has the permission to use
+  // the device or not.
+  //
+  // In a shared environment where the devices are set to EXCLUSIVE_PROCESS
+  // or EXCLUSIVE_THREAD mode, cudaSetDevice() returns cudaSuccess
+  // even if the device is exclusively occupied by another process or thread.
+  // Cuda operations that initialize the context are needed to check
+  // the permission. cudaFree(0) is one of those with no side effect,
+  // except the context initialization.
+  bool r = ((cudaSuccess == cudaSetDevice(device_id)) &&
+            (cudaSuccess == cudaFree(0)));
+  // reset any error that may have occurred.
+  cudaGetLastError();
+  return r;
+}
+
+int Caffe::FindDevice(const int start_id) {
+  // This function finds the first available device by checking devices with
+  // ordinal from start_id to the highest available value. In the
+  // EXCLUSIVE_PROCESS or EXCLUSIVE_THREAD mode, if it succeeds, it also
+  // claims the device due to the initialization of the context.
+  int count = 0;
+  CUDA_CHECK(cudaGetDeviceCount(&count));
+  for (int i = start_id; i < count; i++) {
+    if (CheckDevice(i)) return i;
+  }
+  return -1;
+}
 
 class Caffe::RNG::Generator {
  public:
diff --git a/src/caffe/data_reader.cpp b/src/caffe/data_reader.cpp
new file mode 100644
index 00000000000..9f019bbfcb7
--- /dev/null
+++ b/src/caffe/data_reader.cpp
@@ -0,0 +1,119 @@
+#include <boost/thread.hpp>
+#include <map>
+#include <string>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/data_reader.hpp"
+#include "caffe/layers/data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+
+namespace caffe {
+
+using boost::weak_ptr;
+
+map<const string, weak_ptr<DataReader::Body> > DataReader::bodies_;
+static boost::mutex bodies_mutex_;
+
+DataReader::DataReader(const LayerParameter& param)
+    : queue_pair_(new QueuePair(  //
+        param.data_param().prefetch() * param.data_param().batch_size())) {
+  // Get or create a body
+  boost::mutex::scoped_lock lock(bodies_mutex_);
+  string key = source_key(param);
+  weak_ptr<Body>& weak = bodies_[key];
+  body_ = weak.lock();
+  if (!body_) {
+    body_.reset(new Body(param));
+    bodies_[key] = weak_ptr<Body>(body_);
+  }
+  body_->new_queue_pairs_.push(queue_pair_);
+}
+
+DataReader::~DataReader() {
+  string key = source_key(body_->param_);
+  body_.reset();
+  boost::mutex::scoped_lock lock(bodies_mutex_);
+  if (bodies_[key].expired()) {
+    bodies_.erase(key);
+  }
+}
+
+//
+
+DataReader::QueuePair::QueuePair(int size) {
+  // Initialize the free queue with requested number of datums
+  for (int i = 0; i < size; ++i) {
+    free_.push(new Datum());
+  }
+}
+
+DataReader::QueuePair::~QueuePair() {
+  Datum* datum;
+  while (free_.try_pop(&datum)) {
+    delete datum;
+  }
+  while (full_.try_pop(&datum)) {
+    delete datum;
+  }
+}
+
+//
+
+DataReader::Body::Body(const LayerParameter& param)
+    : param_(param),
+      new_queue_pairs_() {
+  StartInternalThread();
+}
+
+DataReader::Body::~Body() {
+  StopInternalThread();
+}
+
+void DataReader::Body::InternalThreadEntry() {
+  shared_ptr<db::DB> db(db::GetDB(param_.data_param().backend()));
+  db->Open(param_.data_param().source(), db::READ);
+  shared_ptr<db::Cursor> cursor(db->NewCursor());
+  vector<shared_ptr<QueuePair> > qps;
+  try {
+    int solver_count = param_.phase() == TRAIN ? Caffe::solver_count() : 1;
+
+    // To ensure deterministic runs, only start running once all solvers
+    // are ready. But solvers need to peek on one item during initialization,
+    // so read one item, then wait for the next solver.
+    for (int i = 0; i < solver_count; ++i) {
+      shared_ptr<QueuePair> qp(new_queue_pairs_.pop());
+      read_one(cursor.get(), qp.get());
+      qps.push_back(qp);
+    }
+    // Main loop
+    while (!must_stop()) {
+      for (int i = 0; i < solver_count; ++i) {
+        read_one(cursor.get(), qps[i].get());
+      }
+      // Check no additional readers have been created. This can happen if
+      // more than one net is trained at a time per process, whether single
+      // or multi solver. It might also happen if two data layers have same
+      // name and same source.
+      CHECK_EQ(new_queue_pairs_.size(), 0);
+    }
+  } catch (boost::thread_interrupted&) {
+    // Interrupted exception is expected on shutdown
+  }
+}
+
+void DataReader::Body::read_one(db::Cursor* cursor, QueuePair* qp) {
+  Datum* datum = qp->free_.pop();
+  // TODO deserialize in-place instead of copy?
+  datum->ParseFromString(cursor->value());
+  qp->full_.push(datum);
+
+  // go to the next iter
+  cursor->Next();
+  if (!cursor->valid()) {
+    DLOG(INFO) << "Restarting data prefetching from start.";
+    cursor->SeekToFirst();
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/data_transformer.cpp b/src/caffe/data_transformer.cpp
index b0b98e478c1..7189d67e289 100644
--- a/src/caffe/data_transformer.cpp
+++ b/src/caffe/data_transformer.cpp
@@ -1,4 +1,6 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <string>
 #include <vector>
@@ -19,7 +21,9 @@ DataTransformer<Dtype>::DataTransformer(const TransformationParameter& param,
     CHECK_EQ(param_.mean_value_size(), 0) <<
       "Cannot specify mean_file and mean_value at the same time";
     const string& mean_file = param.mean_file();
-    LOG(INFO) << "Loading mean file from: " << mean_file;
+    if (Caffe::root_solver()) {
+      LOG(INFO) << "Loading mean file from: " << mean_file;
+    }
     BlobProto blob_proto;
     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
     data_mean_.FromProto(blob_proto);
@@ -122,13 +126,39 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   }
 }
 
+
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const Datum& datum,
                                        Blob<Dtype>* transformed_blob) {
+  // If datum is encoded, decoded and transform the cv::image.
+  if (datum.encoded()) {
+#ifdef USE_OPENCV
+    CHECK(!(param_.force_color() && param_.force_gray()))
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // Transform the cv::image into blob.
+    return Transform(cv_img, transformed_blob);
+#else
+    LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
+  } else {
+    if (param_.force_color() || param_.force_gray()) {
+      LOG(ERROR) << "force_color and force_gray only for encoded datum";
+    }
+  }
+
+  const int crop_size = param_.crop_size();
   const int datum_channels = datum.channels();
   const int datum_height = datum.height();
   const int datum_width = datum.width();
 
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -139,8 +169,6 @@ void DataTransformer<Dtype>::Transform(const Datum& datum,
   CHECK_LE(width, datum_width);
   CHECK_GE(num, 1);
 
-  const int crop_size = param_.crop_size();
-
   if (crop_size) {
     CHECK_EQ(crop_size, height);
     CHECK_EQ(crop_size, width);
@@ -173,6 +201,7 @@ void DataTransformer<Dtype>::Transform(const vector<Datum> & datum_vector,
   }
 }
 
+#ifdef USE_OPENCV
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
                                        Blob<Dtype>* transformed_blob) {
@@ -196,10 +225,12 @@ void DataTransformer<Dtype>::Transform(const vector<cv::Mat> & mat_vector,
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int img_channels = cv_img.channels();
   const int img_height = cv_img.rows;
   const int img_width = cv_img.cols;
 
+  // Check dimensions.
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
   const int width = transformed_blob->width();
@@ -212,7 +243,6 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
 
   CHECK(cv_img.depth() == CV_8U) << "Image data type must be unsigned byte";
 
-  const int crop_size = param_.crop_size();
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
@@ -293,15 +323,28 @@ void DataTransformer<Dtype>::Transform(const cv::Mat& cv_img,
     }
   }
 }
+#endif  // USE_OPENCV
 
 template<typename Dtype>
 void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
                                        Blob<Dtype>* transformed_blob) {
+  const int crop_size = param_.crop_size();
   const int input_num = input_blob->num();
   const int input_channels = input_blob->channels();
   const int input_height = input_blob->height();
   const int input_width = input_blob->width();
 
+  if (transformed_blob->count() == 0) {
+    // Initialize transformed_blob with the right shape.
+    if (crop_size) {
+      transformed_blob->Reshape(input_num, input_channels,
+                                crop_size, crop_size);
+    } else {
+      transformed_blob->Reshape(input_num, input_channels,
+                                input_height, input_width);
+    }
+  }
+
   const int num = transformed_blob->num();
   const int channels = transformed_blob->channels();
   const int height = transformed_blob->height();
@@ -313,7 +356,7 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   CHECK_GE(input_height, height);
   CHECK_GE(input_width, width);
 
-  const int crop_size = param_.crop_size();
+
   const Dtype scale = param_.scale();
   const bool do_mirror = param_.mirror() && Rand(2);
   const bool has_mean_file = param_.has_mean_file();
@@ -395,6 +438,87 @@ void DataTransformer<Dtype>::Transform(Blob<Dtype>* input_blob,
   }
 }
 
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const Datum& datum) {
+  if (datum.encoded()) {
+#ifdef USE_OPENCV
+    CHECK(!(param_.force_color() && param_.force_gray()))
+        << "cannot set both force_color and force_gray";
+    cv::Mat cv_img;
+    if (param_.force_color() || param_.force_gray()) {
+    // If force_color then decode in color otherwise decode in gray.
+      cv_img = DecodeDatumToCVMat(datum, param_.force_color());
+    } else {
+      cv_img = DecodeDatumToCVMatNative(datum);
+    }
+    // InferBlobShape using the cv::image.
+    return InferBlobShape(cv_img);
+#else
+    LOG(FATAL) << "Encoded datum requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
+  }
+  const int crop_size = param_.crop_size();
+  const int datum_channels = datum.channels();
+  const int datum_height = datum.height();
+  const int datum_width = datum.width();
+  // Check dimensions.
+  CHECK_GT(datum_channels, 0);
+  CHECK_GE(datum_height, crop_size);
+  CHECK_GE(datum_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = datum_channels;
+  shape[2] = (crop_size)? crop_size: datum_height;
+  shape[3] = (crop_size)? crop_size: datum_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<Datum> & datum_vector) {
+  const int num = datum_vector.size();
+  CHECK_GT(num, 0) << "There is no datum to in the vector";
+  // Use first datum in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(datum_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+
+#ifdef USE_OPENCV
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(const cv::Mat& cv_img) {
+  const int crop_size = param_.crop_size();
+  const int img_channels = cv_img.channels();
+  const int img_height = cv_img.rows;
+  const int img_width = cv_img.cols;
+  // Check dimensions.
+  CHECK_GT(img_channels, 0);
+  CHECK_GE(img_height, crop_size);
+  CHECK_GE(img_width, crop_size);
+  // Build BlobShape.
+  vector<int> shape(4);
+  shape[0] = 1;
+  shape[1] = img_channels;
+  shape[2] = (crop_size)? crop_size: img_height;
+  shape[3] = (crop_size)? crop_size: img_width;
+  return shape;
+}
+
+template<typename Dtype>
+vector<int> DataTransformer<Dtype>::InferBlobShape(
+    const vector<cv::Mat> & mat_vector) {
+  const int num = mat_vector.size();
+  CHECK_GT(num, 0) << "There is no cv_img to in the vector";
+  // Use first cv_img in the vector to InferBlobShape.
+  vector<int> shape = InferBlobShape(mat_vector[0]);
+  // Adjust num to the size of the vector.
+  shape[0] = num;
+  return shape;
+}
+#endif  // USE_OPENCV
+
 template <typename Dtype>
 void DataTransformer<Dtype>::InitRand() {
   const bool needs_rand = param_.mirror() ||
diff --git a/src/caffe/internal_thread.cpp b/src/caffe/internal_thread.cpp
index c2d19d433b4..104884e0295 100644
--- a/src/caffe/internal_thread.cpp
+++ b/src/caffe/internal_thread.cpp
@@ -1,40 +1,66 @@
 #include <boost/thread.hpp>
+#include <exception>
+
 #include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
 InternalThread::~InternalThread() {
-  WaitForInternalThreadToExit();
+  StopInternalThread();
 }
 
 bool InternalThread::is_started() const {
-  return thread_.get() != NULL && thread_->joinable();
+  return thread_ && thread_->joinable();
+}
+
+bool InternalThread::must_stop() {
+  return thread_ && thread_->interruption_requested();
 }
 
+void InternalThread::StartInternalThread() {
+  CHECK(!is_started()) << "Threads should persist and not be restarted.";
+
+  int device = 0;
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaGetDevice(&device));
+#endif
+  Caffe::Brew mode = Caffe::mode();
+  int rand_seed = caffe_rng_rand();
+  int solver_count = Caffe::solver_count();
+  bool root_solver = Caffe::root_solver();
 
-bool InternalThread::StartInternalThread() {
-  if (!WaitForInternalThreadToExit()) {
-    return false;
-  }
   try {
-    thread_.reset(
-        new boost::thread(&InternalThread::InternalThreadEntry, this));
-  } catch (...) {
-    return false;
+    thread_.reset(new boost::thread(&InternalThread::entry, this, device, mode,
+          rand_seed, solver_count, root_solver));
+  } catch (std::exception& e) {
+    LOG(FATAL) << "Thread exception: " << e.what();
   }
-  return true;
 }
 
-/** Will not return until the internal thread has exited. */
-bool InternalThread::WaitForInternalThreadToExit() {
+void InternalThread::entry(int device, Caffe::Brew mode, int rand_seed,
+    int solver_count, bool root_solver) {
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaSetDevice(device));
+#endif
+  Caffe::set_mode(mode);
+  Caffe::set_random_seed(rand_seed);
+  Caffe::set_solver_count(solver_count);
+  Caffe::set_root_solver(root_solver);
+
+  InternalThreadEntry();
+}
+
+void InternalThread::StopInternalThread() {
   if (is_started()) {
+    thread_->interrupt();
     try {
       thread_->join();
-    } catch (...) {
-      return false;
+    } catch (boost::thread_interrupted&) {
+    } catch (std::exception& e) {
+      LOG(FATAL) << "Thread exception: " << e.what();
     }
   }
-  return true;
 }
 
 }  // namespace caffe
diff --git a/src/caffe/layer.cpp b/src/caffe/layer.cpp
new file mode 100644
index 00000000000..3b9128986ae
--- /dev/null
+++ b/src/caffe/layer.cpp
@@ -0,0 +1,27 @@
+#include <boost/thread.hpp>
+#include "caffe/layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void Layer<Dtype>::InitMutex() {
+  forward_mutex_.reset(new boost::mutex());
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Lock() {
+  if (IsShared()) {
+    forward_mutex_->lock();
+  }
+}
+
+template <typename Dtype>
+void Layer<Dtype>::Unlock() {
+  if (IsShared()) {
+    forward_mutex_->unlock();
+  }
+}
+
+INSTANTIATE_CLASS(Layer);
+
+}  // namespace caffe
diff --git a/src/caffe/layer_factory.cpp b/src/caffe/layer_factory.cpp
index d6a1cac5090..e967bd6181c 100644
--- a/src/caffe/layer_factory.cpp
+++ b/src/caffe/layer_factory.cpp
@@ -1,12 +1,34 @@
+// Make sure we include Python.h before any system header
+// to avoid _POSIX_C_SOURCE redefinition
+#ifdef WITH_PYTHON_LAYER
+#include <boost/python.hpp>
+#endif
 #include <string>
 
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
+#include "caffe/layers/conv_layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
 
 #ifdef WITH_PYTHON_LAYER
-#include "caffe/python_layer.hpp"
+#include "caffe/layers/python_layer.hpp"
 #endif
 
 namespace caffe {
@@ -15,17 +37,32 @@ namespace caffe {
 template <typename Dtype>
 shared_ptr<Layer<Dtype> > GetConvolutionLayer(
     const LayerParameter& param) {
-  ConvolutionParameter_Engine engine = param.convolution_param().engine();
+  ConvolutionParameter conv_param = param.convolution_param();
+  ConvolutionParameter_Engine engine = conv_param.engine();
+#ifdef USE_CUDNN
+  bool use_dilation = false;
+  for (int i = 0; i < conv_param.dilation_size(); ++i) {
+    if (conv_param.dilation(i) > 1) {
+      use_dilation = true;
+    }
+  }
+#endif
   if (engine == ConvolutionParameter_Engine_DEFAULT) {
     engine = ConvolutionParameter_Engine_CAFFE;
 #ifdef USE_CUDNN
-    engine = ConvolutionParameter_Engine_CUDNN;
+    if (!use_dilation) {
+      engine = ConvolutionParameter_Engine_CUDNN;
+    }
 #endif
   }
   if (engine == ConvolutionParameter_Engine_CAFFE) {
     return shared_ptr<Layer<Dtype> >(new ConvolutionLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == ConvolutionParameter_Engine_CUDNN) {
+    if (use_dilation) {
+      LOG(FATAL) << "CuDNN doesn't support the dilated convolution at Layer "
+                 << param.name();
+    }
     return shared_ptr<Layer<Dtype> >(new CuDNNConvolutionLayer<Dtype>(param));
 #endif
   } else {
@@ -49,14 +86,21 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
     return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
 #ifdef USE_CUDNN
   } else if (engine == PoolingParameter_Engine_CUDNN) {
-    PoolingParameter p_param = param.pooling_param();
-    if (p_param.pad() || p_param.pad_h() || p_param.pad_w() ||
-        param.top_size() > 1) {
-      LOG(INFO) << "CUDNN does not support padding or multiple tops. "
+    if (param.top_size() > 1) {
+      LOG(INFO) << "cuDNN does not support multiple tops. "
                 << "Using Caffe's own pooling layer.";
       return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
     }
-    return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+    // CuDNN assumes layers are not being modified in place, thus
+    // breaking our index tracking for updates in some cases in Caffe.
+    // Until there is a workaround in Caffe (index management) or
+    // cuDNN, use Caffe layer to max pooling, or don't use in place
+    // layers after max pooling layers
+    if (param.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) {
+        return shared_ptr<Layer<Dtype> >(new PoolingLayer<Dtype>(param));
+    } else {
+        return shared_ptr<Layer<Dtype> >(new CuDNNPoolingLayer<Dtype>(param));
+    }
 #endif
   } else {
     LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
@@ -65,6 +109,43 @@ shared_ptr<Layer<Dtype> > GetPoolingLayer(const LayerParameter& param) {
 
 REGISTER_LAYER_CREATOR(Pooling, GetPoolingLayer);
 
+// Get LRN layer according to engine
+template <typename Dtype>
+shared_ptr<Layer<Dtype> > GetLRNLayer(const LayerParameter& param) {
+  LRNParameter_Engine engine = param.lrn_param().engine();
+
+  if (engine == LRNParameter_Engine_DEFAULT) {
+#ifdef USE_CUDNN
+    engine = LRNParameter_Engine_CUDNN;
+#else
+    engine = LRNParameter_Engine_CAFFE;
+#endif
+  }
+
+  if (engine == LRNParameter_Engine_CAFFE) {
+    return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+#ifdef USE_CUDNN
+  } else if (engine == LRNParameter_Engine_CUDNN) {
+    LRNParameter lrn_param = param.lrn_param();
+
+    if (lrn_param.norm_region() ==LRNParameter_NormRegion_WITHIN_CHANNEL) {
+      return shared_ptr<Layer<Dtype> >(new CuDNNLCNLayer<Dtype>(param));
+    } else {
+      // local size is too big to be handled through cuDNN
+      if (param.lrn_param().local_size() > CUDNN_LRN_MAX_N) {
+        return shared_ptr<Layer<Dtype> >(new LRNLayer<Dtype>(param));
+      } else {
+        return shared_ptr<Layer<Dtype> >(new CuDNNLRNLayer<Dtype>(param));
+      }
+    }
+#endif
+  } else {
+    LOG(FATAL) << "Layer " << param.name() << " has unknown engine.";
+  }
+}
+
+REGISTER_LAYER_CREATOR(LRN, GetLRNLayer);
+
 // Get relu layer according to engine.
 template <typename Dtype>
 shared_ptr<Layer<Dtype> > GetReLULayer(const LayerParameter& param) {
diff --git a/src/caffe/layers/absval_layer.cpp b/src/caffe/layers/absval_layer.cpp
index 5ce28c9e2b4..855bf0bfacb 100644
--- a/src/caffe/layers/absval_layer.cpp
+++ b/src/caffe/layers/absval_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/neuron_layers.hpp"
+#include "caffe/layers/absval_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
diff --git a/src/caffe/layers/absval_layer.cu b/src/caffe/layers/absval_layer.cu
index 91f3c77fe9a..6c927e6fabc 100644
--- a/src/caffe/layers/absval_layer.cu
+++ b/src/caffe/layers/absval_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/absval_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -18,7 +17,6 @@ template <typename Dtype>
 void AbsValLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const int count = top[0]->count();
-  const Dtype* top_data = top[0]->gpu_data();
   const Dtype* top_diff = top[0]->gpu_diff();
   if (propagate_down[0]) {
     const Dtype* bottom_data = bottom[0]->gpu_data();
diff --git a/src/caffe/layers/accuracy_layer.cpp b/src/caffe/layers/accuracy_layer.cpp
index 90aad675ed3..4eddbb5c850 100644
--- a/src/caffe/layers/accuracy_layer.cpp
+++ b/src/caffe/layers/accuracy_layer.cpp
@@ -1,12 +1,9 @@
-#include <algorithm>
 #include <functional>
 #include <utility>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -38,6 +35,13 @@ void AccuracyLayer<Dtype>::Reshape(
       << "with integer values in {0, 1, ..., C-1}.";
   vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.
   top[0]->Reshape(top_shape);
+  if (top.size() > 1) {
+    // Per-class accuracy is a vector; 1 axes.
+    vector<int> top_shape_per_class(1);
+    top_shape_per_class[0] = bottom[0]->shape(label_axis_);
+    top[1]->Reshape(top_shape_per_class);
+    nums_buffer_.Reshape(top_shape_per_class);
+  }
 }
 
 template <typename Dtype>
@@ -50,6 +54,10 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const int num_labels = bottom[0]->shape(label_axis_);
   vector<Dtype> maxval(top_k_+1);
   vector<int> max_id(top_k_+1);
+  if (top.size() > 1) {
+    caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());
+    caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());
+  }
   int count = 0;
   for (int i = 0; i < outer_num_; ++i) {
     for (int j = 0; j < inner_num_; ++j) {
@@ -58,6 +66,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       if (has_ignore_label_ && label_value == ignore_label_) {
         continue;
       }
+      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];
       DCHECK_GE(label_value, 0);
       DCHECK_LT(label_value, num_labels);
       // Top-k accuracy
@@ -73,6 +82,7 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       for (int k = 0; k < top_k_; k++) {
         if (bottom_data_vector[k].second == label_value) {
           ++accuracy;
+          if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];
           break;
         }
       }
@@ -82,6 +92,13 @@ void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 
   // LOG(INFO) << "Accuracy: " << accuracy;
   top[0]->mutable_cpu_data()[0] = accuracy / count;
+  if (top.size() > 1) {
+    for (int i = 0; i < top[1]->count(); ++i) {
+      top[1]->mutable_cpu_data()[i] =
+          nums_buffer_.cpu_data()[i] == 0 ? 0
+          : top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i];
+    }
+  }
   // Accuracy layer should not be used as a loss function.
 }
 
diff --git a/src/caffe/layers/argmax_layer.cpp b/src/caffe/layers/argmax_layer.cpp
index c4040cdcaaa..2d3d6f2d3ff 100644
--- a/src/caffe/layers/argmax_layer.cpp
+++ b/src/caffe/layers/argmax_layer.cpp
@@ -3,31 +3,52 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/argmax_layer.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  out_max_val_ = this->layer_param_.argmax_param().out_max_val();
-  top_k_ = this->layer_param_.argmax_param().top_k();
-  CHECK_GE(top_k_, 1) << " top k must not be less than 1.";
-  CHECK_LE(top_k_, bottom[0]->count() / bottom[0]->num())
-      << "top_k must be less than or equal to the number of classes.";
+  const ArgMaxParameter& argmax_param = this->layer_param_.argmax_param();
+  out_max_val_ = argmax_param.out_max_val();
+  top_k_ = argmax_param.top_k();
+  has_axis_ = argmax_param.has_axis();
+  CHECK_GE(top_k_, 1) << "top k must not be less than 1.";
+  if (has_axis_) {
+    axis_ = bottom[0]->CanonicalAxisIndex(argmax_param.axis());
+    CHECK_GE(axis_, 0) << "axis must not be less than 0.";
+    CHECK_LE(axis_, bottom[0]->num_axes()) <<
+      "axis must be less than or equal to the number of axis.";
+    CHECK_LE(top_k_, bottom[0]->shape(axis_))
+      << "top_k must be less than or equal to the dimension of the axis.";
+  } else {
+    CHECK_LE(top_k_, bottom[0]->count(1))
+      << "top_k must be less than or equal to"
+        " the dimension of the flattened bottom blob per instance.";
+  }
 }
 
 template <typename Dtype>
 void ArgMaxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  if (out_max_val_) {
-    // Produces max_ind and max_val
-    top[0]->Reshape(bottom[0]->num(), 2, top_k_, 1);
+  int num_top_axes = bottom[0]->num_axes();
+  if ( num_top_axes < 3 ) num_top_axes = 3;
+  std::vector<int> shape(num_top_axes, 1);
+  if (has_axis_) {
+    // Produces max_ind or max_val per axis
+    shape = bottom[0]->shape();
+    shape[axis_] = top_k_;
   } else {
-    // Produces only max_ind
-    top[0]->Reshape(bottom[0]->num(), 1, top_k_, 1);
+    shape[0] = bottom[0]->shape(0);
+    // Produces max_ind
+    shape[2] = top_k_;
+    if (out_max_val_) {
+      // Produces max_ind and max_val
+      shape[1] = 2;
+    }
   }
+  top[0]->Reshape(shape);
 }
 
 template <typename Dtype>
@@ -35,23 +56,40 @@ void ArgMaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  int num = bottom[0]->num();
-  int dim = bottom[0]->count() / bottom[0]->num();
+  int dim, axis_dist;
+  if (has_axis_) {
+    dim = bottom[0]->shape(axis_);
+    // Distance between values of axis in blob
+    axis_dist = bottom[0]->count(axis_) / dim;
+  } else {
+    dim = bottom[0]->count(1);
+    axis_dist = 1;
+  }
+  int num = bottom[0]->count() / dim;
+  std::vector<std::pair<Dtype, int> > bottom_data_vector(dim);
   for (int i = 0; i < num; ++i) {
-    std::vector<std::pair<Dtype, int> > bottom_data_vector;
     for (int j = 0; j < dim; ++j) {
-      bottom_data_vector.push_back(
-          std::make_pair(bottom_data[i * dim + j], j));
+      bottom_data_vector[j] = std::make_pair(
+        bottom_data[(i / axis_dist * dim + j) * axis_dist + i % axis_dist], j);
     }
     std::partial_sort(
         bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,
         bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());
     for (int j = 0; j < top_k_; ++j) {
-      top_data[top[0]->offset(i, 0, j)] = bottom_data_vector[j].second;
-    }
-    if (out_max_val_) {
-      for (int j = 0; j < top_k_; ++j) {
-        top_data[top[0]->offset(i, 1, j)] = bottom_data_vector[j].first;
+      if (out_max_val_) {
+        if (has_axis_) {
+          // Produces max_val per axis
+          top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+            = bottom_data_vector[j].first;
+        } else {
+          // Produces max_ind and max_val
+          top_data[2 * i * top_k_ + j] = bottom_data_vector[j].second;
+          top_data[2 * i * top_k_ + top_k_ + j] = bottom_data_vector[j].first;
+        }
+      } else {
+        // Produces max_ind per axis
+        top_data[(i / axis_dist * top_k_ + j) * axis_dist + i % axis_dist]
+          = bottom_data_vector[j].second;
       }
     }
   }
diff --git a/src/caffe/layers/base_conv_layer.cpp b/src/caffe/layers/base_conv_layer.cpp
index ccb3adc7e89..4a4c68e009a 100644
--- a/src/caffe/layers/base_conv_layer.cpp
+++ b/src/caffe/layers/base_conv_layer.cpp
@@ -1,60 +1,121 @@
+#include <algorithm>
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/base_conv_layer.hpp"
 #include "caffe/util/im2col.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
   // Configure the kernel size, padding, stride, and inputs.
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  force_nd_im2col_ = conv_param.force_nd_im2col();
+  channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+  const int first_spatial_axis = channel_axis_ + 1;
+  const int num_axes = bottom[0]->num_axes();
+  num_spatial_axes_ = num_axes - first_spatial_axis;
+  CHECK_GE(num_spatial_axes_, 0);
+  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+  vector<int> spatial_dim_blob_shape(1, std::max(num_spatial_axes_, 1));
+  // Setup filter kernel dimensions (kernel_shape_).
+  kernel_shape_.Reshape(spatial_dim_blob_shape);
+  int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+  if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "kernel_h & kernel_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.kernel_size_size())
+        << "Either kernel_size or kernel_h/w should be specified; not both.";
+    kernel_shape_data[0] = conv_param.kernel_h();
+    kernel_shape_data[1] = conv_param.kernel_w();
   } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
+    const int num_kernel_dims = conv_param.kernel_size_size();
+    CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+        << "kernel_size must be specified once, or once per spatial dimension "
+        << "(kernel_size specified " << num_kernel_dims << " times; "
+        << num_spatial_axes_ << " spatial dims).";
+      for (int i = 0; i < num_spatial_axes_; ++i) {
+        kernel_shape_data[i] =
+            conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+      }
   }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
+  }
+  // Setup stride dimensions (stride_).
+  stride_.Reshape(spatial_dim_blob_shape);
+  int* stride_data = stride_.mutable_cpu_data();
+  if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "stride_h & stride_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.stride_size())
+        << "Either stride or stride_h/w should be specified; not both.";
+    stride_data[0] = conv_param.stride_h();
+    stride_data[1] = conv_param.stride_w();
   } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
+    const int num_stride_dims = conv_param.stride_size();
+    CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+          num_stride_dims == num_spatial_axes_)
+        << "stride must be specified once, or once per spatial dimension "
+        << "(stride specified " << num_stride_dims << " times; "
+        << num_spatial_axes_ << " spatial dims).";
+    const int kDefaultStride = 1;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+          conv_param.stride((num_stride_dims == 1) ? 0 : i);
+      CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+    }
   }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
+  // Setup pad dimensions (pad_).
+  pad_.Reshape(spatial_dim_blob_shape);
+  int* pad_data = pad_.mutable_cpu_data();
+  if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "pad_h & pad_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.pad_size())
+        << "Either pad or pad_h/w should be specified; not both.";
+    pad_data[0] = conv_param.pad_h();
+    pad_data[1] = conv_param.pad_w();
   } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
+    const int num_pad_dims = conv_param.pad_size();
+    CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+          num_pad_dims == num_spatial_axes_)
+        << "pad must be specified once, or once per spatial dimension "
+        << "(pad specified " << num_pad_dims << " times; "
+        << num_spatial_axes_ << " spatial dims).";
+    const int kDefaultPad = 0;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+          conv_param.pad((num_pad_dims == 1) ? 0 : i);
+    }
+  }
+  // Setup dilation dimensions (dilation_).
+  dilation_.Reshape(spatial_dim_blob_shape);
+  int* dilation_data = dilation_.mutable_cpu_data();
+  const int num_dilation_dims = conv_param.dilation_size();
+  CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+        num_dilation_dims == num_spatial_axes_)
+      << "dilation must be specified once, or once per spatial dimension "
+      << "(dilation specified " << num_dilation_dims << " times; "
+      << num_spatial_axes_ << " spatial dims).";
+  const int kDefaultDilation = 1;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+                       conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
   }
   // Special case: im2col is the identity for 1x1 convolution with stride 1
   // and no padding, so flag for skipping the buffer and transformation.
-  is_1x1_ = kernel_w_ == 1 && kernel_h_ == 1
-      && stride_h_ == 1 && stride_w_ == 1 && pad_h_ == 0 && pad_w_ == 0;
+  is_1x1_ = true;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    is_1x1_ &=
+        kernel_shape_data[i] == 1 && stride_data[i] == 1 && pad_data[i] == 0;
+    if (!is_1x1_) { break; }
+  }
   // Configure output channels and groups.
-  channels_ = bottom[0]->channels();
+  channels_ = bottom[0]->shape(channel_axis_);
   num_output_ = this->layer_param_.convolution_param().num_output();
   CHECK_GT(num_output_, 0);
   group_ = this->layer_param_.convolution_param().group();
@@ -71,8 +132,29 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Handle the parameters: weights and biases.
   // - blobs_[0] holds the filter weights
   // - blobs_[1] holds the biases (optional)
+  vector<int> weight_shape(2);
+  weight_shape[0] = conv_out_channels_;
+  weight_shape[1] = conv_in_channels_ / group_;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    weight_shape.push_back(kernel_shape_data[i]);
+  }
   bias_term_ = this->layer_param_.convolution_param().bias_term();
+  vector<int> bias_shape(bias_term_, num_output_);
   if (this->blobs_.size() > 0) {
+    CHECK_EQ(1 + bias_term_, this->blobs_.size())
+        << "Incorrect number of weight blobs.";
+    if (weight_shape != this->blobs_[0]->shape()) {
+      Blob<Dtype> weight_shaped_blob(weight_shape);
+      LOG(FATAL) << "Incorrect weight shape: expected shape "
+          << weight_shaped_blob.shape_string() << "; instead, shape was "
+          << this->blobs_[0]->shape_string();
+    }
+    if (bias_term_ && bias_shape != this->blobs_[1]->shape()) {
+      Blob<Dtype> bias_shaped_blob(bias_shape);
+      LOG(FATAL) << "Incorrect bias shape: expected shape "
+          << bias_shaped_blob.shape_string() << "; instead, shape was "
+          << this->blobs_[1]->shape_string();
+    }
     LOG(INFO) << "Skipping parameter initialization";
   } else {
     if (bias_term_) {
@@ -82,20 +164,20 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     }
     // Initialize and fill the weights:
     // output channels x input channels per-group x kernel height x kernel width
-    this->blobs_[0].reset(new Blob<Dtype>(
-        conv_out_channels_, conv_in_channels_ / group_, kernel_h_, kernel_w_));
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
         this->layer_param_.convolution_param().weight_filler()));
     weight_filler->Fill(this->blobs_[0].get());
     // If necessary, initialize and fill the biases.
     if (bias_term_) {
-      vector<int> bias_shape(1, num_output_);
       this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
       shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
           this->layer_param_.convolution_param().bias_filler()));
       bias_filler->Fill(this->blobs_[1].get());
     }
   }
+  kernel_dim_ = this->blobs_[0]->count(1);
+  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_;
   // Propagate gradients to the parameters (as directed by backward pass).
   this->param_propagate_down_.resize(this->blobs_.size(), true);
 }
@@ -103,52 +185,68 @@ void BaseConvolutionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
-  num_ = bottom[0]->num();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  CHECK_EQ(bottom[0]->channels(), channels_) << "Input size incompatible with"
-    " convolution kernel.";
+  const int first_spatial_axis = channel_axis_ + 1;
+  CHECK_EQ(bottom[0]->num_axes(), first_spatial_axis + num_spatial_axes_)
+      << "bottom num_axes may not change.";
+  num_ = bottom[0]->count(0, channel_axis_);
+  CHECK_EQ(bottom[0]->shape(channel_axis_), channels_)
+      << "Input size incompatible with convolution kernel.";
   // TODO: generalize to handle inputs of different shapes.
   for (int bottom_id = 1; bottom_id < bottom.size(); ++bottom_id) {
-    CHECK_EQ(num_, bottom[bottom_id]->num()) << "Inputs must have same num.";
-    CHECK_EQ(channels_, bottom[bottom_id]->channels())
-        << "Inputs must have same channels.";
-    CHECK_EQ(height_, bottom[bottom_id]->height())
-        << "Inputs must have same height.";
-    CHECK_EQ(width_, bottom[bottom_id]->width())
-        << "Inputs must have same width.";
+    CHECK(bottom[0]->shape() == bottom[bottom_id]->shape())
+        << "All inputs must have the same shape.";
   }
   // Shape the tops.
+  bottom_shape_ = &bottom[0]->shape();
   compute_output_shape();
+  vector<int> top_shape(bottom[0]->shape().begin(),
+      bottom[0]->shape().begin() + channel_axis_);
+  top_shape.push_back(num_output_);
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    top_shape.push_back(output_shape_[i]);
+  }
   for (int top_id = 0; top_id < top.size(); ++top_id) {
-    top[top_id]->Reshape(num_, num_output_, height_out_, width_out_);
+    top[top_id]->Reshape(top_shape);
   }
   if (reverse_dimensions()) {
-    conv_in_height_ = height_out_;
-    conv_in_width_ = width_out_;
-    conv_out_spatial_dim_ = height_ * width_;
+    conv_out_spatial_dim_ = bottom[0]->count(first_spatial_axis);
   } else {
-    conv_in_height_ = height_;
-    conv_in_width_ = width_;
-    conv_out_spatial_dim_ = height_out_ * width_out_;
+    conv_out_spatial_dim_ = top[0]->count(first_spatial_axis);
   }
-  kernel_dim_ = conv_in_channels_ * kernel_h_ * kernel_w_;
-  weight_offset_ = conv_out_channels_ * kernel_dim_ / group_ / group_;
-  col_offset_ = kernel_dim_ * conv_out_spatial_dim_ / group_;
+  col_offset_ = kernel_dim_ * conv_out_spatial_dim_;
   output_offset_ = conv_out_channels_ * conv_out_spatial_dim_ / group_;
+  // Setup input dimensions (conv_input_shape_).
+  vector<int> bottom_dim_blob_shape(1, num_spatial_axes_ + 1);
+  conv_input_shape_.Reshape(bottom_dim_blob_shape);
+  int* conv_input_shape_data = conv_input_shape_.mutable_cpu_data();
+  for (int i = 0; i < num_spatial_axes_ + 1; ++i) {
+    if (reverse_dimensions()) {
+      conv_input_shape_data[i] = top[0]->shape(channel_axis_ + i);
+    } else {
+      conv_input_shape_data[i] = bottom[0]->shape(channel_axis_ + i);
+    }
+  }
   // The im2col result buffer will only hold one image at a time to avoid
   // overly large memory usage. In the special case of 1x1 convolution
   // it goes lazily unused to save memory.
-  if (reverse_dimensions()) {
-    col_buffer_.Reshape(1, kernel_dim_, height_, width_);
-  } else {
-    col_buffer_.Reshape(1, kernel_dim_, height_out_, width_out_);
+  col_buffer_shape_.clear();
+  col_buffer_shape_.push_back(kernel_dim_ * group_);
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    if (reverse_dimensions()) {
+      col_buffer_shape_.push_back(input_shape(i + 1));
+    } else {
+      col_buffer_shape_.push_back(output_shape_[i]);
+    }
   }
+  col_buffer_.Reshape(col_buffer_shape_);
+  bottom_dim_ = bottom[0]->count(channel_axis_);
+  top_dim_ = top[0]->count(channel_axis_);
+  num_kernels_im2col_ = conv_in_channels_ * conv_out_spatial_dim_;
+  num_kernels_col2im_ = reverse_dimensions() ? top_dim_ : bottom_dim_;
   // Set up the all ones "bias multiplier" for adding biases by BLAS
+  out_spatial_dim_ = top[0]->count(first_spatial_axis);
   if (bias_term_) {
-    vector<int> bias_multiplier_shape(1, height_out_ * width_out_);
+    vector<int> bias_multiplier_shape(1, out_spatial_dim_);
     bias_multiplier_.Reshape(bias_multiplier_shape);
     caffe_set(bias_multiplier_.count(), Dtype(1),
         bias_multiplier_.mutable_cpu_data());
@@ -167,7 +265,7 @@ void BaseConvolutionLayer<Dtype>::forward_cpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
-        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        group_, conv_out_spatial_dim_, kernel_dim_,
         (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)0., output + output_offset_ * g);
   }
@@ -177,7 +275,7 @@ template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::forward_cpu_bias(Dtype* output,
     const Dtype* bias) {
   caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
+      out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.cpu_data(),
       (Dtype)1., output);
 }
 
@@ -189,7 +287,7 @@ void BaseConvolutionLayer<Dtype>::backward_cpu_gemm(const Dtype* output,
     col_buff = input;
   }
   for (int g = 0; g < group_; ++g) {
-    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
         conv_out_spatial_dim_, conv_out_channels_ / group_,
         (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
         (Dtype)0., col_buff + col_offset_ * g);
@@ -209,7 +307,7 @@ void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
-        kernel_dim_ / group_, conv_out_spatial_dim_,
+        kernel_dim_, conv_out_spatial_dim_,
         (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)1., weights + weight_offset_ * g);
   }
@@ -218,7 +316,7 @@ void BaseConvolutionLayer<Dtype>::weight_cpu_gemm(const Dtype* input,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::backward_cpu_bias(Dtype* bias,
     const Dtype* input) {
-  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
       input, bias_multiplier_.cpu_data(), 1., bias);
 }
 
@@ -236,7 +334,7 @@ void BaseConvolutionLayer<Dtype>::forward_gpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, conv_out_channels_ /
-        group_, conv_out_spatial_dim_, kernel_dim_ / group_,
+        group_, conv_out_spatial_dim_, kernel_dim_,
         (Dtype)1., weights + weight_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)0., output + output_offset_ * g);
   }
@@ -246,7 +344,7 @@ template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::forward_gpu_bias(Dtype* output,
     const Dtype* bias) {
   caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num_output_,
-      height_out_ * width_out_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
+      out_spatial_dim_, 1, (Dtype)1., bias, bias_multiplier_.gpu_data(),
       (Dtype)1., output);
 }
 
@@ -258,7 +356,7 @@ void BaseConvolutionLayer<Dtype>::backward_gpu_gemm(const Dtype* output,
     col_buff = input;
   }
   for (int g = 0; g < group_; ++g) {
-    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_ / group_,
+    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, kernel_dim_,
         conv_out_spatial_dim_, conv_out_channels_ / group_,
         (Dtype)1., weights + weight_offset_ * g, output + output_offset_ * g,
         (Dtype)0., col_buff + col_offset_ * g);
@@ -278,7 +376,7 @@ void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
   }
   for (int g = 0; g < group_; ++g) {
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, conv_out_channels_ / group_,
-        kernel_dim_ / group_, conv_out_spatial_dim_,
+        kernel_dim_, conv_out_spatial_dim_,
         (Dtype)1., output + output_offset_ * g, col_buff + col_offset_ * g,
         (Dtype)1., weights + weight_offset_ * g);
   }
@@ -287,7 +385,7 @@ void BaseConvolutionLayer<Dtype>::weight_gpu_gemm(const Dtype* input,
 template <typename Dtype>
 void BaseConvolutionLayer<Dtype>::backward_gpu_bias(Dtype* bias,
     const Dtype* input) {
-  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, height_out_ * width_out_, 1.,
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num_output_, out_spatial_dim_, 1.,
       input, bias_multiplier_.gpu_data(), 1., bias);
 }
 
diff --git a/src/caffe/layers/base_data_layer.cpp b/src/caffe/layers/base_data_layer.cpp
index 352200915d7..989319f1a07 100644
--- a/src/caffe/layers/base_data_layer.cpp
+++ b/src/caffe/layers/base_data_layer.cpp
@@ -1,9 +1,13 @@
-#include <string>
+#include <boost/thread.hpp>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
-#include "caffe/net.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/blob.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/proto/caffe.pb.h"
+#include "caffe/util/blocking_queue.hpp"
 
 namespace caffe {
 
@@ -21,61 +25,103 @@ void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
   } else {
     output_labels_ = true;
   }
-  // The subclasses should setup the size of bottom and top
-  DataLayerSetUp(bottom, top);
   data_transformer_.reset(
       new DataTransformer<Dtype>(transform_param_, this->phase_));
   data_transformer_->InitRand();
+  // The subclasses should setup the size of bottom and top
+  DataLayerSetUp(bottom, top);
+}
+
+template <typename Dtype>
+BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(
+    const LayerParameter& param)
+    : BaseDataLayer<Dtype>(param),
+      prefetch_free_(), prefetch_full_() {
+  for (int i = 0; i < PREFETCH_COUNT; ++i) {
+    prefetch_free_.push(&prefetch_[i]);
+  }
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   BaseDataLayer<Dtype>::LayerSetUp(bottom, top);
-  // Now, start the prefetch thread. Before calling prefetch, we make two
-  // cpu_data calls so that the prefetch thread does not accidentally make
-  // simultaneous cudaMalloc calls when the main thread is running. In some
-  // GPUs this seems to cause failures if we do not so.
-  this->prefetch_data_.mutable_cpu_data();
-  if (this->output_labels_) {
-    this->prefetch_label_.mutable_cpu_data();
+  // Before starting the prefetch thread, we make cpu_data and gpu_data
+  // calls so that the prefetch thread does not accidentally make simultaneous
+  // cudaMalloc calls when the main thread is running. In some GPUs this
+  // seems to cause failures if we do not so.
+  for (int i = 0; i < PREFETCH_COUNT; ++i) {
+    prefetch_[i].data_.mutable_cpu_data();
+    if (this->output_labels_) {
+      prefetch_[i].label_.mutable_cpu_data();
+    }
   }
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    for (int i = 0; i < PREFETCH_COUNT; ++i) {
+      prefetch_[i].data_.mutable_gpu_data();
+      if (this->output_labels_) {
+        prefetch_[i].label_.mutable_gpu_data();
+      }
+    }
+  }
+#endif
   DLOG(INFO) << "Initializing prefetch";
-  this->CreatePrefetchThread();
+  this->data_transformer_->InitRand();
+  StartInternalThread();
   DLOG(INFO) << "Prefetch initialized.";
 }
 
 template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::CreatePrefetchThread() {
-  this->data_transformer_->InitRand();
-  CHECK(StartInternalThread()) << "Thread execution failed";
-}
+void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {
+#ifndef CPU_ONLY
+  cudaStream_t stream;
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
+  }
+#endif
 
-template <typename Dtype>
-void BasePrefetchingDataLayer<Dtype>::JoinPrefetchThread() {
-  CHECK(WaitForInternalThreadToExit()) << "Thread joining failed";
+  try {
+    while (!must_stop()) {
+      Batch<Dtype>* batch = prefetch_free_.pop();
+      load_batch(batch);
+#ifndef CPU_ONLY
+      if (Caffe::mode() == Caffe::GPU) {
+        batch->data_.data().get()->async_gpu_push(stream);
+        CUDA_CHECK(cudaStreamSynchronize(stream));
+      }
+#endif
+      prefetch_full_.push(batch);
+    }
+  } catch (boost::thread_interrupted&) {
+    // Interrupted exception is expected on shutdown
+  }
+#ifndef CPU_ONLY
+  if (Caffe::mode() == Caffe::GPU) {
+    CUDA_CHECK(cudaStreamDestroy(stream));
+  }
+#endif
 }
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // First, join the thread
-  JoinPrefetchThread();
-  DLOG(INFO) << "Thread joined";
+  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(batch->data_);
   // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),
              top[0]->mutable_cpu_data());
   DLOG(INFO) << "Prefetch copied";
   if (this->output_labels_) {
-    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
-               top[1]->mutable_cpu_data());
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(batch->label_);
+    // Copy the labels.
+    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),
+        top[1]->mutable_cpu_data());
   }
-  // Start a new prefetch thread
-  DLOG(INFO) << "CreatePrefetchThread";
-  CreatePrefetchThread();
+
+  prefetch_free_.push(batch);
 }
 
 #ifdef CPU_ONLY
diff --git a/src/caffe/layers/base_data_layer.cu b/src/caffe/layers/base_data_layer.cu
index 775f6c47f7e..4056d36a7b4 100644
--- a/src/caffe/layers/base_data_layer.cu
+++ b/src/caffe/layers/base_data_layer.cu
@@ -1,26 +1,29 @@
 #include <vector>
 
-#include "caffe/data_layers.hpp"
+#include "caffe/layers/base_data_layer.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void BasePrefetchingDataLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // First, join the thread
-  JoinPrefetchThread();
+  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");
   // Reshape to loaded data.
-  top[0]->Reshape(this->prefetch_data_.num(), this->prefetch_data_.channels(),
-      this->prefetch_data_.height(), this->prefetch_data_.width());
+  top[0]->ReshapeLike(batch->data_);
   // Copy the data
-  caffe_copy(prefetch_data_.count(), prefetch_data_.cpu_data(),
+  caffe_copy(batch->data_.count(), batch->data_.gpu_data(),
       top[0]->mutable_gpu_data());
   if (this->output_labels_) {
-    caffe_copy(prefetch_label_.count(), prefetch_label_.cpu_data(),
+    // Reshape to loaded labels.
+    top[1]->ReshapeLike(batch->label_);
+    // Copy the labels.
+    caffe_copy(batch->label_.count(), batch->label_.gpu_data(),
         top[1]->mutable_gpu_data());
   }
-  // Start a new prefetch thread
-  CreatePrefetchThread();
+  // Ensure the copy is synchronous wrt the host, so that the next batch isn't
+  // copied in meanwhile.
+  CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+  prefetch_free_.push(batch);
 }
 
 INSTANTIATE_LAYER_GPU_FORWARD(BasePrefetchingDataLayer);
diff --git a/src/caffe/layers/batch_norm_layer.cpp b/src/caffe/layers/batch_norm_layer.cpp
new file mode 100644
index 00000000000..a69d8f99316
--- /dev/null
+++ b/src/caffe/layers/batch_norm_layer.cpp
@@ -0,0 +1,239 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/batch_norm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  BatchNormParameter param = this->layer_param_.batch_norm_param();
+  moving_average_fraction_ = param.moving_average_fraction();
+  use_global_stats_ = this->phase_ == TEST;
+  if (param.has_use_global_stats())
+    use_global_stats_ = param.use_global_stats();
+  if (bottom[0]->num_axes() == 1)
+    channels_ = 1;
+  else
+    channels_ = bottom[0]->shape(1);
+  eps_ = param.eps();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(3);
+    vector<int> sz;
+    sz.push_back(channels_);
+    this->blobs_[0].reset(new Blob<Dtype>(sz));
+    this->blobs_[1].reset(new Blob<Dtype>(sz));
+    sz[0]=1;
+    this->blobs_[2].reset(new Blob<Dtype>(sz));
+    for (int i = 0; i < 3; ++i) {
+      caffe_set(this->blobs_[i]->count(), Dtype(0),
+                this->blobs_[i]->mutable_cpu_data());
+    }
+  }
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  if (bottom[0]->num_axes() >= 1)
+    CHECK_EQ(bottom[0]->shape(1), channels_);
+  top[0]->ReshapeLike(*bottom[0]);
+
+  vector<int> sz;
+  sz.push_back(channels_);
+  mean_.Reshape(sz);
+  variance_.Reshape(sz);
+  temp_.ReshapeLike(*bottom[0]);
+  x_norm_.ReshapeLike(*bottom[0]);
+  sz[0]=bottom[0]->shape(0);
+  batch_sum_multiplier_.Reshape(sz);
+
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+  if (spatial_sum_multiplier_.num_axes() == 0 ||
+      spatial_sum_multiplier_.shape(0) != spatial_dim) {
+    sz[0] = spatial_dim;
+    spatial_sum_multiplier_.Reshape(sz);
+    Dtype* multiplier_data = spatial_sum_multiplier_.mutable_cpu_data();
+    caffe_set(spatial_sum_multiplier_.count(), Dtype(1), multiplier_data);
+  }
+
+  int numbychans = channels_*bottom[0]->shape(0);
+  if (num_by_chans_.num_axes() == 0 ||
+      num_by_chans_.shape(0) != numbychans) {
+    sz[0] = numbychans;
+    num_by_chans_.Reshape(sz);
+    caffe_set(batch_sum_multiplier_.count(), Dtype(1),
+        batch_sum_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int num = bottom[0]->shape(0);
+  int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+
+  if (use_global_stats_) {
+    // use the stored mean/variance estimates.
+    const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+        0 : 1 / this->blobs_[2]->cpu_data()[0];
+    caffe_cpu_scale(variance_.count(), scale_factor,
+        this->blobs_[0]->cpu_data(), mean_.mutable_cpu_data());
+    caffe_cpu_scale(variance_.count(), scale_factor,
+        this->blobs_[1]->cpu_data(), variance_.mutable_cpu_data());
+  } else {
+    // compute mean
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), bottom_data,
+        spatial_sum_multiplier_.cpu_data(), 0.,
+        num_by_chans_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+        mean_.mutable_cpu_data());
+  }
+
+  // subtract mean
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 1., top_data);
+
+  if (!use_global_stats_) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_powx(top[0]->count(), top_data, Dtype(2),
+        temp_.mutable_cpu_data());  // (X-EX)^2
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), temp_.cpu_data(),
+        spatial_sum_multiplier_.cpu_data(), 0.,
+        num_by_chans_.mutable_cpu_data());
+    caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+        variance_.mutable_cpu_data());  // E((X_EX)^2)
+
+    // compute and save moving average
+    this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+    this->blobs_[2]->mutable_cpu_data()[0] += 1;
+    caffe_cpu_axpby(mean_.count(), Dtype(1), mean_.cpu_data(),
+        moving_average_fraction_, this->blobs_[0]->mutable_cpu_data());
+    int m = bottom[0]->count()/channels_;
+    Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+    caffe_cpu_axpby(variance_.count(), bias_correction_factor,
+        variance_.cpu_data(), moving_average_fraction_,
+        this->blobs_[1]->mutable_cpu_data());
+  }
+
+  // normalize variance
+  caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
+  caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
+             variance_.mutable_cpu_data());
+
+  // replicate variance to input size
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), variance_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 0., temp_.mutable_cpu_data());
+  caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
+  // TODO(cdoersch): The caching is only needed because later in-place layers
+  //                 might clobber the data.  Can we skip this if they won't?
+  caffe_copy(x_norm_.count(), top_data,
+      x_norm_.mutable_cpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff;
+  if (bottom[0] != top[0]) {
+    top_diff = top[0]->cpu_diff();
+  } else {
+    caffe_copy(x_norm_.count(), top[0]->cpu_diff(), x_norm_.mutable_cpu_diff());
+    top_diff = x_norm_.cpu_diff();
+  }
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  if (use_global_stats_) {
+    caffe_div(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
+    return;
+  }
+  const Dtype* top_data = x_norm_.cpu_data();
+  int num = bottom[0]->shape()[0];
+  int spatial_dim = bottom[0]->count()/(bottom[0]->shape(0)*channels_);
+  // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+  //
+  // dE(Y)/dX =
+  //   (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+  //     ./ sqrt(var(X) + eps)
+  //
+  // where \cdot and ./ are hadamard product and elementwise division,
+  // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+  // along all dimensions except the channels dimension.  In the above
+  // equation, the operations allow for expansion (i.e. broadcast) along all
+  // dimensions except the channels dimension where required.
+
+  // sum(dE/dY \cdot Y)
+  caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      bottom_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+      mean_.mutable_cpu_data());
+
+  // reshape (broadcast) the above
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 0., bottom_diff);
+
+  // sum(dE/dY \cdot Y) \cdot Y
+  caffe_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      top_diff, spatial_sum_multiplier_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.cpu_data(), batch_sum_multiplier_.cpu_data(), 0.,
+      mean_.mutable_cpu_data());
+  // reshape (broadcast) the above to make
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.cpu_data(), mean_.cpu_data(), 0.,
+      num_by_chans_.mutable_cpu_data());
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+      spatial_dim, 1, 1., num_by_chans_.cpu_data(),
+      spatial_sum_multiplier_.cpu_data(), 1., bottom_diff);
+
+  // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+  caffe_cpu_axpby(temp_.count(), Dtype(1), top_diff,
+      Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+  // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+  // pass.
+  caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchNormLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchNormLayer);
+REGISTER_LAYER_CLASS(BatchNorm);
+}  // namespace caffe
diff --git a/src/caffe/layers/batch_norm_layer.cu b/src/caffe/layers/batch_norm_layer.cu
new file mode 100644
index 00000000000..c21713c81d9
--- /dev/null
+++ b/src/caffe/layers/batch_norm_layer.cu
@@ -0,0 +1,171 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/batch_norm_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  int num = bottom[0]->shape(0);
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+
+  if (bottom[0] != top[0]) {
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+
+
+  if (use_global_stats_) {
+    // use the stored mean/variance estimates.
+    const Dtype scale_factor = this->blobs_[2]->cpu_data()[0] == 0 ?
+        0 : 1 / this->blobs_[2]->cpu_data()[0];
+    caffe_gpu_scale(variance_.count(), scale_factor,
+        this->blobs_[0]->gpu_data(), mean_.mutable_gpu_data());
+    caffe_gpu_scale(variance_.count(), scale_factor,
+        this->blobs_[1]->gpu_data(), variance_.mutable_gpu_data());
+  } else {
+    // compute mean
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), bottom_data,
+        spatial_sum_multiplier_.gpu_data(), 0.,
+        num_by_chans_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+        mean_.mutable_gpu_data());
+  }
+
+  // subtract mean
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, -1, num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 1., top_data);
+
+  if (!use_global_stats_) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_gpu_powx(top[0]->count(), top_data, Dtype(2),
+        temp_.mutable_gpu_data());  // (X-EX)^2
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim,
+        1. / (num * spatial_dim), temp_.gpu_data(),
+        spatial_sum_multiplier_.gpu_data(), 0.,
+        num_by_chans_.mutable_gpu_data());
+    caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+        num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+        variance_.mutable_gpu_data());  // E((X_EX)^2)
+
+    // compute and save moving average
+    this->blobs_[2]->mutable_cpu_data()[0] *= moving_average_fraction_;
+    this->blobs_[2]->mutable_cpu_data()[0] += 1;
+    caffe_gpu_axpby(mean_.count(), Dtype(1), mean_.gpu_data(),
+        moving_average_fraction_, this->blobs_[0]->mutable_gpu_data());
+    int m = bottom[0]->count()/channels_;
+    Dtype bias_correction_factor = m > 1 ? Dtype(m)/(m-1) : 1;
+    caffe_gpu_axpby(variance_.count(), bias_correction_factor,
+        variance_.gpu_data(), moving_average_fraction_,
+        this->blobs_[1]->mutable_gpu_data());
+  }
+
+  // normalize variance
+  caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
+  caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
+      variance_.mutable_gpu_data());
+
+  // replicate variance to input size
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), variance_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 0., temp_.mutable_gpu_data());
+  caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
+  // TODO(cdoersch): The caching is only needed because later in-place layers
+  //                 might clobber the data.  Can we skip this if they won't?
+  caffe_copy(x_norm_.count(), top_data,
+      x_norm_.mutable_gpu_data());
+}
+
+template <typename Dtype>
+void BatchNormLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff;
+  if (bottom[0] != top[0]) {
+    top_diff = top[0]->gpu_diff();
+  } else {
+    caffe_copy(x_norm_.count(), top[0]->gpu_diff(), x_norm_.mutable_gpu_diff());
+    top_diff = x_norm_.gpu_diff();
+  }
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  if (use_global_stats_) {
+    caffe_gpu_div(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
+    return;
+  }
+  const Dtype* top_data = x_norm_.gpu_data();
+  int num = bottom[0]->shape()[0];
+  int spatial_dim = bottom[0]->count()/(channels_*bottom[0]->shape(0));
+  // if Y = (X-mean(X))/(sqrt(var(X)+eps)), then
+  //
+  // dE(Y)/dX =
+  //   (dE/dY - mean(dE/dY) - mean(dE/dY \cdot Y) \cdot Y)
+  //     ./ sqrt(var(X) + eps)
+  //
+  // where \cdot and ./ are hadamard product and elementwise division,
+  // respectively, dE/dY is the top diff, and mean/var/sum are all computed
+  // along all dimensions except the channels dimension.  In the above
+  // equation, the operations allow for expansion (i.e. broadcast) along all
+  // dimensions except the channels dimension where required.
+
+  // sum(dE/dY \cdot Y)
+  caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      bottom_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+      mean_.mutable_gpu_data());
+
+  // reshape (broadcast) the above
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels_ * num,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 0., bottom_diff);
+
+  // sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_mul(temp_.count(), top_data, bottom_diff, bottom_diff);
+
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, channels_ * num, spatial_dim, 1.,
+      top_diff, spatial_sum_multiplier_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemv<Dtype>(CblasTrans, num, channels_, 1.,
+      num_by_chans_.gpu_data(), batch_sum_multiplier_.gpu_data(), 0.,
+      mean_.mutable_gpu_data());
+  // reshape (broadcast) the above to make
+  // sum(dE/dY)-sum(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, channels_, 1, 1,
+      batch_sum_multiplier_.gpu_data(), mean_.gpu_data(), 0.,
+      num_by_chans_.mutable_gpu_data());
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num * channels_,
+      spatial_dim, 1, 1., num_by_chans_.gpu_data(),
+      spatial_sum_multiplier_.gpu_data(), 1., bottom_diff);
+
+  // dE/dY - mean(dE/dY)-mean(dE/dY \cdot Y) \cdot Y
+  caffe_gpu_axpby(temp_.count(), Dtype(1), top_diff,
+      Dtype(-1. / (num * spatial_dim)), bottom_diff);
+
+  // note: temp_ still contains sqrt(var(X)+eps), computed during the forward
+  // pass.
+  caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchNormLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/batch_reindex_layer.cpp b/src/caffe/layers/batch_reindex_layer.cpp
new file mode 100644
index 00000000000..b14e56f7c6b
--- /dev/null
+++ b/src/caffe/layers/batch_reindex_layer.cpp
@@ -0,0 +1,78 @@
+#include <vector>
+
+#include "caffe/layers/batch_reindex_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+                                       const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(1, bottom[1]->num_axes());
+  vector<int> newshape;
+  newshape.push_back(bottom[1]->shape(0));
+  for (int i = 1; i < bottom[0]->shape().size(); ++i) {
+    newshape.push_back(bottom[0]->shape()[i]);
+  }
+  top[0]->Reshape(newshape);
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::check_batch_reindex(int initial_num,
+                                                   int final_num,
+                                                   const Dtype* ridx_data) {
+  for (int i = 0; i < final_num; ++i) {
+    CHECK_GE(ridx_data[i], 0)
+        << "Index specified for reindex layer was negative.";
+    CHECK_LT(ridx_data[i], initial_num)
+        << "Index specified for reindex layer was greater than batch size.";
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                                           const vector<Blob<Dtype>*>& top) {
+  check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+                      bottom[1]->cpu_data());
+  if (top[0]->count() == 0) {
+    return;
+  }
+  int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+  const Dtype* in = bottom[0]->cpu_data();
+  const Dtype* permut = bottom[1]->cpu_data();
+  Dtype* out = top[0]->mutable_cpu_data();
+  for (int index = 0; index < top[0]->count(); ++index) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_cpu(
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+  if (!propagate_down[0]) {
+    return;
+  }
+  int inner_dim = bottom[0]->count() / bottom[0]->shape(0);
+  Dtype* bot_diff = bottom[0]->mutable_cpu_diff();
+  const Dtype* permut = bottom[1]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  caffe_set(bottom[0]->count(), Dtype(0), bot_diff);
+  for (int index = 0; index < top[0]->count(); ++index) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    bot_diff[in_n * (inner_dim) + index % (inner_dim)] += top_diff[index];
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BatchReindexLayer);
+#endif
+
+INSTANTIATE_CLASS(BatchReindexLayer);
+REGISTER_LAYER_CLASS(BatchReindex);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/batch_reindex_layer.cu b/src/caffe/layers/batch_reindex_layer.cu
new file mode 100644
index 00000000000..83054d36d33
--- /dev/null
+++ b/src/caffe/layers/batch_reindex_layer.cu
@@ -0,0 +1,106 @@
+#include <algorithm>
+#include <utility>
+#include <vector>
+
+#include "caffe/layers/batch_reindex_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template<typename Dtype>
+__global__ void BRForward(const int count, const int inner_dim, const Dtype* in,
+                          const Dtype* permut, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / (inner_dim);
+    int in_n = static_cast<int>(permut[n]);
+    out[index] = in[in_n * (inner_dim) + index % (inner_dim)];
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+                                           const vector<Blob<Dtype>*>& top) {
+  check_batch_reindex(bottom[0]->shape(0), bottom[1]->count(),
+                      bottom[1]->cpu_data());
+  if (top[0]->count() == 0) {
+    return;
+  }
+  int threads = top[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  BRForward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+      top[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+      bottom[0]->gpu_data(), bottom[1]->gpu_data(), top[0]->mutable_gpu_data());
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template<typename Dtype>
+__global__ void BRBackward(const int count, const int inner_dim,
+                           const Dtype* in, const Dtype* top_indexes,
+                           const Dtype* begins, const Dtype* counts,
+                           Dtype* out) {
+  CUDA_KERNEL_LOOP(index, count) {
+    int n = index / (inner_dim);
+    out[index] = 0;
+    int lower = static_cast<int>(begins[n]);
+    int upper = lower + static_cast<int>(counts[n]);
+    for (int i = lower; i < upper; ++i) {
+      int in_n = static_cast<int>(top_indexes[i]);
+      out[index] += in[in_n * (inner_dim) + index % (inner_dim)];
+    }
+  }
+}
+
+template<typename Dtype>
+void BatchReindexLayer<Dtype>::Backward_gpu(
+    const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[1]) << "Cannot backprop to index.";
+  if (!propagate_down[0]) {
+    return;
+  }
+
+  vector<std::pair<int, int> > mapping;
+  const Dtype* perm = bottom[1]->cpu_data();
+  for (int i = 0; i < bottom[1]->count(); ++i) {
+    mapping.push_back(pair<int, int>(static_cast<int>(perm[i]), i));
+  }
+  std::sort(mapping.begin(), mapping.end(), pair_sort_first());
+
+  // Each element of the bottom diff is potentially the sum of many top diffs.
+  // However, we'd like each CUDA thread to handle exactly one output.  Hence,
+  // we first pre-compute a list of lists of indices that need to be summed for
+  // each output. `top_indexes` holds the data of this list of lists.  The
+  // k'th element of `begins` points to the location in `top_indexes` where the
+  // list for the k'th example begin, and the k'th element of `counts` is the
+  // length of that list.
+  vector<int> shape;
+  shape.push_back(bottom[1]->count());
+  Blob<Dtype> top_indexes(shape);
+  shape[0] = bottom[0]->shape(0);
+  Blob<Dtype> counts(shape);
+  Blob<Dtype> begins(shape);
+  Dtype* t_i_data = top_indexes.mutable_cpu_data();
+  Dtype* c_data = counts.mutable_cpu_data();
+  Dtype* b_data = begins.mutable_cpu_data();
+  caffe_set(begins.count(), Dtype(-1), b_data);
+  caffe_set(counts.count(), Dtype(0), c_data);
+  for (int i = 0; i < mapping.size(); ++i) {
+    t_i_data[i] = mapping[i].second;
+    if (b_data[mapping[i].first] == -1) {
+      b_data[mapping[i].first] = i;
+    }
+    c_data[mapping[i].first] += 1;
+  }
+
+  int threads = bottom[0]->count();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  BRBackward<Dtype> <<<CAFFE_GET_BLOCKS(threads), CAFFE_CUDA_NUM_THREADS>>>(
+      bottom[0]->count(), bottom[0]->count() / bottom[0]->shape(0),
+      top[0]->gpu_diff(), top_indexes.gpu_data(), begins.gpu_data(),
+      counts.gpu_data(), bottom[0]->mutable_gpu_diff());
+  CUDA_POST_KERNEL_CHECK;
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BatchReindexLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bias_layer.cpp b/src/caffe/layers/bias_layer.cpp
new file mode 100644
index 00000000000..4726a729834
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cpp
@@ -0,0 +1,121 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void BiasLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // bias is a learned parameter; initialize it
+    const BiasParameter& param = this->layer_param_.bias_param();
+    const int axis = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis + num_axes)
+          << "bias blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> bias_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(bias_shape));
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(param.filler()));
+    filler->Fill(this->blobs_[0].get());
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const BiasParameter& param = this->layer_param_.bias_param();
+  Blob<Dtype>* bias = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis == 0 in special case where bias is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis == 0 and (therefore) outer_dim_ == 1.
+  const int axis = (bias->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis + bias->num_axes())
+      << "bias blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis;
+  for (int i = 0; i < bias->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis + i), bias->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis + i
+        << ") and bias->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis);
+  bias_dim_ = bias->count();
+  inner_dim_ = bottom[0]->count(axis + bias->num_axes());
+  dim_ = bias_dim_ * inner_dim_;
+  if (bottom[0] != top[0]) {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  bias_multiplier_.Reshape(vector<int>(1, inner_dim_));
+  if (bias_multiplier_.cpu_data()[inner_dim_ - 1] != Dtype(1)) {
+    caffe_set(inner_dim_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (bottom[0] != top[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    caffe_copy(bottom[0]->count(), bottom_data, top_data);
+  }
+  for (int n = 0; n < outer_dim_; ++n) {
+    caffe_cpu_gemm(CblasNoTrans, CblasNoTrans, bias_dim_,
+        inner_dim_, 1, Dtype(1), bias_data,
+        bias_multiplier_.cpu_data(), Dtype(1), top_data);
+    top_data += dim_;
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_cpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_cpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.cpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(BiasLayer);
+#endif
+
+INSTANTIATE_CLASS(BiasLayer);
+REGISTER_LAYER_CLASS(Bias);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bias_layer.cu b/src/caffe/layers/bias_layer.cu
new file mode 100644
index 00000000000..8ac913a5d7b
--- /dev/null
+++ b/src/caffe/layers/bias_layer.cu
@@ -0,0 +1,59 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void BiasForward(const int n, const Dtype* in,
+    const Dtype* bias, const int bias_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int bias_index = (index / inner_dim) % bias_dim;
+    out[index] = in[index] + bias[bias_index];
+  }
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* bias_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  BiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, bias_data, bias_dim_, inner_dim_, top_data);
+}
+
+template <typename Dtype>
+void BiasLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0] && bottom[0] != top[0]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(bottom[0]->count(), top_diff, bottom_diff);
+  }
+  // in-place, we don't need to do anything with the data diff
+  const bool bias_param = (bottom.size() == 1);
+  if ((!bias_param && propagate_down[1]) ||
+      (bias_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bias_diff = (bias_param ? this->blobs_[0].get() : bottom[1])
+        ->mutable_gpu_diff();
+    bool accum = bias_param;
+    for (int n = 0; n < outer_dim_; ++n) {
+      caffe_gpu_gemv(CblasNoTrans, bias_dim_, inner_dim_, Dtype(1),
+          top_diff, bias_multiplier_.gpu_data(), Dtype(accum), bias_diff);
+      top_diff += dim_;
+      accum = true;
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(BiasLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/bnll_layer.cpp b/src/caffe/layers/bnll_layer.cpp
index 9ba0ea9a715..448d86d752d 100644
--- a/src/caffe/layers/bnll_layer.cpp
+++ b/src/caffe/layers/bnll_layer.cpp
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/bnll_layer.cu b/src/caffe/layers/bnll_layer.cu
index d963d0687d2..8df8ef09afe 100644
--- a/src/caffe/layers/bnll_layer.cu
+++ b/src/caffe/layers/bnll_layer.cu
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/bnll_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/concat_layer.cpp b/src/caffe/layers/concat_layer.cpp
index 1cac8fc3387..580bd47977d 100644
--- a/src/caffe/layers/concat_layer.cpp
+++ b/src/caffe/layers/concat_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -48,11 +47,16 @@ void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
   }
   top[0]->Reshape(top_shape);
   CHECK_EQ(bottom_count_sum, top[0]->count());
+  if (bottom.size() == 1) {
+    top[0]->ShareData(*bottom[0]);
+    top[0]->ShareDiff(*bottom[0]);
+  }
 }
 
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1) { return; }
   Dtype* top_data = top[0]->mutable_cpu_data();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
@@ -72,17 +76,19 @@ void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bottom.size() == 1) { return; }
   const Dtype* top_diff = top[0]->cpu_diff();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
   for (int i = 0; i < bottom.size(); ++i) {
-    if (!propagate_down[i]) { continue; }
-    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
-          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
-          bottom_diff + n * bottom_concat_axis * concat_input_size_);
+    if (propagate_down[i]) {
+      Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
+      for (int n = 0; n < num_concats_; ++n) {
+        caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
+            (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
+            bottom_diff + n * bottom_concat_axis * concat_input_size_);
+      }
     }
     offset_concat_axis += bottom_concat_axis;
   }
diff --git a/src/caffe/layers/concat_layer.cu b/src/caffe/layers/concat_layer.cu
index dbadb5aeb30..a3a0bf6f6ea 100644
--- a/src/caffe/layers/concat_layer.cu
+++ b/src/caffe/layers/concat_layer.cu
@@ -1,26 +1,46 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void Concat(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_concats, const int concat_size,
+    const int top_concat_axis, const int bottom_concat_axis,
+    const int offset_concat_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_concat_size = concat_size * bottom_concat_axis;
+    const int concat_num = index / total_concat_size;
+    const int concat_index = index % total_concat_size;
+    const int top_index = concat_index +
+        (concat_num * top_concat_axis + offset_concat_axis) * concat_size;
+    if (forward) {
+      out_data[top_index] = in_data[index];
+    } else {
+      out_data[index] = in_data[top_index];
+    }
+  }
+}
+
 template <typename Dtype>
 void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (bottom.size() == 1) { return; }
   Dtype* top_data = top[0]->mutable_gpu_data();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = true;
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_,
-          bottom_data + n * bottom_concat_axis * concat_input_size_,
-          top_data + (n * top_concat_axis + offset_concat_axis)
-              * concat_input_size_);
-    }
+    const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+    const int nthreads = bottom_concat_size * num_concats_;
+    Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_concats_, concat_input_size_,
+        top_concat_axis, bottom_concat_axis, offset_concat_axis, top_data);
     offset_concat_axis += bottom_concat_axis;
   }
 }
@@ -28,17 +48,21 @@ void ConcatLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void ConcatLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bottom.size() == 1) { return; }
   const Dtype* top_diff = top[0]->gpu_diff();
   int offset_concat_axis = 0;
   const int top_concat_axis = top[0]->shape(concat_axis_);
+  const bool kForward = false;
   for (int i = 0; i < bottom.size(); ++i) {
-    if (!propagate_down[i]) { continue; }
-    Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
     const int bottom_concat_axis = bottom[i]->shape(concat_axis_);
-    for (int n = 0; n < num_concats_; ++n) {
-      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +
-          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,
-          bottom_diff + n * bottom_concat_axis * concat_input_size_);
+    if (propagate_down[i]) {
+      Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
+      const int bottom_concat_size = bottom_concat_axis * concat_input_size_;
+      const int nthreads = bottom_concat_size * num_concats_;
+      Concat<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+          nthreads, top_diff, kForward, num_concats_, concat_input_size_,
+          top_concat_axis, bottom_concat_axis, offset_concat_axis, bottom_diff);
     }
     offset_concat_axis += bottom_concat_axis;
   }
diff --git a/src/caffe/layers/contrastive_loss_layer.cpp b/src/caffe/layers/contrastive_loss_layer.cpp
index 0692c11c257..599e178e9c4 100644
--- a/src/caffe/layers/contrastive_loss_layer.cpp
+++ b/src/caffe/layers/contrastive_loss_layer.cpp
@@ -1,9 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/loss_layers.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -41,6 +39,8 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
       diff_.mutable_cpu_data());  // a_i-b_i
   const int channels = bottom[0]->channels();
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     dist_sq_.mutable_cpu_data()[i] = caffe_cpu_dot(channels,
@@ -48,7 +48,13 @@ void ContrastiveLossLayer<Dtype>::Forward_cpu(
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq_.cpu_data()[i]),
+          Dtype(0.0));
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -59,6 +65,8 @@ template <typename Dtype>
 void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   for (int i = 0; i < 2; ++i) {
     if (propagate_down[i]) {
       const Dtype sign = (i == 0) ? 1 : -1;
@@ -76,10 +84,20 @@ void ContrastiveLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
               Dtype(0.0),
               bout + (j*channels));
         } else {  // dissimilar pairs
-          if ((margin-dist_sq_.cpu_data()[j]) > Dtype(0.0)) {
+          Dtype mdist(0.0);
+          Dtype beta(0.0);
+          if (legacy_version) {
+            mdist = margin - dist_sq_.cpu_data()[j];
+            beta = -alpha;
+          } else {
+            Dtype dist = sqrt(dist_sq_.cpu_data()[j]);
+            mdist = margin - dist;
+            beta = -alpha * mdist / (dist + Dtype(1e-4));
+          }
+          if (mdist > Dtype(0.0)) {
             caffe_cpu_axpby(
                 channels,
-                -alpha,
+                beta,
                 diff_.cpu_data() + (j*channels),
                 Dtype(0.0),
                 bout + (j*channels));
diff --git a/src/caffe/layers/contrastive_loss_layer.cu b/src/caffe/layers/contrastive_loss_layer.cu
index 78a55995a0a..fd7d67cca94 100644
--- a/src/caffe/layers/contrastive_loss_layer.cu
+++ b/src/caffe/layers/contrastive_loss_layer.cu
@@ -1,10 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -32,12 +30,20 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
       Dtype(0.0),
       dist_sq_.mutable_gpu_data());  // \Sum (a_i-b_i)^2
   Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+  bool legacy_version =
+      this->layer_param_.contrastive_loss_param().legacy_version();
   Dtype loss(0.0);
   for (int i = 0; i < bottom[0]->num(); ++i) {
     if (static_cast<int>(bottom[2]->cpu_data()[i])) {  // similar pairs
       loss += dist_sq_.cpu_data()[i];
     } else {  // dissimilar pairs
-      loss += std::max(margin-dist_sq_.cpu_data()[i], Dtype(0.0));
+      if (legacy_version) {
+        loss += std::max(margin - dist_sq_.cpu_data()[i], Dtype(0.0));
+      } else {
+        Dtype dist = std::max(margin - sqrt(dist_sq_.cpu_data()[i]),
+                              Dtype(0.0));
+        loss += dist*dist;
+      }
     }
   }
   loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2);
@@ -45,8 +51,8 @@ void ContrastiveLossLayer<Dtype>::Forward_gpu(
 }
 
 template <typename Dtype>
-__global__ void CLLForward(const int count, const int channels,
-    const Dtype margin, const Dtype alpha,
+__global__ void CLLBackward(const int count, const int channels,
+    const Dtype margin, const bool legacy_version, const Dtype alpha,
     const Dtype* y, const Dtype* diff, const Dtype* dist_sq,
     Dtype *bottom_diff) {
   CUDA_KERNEL_LOOP(i, count) {
@@ -54,8 +60,18 @@ __global__ void CLLForward(const int count, const int channels,
     if (static_cast<int>(y[n])) {  // similar pairs
       bottom_diff[i] = alpha * diff[i];
     } else {  // dissimilar pairs
-      if ((margin-dist_sq[n]) > 0.0) {
-        bottom_diff[i] = -alpha * diff[i];
+      Dtype mdist(0.0);
+      Dtype beta(0.0);
+      if (legacy_version) {
+        mdist = (margin - dist_sq[n]);
+        beta = -alpha;
+      } else {
+        Dtype dist = sqrt(dist_sq[n]);
+        mdist = (margin - dist);
+        beta = -alpha * mdist / (dist + Dtype(1e-4)) * diff[i];
+      }
+      if (mdist > 0.0) {
+        bottom_diff[i] = beta;
       } else {
         bottom_diff[i] = 0;
       }
@@ -71,12 +87,14 @@ void ContrastiveLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const int count = bottom[0]->count();
       const int channels = bottom[0]->channels();
       Dtype margin = this->layer_param_.contrastive_loss_param().margin();
+      const bool legacy_version =
+          this->layer_param_.contrastive_loss_param().legacy_version();
       const Dtype sign = (i == 0) ? 1 : -1;
       const Dtype alpha = sign * top[0]->cpu_diff()[0] /
           static_cast<Dtype>(bottom[0]->num());
       // NOLINT_NEXT_LINE(whitespace/operators)
-      CLLForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
-          count, channels, margin, alpha,
+      CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+          count, channels, margin, legacy_version, alpha,
           bottom[2]->gpu_data(),  // pair similarity 0 or 1
           diff_.gpu_data(),  // the cached eltwise difference between a and b
           dist_sq_.gpu_data(),  // the cached square distance between a and b
diff --git a/src/caffe/layers/conv_layer.cpp b/src/caffe/layers/conv_layer.cpp
index c0c9f6f3371..5d522ab31f2 100644
--- a/src/caffe/layers/conv_layer.cpp
+++ b/src/caffe/layers/conv_layer.cpp
@@ -1,19 +1,24 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void ConvolutionLayer<Dtype>::compute_output_shape() {
-  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
-      / this->stride_h_ + 1;
-  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
-      / this->stride_w_ + 1;
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int* stride_data = this->stride_.cpu_data();
+  const int* pad_data = this->pad_.cpu_data();
+  const int* dilation_data = this->dilation_.cpu_data();
+  this->output_shape_.clear();
+  for (int i = 0; i < this->num_spatial_axes_; ++i) {
+    // i + 1 to skip channel axis
+    const int input_dim = this->input_shape(i + 1);
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
+        / stride_data[i] + 1;
+    this->output_shape_.push_back(output_dim);
+  }
 }
 
 template <typename Dtype>
@@ -24,11 +29,11 @@ void ConvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->forward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->cpu_data();
-        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -39,13 +44,6 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();
@@ -54,20 +52,20 @@ void ConvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
-              top_diff + top[i]->offset(n), weight_diff);
+          this->weight_cpu_gemm(bottom_data + n * this->bottom_dim_,
+              top_diff + n * this->top_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+          this->backward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_);
         }
       }
     }
diff --git a/src/caffe/layers/conv_layer.cu b/src/caffe/layers/conv_layer.cu
index 3902fdf3930..d06e4b6244e 100644
--- a/src/caffe/layers/conv_layer.cu
+++ b/src/caffe/layers/conv_layer.cu
@@ -1,10 +1,6 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
 
 namespace caffe {
 
@@ -16,11 +12,11 @@ void ConvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->forward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->forward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->gpu_data();
-        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -31,20 +27,13 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     // Bias gradient, if necessary.
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
@@ -53,13 +42,13 @@ void ConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_gpu_gemm(bottom_data + bottom[i]->offset(n),
-              top_diff + top[i]->offset(n), weight_diff);
+          this->weight_gpu_gemm(bottom_data + n * this->bottom_dim_,
+              top_diff + n * this->top_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->backward_gpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+          this->backward_gpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_);
         }
       }
     }
diff --git a/src/caffe/layers/crop_layer.cpp b/src/caffe/layers/crop_layer.cpp
new file mode 100644
index 00000000000..e81bdd732f3
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cpp
@@ -0,0 +1,150 @@
+#include <algorithm>
+#include <functional>
+#include <map>
+#include <set>
+#include <vector>
+
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/crop_layer.hpp"
+#include "caffe/net.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+void CropLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  // All logic that depends only on the number of dimensions is here,
+  // the rest is in Reshape because it depends on Blob size.
+  // bottom[0] supplies the data
+  // bottom[1] supplies the size
+  const CropParameter& param = this->layer_param_.crop_param();
+  CHECK_EQ(bottom.size(), 2) << "Wrong number of bottom blobs.";
+  int input_dim = bottom[0]->num_axes();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_LT(start_axis, input_dim) << "crop axis bigger than input dim";
+  if (param.offset_size() > 1) {
+    // the number of crop values specified must be equal to the number
+    // of dimensions following axis
+    CHECK_EQ(start_axis + param.offset_size(), input_dim)
+      << "number of offset values specified must be equal to the number of "
+      << "dimensions following axis.";
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const CropParameter& param = this->layer_param_.crop_param();
+  int input_dim = bottom[0]->num_axes();
+  const int start_axis = bottom[0]->CanonicalAxisIndex(param.axis());
+
+  // initialize all offsets to 0
+  offsets = vector<int>(input_dim, 0);
+  // initialize new shape to bottom[0]
+  vector<int> new_shape(bottom[0]->shape());
+
+  // apply crops
+  for (int i = 0; i < input_dim; ++i) {
+    int crop_offset = 0;
+    int new_size    = bottom[0]->shape(i);
+    if (i >= start_axis) {
+      new_size = bottom[1]->shape(i);
+
+      if (param.offset_size() == 1) {
+        // if only one crop value is supplied, crop all dimensions after axis
+        // by this crop value
+        crop_offset = param.offset(0);
+      } else if (param.offset_size() > 1) {
+        // crop values specified must be equal to the number of dimensions
+        // following axis
+        crop_offset = param.offset(i - start_axis);
+      }
+    }
+    // Check that the image we are cropping minus the margin is bigger
+    // than the destination image.
+    CHECK_GE(bottom[0]->shape(i) - crop_offset,
+             bottom[1]->shape(i))
+        << "invalid crop parameters in dimension: " << i;
+    // Now set new size and offsets
+    new_shape[i] = new_size;
+    offsets[i] = crop_offset;
+  }
+  top[0]->Reshape(new_shape);
+}
+
+// recursive copy function
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 1 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursively
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last dimensions, which is stored continously in memory
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      // prepare index vector reduced(red) and with offsets(off)
+      std::vector<int> ind_red(cur_dim, 0);
+      std::vector<int> ind_off(cur_dim+1, 0);
+      for (int j = 0; j < cur_dim; ++j) {
+          ind_red[j] = indices[j];
+          ind_off[j] = indices[j] + offsets[j];
+      }
+      ind_off[cur_dim] = offsets[cur_dim];
+      // do the copy
+      if (is_forward) {
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + bottom[0]->offset(ind_off),
+            dest_data + top[0]->offset(ind_red));
+      } else {
+        // in the backwards pass the src_data is top_diff
+        // and the dest_data is bottom_diff
+        caffe_copy(top[0]->shape(cur_dim),
+            src_data + top[0]->offset(ind_red),
+            dest_data + bottom[0]->offset(ind_off));
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  crop_copy(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+
+  if (propagate_down[0]) {
+    caffe_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy(bottom, top, offsets, indices, 0, top_diff, bottom_diff, false);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(CropLayer);
+#endif
+
+INSTANTIATE_CLASS(CropLayer);
+REGISTER_LAYER_CLASS(Crop);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/crop_layer.cu b/src/caffe/layers/crop_layer.cu
new file mode 100644
index 00000000000..9ed8f7cce57
--- /dev/null
+++ b/src/caffe/layers/crop_layer.cu
@@ -0,0 +1,124 @@
+#include <vector>
+
+#include "caffe/layers/crop_layer.hpp"
+
+namespace caffe {
+
+// Copy (one line per thread) from one array to another, with arbitrary
+// strides in the last two dimensions.
+template <typename Dtype>
+__global__ void copy_kernel(const int n, const int height, const int width,
+    const int src_outer_stride, const int src_inner_stride,
+    const int dest_outer_stride, const int dest_inner_stride,
+    const Dtype* src, Dtype* dest) {
+  CUDA_KERNEL_LOOP(index, n) {
+    int src_start = index / height * src_outer_stride
+                  + index % height * src_inner_stride;
+    int dest_start = index / height * dest_outer_stride
+                   + index % height * dest_inner_stride;
+    for (int i = 0; i < width; ++i) {
+      dest[dest_start + i] = src[src_start + i];
+    }
+  }
+}
+
+// recursive copy function, this function is similar to crop_copy but loops
+// over all but the last two dimensions. It is implemented this way to allow
+// for ND cropping while still relying on a CUDA kernel for the innermost
+// two dimensions for performance reasons.
+// An alternative way to implement ND cropping relying more on the kernel
+// would require passing offsets to the kernel, which is a bit problematic
+// because it is of variable length. Since in the standard (N,C,W,H) case
+// N,C are usually not cropped a speedup could be achieved by not looping
+// the application of the copy_kernel around these dimensions.
+template <typename Dtype>
+void CropLayer<Dtype>::crop_copy_gpu(const vector<Blob<Dtype>*>& bottom,
+             const vector<Blob<Dtype>*>& top,
+             const vector<int>& offsets,
+             vector<int> indices,
+             int cur_dim,
+             const Dtype* src_data,
+             Dtype* dest_data,
+             bool is_forward) {
+  if (cur_dim + 2 < top[0]->num_axes()) {
+    // We are not yet at the final dimension, call copy recursivley
+    for (int i = 0; i < top[0]->shape(cur_dim); ++i) {
+      indices[cur_dim] = i;
+      crop_copy_gpu(bottom, top, offsets, indices, cur_dim+1,
+                src_data, dest_data, is_forward);
+    }
+  } else {
+    // We are at the last two dimensions, which are stored continously in memory
+    // With (N,C,H,W)
+    //      (0,1,2,3) cur_dim   -> H
+    //                cur_dim+1 -> W
+    const int lines = top[0]->shape(cur_dim);
+    const int height = top[0]->shape(cur_dim);
+    const int width = top[0]->shape(cur_dim+1);
+    std::vector<int> ind_off(cur_dim+2, 0);
+    for (int j = 0; j < cur_dim; ++j) {
+        ind_off[j] = indices[j] + offsets[j];
+    }
+    ind_off[cur_dim] = offsets[cur_dim];
+    ind_off[cur_dim+1] = offsets[cur_dim+1];
+    // Compute copy strides
+    const int src_outer_stride =
+        bottom[0]->shape(cur_dim)*bottom[0]->shape(cur_dim+1);
+    const int src_inner_stride = bottom[0]->shape(cur_dim+1);
+    const int dest_outer_stride =
+        top[0]->shape(cur_dim)*top[0]->shape(cur_dim+1);
+    const int dest_inner_stride = top[0]->shape(cur_dim+1);
+
+    if (is_forward) {
+      const Dtype* bottom_data = bottom[0]->gpu_data() +
+          bottom[0]->offset(ind_off);
+      Dtype* top_data = top[0]->mutable_gpu_data() +
+          top[0]->offset(indices);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          src_outer_stride, src_inner_stride,
+          dest_outer_stride, dest_inner_stride,
+          bottom_data, top_data);
+
+    } else {
+      const Dtype* top_diff = top[0]->gpu_diff() +
+          top[0]->offset(indices);
+      Dtype* bottom_diff = bottom[0]->mutable_gpu_diff() +
+          bottom[0]->offset(ind_off);
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      copy_kernel<<<CAFFE_GET_BLOCKS(lines), CAFFE_CUDA_NUM_THREADS>>>(
+          lines, height, width,
+          dest_outer_stride, dest_inner_stride,
+          src_outer_stride, src_inner_stride,
+          top_diff, bottom_diff);
+    }
+  }
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  std::vector<int> indices(top[0]->num_axes(), 0);
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  crop_copy_gpu(bottom, top, offsets, indices, 0, bottom_data, top_data, true);
+}
+
+template <typename Dtype>
+void CropLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  if (propagate_down[0]) {
+    caffe_gpu_set(bottom[0]->count(), static_cast<Dtype>(0), bottom_diff);
+    std::vector<int> indices(top[0]->num_axes(), 0);
+    crop_copy_gpu(bottom, top, offsets, indices, 0, top_diff, bottom_diff,
+                  false);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CropLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/cudnn_conv_layer.cpp b/src/caffe/layers/cudnn_conv_layer.cpp
index 104d2b9d669..1987fb096b0 100644
--- a/src/caffe/layers/cudnn_conv_layer.cpp
+++ b/src/caffe/layers/cudnn_conv_layer.cpp
@@ -1,11 +1,8 @@
 #ifdef USE_CUDNN
+#include <algorithm>
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
 
 namespace caffe {
 
@@ -24,24 +21,50 @@ void CuDNNConvolutionLayer<Dtype>::LayerSetUp(
   // Initialize CUDA streams and cuDNN.
   stream_         = new cudaStream_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
   handle_         = new cudnnHandle_t[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  // Initialize algorithm arrays
+  fwd_algo_       = new cudnnConvolutionFwdAlgo_t[bottom.size()];
+  bwd_filter_algo_= new cudnnConvolutionBwdFilterAlgo_t[bottom.size()];
+  bwd_data_algo_  = new cudnnConvolutionBwdDataAlgo_t[bottom.size()];
+
+  // initialize size arrays
+  workspace_fwd_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_filter_sizes_ = new size_t[bottom.size()];
+  workspace_bwd_data_sizes_ = new size_t[bottom.size()];
+
+  // workspace data
   workspaceSizeInBytes = 0;
-  workspace = NULL;
+  workspaceData = NULL;
+  workspace = new void*[this->group_ * CUDNN_STREAMS_PER_GROUP];
+
+  for (size_t i = 0; i < bottom.size(); ++i) {
+    // initialize all to default algorithms
+    fwd_algo_[i] = (cudnnConvolutionFwdAlgo_t)0;
+    bwd_filter_algo_[i] = (cudnnConvolutionBwdFilterAlgo_t)0;
+    bwd_data_algo_[i] = (cudnnConvolutionBwdDataAlgo_t)0;
+    // default algorithms don't require workspace
+    workspace_fwd_sizes_[i] = 0;
+    workspace_bwd_data_sizes_[i] = 0;
+    workspace_bwd_filter_sizes_[i] = 0;
+  }
 
   for (int g = 0; g < this->group_ * CUDNN_STREAMS_PER_GROUP; g++) {
     CUDA_CHECK(cudaStreamCreate(&stream_[g]));
     CUDNN_CHECK(cudnnCreate(&handle_[g]));
     CUDNN_CHECK(cudnnSetStream(handle_[g], stream_[g]));
+    workspace[g] = NULL;
   }
 
   // Set the indexing parameters.
-  weight_offset_ = (this->num_output_ / this->group_)
-      * (this->channels_ / this->group_) * this->kernel_h_ * this->kernel_w_;
   bias_offset_ = (this->num_output_ / this->group_);
 
   // Create filter descriptor.
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int kernel_h = kernel_shape_data[0];
+  const int kernel_w = kernel_shape_data[1];
   cudnn::createFilterDesc<Dtype>(&filter_desc_,
       this->num_output_ / this->group_, this->channels_ / this->group_,
-      this->kernel_h_, this->kernel_w_);
+      kernel_h, kernel_w);
 
   // Create tensor descriptor(s) for data and corresponding convolution(s).
   for (int i = 0; i < bottom.size(); i++) {
@@ -68,29 +91,137 @@ template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Reshape(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
   ConvolutionLayer<Dtype>::Reshape(bottom, top);
-  bottom_offset_ = (this->channels_ / this->group_)
-      * this->height_ * this->width_;
-  top_offset_ = (this->num_output_ / this->group_)
-      * this->height_out_ * this->width_out_;
+  CHECK_EQ(2, this->num_spatial_axes_)
+      << "CuDNNConvolution input must have 2 spatial axes "
+      << "(e.g., height and width). "
+      << "Use 'engine: CAFFE' for general ND convolution.";
+  bottom_offset_ = this->bottom_dim_ / this->group_;
+  top_offset_ = this->top_dim_ / this->group_;
+  const int height = bottom[0]->shape(this->channel_axis_ + 1);
+  const int width = bottom[0]->shape(this->channel_axis_ + 2);
+  const int height_out = top[0]->shape(this->channel_axis_ + 1);
+  const int width_out = top[0]->shape(this->channel_axis_ + 2);
+  const int* pad_data = this->pad_.cpu_data();
+  const int pad_h = pad_data[0];
+  const int pad_w = pad_data[1];
+  const int* stride_data = this->stride_.cpu_data();
+  const int stride_h = stride_data[0];
+  const int stride_w = stride_data[1];
+
+  // Specify workspace limit for kernels directly until we have a
+  // planning strategy and a rewrite of Caffe's GPU memory mangagement
+  size_t workspace_limit_bytes = 8*1024*1024;
 
   for (int i = 0; i < bottom.size(); i++) {
     cudnn::setTensor4dDesc<Dtype>(&bottom_descs_[i],
         this->num_,
-        this->channels_ / this->group_,
-        this->height_, this->width_,
-        this->channels_ * this->height_ * this->width_,
-        this->height_ * this->width_,
-        this->width_, 1);
+        this->channels_ / this->group_, height, width,
+        this->channels_ * height * width,
+        height * width, width, 1);
     cudnn::setTensor4dDesc<Dtype>(&top_descs_[i],
         this->num_,
-        this->num_output_ / this->group_,
-        this->height_out_, this->width_out_,
-        this->num_output_ * this->height_out_ * this->width_out_,
-        this->height_out_ * this->width_out_,
-        this->width_out_, 1);
+        this->num_output_ / this->group_, height_out, width_out,
+        this->num_output_ * this->out_spatial_dim_,
+        this->out_spatial_dim_, width_out, 1);
     cudnn::setConvolutionDesc<Dtype>(&conv_descs_[i], bottom_descs_[i],
-        filter_desc_, this->pad_h_, this->pad_w_,
-        this->stride_h_, this->stride_w_);
+        filter_desc_, pad_h, pad_w,
+        stride_h, stride_w);
+
+    // choose forward and backward algorithms + workspace(s)
+    CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[0],
+      bottom_descs_[i],
+      filter_desc_,
+      conv_descs_[i],
+      top_descs_[i],
+      CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
+      workspace_limit_bytes,
+      &fwd_algo_[i]));
+
+    CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[0],
+      bottom_descs_[i],
+      filter_desc_,
+      conv_descs_[i],
+      top_descs_[i],
+      fwd_algo_[i],
+      &(workspace_fwd_sizes_[i])));
+
+    // choose backward algorithm for filter
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterAlgorithm(handle_[0],
+          bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+          CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
+          workspace_limit_bytes, &bwd_filter_algo_[i]) );
+
+    // get workspace for backwards filter algorithm
+    CUDNN_CHECK(cudnnGetConvolutionBackwardFilterWorkspaceSize(handle_[0],
+          bottom_descs_[i], top_descs_[i], conv_descs_[i], filter_desc_,
+          bwd_filter_algo_[i], &workspace_bwd_filter_sizes_[i]));
+
+    // choose backward algo for data
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataAlgorithm(handle_[0],
+          filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+          CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
+        workspace_limit_bytes, &bwd_data_algo_[i]));
+
+    // get workspace size
+    CUDNN_CHECK(cudnnGetConvolutionBackwardDataWorkspaceSize(handle_[0],
+          filter_desc_, top_descs_[i], conv_descs_[i], bottom_descs_[i],
+          bwd_data_algo_[i], &workspace_bwd_data_sizes_[i]) );
+  }
+
+  // reduce over all workspace sizes to get a maximum to allocate / reallocate
+  size_t total_workspace_fwd = 0;
+  size_t total_workspace_bwd_data = 0;
+  size_t total_workspace_bwd_filter = 0;
+
+  for (size_t i = 0; i < bottom.size(); i++) {
+    total_workspace_fwd        = std::max(total_workspace_fwd,
+                                     workspace_fwd_sizes_[i]);
+    total_workspace_bwd_data   = std::max(total_workspace_bwd_data,
+                                     workspace_bwd_data_sizes_[i]);
+    total_workspace_bwd_filter = std::max(total_workspace_bwd_filter,
+                                     workspace_bwd_filter_sizes_[i]);
+  }
+  // get max over all operations
+  size_t max_workspace = std::max(total_workspace_fwd,
+                             total_workspace_bwd_data);
+  max_workspace = std::max(max_workspace, total_workspace_bwd_filter);
+  // ensure all groups have enough workspace
+  size_t total_max_workspace = max_workspace *
+                               (this->group_ * CUDNN_STREAMS_PER_GROUP);
+
+  // this is the total amount of storage needed over all groups + streams
+  if (total_max_workspace > workspaceSizeInBytes) {
+    DLOG(INFO) << "Reallocating workspace storage: " << total_max_workspace;
+    workspaceSizeInBytes = total_max_workspace;
+
+    // free the existing workspace and allocate a new (larger) one
+    cudaFree(this->workspaceData);
+
+    cudaError_t err = cudaMalloc(&(this->workspaceData), workspaceSizeInBytes);
+    if (err != cudaSuccess) {
+      // force zero memory path
+      for (int i = 0; i < bottom.size(); i++) {
+        workspace_fwd_sizes_[i] = 0;
+        workspace_bwd_filter_sizes_[i] = 0;
+        workspace_bwd_data_sizes_[i] = 0;
+        fwd_algo_[i] = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
+        bwd_filter_algo_[i] = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_0;
+        bwd_data_algo_[i] = CUDNN_CONVOLUTION_BWD_DATA_ALGO_0;
+      }
+
+      // NULL out all workspace pointers
+      for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+        workspace[g] = NULL;
+      }
+      // NULL out underlying data
+      workspaceData = NULL;
+      workspaceSizeInBytes = 0;
+    }
+
+    // if we succeed in the allocation, set pointer aliases for workspaces
+    for (int g = 0; g < (this->group_ * CUDNN_STREAMS_PER_GROUP); g++) {
+      workspace[g] = reinterpret_cast<char *>(workspaceData) + g*max_workspace;
+    }
   }
 
   // Tensor descriptor for bias.
@@ -120,8 +251,15 @@ CuDNNConvolutionLayer<Dtype>::~CuDNNConvolutionLayer() {
     cudnnDestroy(handle_[g]);
   }
 
+  cudaFree(workspaceData);
   delete [] stream_;
   delete [] handle_;
+  delete [] fwd_algo_;
+  delete [] bwd_filter_algo_;
+  delete [] bwd_data_algo_;
+  delete [] workspace_fwd_sizes_;
+  delete [] workspace_bwd_data_sizes_;
+  delete [] workspace_bwd_filter_sizes_;
 }
 
 INSTANTIATE_CLASS(CuDNNConvolutionLayer);
diff --git a/src/caffe/layers/cudnn_conv_layer.cu b/src/caffe/layers/cudnn_conv_layer.cu
index 4a1a4c4f4f2..42c4fd0260c 100644
--- a/src/caffe/layers/cudnn_conv_layer.cu
+++ b/src/caffe/layers/cudnn_conv_layer.cu
@@ -1,11 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_conv_layer.hpp"
 
 namespace caffe {
 
@@ -14,75 +10,39 @@ __global__ void sync_conv_groups() { }
 template <typename Dtype>
 void CuDNNConvolutionLayer<Dtype>::Forward_gpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* weight = this->blobs_[0]->gpu_data();
   for (int i = 0; i < bottom.size(); ++i) {
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
-    const Dtype* weight = this->blobs_[0]->gpu_data();
-
-    size_t workspace_limit_bytes = this->kernel_h_ *
-                                   this->kernel_w_ *
-                                   this->channels_ *
-                                   sizeof(int) + 1;
 
     // Forward through cuDNN in parallel over groups.
     for (int g = 0; g < this->group_; g++) {
-      cudnnConvolutionFwdAlgo_t algo;
-
-      // pick the convolution algorithm
-      // TODO(shelhamer) this should be done during reshape
-      // TODO(shelhamer) the choice of automatic or manual algorithm picking
-      // should be exposed in proto
-      CUDNN_CHECK(cudnnGetConvolutionForwardAlgorithm(handle_[g],
-        bottom_descs_[i],
-        filter_desc_,
-        conv_descs_[i],
-        top_descs_[i],
-        CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
-        workspace_limit_bytes,  // memoryLimitInBytes,
-        &algo));
-
-      // get minimum size of the workspace needed for the desired algorithm
-      size_t workspaceSizeInBytes_temp = 0;
-
-      CUDNN_CHECK(cudnnGetConvolutionForwardWorkspaceSize(handle_[g],
-        bottom_descs_[i],
-        filter_desc_,
-        conv_descs_[i],
-        top_descs_[i],
-        algo,
-        &workspaceSizeInBytes_temp));
-
-      if (workspaceSizeInBytes_temp > workspaceSizeInBytes) {
-        workspaceSizeInBytes = workspaceSizeInBytes_temp;
-        // free the existing workspace and allocate a new (larger) one
-        cudaFree(this->workspace);
-        cudaError_t err = cudaMalloc(&(this->workspace), workspaceSizeInBytes);
-        if (err != cudaSuccess) {
-          // force zero memory path
-          algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM;
-          workspace = NULL;
-          workspaceSizeInBytes = 0;
-        }
-      }
-
       // Filters.
       CUDNN_CHECK(cudnnConvolutionForward(handle_[g],
             cudnn::dataType<Dtype>::one,
             bottom_descs_[i], bottom_data + bottom_offset_ * g,
-            filter_desc_, weight + weight_offset_ * g,
+            filter_desc_, weight + this->weight_offset_ * g,
             conv_descs_[i],
-            algo, workspace, workspaceSizeInBytes,
+            fwd_algo_[i], workspace[g], workspace_fwd_sizes_[i],
             cudnn::dataType<Dtype>::zero,
             top_descs_[i], top_data + top_offset_ * g));
 
       // Bias.
       if (this->bias_term_) {
         const Dtype* bias_data = this->blobs_[1]->gpu_data();
+#if CUDNN_VERSION_MIN(4, 0, 0)
+        CUDNN_CHECK(cudnnAddTensor(handle_[g],
+              cudnn::dataType<Dtype>::one,
+              bias_desc_, bias_data + bias_offset_ * g,
+              cudnn::dataType<Dtype>::one,
+              top_descs_[i], top_data + top_offset_ * g));
+#else
         CUDNN_CHECK(cudnnAddTensor(handle_[g], CUDNN_ADD_SAME_C,
               cudnn::dataType<Dtype>::one,
               bias_desc_, bias_data + bias_offset_ * g,
               cudnn::dataType<Dtype>::one,
               top_descs_[i], top_data + top_offset_ * g));
+#endif
       }
     }
 
@@ -101,12 +61,10 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   if (this->param_propagate_down_[0]) {
     weight = this->blobs_[0]->gpu_data();
     weight_diff = this->blobs_[0]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
   }
   Dtype* bias_diff = NULL;
   if (this->bias_term_ && this->param_propagate_down_[1]) {
     bias_diff = this->blobs_[1]->mutable_gpu_diff();
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0), bias_diff);
   }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
@@ -124,13 +82,16 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       // Gradient w.r.t. weights.
       if (this->param_propagate_down_[0]) {
         const Dtype* bottom_data = bottom[i]->gpu_data();
-        CUDNN_CHECK(cudnnConvolutionBackwardFilter(handle_[1*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardFilter_v3(
+              handle_[1*this->group_ + g],
               cudnn::dataType<Dtype>::one,
               bottom_descs_[i], bottom_data + bottom_offset_ * g,
               top_descs_[i],    top_diff + top_offset_ * g,
               conv_descs_[i],
+              bwd_filter_algo_[i], workspace[1*this->group_ + g],
+              workspace_bwd_filter_sizes_[i],
               cudnn::dataType<Dtype>::one,
-              filter_desc_, weight_diff + weight_offset_ * g));
+              filter_desc_, weight_diff + this->weight_offset_ * g));
       }
 
       // Gradient w.r.t. bottom data.
@@ -139,11 +100,14 @@ void CuDNNConvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
           weight = this->blobs_[0]->gpu_data();
         }
         Dtype* bottom_diff = bottom[i]->mutable_gpu_diff();
-        CUDNN_CHECK(cudnnConvolutionBackwardData(handle_[2*this->group_ + g],
+        CUDNN_CHECK(cudnnConvolutionBackwardData_v3(
+              handle_[2*this->group_ + g],
               cudnn::dataType<Dtype>::one,
-              filter_desc_, weight + weight_offset_ * g,
+              filter_desc_, weight + this->weight_offset_ * g,
               top_descs_[i], top_diff + top_offset_ * g,
               conv_descs_[i],
+              bwd_data_algo_[i], workspace[2*this->group_ + g],
+              workspace_bwd_data_sizes_[i],
               cudnn::dataType<Dtype>::zero,
               bottom_descs_[i], bottom_diff + bottom_offset_ * g));
       }
diff --git a/src/caffe/layers/cudnn_lcn_layer.cpp b/src/caffe/layers/cudnn_lcn_layer.cpp
new file mode 100644
index 00000000000..9c09bf26b4d
--- /dev/null
+++ b/src/caffe/layers/cudnn_lcn_layer.cpp
@@ -0,0 +1,73 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+  // create a LRN handle
+  handles_setup_ = true;
+
+  size_ = this->layer_param().lrn_param().local_size();
+  pre_pad_ = (size_ - 1) / 2;
+  alpha_ = this->layer_param().lrn_param().alpha();
+  beta_ = this->layer_param().lrn_param().beta();
+  k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::Reshape(bottom, top);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+
+  // allocate / reallocate tempData buffers
+  size_t totalSizeInBytes = sizeof(Dtype)*bottom[0]->num()* \
+                            this->channels_*this->height_*this->width_;
+
+  if (totalSizeInBytes > tempDataSize) {
+    tempDataSize = totalSizeInBytes;
+
+    cudaFree(tempData1);
+    cudaFree(tempData2);
+
+    // allocate new buffers
+    CUDA_CHECK(cudaMalloc(&tempData1, totalSizeInBytes));
+    CUDA_CHECK(cudaMalloc(&tempData2, totalSizeInBytes));
+  }
+}
+
+template <typename Dtype>
+CuDNNLCNLayer<Dtype>::~CuDNNLCNLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
+
+  // destroy LRN handle
+  cudnnDestroy(handle_);
+
+  // free temp buffers
+  cudaFree(tempData1);
+  cudaFree(tempData2);
+}
+
+INSTANTIATE_CLASS(CuDNNLCNLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lcn_layer.cu b/src/caffe/layers/cudnn_lcn_layer.cu
new file mode 100644
index 00000000000..b44ef4730ef
--- /dev/null
+++ b/src/caffe/layers/cudnn_lcn_layer.cu
@@ -0,0 +1,46 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+
+  CUDNN_CHECK(cudnnDivisiveNormalizationForward(
+        handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        NULL,  // srcMeansData
+        this->tempData1, this->tempData2,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLCNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  CUDNN_CHECK(cudnnDivisiveNormalizationBackward(
+        handle_, norm_desc_, CUDNN_DIVNORM_PRECOMPUTED_MEANS,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        NULL, top_diff,  // NULL - srcMeansData
+        this->tempData1, this->tempData2,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_diff,
+        NULL) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLCNLayer);
+
+}  // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lrn_layer.cpp b/src/caffe/layers/cudnn_lrn_layer.cpp
new file mode 100644
index 00000000000..0495b802baf
--- /dev/null
+++ b/src/caffe/layers/cudnn_lrn_layer.cpp
@@ -0,0 +1,53 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::LayerSetUp(bottom, top);
+
+  CUDNN_CHECK(cudnnCreate(&handle_));
+  CUDNN_CHECK(cudnnCreateLRNDescriptor(&norm_desc_));
+  cudnn::createTensor4dDesc<Dtype>(&bottom_desc_);
+  cudnn::createTensor4dDesc<Dtype>(&top_desc_);
+
+  // create a LRN handle
+  handles_setup_ = true;
+
+  size_ = this->layer_param().lrn_param().local_size();
+  alpha_ = this->layer_param().lrn_param().alpha();
+  beta_ = this->layer_param().lrn_param().beta();
+  k_ = this->layer_param().lrn_param().k();
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  LRNLayer<Dtype>::Reshape(bottom, top);
+  cudnn::setTensor4dDesc<Dtype>(&bottom_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  cudnn::setTensor4dDesc<Dtype>(&top_desc_, bottom[0]->num(),
+      this->channels_, this->height_, this->width_);
+  CUDNN_CHECK(cudnnSetLRNDescriptor(norm_desc_, size_, alpha_, beta_, k_));
+}
+
+template <typename Dtype>
+CuDNNLRNLayer<Dtype>::~CuDNNLRNLayer() {
+  // Check that handles have been setup before destroying.
+  if (!handles_setup_) { return; }
+
+  cudnnDestroyTensorDescriptor(bottom_desc_);
+  cudnnDestroyTensorDescriptor(top_desc_);
+
+  // destroy LRN handle
+  cudnnDestroy(handle_);
+}
+
+INSTANTIATE_CLASS(CuDNNLRNLayer);
+
+}   // namespace caffe
+#endif
diff --git a/src/caffe/layers/cudnn_lrn_layer.cu b/src/caffe/layers/cudnn_lrn_layer.cu
new file mode 100644
index 00000000000..ca647f3c64d
--- /dev/null
+++ b/src/caffe/layers/cudnn_lrn_layer.cu
@@ -0,0 +1,44 @@
+#ifdef USE_CUDNN
+#include <vector>
+
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+
+  CUDNN_CHECK(cudnnLRNCrossChannelForward(
+        handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+        cudnn::dataType<Dtype>::one,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        top_desc_, top_data) );
+}
+
+template <typename Dtype>
+void CuDNNLRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+
+  CUDNN_CHECK(cudnnLRNCrossChannelBackward(
+        handle_, norm_desc_, CUDNN_LRN_CROSS_CHANNEL_DIM1,
+        cudnn::dataType<Dtype>::one,
+        top_desc_, top_data,
+        top_desc_, top_diff,
+        bottom_desc_, bottom_data,
+        cudnn::dataType<Dtype>::zero,
+        bottom_desc_, bottom_diff) );
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(CuDNNLRNLayer);
+
+};  // namespace caffe
+
+#endif
diff --git a/src/caffe/layers/cudnn_pooling_layer.cpp b/src/caffe/layers/cudnn_pooling_layer.cpp
index c92c4e477b5..24f14780b4f 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cpp
+++ b/src/caffe/layers/cudnn_pooling_layer.cpp
@@ -1,11 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_pooling_layer.cu b/src/caffe/layers/cudnn_pooling_layer.cu
index a952b855a48..6f00195fa2d 100644
--- a/src/caffe/layers/cudnn_pooling_layer.cu
+++ b/src/caffe/layers/cudnn_pooling_layer.cu
@@ -1,11 +1,7 @@
 #ifdef USE_CUDNN
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_pooling_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cpp b/src/caffe/layers/cudnn_relu_layer.cpp
index 759d83984ef..c86c6907113 100644
--- a/src/caffe/layers/cudnn_relu_layer.cpp
+++ b/src/caffe/layers/cudnn_relu_layer.cpp
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_relu_layer.cu b/src/caffe/layers/cudnn_relu_layer.cu
index 21d14857dd2..9f617183baa 100644
--- a/src/caffe/layers/cudnn_relu_layer.cu
+++ b/src/caffe/layers/cudnn_relu_layer.cu
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cpp b/src/caffe/layers/cudnn_sigmoid_layer.cpp
index 32637873d46..ccb955cdaff 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cpp
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cpp
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_sigmoid_layer.cu b/src/caffe/layers/cudnn_sigmoid_layer.cu
index 7a06cf721da..e2a4b460c6c 100644
--- a/src/caffe/layers/cudnn_sigmoid_layer.cu
+++ b/src/caffe/layers/cudnn_sigmoid_layer.cu
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cpp b/src/caffe/layers/cudnn_softmax_layer.cpp
index 77a3225adcd..6440df9805b 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cpp
+++ b/src/caffe/layers/cudnn_softmax_layer.cpp
@@ -1,13 +1,9 @@
 #ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_softmax_layer.cu b/src/caffe/layers/cudnn_softmax_layer.cu
index a9e2fcefaf7..7283eb71558 100644
--- a/src/caffe/layers/cudnn_softmax_layer.cu
+++ b/src/caffe/layers/cudnn_softmax_layer.cu
@@ -1,13 +1,9 @@
 #ifdef USE_CUDNN
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_softmax_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cpp b/src/caffe/layers/cudnn_tanh_layer.cpp
index 376faad324d..1a56418227c 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cpp
+++ b/src/caffe/layers/cudnn_tanh_layer.cpp
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/cudnn_tanh_layer.cu b/src/caffe/layers/cudnn_tanh_layer.cu
index d287f6fee85..89df28a3e8b 100644
--- a/src/caffe/layers/cudnn_tanh_layer.cu
+++ b/src/caffe/layers/cudnn_tanh_layer.cu
@@ -1,9 +1,7 @@
 #ifdef USE_CUDNN
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/data_layer.cpp b/src/caffe/layers/data_layer.cpp
index 0f2d66776a9..66e6301fd45 100644
--- a/src/caffe/layers/data_layer.cpp
+++ b/src/caffe/layers/data_layer.cpp
@@ -1,159 +1,103 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
-
+#endif  // USE_OPENCV
 #include <stdint.h>
 
-#include <string>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/proto/caffe.pb.h"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/util/rng.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-DataLayer<Dtype>::~DataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+DataLayer<Dtype>::DataLayer(const LayerParameter& param)
+  : BasePrefetchingDataLayer<Dtype>(param),
+    reader_(param) {
+}
+
+template <typename Dtype>
+DataLayer<Dtype>::~DataLayer() {
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
 void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  // Initialize DB
-  db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
-  db_->Open(this->layer_param_.data_param().source(), db::READ);
-  cursor_.reset(db_->NewCursor());
-
-  // Check if we should randomly skip a few data points
-  if (this->layer_param_.data_param().rand_skip()) {
-    unsigned int skip = caffe_rng_rand() %
-                        this->layer_param_.data_param().rand_skip();
-    LOG(INFO) << "Skipping first " << skip << " data points.";
-    while (skip-- > 0) {
-      cursor_->Next();
-    }
-  }
+  const int batch_size = this->layer_param_.data_param().batch_size();
   // Read a data point, and use it to initialize the top blob.
-  Datum datum;
-  datum.ParseFromString(cursor_->value());
-
-  bool force_color = this->layer_param_.data_param().force_encoded_color();
-  if ((force_color && DecodeDatum(&datum, true)) ||
-      DecodeDatumNative(&datum)) {
-    LOG(INFO) << "Decoding Datum";
-  }
-  // image
-  int crop_size = this->layer_param_.transform_param().crop_size();
-  if (crop_size > 0) {
-    top[0]->Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), crop_size, crop_size);
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), crop_size, crop_size);
-    this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
-  } else {
-    top[0]->Reshape(
-        this->layer_param_.data_param().batch_size(), datum.channels(),
-        datum.height(), datum.width());
-    this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
-        datum.channels(), datum.height(), datum.width());
-    this->transformed_data_.Reshape(1, datum.channels(),
-      datum.height(), datum.width());
+  Datum& datum = *(reader_.full().peek());
+
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape top[0] and prefetch_data according to the batch_size.
+  top_shape[0] = batch_size;
+  top[0]->Reshape(top_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].data_.Reshape(top_shape);
   }
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
   if (this->output_labels_) {
-    vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
+    vector<int> label_shape(1, batch_size);
     top[1]->Reshape(label_shape);
-    this->prefetch_label_.Reshape(label_shape);
+    for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+      this->prefetch_[i].label_.Reshape(label_shape);
+    }
   }
 }
 
-// This function is used to create a thread that prefetches the data.
-template <typename Dtype>
-void DataLayer<Dtype>::InternalThreadEntry() {
+// This function is called on prefetch thread
+template<typename Dtype>
+void DataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer batch_timer;
   batch_timer.Start();
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  CHECK(this->prefetch_data_.count());
+  CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
 
-  // Reshape on single input batches for inputs of varying dimension.
+  // Reshape according to the first datum of each batch
+  // on single input batches allows for inputs of varying dimension.
   const int batch_size = this->layer_param_.data_param().batch_size();
-  const int crop_size = this->layer_param_.transform_param().crop_size();
-  bool force_color = this->layer_param_.data_param().force_encoded_color();
-  if (batch_size == 1 && crop_size == 0) {
-    Datum datum;
-    datum.ParseFromString(cursor_->value());
-    if (datum.encoded()) {
-      if (force_color) {
-        DecodeDatum(&datum, true);
-      } else {
-        DecodeDatumNative(&datum);
-      }
-    }
-    this->prefetch_data_.Reshape(1, datum.channels(),
-        datum.height(), datum.width());
-    this->transformed_data_.Reshape(1, datum.channels(),
-        datum.height(), datum.width());
-  }
-
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
+  Datum& datum = *(reader_.full().peek());
+  // Use data_transformer to infer the expected blob shape from datum.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape batch according to the batch_size.
+  top_shape[0] = batch_size;
+  batch->data_.Reshape(top_shape);
+
+  Dtype* top_data = batch->data_.mutable_cpu_data();
   Dtype* top_label = NULL;  // suppress warnings about uninitialized variables
 
   if (this->output_labels_) {
-    top_label = this->prefetch_label_.mutable_cpu_data();
+    top_label = batch->label_.mutable_cpu_data();
   }
   for (int item_id = 0; item_id < batch_size; ++item_id) {
     timer.Start();
-    // get a blob
-    Datum datum;
-    datum.ParseFromString(cursor_->value());
-
-    cv::Mat cv_img;
-    if (datum.encoded()) {
-      if (force_color) {
-        cv_img = DecodeDatumToCVMat(datum, true);
-      } else {
-        cv_img = DecodeDatumToCVMatNative(datum);
-      }
-      if (cv_img.channels() != this->transformed_data_.channels()) {
-        LOG(WARNING) << "Your dataset contains encoded images with mixed "
-        << "channel sizes. Consider adding a 'force_color' flag to the "
-        << "model definition, or rebuild your dataset using "
-        << "convert_imageset.";
-      }
-    }
+    // get a datum
+    Datum& datum = *(reader_.full().pop("Waiting for data"));
     read_time += timer.MicroSeconds();
     timer.Start();
-
     // Apply data transformations (mirror, scale, crop...)
-    int offset = this->prefetch_data_.offset(item_id);
+    int offset = batch->data_.offset(item_id);
     this->transformed_data_.set_cpu_data(top_data + offset);
-    if (datum.encoded()) {
-      this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
-    } else {
-      this->data_transformer_->Transform(datum, &(this->transformed_data_));
-    }
+    this->data_transformer_->Transform(datum, &(this->transformed_data_));
+    // Copy label.
     if (this->output_labels_) {
       top_label[item_id] = datum.label();
     }
     trans_time += timer.MicroSeconds();
-    // go to the next iter
-    cursor_->Next();
-    if (!cursor_->valid()) {
-      DLOG(INFO) << "Restarting data prefetching from start.";
-      cursor_->SeekToFirst();
-    }
+
+    reader_.free().push(const_cast<Datum*>(&datum));
   }
+  timer.Stop();
   batch_timer.Stop();
   DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
   DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
diff --git a/src/caffe/layers/deconv_layer.cpp b/src/caffe/layers/deconv_layer.cpp
index e6d65ab526b..20a460fbdea 100644
--- a/src/caffe/layers/deconv_layer.cpp
+++ b/src/caffe/layers/deconv_layer.cpp
@@ -1,19 +1,24 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void DeconvolutionLayer<Dtype>::compute_output_shape() {
-  this->height_out_ = this->stride_h_ * (this->height_ - 1) + this->kernel_h_
-      - 2 * this->pad_h_;
-  this->width_out_ = this->stride_w_ * (this->width_ - 1) + this->kernel_w_
-      - 2 * this->pad_w_;
+  const int* kernel_shape_data = this->kernel_shape_.cpu_data();
+  const int* stride_data = this->stride_.cpu_data();
+  const int* pad_data = this->pad_.cpu_data();
+  const int* dilation_data = this->dilation_.cpu_data();
+  this->output_shape_.clear();
+  for (int i = 0; i < this->num_spatial_axes_; ++i) {
+    // i + 1 to skip channel axis
+    const int input_dim = this->input_shape(i + 1);
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+    const int output_dim = stride_data[i] * (input_dim - 1)
+        + kernel_extent - 2 * pad_data[i];
+    this->output_shape_.push_back(output_dim);
+  }
 }
 
 template <typename Dtype>
@@ -24,11 +29,11 @@ void DeconvolutionLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->cpu_data();
     Dtype* top_data = top[i]->mutable_cpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->backward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->backward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->cpu_data();
-        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -39,13 +44,6 @@ void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->cpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_cpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->cpu_diff();
     const Dtype* bottom_data = bottom[i]->cpu_data();
@@ -54,21 +52,21 @@ void DeconvolutionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_cpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // Gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_cpu_gemm(top_diff + top[i]->offset(n),
-              bottom_data + bottom[i]->offset(n), weight_diff);
+          this->weight_cpu_gemm(top_diff + n * this->top_dim_,
+              bottom_data + n * this->bottom_dim_, weight_diff);
         }
         // Gradient w.r.t. bottom data, if necessary, reusing the column buffer
         // we might have just computed above.
         if (propagate_down[i]) {
-          this->forward_cpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n),
+          this->forward_cpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_,
               this->param_propagate_down_[0]);
         }
       }
diff --git a/src/caffe/layers/deconv_layer.cu b/src/caffe/layers/deconv_layer.cu
index 9198dd64c72..226763223fa 100644
--- a/src/caffe/layers/deconv_layer.cu
+++ b/src/caffe/layers/deconv_layer.cu
@@ -1,10 +1,6 @@
 #include <vector>
 
-#include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/im2col.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 namespace caffe {
 
@@ -16,11 +12,11 @@ void DeconvolutionLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
     const Dtype* bottom_data = bottom[i]->gpu_data();
     Dtype* top_data = top[i]->mutable_gpu_data();
     for (int n = 0; n < this->num_; ++n) {
-      this->backward_gpu_gemm(bottom_data + bottom[i]->offset(n), weight,
-          top_data + top[i]->offset(n));
+      this->backward_gpu_gemm(bottom_data + n * this->bottom_dim_, weight,
+          top_data + n * this->top_dim_);
       if (this->bias_term_) {
         const Dtype* bias = this->blobs_[1]->gpu_data();
-        this->forward_gpu_bias(top_data + top[i]->offset(n), bias);
+        this->forward_gpu_bias(top_data + n * this->top_dim_, bias);
       }
     }
   }
@@ -31,13 +27,6 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* weight = this->blobs_[0]->gpu_data();
   Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
-  if (this->param_propagate_down_[0]) {
-    caffe_gpu_set(this->blobs_[0]->count(), Dtype(0), weight_diff);
-  }
-  if (this->bias_term_ && this->param_propagate_down_[1]) {
-    caffe_gpu_set(this->blobs_[1]->count(), Dtype(0),
-        this->blobs_[1]->mutable_gpu_diff());
-  }
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     const Dtype* bottom_data = bottom[i]->gpu_data();
@@ -46,20 +35,21 @@ void DeconvolutionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     if (this->bias_term_ && this->param_propagate_down_[1]) {
       Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
       for (int n = 0; n < this->num_; ++n) {
-        this->backward_gpu_bias(bias_diff, top_diff + top[i]->offset(n));
+        this->backward_gpu_bias(bias_diff, top_diff + n * this->top_dim_);
       }
     }
     if (this->param_propagate_down_[0] || propagate_down[i]) {
       for (int n = 0; n < this->num_; ++n) {
         // gradient w.r.t. weight. Note that we will accumulate diffs.
         if (this->param_propagate_down_[0]) {
-          this->weight_gpu_gemm(top_diff + top[i]->offset(n),
-              bottom_data + bottom[i]->offset(n), weight_diff);
+          this->weight_gpu_gemm(top_diff + n * this->top_dim_,
+              bottom_data + n * this->bottom_dim_, weight_diff);
         }
         // gradient w.r.t. bottom data, if necessary.
         if (propagate_down[i]) {
-          this->forward_gpu_gemm(top_diff + top[i]->offset(n), weight,
-              bottom_diff + bottom[i]->offset(n));
+          this->forward_gpu_gemm(top_diff + n * this->top_dim_, weight,
+              bottom_diff + n * this->bottom_dim_,
+              this->param_propagate_down_[0]);
         }
       }
     }
diff --git a/src/caffe/layers/dropout_layer.cpp b/src/caffe/layers/dropout_layer.cpp
index ec1256fd2fa..533ab26c04d 100644
--- a/src/caffe/layers/dropout_layer.cpp
+++ b/src/caffe/layers/dropout_layer.cpp
@@ -2,11 +2,8 @@
 
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/dropout_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -26,8 +23,8 @@ void DropoutLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   NeuronLayer<Dtype>::Reshape(bottom, top);
   // Set up the cache for random number generation
-  rand_vec_.Reshape(bottom[0]->num(), bottom[0]->channels(),
-      bottom[0]->height(), bottom[0]->width());
+  // ReshapeLike does not work because rand_vec_ is of Dtype uint
+  rand_vec_.Reshape(bottom[0]->shape());
 }
 
 template <typename Dtype>
diff --git a/src/caffe/layers/dropout_layer.cu b/src/caffe/layers/dropout_layer.cu
index f9ea04f4acf..186c10ca489 100644
--- a/src/caffe/layers/dropout_layer.cu
+++ b/src/caffe/layers/dropout_layer.cu
@@ -1,16 +1,10 @@
-#include <algorithm>
-#include <limits>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/dropout_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
-
 template <typename Dtype>
 __global__ void DropoutForward(const int n, const Dtype* in,
     const unsigned int* mask, const unsigned int threshold, const float scale,
@@ -73,5 +67,4 @@ void DropoutLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
 INSTANTIATE_LAYER_GPU_FUNCS(DropoutLayer);
 
-
 }  // namespace caffe
diff --git a/src/caffe/layers/dummy_data_layer.cpp b/src/caffe/layers/dummy_data_layer.cpp
index 6b0d617464c..e382bfea802 100644
--- a/src/caffe/layers/dummy_data_layer.cpp
+++ b/src/caffe/layers/dummy_data_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cpp b/src/caffe/layers/eltwise_layer.cpp
index a80700736bd..21256166bfa 100644
--- a/src/caffe/layers/eltwise_layer.cpp
+++ b/src/caffe/layers/eltwise_layer.cpp
@@ -1,9 +1,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/eltwise_layer.cu b/src/caffe/layers/eltwise_layer.cu
index 2247870d97f..c142852e03d 100644
--- a/src/caffe/layers/eltwise_layer.cu
+++ b/src/caffe/layers/eltwise_layer.cu
@@ -1,9 +1,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/elu_layer.cpp b/src/caffe/layers/elu_layer.cpp
new file mode 100644
index 00000000000..a0f87635a5a
--- /dev/null
+++ b/src/caffe/layers/elu_layer.cpp
@@ -0,0 +1,47 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int count = bottom[0]->count();
+  Dtype alpha = this->layer_param_.elu_param().alpha();
+  for (int i = 0; i < count; ++i) {
+    top_data[i] = std::max(bottom_data[i], Dtype(0))
+        + alpha * (exp(std::min(bottom_data[i], Dtype(0))) - Dtype(1));
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    const Dtype* top_data = top[0]->cpu_data();
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    const int count = bottom[0]->count();
+    Dtype alpha = this->layer_param_.elu_param().alpha();
+    for (int i = 0; i < count; ++i) {
+      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
+          + (alpha + top_data[i]) * (bottom_data[i] <= 0));
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(ELULayer);
+#endif
+
+INSTANTIATE_CLASS(ELULayer);
+REGISTER_LAYER_CLASS(ELU);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/elu_layer.cu b/src/caffe/layers/elu_layer.cu
new file mode 100644
index 00000000000..12545aa8253
--- /dev/null
+++ b/src/caffe/layers/elu_layer.cu
@@ -0,0 +1,62 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layers/elu_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ELUForward(const int n, const Dtype* in, Dtype* out,
+    Dtype alpha) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out[index] = in[index] > 0 ? in[index] :
+        alpha * (exp(in[index]) - 1);
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = bottom[0]->count();
+  Dtype alpha = this->layer_param_.elu_param().alpha();
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  ELUForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, top_data, alpha);
+  CUDA_POST_KERNEL_CHECK;
+}
+
+template <typename Dtype>
+__global__ void ELUBackward(const int n, const Dtype* in_diff,
+    const Dtype* out_data, const Dtype* in_data,
+    Dtype* out_diff, Dtype alpha) {
+  CUDA_KERNEL_LOOP(index, n) {
+    out_diff[index] = in_data[index] > 0 ? in_diff[index] :
+        in_diff[index] * (out_data[index] + alpha);
+  }
+}
+
+template <typename Dtype>
+void ELULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down,
+    const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[0]) {
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* top_data = top[0]->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    const int count = bottom[0]->count();
+    Dtype alpha = this->layer_param_.elu_param().alpha();
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    ELUBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, top_data, bottom_data, bottom_diff, alpha);
+    CUDA_POST_KERNEL_CHECK;
+  }
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(ELULayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/embed_layer.cpp b/src/caffe/layers/embed_layer.cpp
new file mode 100644
index 00000000000..36b40d700fd
--- /dev/null
+++ b/src/caffe/layers/embed_layer.cpp
@@ -0,0 +1,119 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  N_ = this->layer_param_.embed_param().num_output();
+  CHECK_GT(N_, 0) << "EmbedLayer num_output must be positive.";
+  K_ = this->layer_param_.embed_param().input_dim();
+  CHECK_GT(K_, 0) << "EmbedLayer input_dim must be positive.";
+  bias_term_ = this->layer_param_.embed_param().bias_term();
+  // Check if we need to set up the weights
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    if (bias_term_) {
+      this->blobs_.resize(2);
+    } else {
+      this->blobs_.resize(1);
+    }
+    // Initialize the weights --
+    // transposed from InnerProductLayer for spatial locality.
+    vector<int> weight_shape(2);
+    weight_shape[0] = K_;
+    weight_shape[1] = N_;
+    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
+    // fill the weights
+    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
+        this->layer_param_.embed_param().weight_filler()));
+    weight_filler->Fill(this->blobs_[0].get());
+    // If necessary, initialize and fill the bias term
+    if (bias_term_) {
+      vector<int> bias_shape(1, N_);
+      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
+      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
+          this->layer_param_.embed_param().bias_filler()));
+      bias_filler->Fill(this->blobs_[1].get());
+    }
+  }  // parameter initialization
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // Figure out the dimensions
+  M_ = bottom[0]->count();
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape.push_back(N_);
+  top[0]->Reshape(top_shape);
+  // Set up the bias multiplier
+  if (bias_term_) {
+    vector<int> bias_shape(1, M_);
+    bias_multiplier_.Reshape(bias_shape);
+    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* weight = this->blobs_[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  int index;
+  for (int n = 0; n < M_; ++n) {
+    index = static_cast<int>(bottom_data[n]);
+    DCHECK_GE(index, 0);
+    DCHECK_LT(index, K_);
+    DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n]) << "non-integer input";
+    caffe_copy(N_, weight + index * N_, top_data + n * N_);
+  }
+  if (bias_term_) {
+    const Dtype* bias = this->blobs_[1]->cpu_data();
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+        bias_multiplier_.cpu_data(), bias, Dtype(1), top_data);
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+  if (this->param_propagate_down_[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* bottom_data = bottom[0]->cpu_data();
+    // Gradient with respect to weight
+    Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
+    int index;
+    for (int n = 0; n < M_; ++n) {
+      index = static_cast<int>(bottom_data[n]);
+      DCHECK_GE(index, 0);
+      DCHECK_LT(index, K_);
+      DCHECK_EQ(static_cast<Dtype>(index), bottom_data[n])
+          << "non-integer input";
+      caffe_axpy(N_, Dtype(1), top_diff + n * N_, weight_diff + index * N_);
+    }
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
+    caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+        bias_multiplier_.cpu_data(), Dtype(1), bias_diff);
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(EmbedLayer);
+#endif
+
+INSTANTIATE_CLASS(EmbedLayer);
+REGISTER_LAYER_CLASS(Embed);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/embed_layer.cu b/src/caffe/layers/embed_layer.cu
new file mode 100644
index 00000000000..6324a3a8937
--- /dev/null
+++ b/src/caffe/layers/embed_layer.cu
@@ -0,0 +1,81 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+#include "caffe/util/gpu_util.cuh"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void EmbedForward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* weight, const int M, const int N, const int K,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(top_index, nthreads) {
+    const int n = top_index / N;
+    const int d = top_index % N;
+    const int index = static_cast<int>(bottom_data[n]);
+    const int weight_index = index * N + d;
+    top_data[top_index] = weight[weight_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* top_diff, const int M, const int N, const int K,
+    Dtype* weight_diff);
+
+template <typename Dtype>
+__global__ void EmbedBackward(const int nthreads, const Dtype* bottom_data,
+    const Dtype* top_diff, const int M, const int N, const int K,
+    Dtype* weight_diff) {
+  CUDA_KERNEL_LOOP(top_index, nthreads) {
+    const int n = top_index / N;
+    const int d = top_index % N;
+    const int index = static_cast<int>(bottom_data[n]);
+    const int weight_index = index * N + d;
+    caffe_gpu_atomic_add(top_diff[top_index], weight_diff + weight_index);
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const Dtype* weight = this->blobs_[0]->gpu_data();
+  const int count = top[0]->count();
+  EmbedForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+      count, bottom_data, weight, M_, N_, K_, top_data);
+  if (bias_term_) {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, Dtype(1),
+        bias_multiplier_.gpu_data(),
+        this->blobs_[1]->gpu_data(), Dtype(1), top_data);
+  }
+}
+
+template <typename Dtype>
+void EmbedLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  CHECK(!propagate_down[0]) << "Can't backpropagate to EmbedLayer input.";
+  if (this->param_propagate_down_[0]) {
+    const int top_count = top[0]->count();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    Dtype* weight_diff = this->blobs_[0]->mutable_gpu_diff();
+    EmbedBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(top_count), CAFFE_CUDA_NUM_THREADS>>>(
+        top_count, bottom_data, top_diff, M_, N_, K_, weight_diff);
+  }
+  if (bias_term_ && this->param_propagate_down_[1]) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bias_diff = this->blobs_[1]->mutable_gpu_diff();
+    caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, Dtype(1), top_diff,
+        bias_multiplier_.gpu_data(), Dtype(1), bias_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(EmbedLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/euclidean_loss_layer.cpp b/src/caffe/layers/euclidean_loss_layer.cpp
index 80efa31b22c..300d991e765 100644
--- a/src/caffe/layers/euclidean_loss_layer.cpp
+++ b/src/caffe/layers/euclidean_loss_layer.cpp
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/euclidean_loss_layer.cu b/src/caffe/layers/euclidean_loss_layer.cu
index 5b1de3ad2d9..4c221b64faf 100644
--- a/src/caffe/layers/euclidean_loss_layer.cu
+++ b/src/caffe/layers/euclidean_loss_layer.cu
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/exp_layer.cpp b/src/caffe/layers/exp_layer.cpp
index c7e7c60cfad..1f4a309fe25 100644
--- a/src/caffe/layers/exp_layer.cpp
+++ b/src/caffe/layers/exp_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/exp_layer.cu b/src/caffe/layers/exp_layer.cu
index 2d75d8dd6c7..61f7f11dd46 100644
--- a/src/caffe/layers/exp_layer.cu
+++ b/src/caffe/layers/exp_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/filter_layer.cpp b/src/caffe/layers/filter_layer.cpp
new file mode 100644
index 00000000000..e226c0b6c9b
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cpp
@@ -0,0 +1,125 @@
+#include <vector>
+
+#include "caffe/layers/filter_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(top.size(), bottom.size() - 1);
+  first_reshape_ = true;
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // bottom[0...k-1] are the blobs to filter
+  // bottom[last] is the "selector_blob"
+  int selector_index = bottom.size() - 1;
+  for (int i = 1; i < bottom[selector_index]->num_axes(); ++i) {
+    CHECK_EQ(bottom[selector_index]->shape(i), 1)
+        << "Selector blob dimensions must be singletons (1), except the first";
+  }
+  for (int i = 0; i < bottom.size() - 1; ++i) {
+    CHECK_EQ(bottom[selector_index]->shape(0), bottom[i]->shape(0)) <<
+        "Each bottom should have the same 0th dimension as the selector blob";
+  }
+
+  const Dtype* bottom_data_selector = bottom[selector_index]->cpu_data();
+  indices_to_forward_.clear();
+
+  // look for non-zero elements in bottom[0]. Items of each bottom that
+  // have the same index as the items in bottom[0] with value == non-zero
+  // will be forwarded
+  for (int item_id = 0; item_id < bottom[selector_index]->shape(0); ++item_id) {
+    // we don't need an offset because item size == 1
+    const Dtype* tmp_data_selector = bottom_data_selector + item_id;
+    if (*tmp_data_selector) {
+      indices_to_forward_.push_back(item_id);
+    }
+  }
+  // only filtered items will be forwarded
+  int new_tops_num = indices_to_forward_.size();
+  // init
+  if (first_reshape_) {
+    new_tops_num = bottom[0]->shape(0);
+    first_reshape_ = false;
+  }
+  for (int t = 0; t < top.size(); ++t) {
+    int num_axes = bottom[t]->num_axes();
+    vector<int> shape_top(num_axes);
+    shape_top[0] = new_tops_num;
+    for (int ts = 1; ts < num_axes; ++ts)
+      shape_top[ts] = bottom[t]->shape(ts);
+    top[t]->Reshape(shape_top);
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); ++t) {
+    const Dtype* bottom_data = bottom[t]->cpu_data();
+    Dtype* top_data = top[t]->mutable_cpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * bottom[t]->count(1);
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+               << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); i++) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); n++) {
+        data_offset_bottom = n * dim;
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          caffe_set(dim, Dtype(0),
+              bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_set(dim, Dtype(0),
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = next_to_backward_offset * dim;
+            next_to_backward_offset++;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_cpu_diff() + data_offset_top,
+                bottom[i]->mutable_cpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(FilterLayer);
+#endif
+
+INSTANTIATE_CLASS(FilterLayer);
+REGISTER_LAYER_CLASS(Filter);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/filter_layer.cu b/src/caffe/layers/filter_layer.cu
new file mode 100644
index 00000000000..b01b16f840c
--- /dev/null
+++ b/src/caffe/layers/filter_layer.cu
@@ -0,0 +1,69 @@
+#include <vector>
+
+#include "caffe/layers/filter_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  int new_tops_num = indices_to_forward_.size();
+  // forward all filtered items for all bottoms but the Selector (bottom[last])
+  for (int t = 0; t < top.size(); ++t) {
+    const Dtype* bottom_data = bottom[t]->gpu_data();
+    Dtype* top_data = top[t]->mutable_gpu_data();
+    int dim = bottom[t]->count() / bottom[t]->shape(0);
+    for (int n = 0; n < new_tops_num; ++n) {
+      int data_offset_top = n * dim;
+      int data_offset_bottom = indices_to_forward_[n] * dim;
+      caffe_copy(dim, bottom_data + data_offset_bottom,
+          top_data + data_offset_top);
+    }
+  }
+}
+
+template <typename Dtype>
+void FilterLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (propagate_down[bottom.size() - 1]) {
+    LOG(FATAL) << this->type()
+               << "Layer cannot backpropagate to filter index inputs";
+  }
+  for (int i = 0; i < top.size(); ++i) {
+    // bottom[last] is the selector and never needs backpropagation
+    // so we can iterate over top vector because top.size() == bottom.size() -1
+    if (propagate_down[i]) {
+      const int dim = top[i]->count() / top[i]->shape(0);
+      int next_to_backward_offset = 0;
+      int batch_offset = 0;
+      int data_offset_bottom = 0;
+      int data_offset_top = 0;
+      for (int n = 0; n < bottom[i]->shape(0); ++n) {
+        if (next_to_backward_offset >= indices_to_forward_.size()) {
+          // we already visited all items that were been forwarded, so
+          // just set to zero remaining ones
+          data_offset_bottom = n * dim;
+          caffe_gpu_set(dim, Dtype(0),
+              bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+        } else {
+          batch_offset = indices_to_forward_[next_to_backward_offset];
+          data_offset_bottom = n * dim;
+          if (n != batch_offset) {  // this data was not been forwarded
+            caffe_gpu_set(dim, Dtype(0),
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          } else {  // this data was been forwarded
+            data_offset_top = next_to_backward_offset * dim;
+            ++next_to_backward_offset;  // point to next forwarded item index
+            caffe_copy(dim, top[i]->mutable_gpu_diff() + data_offset_top,
+                bottom[i]->mutable_gpu_diff() + data_offset_bottom);
+          }
+        }
+      }
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(FilterLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/flatten_layer.cpp b/src/caffe/layers/flatten_layer.cpp
index 745f271ea45..d4ab3935760 100644
--- a/src/caffe/layers/flatten_layer.cpp
+++ b/src/caffe/layers/flatten_layer.cpp
@@ -1,17 +1,27 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/flatten_layer.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
 void FlattenLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  vector<int> top_shape(2);
-  top_shape[0] = bottom[0]->num();
-  top_shape[1] = bottom[0]->count() / bottom[0]->num();
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+      "allow in-place computation.";
+  const int start_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().axis());
+  const int end_axis = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.flatten_param().end_axis());
+  vector<int> top_shape;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
+  const int flattened_dim = bottom[0]->count(start_axis, end_axis + 1);
+  top_shape.push_back(flattened_dim);
+  for (int i = end_axis + 1; i < bottom[0]->num_axes(); ++i) {
+    top_shape.push_back(bottom[0]->shape(i));
+  }
   top[0]->Reshape(top_shape);
   CHECK_EQ(top[0]->count(), bottom[0]->count());
 }
diff --git a/src/caffe/layers/hdf5_data_layer.cpp b/src/caffe/layers/hdf5_data_layer.cpp
index 8a782f7e524..2f13dc641df 100644
--- a/src/caffe/layers/hdf5_data_layer.cpp
+++ b/src/caffe/layers/hdf5_data_layer.cpp
@@ -14,9 +14,8 @@
 #include "hdf5_hl.h"
 #include "stdint.h"
 
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
+#include "caffe/util/hdf5.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_data_layer.cu b/src/caffe/layers/hdf5_data_layer.cu
index 5e3e4ced141..595d2230220 100644
--- a/src/caffe/layers/hdf5_data_layer.cu
+++ b/src/caffe/layers/hdf5_data_layer.cu
@@ -4,15 +4,12 @@ TODO:
 */
 
 #include <stdint.h>
-#include <string>
 #include <vector>
 
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cpp b/src/caffe/layers/hdf5_output_layer.cpp
index f63375c3dc6..f8f1edcd18e 100644
--- a/src/caffe/layers/hdf5_output_layer.cpp
+++ b/src/caffe/layers/hdf5_output_layer.cpp
@@ -3,11 +3,8 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
+#include "caffe/util/hdf5.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hdf5_output_layer.cu b/src/caffe/layers/hdf5_output_layer.cu
index ae497c34fc2..c1685cd34a7 100644
--- a/src/caffe/layers/hdf5_output_layer.cu
+++ b/src/caffe/layers/hdf5_output_layer.cu
@@ -3,11 +3,7 @@
 #include "hdf5.h"
 #include "hdf5_hl.h"
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/hinge_loss_layer.cpp b/src/caffe/layers/hinge_loss_layer.cpp
index a2fb2a18309..374aed3c98f 100644
--- a/src/caffe/layers/hinge_loss_layer.cpp
+++ b/src/caffe/layers/hinge_loss_layer.cpp
@@ -1,12 +1,8 @@
 #include <algorithm>
-#include <cfloat>
-#include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/im2col_layer.cpp b/src/caffe/layers/im2col_layer.cpp
index 1c802714e33..2fb9b3c1099 100644
--- a/src/caffe/layers/im2col_layer.cpp
+++ b/src/caffe/layers/im2col_layer.cpp
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -11,54 +9,122 @@ template <typename Dtype>
 void Im2colLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   ConvolutionParameter conv_param = this->layer_param_.convolution_param();
-  CHECK(!conv_param.has_kernel_size() !=
-      !(conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "Filter size is kernel_size OR kernel_h and kernel_w; not both";
-  CHECK(conv_param.has_kernel_size() ||
-      (conv_param.has_kernel_h() && conv_param.has_kernel_w()))
-      << "For non-square filters both kernel_h and kernel_w are required.";
-  CHECK((!conv_param.has_pad() && conv_param.has_pad_h()
-      && conv_param.has_pad_w())
-      || (!conv_param.has_pad_h() && !conv_param.has_pad_w()))
-      << "pad is pad OR pad_h and pad_w are required.";
-  CHECK((!conv_param.has_stride() && conv_param.has_stride_h()
-      && conv_param.has_stride_w())
-      || (!conv_param.has_stride_h() && !conv_param.has_stride_w()))
-      << "Stride is stride OR stride_h and stride_w are required.";
-  if (conv_param.has_kernel_size()) {
-    kernel_h_ = kernel_w_ = conv_param.kernel_size();
+  force_nd_im2col_ = conv_param.force_nd_im2col();
+  const int input_num_dims = bottom[0]->shape().size();
+  channel_axis_ = bottom[0]->CanonicalAxisIndex(conv_param.axis());
+  const int first_spatial_dim = channel_axis_ + 1;
+  num_spatial_axes_ = input_num_dims - first_spatial_dim;
+  CHECK_GE(num_spatial_axes_, 1);
+  vector<int> dim_blob_shape(1, num_spatial_axes_);
+  // Setup filter kernel dimensions (kernel_shape_).
+  kernel_shape_.Reshape(dim_blob_shape);
+  int* kernel_shape_data = kernel_shape_.mutable_cpu_data();
+  if (conv_param.has_kernel_h() || conv_param.has_kernel_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "kernel_h & kernel_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.kernel_size_size())
+        << "Either kernel_size or kernel_h/w should be specified; not both.";
+    kernel_shape_data[0] = conv_param.kernel_h();
+    kernel_shape_data[1] = conv_param.kernel_w();
   } else {
-    kernel_h_ = conv_param.kernel_h();
-    kernel_w_ = conv_param.kernel_w();
+    const int num_kernel_dims = conv_param.kernel_size_size();
+    CHECK(num_kernel_dims == 1 || num_kernel_dims == num_spatial_axes_)
+        << "kernel_size must be specified once, or once per spatial dimension "
+        << "(kernel_size specified " << num_kernel_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+      for (int i = 0; i < num_spatial_axes_; ++i) {
+        kernel_shape_data[i] =
+            conv_param.kernel_size((num_kernel_dims == 1) ? 0 : i);
+      }
   }
-  CHECK_GT(kernel_h_, 0) << "Filter dimensions cannot be zero.";
-  CHECK_GT(kernel_w_, 0) << "Filter dimensions cannot be zero.";
-  if (!conv_param.has_pad_h()) {
-    pad_h_ = pad_w_ = conv_param.pad();
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    CHECK_GT(kernel_shape_data[i], 0) << "Filter dimensions must be nonzero.";
+  }
+  // Setup stride dimensions (stride_).
+  stride_.Reshape(dim_blob_shape);
+  int* stride_data = stride_.mutable_cpu_data();
+  if (conv_param.has_stride_h() || conv_param.has_stride_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "stride_h & stride_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.stride_size())
+        << "Either stride or stride_h/w should be specified; not both.";
+    stride_data[0] = conv_param.stride_h();
+    stride_data[1] = conv_param.stride_w();
   } else {
-    pad_h_ = conv_param.pad_h();
-    pad_w_ = conv_param.pad_w();
+    const int num_stride_dims = conv_param.stride_size();
+    CHECK(num_stride_dims == 0 || num_stride_dims == 1 ||
+          num_stride_dims == num_spatial_axes_)
+        << "stride must be specified once, or once per spatial dimension "
+        << "(stride specified " << num_stride_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultStride = 1;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      stride_data[i] = (num_stride_dims == 0) ? kDefaultStride :
+          conv_param.stride((num_stride_dims == 1) ? 0 : i);
+      CHECK_GT(stride_data[i], 0) << "Stride dimensions must be nonzero.";
+    }
   }
-  if (!conv_param.has_stride_h()) {
-    stride_h_ = stride_w_ = conv_param.stride();
+  // Setup pad dimensions (pad_).
+  pad_.Reshape(dim_blob_shape);
+  int* pad_data = pad_.mutable_cpu_data();
+  if (conv_param.has_pad_h() || conv_param.has_pad_w()) {
+    CHECK_EQ(num_spatial_axes_, 2)
+        << "pad_h & pad_w can only be used for 2D convolution.";
+    CHECK_EQ(0, conv_param.pad_size())
+        << "Either pad or pad_h/w should be specified; not both.";
+    pad_data[0] = conv_param.pad_h();
+    pad_data[1] = conv_param.pad_w();
   } else {
-    stride_h_ = conv_param.stride_h();
-    stride_w_ = conv_param.stride_w();
+    const int num_pad_dims = conv_param.pad_size();
+    CHECK(num_pad_dims == 0 || num_pad_dims == 1 ||
+          num_pad_dims == num_spatial_axes_)
+        << "pad must be specified once, or once per spatial dimension "
+        << "(pad specified " << num_pad_dims << " times; "
+        << num_spatial_axes_ << " spatial dims);";
+    const int kDefaultPad = 0;
+    for (int i = 0; i < num_spatial_axes_; ++i) {
+      pad_data[i] = (num_pad_dims == 0) ? kDefaultPad :
+          conv_param.pad((num_pad_dims == 1) ? 0 : i);
+    }
+  }
+  // Setup dilation dimensions (dilation_).
+  dilation_.Reshape(dim_blob_shape);
+  int* dilation_data = dilation_.mutable_cpu_data();
+  const int num_dilation_dims = conv_param.dilation_size();
+  CHECK(num_dilation_dims == 0 || num_dilation_dims == 1 ||
+        num_dilation_dims == num_spatial_axes_)
+      << "dilation must be specified once, or once per spatial dimension "
+      << "(dilation specified " << num_dilation_dims << " times; "
+      << num_spatial_axes_ << " spatial dims).";
+  const int kDefaultDilation = 1;
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    dilation_data[i] = (num_dilation_dims == 0) ? kDefaultDilation :
+                       conv_param.dilation((num_dilation_dims == 1) ? 0 : i);
   }
 }
 
 template <typename Dtype>
 void Im2colLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
-  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
-      << "corresponding to (num, channels, height, width)";
-  channels_ = bottom[0]->channels();
-  height_ = bottom[0]->height();
-  width_ = bottom[0]->width();
-  top[0]->Reshape(
-      bottom[0]->num(), channels_ * kernel_h_ * kernel_w_,
-      (height_ + 2 * pad_h_ - kernel_h_) / stride_h_ + 1,
-      (width_ + 2 * pad_w_ - kernel_w_) / stride_w_ + 1);
+  vector<int> top_shape = bottom[0]->shape();
+  const int* kernel_shape_data = kernel_shape_.cpu_data();
+  const int* stride_data = stride_.cpu_data();
+  const int* pad_data = pad_.cpu_data();
+  const int* dilation_data = dilation_.cpu_data();
+  for (int i = 0; i < num_spatial_axes_; ++i) {
+    top_shape[channel_axis_] *= kernel_shape_data[i];
+    const int input_dim = bottom[0]->shape(channel_axis_ + i + 1);
+    const int kernel_extent = dilation_data[i] * (kernel_shape_data[i] - 1) + 1;
+    const int output_dim = (input_dim + 2 * pad_data[i] - kernel_extent)
+        / stride_data[i] + 1;
+    top_shape[channel_axis_ + i + 1] = output_dim;
+  }
+  top[0]->Reshape(top_shape);
+  num_ = bottom[0]->count(0, channel_axis_);
+  bottom_dim_ = bottom[0]->count(channel_axis_);
+  top_dim_ = top[0]->count(channel_axis_);
+
+  channels_ = bottom[0]->shape(channel_axis_);
 }
 
 template <typename Dtype>
@@ -66,10 +132,29 @@ void Im2colLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
-  for (int n = 0; n < bottom[0]->num(); ++n) {
-    im2col_cpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + top[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    DCHECK_EQ(bottom[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+    DCHECK_EQ(top[0]->shape().size() - channel_axis_, num_spatial_axes_ + 1);
+    DCHECK_EQ(kernel_shape_.count(), num_spatial_axes_);
+    DCHECK_EQ(pad_.count(), num_spatial_axes_);
+    DCHECK_EQ(stride_.count(), num_spatial_axes_);
+    DCHECK_EQ(dilation_.count(), num_spatial_axes_);
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_cpu(bottom_data + n * bottom_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+          top_data + n * top_dim_);
+    } else {
+      im2col_nd_cpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+          bottom[0]->shape().data() + channel_axis_,
+          top[0]->shape().data() + channel_axis_,
+          kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+          dilation_.cpu_data(), top_data + n * top_dim_);
+    }
   }
 }
 
@@ -78,10 +163,23 @@ void Im2colLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->cpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    col2im_cpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_cpu(top_diff + n * top_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+          bottom_diff + n * bottom_dim_);
+    } else {
+      col2im_nd_cpu(top_diff + n * top_dim_, num_spatial_axes_,
+          bottom[0]->shape().data() + channel_axis_,
+          top[0]->shape().data() + channel_axis_,
+          kernel_shape_.cpu_data(), pad_.cpu_data(), stride_.cpu_data(),
+          dilation_.cpu_data(), bottom_diff + n * bottom_dim_);
+    }
   }
 }
 
diff --git a/src/caffe/layers/im2col_layer.cu b/src/caffe/layers/im2col_layer.cu
index 9c338b14cb7..792c97f70f9 100644
--- a/src/caffe/layers/im2col_layer.cu
+++ b/src/caffe/layers/im2col_layer.cu
@@ -1,9 +1,7 @@
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -12,10 +10,24 @@ void Im2colLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
-  for (int n = 0; n < bottom[0]->num(); ++n) {
-    im2col_gpu(bottom_data + bottom[0]->offset(n), channels_, height_,
-        width_, kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, top_data + top[0]->offset(n));
+  const int num_kernels = channels_ * top[0]->count(channel_axis_ + 1);
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      im2col_gpu(bottom_data + n * bottom_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+          top_data + n * top_dim_);
+    } else {
+      im2col_nd_gpu(bottom_data + n * bottom_dim_, num_spatial_axes_,
+          num_kernels, bottom[0]->gpu_shape() + channel_axis_,
+          top[0]->gpu_shape() + channel_axis_,
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          dilation_.gpu_data(), top_data + n * top_dim_);
+    }
   }
 }
 
@@ -24,10 +36,23 @@ void Im2colLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
   const Dtype* top_diff = top[0]->gpu_diff();
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
-  for (int n = 0; n < top[0]->num(); ++n) {
-    col2im_gpu(top_diff + top[0]->offset(n), channels_, height_, width_,
-        kernel_h_, kernel_w_, pad_h_, pad_w_,
-        stride_h_, stride_w_, bottom_diff + bottom[0]->offset(n));
+  for (int n = 0; n < num_; ++n) {
+    if (!force_nd_im2col_ && num_spatial_axes_ == 2) {
+      col2im_gpu(top_diff + n * top_dim_, channels_,
+          bottom[0]->shape(channel_axis_ + 1),
+          bottom[0]->shape(channel_axis_ + 2),
+          kernel_shape_.cpu_data()[0], kernel_shape_.cpu_data()[1],
+          pad_.cpu_data()[0], pad_.cpu_data()[1],
+          stride_.cpu_data()[0], stride_.cpu_data()[1],
+          dilation_.cpu_data()[0], dilation_.cpu_data()[1],
+          bottom_diff + n * bottom_dim_);
+    } else {
+      col2im_nd_gpu(top_diff + n * top_dim_, num_spatial_axes_, bottom_dim_,
+          bottom[0]->gpu_shape() + channel_axis_,
+          top[0]->gpu_shape() + channel_axis_,
+          kernel_shape_.gpu_data(), pad_.gpu_data(), stride_.gpu_data(),
+          dilation_.gpu_data(), bottom_diff + n * bottom_dim_);
+    }
   }
 }
 
diff --git a/src/caffe/layers/image_data_layer.cpp b/src/caffe/layers/image_data_layer.cpp
index 38ebbd5ec14..62fda4accce 100644
--- a/src/caffe/layers/image_data_layer.cpp
+++ b/src/caffe/layers/image_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 
 #include <fstream>  // NOLINT(readability/streams)
@@ -6,8 +7,9 @@
 #include <utility>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/image_data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -17,7 +19,7 @@ namespace caffe {
 
 template <typename Dtype>
 ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
@@ -62,28 +64,28 @@ void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // Read an image, and use it to initialize the top blob.
   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
                                     new_height, new_width, is_color);
-  const int channels = cv_img.channels();
-  const int height = cv_img.rows;
-  const int width = cv_img.cols;
-  // image
-  const int crop_size = this->layer_param_.transform_param().crop_size();
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
+  // Use data_transformer to infer the expected blob shape from a cv_image.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape prefetch_data and top[0] according to the batch_size.
   const int batch_size = this->layer_param_.image_data_param().batch_size();
-  if (crop_size > 0) {
-    top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-    this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
-    this->transformed_data_.Reshape(1, channels, crop_size, crop_size);
-  } else {
-    top[0]->Reshape(batch_size, channels, height, width);
-    this->prefetch_data_.Reshape(batch_size, channels, height, width);
-    this->transformed_data_.Reshape(1, channels, height, width);
+  CHECK_GT(batch_size, 0) << "Positive batch size required";
+  top_shape[0] = batch_size;
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].data_.Reshape(top_shape);
   }
+  top[0]->Reshape(top_shape);
+
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
       << top[0]->width();
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  this->prefetch_label_.Reshape(label_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].label_.Reshape(label_shape);
+  }
 }
 
 template <typename Dtype>
@@ -93,36 +95,37 @@ void ImageDataLayer<Dtype>::ShuffleImages() {
   shuffle(lines_.begin(), lines_.end(), prefetch_rng);
 }
 
-// This function is used to create a thread that prefetches the data.
+// This function is called on prefetch thread
 template <typename Dtype>
-void ImageDataLayer<Dtype>::InternalThreadEntry() {
+void ImageDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   CPUTimer batch_timer;
   batch_timer.Start();
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  CHECK(this->prefetch_data_.count());
+  CHECK(batch->data_.count());
   CHECK(this->transformed_data_.count());
   ImageDataParameter image_data_param = this->layer_param_.image_data_param();
   const int batch_size = image_data_param.batch_size();
   const int new_height = image_data_param.new_height();
   const int new_width = image_data_param.new_width();
-  const int crop_size = this->layer_param_.transform_param().crop_size();
   const bool is_color = image_data_param.is_color();
   string root_folder = image_data_param.root_folder();
 
-  // Reshape on single input batches for inputs of varying dimension.
-  if (batch_size == 1 && crop_size == 0 && new_height == 0 && new_width == 0) {
-    cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
-        0, 0, is_color);
-    this->prefetch_data_.Reshape(1, cv_img.channels(),
-        cv_img.rows, cv_img.cols);
-    this->transformed_data_.Reshape(1, cv_img.channels(),
-        cv_img.rows, cv_img.cols);
-  }
-
-  Dtype* prefetch_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* prefetch_label = this->prefetch_label_.mutable_cpu_data();
+  // Reshape according to the first image of each batch
+  // on single input batches allows for inputs of varying dimension.
+  cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,
+      new_height, new_width, is_color);
+  CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;
+  // Use data_transformer to infer the expected blob shape from a cv_img.
+  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);
+  this->transformed_data_.Reshape(top_shape);
+  // Reshape batch according to the batch_size.
+  top_shape[0] = batch_size;
+  batch->data_.Reshape(top_shape);
+
+  Dtype* prefetch_data = batch->data_.mutable_cpu_data();
+  Dtype* prefetch_label = batch->label_.mutable_cpu_data();
 
   // datum scales
   const int lines_size = lines_.size();
@@ -136,7 +139,7 @@ void ImageDataLayer<Dtype>::InternalThreadEntry() {
     read_time += timer.MicroSeconds();
     timer.Start();
     // Apply transformations (mirror, crop...) to the image
-    int offset = this->prefetch_data_.offset(item_id);
+    int offset = batch->data_.offset(item_id);
     this->transformed_data_.set_cpu_data(prefetch_data + offset);
     this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
     trans_time += timer.MicroSeconds();
@@ -163,3 +166,4 @@ INSTANTIATE_CLASS(ImageDataLayer);
 REGISTER_LAYER_CLASS(ImageData);
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/layers/infogain_loss_layer.cpp b/src/caffe/layers/infogain_loss_layer.cpp
index a1e0b40de0e..624d3118124 100644
--- a/src/caffe/layers/infogain_loss_layer.cpp
+++ b/src/caffe/layers/infogain_loss_layer.cpp
@@ -1,12 +1,9 @@
 #include <algorithm>
-#include <cfloat>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/inner_product_layer.cpp b/src/caffe/layers/inner_product_layer.cpp
index 89e0c8fbad7..e65349f0055 100644
--- a/src/caffe/layers/inner_product_layer.cpp
+++ b/src/caffe/layers/inner_product_layer.cpp
@@ -1,11 +1,8 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -14,6 +11,7 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
   const int num_output = this->layer_param_.inner_product_param().num_output();
   bias_term_ = this->layer_param_.inner_product_param().bias_term();
+  transpose_ = this->layer_param_.inner_product_param().transpose();
   N_ = num_output;
   const int axis = bottom[0]->CanonicalAxisIndex(
       this->layer_param_.inner_product_param().axis());
@@ -30,10 +28,15 @@ void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
     } else {
       this->blobs_.resize(1);
     }
-    // Intialize the weight
+    // Initialize the weights
     vector<int> weight_shape(2);
-    weight_shape[0] = N_;
-    weight_shape[1] = K_;
+    if (transpose_) {
+      weight_shape[0] = K_;
+      weight_shape[1] = N_;
+    } else {
+      weight_shape[0] = N_;
+      weight_shape[1] = K_;
+    }
     this->blobs_[0].reset(new Blob<Dtype>(weight_shape));
     // fill the weights
     shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
@@ -83,7 +86,8 @@ void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->cpu_data();
   Dtype* top_data = top[0]->mutable_cpu_data();
   const Dtype* weight = this->blobs_[0]->cpu_data();
-  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans,
+      M_, N_, K_, (Dtype)1.,
       bottom_data, weight, (Dtype)0., top_data);
   if (bias_term_) {
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
@@ -100,22 +104,39 @@ void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->cpu_diff();
     const Dtype* bottom_data = bottom[0]->cpu_data();
     // Gradient with respect to weight
-    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_cpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bias
     caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.cpu_data(), (Dtype)0.,
+        bias_multiplier_.cpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_cpu_diff());
   }
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->cpu_diff();
     // Gradient with respect to bottom data
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
-        bottom[0]->mutable_cpu_diff());
+    if (transpose_) {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->cpu_data(),
+          (Dtype)0., bottom[0]->mutable_cpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/layers/inner_product_layer.cu b/src/caffe/layers/inner_product_layer.cu
index a9e1784a205..a58b56e3281 100644
--- a/src/caffe/layers/inner_product_layer.cu
+++ b/src/caffe/layers/inner_product_layer.cu
@@ -1,11 +1,8 @@
 #include <vector>
 
-#include "caffe/blob.hpp"
-#include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -15,12 +12,21 @@ void InnerProductLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
   const Dtype* bottom_data = bottom[0]->gpu_data();
   Dtype* top_data = top[0]->mutable_gpu_data();
   const Dtype* weight = this->blobs_[0]->gpu_data();
-  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
-      bottom_data, weight, (Dtype)0., top_data);
-  if (bias_term_) {
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
-        bias_multiplier_.gpu_data(),
-        this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
+  if (M_ == 1) {
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, N_, K_, (Dtype)1.,
+                         weight, bottom_data, (Dtype)0., top_data);
+    if (bias_term_)
+      caffe_gpu_axpy<Dtype>(N_, bias_multiplier_.cpu_data()[0],
+                            this->blobs_[1]->gpu_data(), top_data);
+  } else {
+    caffe_gpu_gemm<Dtype>(CblasNoTrans,
+                          transpose_ ? CblasNoTrans : CblasTrans,
+                          M_, N_, K_, (Dtype)1.,
+                          bottom_data, weight, (Dtype)0., top_data);
+    if (bias_term_)
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
+                            bias_multiplier_.gpu_data(),
+                            this->blobs_[1]->gpu_data(), (Dtype)1., top_data);
   }
 }
 
@@ -32,22 +38,39 @@ void InnerProductLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     const Dtype* top_diff = top[0]->gpu_diff();
     const Dtype* bottom_data = bottom[0]->gpu_data();
     // Gradient with respect to weight
-    caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
-        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          K_, N_, M_,
+          (Dtype)1., bottom_data, top_diff,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasTrans, CblasNoTrans,
+          N_, K_, M_,
+          (Dtype)1., top_diff, bottom_data,
+          (Dtype)1., this->blobs_[0]->mutable_gpu_diff());
+    }
   }
   if (bias_term_ && this->param_propagate_down_[1]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bias
     caffe_gpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
-        bias_multiplier_.gpu_data(), (Dtype)0.,
+        bias_multiplier_.gpu_data(), (Dtype)1.,
         this->blobs_[1]->mutable_gpu_diff());
   }
   if (propagate_down[0]) {
     const Dtype* top_diff = top[0]->gpu_diff();
     // Gradient with respect to bottom data
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
-        top_diff, this->blobs_[0]->gpu_data(), (Dtype)0.,
-        bottom[0]->mutable_gpu_diff());
+    if (transpose_) {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasTrans,
+          M_, K_, N_,
+          (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+          (Dtype)0., bottom[0]->mutable_gpu_diff());
+    } else {
+      caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans,
+          M_, K_, N_,
+         (Dtype)1., top_diff, this->blobs_[0]->gpu_data(),
+         (Dtype)0., bottom[0]->mutable_gpu_diff());
+    }
   }
 }
 
diff --git a/src/caffe/layers/input_layer.cpp b/src/caffe/layers/input_layer.cpp
new file mode 100644
index 00000000000..667d8ad67ef
--- /dev/null
+++ b/src/caffe/layers/input_layer.cpp
@@ -0,0 +1,27 @@
+#include <vector>
+
+#include "caffe/layers/input_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void InputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const int num_top = top.size();
+  const InputParameter& param = this->layer_param_.input_param();
+  const int num_shape = param.shape_size();
+  CHECK(num_shape == 0 || num_shape == 1 || num_shape == num_top)
+      << "Must specify 'shape' once, once per top blob, or not at all: "
+      << num_top << " tops vs. " << num_shape << " shapes.";
+  if (num_shape > 0) {
+    for (int i = 0; i < num_top; ++i) {
+      const int shape_index = (param.shape_size() == 1) ? 0 : i;
+      top[i]->Reshape(param.shape(shape_index));
+    }
+  }
+}
+
+INSTANTIATE_CLASS(InputLayer);
+REGISTER_LAYER_CLASS(Input);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/log_layer.cpp b/src/caffe/layers/log_layer.cpp
new file mode 100644
index 00000000000..c70a795cf53
--- /dev/null
+++ b/src/caffe/layers/log_layer.cpp
@@ -0,0 +1,85 @@
+#include <vector>
+
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  NeuronLayer<Dtype>::LayerSetUp(bottom, top);
+  const Dtype base = this->layer_param_.log_param().base();
+  if (base != Dtype(-1)) {
+    CHECK_GT(base, 0) << "base must be strictly positive.";
+  }
+  // If base == -1, interpret the base as e and set log_base = 1 exactly.
+  // Otherwise, calculate its log explicitly.
+  const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);
+  CHECK(!isnan(log_base))
+      << "NaN result: log(base) = log(" << base << ") = " << log_base;
+  CHECK(!isinf(log_base))
+      << "Inf result: log(base) = log(" << base << ") = " << log_base;
+  base_scale_ = Dtype(1) / log_base;
+  CHECK(!isnan(base_scale_))
+      << "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  CHECK(!isinf(base_scale_))
+      << "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;
+  input_scale_ = this->layer_param_.log_param().scale();
+  input_shift_ = this->layer_param_.log_param().shift();
+  backward_num_scale_ = input_scale_ / log_base;
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_copy(count, bottom_data, bottom_diff);
+  if (input_scale_ != Dtype(1)) {
+    caffe_scal(count, input_scale_, bottom_diff);
+  }
+  if (input_shift_ != Dtype(0)) {
+    caffe_add_scalar(count, input_shift_, bottom_diff);
+  }
+  caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+  if (backward_num_scale_ != Dtype(1)) {
+    caffe_scal(count, backward_num_scale_, bottom_diff);
+  }
+  caffe_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(LogLayer);
+#endif
+
+INSTANTIATE_CLASS(LogLayer);
+REGISTER_LAYER_CLASS(Log);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/log_layer.cu b/src/caffe/layers/log_layer.cu
new file mode 100644
index 00000000000..db466dbac29
--- /dev/null
+++ b/src/caffe/layers/log_layer.cu
@@ -0,0 +1,55 @@
+#include <vector>
+
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void LogLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int count = bottom[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {
+    caffe_gpu_log(count, bottom_data, top_data);
+  } else {
+    caffe_copy(count, bottom_data, top_data);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, top_data);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, top_data);
+    }
+    caffe_gpu_log(count, top_data, top_data);
+  }
+  if (base_scale_ != Dtype(1)) {
+    caffe_gpu_scal(count, base_scale_, top_data);
+  }
+}
+
+template <typename Dtype>
+void LogLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+    const int count = bottom[0]->count();
+    const Dtype* bottom_data = bottom[0]->gpu_data();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    caffe_copy(count, bottom_data, bottom_diff);
+    if (input_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, input_scale_, bottom_diff);
+    }
+    if (input_shift_ != Dtype(0)) {
+      caffe_gpu_add_scalar(count, input_shift_, bottom_diff);
+    }
+    caffe_gpu_powx(count, bottom_diff, Dtype(-1), bottom_diff);
+    if (backward_num_scale_ != Dtype(1)) {
+      caffe_gpu_scal(count, backward_num_scale_, bottom_diff);
+    }
+    caffe_gpu_mul(count, top_diff, bottom_diff, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(LogLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 3496a5c2a8a..c0b7a862181 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -1,12 +1,6 @@
-#include <algorithm>
-#include <cfloat>
-#include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/loss_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/lrn_layer.cpp b/src/caffe/layers/lrn_layer.cpp
index 36c1ace4c99..210525e20f3 100644
--- a/src/caffe/layers/lrn_layer.cpp
+++ b/src/caffe/layers/lrn_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -254,6 +253,5 @@ STUB_GPU_BACKWARD(LRNLayer, CrossChannelBackward);
 #endif
 
 INSTANTIATE_CLASS(LRNLayer);
-REGISTER_LAYER_CLASS(LRN);
 
 }  // namespace caffe
diff --git a/src/caffe/layers/lrn_layer.cu b/src/caffe/layers/lrn_layer.cu
index 24aa6a30130..26e619c7569 100644
--- a/src/caffe/layers/lrn_layer.cu
+++ b/src/caffe/layers/lrn_layer.cu
@@ -1,50 +1,51 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/lrn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-__global__ void LRNFillScale(const int nthreads, const Dtype* in,
+__global__ void LRNFillScale(const int nthreads, const Dtype* const in,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype alpha_over_size,
-    const Dtype k, Dtype* scale) {
+    const Dtype k, Dtype* const scale) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
-    in += offset;
-    scale += offset;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
+    const Dtype* const in_off = in + offset;
+    Dtype* const scale_off = scale + offset;
     int head = 0;
-    int pre_pad = (size - 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = (size - 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_scale = 0;
     // fill the scale at [n, :, h, w]
     // accumulate values
     while (head < post_pad && head < channels) {
-      accum_scale += in[head * step] * in[head * step];
+      accum_scale += in_off[head * step] * in_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_scale += in[head * step] * in[head * step];
+      accum_scale += in_off[head * step] * in_off[head * step];
       if (head - size >= 0) {
-        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
       }
-      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
       if (head - size >= 0) {
-        accum_scale -= in[(head - size) * step] * in[(head - size) * step];
+        accum_scale -= in_off[(head - size) * step]
+                       * in_off[(head - size) * step];
       }
-      scale[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
+      scale_off[(head - post_pad) * step] = k + accum_scale * alpha_over_size;
       ++head;
     }
   }
@@ -68,8 +69,8 @@ void LRNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 // TODO: check if it would be faster to just put it into the previous kernel.
 template <typename Dtype>
-__global__ void LRNComputeOutput(const int nthreads, const Dtype* in,
-    const Dtype* scale, const Dtype negative_beta, Dtype* out) {
+__global__ void LRNComputeOutput(const int nthreads, const Dtype* const in,
+    const Dtype* const scale, const Dtype negative_beta, Dtype* const out) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     out[index] = in[index] * pow(scale[index], negative_beta);
   }
@@ -118,56 +119,58 @@ void LRNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 }
 
 template <typename Dtype>
-__global__ void LRNComputeDiff(const int nthreads, const Dtype* bottom_data,
-    const Dtype* top_data, const Dtype* scale, const Dtype* top_diff,
+__global__ void LRNComputeDiff(const int nthreads,
+    const Dtype* const bottom_data, const Dtype* const top_data,
+    const Dtype* const scale, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int size, const Dtype negative_beta,
-    const Dtype cache_ratio,
-    Dtype* bottom_diff) {
+    const Dtype cache_ratio, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int n = index / width / height;
-    int offset = (n * channels * height + h) * width + w;
-    int step = height * width;
-    bottom_data += offset;
-    top_data += offset;
-    scale += offset;
-    top_diff += offset;
-    bottom_diff += offset;
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int n = index / width / height;
+    const int offset = (n * channels * height + h) * width + w;
+    const int step = height * width;
+    const Dtype* const bottom_off = bottom_data + offset;
+    const Dtype* const top_off = top_data + offset;
+    const Dtype* const scale_off = scale + offset;
+    const Dtype* const top_diff_off = top_diff + offset;
+    Dtype* const bottom_diff_off = bottom_diff + offset;
     int head = 0;
-    int pre_pad = size - (size + 1) / 2;
-    int post_pad = size - pre_pad - 1;
+    const int pre_pad = size - (size + 1) / 2;
+    const int post_pad = size - pre_pad - 1;
     Dtype accum_ratio = 0;
     // accumulate values
     while (head < post_pad && head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
       ++head;
     }
     // both add and subtract
     while (head < channels) {
-      accum_ratio += top_diff[head * step] * top_data[head * step] /
-          scale[head * step];
+      accum_ratio += top_diff_off[head * step] * top_off[head * step] /
+          scale_off[head * step];
       if (head - size >= 0) {
-        accum_ratio -= top_diff[(head - size) * step] *
-            top_data[(head - size) * step] / scale[(head - size) * step];
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
       }
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
     // subtract only
     while (head < channels + post_pad) {
       if (head - size >= 0) {
-        accum_ratio -= top_diff[(head - size) * step] *
-            top_data[(head - size) * step] / scale[(head - size) * step];
+        accum_ratio -= top_diff_off[(head - size) * step] *
+            top_off[(head - size) * step] / scale_off[(head - size) * step];
       }
-      bottom_diff[(head - post_pad) * step] = top_diff[(head - post_pad) * step]
-          * pow(scale[(head - post_pad) * step], negative_beta) - cache_ratio *
-          bottom_data[(head - post_pad) * step] * accum_ratio;
+      bottom_diff_off[(head - post_pad) * step] =
+          top_diff_off[(head - post_pad) * step]
+            * pow(scale_off[(head - post_pad) * step], negative_beta)
+          - cache_ratio * bottom_off[(head - post_pad) * step] * accum_ratio;
       ++head;
     }
   }
diff --git a/src/caffe/layers/memory_data_layer.cpp b/src/caffe/layers/memory_data_layer.cpp
index 42de4198bc4..82909874054 100644
--- a/src/caffe/layers/memory_data_layer.cpp
+++ b/src/caffe/layers/memory_data_layer.cpp
@@ -1,10 +1,10 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <vector>
 
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
 
 namespace caffe {
 
@@ -53,6 +53,7 @@ void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
   has_new_data_ = true;
 }
 
+#ifdef USE_OPENCV
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
     const vector<int>& labels) {
@@ -76,6 +77,7 @@ void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
   Reset(top_data, top_label, num);
   has_new_data_ = true;
 }
+#endif  // USE_OPENCV
 
 template <typename Dtype>
 void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
diff --git a/src/caffe/layers/multinomial_logistic_loss_layer.cpp b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
index 4267a594a0f..65664998d2c 100644
--- a/src/caffe/layers/multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/layers/multinomial_logistic_loss_layer.cpp
@@ -1,12 +1,9 @@
 #include <algorithm>
-#include <cfloat>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/util/io.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/mvn_layer.cpp b/src/caffe/layers/mvn_layer.cpp
index b74d7b4f300..8fe4ef8c0a8 100644
--- a/src/caffe/layers/mvn_layer.cpp
+++ b/src/caffe/layers/mvn_layer.cpp
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/mvn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -18,10 +16,15 @@ void MVNLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
       1, 1);
   temp_.Reshape(bottom[0]->num(), bottom[0]->channels(),
       bottom[0]->height(), bottom[0]->width());
-  sum_multiplier_.Reshape(1, 1,
-      bottom[0]->height(), bottom[0]->width());
+  if ( this->layer_param_.mvn_param().across_channels() ) {
+    sum_multiplier_.Reshape(1, bottom[0]->channels(), bottom[0]->height(),
+                            bottom[0]->width());
+  } else {
+    sum_multiplier_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+  }
   Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
   caffe_set(sum_multiplier_.count(), Dtype(1), multiplier_data);
+  eps_ = this->layer_param_.mvn_param().eps();
 }
 
 template <typename Dtype>
@@ -36,53 +39,34 @@ void MVNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
     num = bottom[0]->num() * bottom[0]->channels();
 
   int dim = bottom[0]->count() / num;
-  Dtype eps = 1e-10;
 
-  if (this->layer_param_.mvn_param().normalize_variance()) {
-    // put the squares of bottom into temp_
-    caffe_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        temp_.mutable_cpu_data());
+  // subtract mean
+  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+      temp_.mutable_cpu_data());
+  caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);  // X-EX
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_powx(bottom[0]->count(), top_data, Dtype(2),
+        temp_.mutable_cpu_data());  // (X-EX)^2
     caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
         sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());  // E(X^2)
-    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-        temp_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-        variance_.mutable_cpu_data());  // variance
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-            temp_.mutable_cpu_data());
-
-    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
+        variance_.mutable_cpu_data());  // E((X-EX)^2)
 
     // normalize variance
     caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
           variance_.mutable_cpu_data());
 
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
+    caffe_add_scalar(variance_.count(), eps_, variance_.mutable_cpu_data());
 
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
           temp_.mutable_cpu_data());
 
     caffe_div(temp_.count(), top_data, temp_.cpu_data(), top_data);
-  } else {
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-            sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-
-    // subtract mean
-    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
-            temp_.mutable_cpu_data());
-
-    caffe_add(temp_.count(), bottom_data, temp_.cpu_data(), top_data);
   }
 }
 
@@ -102,7 +86,6 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     num = bottom[0]->num() * bottom[0]->channels();
 
   int dim = bottom[0]->count() / num;
-  Dtype eps = 1e-10;
 
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_mul(temp_.count(), top_data, top_diff, bottom_diff);
@@ -125,31 +108,18 @@ void MVNLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
     // put the squares of bottom into temp_
     caffe_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_cpu_data());
-
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());  // EX
-    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.cpu_data(),
-        sum_multiplier_.cpu_data(), 0.,
-        variance_.mutable_cpu_data());  // E(X^2)
-    caffe_powx(mean_.count(), mean_.cpu_data(), Dtype(2),
-        temp_.mutable_cpu_data());  // (EX)^2
-    caffe_sub(mean_.count(), variance_.cpu_data(), temp_.cpu_data(),
-        variance_.mutable_cpu_data());  // variance
-
-    // normalize variance
-    caffe_powx(variance_.count(), variance_.cpu_data(), Dtype(0.5),
-          variance_.mutable_cpu_data());
-
-    caffe_add_scalar(variance_.count(), eps, variance_.mutable_cpu_data());
-
     caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
         temp_.mutable_cpu_data());
 
     caffe_div(temp_.count(), bottom_diff, temp_.cpu_data(), bottom_diff);
   } else {
-    caffe_copy(temp_.count(), top_diff, bottom_diff);
+    caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+      sum_multiplier_.cpu_data(), 0., mean_.mutable_cpu_data());
+    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.cpu_data(), sum_multiplier_.cpu_data(), 0.,
+      temp_.mutable_cpu_data());
+    caffe_add(temp_.count(), top_diff, temp_.cpu_data(), bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/mvn_layer.cu b/src/caffe/layers/mvn_layer.cu
index 0667f50380f..739293be00e 100644
--- a/src/caffe/layers/mvn_layer.cu
+++ b/src/caffe/layers/mvn_layer.cu
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/mvn_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -20,53 +18,34 @@ void MVNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
   int dim = bottom[0]->count() / num;
 
-  if (this->layer_param_.mvn_param().normalize_variance()) {
-    // put the squares of bottom into temp_
-    caffe_gpu_powx(bottom[0]->count(), bottom_data, Dtype(2),
-        temp_.mutable_gpu_data());
+  // subtract mean
+  caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
+      sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+  caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+      mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+      temp_.mutable_gpu_data());
+  caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(),
+      top_data);  // X-EX
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
+  if (this->layer_param_.mvn_param().normalize_variance()) {
+    // compute variance using var(X) = E((X-EX)^2)
+    caffe_gpu_powx(bottom[0]->count(), top_data, Dtype(2),
+        temp_.mutable_gpu_data());  // (X-EX)^2
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
         sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());  // E(X^2)
-    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-        temp_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-        variance_.mutable_gpu_data());  // variance
-
-    Dtype eps = 1e-10;
-
-    // do mean and variance normalization
-    // subtract mean
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-            temp_.mutable_gpu_data());
-
-    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
+        variance_.mutable_gpu_data());  // E((X-EX)^2)
 
     // normalize variance
     caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
           variance_.mutable_gpu_data());
 
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
+    caffe_gpu_add_scalar(variance_.count(), eps_, variance_.mutable_gpu_data());
 
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
           variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
           temp_.mutable_gpu_data());
 
     caffe_gpu_div(temp_.count(), top_data, temp_.gpu_data(), top_data);
-  } else {
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-
-    // subtract mean
-    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
-            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
-            temp_.mutable_gpu_data());
-
-    caffe_gpu_add(temp_.count(), bottom_data, temp_.gpu_data(), top_data);
   }
 }
 
@@ -87,8 +66,6 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
 
   int dim = bottom[0]->count() / num;
 
-  Dtype eps = 1e-10;
-
   if (this->layer_param_.mvn_param().normalize_variance()) {
     caffe_gpu_mul(temp_.count(), top_data, top_diff, bottom_diff);
     caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., bottom_diff,
@@ -111,30 +88,18 @@ void MVNLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     caffe_gpu_powx(temp_.count(), bottom_data, Dtype(2),
         temp_.mutable_gpu_data());
 
-    // computes variance using var(X) = E(X^2) - (EX)^2
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, bottom_data,
-        sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());  // EX
-    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, temp_.gpu_data(),
-        sum_multiplier_.gpu_data(), 0.,
-        variance_.mutable_gpu_data());  // E(X^2)
-    caffe_gpu_powx(mean_.count(), mean_.gpu_data(), Dtype(2),
-        temp_.mutable_gpu_data());  // (EX)^2
-    caffe_gpu_sub(mean_.count(), variance_.gpu_data(), temp_.gpu_data(),
-        variance_.mutable_gpu_data());  // variance
-
-    // normalize variance
-    caffe_gpu_powx(variance_.count(), variance_.gpu_data(), Dtype(0.5),
-          variance_.mutable_gpu_data());
-
-    caffe_gpu_add_scalar(variance_.count(), eps, variance_.mutable_gpu_data());
-
     caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, 1.,
         variance_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
         temp_.mutable_gpu_data());
 
     caffe_gpu_div(temp_.count(), bottom_diff, temp_.gpu_data(), bottom_diff);
   } else {
-    caffe_copy(temp_.count(), top_diff, bottom_diff);
+    caffe_gpu_gemv<Dtype>(CblasNoTrans, num, dim, 1. / dim, top_diff,
+            sum_multiplier_.gpu_data(), 0., mean_.mutable_gpu_data());
+    caffe_gpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
+            mean_.gpu_data(), sum_multiplier_.gpu_data(), 0.,
+            temp_.mutable_gpu_data());
+    caffe_gpu_add(temp_.count(), top_diff, temp_.gpu_data(), bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/neuron_layer.cpp b/src/caffe/layers/neuron_layer.cpp
index ba67b43878e..d7b5f389310 100644
--- a/src/caffe/layers/neuron_layer.cpp
+++ b/src/caffe/layers/neuron_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/normalize_layer.cpp b/src/caffe/layers/normalize_layer.cpp
new file mode 100644
index 00000000000..6d0b7bc36f8
--- /dev/null
+++ b/src/caffe/layers/normalize_layer.cpp
@@ -0,0 +1,234 @@
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/normalize_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  buffer_.Reshape(1, bottom[0]->channels(),
+                   bottom[0]->height(), bottom[0]->width());
+  buffer_channel_.Reshape(1, bottom[0]->channels(), 1, 1);
+  buffer_spatial_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+  NormalizeParameter norm_param = this->layer_param().norm_param();
+  across_spatial_ = norm_param.across_spatial();
+  if (across_spatial_) {
+    norm_.Reshape(bottom[0]->num(), 1, 1, 1);
+  } else {
+    norm_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  }
+  eps_ = norm_param.eps();
+  int channels = bottom[0]->channels();
+  int spatial_dim = bottom[0]->width() * bottom[0]->height();
+  sum_channel_multiplier_.Reshape(1, channels, 1, 1);
+  caffe_set(channels, Dtype(1), sum_channel_multiplier_.mutable_cpu_data());
+  sum_spatial_multiplier_.Reshape(
+      1, 1, bottom[0]->height(), bottom[0]->width());
+  caffe_set(spatial_dim, Dtype(1), sum_spatial_multiplier_.mutable_cpu_data());
+  channel_shared_ = norm_param.channel_shared();
+  if (this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else {
+    this->blobs_.resize(1);
+    if (channel_shared_) {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(0)));
+    } else {
+      this->blobs_[0].reset(new Blob<Dtype>(vector<int>(1, channels)));
+    }
+    shared_ptr<Filler<Dtype> > scale_filler;
+    if (norm_param.has_scale_filler()) {
+      scale_filler.reset(GetFiller<Dtype>(norm_param.scale_filler()));
+    } else {
+      FillerParameter filler_param;
+      filler_param.set_type("constant");
+      filler_param.set_value(1.0);
+      scale_filler.reset(GetFiller<Dtype>(filler_param));
+    }
+    scale_filler->Fill(this->blobs_[0].get());
+  }
+  if (channel_shared_) {
+    CHECK_EQ(this->blobs_[0]->count(), 1)
+        << "Scale size is inconsistent with prototxt config";
+  } else {
+    CHECK_EQ(this->blobs_[0]->count(), channels)
+        << "Scale size is inconsistent with prototxt config";
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_GE(bottom[0]->num_axes(), 2)
+      << "Number of axes of bottom blob must be >=2.";
+  top[0]->ReshapeLike(*bottom[0]);
+  buffer_.Reshape(1, bottom[0]->channels(),
+                   bottom[0]->height(), bottom[0]->width());
+  if (!across_spatial_) {
+    norm_.Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  }
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  if (spatial_dim != sum_spatial_multiplier_.count()) {
+    sum_spatial_multiplier_.Reshape(
+        1, 1, bottom[0]->height(), bottom[0]->width());
+    caffe_set(spatial_dim, Dtype(1),
+              sum_spatial_multiplier_.mutable_cpu_data());
+    buffer_spatial_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const Dtype* scale = this->blobs_[0]->cpu_data();
+  Dtype* buffer_data = buffer_.mutable_cpu_data();
+  Dtype* norm_data = norm_.mutable_cpu_data();
+  // add eps to avoid overflow
+  caffe_set<Dtype>(norm_.count(), Dtype(eps_), norm_data);
+  const Dtype* sum_channel_multiplier = sum_channel_multiplier_.cpu_data();
+  const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.cpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / num;
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  int channels = bottom[0]->channels();
+  for (int n = 0; n < num; ++n) {
+    caffe_sqr<Dtype>(dim, bottom_data, buffer_data);
+    if (across_spatial_) {
+      // add eps to avoid overflow
+      norm_data[n] = pow(caffe_cpu_asum<Dtype>(dim, buffer_data)+eps_,
+                         Dtype(0.5));
+      caffe_cpu_scale<Dtype>(dim, Dtype(1.0 / norm_data[n]), bottom_data,
+                             top_data);
+    } else {
+      caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, Dtype(1),
+                            buffer_data, sum_channel_multiplier, Dtype(1),
+                            norm_data);
+      // compute norm
+      caffe_powx<Dtype>(spatial_dim, norm_data, Dtype(0.5), norm_data);
+      // scale the layer
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                            1, Dtype(1), sum_channel_multiplier, norm_data,
+                            Dtype(0), buffer_data);
+      caffe_div<Dtype>(dim, bottom_data, buffer_data, top_data);
+      norm_data += spatial_dim;
+    }
+    // scale the output
+    if (channel_shared_) {
+      caffe_scal<Dtype>(dim, scale[0], top_data);
+    } else {
+      caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                            1, Dtype(1), scale, sum_spatial_multiplier,
+                            Dtype(0),
+                            buffer_data);
+      caffe_mul<Dtype>(dim, top_data, buffer_data, top_data);
+    }
+    bottom_data += dim;
+    top_data += dim;
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->cpu_diff();
+  const Dtype* top_data = top[0]->cpu_data();
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  const Dtype* scale = this->blobs_[0]->cpu_data();
+  const Dtype* norm_data = norm_.cpu_data();
+  Dtype* buffer_data = buffer_.mutable_cpu_data();
+  Dtype* buffer_channel = buffer_channel_.mutable_cpu_data();
+  Dtype* buffer_spatial = buffer_spatial_.mutable_cpu_data();
+  const Dtype* sum_channel_multiplier = sum_channel_multiplier_.cpu_data();
+  const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.cpu_data();
+  int count = top[0]->count();
+  int num = top[0]->num();
+  int dim = count / num;
+  int spatial_dim = top[0]->height() * top[0]->width();
+  int channels = top[0]->channels();
+
+  // Propagate to param
+  if (this->param_propagate_down_[0]) {
+    Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff();
+    if (channel_shared_) {
+      scale_diff[0] +=
+          caffe_cpu_dot<Dtype>(count, top_data, top_diff) / scale[0];
+    } else {
+      for (int n = 0; n < num; ++n) {
+        caffe_mul<Dtype>(dim, top_data+n*dim, top_diff+n*dim, buffer_data);
+        caffe_cpu_gemv<Dtype>(CblasNoTrans, channels, spatial_dim, Dtype(1),
+                              buffer_data, sum_spatial_multiplier, Dtype(0),
+                              buffer_channel);
+        // store a / scale[i] in buffer_data temporary
+        caffe_div<Dtype>(channels, buffer_channel, scale, buffer_channel);
+        caffe_add<Dtype>(channels, buffer_channel, scale_diff, scale_diff);
+      }
+    }
+  }
+
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    for (int n = 0; n < num; ++n) {
+      if (across_spatial_) {
+        Dtype a = caffe_cpu_dot<Dtype>(dim, bottom_data, top_diff);
+        caffe_cpu_scale<Dtype>(dim, a / norm_data[n] / norm_data[n],
+                               bottom_data, bottom_diff);
+        caffe_sub<Dtype>(dim, top_diff, bottom_diff, bottom_diff);
+        caffe_scal<Dtype>(dim, Dtype(1.0 / norm_data[n]), bottom_diff);
+      } else {
+        // dot product between bottom_data and top_diff
+        caffe_mul<Dtype>(dim, bottom_data, top_diff, buffer_data);
+        caffe_cpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, Dtype(1),
+                              buffer_data, sum_channel_multiplier, Dtype(0),
+                              buffer_spatial);
+        // scale bottom_diff
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                              1, Dtype(1), sum_channel_multiplier,
+                              buffer_spatial, Dtype(0), buffer_data);
+        caffe_mul<Dtype>(dim, bottom_data, buffer_data, bottom_diff);
+        // divide by square of norm
+        caffe_powx<Dtype>(spatial_dim, norm_data, Dtype(2), buffer_spatial);
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                              1, Dtype(1), sum_channel_multiplier,
+                              buffer_spatial, Dtype(0), buffer_data);
+        caffe_div<Dtype>(dim, bottom_diff, buffer_data, bottom_diff);
+        // subtract
+        caffe_sub<Dtype>(dim, top_diff, bottom_diff, bottom_diff);
+        // divide by norm
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                              1, Dtype(1), sum_channel_multiplier, norm_data,
+                              Dtype(0), buffer_data);
+        caffe_div<Dtype>(dim, bottom_diff, buffer_data, bottom_diff);
+        norm_data += spatial_dim;
+      }
+      // scale the diff
+      if (channel_shared_) {
+        caffe_scal<Dtype>(dim, scale[0], bottom_diff);
+      } else {
+        caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, spatial_dim,
+                              1, Dtype(1), scale, sum_spatial_multiplier,
+                              Dtype(0), buffer_data);
+        caffe_mul<Dtype>(dim, bottom_diff, buffer_data, bottom_diff);
+      }
+      bottom_data += dim;
+      top_diff += dim;
+      bottom_diff += dim;
+    }
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(NormalizeLayer);
+#endif
+
+INSTANTIATE_CLASS(NormalizeLayer);
+REGISTER_LAYER_CLASS(Normalize);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/normalize_layer.cu b/src/caffe/layers/normalize_layer.cu
new file mode 100644
index 00000000000..659f3eb4faa
--- /dev/null
+++ b/src/caffe/layers/normalize_layer.cu
@@ -0,0 +1,220 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "thrust/device_vector.h"
+
+#include "caffe/filler.hpp"
+#include "caffe/layers/normalize_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+// divid a matrix with vector
+template <typename Dtype>
+__global__ void DivBsx(const int nthreads, const Dtype* A,
+    const Dtype* v, const int rows, const int cols, const CBLAS_TRANSPOSE trans,
+    Dtype* B) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int c = index % cols;
+    int r = (index / cols) % rows;
+    if (trans == CblasNoTrans) {
+      B[index] = A[index] / v[c];
+    } else {
+      B[index] = A[index] / v[r];
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void MulBsx(const int nthreads, const Dtype* A,
+    const Dtype* v, const int rows, const int cols, const CBLAS_TRANSPOSE trans,
+    Dtype* B) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    int c = index % cols;
+    int r = (index / cols) % rows;
+    if (trans == CblasNoTrans) {
+      B[index] = A[index] * v[c];
+    } else {
+      B[index] = A[index] * v[r];
+    }
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  Dtype* buffer_data = buffer_.mutable_gpu_data();
+  Dtype* norm_data;
+  if (across_spatial_) {
+    // need to index it
+    norm_data = norm_.mutable_cpu_data();
+  } else {
+    norm_data = norm_.mutable_gpu_data();
+    // add eps to avoid overflow
+    caffe_gpu_set<Dtype>(norm_.count(), Dtype(eps_), norm_data);
+  }
+  const Dtype* scale;
+  if (channel_shared_) {
+    scale = this->blobs_[0]->cpu_data();
+  } else {
+    scale = this->blobs_[0]->gpu_data();
+  }
+  const Dtype* sum_channel_multiplier = sum_channel_multiplier_.gpu_data();
+  int num = bottom[0]->num();
+  int dim = bottom[0]->count() / num;
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  int channels = bottom[0]->channels();
+  for (int n = 0; n < num; ++n) {
+    caffe_gpu_powx<Dtype>(dim, bottom_data, Dtype(2), buffer_data);
+    if (across_spatial_) {
+      Dtype normsqr;
+      caffe_gpu_asum<Dtype>(dim, buffer_data, &normsqr);
+      // add eps to avoid overflow
+      norm_data[n] = pow(normsqr+eps_, Dtype(0.5));
+      caffe_gpu_scale<Dtype>(dim, Dtype(1.0 / norm_data[n]), bottom_data,
+                             top_data);
+    } else {
+      // compute norm
+      caffe_gpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, Dtype(1),
+                            buffer_data, sum_channel_multiplier, Dtype(1),
+                            norm_data);
+      caffe_gpu_powx<Dtype>(spatial_dim, norm_data, Dtype(0.5), norm_data);
+      // scale the layer
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      DivBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+          dim, bottom_data, norm_data, channels, spatial_dim, CblasNoTrans,
+          top_data);
+      CUDA_POST_KERNEL_CHECK;
+      norm_data += spatial_dim;
+    }
+    // scale the output
+    if (channel_shared_) {
+      caffe_gpu_scal<Dtype>(dim, scale[0], top_data);
+    } else {
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      MulBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+          dim, top_data, scale, channels, spatial_dim, CblasTrans,
+          top_data);
+      CUDA_POST_KERNEL_CHECK;
+    }
+    bottom_data += dim;
+    top_data += dim;
+  }
+}
+
+template <typename Dtype>
+void NormalizeLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  const Dtype* top_diff = top[0]->gpu_diff();
+  const Dtype* top_data = top[0]->gpu_data();
+  const Dtype* bottom_data = bottom[0]->mutable_gpu_data();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const Dtype* norm_data;
+  if (across_spatial_) {
+    // need to index it
+    norm_data = norm_.cpu_data();
+  } else {
+    norm_data = norm_.gpu_data();
+  }
+  const Dtype* scale;
+  if (channel_shared_) {
+    scale = this->blobs_[0]->cpu_data();
+  } else {
+    scale = this->blobs_[0]->gpu_data();
+  }
+  Dtype* buffer_data = buffer_.mutable_gpu_data();
+  Dtype* buffer_channel = buffer_channel_.mutable_gpu_data();
+  Dtype* buffer_spatial = buffer_spatial_.mutable_gpu_data();
+  const Dtype* sum_channel_multiplier = sum_channel_multiplier_.gpu_data();
+  const Dtype* sum_spatial_multiplier = sum_spatial_multiplier_.gpu_data();
+  int count = top[0]->count();
+  int num = top[0]->num();
+  int dim = count / num;
+  int spatial_dim = top[0]->height() * top[0]->width();
+  int channels = top[0]->channels();
+
+  // Propagate to param
+  if (this->param_propagate_down_[0]) {
+    if (channel_shared_) {
+      Dtype* scale_diff = this->blobs_[0]->mutable_cpu_diff();
+      Dtype a;
+      caffe_gpu_dot<Dtype>(count, top_data, top_diff, &a);
+      scale_diff[0] += a / scale[0];
+    } else {
+      Dtype* scale_diff = this->blobs_[0]->mutable_gpu_diff();
+      for (int n = 0; n < num; ++n) {
+        // compute a
+        caffe_gpu_mul<Dtype>(dim, top_data+n*dim, top_diff+n*dim, buffer_data);
+        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, spatial_dim, Dtype(1),
+                              buffer_data, sum_spatial_multiplier, Dtype(0),
+                              buffer_channel);
+        // store a / scale[i] in buffer_data temporary
+        caffe_gpu_div<Dtype>(channels, buffer_channel, scale, buffer_channel);
+        caffe_gpu_add<Dtype>(channels, buffer_channel, scale_diff, scale_diff);
+      }
+    }
+  }
+
+  // Propagate to bottom
+  if (propagate_down[0]) {
+    for (int n = 0; n < num; ++n) {
+      if (across_spatial_) {
+        Dtype a;
+        caffe_gpu_dot<Dtype>(dim, bottom_data, top_diff, &a);
+        caffe_gpu_scale<Dtype>(dim, a / norm_data[n] / norm_data[n],
+                               bottom_data, bottom_diff);
+        caffe_gpu_sub<Dtype>(dim, top_diff, bottom_diff, bottom_diff);
+        caffe_gpu_scale<Dtype>(dim, Dtype(1.0 / norm_data[n]), bottom_diff,
+                               bottom_diff);
+      } else {
+        // dot product between bottom_data and top_diff
+        caffe_gpu_mul<Dtype>(dim, bottom_data, top_diff, buffer_data);
+        caffe_gpu_gemv<Dtype>(CblasTrans, channels, spatial_dim, Dtype(1),
+                              buffer_data, sum_channel_multiplier, Dtype(0),
+                              buffer_spatial);
+        // scale botom_diff
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        MulBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+            dim, bottom_data, buffer_spatial, channels, spatial_dim,
+            CblasNoTrans, bottom_diff);
+        CUDA_POST_KERNEL_CHECK;
+        // divide by square of norm
+        caffe_gpu_powx<Dtype>(spatial_dim, norm_data, Dtype(2), buffer_spatial);
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        DivBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+            dim, bottom_diff, buffer_spatial, channels, spatial_dim,
+            CblasNoTrans, bottom_diff);
+        CUDA_POST_KERNEL_CHECK;
+        caffe_gpu_sub<Dtype>(dim, top_diff, bottom_diff, bottom_diff);
+        // divide by norm
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        DivBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+            dim, bottom_diff, norm_data, channels, spatial_dim, CblasNoTrans,
+            bottom_diff);
+        CUDA_POST_KERNEL_CHECK;
+        norm_data += spatial_dim;
+      }
+      // scale the diff
+      if (channel_shared_) {
+        caffe_gpu_scal<Dtype>(dim, scale[0], bottom_diff);
+      } else {
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        MulBsx<Dtype> <<<CAFFE_GET_BLOCKS(dim), CAFFE_CUDA_NUM_THREADS>>>(
+            dim, bottom_diff, scale, channels, spatial_dim, CblasTrans,
+            bottom_diff);
+        CUDA_POST_KERNEL_CHECK;
+      }
+      bottom_data += dim;
+      top_diff += dim;
+      bottom_diff += dim;
+    }
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(NormalizeLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/parse_evaluate_layer.cpp b/src/caffe/layers/parse_evaluate_layer.cpp
new file mode 100644
index 00000000000..3693e52c3f7
--- /dev/null
+++ b/src/caffe/layers/parse_evaluate_layer.cpp
@@ -0,0 +1,75 @@
+#include <algorithm>
+#include <functional>
+#include <utility>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/parse_evaluate_layer.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ParseEvaluateLayer<Dtype>::LayerSetUp(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const ParseEvaluateParameter& parse_evaluate_param =
+      this->layer_param_.parse_evaluate_param();
+  CHECK(parse_evaluate_param.has_num_labels()) << "Must have num_labels!!";
+  num_labels_ = parse_evaluate_param.num_labels();
+  ignore_labels_.clear();
+  int num_ignore_label = parse_evaluate_param.ignore_label().size();
+  for (int i = 0; i < num_ignore_label; ++i) {
+    ignore_labels_.insert(parse_evaluate_param.ignore_label(i));
+  }
+}
+
+template <typename Dtype>
+void ParseEvaluateLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num())
+      << "The data and label should have the same number.";
+  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels());
+  CHECK_EQ(bottom[0]->channels(), 1);
+  CHECK_EQ(bottom[0]->height(), bottom[1]->height());
+  CHECK_GE(bottom[0]->width(), bottom[1]->width());
+  top[0]->Reshape(1, num_labels_, 1, 3);
+}
+
+template <typename Dtype>
+void ParseEvaluateLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+                                            const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(bottom[0]->num(), bottom[1]->num());
+  CHECK_EQ(bottom[0]->count(), bottom[1]->count());
+  const Dtype* bottom_pred = bottom[0]->cpu_data();
+  const Dtype* bottom_gt = bottom[1]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  caffe_set(top[0]->count(), Dtype(0), top_data);
+  int num = bottom[0]->num();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  for (int i = 0; i < num; ++i) {
+    // count the number of ground truth labels, the predicted labels, and
+    // predicted labels happens to be ground truth labels
+    for (int j = 0; j < spatial_dim; ++j) {
+      int gt_label = bottom_gt[j];
+      int pred_label = bottom_pred[j];
+      CHECK_LT(pred_label, num_labels_);
+      if (ignore_labels_.find(gt_label) != ignore_labels_.end()) {
+        continue;
+      }
+      if (gt_label == pred_label) {
+        top_data[gt_label * 3]++;
+      }
+      top_data[gt_label * 3 + 1]++;
+      top_data[pred_label * 3 + 2]++;
+    }
+    bottom_pred += bottom[0]->offset(1);
+    bottom_gt += bottom[1]->offset(1);
+  }
+  // ParseEvaluate layer should not be used as a loss function.
+}
+
+INSTANTIATE_CLASS(ParseEvaluateLayer);
+REGISTER_LAYER_CLASS(ParseEvaluate);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/parse_output_layer.cpp b/src/caffe/layers/parse_output_layer.cpp
new file mode 100644
index 00000000000..0f66e74e5b7
--- /dev/null
+++ b/src/caffe/layers/parse_output_layer.cpp
@@ -0,0 +1,67 @@
+#include <algorithm>
+#include <functional>
+#include <utility>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/parse_output_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ParseOutputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  out_max_val_ = top.size() > 1;
+}
+
+template <typename Dtype>
+void ParseOutputLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // Produces max_ind and max_val
+  top[0]->Reshape(bottom[0]->num(), 1, bottom[0]->height(), bottom[0]->width());
+  if (out_max_val_) {
+    top[1]->ReshapeLike(*top[0]);
+  }
+  max_prob_.Reshape(1, 1, bottom[0]->height(), bottom[0]->width());
+}
+
+template <typename Dtype>
+void ParseOutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_label_data = top[0]->mutable_cpu_data();
+  Dtype* top_prob_data = NULL;
+  if (out_max_val_) {
+    top_prob_data = top[1]->mutable_cpu_data();
+  }
+  Dtype* max_prob_data = max_prob_.mutable_cpu_data();
+  int num = bottom[0]->num();
+  int channels = bottom[0]->channels();
+  int spatial_dim = bottom[0]->height() * bottom[0]->width();
+  for (int i = 0; i < num; ++i) {
+    caffe_set(spatial_dim, Dtype(0), top_label_data);
+    // initialize max value from first plane
+    caffe_copy(spatial_dim, bottom_data, max_prob_data);
+    bottom_data += bottom[0]->offset(0, 1);
+    for (int j = 1; j < channels; ++j) {
+      for (int k = 0; k < spatial_dim; ++k) {
+        Dtype prob = bottom_data[k];
+        if (prob > max_prob_data[k]) {
+          max_prob_data[k] = prob;
+          top_label_data[k] = j;
+        }
+      }
+      bottom_data += bottom[0]->offset(0, 1);
+    }
+    top_label_data += top[0]->offset(1);
+    if (out_max_val_) {
+      caffe_copy(spatial_dim, max_prob_data, top_prob_data);
+      top_prob_data += top[1]->offset(1);
+    }
+  }
+}
+
+INSTANTIATE_CLASS(ParseOutputLayer);
+REGISTER_LAYER_CLASS(ParseOutput);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/pooling_layer.cpp b/src/caffe/layers/pooling_layer.cpp
index c8d41499455..90897db0f45 100644
--- a/src/caffe/layers/pooling_layer.cpp
+++ b/src/caffe/layers/pooling_layer.cpp
@@ -2,11 +2,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/common.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/syncedmem.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/pooling_layer.cu b/src/caffe/layers/pooling_layer.cu
index d1d48501af3..1ea46cc81b1 100644
--- a/src/caffe/layers/pooling_layer.cu
+++ b/src/caffe/layers/pooling_layer.cu
@@ -2,38 +2,38 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-__global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data,
-    int* mask, Dtype* top_mask) {
+__global__ void MaxPoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data, int* mask, Dtype* top_mask) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
-    int hend = min(hstart + kernel_h, height);
-    int wend = min(wstart + kernel_w, width);
+    const int hend = min(hstart + kernel_h, height);
+    const int wend = min(wstart + kernel_w, width);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     Dtype maxval = -FLT_MAX;
     int maxidx = -1;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        if (bottom_data[h * width + w] > maxval) {
+        if (bottom_slice[h * width + w] > maxval) {
           maxidx = h * width + w;
-          maxval = bottom_data[maxidx];
+          maxval = bottom_slice[maxidx];
         }
       }
     }
@@ -47,30 +47,32 @@ __global__ void MaxPoolForward(const int nthreads, const Dtype* bottom_data,
 }
 
 template <typename Dtype>
-__global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
-    const int num, const int channels, const int height,
-    const int width, const int pooled_height, const int pooled_width,
-    const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, const int pad_h, const int pad_w, Dtype* top_data) {
+__global__ void AvePoolForward(const int nthreads,
+    const Dtype* const bottom_data, const int num, const int channels,
+    const int height, const int width, const int pooled_height,
+    const int pooled_width, const int kernel_h, const int kernel_w,
+    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
+    Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
     int hstart = ph * stride_h - pad_h;
     int wstart = pw * stride_w - pad_w;
     int hend = min(hstart + kernel_h, height + pad_h);
     int wend = min(wstart + kernel_w, width + pad_w);
-    int pool_size = (hend - hstart) * (wend - wstart);
+    const int pool_size = (hend - hstart) * (wend - wstart);
     hstart = max(hstart, 0);
     wstart = max(wstart, 0);
     hend = min(hend, height);
     wend = min(wend, width);
     Dtype aveval = 0;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        aveval += bottom_data[h * width + w];
+        aveval += bottom_slice[h * width + w];
       }
     }
     top_data[index] = aveval / pool_size;
@@ -79,37 +81,38 @@ __global__ void AvePoolForward(const int nthreads, const Dtype* bottom_data,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTrain(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* rand_idx, Dtype* top_data) {
+    const int stride_w, Dtype* const rand_idx, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     Dtype cumsum = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
       }
     }
-    float thres = rand_idx[index] * cumsum;
+    const float thres = rand_idx[index] * cumsum;
     // Second pass: get value, and set index.
     cumsum = 0;
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
         if (cumsum >= thres) {
           rand_idx[index] = ((n * channels + c) * height + h) * width + w;
-          top_data[index] = bottom_data[h * width + w];
+          top_data[index] = bottom_slice[h * width + w];
           return;
         }
       }
@@ -120,29 +123,30 @@ __global__ void StoPoolForwardTrain(const int nthreads,
 
 template <typename Dtype>
 __global__ void StoPoolForwardTest(const int nthreads,
-    const Dtype* bottom_data,
+    const Dtype* const bottom_data,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* top_data) {
+    const int stride_w, Dtype* const top_data) {
   CUDA_KERNEL_LOOP(index, nthreads) {
-    int pw = index % pooled_width;
-    int ph = (index / pooled_width) % pooled_height;
-    int c = (index / pooled_width / pooled_height) % channels;
-    int n = index / pooled_width / pooled_height / channels;
-    int hstart = ph * stride_h;
-    int hend = min(hstart + kernel_h, height);
-    int wstart = pw * stride_w;
-    int wend = min(wstart + kernel_w, width);
+    const int pw = index % pooled_width;
+    const int ph = (index / pooled_width) % pooled_height;
+    const int c = (index / pooled_width / pooled_height) % channels;
+    const int n = index / pooled_width / pooled_height / channels;
+    const int hstart = ph * stride_h;
+    const int hend = min(hstart + kernel_h, height);
+    const int wstart = pw * stride_w;
+    const int wend = min(wstart + kernel_w, width);
     // We set cumsum to be 0 to avoid divide-by-zero problems
     Dtype cumsum = FLT_MIN;
     Dtype cumvalues = 0.;
-    bottom_data += (n * channels + c) * height * width;
+    const Dtype* const bottom_slice =
+        bottom_data + (n * channels + c) * height * width;
     // First pass: get sum
     for (int h = hstart; h < hend; ++h) {
       for (int w = wstart; w < wend; ++w) {
-        cumsum += bottom_data[h * width + w];
-        cumvalues += bottom_data[h * width + w] * bottom_data[h * width + w];
+        cumsum += bottom_slice[h * width + w];
+        cumvalues += bottom_slice[h * width + w] * bottom_slice[h * width + w];
       }
     }
     top_data[index] = cumvalues / cumsum;
@@ -210,43 +214,43 @@ void PoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 
 
 template <typename Dtype>
-__global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
-    const int* mask, const Dtype* top_mask, const int num, const int channels,
-    const int height, const int width, const int pooled_height,
-    const int pooled_width, const int kernel_h, const int kernel_w,
-    const int stride_h, const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+__global__ void MaxPoolBackward(const int nthreads, const Dtype* const top_diff,
+    const int* const mask, const Dtype* const top_mask, const int num,
+    const int channels, const int height, const int width,
+    const int pooled_height, const int pooled_width, const int kernel_h,
+    const int kernel_w, const int stride_h, const int stride_w, const int pad_h,
+    const int pad_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart =
-        (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
-    int phend = min((h + pad_h) / stride_h + 1, pooled_height);
-    int pwstart =
-        (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
-    int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart =
+         (h + pad_h < kernel_h) ? 0 : (h + pad_h - kernel_h) / stride_h + 1;
+    const int phend = min((h + pad_h) / stride_h + 1, pooled_height);
+    const int pwstart =
+         (w + pad_w < kernel_w) ? 0 : (w + pad_w - kernel_w) / stride_w + 1;
+    const int pwend = min((w + pad_w) / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    int offset = (n * channels + c) * pooled_height * pooled_width;
-    top_diff += offset;
+    const int offset = (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice = top_diff + offset;
     if (mask) {
-      mask += offset;
+      const int* const mask_slice = mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
     } else {
-      top_mask += offset;
+      const Dtype* const top_mask_slice = top_mask + offset;
       for (int ph = phstart; ph < phend; ++ph) {
         for (int pw = pwstart; pw < pwend; ++pw) {
-          if (top_mask[ph * pooled_width + pw] == h * width + w) {
-            gradient += top_diff[ph * pooled_width + pw];
+          if (top_mask_slice[ph * pooled_width + pw] == h * width + w) {
+            gradient += top_diff_slice[ph * pooled_width + pw];
           }
         }
       }
@@ -256,25 +260,26 @@ __global__ void MaxPoolBackward(const int nthreads, const Dtype* top_diff,
 }
 
 template <typename Dtype>
-__global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
+__global__ void AvePoolBackward(const int nthreads, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
     const int stride_w, const int pad_h, const int pad_w,
-    Dtype* bottom_diff) {
+    Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width + pad_w;
-    int h = (index / width) % height + pad_h;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width + pad_w;
+    const int h = (index / width) % height + pad_h;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
         // figure out the pooling size
@@ -283,7 +288,7 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
         int hend = min(hstart + kernel_h, height + pad_h);
         int wend = min(wstart + kernel_w, width + pad_w);
         int pool_size = (hend - hstart) * (wend - wstart);
-        gradient += top_diff[ph * pooled_width + pw] / pool_size;
+        gradient += top_diff_slice[ph * pooled_width + pw] / pool_size;
       }
     }
     bottom_diff[index] = gradient;
@@ -293,29 +298,31 @@ __global__ void AvePoolBackward(const int nthreads, const Dtype* top_diff,
 
 template <typename Dtype>
 __global__ void StoPoolBackward(const int nthreads,
-    const Dtype* rand_idx, const Dtype* top_diff,
+    const Dtype* const rand_idx, const Dtype* const top_diff,
     const int num, const int channels, const int height,
     const int width, const int pooled_height, const int pooled_width,
     const int kernel_h, const int kernel_w, const int stride_h,
-    const int stride_w, Dtype* bottom_diff) {
+    const int stride_w, Dtype* const bottom_diff) {
   CUDA_KERNEL_LOOP(index, nthreads) {
     // find out the local index
     // find out the local offset
-    int w = index % width;
-    int h = (index / width) % height;
-    int c = (index / width / height) % channels;
-    int n = index / width / height / channels;
-    int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
-    int phend = min(h / stride_h + 1, pooled_height);
-    int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
-    int pwend = min(w / stride_w + 1, pooled_width);
+    const int w = index % width;
+    const int h = (index / width) % height;
+    const int c = (index / width / height) % channels;
+    const int n = index / width / height / channels;
+    const int phstart = (h < kernel_h) ? 0 : (h - kernel_h) / stride_h + 1;
+    const int phend = min(h / stride_h + 1, pooled_height);
+    const int pwstart = (w < kernel_w) ? 0 : (w - kernel_w) / stride_w + 1;
+    const int pwend = min(w / stride_w + 1, pooled_width);
     Dtype gradient = 0;
-    rand_idx += (n * channels + c) * pooled_height * pooled_width;
-    top_diff += (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const rand_idx_slice =
+        rand_idx + (n * channels + c) * pooled_height * pooled_width;
+    const Dtype* const top_diff_slice =
+        top_diff + (n * channels + c) * pooled_height * pooled_width;
     for (int ph = phstart; ph < phend; ++ph) {
       for (int pw = pwstart; pw < pwend; ++pw) {
-        gradient += top_diff[ph * pooled_width + pw] *
-            (index == static_cast<int>(rand_idx[ph * pooled_width + pw]));
+        gradient += top_diff_slice[ph * pooled_width + pw] *
+            (index == static_cast<int>(rand_idx_slice[ph * pooled_width + pw]));
       }
     }
     bottom_diff[index] = gradient;
diff --git a/src/caffe/layers/power_layer.cpp b/src/caffe/layers/power_layer.cpp
index 4fe34c49f32..d99b77ca839 100644
--- a/src/caffe/layers/power_layer.cpp
+++ b/src/caffe/layers/power_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/power_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/power_layer.cu b/src/caffe/layers/power_layer.cu
index 90d944059b6..07711c4213d 100644
--- a/src/caffe/layers/power_layer.cu
+++ b/src/caffe/layers/power_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/power_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/prelu_layer.cpp b/src/caffe/layers/prelu_layer.cpp
index 7119a274dd3..853181bd5a2 100644
--- a/src/caffe/layers/prelu_layer.cpp
+++ b/src/caffe/layers/prelu_layer.cpp
@@ -2,8 +2,9 @@
 #include <vector>
 
 #include "caffe/filler.hpp"
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
 
 namespace caffe {
 
@@ -45,7 +46,8 @@ void PReLULayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 
   // Propagate gradients to the parameters (as directed by backward pass).
   this->param_propagate_down_.resize(this->blobs_.size(), true);
-  multiplier_.Reshape(vector<int>(1, bottom[0]->count() / bottom[0]->num()));
+  multiplier_.Reshape(vector<int>(1, bottom[0]->count(1)));
+  backward_buff_.Reshape(vector<int>(1, bottom[0]->count(1)));
   caffe_set(multiplier_.count(), Dtype(1), multiplier_.mutable_cpu_data());
 }
 
@@ -112,7 +114,6 @@ void PReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   // keep top_diff unchanged.
   if (this->param_propagate_down_[0]) {
     Dtype* slope_diff = this->blobs_[0]->mutable_cpu_diff();
-    caffe_set(this->blobs_[0]->count(), Dtype(0), slope_diff);
     for (int i = 0; i < count; ++i) {
       int c = (i / dim) % channels / div_factor;
       slope_diff[c] += top_diff[i] * bottom_data[i] * (bottom_data[i] <= 0);
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu
index fd0eda5d191..aeb80eacd03 100644
--- a/src/caffe/layers/prelu_layer.cu
+++ b/src/caffe/layers/prelu_layer.cu
@@ -1,8 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/neuron_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
 
 namespace caffe {
 
@@ -31,10 +31,15 @@ __global__ void PReLUBackward(const int n, const int channels, const int dim,
 
 // CUDA kernel for element-wise parameter backward
 template <typename Dtype>
-__global__ void PReLUParamBackward(const int n, const Dtype* in_diff,
+__global__ void PReLUParamBackward(const int n,
+    const int rows, const int rowPitch, const Dtype* in_diff,
     const Dtype* in_data, Dtype* out_diff) {
   CUDA_KERNEL_LOOP(index, n) {
     out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0);
+    for ( int k = 1; k < rows; k++ ) {
+        out_diff[index] += in_diff[index + k*rowPitch]
+           * in_data[index + k*rowPitch] * (in_data[index + k*rowPitch] <= 0);
+    }
   }
 }
 
@@ -75,38 +80,31 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     bottom_data = bottom_memory_.gpu_data();
   }
 
-  // Propagte to param
+  // Propagate to param
   // Since to write bottom diff will affect top diff if top and bottom blobs
   // are identical (in-place computaion), we first compute param backward to
   // keep top_diff unchanged.
   if (this->param_propagate_down_[0]) {
     Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff();
-    // slope_diff is set as 0, then accumulated over batches
-    caffe_gpu_set<Dtype>(this->blobs_[0]->count(), Dtype(0), slope_diff);
     int cdim = channels * dim;
-    Dtype dsum = 0.;
-    for (int n = 0; n < bottom[0]->num(); ++n) {
-      Dtype* temp_buff = multiplier_.mutable_gpu_diff();
-      // compute element-wise diff
-      // NOLINT_NEXT_LINE(whitespace/operators)
-      PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(count),
-          CAFFE_CUDA_NUM_THREADS>>>(
-          cdim, top_diff + top[0]->offset(n),
-          bottom_data + bottom[0]->offset(n), multiplier_.mutable_gpu_diff());
-      CUDA_POST_KERNEL_CHECK;
-      if (channel_shared_) {
-        Dtype d;
-        caffe_gpu_dot<Dtype>(channels * dim, multiplier_.gpu_diff(),
-            multiplier_.gpu_data(), &d);
-        dsum += d;
-      } else {
-        caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
-            multiplier_.gpu_diff(), multiplier_.gpu_data(), 1.,
-            slope_diff);
-      }
-    }
+
+    // compute element-wise diff
+    // NOLINT_NEXT_LINE(whitespace/operators)
+    PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim),
+      CAFFE_CUDA_NUM_THREADS>>>(
+      cdim, bottom[0]->num(), top[0]->offset(1), top_diff ,
+      bottom_data ,
+      backward_buff_.mutable_gpu_diff());
+    CUDA_POST_KERNEL_CHECK;
     if (channel_shared_) {
-      caffe_gpu_set(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+      Dtype dsum;
+      caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(),
+       multiplier_.gpu_data(), &dsum);
+      caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff);
+    } else {
+      caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1.,
+        backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1.,
+        slope_diff);
     }
   }
   // Propagate to bottom
diff --git a/src/caffe/layers/reduction_layer.cpp b/src/caffe/layers/reduction_layer.cpp
new file mode 100644
index 00000000000..fa46487e6a3
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cpp
@@ -0,0 +1,129 @@
+#include <vector>
+
+#include "caffe/layers/reduction_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  op_ = this->layer_param_.reduction_param().operation();
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  axis_ = bottom[0]->CanonicalAxisIndex(
+      this->layer_param_.reduction_param().axis());
+  // In the output, we'll keep all axes up to the reduction axis, but
+  // throw away any after that.
+  // Note: currently reducing along non-tail axes is not supported; otherwise,
+  // we'd need to also copy any axes following an "end_axis".
+  vector<int> top_shape(bottom[0]->shape().begin(),
+                        bottom[0]->shape().begin() + axis_);
+  top[0]->Reshape(top_shape);
+  num_ = bottom[0]->count(0, axis_);
+  dim_ = bottom[0]->count(axis_);
+  CHECK_EQ(num_, top[0]->count());
+  if (op_ == ReductionParameter_ReductionOp_SUM ||
+      op_ == ReductionParameter_ReductionOp_MEAN) {
+    vector<int> sum_mult_shape(1, dim_);
+    sum_multiplier_.Reshape(sum_mult_shape);
+    caffe_set(dim_, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  coeff_ = this->layer_param().reduction_param().coeff();
+  if (op_ == ReductionParameter_ReductionOp_MEAN) {
+    coeff_ /= dim_;
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.cpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      *top_data = caffe_cpu_dot(dim_, mult_data, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      *top_data = caffe_cpu_asum(dim_, bottom_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      *top_data = caffe_cpu_dot(dim_, bottom_data, bottom_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_cpu_data();
+    caffe_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->cpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_cpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_cpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ReductionLayer);
+#endif
+
+INSTANTIATE_CLASS(ReductionLayer);
+REGISTER_LAYER_CLASS(Reduction);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/reduction_layer.cu b/src/caffe/layers/reduction_layer.cu
new file mode 100644
index 00000000000..4a6b2b73fc7
--- /dev/null
+++ b/src/caffe/layers/reduction_layer.cu
@@ -0,0 +1,91 @@
+#include <vector>
+
+#include "caffe/layers/reduction_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  const Dtype* mult_data = NULL;
+  if (sum_multiplier_.count() > 0) {
+    mult_data = sum_multiplier_.gpu_data();
+  }
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < num_; ++i) {
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_dot(dim_, mult_data, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_asum(dim_, bottom_data, top_data);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_dot(dim_, bottom_data, bottom_data, top_data);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    ++top_data;
+  }
+  if (coeff_ != Dtype(1)) {
+    // Reset the top_data pointer.
+    top_data = top[0]->mutable_gpu_data();
+    caffe_gpu_scal(num_, coeff_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ReductionLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  // Get bottom_data, if needed.
+  const Dtype* bottom_data = NULL;
+  switch (op_) {
+  // Operations that don't need bottom_data
+  case ReductionParameter_ReductionOp_SUM:
+  case ReductionParameter_ReductionOp_MEAN:
+    break;
+  // Operations that need bottom_data
+  case ReductionParameter_ReductionOp_ASUM:
+  case ReductionParameter_ReductionOp_SUMSQ:
+    bottom_data = bottom[0]->gpu_data();
+    break;
+  default:
+    LOG(FATAL) << "Unknown reduction op: "
+        << ReductionParameter_ReductionOp_Name(op_);
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  for (int i = 0; i < num_; ++i) {
+    const Dtype bottom_coeff = (*top_diff) * coeff_;
+    switch (op_) {
+    case ReductionParameter_ReductionOp_SUM:
+    case ReductionParameter_ReductionOp_MEAN:
+      caffe_gpu_set(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_ASUM:
+      caffe_gpu_sign(dim_, bottom_data, bottom_diff);
+      caffe_gpu_scal(dim_, bottom_coeff, bottom_diff);
+      break;
+    case ReductionParameter_ReductionOp_SUMSQ:
+      caffe_gpu_scale(dim_, 2 * bottom_coeff, bottom_data, bottom_diff);
+      break;
+    default:
+      LOG(FATAL) << "Unknown reduction op: "
+          << ReductionParameter_ReductionOp_Name(op_);
+    }
+    bottom_data += dim_;
+    bottom_diff += dim_;
+    ++top_diff;
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ReductionLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/relu_layer.cpp b/src/caffe/layers/relu_layer.cpp
index cc00319a578..92a729c81bd 100644
--- a/src/caffe/layers/relu_layer.cpp
+++ b/src/caffe/layers/relu_layer.cpp
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/relu_layer.cu b/src/caffe/layers/relu_layer.cu
index b8924c855e5..4bf15b3aad3 100644
--- a/src/caffe/layers/relu_layer.cu
+++ b/src/caffe/layers/relu_layer.cu
@@ -1,8 +1,7 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/relu_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/reshape_layer.cpp b/src/caffe/layers/reshape_layer.cpp
new file mode 100644
index 00000000000..45dd0902d6a
--- /dev/null
+++ b/src/caffe/layers/reshape_layer.cpp
@@ -0,0 +1,96 @@
+#include <vector>
+
+#include "caffe/layers/reshape_layer.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "
+      "allow in-place computation.";
+  inferred_axis_ = -1;
+  copy_axes_.clear();
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int top_num_axes = top_blob_shape.dim_size();
+  constant_count_ = 1;
+  for (int i = 0; i < top_num_axes; ++i) {
+    const int top_dim = top_blob_shape.dim(i);
+    if (top_dim == 0) {
+      copy_axes_.push_back(i);
+    } else if (top_dim == -1) {
+      CHECK_EQ(inferred_axis_, -1) << "new shape contains multiple "
+          << "-1 dims; at most a single (1) value of -1 may be specified";
+      inferred_axis_ = i;
+    } else {
+      constant_count_ *= top_dim;
+    }
+  }
+}
+
+template <typename Dtype>
+void ReshapeLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+    const vector<Blob<Dtype>*>& top) {
+  const int input_start_axis = this->layer_param_.reshape_param().axis();
+  const int start_axis = (input_start_axis >= 0) ? input_start_axis :
+      bottom[0]->num_axes() + input_start_axis + 1;
+  CHECK_GE(start_axis, 0) << "axis " << input_start_axis << " out of range";
+  CHECK_LE(start_axis, bottom[0]->num_axes()) << "axis " << input_start_axis
+      << " out of range for " << bottom[0]->num_axes() << "-D input blob";
+  const int num_axes = this->layer_param_.reshape_param().num_axes();
+  CHECK_GE(num_axes, -1) << "num_axes must be >= 0, or -1 for all";
+  const int end_axis =
+      (num_axes == -1) ? bottom[0]->num_axes() : (start_axis + num_axes);
+  CHECK_LE(end_axis, bottom[0]->num_axes())
+      << "end_axis = axis + num_axes is out of range";
+  const int num_axes_replaced = end_axis - start_axis;
+  const int num_axes_retained = bottom[0]->num_axes() - num_axes_replaced;
+  const BlobShape& top_blob_shape = this->layer_param_.reshape_param().shape();
+  const int num_new_axes = top_blob_shape.dim_size();
+  vector<int> top_shape(num_axes_retained + num_new_axes);
+  int top_shape_index = 0;
+  for (int i = 0; i < start_axis; ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  for (int i = 0; i < num_new_axes; ++i) {
+    top_shape[top_shape_index++] = top_blob_shape.dim(i);
+  }
+  for (int i = end_axis; i < bottom[0]->num_axes(); ++i) {
+    top_shape[top_shape_index++] = bottom[0]->shape(i);
+  }
+  CHECK_EQ(top_shape_index, top_shape.size());
+  for (int i = 0; i < copy_axes_.size(); ++i) {
+    const int copy_axis_index = copy_axes_[i];
+    CHECK_GT(bottom[0]->num_axes(), start_axis + copy_axis_index)
+        << "new shape contains a 0, but there was no corresponding bottom axis "
+        << "to copy";
+    top_shape[start_axis + copy_axis_index] =
+        bottom[0]->shape(start_axis + copy_axis_index);
+  }
+  if (inferred_axis_ >= 0) {
+    // A -1 dim was specified; infer the correct dimension by computing the
+    // product of the other dimensions.
+    int explicit_count = constant_count_;
+    explicit_count *= bottom[0]->count(0, start_axis);
+    explicit_count *= bottom[0]->count(end_axis);
+    for (int i = 0; i < copy_axes_.size(); ++i) {
+      const int copy_axis_index = copy_axes_[i];
+      explicit_count *= top_shape[start_axis + copy_axis_index];
+    }
+    CHECK_EQ(0, bottom[0]->count() % explicit_count) << "bottom count ("
+        << bottom[0]->count() << ") must be divisible by the product of "
+        << "the specified dimensions (" << explicit_count << ")";
+    const int inferred_dim = bottom[0]->count() / explicit_count;
+    top_shape[start_axis + inferred_axis_] = inferred_dim;
+  }
+  top[0]->Reshape(top_shape);
+  CHECK_EQ(top[0]->count(), bottom[0]->count())
+      << "output count must match input count";
+  top[0]->ShareData(*bottom[0]);
+  top[0]->ShareDiff(*bottom[0]);
+}
+
+INSTANTIATE_CLASS(ReshapeLayer);
+REGISTER_LAYER_CLASS(Reshape);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cpp b/src/caffe/layers/scale_layer.cpp
new file mode 100644
index 00000000000..ecdbb123e31
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cpp
@@ -0,0 +1,219 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/filler.hpp"
+#include "caffe/layer_factory.hpp"
+#include "caffe/layers/scale_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  if (bottom.size() == 1 && this->blobs_.size() > 0) {
+    LOG(INFO) << "Skipping parameter initialization";
+  } else if (bottom.size() == 1) {
+    // scale is a learned parameter; initialize it
+    axis_ = bottom[0]->CanonicalAxisIndex(param.axis());
+    const int num_axes = param.num_axes();
+    CHECK_GE(num_axes, -1) << "num_axes must be non-negative, "
+                           << "or -1 to extend to the end of bottom[0]";
+    if (num_axes >= 0) {
+      CHECK_GE(bottom[0]->num_axes(), axis_ + num_axes)
+          << "scale blob's shape extends past bottom[0]'s shape when applied "
+          << "starting with bottom[0] axis = " << axis_;
+    }
+    this->blobs_.resize(1);
+    const vector<int>::const_iterator& shape_start =
+        bottom[0]->shape().begin() + axis_;
+    const vector<int>::const_iterator& shape_end =
+        (num_axes == -1) ? bottom[0]->shape().end() : (shape_start + num_axes);
+    vector<int> scale_shape(shape_start, shape_end);
+    this->blobs_[0].reset(new Blob<Dtype>(scale_shape));
+    FillerParameter filler_param(param.filler());
+    if (!param.has_filler()) {
+      // Default to unit (1) filler for identity operation.
+      filler_param.set_type("constant");
+      filler_param.set_value(1);
+    }
+    shared_ptr<Filler<Dtype> > filler(GetFiller<Dtype>(filler_param));
+    filler->Fill(this->blobs_[0].get());
+  }
+  if (param.bias_term()) {
+    LayerParameter layer_param(this->layer_param_);
+    layer_param.set_type("Bias");
+    BiasParameter* bias_param = layer_param.mutable_bias_param();
+    bias_param->set_axis(param.axis());
+    if (bottom.size() > 1) {
+      bias_param->set_num_axes(bottom[1]->num_axes());
+    } else {
+      bias_param->set_num_axes(param.num_axes());
+    }
+    bias_param->mutable_filler()->CopyFrom(param.bias_filler());
+    bias_layer_ = LayerRegistry<Dtype>::CreateLayer(layer_param);
+    bias_bottom_vec_.resize(1);
+    bias_bottom_vec_[0] = bottom[0];
+    bias_layer_->SetUp(bias_bottom_vec_, top);
+    bias_param_id_ = this->blobs_.size();
+    this->blobs_.resize(bias_param_id_ + 1);
+    this->blobs_[bias_param_id_] = bias_layer_->blobs()[0];
+    bias_propagate_down_.resize(1, false);
+  }
+  this->param_propagate_down_.resize(this->blobs_.size(), true);
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const ScaleParameter& param = this->layer_param_.scale_param();
+  Blob<Dtype>* scale = (bottom.size() > 1) ? bottom[1] : this->blobs_[0].get();
+  // Always set axis_ == 0 in special case where scale is a scalar
+  // (num_axes == 0). Mathematically equivalent for any choice of axis_, so the
+  // actual setting can be safely ignored; and computation is most efficient
+  // with axis_ == 0 and (therefore) outer_dim_ == 1. (Setting axis_ to
+  // bottom[0]->num_axes() - 1, giving inner_dim_ == 1, would be equally
+  // performant.)
+  axis_ = (scale->num_axes() == 0) ?
+      0 : bottom[0]->CanonicalAxisIndex(param.axis());
+  CHECK_GE(bottom[0]->num_axes(), axis_ + scale->num_axes())
+      << "scale blob's shape extends past bottom[0]'s shape when applied "
+      << "starting with bottom[0] axis = " << axis_;
+  for (int i = 0; i < scale->num_axes(); ++i) {
+    CHECK_EQ(bottom[0]->shape(axis_ + i), scale->shape(i))
+        << "dimension mismatch between bottom[0]->shape(" << axis_ + i
+        << ") and scale->shape(" << i << ")";
+  }
+  outer_dim_ = bottom[0]->count(0, axis_);
+  scale_dim_ = scale->count();
+  inner_dim_ = bottom[0]->count(axis_ + scale->num_axes());
+  if (bottom[0] == top[0]) {  // in-place computation
+    temp_.ReshapeLike(*bottom[0]);
+  } else {
+    top[0]->ReshapeLike(*bottom[0]);
+  }
+  sum_result_.Reshape(vector<int>(1, outer_dim_ * scale_dim_));
+  const int sum_mult_size = std::max(outer_dim_, inner_dim_);
+  sum_multiplier_.Reshape(vector<int>(1, sum_mult_size));
+  if (sum_multiplier_.cpu_data()[sum_mult_size - 1] != Dtype(1)) {
+    caffe_set(sum_mult_size, Dtype(1), sum_multiplier_.mutable_cpu_data());
+  }
+  if (bias_layer_) {
+    bias_bottom_vec_[0] = top[0];
+    bias_layer_->Reshape(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  if (bottom[0] == top[0]) {
+    // In-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->cpu_data(),
+               temp_.mutable_cpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int n = 0; n < outer_dim_; ++n) {
+    for (int d = 0; d < scale_dim_; ++d) {
+      const Dtype factor = scale_data[d];
+      caffe_cpu_scale(inner_dim_, factor, bottom_data, top_data);
+      bottom_data += inner_dim_;
+      top_data += inner_dim_;
+    }
+  }
+  if (bias_layer_) {
+    bias_layer_->Forward(bias_bottom_vec_, top);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->cpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_cpu_diff() :
+        (in_place ? temp_.mutable_cpu_data() : bottom[0]->mutable_cpu_diff()));
+    caffe_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result = caffe_cpu_dot(inner_dim_, product, sum_mult);
+          *scale_diff += result;
+        } else {
+          *scale_diff = caffe_cpu_dot(inner_dim_, product, sum_mult);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_cpu_diff() : sum_result_.mutable_cpu_data();
+        caffe_cpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.cpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_dim_ == 1) {
+          if (scale_param) {
+            Dtype result = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+            *scale_diff += result;
+          } else {
+            *scale_diff = caffe_cpu_dot(outer_dim_, sum_mult, sum_result);
+          }
+        } else {
+          caffe_cpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const Dtype* top_diff = top[0]->cpu_diff();
+    const Dtype* scale_data = scale->cpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+    for (int n = 0; n < outer_dim_; ++n) {
+      for (int d = 0; d < scale_dim_; ++d) {
+        const Dtype factor = scale_data[d];
+        caffe_cpu_scale(inner_dim_, factor, top_diff, bottom_diff);
+        bottom_diff += inner_dim_;
+        top_diff += inner_dim_;
+      }
+    }
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(ScaleLayer);
+#endif
+
+INSTANTIATE_CLASS(ScaleLayer);
+REGISTER_LAYER_CLASS(Scale);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/scale_layer.cu b/src/caffe/layers/scale_layer.cu
new file mode 100644
index 00000000000..fc9a8064db5
--- /dev/null
+++ b/src/caffe/layers/scale_layer.cu
@@ -0,0 +1,135 @@
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layers/scale_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void ScaleForward(const int n, const Dtype* in,
+    const Dtype* scale, const int scale_dim, const int inner_dim,
+    Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index];
+  }
+}
+
+template <typename Dtype>
+__global__ void ScaleBiasForward(const int n, const Dtype* in,
+    const Dtype* scale, const Dtype* bias,
+    const int scale_dim, const int inner_dim, Dtype* out) {
+  CUDA_KERNEL_LOOP(index, n) {
+    const int scale_index = (index / inner_dim) % scale_dim;
+    out[index] = in[index] * scale[scale_index] + bias[scale_index];
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const int count = top[0]->count();
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  if (bottom[0] == top[0]) {
+    // in-place computation; need to store bottom data before overwriting it.
+    // Note that this is only necessary for Backward; we could skip this if not
+    // doing Backward, but Caffe currently provides no way of knowing whether
+    // we'll need to do Backward at the time of the Forward call.
+    caffe_copy(bottom[0]->count(), bottom[0]->gpu_data(),
+               temp_.mutable_gpu_data());
+  }
+  const Dtype* scale_data =
+      ((bottom.size() > 1) ? bottom[1] : this->blobs_[0].get())->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  if (bias_layer_) {
+    const Dtype* bias_data = this->blobs_[bias_param_id_]->gpu_data();
+    ScaleBiasForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, bias_data, scale_dim_, inner_dim_,
+        top_data);
+  } else {
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, scale_data, scale_dim_, inner_dim_, top_data);
+  }
+}
+
+template <typename Dtype>
+void ScaleLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (bias_layer_ &&
+      this->param_propagate_down_[this->param_propagate_down_.size() - 1]) {
+    bias_layer_->Backward(top, bias_propagate_down_, bias_bottom_vec_);
+  }
+  const bool scale_param = (bottom.size() == 1);
+  Blob<Dtype>* scale = scale_param ? this->blobs_[0].get() : bottom[1];
+  if ((!scale_param && propagate_down[1]) ||
+      (scale_param && this->param_propagate_down_[0])) {
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const bool in_place = (bottom[0] == top[0]);
+    const Dtype* bottom_data = (in_place ? &temp_ : bottom[0])->gpu_data();
+    // Hack: store big eltwise product in bottom[0] diff, except in the special
+    // case where this layer itself does the eltwise product, in which case we
+    // can store it directly in the scale diff, and we're done.
+    // If we're computing in-place (and not doing eltwise computation), this
+    // hack doesn't work and we store the product in temp_.
+    const bool is_eltwise = (bottom[0]->count() == scale->count());
+    Dtype* product = (is_eltwise ? scale->mutable_gpu_diff() :
+        (in_place ? temp_.mutable_gpu_data() : bottom[0]->mutable_gpu_diff()));
+    caffe_gpu_mul(top[0]->count(), top_diff, bottom_data, product);
+    if (!is_eltwise) {
+      Dtype* sum_result = NULL;
+      if (inner_dim_ == 1) {
+        sum_result = product;
+      } else if (sum_result_.count() == 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        Dtype* scale_diff = scale->mutable_cpu_diff();
+        if (scale_param) {
+          Dtype result;
+          caffe_gpu_dot(inner_dim_, product, sum_mult, &result);
+          *scale_diff += result;
+        } else {
+          caffe_gpu_dot(inner_dim_, product, sum_mult, scale_diff);
+        }
+      } else {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        sum_result = (outer_dim_ == 1) ?
+            scale->mutable_gpu_diff() : sum_result_.mutable_gpu_data();
+        caffe_gpu_gemv(CblasNoTrans, sum_result_.count(), inner_dim_,
+                       Dtype(1), product, sum_mult, Dtype(0), sum_result);
+      }
+      if (outer_dim_ != 1) {
+        const Dtype* sum_mult = sum_multiplier_.gpu_data();
+        if (scale_dim_ == 1) {
+          Dtype* scale_diff = scale->mutable_cpu_diff();
+          if (scale_param) {
+            Dtype result;
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, &result);
+            *scale_diff += result;
+          } else {
+            caffe_gpu_dot(outer_dim_, sum_mult, sum_result, scale_diff);
+          }
+        } else {
+          Dtype* scale_diff = scale->mutable_gpu_diff();
+          caffe_gpu_gemv(CblasTrans, outer_dim_, scale_dim_,
+                         Dtype(1), sum_result, sum_mult, Dtype(scale_param),
+                         scale_diff);
+        }
+      }
+    }
+  }
+  if (propagate_down[0]) {
+    const int count = top[0]->count();
+    const Dtype* top_diff = top[0]->gpu_diff();
+    const Dtype* scale_data = scale->gpu_data();
+    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+    ScaleForward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, scale_data, scale_dim_, inner_dim_, bottom_diff);
+  }
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(ScaleLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
index 077d949981c..10ac9470832 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
@@ -1,10 +1,7 @@
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -71,7 +68,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
 }
 
 #ifdef CPU_ONLY
-STUB_GPU(SigmoidCrossEntropyLossLayer);
+STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
 #endif
 
 INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
diff --git a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
index 08f7f492297..046cb9d3a31 100644
--- a/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
+++ b/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cu
@@ -1,33 +1,10 @@
-#include <algorithm>
-#include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
-template <typename Dtype>
-void SigmoidCrossEntropyLossLayer<Dtype>::Forward_gpu(
-    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
-  // The forward pass computes the sigmoid outputs.
-  sigmoid_bottom_vec_[0] = bottom[0];
-  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
-  // Compute the loss (negative log likelihood)
-  const int count = bottom[0]->count();
-  const int num = bottom[0]->num();
-  // Stable version of loss computation from input data
-  const Dtype* input_data = bottom[0]->cpu_data();
-  const Dtype* target = bottom[1]->cpu_data();
-  Dtype loss = 0;
-  for (int i = 0; i < count; ++i) {
-    loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
-        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
-  }
-  top[0]->mutable_cpu_data()[0] = loss / num;
-}
-
 template <typename Dtype>
 void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
     const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
@@ -51,7 +28,7 @@ void SigmoidCrossEntropyLossLayer<Dtype>::Backward_gpu(
   }
 }
 
-INSTANTIATE_LAYER_GPU_FUNCS(SigmoidCrossEntropyLossLayer);
+INSTANTIATE_LAYER_GPU_BACKWARD(SigmoidCrossEntropyLossLayer);
 
 
 }  // namespace caffe
diff --git a/src/caffe/layers/sigmoid_layer.cpp b/src/caffe/layers/sigmoid_layer.cpp
index 48c384905bf..85fd9676812 100644
--- a/src/caffe/layers/sigmoid_layer.cpp
+++ b/src/caffe/layers/sigmoid_layer.cpp
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/sigmoid_layer.cu b/src/caffe/layers/sigmoid_layer.cu
index e1af0657ec1..184c61ede83 100644
--- a/src/caffe/layers/sigmoid_layer.cu
+++ b/src/caffe/layers/sigmoid_layer.cu
@@ -1,9 +1,7 @@
-#include <algorithm>
 #include <cmath>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/silence_layer.cpp b/src/caffe/layers/silence_layer.cpp
index 4abf9eff4a2..b2f85c52a0f 100644
--- a/src/caffe/layers/silence_layer.cpp
+++ b/src/caffe/layers/silence_layer.cpp
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/silence_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -12,7 +11,7 @@ void SilenceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
       caffe_set(bottom[i]->count(), Dtype(0),
-                bottom[i]->mutable_cpu_data());
+                bottom[i]->mutable_cpu_diff());
     }
   }
 }
diff --git a/src/caffe/layers/silence_layer.cu b/src/caffe/layers/silence_layer.cu
index 8d044ee7307..3494f6f6731 100644
--- a/src/caffe/layers/silence_layer.cu
+++ b/src/caffe/layers/silence_layer.cu
@@ -1,7 +1,6 @@
 #include <vector>
 
-#include "caffe/common_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/layers/silence_layer.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
@@ -18,7 +17,7 @@ void SilenceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
   for (int i = 0; i < bottom.size(); ++i) {
     if (propagate_down[i]) {
       caffe_gpu_set(bottom[i]->count(), Dtype(0),
-                    bottom[i]->mutable_gpu_data());
+                    bottom[i]->mutable_gpu_diff());
     }
   }
 }
diff --git a/src/caffe/layers/slice_layer.cpp b/src/caffe/layers/slice_layer.cpp
index e4418c9cf9c..759beafe0d9 100644
--- a/src/caffe/layers/slice_layer.cpp
+++ b/src/caffe/layers/slice_layer.cpp
@@ -1,9 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/slice_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -67,11 +66,16 @@ void SliceLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
     }
   }
   CHECK_EQ(count, bottom[0]->count());
+  if (top.size() == 1) {
+    top[0]->ShareData(*bottom[0]);
+    top[0]->ShareDiff(*bottom[0]);
+  }
 }
 
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) { return; }
   int offset_slice_axis = 0;
   const Dtype* bottom_data = bottom[0]->cpu_data();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
@@ -92,7 +96,7 @@ void SliceLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  if (!propagate_down[0]) { return; }
+  if (!propagate_down[0] || top.size() == 1) { return; }
   int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
diff --git a/src/caffe/layers/slice_layer.cu b/src/caffe/layers/slice_layer.cu
index e6e65677bd8..1be3a797d3e 100644
--- a/src/caffe/layers/slice_layer.cu
+++ b/src/caffe/layers/slice_layer.cu
@@ -1,27 +1,46 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/slice_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
+template <typename Dtype>
+__global__ void Slice(const int nthreads, const Dtype* in_data,
+    const bool forward, const int num_slices, const int slice_size,
+    const int bottom_slice_axis, const int top_slice_axis,
+    const int offset_slice_axis, Dtype* out_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int total_slice_size = slice_size * top_slice_axis;
+    const int slice_num = index / total_slice_size;
+    const int slice_index = index % total_slice_size;
+    const int bottom_index = slice_index +
+        (slice_num * bottom_slice_axis + offset_slice_axis) * slice_size;
+    if (forward) {
+      out_data[index] = in_data[bottom_index];
+    } else {
+      out_data[bottom_index] = in_data[index];
+    }
+  }
+}
+
 template <typename Dtype>
 void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
+  if (top.size() == 1) { return; }
   int offset_slice_axis = 0;
   const Dtype* bottom_data = bottom[0]->gpu_data();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = true;
   for (int i = 0; i < top.size(); ++i) {
     Dtype* top_data = top[i]->mutable_gpu_data();
     const int top_slice_axis = top[i]->shape(slice_axis_);
-    for (int n = 0; n < num_slices_; ++n) {
-      const int top_offset = n * top_slice_axis * slice_size_;
-      const int bottom_offset =
-          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
-      caffe_copy(top_slice_axis * slice_size_,
-          bottom_data + bottom_offset, top_data + top_offset);
-    }
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, bottom_data, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, top_data);
     offset_slice_axis += top_slice_axis;
   }
 }
@@ -29,20 +48,20 @@ void SliceLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
 template <typename Dtype>
 void SliceLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
-  if (!propagate_down[0]) { return; }
+  if (!propagate_down[0] || top.size() == 1) { return; }
   int offset_slice_axis = 0;
   Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
   const int bottom_slice_axis = bottom[0]->shape(slice_axis_);
+  const bool kForward = false;
   for (int i = 0; i < top.size(); ++i) {
     const Dtype* top_diff = top[i]->gpu_diff();
     const int top_slice_axis = top[i]->shape(slice_axis_);
-    for (int n = 0; n < num_slices_; ++n) {
-      const int top_offset = n * top_slice_axis * slice_size_;
-      const int bottom_offset =
-          (n * bottom_slice_axis + offset_slice_axis) * slice_size_;
-      caffe_copy(top_slice_axis * slice_size_,
-          top_diff + top_offset, bottom_diff + bottom_offset);
-    }
+    const int top_slice_size = top_slice_axis * slice_size_;
+    const int nthreads = top_slice_size * num_slices_;
+    Slice<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+        nthreads, top_diff, kForward, num_slices_, slice_size_,
+        bottom_slice_axis, top_slice_axis, offset_slice_axis, bottom_diff);
     offset_slice_axis += top_slice_axis;
   }
 }
diff --git a/src/caffe/layers/softmax_layer.cpp b/src/caffe/layers/softmax_layer.cpp
index 04712c9e653..f60e9b03ebf 100644
--- a/src/caffe/layers/softmax_layer.cpp
+++ b/src/caffe/layers/softmax_layer.cpp
@@ -1,9 +1,8 @@
 #include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_layer.cu b/src/caffe/layers/softmax_layer.cu
index 1f9c3a41203..7a9e6833bf6 100644
--- a/src/caffe/layers/softmax_layer.cu
+++ b/src/caffe/layers/softmax_layer.cu
@@ -4,9 +4,8 @@
 
 #include "thrust/device_vector.h"
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/softmax_loss_layer.cpp b/src/caffe/layers/softmax_loss_layer.cpp
index ba312f67fbc..dddb7606573 100644
--- a/src/caffe/layers/softmax_loss_layer.cpp
+++ b/src/caffe/layers/softmax_loss_layer.cpp
@@ -2,10 +2,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/layer_factory.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -27,7 +25,14 @@ void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
   if (has_ignore_label_) {
     ignore_label_ = this->layer_param_.loss_param().ignore_label();
   }
-  normalize_ = this->layer_param_.loss_param().normalize();
+  if (!this->layer_param_.loss_param().has_normalization() &&
+      this->layer_param_.loss_param().has_normalize()) {
+    normalization_ = this->layer_param_.loss_param().normalize() ?
+                     LossParameter_NormalizationMode_VALID :
+                     LossParameter_NormalizationMode_BATCH_SIZE;
+  } else {
+    normalization_ = this->layer_param_.loss_param().normalization();
+  }
 }
 
 template <typename Dtype>
@@ -50,6 +55,36 @@ void SoftmaxWithLossLayer<Dtype>::Reshape(
   }
 }
 
+template <typename Dtype>
+Dtype SoftmaxWithLossLayer<Dtype>::get_normalizer(
+    LossParameter_NormalizationMode normalization_mode, int valid_count) {
+  Dtype normalizer;
+  switch (normalization_mode) {
+    case LossParameter_NormalizationMode_FULL:
+      normalizer = Dtype(outer_num_ * inner_num_);
+      break;
+    case LossParameter_NormalizationMode_VALID:
+      if (valid_count == -1) {
+        normalizer = Dtype(outer_num_ * inner_num_);
+      } else {
+        normalizer = Dtype(valid_count);
+      }
+      break;
+    case LossParameter_NormalizationMode_BATCH_SIZE:
+      normalizer = Dtype(outer_num_);
+      break;
+    case LossParameter_NormalizationMode_NONE:
+      normalizer = Dtype(1);
+      break;
+    default:
+      LOG(FATAL) << "Unknown normalization mode: "
+          << LossParameter_NormalizationMode_Name(normalization_mode);
+  }
+  // Some users will have no labels for some examples in order to 'turn off' a
+  // particular loss in a multi-task setup. The max prevents NaNs in that case.
+  return std::max(Dtype(1.0), normalizer);
+}
+
 template <typename Dtype>
 void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
     const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
@@ -73,11 +108,7 @@ void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
       ++count;
     }
   }
-  if (normalize_) {
-    top[0]->mutable_cpu_data()[0] = loss / count;
-  } else {
-    top[0]->mutable_cpu_data()[0] = loss / outer_num_;
-  }
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
@@ -111,12 +142,9 @@ void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
       }
     }
     // Scale gradient
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    if (normalize_) {
-      caffe_scal(prob_.count(), loss_weight / count, bottom_diff);
-    } else {
-      caffe_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
-    }
+    Dtype loss_weight = top[0]->cpu_diff()[0] /
+                        get_normalizer(normalization_, count);
+    caffe_scal(prob_.count(), loss_weight, bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/softmax_loss_layer.cu b/src/caffe/layers/softmax_loss_layer.cu
index 7e0f3da4552..660e1b39fe0 100644
--- a/src/caffe/layers/softmax_loss_layer.cu
+++ b/src/caffe/layers/softmax_loss_layer.cu
@@ -2,9 +2,8 @@
 #include <cfloat>
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
@@ -50,14 +49,15 @@ void SoftmaxWithLossLayer<Dtype>::Forward_gpu(
       outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
   Dtype loss;
   caffe_gpu_asum(nthreads, loss_data, &loss);
-  if (normalize_) {
-    Dtype count;
-    caffe_gpu_asum(nthreads, counts, &count);
-    loss /= count;
-  } else {
-    loss /= outer_num_;
+  Dtype valid_count = -1;
+  // Only launch another CUDA kernel if we actually need the count of valid
+  // outputs.
+  if (normalization_ == LossParameter_NormalizationMode_VALID &&
+      has_ignore_label_) {
+    caffe_gpu_asum(nthreads, counts, &valid_count);
   }
-  top[0]->mutable_cpu_data()[0] = loss;
+  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_,
+                                                        valid_count);
   if (top.size() == 2) {
     top[1]->ShareData(prob_);
   }
@@ -109,14 +109,17 @@ void SoftmaxWithLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
     SoftmaxLossBackwardGPU<Dtype><<<CAFFE_GET_BLOCKS(nthreads),
         CAFFE_CUDA_NUM_THREADS>>>(nthreads, top_data, label, bottom_diff,
         outer_num_, dim, inner_num_, has_ignore_label_, ignore_label_, counts);
-    const Dtype loss_weight = top[0]->cpu_diff()[0];
-    if (normalize_) {
-      Dtype count;
-      caffe_gpu_asum(nthreads, counts, &count);
-      caffe_gpu_scal(prob_.count(), loss_weight / count, bottom_diff);
-    } else {
-      caffe_gpu_scal(prob_.count(), loss_weight / outer_num_, bottom_diff);
+
+    Dtype valid_count = -1;
+    // Only launch another CUDA kernel if we actually need the count of valid
+    // outputs.
+    if (normalization_ == LossParameter_NormalizationMode_VALID &&
+        has_ignore_label_) {
+      caffe_gpu_asum(nthreads, counts, &valid_count);
     }
+    const Dtype loss_weight = top[0]->cpu_diff()[0] /
+                              get_normalizer(normalization_, valid_count);
+    caffe_gpu_scal(prob_.count(), loss_weight , bottom_diff);
   }
 }
 
diff --git a/src/caffe/layers/split_layer.cpp b/src/caffe/layers/split_layer.cpp
index 272cb59cd37..1a27a9af0a1 100644
--- a/src/caffe/layers/split_layer.cpp
+++ b/src/caffe/layers/split_layer.cpp
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/split_layer.cu b/src/caffe/layers/split_layer.cu
index a4f5df26452..bec9987c7cc 100644
--- a/src/caffe/layers/split_layer.cu
+++ b/src/caffe/layers/split_layer.cu
@@ -1,8 +1,7 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/util/math_functions.hpp"
-#include "caffe/vision_layers.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/spp_layer.cpp b/src/caffe/layers/spp_layer.cpp
new file mode 100644
index 00000000000..b9af8e8af0e
--- /dev/null
+++ b/src/caffe/layers/spp_layer.cpp
@@ -0,0 +1,228 @@
+#include <algorithm>
+#include <vector>
+
+#include "caffe/layer.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+LayerParameter SPPLayer<Dtype>::GetPoolingParam(const int pyramid_level,
+      const int bottom_h, const int bottom_w, const SPPParameter spp_param) {
+  LayerParameter pooling_param;
+  int num_bins = pow(2, pyramid_level);
+
+  // find padding and kernel size so that the pooling is
+  // performed across the entire image
+  int kernel_h = ceil(bottom_h / static_cast<double>(num_bins));
+  // remainder_h is the min number of pixels that need to be padded before
+  // entire image height is pooled over with the chosen kernel dimension
+  int remainder_h = kernel_h * num_bins - bottom_h;
+  // pooling layer pads (2 * pad_h) pixels on the top and bottom of the
+  // image.
+  int pad_h = (remainder_h + 1) / 2;
+
+  // similar logic for width
+  int kernel_w = ceil(bottom_w / static_cast<double>(num_bins));
+  int remainder_w = kernel_w * num_bins - bottom_w;
+  int pad_w = (remainder_w + 1) / 2;
+
+  pooling_param.mutable_pooling_param()->set_pad_h(pad_h);
+  pooling_param.mutable_pooling_param()->set_pad_w(pad_w);
+  pooling_param.mutable_pooling_param()->set_kernel_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_kernel_w(kernel_w);
+  pooling_param.mutable_pooling_param()->set_stride_h(kernel_h);
+  pooling_param.mutable_pooling_param()->set_stride_w(kernel_w);
+
+  switch (spp_param.pool()) {
+  case SPPParameter_PoolMethod_MAX:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_MAX);
+    break;
+  case SPPParameter_PoolMethod_AVE:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_AVE);
+    break;
+  case SPPParameter_PoolMethod_STOCHASTIC:
+    pooling_param.mutable_pooling_param()->set_pool(
+        PoolingParameter_PoolMethod_STOCHASTIC);
+    break;
+  default:
+    LOG(FATAL) << "Unknown pooling method.";
+  }
+
+  return pooling_param;
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  SPPParameter spp_param = this->layer_param_.spp_param();
+
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  reshaped_first_time_ = false;
+  CHECK_GT(bottom_h_, 0) << "Input dimensions cannot be zero.";
+  CHECK_GT(bottom_w_, 0) << "Input dimensions cannot be zero.";
+
+  pyramid_height_ = spp_param.pyramid_height();
+  split_top_vec_.clear();
+  pooling_bottom_vecs_.clear();
+  pooling_layers_.clear();
+  pooling_top_vecs_.clear();
+  pooling_outputs_.clear();
+  flatten_layers_.clear();
+  flatten_top_vecs_.clear();
+  flatten_outputs_.clear();
+  concat_bottom_vec_.clear();
+
+  if (pyramid_height_ == 1) {
+    // pooling layer setup
+    LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+        spp_param);
+    pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+        new PoolingLayer<Dtype>(pooling_param)));
+    pooling_layers_[0]->SetUp(bottom, top);
+    return;
+  }
+  // split layer output holders setup
+  for (int i = 0; i < pyramid_height_; i++) {
+    split_top_vec_.push_back(new Blob<Dtype>());
+  }
+
+  // split layer setup
+  LayerParameter split_param;
+  split_layer_.reset(new SplitLayer<Dtype>(split_param));
+  split_layer_->SetUp(bottom, split_top_vec_);
+
+  for (int i = 0; i < pyramid_height_; i++) {
+    // pooling layer input holders setup
+    pooling_bottom_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_bottom_vecs_[i]->push_back(split_top_vec_[i]);
+
+    // pooling layer output holders setup
+    pooling_outputs_.push_back(new Blob<Dtype>());
+    pooling_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    pooling_top_vecs_[i]->push_back(pooling_outputs_[i]);
+
+    // pooling layer setup
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_.push_back(shared_ptr<PoolingLayer<Dtype> > (
+        new PoolingLayer<Dtype>(pooling_param)));
+    pooling_layers_[i]->SetUp(*pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+
+    // flatten layer output holders setup
+    flatten_outputs_.push_back(new Blob<Dtype>());
+    flatten_top_vecs_.push_back(new vector<Blob<Dtype>*>);
+    flatten_top_vecs_[i]->push_back(flatten_outputs_[i]);
+
+    // flatten layer setup
+    LayerParameter flatten_param;
+    flatten_layers_.push_back(new FlattenLayer<Dtype>(flatten_param));
+    flatten_layers_[i]->SetUp(*pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+
+    // concat layer input holders setup
+    concat_bottom_vec_.push_back(flatten_outputs_[i]);
+  }
+
+  // concat layer setup
+  LayerParameter concat_param;
+  concat_layer_.reset(new ConcatLayer<Dtype>(concat_param));
+  concat_layer_->SetUp(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  CHECK_EQ(4, bottom[0]->num_axes()) << "Input must have 4 axes, "
+      << "corresponding to (num, channels, height, width)";
+  // Do nothing if bottom shape is unchanged since last Reshape
+  if (num_ == bottom[0]->num() && channels_ == bottom[0]->channels() &&
+      bottom_h_ == bottom[0]->height() && bottom_w_ == bottom[0]->width() &&
+      reshaped_first_time_) {
+    return;
+  }
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  bottom_h_ = bottom[0]->height();
+  bottom_w_ = bottom[0]->width();
+  reshaped_first_time_ = true;
+  SPPParameter spp_param = this->layer_param_.spp_param();
+  if (pyramid_height_ == 1) {
+    LayerParameter pooling_param = GetPoolingParam(0, bottom_h_, bottom_w_,
+        spp_param);
+    pooling_layers_[0].reset(new PoolingLayer<Dtype>(pooling_param));
+    pooling_layers_[0]->SetUp(bottom, top);
+    pooling_layers_[0]->Reshape(bottom, top);
+    return;
+  }
+  split_layer_->Reshape(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    LayerParameter pooling_param = GetPoolingParam(
+        i, bottom_h_, bottom_w_, spp_param);
+
+    pooling_layers_[i].reset(
+        new PoolingLayer<Dtype>(pooling_param));
+    pooling_layers_[i]->SetUp(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Reshape(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Reshape(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Reshape(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  if (pyramid_height_ == 1) {
+    pooling_layers_[0]->Forward(bottom, top);
+    return;
+  }
+  split_layer_->Forward(bottom, split_top_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    pooling_layers_[i]->Forward(
+        *pooling_bottom_vecs_[i], *pooling_top_vecs_[i]);
+    flatten_layers_[i]->Forward(
+        *pooling_top_vecs_[i], *flatten_top_vecs_[i]);
+  }
+  concat_layer_->Forward(concat_bottom_vec_, top);
+}
+
+template <typename Dtype>
+void SPPLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  if (pyramid_height_ == 1) {
+    pooling_layers_[0]->Backward(top, propagate_down, bottom);
+    return;
+  }
+  vector<bool> concat_propagate_down(pyramid_height_, true);
+  concat_layer_->Backward(top, concat_propagate_down, concat_bottom_vec_);
+  for (int i = 0; i < pyramid_height_; i++) {
+    flatten_layers_[i]->Backward(
+        *flatten_top_vecs_[i], propagate_down, *pooling_top_vecs_[i]);
+    pooling_layers_[i]->Backward(
+        *pooling_top_vecs_[i], propagate_down, *pooling_bottom_vecs_[i]);
+  }
+  split_layer_->Backward(split_top_vec_, propagate_down, bottom);
+}
+
+INSTANTIATE_CLASS(SPPLayer);
+REGISTER_LAYER_CLASS(SPP);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/tanh_layer.cpp b/src/caffe/layers/tanh_layer.cpp
index ee5ed773c74..184e926d22a 100644
--- a/src/caffe/layers/tanh_layer.cpp
+++ b/src/caffe/layers/tanh_layer.cpp
@@ -1,11 +1,9 @@
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tanh_layer.cu b/src/caffe/layers/tanh_layer.cu
index ccd6e63ee7c..cbfc178e6db 100644
--- a/src/caffe/layers/tanh_layer.cu
+++ b/src/caffe/layers/tanh_layer.cu
@@ -1,11 +1,9 @@
 // TanH neuron activation function layer.
 // Adapted from ReLU layer code written by Yangqing Jia
 
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/threshold_layer.cpp b/src/caffe/layers/threshold_layer.cpp
index 2365e7b9c72..63822ee5520 100644
--- a/src/caffe/layers/threshold_layer.cpp
+++ b/src/caffe/layers/threshold_layer.cpp
@@ -1,8 +1,6 @@
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
-
+#include "caffe/layers/threshold_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/threshold_layer.cu b/src/caffe/layers/threshold_layer.cu
index bfa7f159460..b0b0665589f 100644
--- a/src/caffe/layers/threshold_layer.cu
+++ b/src/caffe/layers/threshold_layer.cu
@@ -1,8 +1,6 @@
-#include <algorithm>
 #include <vector>
 
-#include "caffe/layer.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
 
 namespace caffe {
 
diff --git a/src/caffe/layers/tile_layer.cpp b/src/caffe/layers/tile_layer.cpp
new file mode 100644
index 00000000000..cf0c187005c
--- /dev/null
+++ b/src/caffe/layers/tile_layer.cpp
@@ -0,0 +1,61 @@
+#include <vector>
+
+#include "caffe/layers/tile_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void TileLayer<Dtype>::Reshape(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const TileParameter& tile_param = this->layer_param_.tile_param();
+  axis_ = bottom[0]->CanonicalAxisIndex(tile_param.axis());
+  CHECK(tile_param.has_tiles()) << "Number of tiles must be specified";
+  tiles_ = tile_param.tiles();
+  CHECK_GT(tiles_, 0) << "Number of tiles must be positive.";
+  vector<int> top_shape = bottom[0]->shape();
+  top_shape[axis_] = bottom[0]->shape(axis_) * tiles_;
+  top[0]->Reshape(top_shape);
+  outer_dim_ = bottom[0]->count(0, axis_);
+  inner_dim_ = bottom[0]->count(axis_);
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_cpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  for (int i = 0; i < outer_dim_; ++i) {
+    for (int t = 0; t < tiles_; ++t) {
+      caffe_copy(inner_dim_, bottom_data, top_data);
+      top_data += inner_dim_;
+    }
+    bottom_data += inner_dim_;
+  }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  for (int i = 0; i < outer_dim_; ++i) {
+    caffe_copy(inner_dim_, top_diff, bottom_diff);
+    top_diff += inner_dim_;
+    for (int t = 1; t < tiles_; ++t) {
+      caffe_axpy(inner_dim_, Dtype(1), top_diff, bottom_diff);
+      top_diff += inner_dim_;
+    }
+    bottom_diff += inner_dim_;
+  }
+}
+
+#ifdef CPU_ONLY
+STUB_GPU(TileLayer);
+#endif
+
+INSTANTIATE_CLASS(TileLayer);
+REGISTER_LAYER_CLASS(Tile);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/tile_layer.cu b/src/caffe/layers/tile_layer.cu
new file mode 100644
index 00000000000..282049ebd7b
--- /dev/null
+++ b/src/caffe/layers/tile_layer.cu
@@ -0,0 +1,66 @@
+#include <vector>
+
+#include "caffe/layers/tile_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void Tile(const int nthreads, const Dtype* bottom_data,
+    const int tile_size, const int num_tiles, const int bottom_tile_axis,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int d = index % tile_size;
+    const int b = (index / tile_size / num_tiles) % bottom_tile_axis;
+    const int n = index / tile_size / num_tiles / bottom_tile_axis;
+    const int bottom_index = (n * bottom_tile_axis + b) * tile_size + d;
+    top_data[index] = bottom_data[bottom_index];
+  }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Forward_gpu(
+    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int bottom_tile_axis = bottom[0]->shape(axis_);
+  const int nthreads = top[0]->count();
+  Tile<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+      nthreads, bottom_data, inner_dim_, tiles_, bottom_tile_axis, top_data);
+}
+
+template <typename Dtype>
+__global__ void TileBackward(const int nthreads, const Dtype* top_diff,
+    const int tile_size, const int num_tiles, const int bottom_tile_axis,
+    Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    const int d = index % tile_size;
+    const int b = (index / tile_size) % bottom_tile_axis;
+    const int n = index / tile_size / bottom_tile_axis;
+    bottom_diff[index] = 0;
+    int top_index = (n * num_tiles * bottom_tile_axis + b) * tile_size + d;
+    for (int t = 0; t < num_tiles; ++t) {
+      bottom_diff[index] += top_diff[top_index];
+      top_index += bottom_tile_axis * tile_size;
+    }
+  }
+}
+
+template <typename Dtype>
+void TileLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) { return; }
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int bottom_tile_axis = bottom[0]->shape(axis_);
+  const int tile_size = inner_dim_ / bottom_tile_axis;
+  const int nthreads = bottom[0]->count();
+  TileBackward<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(nthreads), CAFFE_CUDA_NUM_THREADS>>>(
+      nthreads, top_diff, tile_size, tiles_, bottom_tile_axis, bottom_diff);
+}
+
+INSTANTIATE_LAYER_GPU_FUNCS(TileLayer);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/unpooling_layer.cpp b/src/caffe/layers/unpooling_layer.cpp
new file mode 100644
index 00000000000..df081e9244a
--- /dev/null
+++ b/src/caffe/layers/unpooling_layer.cpp
@@ -0,0 +1,378 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/common.hpp"
+#include "caffe/layers/unpooling_layer.hpp"
+#include "caffe/syncedmem.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+using std::min;
+using std::max;
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  UnPoolingParameter unpool_param = this->layer_param_.unpooling_param();
+  CHECK(!unpool_param.has_out_kernel_size() !=
+      !(unpool_param.has_out_kernel_h() && unpool_param.has_out_kernel_w()))
+      << "Out filter size is out_kernel_size OR out_kernel_h and out_kernel_w; "
+      << "not both";
+  CHECK(unpool_param.has_out_kernel_size() ||
+      (unpool_param.has_out_kernel_h() && unpool_param.has_out_kernel_w()))
+      << "For non-square filters both out_kernel_h and out_kernel_w are "
+      << "required.";
+  CHECK((!unpool_param.has_out_pad() && unpool_param.has_out_pad_h()
+      && unpool_param.has_out_pad_w())
+      || (!unpool_param.has_out_pad_h() && !unpool_param.has_out_pad_w()))
+      << "Out pad is out_pad OR out_pad_h and out_pad_w are required.";
+  CHECK((!unpool_param.has_out_stride() && unpool_param.has_out_stride_h()
+      && unpool_param.has_out_stride_w())
+      || (!unpool_param.has_out_stride_h() && !unpool_param.has_out_stride_w()))
+      << "Out stride is out_stride OR out_stride_h and out_stride_w are "
+      << "required.";
+  if (bottom.size() == 1) {
+    if (unpool_param.has_out_kernel_size()) {
+      out_kernel_h_ = out_kernel_w_ = unpool_param.out_kernel_size();
+    } else {
+      out_kernel_h_ = unpool_param.out_kernel_h();
+      out_kernel_w_ = unpool_param.out_kernel_w();
+    }
+    CHECK_GT(out_kernel_h_, 0) << "Out filter dimensions cannot be zero.";
+    CHECK_GT(out_kernel_w_, 0) << "Out filter dimensions cannot be zero.";
+    if (!unpool_param.has_out_stride_h()) {
+      out_stride_h_ = out_stride_w_ = unpool_param.out_stride();
+    } else {
+      out_stride_h_ = unpool_param.out_stride_h();
+      out_stride_w_ = unpool_param.out_stride_w();
+    }
+    if (!unpool_param.has_out_pad_h()) {
+      out_pad_h_ = out_pad_w_ = unpool_param.out_pad();
+    } else {
+      out_pad_h_ = unpool_param.out_pad_h();
+      out_pad_w_ = unpool_param.out_pad_w();
+    }
+  } else {
+    // Compute out_kernel and out_stride automatically
+    out_kernel_h_ = static_cast<int>(ceil(static_cast<float>(
+                bottom[1]->height()) / bottom[0]->height()));
+    out_kernel_w_ = static_cast<int>(ceil(static_cast<float>(
+                bottom[1]->width()) / bottom[0]->width()));
+
+    out_stride_h_ = static_cast<int>(ceil(static_cast<float>(
+                bottom[1]->height()) / bottom[0]->height()));
+    out_stride_w_ = static_cast<int>(ceil(static_cast<float>(
+                bottom[1]->width()) / bottom[0]->width()));
+
+    // In case either width or height of bottom[0] is 1, we set stride to 1
+    if (out_stride_h_ == bottom[1]->height()) {
+      out_stride_h_ = 1;
+    }
+    if (out_stride_w_ == bottom[1]->width()) {
+      out_stride_w_ = 1;
+    }
+
+    out_pad_h_ = static_cast<int>(floor(static_cast<float>(
+                (bottom[0]->height()-1)*out_stride_h_+out_kernel_h_
+                -bottom[1]->height())/2));
+    out_pad_w_ = static_cast<int>(floor(static_cast<float>(
+                (bottom[0]->width()-1)*out_stride_w_+out_kernel_w_
+                -bottom[1]->width())/2));
+  }
+  if (out_pad_h_ != 0 || out_pad_w_ != 0) {
+    CHECK_LT(out_pad_h_, out_kernel_h_);
+    CHECK_LT(out_pad_w_, out_kernel_w_);
+  }
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  // reset the out_kernel_size and out_stride, etc.
+  this->LayerSetUp(bottom, top);
+
+  num_ = bottom[0]->num();
+  channels_ = bottom[0]->channels();
+  height_ = bottom[0]->height();
+  width_ = bottom[0]->width();
+  unpooled_height_ = (height_ - 1) * out_stride_h_ - 2 * out_pad_h_ +
+      out_kernel_h_;
+  unpooled_width_ = (width_ - 1) * out_stride_w_ - 2 * out_pad_w_ +
+      out_kernel_w_;
+  top[0]->Reshape(num_, channels_, unpooled_height_, unpooled_width_);
+
+  // fill the mask
+  this->FillMask();
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::FillMask() {
+  // Different unpool method needs different mask, but they are same across
+  // channels and samples
+  mask_.Reshape(1, 1, unpooled_height_, unpooled_width_);
+  int* mask = mask_.mutable_cpu_data();
+  switch (this->layer_param_.unpooling_param().unpool()) {
+  case UnPoolingParameter_UnPoolMethod_FIXED:
+    // mask_ records map of positions from bottom to top
+    caffe_set(mask_.count(), -1, mask);
+    for (int h = 0; h < height_; ++h) {
+      for (int w = 0; w < width_; ++w) {
+        int uhstart = h * out_stride_h_ - out_pad_h_;
+        int uwstart = w * out_stride_w_ - out_pad_w_;
+        int uhend = uhstart + out_kernel_h_;
+        int uwend = uwstart + out_kernel_w_;
+        int uhmid = floor((uhstart + uhend - 1) / 2);
+        int uwmid = floor((uwstart + uwend - 1) / 2);
+        uhmid = min(max(uhmid, 0), unpooled_height_-1);
+        uwmid = min(max(uwmid, 0), unpooled_width_-1);
+        const int unpool_index = uhmid * unpooled_width_ + uwmid;
+        const int index = h * width_ + w;
+        mask[unpool_index] = index;
+      }
+    }
+    break;
+  case UnPoolingParameter_UnPoolMethod_DIV:
+  case UnPoolingParameter_UnPoolMethod_REP:
+    // mask_ records counts of contributions to each unpooled position
+    // same for DIV and REP unpool operation
+    caffe_set(mask_.count(), 0, mask);
+    for (int h = 0; h < height_; ++h) {
+      for (int w = 0; w < width_; ++w) {
+        int uhstart = h * out_stride_h_ - out_pad_h_;
+        int uwstart = w * out_stride_w_ - out_pad_w_;
+        int uhend = min(uhstart + out_kernel_h_, unpooled_height_);
+        int uwend = min(uwstart + out_kernel_w_, unpooled_width_);
+        uhstart = max(uhstart, 0);
+        uwstart = max(uwstart, 0);
+        for (int uh = uhstart; uh < uhend; ++uh) {
+          for (int uw = uwstart; uw < uwend; ++uw) {
+            const int unpool_index = uh * unpooled_width_ + uw;
+            mask[unpool_index] += 1;
+          }
+        }
+      }
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown unpooling method.";
+  }
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->cpu_data();
+  Dtype* top_data = top[0]->mutable_cpu_data();
+  const int top_count = top[0]->count();
+  caffe_set(top_count, Dtype(0), top_data);
+  const int* mask = mask_.cpu_data();
+  // Different pooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more code.
+  switch (this->layer_param_.unpooling_param().unpool()) {
+  case UnPoolingParameter_UnPoolMethod_FIXED:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int h = 0; h < height_; ++h) {
+          for (int w = 0; w < width_; ++w) {
+            int uhstart = h * out_stride_h_ - out_pad_h_;
+            int uwstart = w * out_stride_w_ - out_pad_w_;
+            int uhend = uhstart + out_kernel_h_;
+            int uwend = uwstart + out_kernel_w_;
+            int uhmid = floor((uhstart + uhend - 1) / 2);
+            int uwmid = floor((uwstart + uwend - 1) / 2);
+            uhmid = min(max(uhmid, 0), unpooled_height_-1);
+            uwmid = min(max(uwmid, 0), unpooled_width_-1);
+            const int unpool_index = uhmid * unpooled_width_ + uwmid;
+            const int index = h * width_ + w;
+            top_data[unpool_index] = bottom_data[index];
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case UnPoolingParameter_UnPoolMethod_DIV:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int h = 0; h < height_; ++h) {
+          for (int w = 0; w < width_; ++w) {
+            int uhstart = h * out_stride_h_ - out_pad_h_;
+            int uwstart = w * out_stride_w_ - out_pad_w_;
+            int uhend = min(uhstart + out_kernel_h_,
+                            unpooled_height_ + out_pad_h_);
+            int uwend = min(uwstart + out_kernel_w_,
+                            unpooled_width_ + out_pad_w_);
+            int unpool_size = (uhend - uhstart) * (uwend - uwstart);
+            uhstart = max(uhstart, 0);
+            uwstart = max(uwstart, 0);
+            uhend = min(uhend, unpooled_height_);
+            uwend = min(uwend, unpooled_width_);
+            Dtype div_data = bottom_data[h * width_ + w] / unpool_size;
+            for (int uh = uhstart; uh < uhend; ++uh) {
+              for (int uw = uwstart; uw < uwend; ++uw) {
+                int unpool_index = uh * unpooled_width_ + uw;
+                CHECK_GT(mask[unpool_index], 0);
+                top_data[unpool_index] += div_data / mask[unpool_index];
+              }
+            }
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case UnPoolingParameter_UnPoolMethod_REP:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int h = 0; h < height_; ++h) {
+          for (int w = 0; w < width_; ++w) {
+            int uhstart = h * out_stride_h_ - out_pad_h_;
+            int uwstart = w * out_stride_w_ - out_pad_w_;
+            int uhend = min(uhstart + out_kernel_h_,
+                            unpooled_height_ + out_pad_h_);
+            int uwend = min(uwstart + out_kernel_w_,
+                            unpooled_width_ + out_pad_w_);
+            uhstart = max(uhstart, 0);
+            uwstart = max(uwstart, 0);
+            uhend = min(uhend, unpooled_height_);
+            uwend = min(uwend, unpooled_width_);
+            Dtype data = bottom_data[h * width_ + w];
+            for (int uh = uhstart; uh < uhend; ++uh) {
+              for (int uw = uwstart; uw < uwend; ++uw) {
+                int unpool_index = uh * unpooled_width_ + uw;
+                CHECK_GT(mask[unpool_index], 0);
+                top_data[unpool_index] += data / mask[unpool_index];
+              }
+            }
+          }
+        }
+        // compute offset
+        bottom_data += bottom[0]->offset(0, 1);
+        top_data += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown unpooling method.";
+  }
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  const Dtype* top_diff = top[0]->cpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
+  caffe_set(bottom[0]->count(), Dtype(0), bottom_diff);
+  // Different unpooling methods. We explicitly do the switch outside the for
+  // loop to save time, although this results in more codes.
+  const int* mask = mask_.cpu_data();
+  switch (this->layer_param_.unpooling_param().unpool()) {
+  case UnPoolingParameter_UnPoolMethod_FIXED:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int uh = 0; uh < unpooled_height_; ++uh) {
+          for (int uw = 0; uw < unpooled_width_; ++uw) {
+            const int unpool_index = uh * unpooled_width_ + uw;
+            const int index = mask[unpool_index];
+            if (index != -1) {
+              bottom_diff[index] = top_diff[unpool_index];
+            }
+          }
+        }
+        bottom_diff += bottom[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case UnPoolingParameter_UnPoolMethod_DIV:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int h = 0; h < height_; ++h) {
+          for (int w = 0; w < width_; ++w) {
+            int uhstart = h * out_stride_h_ - out_pad_h_;
+            int uwstart = w * out_stride_w_ - out_pad_w_;
+            int uhend = min(uhstart + out_kernel_h_,
+                            unpooled_height_ + out_pad_h_);
+            int uwend = min(uwstart + out_kernel_w_,
+                            unpooled_width_ + out_pad_w_);
+            int unpool_size = (uhend - uhstart) * (uwend - uwstart);
+            uhstart = max(uhstart, 0);
+            uwstart = max(uwstart, 0);
+            uhend = min(uhend, unpooled_height_);
+            uwend = min(uwend, unpooled_width_);
+            for (int uh = uhstart; uh < uhend; ++uh) {
+              for (int uw = uwstart; uw < uwend; ++uw) {
+                const int unpool_index = uh * unpooled_width_ + uw;
+                CHECK_GT(mask[unpool_index], 0);
+                bottom_diff[h * width_ + w] +=
+                  top_diff[unpool_index] / unpool_size / mask[unpool_index];
+              }
+            }
+          }
+        }
+        // offset
+        bottom_diff += bottom[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  case UnPoolingParameter_UnPoolMethod_REP:
+    // The main loop
+    for (int n = 0; n < num_; ++n) {
+      for (int c = 0; c < channels_; ++c) {
+        for (int h = 0; h < height_; ++h) {
+          for (int w = 0; w < width_; ++w) {
+            int uhstart = h * out_stride_h_ - out_pad_h_;
+            int uwstart = w * out_stride_w_ - out_pad_w_;
+            int uhend = min(uhstart + out_kernel_h_,
+                            unpooled_height_ + out_pad_h_);
+            int uwend = min(uwstart + out_kernel_w_,
+                            unpooled_width_ + out_pad_w_);
+            uhstart = max(uhstart, 0);
+            uwstart = max(uwstart, 0);
+            uhend = min(uhend, unpooled_height_);
+            uwend = min(uwend, unpooled_width_);
+            for (int uh = uhstart; uh < uhend; ++uh) {
+              for (int uw = uwstart; uw < uwend; ++uw) {
+                const int unpool_index = uh * unpooled_width_ + uw;
+                CHECK_GT(mask[unpool_index], 0);
+                bottom_diff[h * width_ + w] +=
+                  top_diff[unpool_index] / mask[unpool_index];
+              }
+            }
+          }
+        }
+        // offset
+        bottom_diff += bottom[0]->offset(0, 1);
+        top_diff += top[0]->offset(0, 1);
+      }
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown unpooling method.";
+  }
+}
+
+
+#ifdef CPU_ONLY
+STUB_GPU(UnPoolingLayer);
+#endif
+
+INSTANTIATE_CLASS(UnPoolingLayer);
+REGISTER_LAYER_CLASS(UnPooling);
+
+}  // namespace caffe
diff --git a/src/caffe/layers/unpooling_layer.cu b/src/caffe/layers/unpooling_layer.cu
new file mode 100644
index 00000000000..d8c2a31cc89
--- /dev/null
+++ b/src/caffe/layers/unpooling_layer.cu
@@ -0,0 +1,307 @@
+#include <algorithm>
+#include <cfloat>
+#include <vector>
+
+#include "caffe/layers/unpooling_layer.hpp"
+#include "caffe/util/math_functions.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void FixedUnPoolForward(const int nthreads, const Dtype* bottom_data,
+    const int num, const int channels, const int height, const int width,
+    const int unpooled_height, const int unpooled_width, const int out_kernel_h,
+    const int out_kernel_w, const int out_stride_h, const int out_stride_w,
+    const int out_pad_h, const int out_pad_w, Dtype* top_data) {
+  CUDA_KERNEL_LOOP(unpool_index, nthreads) {
+    int uw = unpool_index % unpooled_width;
+    int uh = (unpool_index / unpooled_width) % unpooled_height;
+    int c = (unpool_index / unpooled_width / unpooled_height) % channels;
+    int n = unpool_index / unpooled_width / unpooled_height / channels;
+    int hstart = (uh + out_pad_h < out_kernel_h) ? 0 :
+      (uh + out_pad_h - out_kernel_h) / out_stride_h + 1;
+    int hend = min((uh + out_pad_h) / out_stride_h + 1, height);
+    int wstart = (uw + out_pad_w < out_kernel_w) ? 0 :
+      (uw + out_pad_w - out_kernel_w) / out_stride_w + 1;
+    int wend = min((uw + out_pad_w) / out_stride_w + 1, width);
+    int offset = (n * channels + c) * height * width;
+    int unpool_offset = (n * channels + c) * unpooled_height * unpooled_width;
+    bottom_data += offset;
+    for (int h = hstart; h < hend; ++h) {
+      for (int w = wstart; w < wend; ++w) {
+        int uhstart = h * out_stride_h - out_pad_h;
+        int uwstart = w * out_stride_w - out_pad_w;
+        int uhend = uhstart + out_kernel_h;
+        int uwend = uwstart + out_kernel_w;
+        int uhmid = (uhstart + uhend - 1) / 2;
+        int uwmid = (uwstart + uwend - 1) / 2;
+        uhmid = min(max(uhmid, 0), unpooled_height);
+        uwmid = min(max(uwmid, 0), unpooled_width);
+        if (unpool_offset + uhmid * unpooled_width + uwmid == unpool_index) {
+          // find the mapping, assign & return
+          int index = h * width + w;
+          top_data[unpool_index] = bottom_data[index];
+          return;
+        }
+      }
+    }
+  }
+}
+
+template <typename Dtype>
+__global__ void DivUnPoolForward(const int nthreads, const Dtype* bottom_data,
+    const int* mask, const int num, const int channels, const int height,
+    const int width, const int unpooled_height, const int unpooled_width,
+    const int out_kernel_h, const int out_kernel_w, const int out_stride_h,
+    const int out_stride_w, const int out_pad_h, const int out_pad_w,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(unpool_index, nthreads) {
+    int uw = unpool_index % unpooled_width + out_pad_w;
+    int uh = (unpool_index / unpooled_width) % unpooled_height + out_pad_h;
+    int c = (unpool_index / unpooled_width / unpooled_height) % channels;
+    int n = unpool_index / unpooled_width / unpooled_height / channels;
+    int spatial_dim = unpooled_height * unpooled_width;
+    int hstart = (uh < out_kernel_h) ? 0 :
+      (uh - out_kernel_h) / out_stride_h + 1;
+    int hend = min(uh / out_stride_h + 1, height);
+    int wstart = (uw < out_kernel_w) ? 0 :
+      (uw - out_kernel_w) / out_stride_w + 1;
+    int wend = min(uw / out_stride_w + 1, width);
+    Dtype divval = 0;
+    bottom_data += (n * channels + c) * height * width;
+    for (int h = hstart; h < hend; ++h) {
+      for (int w = wstart; w < wend; ++w) {
+        int uhstart = h * out_stride_h - out_pad_h;
+        int uwstart = w * out_stride_w - out_pad_w;
+        int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h);
+        int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w);
+        int unpool_size = (uhend - uhstart) * (uwend - uwstart);
+        divval += bottom_data[h * width + w] / unpool_size;
+      }
+    }
+    top_data[unpool_index] = divval / mask[unpool_index % spatial_dim];
+  }
+}
+
+template <typename Dtype>
+__global__ void RepUnPoolForward(const int nthreads, const Dtype* bottom_data,
+    const int* mask, const int num, const int channels, const int height,
+    const int width, const int unpooled_height, const int unpooled_width,
+    const int out_kernel_h, const int out_kernel_w, const int out_stride_h,
+    const int out_stride_w, const int out_pad_h, const int out_pad_w,
+    Dtype* top_data) {
+  CUDA_KERNEL_LOOP(unpool_index, nthreads) {
+    int uw = unpool_index % unpooled_width + out_pad_w;
+    int uh = (unpool_index / unpooled_width) % unpooled_height + out_pad_h;
+    int c = (unpool_index / unpooled_width / unpooled_height) % channels;
+    int n = unpool_index / unpooled_width / unpooled_height / channels;
+    int spatial_dim = unpooled_height * unpooled_width;
+    int hstart = (uh < out_kernel_h) ? 0 :
+      (uh - out_kernel_h) / out_stride_h + 1;
+    int hend = min(uh / out_stride_h + 1, height);
+    int wstart = (uw < out_kernel_w) ? 0 :
+      (uw - out_kernel_w) / out_stride_w + 1;
+    int wend = min(uw / out_stride_w + 1, width);
+    Dtype val = 0;
+    bottom_data += (n * channels + c) * height * width;
+    for (int h = hstart; h < hend; ++h) {
+      for (int w = wstart; w < wend; ++w) {
+        int uhstart = h * out_stride_h - out_pad_h;
+        int uwstart = w * out_stride_w - out_pad_w;
+        int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h);
+        int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w);
+        val += bottom_data[h * width + w];
+      }
+    }
+    top_data[unpool_index] = val / mask[unpool_index % spatial_dim];
+  }
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
+      const vector<Blob<Dtype>*>& top) {
+  const Dtype* bottom_data = bottom[0]->gpu_data();
+  Dtype* top_data = top[0]->mutable_gpu_data();
+  const int count = top[0]->count();
+  caffe_gpu_set(count, Dtype(0), top_data);
+  const int* mask = mask_.gpu_data();
+  switch (this->layer_param_.unpooling_param().unpool()) {
+  case UnPoolingParameter_UnPoolMethod_FIXED:
+    FixedUnPoolForward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data);
+    break;
+  case UnPoolingParameter_UnPoolMethod_DIV:
+    DivUnPoolForward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, mask, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data);
+    break;
+  case UnPoolingParameter_UnPoolMethod_REP:
+    RepUnPoolForward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, bottom_data, mask, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, top_data);
+    break;
+  default:
+    LOG(FATAL) << "Unknown unpooling method.";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+
+template <typename Dtype>
+__global__ void FixedUnPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int* mask, const int num, const int channels, const int height,
+    const int width, const int unpooled_height, const int unpooled_width,
+    const int out_kernel_h, const int out_kernel_w, const int out_stride_h,
+    const int out_stride_w, const int out_pad_h, const int out_pad_w,
+    Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // find out the local index
+    // find out the local offset
+    int w = index % width;
+    int h = (index / width) % height;
+    int c = (index / width / height) % channels;
+    int n = index / width / height / channels;
+    int uhstart = h * out_stride_h - out_pad_h;
+    int uwstart = w * out_stride_w - out_pad_w;
+    int uhend = uhstart + out_kernel_h;
+    int uwend = uwstart + out_kernel_w;
+    int uhmid = (uhstart + uhend - 1) / 2;
+    int uwmid = (uwstart + uwend - 1) / 2;
+    uhmid = min(max(uhmid, 0), unpooled_height-1);
+    uwmid = min(max(uwmid, 0), unpooled_width-1);
+    int offset = (n * channels + c) * unpooled_height * unpooled_width;
+    int unpool_index = uhmid * unpooled_width + uwmid;
+    Dtype gradient = 0;
+    if (mask[unpool_index] == h * width + w) {
+      gradient += top_diff[unpool_index + offset];
+    }
+    bottom_diff[index] = gradient;
+  }
+}
+
+template <typename Dtype>
+__global__ void DivUnPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int* mask, const int num, const int channels, const int height,
+    const int width, const int unpooled_height, const int unpooled_width,
+    const int out_kernel_h, const int out_kernel_w, const int out_stride_h,
+    const int out_stride_w, const int out_pad_h, const int out_pad_w,
+    Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // find out the local index
+    // find out the local offset
+    int w = index % width;
+    int h = (index / width) % height;
+    int c = (index / width / height) % channels;
+    int n = index / width / height / channels;
+    int uhstart = h * out_stride_h - out_pad_h;
+    int uwstart = w * out_stride_w - out_pad_w;
+    int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h);
+    int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w);
+    int unpool_size = (uhend - uhstart) * (uwend - uwstart);
+    uhstart = max(uhstart, 0);
+    uwstart = max(uwstart, 0);
+    uhend = min(uhend, unpooled_height);
+    uwend = min(uwend, unpooled_width);
+    Dtype gradient = 0;
+    int offset = (n * channels + c) * unpooled_height * unpooled_width;
+    for (int uh = uhstart; uh < uhend; ++uh) {
+      for (int uw = uwstart; uw < uwend; ++uw) {
+        int unpool_index = uh * unpooled_width + uw;
+        gradient += top_diff[unpool_index + offset] / mask[unpool_index];
+      }
+    }
+    bottom_diff[index] = gradient / unpool_size;
+  }
+}
+
+template <typename Dtype>
+__global__ void RepUnPoolBackward(const int nthreads, const Dtype* top_diff,
+    const int* mask, const int num, const int channels, const int height,
+    const int width, const int unpooled_height, const int unpooled_width,
+    const int out_kernel_h, const int out_kernel_w, const int out_stride_h,
+    const int out_stride_w, const int out_pad_h, const int out_pad_w,
+    Dtype* bottom_diff) {
+  CUDA_KERNEL_LOOP(index, nthreads) {
+    // find out the local index
+    // find out the local offset
+    int w = index % width;
+    int h = (index / width) % height;
+    int c = (index / width / height) % channels;
+    int n = index / width / height / channels;
+    int uhstart = h * out_stride_h - out_pad_h;
+    int uwstart = w * out_stride_w - out_pad_w;
+    int uhend = min(uhstart + out_kernel_h, unpooled_height + out_pad_h);
+    int uwend = min(uwstart + out_kernel_w, unpooled_width + out_pad_w);
+    uhstart = max(uhstart, 0);
+    uwstart = max(uwstart, 0);
+    uhend = min(uhend, unpooled_height);
+    uwend = min(uwend, unpooled_width);
+    Dtype gradient = 0;
+    int offset = (n * channels + c) * unpooled_height * unpooled_width;
+    for (int uh = uhstart; uh < uhend; ++uh) {
+      for (int uw = uwstart; uw < uwend; ++uw) {
+        int unpool_index = uh * unpooled_width + uw;
+        gradient += top_diff[unpool_index + offset] / mask[unpool_index];
+      }
+    }
+    bottom_diff[index] = gradient;
+  }
+}
+
+template <typename Dtype>
+void UnPoolingLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
+      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
+  if (!propagate_down[0]) {
+    return;
+  }
+  const Dtype* top_diff = top[0]->gpu_diff();
+  Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
+  const int count = bottom[0]->count();
+  caffe_gpu_set(count, Dtype(0.), bottom_diff);
+  const int* mask = mask_.gpu_data();
+  switch (this->layer_param_.unpooling_param().unpool()) {
+  case UnPoolingParameter_UnPoolMethod_FIXED:
+    FixedUnPoolBackward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, mask, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff);
+    break;
+  case UnPoolingParameter_UnPoolMethod_DIV:
+    DivUnPoolBackward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, mask, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff);
+    break;
+  case UnPoolingParameter_UnPoolMethod_REP:
+    RepUnPoolBackward<Dtype>
+        // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
+        count, top_diff, mask, num_, channels_, height_, width_,
+        unpooled_height_, unpooled_width_, out_kernel_h_, out_kernel_w_,
+        out_stride_h_, out_stride_w_, out_pad_h_, out_pad_w_, bottom_diff);
+    break;
+  default:
+    LOG(FATAL) << "Unknown unpooling method.";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+
+INSTANTIATE_LAYER_GPU_FUNCS(UnPoolingLayer);
+
+
+}  // namespace caffe
diff --git a/src/caffe/layers/window_data_layer.cpp b/src/caffe/layers/window_data_layer.cpp
index c127d56bc46..4ca8315d791 100644
--- a/src/caffe/layers/window_data_layer.cpp
+++ b/src/caffe/layers/window_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/highgui/highgui_c.h>
 #include <stdint.h>
 
@@ -11,9 +12,10 @@
 #include "opencv2/highgui/highgui.hpp"
 #include "opencv2/imgproc/imgproc.hpp"
 
-#include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
-#include "caffe/layer.hpp"
+#include "caffe/data_transformer.hpp"
+#include "caffe/internal_thread.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/layers/window_data_layer.hpp"
 #include "caffe/util/benchmark.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -27,7 +29,7 @@ namespace caffe {
 
 template <typename Dtype>
 WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {
-  this->JoinPrefetchThread();
+  this->StopInternalThread();
 }
 
 template <typename Dtype>
@@ -171,7 +173,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   CHECK_GT(crop_size, 0);
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   top[0]->Reshape(batch_size, channels, crop_size, crop_size);
-  this->prefetch_data_.Reshape(batch_size, channels, crop_size, crop_size);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i)
+    this->prefetch_[i].data_.Reshape(
+        batch_size, channels, crop_size, crop_size);
 
   LOG(INFO) << "output data size: " << top[0]->num() << ","
       << top[0]->channels() << "," << top[0]->height() << ","
@@ -179,7 +183,9 @@ void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
   // label
   vector<int> label_shape(1, batch_size);
   top[1]->Reshape(label_shape);
-  this->prefetch_label_.Reshape(label_shape);
+  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
+    this->prefetch_[i].label_.Reshape(label_shape);
+  }
 
   // data mean
   has_mean_file_ = this->transform_param_.has_mean_file();
@@ -217,9 +223,9 @@ unsigned int WindowDataLayer<Dtype>::PrefetchRand() {
   return (*prefetch_rng)();
 }
 
-// Thread fetching the data
+// This function is called on prefetch thread
 template <typename Dtype>
-void WindowDataLayer<Dtype>::InternalThreadEntry() {
+void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {
   // At each iteration, sample N windows where N*p are foreground (object)
   // windows and N*(1-p) are background (non-object) windows
   CPUTimer batch_timer;
@@ -227,8 +233,8 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   double read_time = 0;
   double trans_time = 0;
   CPUTimer timer;
-  Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
-  Dtype* top_label = this->prefetch_label_.mutable_cpu_data();
+  Dtype* top_data = batch->data_.mutable_cpu_data();
+  Dtype* top_label = batch->label_.mutable_cpu_data();
   const Dtype scale = this->layer_param_.window_data_param().scale();
   const int batch_size = this->layer_param_.window_data_param().batch_size();
   const int context_pad = this->layer_param_.window_data_param().context_pad();
@@ -252,7 +258,7 @@ void WindowDataLayer<Dtype>::InternalThreadEntry() {
   bool use_square = (crop_mode == "square") ? true : false;
 
   // zero out batch
-  caffe_set(this->prefetch_data_.count(), Dtype(0), top_data);
+  caffe_set(batch->data_.count(), Dtype(0), top_data);
 
   const int num_fg = static_cast<int>(static_cast<float>(batch_size)
       * fg_fraction);
@@ -464,3 +470,4 @@ INSTANTIATE_CLASS(WindowDataLayer);
 REGISTER_LAYER_CLASS(WindowData);
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index fd00b122630..23d94c97c07 100644
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -5,12 +5,15 @@
 #include <utility>
 #include <vector>
 
+#include "hdf5.h"
+
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/net.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/insert_splits.hpp"
-#include "caffe/util/io.hpp"
 #include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
@@ -19,12 +22,14 @@
 namespace caffe {
 
 template <typename Dtype>
-Net<Dtype>::Net(const NetParameter& param) {
+Net<Dtype>::Net(const NetParameter& param, const Net* root_net)
+    : root_net_(root_net) {
   Init(param);
 }
 
 template <typename Dtype>
-Net<Dtype>::Net(const string& param_file, Phase phase) {
+Net<Dtype>::Net(const string& param_file, Phase phase, const Net* root_net)
+    : root_net_(root_net) {
   NetParameter param;
   ReadNetParamsFromTextFileOrDie(param_file, &param);
   param.mutable_state()->set_phase(phase);
@@ -33,14 +38,17 @@ Net<Dtype>::Net(const string& param_file, Phase phase) {
 
 template <typename Dtype>
 void Net<Dtype>::Init(const NetParameter& in_param) {
+  CHECK(Caffe::root_solver() || root_net_)
+      << "root_net_ needs to be set for all non-root solvers";
   // Set phase from the state.
   phase_ = in_param.state().phase();
   // Filter layers based on their include/exclude rules and
   // the current NetState.
   NetParameter filtered_param;
   FilterNet(in_param, &filtered_param);
-  LOG(INFO) << "Initializing net from parameters: " << std::endl
-            << filtered_param.DebugString();
+  LOG_IF(INFO, Caffe::root_solver())
+      << "Initializing net from parameters: " << std::endl
+      << filtered_param.DebugString();
   // Create a copy of filtered_param with splits added where necessary.
   NetParameter param;
   InsertSplits(filtered_param, &param);
@@ -48,23 +56,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   name_ = param.name();
   map<string, int> blob_name_to_idx;
   set<string> available_blobs;
-  CHECK(param.input_dim_size() == 0 || param.input_shape_size() == 0)
-      << "Must specify either input_shape OR deprecated input_dim, not both.";
-  if (param.input_dim_size() > 0) {
-    // Deprecated 4D dimensions.
-    CHECK_EQ(param.input_size() * 4, param.input_dim_size())
-        << "Incorrect input blob dimension specifications.";
-  } else {
-    CHECK_EQ(param.input_size(), param.input_shape_size())
-        << "Exactly one input_shape must be specified per input.";
-  }
   memory_used_ = 0;
-  // set the input blobs
-  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
-    const int layer_id = -1;  // inputs have fake layer ID -1
-    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
-  }
-  DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
   // For each layer, set up its input and output
   bottom_vecs_.resize(param.layer_size());
   top_vecs_.resize(param.layer_size());
@@ -73,16 +65,33 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   top_id_vecs_.resize(param.layer_size());
   bottom_need_backward_.resize(param.layer_size());
   for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
+    // For non-root solvers, whether this layer is shared from root_net_.
+    bool share_from_root = !Caffe::root_solver()
+        && root_net_->layers_[layer_id]->ShareInParallel();
     // Inherit phase from net if unset.
     if (!param.layer(layer_id).has_phase()) {
       param.mutable_layer(layer_id)->set_phase(phase_);
     }
     // Setup layer.
     const LayerParameter& layer_param = param.layer(layer_id);
-    layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    if (layer_param.propagate_down_size() > 0) {
+      CHECK_EQ(layer_param.propagate_down_size(),
+          layer_param.bottom_size())
+          << "propagate_down param must be specified "
+          << "either 0 or bottom_size times ";
+    }
+    if (share_from_root) {
+      LOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
+      layers_.push_back(root_net_->layers_[layer_id]);
+      layers_[layer_id]->SetShared(true);
+    } else {
+      layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
+    }
     layer_names_.push_back(layer_param.name());
-    LOG(INFO) << "Creating Layer " << layer_param.name();
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating Layer " << layer_param.name();
     bool need_backward = false;
+
     // Figure out this layer's input and output
     for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
          ++bottom_id) {
@@ -94,6 +103,12 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     int num_top = layer_param.top_size();
     for (int top_id = 0; top_id < num_top; ++top_id) {
       AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
+      // Collect Input layer tops as Net inputs.
+      if (layer_param.type() == "Input") {
+        const int blob_id = blobs_.size() - 1;
+        net_input_blob_indices_.push_back(blob_id);
+        net_input_blobs_.push_back(blobs_[blob_id].get());
+      }
     }
     // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
     // specified fewer than the required number (as specified by
@@ -110,20 +125,36 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       }
     }
     // After this layer is connected, set it up.
-    LOG(INFO) << "Setting up " << layer_names_[layer_id];
-    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
+    if (share_from_root) {
+      // Set up size of top blobs using root_net_
+      const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
+      const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
+      for (int top_id = 0; top_id < base_top.size(); ++top_id) {
+        this_top[top_id]->ReshapeLike(*base_top[top_id]);
+        LOG(INFO) << "Created top blob " << top_id << " (shape: "
+            << this_top[top_id]->shape_string() <<  ") for shared layer "
+            << layer_param.name();
+      }
+    } else {
+      layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
+    }
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Setting up " << layer_names_[layer_id];
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
         blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
       }
       blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
-      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
+      LOG_IF(INFO, Caffe::root_solver())
+          << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
       if (layer->loss(top_id)) {
-        LOG(INFO) << "    with loss weight " << layer->loss(top_id);
+        LOG_IF(INFO, Caffe::root_solver())
+            << "    with loss weight " << layer->loss(top_id);
       }
       memory_used_ += top_vecs_[layer_id][top_id]->count();
     }
-    DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Memory required for data: " << memory_used_ * sizeof(Dtype);
     const int param_size = layer_param.param_size();
     const int num_param_blobs = layers_[layer_id]->blobs().size();
     CHECK_LE(param_size, num_param_blobs)
@@ -132,7 +163,7 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
     for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
       const ParamSpec* param_spec = (param_id < param_size) ?
           &layer_param.param(param_id) : &default_param_spec;
-      const bool param_need_backward = param_spec->lr_mult() > 0;
+      const bool param_need_backward = param_spec->lr_mult() != 0;
       need_backward |= param_need_backward;
       layers_[layer_id]->set_param_propagate_down(param_id,
                                                   param_need_backward);
@@ -151,23 +182,43 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // Go through the net backwards to determine which blobs contribute to the
   // loss.  We can skip backward computation for blobs that don't contribute
   // to the loss.
+  // Also checks if all bottom blobs don't need backward computation (possible
+  // because the skip_propagate_down param) and so we can skip bacward
+  // computation for the entire layer
   set<string> blobs_under_loss;
+  set<string> blobs_skip_backp;
   for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
     bool layer_contributes_loss = false;
+    bool layer_skip_propagate_down = true;
     for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
       const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
       if (layers_[layer_id]->loss(top_id) ||
           (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
         layer_contributes_loss = true;
+      }
+      if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
+        layer_skip_propagate_down = false;
+      }
+      if (layer_contributes_loss && !layer_skip_propagate_down)
         break;
+    }
+    // If this layer can skip backward computation, also all his bottom blobs
+    // don't need backpropagation
+    if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {
+      layer_need_backward_[layer_id] = false;
+      for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
+               ++bottom_id) {
+        bottom_need_backward_[layer_id][bottom_id] = false;
       }
     }
     if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
-    if (layer_need_backward_[layer_id]) {
-      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
-    } else {
-      LOG(INFO) << layer_names_[layer_id]
-                << " does not need backward computation.";
+    if (Caffe::root_solver()) {
+      if (layer_need_backward_[layer_id]) {
+        LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
+      } else {
+        LOG(INFO) << layer_names_[layer_id]
+            << " does not need backward computation.";
+      }
     }
     for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
          ++bottom_id) {
@@ -178,6 +229,11 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
       } else {
         bottom_need_backward_[layer_id][bottom_id] = false;
       }
+      if (!bottom_need_backward_[layer_id][bottom_id]) {
+        const string& blob_name =
+                   blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
+        blobs_skip_backp.insert(blob_name);
+      }
     }
   }
   // Handle force_backward if needed.
@@ -202,7 +258,8 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   // In the end, all remaining blobs are considered output blobs.
   for (set<string>::iterator it = available_blobs.begin();
       it != available_blobs.end(); ++it) {
-    LOG(INFO) << "This network produces output " << *it;
+    LOG_IF(INFO, Caffe::root_solver())
+        << "This network produces output " << *it;
     net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
     net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
   }
@@ -212,10 +269,9 @@ void Net<Dtype>::Init(const NetParameter& in_param) {
   for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
     layer_names_index_[layer_names_[layer_id]] = layer_id;
   }
-  GetLearningRateAndWeightDecay();
+  ShareWeights();
   debug_info_ = param.debug_info();
-  LOG(INFO) << "Network initialization done.";
-  LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
+  LOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
 }
 
 template <typename Dtype>
@@ -254,16 +310,18 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   // Check whether the rule is broken due to phase.
   if (rule.has_phase()) {
       if (rule.phase() != state.phase()) {
-        LOG(INFO) << "The NetState phase (" << state.phase()
-          << ") differed from the phase (" << rule.phase()
-          << ") specified by a rule in layer " << layer_name;
+        LOG_IF(INFO, Caffe::root_solver())
+            << "The NetState phase (" << state.phase()
+            << ") differed from the phase (" << rule.phase()
+            << ") specified by a rule in layer " << layer_name;
         return false;
       }
   }
   // Check whether the rule is broken due to min level.
   if (rule.has_min_level()) {
     if (state.level() < rule.min_level()) {
-      LOG(INFO) << "The NetState level (" << state.level()
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState level (" << state.level()
           << ") is above the min_level (" << rule.min_level()
           << ") specified by a rule in layer " << layer_name;
       return false;
@@ -272,7 +330,8 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
   // Check whether the rule is broken due to max level.
   if (rule.has_max_level()) {
     if (state.level() > rule.max_level()) {
-      LOG(INFO) << "The NetState level (" << state.level()
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState level (" << state.level()
           << ") is above the max_level (" << rule.max_level()
           << ") specified by a rule in layer " << layer_name;
       return false;
@@ -287,8 +346,9 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (!has_stage) {
-      LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
-                << "' specified by a rule in layer " << layer_name;
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState did not contain stage '" << rule.stage(i)
+          << "' specified by a rule in layer " << layer_name;
       return false;
     }
   }
@@ -301,43 +361,42 @@ bool Net<Dtype>::StateMeetsRule(const NetState& state,
       if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
     }
     if (has_stage) {
-      LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
-                << "' specified by a rule in layer " << layer_name;
+      LOG_IF(INFO, Caffe::root_solver())
+          << "The NetState contained a not_stage '" << rule.not_stage(i)
+          << "' specified by a rule in layer " << layer_name;
       return false;
     }
   }
   return true;
 }
 
-// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
-// layer_id == -1, tops have layer_id >= 0.)
+// Helper for Net::Init: add a new top blob to the net.
 template <typename Dtype>
 void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                            const int top_id, set<string>* available_blobs,
                            map<string, int>* blob_name_to_idx) {
-  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
-    (new LayerParameter(param.layer(layer_id))) : NULL);
-  const string& blob_name = layer_param ?
-      (layer_param->top_size() > top_id ?
-          layer_param->top(top_id) : "(automatic)") : param.input(top_id);
+  shared_ptr<LayerParameter> layer_param(
+      new LayerParameter(param.layer(layer_id)));
+  const string& blob_name = (layer_param->top_size() > top_id) ?
+      layer_param->top(top_id) : "(automatic)";
   // Check if we are doing in-place computation
-  if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
+  if (blob_name_to_idx && layer_param->bottom_size() > top_id &&
       blob_name == layer_param->bottom(top_id)) {
     // In-place computation
-    LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
+    LOG_IF(INFO, Caffe::root_solver())
+        << layer_param->name() << " -> " << blob_name << " (in-place)";
     top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
     top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
   } else if (blob_name_to_idx &&
              blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
     // If we are not doing in-place computation but have duplicated blobs,
     // raise an error.
-    LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
+    LOG(FATAL) << "Top blob '" << blob_name
+               << "' produced by multiple sources.";
   } else {
     // Normal output.
-    if (layer_param) {
+    if (Caffe::root_solver()) {
       LOG(INFO) << layer_param->name() << " -> " << blob_name;
-    } else {
-      LOG(INFO) << "Input " << top_id << " -> " << blob_name;
     }
     shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
     const int blob_id = blobs_.size();
@@ -345,43 +404,35 @@ void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
     blob_names_.push_back(blob_name);
     blob_need_backward_.push_back(false);
     if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
-    if (layer_id == -1) {
-      // Set the (explicitly specified) dimensions of the input blob.
-      if (param.input_dim_size() > 0) {
-        blob_pointer->Reshape(param.input_dim(top_id * 4),
-                              param.input_dim(top_id * 4 + 1),
-                              param.input_dim(top_id * 4 + 2),
-                              param.input_dim(top_id * 4 + 3));
-      } else {
-        blob_pointer->Reshape(param.input_shape(top_id));
-      }
-      net_input_blob_indices_.push_back(blob_id);
-      net_input_blobs_.push_back(blob_pointer.get());
-    } else {
-      top_id_vecs_[layer_id].push_back(blob_id);
-      top_vecs_[layer_id].push_back(blob_pointer.get());
-    }
+    top_id_vecs_[layer_id].push_back(blob_id);
+    top_vecs_[layer_id].push_back(blob_pointer.get());
   }
   if (available_blobs) { available_blobs->insert(blob_name); }
 }
 
 // Helper for Net::Init: add a new bottom blob to the net.
 template <typename Dtype>
-int Net<Dtype>::AppendBottom(const NetParameter& param,
-    const int layer_id, const int bottom_id,
-    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
+int Net<Dtype>::AppendBottom(const NetParameter& param, const int layer_id,
+    const int bottom_id, set<string>* available_blobs,
+    map<string, int>* blob_name_to_idx) {
   const LayerParameter& layer_param = param.layer(layer_id);
   const string& blob_name = layer_param.bottom(bottom_id);
   if (available_blobs->find(blob_name) == available_blobs->end()) {
-    LOG(FATAL) << "Unknown blob input " << blob_name
-               << " (at index " << bottom_id << ") to layer " << layer_id;
+    LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+               << layer_param.name() << "', bottom index " << bottom_id << ")";
   }
   const int blob_id = (*blob_name_to_idx)[blob_name];
-  LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
+  LOG_IF(INFO, Caffe::root_solver())
+      << layer_names_[layer_id] << " <- " << blob_name;
   bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
   bottom_id_vecs_[layer_id].push_back(blob_id);
   available_blobs->erase(blob_name);
-  const bool need_backward = blob_need_backward_[blob_id];
+  bool propagate_down = true;
+  // Check if the backpropagation on bottom_id should be skipped
+  if (layer_param.propagate_down_size() > 0)
+    propagate_down = layer_param.propagate_down(bottom_id);
+  const bool need_backward = blob_need_backward_[blob_id] &&
+                          propagate_down;
   bottom_need_backward_[layer_id].push_back(need_backward);
   return blob_id;
 }
@@ -404,15 +455,25 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
   params_.push_back(layers_[layer_id]->blobs()[param_id]);
   param_id_vecs_[layer_id].push_back(net_param_id);
   param_layer_indices_.push_back(make_pair(layer_id, param_id));
+  ParamSpec default_param_spec;
+  const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?
+      &layer_param.param(param_id) : &default_param_spec;
   if (!param_size || !param_name.size() || (param_name.size() &&
       param_names_index_.find(param_name) == param_names_index_.end())) {
     // This layer "owns" this parameter blob -- it is either anonymous
     // (i.e., not given a param_name) or explicitly given a name that we
     // haven't already seen.
     param_owners_.push_back(-1);
-    if (param_size) {
+    if (param_name.size()) {
       param_names_index_[param_name] = net_param_id;
     }
+    const int learnable_param_id = learnable_params_.size();
+    learnable_params_.push_back(params_[net_param_id].get());
+    learnable_param_ids_.push_back(learnable_param_id);
+    has_params_lr_.push_back(param_spec->has_lr_mult());
+    has_params_decay_.push_back(param_spec->has_decay_mult());
+    params_lr_.push_back(param_spec->lr_mult());
+    params_weight_decay_.push_back(param_spec->decay_mult());
   } else {
     // Named param blob with name we've seen before: share params
     const int owner_net_param_id = param_names_index_[param_name];
@@ -421,9 +482,10 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
         param_layer_indices_[owner_net_param_id];
     const int owner_layer_id = owner_index.first;
     const int owner_param_id = owner_index.second;
-    LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
-              << "layer '" << layer_names_[owner_layer_id] << "', param "
-              << "index " << owner_param_id;
+    LOG_IF(INFO, Caffe::root_solver()) << "Sharing parameters '" << param_name
+        << "' owned by "
+        << "layer '" << layer_names_[owner_layer_id] << "', param "
+        << "index " << owner_param_id;
     Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
     Blob<Dtype>* owner_blob =
         layers_[owner_layer_id]->blobs()[owner_param_id].get();
@@ -432,28 +494,40 @@ void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                                   ParamSpec_DimCheckMode_PERMISSIVE)) {
       // Permissive dimension checking -- only check counts are the same.
       CHECK_EQ(this_blob->count(), owner_blob->count())
-          << "Shared parameter blobs must have the same count.";
+          << "Cannot share param '" << param_name << "' owned by layer '"
+          << layer_names_[owner_layer_id] << "' with layer '"
+          << layer_names_[layer_id] << "'; count mismatch.  Owner layer param "
+          << "shape is " << owner_blob->shape_string() << "; sharing layer "
+          << "shape is " << this_blob->shape_string();
     } else {
       // Strict dimension checking -- all dims must be the same.
-      CHECK(this_blob->shape() == owner_blob->shape());
+      CHECK(this_blob->shape() == owner_blob->shape())
+          << "Cannot share param '" << param_name << "' owned by layer '"
+          << layer_names_[owner_layer_id] << "' with layer '"
+          << layer_names_[layer_id] << "'; shape mismatch.  Owner layer param "
+          << "shape is " << owner_blob->shape_string() << "; sharing layer "
+          << "expects shape " << this_blob->shape_string();
+    }
+    const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
+    learnable_param_ids_.push_back(learnable_param_id);
+    if (param_spec->has_lr_mult()) {
+      if (has_params_lr_[learnable_param_id]) {
+        CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])
+            << "Shared param '" << param_name << "' has mismatched lr_mult.";
+      } else {
+        has_params_lr_[learnable_param_id] = true;
+        params_lr_[learnable_param_id] = param_spec->lr_mult();
+      }
     }
-    layers_[layer_id]->blobs()[param_id]->ShareData(
-        *layers_[owner_layer_id]->blobs()[owner_param_id]);
-  }
-}
-
-template <typename Dtype>
-void Net<Dtype>::GetLearningRateAndWeightDecay() {
-  LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
-  ParamSpec default_param_spec;
-  for (int i = 0; i < layers_.size(); ++i) {
-    vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
-    for (int j = 0; j < layer_blobs.size(); ++j) {
-      const ParamSpec* param_spec =
-          (layers_[i]->layer_param().param_size() > j) ?
-          &layers_[i]->layer_param().param(j) : &default_param_spec;
-      params_lr_.push_back(param_spec->lr_mult());
-      params_weight_decay_.push_back(param_spec->decay_mult());
+    if (param_spec->has_decay_mult()) {
+      if (has_params_decay_[learnable_param_id]) {
+        CHECK_EQ(param_spec->decay_mult(),
+                 params_weight_decay_[learnable_param_id])
+            << "Shared param '" << param_name << "' has mismatched decay_mult.";
+      } else {
+        has_params_decay_[learnable_param_id] = true;
+        params_weight_decay_[learnable_param_id] = param_spec->decay_mult();
+      }
     }
   }
 }
@@ -463,14 +537,8 @@ Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
   CHECK_GE(start, 0);
   CHECK_LT(end, layers_.size());
   Dtype loss = 0;
-  if (debug_info_) {
-    for (int i = 0; i < net_input_blobs_.size(); ++i) {
-      InputDebugInfo(i);
-    }
-  }
   for (int i = start; i <= end; ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
-    layers_[i]->Reshape(bottom_vecs_[i], top_vecs_[i]);
     Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
     loss += layer_loss;
     if (debug_info_) { ForwardDebugInfo(i); }
@@ -489,7 +557,7 @@ Dtype Net<Dtype>::ForwardTo(int end) {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
+const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
   if (loss != NULL) {
     *loss = ForwardFromTo(0, layers_.size() - 1);
   } else {
@@ -501,32 +569,13 @@ const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
 template <typename Dtype>
 const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
     const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
-  // Copy bottom to internal bottom
+  LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "
+      << "will be removed in a future version. Use Forward(loss).";
+  // Copy bottom to net bottoms
   for (int i = 0; i < bottom.size(); ++i) {
     net_input_blobs_[i]->CopyFrom(*bottom[i]);
   }
-  return ForwardPrefilled(loss);
-}
-
-template <typename Dtype>
-string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss) {
-  BlobProtoVector blob_proto_vec;
-  if (net_input_blobs_.size()) {
-    blob_proto_vec.ParseFromString(input_blob_protos);
-    CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())
-        << "Incorrect input size.";
-    for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {
-      net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));
-    }
-  }
-  ForwardPrefilled(loss);
-  blob_proto_vec.Clear();
-  for (int i = 0; i < net_output_blobs_.size(); ++i) {
-    net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());
-  }
-  string output;
-  blob_proto_vec.SerializeToString(&output);
-  return output;
+  return Forward(loss);
 }
 
 template <typename Dtype>
@@ -542,24 +591,17 @@ void Net<Dtype>::BackwardFromTo(int start, int end) {
   }
 }
 
-template <typename Dtype>
-void Net<Dtype>::InputDebugInfo(const int input_id) {
-  const Blob<Dtype>& blob = *net_input_blobs_[input_id];
-  const string& blob_name = blob_names_[net_input_blob_indices_[input_id]];
-  const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-  LOG(INFO) << "    [Forward] "
-     << "Input " << blob_name << " data: " << data_abs_val_mean;
-}
-
 template <typename Dtype>
 void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
   for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
     const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
     const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Forward] "
-       << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
-       << " data: " << data_abs_val_mean;
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Forward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", top blob " << blob_name
+        << " data: " << data_abs_val_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
        ++param_id) {
@@ -567,9 +609,11 @@ void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
     const int net_param_id = param_id_vecs_[layer_id][param_id];
     const string& blob_name = param_display_names_[net_param_id];
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Forward] "
-       << "Layer " << layer_names_[layer_id] << ", param blob " << blob_name
-       << " data: " << data_abs_val_mean;
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Forward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", param blob " << blob_name
+        << " data: " << data_abs_val_mean;
   }
 }
 
@@ -581,8 +625,10 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     const Blob<Dtype>& blob = *bottom_vec[bottom_id];
     const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    LOG(INFO) << "    [Backward] "
-        << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Backward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", bottom blob " << blob_name
         << " diff: " << diff_abs_val_mean;
   }
   for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
@@ -590,8 +636,10 @@ void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
     if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
     const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
     const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
-    LOG(INFO) << "    [Backward] "
-        << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Backward] "
+        << "Layer " << layer_names_[layer_id]
+        << ", param blob " << param_id
         << " diff: " << diff_abs_val_mean;
   }
 }
@@ -605,16 +653,19 @@ void Net<Dtype>::UpdateDebugInfo(const int param_id) {
   const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
   if (param_owner < 0) {
     const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
-    LOG(INFO) << "    [Update] Layer " << layer_name
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Update] Layer " << layer_name
         << ", param " << param_display_name
-        << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
+        << " data: " << data_abs_val_mean
+        << "; diff: " << diff_abs_val_mean;
   } else {
     const string& owner_layer_name =
         layer_names_[param_layer_indices_[param_owner].first];
-    LOG(INFO) << "    [Update] Layer " << layer_name
+    LOG_IF(INFO, Caffe::root_solver())
+        << "    [Update] Layer " << layer_name
         << ", param blob " << param_display_name
-        << " (owned by layer " << owner_layer_name << ", "
-        << "param " << param_display_names_[param_owners_[param_id]] << ")"
+        << " (owned by layer " << owner_layer_name << ", " << "param "
+        << param_display_names_[param_owners_[param_id]] << ")"
         << " diff: " << diff_abs_val_mean;
   }
 }
@@ -631,7 +682,7 @@ void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
       ++target_layer_id;
     }
     if (target_layer_id == layer_names_.size()) {
-      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
     DLOG(INFO) << "Copying source layer " << source_layer_name;
@@ -641,7 +692,11 @@ void Net<Dtype>::ShareTrainedLayersWith(const Net* other) {
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
       Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
-      CHECK(target_blobs[j]->shape() == source_blob->shape());
+      CHECK(target_blobs[j]->shape() == source_blob->shape())
+          << "Cannot share param " << j << " weights from layer '"
+          << source_layer_name << "'; shape mismatch.  Source param shape is "
+          << source_blob->shape_string() << "; target param shape is "
+          << target_blobs[j]->shape_string();
       target_blobs[j]->ShareData(*source_blob);
     }
   }
@@ -662,18 +717,17 @@ void Net<Dtype>::Backward() {
   BackwardFromTo(layers_.size() - 1, 0);
   if (debug_info_) {
     Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
-    for (int i = 0; i < params_.size(); ++i) {
-      if (param_owners_[i] >= 0) { continue; }
-      asum_data += params_[i]->asum_data();
-      asum_diff += params_[i]->asum_diff();
-      sumsq_data += params_[i]->sumsq_data();
-      sumsq_diff += params_[i]->sumsq_diff();
+    for (int i = 0; i < learnable_params_.size(); ++i) {
+      asum_data += learnable_params_[i]->asum_data();
+      asum_diff += learnable_params_[i]->asum_diff();
+      sumsq_data += learnable_params_[i]->sumsq_data();
+      sumsq_diff += learnable_params_[i]->sumsq_diff();
     }
     const Dtype l2norm_data = std::sqrt(sumsq_data);
     const Dtype l2norm_diff = std::sqrt(sumsq_diff);
     LOG(ERROR) << "    [Backward] All net params (data, diff): "
-        << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
-        << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
+               << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
+               << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
   }
 }
 
@@ -696,7 +750,7 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
       ++target_layer_id;
     }
     if (target_layer_id == layer_names_.size()) {
-      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
       continue;
     }
     DLOG(INFO) << "Copying source layer " << source_layer_name;
@@ -705,6 +759,17 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
     CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
         << "Incompatible number of blobs for layer " << source_layer_name;
     for (int j = 0; j < target_blobs.size(); ++j) {
+      if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
+        Blob<Dtype> source_blob;
+        const bool kReshape = true;
+        source_blob.FromProto(source_layer.blobs(j), kReshape);
+        LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"
+            << source_layer_name << "'; shape mismatch.  Source param shape is "
+            << source_blob.shape_string() << "; target param shape is "
+            << target_blobs[j]->shape_string() << ". "
+            << "To learn this layer's parameters from scratch rather than "
+            << "copying from a saved net, rename the layer.";
+      }
       const bool kReshape = false;
       target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
     }
@@ -713,67 +778,175 @@ void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
 
 template <typename Dtype>
 void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
+  if (trained_filename.size() >= 3 &&
+      trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
+    CopyTrainedLayersFromHDF5(trained_filename);
+  } else {
+    CopyTrainedLayersFromBinaryProto(trained_filename);
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
+    const string trained_filename) {
   NetParameter param;
   ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
   CopyTrainedLayersFrom(param);
 }
 
+template <typename Dtype>
+void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
+  hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
+                           H5P_DEFAULT);
+  CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
+  hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT);
+  CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename;
+  int num_layers = hdf5_get_num_links(data_hid);
+  for (int i = 0; i < num_layers; ++i) {
+    string source_layer_name = hdf5_get_name_by_idx(data_hid, i);
+    if (!layer_names_index_.count(source_layer_name)) {
+      LOG(INFO) << "Ignoring source layer " << source_layer_name;
+      continue;
+    }
+    int target_layer_id = layer_names_index_[source_layer_name];
+    DLOG(INFO) << "Copying source layer " << source_layer_name;
+    vector<shared_ptr<Blob<Dtype> > >& target_blobs =
+        layers_[target_layer_id]->blobs();
+    hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(),
+        H5P_DEFAULT);
+    CHECK_GE(layer_hid, 0)
+        << "Error reading weights from " << trained_filename;
+    // Check that source layer doesn't have more params than target layer
+    int num_source_params = hdf5_get_num_links(layer_hid);
+    CHECK_LE(num_source_params, target_blobs.size())
+        << "Incompatible number of blobs for layer " << source_layer_name;
+    for (int j = 0; j < target_blobs.size(); ++j) {
+      ostringstream oss;
+      oss << j;
+      string dataset_name = oss.str();
+      int target_net_param_id = param_id_vecs_[target_layer_id][j];
+      if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) {
+        // Target param doesn't exist in source weights...
+        if (param_owners_[target_net_param_id] != -1) {
+          // ...but it's weight-shared in target, so that's fine.
+          continue;
+        } else {
+          LOG(FATAL) << "Incompatible number of blobs for layer "
+              << source_layer_name;
+        }
+      }
+      hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes,
+          target_blobs[j].get());
+    }
+    H5Gclose(layer_hid);
+  }
+  H5Gclose(data_hid);
+  H5Fclose(file_hid);
+}
+
 template <typename Dtype>
 void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
   param->Clear();
   param->set_name(name_);
   // Add bottom and top
-  for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
-    param->add_input(blob_names_[net_input_blob_indices_[i]]);
-  }
   DLOG(INFO) << "Serializing " << layers_.size() << " layers";
   for (int i = 0; i < layers_.size(); ++i) {
     LayerParameter* layer_param = param->add_layer();
-    for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
-      layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
+    layers_[i]->ToProto(layer_param, write_diff);
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
+  hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(file_hid, 0)
+      << "Couldn't open " << filename << " to save weights.";
+  hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << ".";
+  hid_t diff_hid = -1;
+  if (write_diff) {
+    diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT,
+        H5P_DEFAULT);
+    CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << ".";
+  }
+  for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
+    const LayerParameter& layer_param = layers_[layer_id]->layer_param();
+    string layer_name = layer_param.name();
+    hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(),
+        H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+    CHECK_GE(layer_data_hid, 0)
+        << "Error saving weights to " << filename << ".";
+    hid_t layer_diff_hid = -1;
+    if (write_diff) {
+      layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(),
+          H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
+      CHECK_GE(layer_diff_hid, 0)
+          << "Error saving weights to " << filename << ".";
+    }
+    int num_params = layers_[layer_id]->blobs().size();
+    for (int param_id = 0; param_id < num_params; ++param_id) {
+      ostringstream dataset_name;
+      dataset_name << param_id;
+      const int net_param_id = param_id_vecs_[layer_id][param_id];
+      if (param_owners_[net_param_id] == -1) {
+        // Only save params that own themselves
+        hdf5_save_nd_dataset<Dtype>(layer_data_hid, dataset_name.str(),
+            *params_[net_param_id]);
+      }
+      if (write_diff) {
+        // Write diffs regardless of weight-sharing
+        hdf5_save_nd_dataset<Dtype>(layer_diff_hid, dataset_name.str(),
+            *params_[net_param_id], true);
+      }
     }
-    for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
-      layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);
+    H5Gclose(layer_data_hid);
+    if (write_diff) {
+      H5Gclose(layer_diff_hid);
     }
-    layers_[i]->ToProto(layer_param, write_diff);
   }
+  H5Gclose(data_hid);
+  if (write_diff) {
+    H5Gclose(diff_hid);
+  }
+  H5Fclose(file_hid);
 }
 
 template <typename Dtype>
 void Net<Dtype>::Update() {
-  // First, accumulate the diffs of any shared parameters into their owner's
-  // diff. (Assumes that the learning rate, weight decay, etc. have already been
-  // accounted for in the current diff.)
-  for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] < 0) { continue; }
-    if (debug_info_) { UpdateDebugInfo(i); }
-    const int count = params_[i]->count();
-    const Dtype* this_diff;
-    Dtype* owner_diff;
+  for (int i = 0; i < learnable_params_.size(); ++i) {
+    learnable_params_[i]->Update();
+  }
+}
+
+template <typename Dtype>
+void Net<Dtype>::ClearParamDiffs() {
+  for (int i = 0; i < learnable_params_.size(); ++i) {
+    Blob<Dtype>* blob = learnable_params_[i];
     switch (Caffe::mode()) {
     case Caffe::CPU:
-      this_diff = params_[i]->cpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
-      caffe_add(count, this_diff, owner_diff, owner_diff);
+      caffe_set(blob->count(), static_cast<Dtype>(0),
+                blob->mutable_cpu_diff());
       break;
-#ifndef CPU_ONLY
     case Caffe::GPU:
-      this_diff = params_[i]->gpu_diff();
-      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
-      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
-      break;
+#ifndef CPU_ONLY
+      caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
+                    blob->mutable_gpu_diff());
 #else
       NO_GPU;
 #endif
-    default:
-      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+      break;
     }
   }
-  // Now, update the owned parameters.
+}
+
+template <typename Dtype>
+void Net<Dtype>::ShareWeights() {
   for (int i = 0; i < params_.size(); ++i) {
-    if (param_owners_[i] >= 0) { continue; }
-    if (debug_info_) { UpdateDebugInfo(i); }
-    params_[i]->Update();
+    if (param_owners_[i] < 0) { continue; }
+    params_[i]->ShareData(*params_[param_owners_[i]]);
+    params_[i]->ShareDiff(*params_[param_owners_[i]]);
   }
 }
 
diff --git a/src/caffe/parallel.cpp b/src/caffe/parallel.cpp
new file mode 100644
index 00000000000..5bc41c6a6e5
--- /dev/null
+++ b/src/caffe/parallel.cpp
@@ -0,0 +1,443 @@
+#ifndef CPU_ONLY
+#include <cuda_runtime.h>
+#endif
+#include <glog/logging.h>
+#include <stdio.h>
+
+#include <sstream>
+#include <string>
+#include <vector>
+
+#include "boost/thread.hpp"
+#include "caffe/caffe.hpp"
+#include "caffe/parallel.hpp"
+
+namespace caffe {
+
+enum Op {
+  copy,
+  replace_cpu,
+  replace_gpu,
+  replace_cpu_diff,
+  replace_gpu_diff
+};
+
+template<typename Dtype>
+static void apply_buffers(const vector<Blob<Dtype>*>& blobs,
+                          Dtype* buffer, size_t total_size, Op op) {
+  Dtype* ptr = buffer;
+  for (int i = 0; i < blobs.size(); ++i) {
+    int size = blobs[i]->count();
+    switch (op) {
+      case copy: {
+        // Init buffer to current values of blobs
+        caffe_copy(size,
+                   reinterpret_cast<const Dtype*>(blobs[i]->data()->cpu_data()),
+                   ptr);
+        break;
+      }
+      case replace_cpu:
+        blobs[i]->data()->set_cpu_data(ptr);
+        break;
+      case replace_gpu:
+        blobs[i]->data()->set_gpu_data(ptr);
+        break;
+      case replace_cpu_diff:
+        blobs[i]->diff()->set_cpu_data(ptr);
+        break;
+      case replace_gpu_diff:
+        blobs[i]->diff()->set_gpu_data(ptr);
+        break;
+    }
+    ptr += size;
+  }
+  // total_size is at least one byte
+  CHECK_EQ(total_size, (ptr == buffer ? 1 : ptr - buffer));
+}
+
+// Buffer size necessary to store given blobs
+template<typename Dtype>
+static size_t total_size(const vector<Blob<Dtype>*>& params) {
+  size_t size = 0;
+  for (int i = 0; i < params.size(); ++i)
+    size += params[i]->count();
+  // Size have at least one byte, otherwise cudaMalloc fails if net has no
+  // learnable parameters.
+  return (size > 0) ? size : 1;
+}
+
+template<typename Dtype>
+Params<Dtype>::Params(shared_ptr<Solver<Dtype> > root_solver)
+    : size_(total_size<Dtype>(root_solver->net()->learnable_params())),
+      data_(),
+      diff_() {
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::GPUParams(shared_ptr<Solver<Dtype> > root_solver, int device)
+    : Params<Dtype>(root_solver) {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+
+  // Allocate device buffers
+  CUDA_CHECK(cudaSetDevice(device));
+  CUDA_CHECK(cudaMalloc(&data_, size_ * sizeof(Dtype)));
+
+  // Copy blob values
+  const vector<Blob<Dtype>*>& net =
+      root_solver->net()->learnable_params();
+  apply_buffers(net, data_, size_, copy);
+
+  CUDA_CHECK(cudaMalloc(&diff_, size_ * sizeof(Dtype)));
+  caffe_gpu_set(size_, Dtype(0), diff_);
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+  NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+GPUParams<Dtype>::~GPUParams() {
+#ifndef CPU_ONLY
+  CUDA_CHECK(cudaFree(data_));
+  CUDA_CHECK(cudaFree(diff_));
+#endif
+}
+
+template<typename Dtype>
+void GPUParams<Dtype>::configure(Solver<Dtype>* solver) const {
+  const vector<Blob<Dtype>*>& net =
+      solver->net()->learnable_params();
+  apply_buffers(net, data_, size_, replace_gpu);
+  apply_buffers(net, diff_, size_, replace_gpu_diff);
+}
+
+void DevicePair::compute(const vector<int> devices, vector<DevicePair>* pairs) {
+#ifndef CPU_ONLY
+  vector<int> remaining(devices);
+
+  // Depth for reduction tree
+  int remaining_depth = static_cast<int>(ceil(log2(remaining.size())));
+
+  // Group GPUs by board
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      for (int j = i + 1; j < remaining.size(); ++j) {
+        cudaDeviceProp a, b;
+        CUDA_CHECK(cudaGetDeviceProperties(&a, remaining[i]));
+        CUDA_CHECK(cudaGetDeviceProperties(&b, remaining[j]));
+        if (a.isMultiGpuBoard && b.isMultiGpuBoard) {
+          if (a.multiGpuBoardGroupID == b.multiGpuBoardGroupID) {
+            pairs->push_back(DevicePair(remaining[i], remaining[j]));
+            DLOG(INFO) << "GPU board: " << remaining[i] << ":" << remaining[j];
+            remaining.erase(remaining.begin() + j);
+            break;
+          }
+        }
+      }
+    }
+  }
+  ostringstream s;
+  for (int i = 0; i < remaining.size(); ++i) {
+    s << (i ? ", " : "") << remaining[i];
+  }
+  DLOG(INFO) << "GPUs paired by boards, remaining: " << s.str();
+
+  // Group by P2P accessibility
+  remaining_depth = ceil(log2(remaining.size()));
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      for (int j = i + 1; j < remaining.size(); ++j) {
+        int access;
+        CUDA_CHECK(
+            cudaDeviceCanAccessPeer(&access, remaining[i], remaining[j]));
+        if (access) {
+          pairs->push_back(DevicePair(remaining[i], remaining[j]));
+          DLOG(INFO) << "P2P pair: " << remaining[i] << ":" << remaining[j];
+          remaining.erase(remaining.begin() + j);
+          break;
+        }
+      }
+    }
+  }
+  s.str("");
+  for (int i = 0; i < remaining.size(); ++i) {
+    s << (i ? ", " : "") << remaining[i];
+  }
+  DLOG(INFO) << "GPUs paired by P2P access, remaining: " << s.str();
+
+  // Group remaining
+  remaining_depth = ceil(log2(remaining.size()));
+  for (int d = 0; d < remaining_depth; ++d) {
+    for (int i = 0; i < remaining.size(); ++i) {
+      pairs->push_back(DevicePair(remaining[i], remaining[i + 1]));
+      DLOG(INFO) << "Remaining pair: " << remaining[i] << ":"
+                 << remaining[i + 1];
+      remaining.erase(remaining.begin() + i + 1);
+    }
+  }
+
+  // Should only be the parent node remaining
+  CHECK_EQ(remaining.size(), 1);
+
+  pairs->insert(pairs->begin(), DevicePair(-1, remaining[0]));
+
+  CHECK(pairs->size() == devices.size());
+  for (int i = 0; i < pairs->size(); ++i) {
+    CHECK((*pairs)[i].parent() != (*pairs)[i].device());
+    for (int j = i + 1; j < pairs->size(); ++j) {
+      CHECK((*pairs)[i].device() != (*pairs)[j].device());
+    }
+  }
+#else
+  NO_GPU;
+#endif
+}
+
+//
+
+template<typename Dtype>
+P2PSync<Dtype>::P2PSync(shared_ptr<Solver<Dtype> > root_solver,
+                        P2PSync<Dtype>* parent, const SolverParameter& param)
+    : GPUParams<Dtype>(root_solver, param.device_id()),
+      parent_(parent),
+      children_(),
+      queue_(),
+      initial_iter_(root_solver->iter()),
+      solver_() {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+  const int self = param.device_id();
+  CUDA_CHECK(cudaSetDevice(self));
+
+  if (parent == NULL) {
+    solver_ = root_solver;
+  } else {
+    Caffe::set_root_solver(false);
+    solver_.reset(new WorkerSolver<Dtype>(param, root_solver.get()));
+    Caffe::set_root_solver(true);
+  }
+  this->configure(solver_.get());
+  solver_->add_callback(this);
+
+  if (parent) {
+    // Enable p2p access between devices
+    const int peer = parent->solver_->param().device_id();
+    int access;
+    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+    if (access) {
+      CUDA_CHECK(cudaDeviceEnablePeerAccess(peer, 0));
+    } else {
+      LOG(INFO)<< "GPU " << self << " does not have p2p access to GPU " << peer;
+    }
+    // Allocate receiving buffer on parent
+    CUDA_CHECK(cudaSetDevice(peer));
+    CUDA_CHECK(cudaMalloc(&parent_grads_, size_ * sizeof(Dtype)));
+    CUDA_CHECK(cudaSetDevice(self));
+  }
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#else
+  NO_GPU;
+#endif
+}
+
+template<typename Dtype>
+P2PSync<Dtype>::~P2PSync() {
+#ifndef CPU_ONLY
+  int initial_device;
+  CUDA_CHECK(cudaGetDevice(&initial_device));
+  const int self = solver_->param().device_id();
+  CUDA_CHECK(cudaSetDevice(self));
+
+  if (parent_) {
+    CUDA_CHECK(cudaFree(parent_grads_));
+    const int peer = parent_->solver_->param().device_id();
+    int access;
+    CUDA_CHECK(cudaDeviceCanAccessPeer(&access, self, peer));
+    if (access) {
+      CUDA_CHECK(cudaDeviceDisablePeerAccess(peer));
+    }
+  }
+
+  CUDA_CHECK(cudaSetDevice(initial_device));
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::InternalThreadEntry() {
+  Caffe::SetDevice(solver_->param().device_id());
+  CHECK(Caffe::root_solver());
+  Caffe::set_root_solver(false);
+  // See if there is a defined seed and reset random state if so
+  if (solver_->param().random_seed() >= 0) {
+    // Fetch random seed and modulate by device ID to make sure
+    // everyone doesn't have the same seed.  We seem to have some
+    // solver instability if we have everyone with the same seed
+    Caffe::set_random_seed(
+        solver_->param().random_seed() + solver_->param().device_id());
+  }
+  solver_->Step(solver_->param().max_iter() - initial_iter_);
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_start() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  CUDA_CHECK(cudaGetDevice(&device));
+  CHECK(device == solver_->param().device_id());
+#else
+//  CHECK(false);
+#endif
+
+  // Wait for update from parent
+  if (parent_) {
+    P2PSync<Dtype> *parent = queue_.pop();
+    CHECK(parent == parent_);
+  }
+
+  // Update children
+  for (int i = children_.size() - 1; i >= 0; i--) {
+    Dtype* src = data_;
+    Dtype* dst = children_[i]->data_;
+
+#ifdef DEBUG
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == children_[i]->solver_->param().device_id());
+#endif
+
+    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),
+        cudaMemcpyDeviceToDevice, cudaStreamDefault));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+    children_[i]->queue_.push(this);
+  }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::on_gradients_ready() {
+#ifndef CPU_ONLY
+#ifdef DEBUG
+  int device;
+  CUDA_CHECK(cudaGetDevice(&device));
+  CHECK(device == solver_->param().device_id());
+#endif
+
+  // Sum children gradients as they appear in the queue
+  for (int i = 0; i < children_.size(); ++i) {
+    P2PSync<Dtype> *child = queue_.pop();
+    Dtype* src = child->parent_grads_;
+    Dtype* dst = diff_;
+
+#ifdef DEBUG
+    bool ok = false;
+    for (int j = 0; j < children_.size(); ++j) {
+      if (child == children_[j]) {
+        ok = true;
+      }
+    }
+    CHECK(ok);
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == device);
+#endif
+
+    caffe_gpu_add(size_, src, dst, dst);
+  }
+
+  // Send gradients to parent
+  if (parent_) {
+    Dtype* src = diff_;
+    Dtype* dst = parent_grads_;
+
+#ifdef DEBUG
+    cudaPointerAttributes attributes;
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, src));
+    CHECK(attributes.device == device);
+    CUDA_CHECK(cudaPointerGetAttributes(&attributes, dst));
+    CHECK(attributes.device == parent_->solver_->param().device_id());
+#endif
+
+    CUDA_CHECK(cudaMemcpyAsync(dst, src, size_ * sizeof(Dtype),  //
+        cudaMemcpyDeviceToDevice, cudaStreamDefault));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamDefault));
+    parent_->queue_.push(this);
+  } else {
+    // Loss functions divide gradients by the batch size, so to compensate
+    // for split batch, the root solver divides by number of solvers.
+    caffe_gpu_scal(size_, Dtype(1.0 / Caffe::solver_count()), diff_);
+  }
+#endif
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::Prepare(const vector<int>& gpus,
+            vector<shared_ptr<P2PSync<Dtype> > >* syncs) {
+  // Pair devices for map-reduce synchronization
+  vector<DevicePair> pairs;
+  DevicePair::compute(gpus, &pairs);
+  ostringstream s;
+  for (int i = 1; i < pairs.size(); ++i) {
+    s << (i == 1 ? "" : ", ") << pairs[i].parent() << ":" << pairs[i].device();
+  }
+  LOG(INFO)<< "GPUs pairs " << s.str();
+
+  SolverParameter param(solver_->param());
+
+  // Build the GPU tree by finding the parent for each solver
+  for (int attempts = 0; attempts < pairs.size(); ++attempts) {
+    for (int i = 1; i < pairs.size(); ++i) {
+      if (!syncs->at(i).get()) {
+        P2PSync<Dtype>* parent = NULL;
+        for (int j = 0; j < syncs->size(); ++j) {
+          P2PSync<Dtype>* sync = j == 0 ? this : syncs->at(j).get();
+          if (sync) {
+            const SolverParameter& p = sync->solver()->param();
+            if (p.device_id() == pairs[i].parent()) {
+              parent = sync;
+            }
+          }
+        }
+        if (parent) {
+          param.set_device_id(pairs[i].device());
+          syncs->at(i).reset(new P2PSync<Dtype>(solver_, parent, param));
+          parent->children_.push_back((P2PSync<Dtype>*) syncs->at(i).get());
+        }
+      }
+    }
+  }
+}
+
+template<typename Dtype>
+void P2PSync<Dtype>::Run(const vector<int>& gpus) {
+  vector<shared_ptr<P2PSync<Dtype> > > syncs(gpus.size());
+  Prepare(gpus, &syncs);
+
+  LOG(INFO)<< "Starting Optimization";
+
+  for (int i = 1; i < syncs.size(); ++i) {
+    syncs[i]->StartInternalThread();
+  }
+
+  // Run root solver on current thread
+  solver_->Solve();
+
+  for (int i = 1; i < syncs.size(); ++i) {
+    syncs[i]->StopInternalThread();
+  }
+}
+
+INSTANTIATE_CLASS(Params);
+INSTANTIATE_CLASS(GPUParams);
+INSTANTIATE_CLASS(P2PSync);
+
+}  // namespace caffe
diff --git a/src/caffe/proto/caffe.proto b/src/caffe/proto/caffe.proto
index 5b21cf20028..2daf238f1ad 100644
--- a/src/caffe/proto/caffe.proto
+++ b/src/caffe/proto/caffe.proto
@@ -11,6 +11,8 @@ message BlobProto {
   optional BlobShape shape = 7;
   repeated float data = 5 [packed = true];
   repeated float diff = 6 [packed = true];
+  repeated double double_data = 8 [packed = true];
+  repeated double double_diff = 9 [packed = true];
 
   // 4D dimensions -- deprecated.  Use "shape" instead.
   optional int32 num = 1 [default = 0];
@@ -49,16 +51,24 @@ message FillerParameter {
   // The expected number of non-zero output weights for a given input in
   // Gaussian filler -- the default -1 means don't perform sparsification.
   optional int32 sparse = 7 [default = -1];
+  // Normalize the filler variance by fan_in, fan_out, or their average.
+  // Applies to 'xavier' and 'msra' fillers.
+  enum VarianceNorm {
+    FAN_IN = 0;
+    FAN_OUT = 1;
+    AVERAGE = 2;
+  }
+  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
 }
 
 message NetParameter {
   optional string name = 1; // consider giving the network a name
-  // The input blobs to the network.
+  // DEPRECATED. See InputParameter. The input blobs to the network.
   repeated string input = 3;
-  // The shape of the input blobs.
+  // DEPRECATED. See InputParameter. The shape of the input blobs.
   repeated BlobShape input_shape = 8;
 
-  // 4D input dimensions -- deprecated.  Use "shape" instead.
+  // 4D input dimensions -- deprecated.  Use "input_shape" instead.
   // If specified, for each input blob there should be four
   // values specifying the num, channels, height and width of the input blob.
   // Thus, there should be a total of (4 * #input) numbers.
@@ -88,7 +98,7 @@ message NetParameter {
 // NOTE
 // Update the next available ID when you add a new SolverParameter field.
 //
-// SolverParameter next available ID: 36 (last added: clip_gradients)
+// SolverParameter next available ID: 42 (last added: eval_type)
 message SolverParameter {
   //////////////////////////////////////////////////////////////////////////////
   // Specifying the train and test networks
@@ -141,7 +151,25 @@ message SolverParameter {
   // Display the loss averaged over the last average_loss iterations
   optional int32 average_loss = 33 [default = 1];
   optional int32 max_iter = 7; // the maximum number of iterations
-  optional string lr_policy = 8; // The learning rate decay policy.
+  // accumulate gradients over `iter_size` x `batch_size` instances
+  optional int32 iter_size = 36 [default = 1];
+
+  // The learning rate decay policy. The currently implemented learning rate
+  // policies are as follows:
+  //    - fixed: always return base_lr.
+  //    - step: return base_lr * gamma ^ (floor(iter / step))
+  //    - exp: return base_lr * gamma ^ iter
+  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+  //    - multistep: similar to step but it allows non uniform steps defined by
+  //      stepvalue
+  //    - poly: the effective learning rate follows a polynomial decay, to be
+  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+  //    - sigmoid: the effective learning rate follows a sigmod decay
+  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+  //
+  // where base_lr, max_iter, gamma, step, stepvalue and power are defined
+  // in the solver parameter protocol buffer, and iter is the current iteration.
+  optional string lr_policy = 8;
   optional float gamma = 9; // The parameter to compute the learning rate.
   optional float power = 10; // The parameter to compute the learning rate.
   optional float momentum = 11; // The momentum value.
@@ -163,6 +191,11 @@ message SolverParameter {
   // whether to snapshot diff in the results or not. Snapshotting diff will help
   // debugging but the final protocol buffer size will be much larger.
   optional bool snapshot_diff = 16 [default = false];
+  enum SnapshotFormat {
+    HDF5 = 0;
+    BINARYPROTO = 1;
+  }
+  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
   // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
   enum SolverMode {
     CPU = 0;
@@ -176,15 +209,17 @@ message SolverParameter {
   // (and by default) initialize using a seed derived from the system clock.
   optional int64 random_seed = 20 [default = -1];
 
-  // Solver type
-  enum SolverType {
-    SGD = 0;
-    NESTEROV = 1;
-    ADAGRAD = 2;
-  }
-  optional SolverType solver_type = 30 [default = SGD];
-  // numerical stability for AdaGrad
+  // type of the solver
+  optional string type = 40 [default = "SGD"];
+
+  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
   optional float delta = 31 [default = 1e-8];
+  // parameters for the Adam solver
+  optional float momentum2 = 39 [default = 0.999];
+
+  // RMSProp decay value
+  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
+  optional float rms_decay = 38;
 
   // If true, print information about the state of the net that may help with
   // debugging learning problems.
@@ -192,6 +227,21 @@ message SolverParameter {
 
   // If false, don't save a snapshot after training finishes.
   optional bool snapshot_after_train = 28 [default = true];
+
+// Evaluation type
+  optional string eval_type = 41 [default = "classification"];
+
+  // DEPRECATED: old solver enum types, use string instead
+  enum SolverType {
+    SGD = 0;
+    NESTEROV = 1;
+    ADAGRAD = 2;
+    RMSPROP = 3;
+    ADADELTA = 4;
+    ADAM = 5;
+  }
+  // DEPRECATED: use type instead of solver_type
+  optional SolverType solver_type = 30 [default = SGD];
 }
 
 // A message that stores the solver snapshots
@@ -259,7 +309,7 @@ message ParamSpec {
 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 //
-// LayerParameter next available layer-specific ID: 132 (last added: prelu_param)
+// LayerParameter next available layer-specific ID: 148 (last added: parse_evaluate_param)
 message LayerParameter {
   optional string name = 1; // the layer name
   optional string type = 2; // the layer type
@@ -281,6 +331,10 @@ message LayerParameter {
   // The blobs containing the numeric parameters of the layer.
   repeated BlobProto blobs = 7;
 
+  // Specifies on which bottoms the backpropagation should be skipped.
+  // The size must be either 0 or equal to the number of bottoms.
+  repeated bool propagate_down = 11;
+
   // Rules controlling whether and when a layer is included in the network,
   // based on the current NetState.  You may specify a non-zero number of rules
   // to include OR exclude, but not both.  If no include or exclude rules are
@@ -304,33 +358,49 @@ message LayerParameter {
   // The default for the engine is set by the ENGINE switch at compile-time.
   optional AccuracyParameter accuracy_param = 102;
   optional ArgMaxParameter argmax_param = 103;
+  optional BatchNormParameter batch_norm_param = 139;
+  optional BiasParameter bias_param = 141;
   optional ConcatParameter concat_param = 104;
   optional ContrastiveLossParameter contrastive_loss_param = 105;
   optional ConvolutionParameter convolution_param = 106;
+  optional CropParameter crop_param = 144;
   optional DataParameter data_param = 107;
   optional DropoutParameter dropout_param = 108;
   optional DummyDataParameter dummy_data_param = 109;
   optional EltwiseParameter eltwise_param = 110;
+  optional ELUParameter elu_param = 140;
+  optional EmbedParameter embed_param = 137;
   optional ExpParameter exp_param = 111;
+  optional FlattenParameter flatten_param = 135;
   optional HDF5DataParameter hdf5_data_param = 112;
   optional HDF5OutputParameter hdf5_output_param = 113;
   optional HingeLossParameter hinge_loss_param = 114;
   optional ImageDataParameter image_data_param = 115;
   optional InfogainLossParameter infogain_loss_param = 116;
   optional InnerProductParameter inner_product_param = 117;
+  optional InputParameter input_param = 143;
+  optional LogParameter log_param = 134;
   optional LRNParameter lrn_param = 118;
   optional MemoryDataParameter memory_data_param = 119;
   optional MVNParameter mvn_param = 120;
+  optional NormalizeParameter norm_param = 145;
+  optional ParseEvaluateParameter parse_evaluate_param = 147;
   optional PoolingParameter pooling_param = 121;
   optional PowerParameter power_param = 122;
   optional PReLUParameter prelu_param = 131;
   optional PythonParameter python_param = 130;
+  optional ReductionParameter reduction_param = 136;
   optional ReLUParameter relu_param = 123;
+  optional ReshapeParameter reshape_param = 133;
+  optional ScaleParameter scale_param = 142;
   optional SigmoidParameter sigmoid_param = 124;
   optional SoftmaxParameter softmax_param = 125;
+  optional SPPParameter spp_param = 132;
   optional SliceParameter slice_param = 126;
   optional TanHParameter tanh_param = 127;
   optional ThresholdParameter threshold_param = 128;
+  optional TileParameter tile_param = 138;
+  optional UnPoolingParameter unpooling_param = 146;
   optional WindowDataParameter window_data_param = 129;
 }
 
@@ -351,18 +421,42 @@ message TransformationParameter {
   // or can be repeated the same number of times as channels
   // (would subtract them from the corresponding channel)
   repeated float mean_value = 5;
+  // Force the decoded image to have 3 color channels.
+  optional bool force_color = 6 [default = false];
+  // Force the decoded image to have 1 color channels.
+  optional bool force_gray = 7 [default = false];
 }
 
 // Message that stores parameters shared by loss layers
 message LossParameter {
   // If specified, ignore instances with the given label.
   optional int32 ignore_label = 1;
-  // If true, normalize each batch across all instances (including spatial
-  // dimesions, but not ignored instances); else, divide by batch size only.
-  optional bool normalize = 2 [default = true];
+  // How to normalize the loss for loss layers that aggregate across batches,
+  // spatial dimensions, or other dimensions.  Currently only implemented in
+  // SoftmaxWithLoss layer.
+  enum NormalizationMode {
+    // Divide by the number of examples in the batch times spatial dimensions.
+    // Outputs that receive the ignore label will NOT be ignored in computing
+    // the normalization factor.
+    FULL = 0;
+    // Divide by the total number of output locations that do not take the
+    // ignore_label.  If ignore_label is not set, this behaves like FULL.
+    VALID = 1;
+    // Divide by the batch size.
+    BATCH_SIZE = 2;
+    // Do not normalize the loss.
+    NONE = 3;
+  }
+  optional NormalizationMode normalization = 3 [default = VALID];
+  // Deprecated.  Ignored if normalization is specified.  If normalization
+  // is not specified, then setting this to false will be equivalent to
+  // normalization = BATCH_SIZE to be consistent with previous behavior.
+  optional bool normalize = 2;
 }
 
-// Message that stores parameters used by AccuracyLayer
+// Messages that store parameters used by individual layer types follow, in
+// alphabetical order.
+
 message AccuracyParameter {
   // When computing accuracy, count as correct by comparing the true label to
   // the top k scoring classes.  By default, only compare to the top scoring
@@ -380,14 +474,17 @@ message AccuracyParameter {
   optional int32 ignore_label = 3;
 }
 
-// Message that stores parameters used by ArgMaxLayer
 message ArgMaxParameter {
   // If true produce pairs (argmax, maxval)
   optional bool out_max_val = 1 [default = false];
   optional uint32 top_k = 2 [default = 1];
+  // The axis along which to maximise -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
+  // for each index of the first / num dimension.
+  optional int32 axis = 3;
 }
 
-// Message that stores parameters used by ConcatLayer
 message ConcatParameter {
   // The axis along which to concatenate -- may be negative to index from the
   // end (e.g., -1 for the last axis).  Other axes must have the
@@ -399,28 +496,87 @@ message ConcatParameter {
   optional uint32 concat_dim = 1 [default = 1];
 }
 
-// Message that stores parameters used by ContrastiveLossLayer
+message BatchNormParameter {
+  // If false, accumulate global mean/variance values via a moving average. If
+  // true, use those accumulated values instead of computing mean/variance
+  // across the batch.
+  optional bool use_global_stats = 1;
+  // How much does the moving average decay each iteration?
+  optional float moving_average_fraction = 2 [default = .999];
+  // Small value to add to the variance estimate so that we don't divide by
+  // zero.
+  optional float eps = 3 [default = 1e-5];
+}
+
+message BiasParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar bias.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the bias
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the bias is
+  // a learned parameter of the layer.)
+  // The initialization for the learned bias parameter.
+  // Default is the zero (0) initialization, resulting in the BiasLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+}
+
 message ContrastiveLossParameter {
-  //margin for dissimilar pair
+  // margin for dissimilar pair
   optional float margin = 1 [default = 1.0];
+  // The first implementation of this cost did not exactly match the cost of
+  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
+  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
+  // Hadsell paper. New models should probably use this version.
+  // legacy_version = true uses (margin - d^2). This is kept to support /
+  // reproduce existing models and results
+  optional bool legacy_version = 2 [default = false];
 }
 
-// Message that stores parameters used by ConvolutionLayer
 message ConvolutionParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
+
   // Pad, kernel size, and stride are all given as a single value for equal
-  // dimensions in height and width or as Y, X pairs.
-  optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
-  optional uint32 pad_h = 9 [default = 0]; // The padding height
-  optional uint32 pad_w = 10 [default = 0]; // The padding width
-  optional uint32 kernel_size = 4; // The kernel size (square)
-  optional uint32 kernel_h = 11; // The kernel height
-  optional uint32 kernel_w = 12; // The kernel width
+  // dimensions in all spatial dimensions, or once per spatial dimension.
+  repeated uint32 pad = 3; // The padding size; defaults to 0
+  repeated uint32 kernel_size = 4; // The kernel size
+  repeated uint32 stride = 6; // The stride; defaults to 1
+  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
+  // holes. (Kernel dilation is sometimes referred to by its use in the
+  // algorithme à trous from Holschneider et al. 1987.)
+  repeated uint32 dilation = 18; // The dilation; defaults to 1
+
+  // For 2D convolution only, the *_h and *_w versions may also be used to
+  // specify both spatial dimensions.
+  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
+  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
+  optional uint32 kernel_h = 11; // The kernel height (2D only)
+  optional uint32 kernel_w = 12; // The kernel width (2D only)
+  optional uint32 stride_h = 13; // The stride height (2D only)
+  optional uint32 stride_w = 14; // The stride width (2D only)
+
   optional uint32 group = 5 [default = 1]; // The group size for group conv
-  optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
-  optional uint32 stride_h = 13; // The stride height
-  optional uint32 stride_w = 14; // The stride width
+
   optional FillerParameter weight_filler = 7; // The filler for the weight
   optional FillerParameter bias_filler = 8; // The filler for the bias
   enum Engine {
@@ -429,9 +585,44 @@ message ConvolutionParameter {
     CUDNN = 2;
   }
   optional Engine engine = 15 [default = DEFAULT];
+
+  // The axis to interpret as "channels" when performing convolution.
+  // Preceding dimensions are treated as independent inputs;
+  // succeeding dimensions are treated as "spatial".
+  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
+  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
+  // groups g>1) filters across the spatial axes (H, W) of the input.
+  // With (N, C, D, H, W) inputs, and axis == 1, we perform
+  // N independent 3D convolutions, sliding (C/g)-channels
+  // filters across the spatial axes (D, H, W) of the input.
+  optional int32 axis = 16 [default = 1];
+
+  // Whether to force use of the general ND convolution, even if a specific
+  // implementation for blobs of the appropriate number of spatial dimensions
+  // is available. (Currently, there is only a 2D-specific convolution
+  // implementation; for input blobs with num_axes != 2, this option is
+  // ignored and the ND implementation will be used.)
+  optional bool force_nd_im2col = 17 [default = false];
+}
+
+message CropParameter {
+  // To crop, elements of the first bottom are selected to fit the dimensions
+  // of the second, reference bottom. The crop is configured by
+  // - the crop `axis` to pick the dimensions for cropping
+  // - the crop `offset` to set the shift for all/each dimension
+  // to align the cropped bottom with the reference bottom.
+  // All dimensions up to but excluding `axis` are preserved, while
+  // the dimensions including and trailing `axis` are cropped.
+  // If only one `offset` is set, then all dimensions are offset by this amount.
+  // Otherwise, the number of offsets must equal the number of cropped axes to
+  // shift the crop in each dimension accordingly.
+  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
+  // and `axis` may be negative to index from the end (e.g., -1 for the last
+  // axis).
+  optional int32 axis = 1 [default = 2];
+  repeated uint32 offset = 2;
 }
 
-// Message that stores parameters used by DataLayer
 message DataParameter {
   enum DB {
     LEVELDB = 0;
@@ -445,6 +636,7 @@ message DataParameter {
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
   // be larger than the number of keys in the database.
+  // DEPRECATED. Each solver accesses a different subset of the database.
   optional uint32 rand_skip = 7 [default = 0];
   optional DB backend = 8 [default = LEVELDB];
   // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
@@ -460,14 +652,15 @@ message DataParameter {
   optional bool mirror = 6 [default = false];
   // Force the encoded image to have 3 color channels
   optional bool force_encoded_color = 9 [default = false];
+  // Prefetch queue (Number of batches to prefetch to host memory, increase if
+  // data access bandwidth varies).
+  optional uint32 prefetch = 10 [default = 4];
 }
 
-// Message that stores parameters used by DropoutLayer
 message DropoutParameter {
   optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
 }
 
-// Message that stores parameters used by DummyDataLayer.
 // DummyDataLayer fills any number of arbitrarily shaped blobs with random
 // (or constant) data generated by "Fillers" (see "message FillerParameter").
 message DummyDataParameter {
@@ -487,7 +680,6 @@ message DummyDataParameter {
   repeated uint32 width = 5;
 }
 
-// Message that stores parameters used by EltwiseLayer
 message EltwiseParameter {
   enum EltwiseOp {
     PROD = 0;
@@ -502,6 +694,28 @@ message EltwiseParameter {
   optional bool stable_prod_grad = 3 [default = true];
 }
 
+// Message that stores parameters used by ELULayer
+message ELUParameter {
+  // Described in:
+  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
+  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
+  optional float alpha = 1 [default = 1];
+}
+
+// Message that stores parameters used by EmbedLayer
+message EmbedParameter {
+  optional uint32 num_output = 1; // The number of outputs for the layer
+  // The input is given as integers to be interpreted as one-hot
+  // vector indices with dimension num_input.  Hence num_input should be
+  // 1 greater than the maximum possible input value.
+  optional uint32 input_dim = 2;
+
+  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
+  optional FillerParameter weight_filler = 4; // The filler for the weight
+  optional FillerParameter bias_filler = 5; // The filler for the bias
+
+}
+
 // Message that stores parameters used by ExpLayer
 message ExpParameter {
   // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
@@ -512,6 +726,18 @@ message ExpParameter {
   optional float shift = 3 [default = 0.0];
 }
 
+/// Message that stores parameters used by FlattenLayer
+message FlattenParameter {
+  // The first axis to flatten: all preceding axes are retained in the output.
+  // May be negative to index from the end (e.g., -1 for the last axis).
+  optional int32 axis = 1 [default = 1];
+
+  // The last axis to flatten: all following axes are retained in the output.
+  // May be negative to index from the end (e.g., the default -1 for the last
+  // axis).
+  optional int32 end_axis = 2 [default = -1];
+}
+
 // Message that stores parameters used by HDF5DataLayer
 message HDF5DataParameter {
   // Specify the data source.
@@ -527,7 +753,6 @@ message HDF5DataParameter {
   optional bool shuffle = 3 [default = false];
 }
 
-// Message that stores parameters used by HDF5OutputLayer
 message HDF5OutputParameter {
   optional string file_name = 1;
 }
@@ -541,12 +766,11 @@ message HingeLossParameter {
   optional Norm norm = 1 [default = L1];
 }
 
-// Message that stores parameters used by ImageDataLayer
 message ImageDataParameter {
   // Specify the data source.
   optional string source = 1;
   // Specify the batch size.
-  optional uint32 batch_size = 4;
+  optional uint32 batch_size = 4 [default = 1];
   // The rand_skip variable is for the data layer to skip a few data points
   // to avoid all asynchronous sgd clients to start at the same point. The skip
   // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
@@ -573,13 +797,11 @@ message ImageDataParameter {
   optional string root_folder = 12 [default = ""];
 }
 
-// Message that stores parameters InfogainLossLayer
 message InfogainLossParameter {
   // Specify the infogain matrix source.
   optional string source = 1;
 }
 
-// Message that stores parameters used by InnerProductLayer
 message InnerProductParameter {
   optional uint32 num_output = 1; // The number of outputs for the layer
   optional bool bias_term = 2 [default = true]; // whether to have bias terms
@@ -590,6 +812,29 @@ message InnerProductParameter {
   // all preceding axes are retained in the output.
   // May be negative to index from the end (e.g., -1 for the last axis).
   optional int32 axis = 5 [default = 1];
+  // Specify whether to transpose the weight matrix or not.
+  // If transpose == true, any operations will be performed on the transpose
+  // of the weight matrix. The weight matrix itself is not going to be transposed
+  // but rather the transfer flag of operations will be toggled accordingly.
+  optional bool transpose = 6 [default = false];
+}
+
+message InputParameter {
+  // This layer produces N >= 1 top blob(s) to be assigned manually.
+  // Define N shapes to set a shape for each top.
+  // Define 1 shape to set the same shape for every top.
+  // Define no shape to defer to reshaping manually.
+  repeated BlobShape shape = 1;
+}
+
+// Message that stores parameters used by LogLayer
+message LogParameter {
+  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
+  // Or if base is set to the default (-1), base is set to e,
+  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
+  optional float base = 1 [default = -1.0];
+  optional float scale = 2 [default = 1.0];
+  optional float shift = 3 [default = 0.0];
 }
 
 // Message that stores parameters used by LRNLayer
@@ -603,9 +848,14 @@ message LRNParameter {
   }
   optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
   optional float k = 5 [default = 1.];
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
 }
 
-// Message that stores parameters used by MemoryDataLayer
 message MemoryDataParameter {
   optional uint32 batch_size = 1;
   optional uint32 channels = 2;
@@ -613,16 +863,36 @@ message MemoryDataParameter {
   optional uint32 width = 4;
 }
 
-// Message that stores parameters used by MVNLayer
 message MVNParameter {
   // This parameter can be set to false to normalize mean only
   optional bool normalize_variance = 1 [default = true];
 
   // This parameter can be set to true to perform DNN-like MVN
   optional bool across_channels = 2 [default = false];
+
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 3 [default = 1e-9];
+}
+
+// Message that stores parameters used by NormalizeLayer
+message NormalizeParameter {
+  optional bool across_spatial = 1 [default = true];
+  // Initial value of scale. Default is 1.0 for all
+  optional FillerParameter scale_filler = 2;
+  // Whether or not scale parameters are shared across channels.
+  optional bool channel_shared = 3 [default = true];
+  // Epsilon for not dividing by zero while normalizing variance
+  optional float eps = 4 [default = 1e-10];
+}
+
+// Message that stores parameters used by ParseEvaluateLayer
+message ParseEvaluateParameter {
+  // Number of total labels. Must provide.
+  optional int32 num_labels = 1;
+  // Ignore evaluating following labels.
+  repeated int32 ignore_label = 2;
 }
 
-// Message that stores parameters used by PoolingLayer
 message PoolingParameter {
   enum PoolMethod {
     MAX = 0;
@@ -652,7 +922,6 @@ message PoolingParameter {
   optional bool global_pooling = 12 [default = false];
 }
 
-// Message that stores parameters used by PowerLayer
 message PowerParameter {
   // PowerLayer computes outputs y = (shift + scale * x) ^ power.
   optional float power = 1 [default = 1.0];
@@ -660,10 +929,47 @@ message PowerParameter {
   optional float shift = 3 [default = 0.0];
 }
 
-// Message that stores parameters used by PythonLayer
 message PythonParameter {
   optional string module = 1;
   optional string layer = 2;
+  // This value is set to the attribute `param_str` of the `PythonLayer` object
+  // in Python before calling the `setup()` method. This could be a number,
+  // string, dictionary in Python dict format, JSON, etc. You may parse this
+  // string in `setup` method and use it in `forward` and `backward`.
+  optional string param_str = 3 [default = ''];
+  // Whether this PythonLayer is shared among worker solvers during data parallelism.
+  // If true, each worker solver sequentially run forward from this layer.
+  // This value should be set true if you are using it as a data layer.
+  optional bool share_in_parallel = 4 [default = false];
+}
+
+// Message that stores parameters used by ReductionLayer
+message ReductionParameter {
+  enum ReductionOp {
+    SUM = 1;
+    ASUM = 2;
+    SUMSQ = 3;
+    MEAN = 4;
+  }
+
+  optional ReductionOp operation = 1 [default = SUM]; // reduction operation
+
+  // The first axis to reduce to a scalar -- may be negative to index from the
+  // end (e.g., -1 for the last axis).
+  // (Currently, only reduction along ALL "tail" axes is supported; reduction
+  // of axis M through N, where N < num_axes - 1, is unsupported.)
+  // Suppose we have an n-axis bottom Blob with shape:
+  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
+  // If axis == m, the output Blob will have shape
+  //     (d0, d1, d2, ..., d(m-1)),
+  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
+  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
+  // If axis == 0 (the default), the output Blob always has the empty shape
+  // (count 1), performing reduction across the entire input --
+  // often useful for creating new loss functions.
+  optional int32 axis = 2 [default = 0];
+
+  optional float coeff = 3 [default = 1.0]; // coefficient for output
 }
 
 // Message that stores parameters used by ReLULayer
@@ -682,7 +988,107 @@ message ReLUParameter {
   optional Engine engine = 2 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SigmoidLayer
+message ReshapeParameter {
+  // Specify the output dimensions. If some of the dimensions are set to 0,
+  // the corresponding dimension from the bottom layer is used (unchanged).
+  // Exactly one dimension may be set to -1, in which case its value is
+  // inferred from the count of the bottom blob and the remaining dimensions.
+  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
+  //
+  //   layer {
+  //     type: "Reshape" bottom: "input" top: "output"
+  //     reshape_param { ... }
+  //   }
+  //
+  // If "input" is 2D with shape 2 x 8, then the following reshape_param
+  // specifications are all equivalent, producing a 3D blob "output" with shape
+  // 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
+  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
+  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
+  //
+  optional BlobShape shape = 1;
+
+  // axis and num_axes control the portion of the bottom blob's shape that are
+  // replaced by (included in) the reshape. By default (axis == 0 and
+  // num_axes == -1), the entire bottom blob shape is included in the reshape,
+  // and hence the shape field must specify the entire output shape.
+  //
+  // axis may be non-zero to retain some portion of the beginning of the input
+  // shape (and may be negative to index from the end; e.g., -1 to begin the
+  // reshape after the last axis, including nothing in the reshape,
+  // -2 to include only the last axis, etc.).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are all equivalent,
+  // producing a blob "output" with shape 2 x 2 x 4:
+  //
+  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
+  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
+  //
+  // num_axes specifies the extent of the reshape.
+  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
+  // input axes in the range [axis, axis+num_axes].
+  // num_axes may also be -1, the default, to include all remaining axes
+  // (starting from axis).
+  //
+  // For example, suppose "input" is a 2D blob with shape 2 x 8.
+  // Then the following ReshapeLayer specifications are equivalent,
+  // producing a blob "output" with shape 1 x 2 x 8.
+  //
+  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
+  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
+  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
+  //
+  // On the other hand, these would produce output blob shape 2 x 1 x 8:
+  //
+  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
+  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
+  //
+  optional int32 axis = 2 [default = 0];
+  optional int32 num_axes = 3 [default = -1];
+}
+
+message ScaleParameter {
+  // The first axis of bottom[0] (the first input Blob) along which to apply
+  // bottom[1] (the second input Blob).  May be negative to index from the end
+  // (e.g., -1 for the last axis).
+  //
+  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
+  // top[0] will have the same shape, and bottom[1] may have any of the
+  // following shapes (for the given value of axis):
+  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
+  //    (axis == 1 == -3)          3;     3x40;     3x40x60
+  //    (axis == 2 == -2)                   40;       40x60
+  //    (axis == 3 == -1)                                60
+  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
+  // "axis") -- a scalar multiplier.
+  optional int32 axis = 1 [default = 1];
+
+  // (num_axes is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
+  // number of axes by the second bottom.)
+  // The number of axes of the input (bottom[0]) covered by the scale
+  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
+  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
+  optional int32 num_axes = 2 [default = 1];
+
+  // (filler is ignored unless just one bottom is given and the scale is
+  // a learned parameter of the layer.)
+  // The initialization for the learned scale parameter.
+  // Default is the unit (1) initialization, resulting in the ScaleLayer
+  // initially performing the identity operation.
+  optional FillerParameter filler = 3;
+
+  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
+  // may be more efficient).  Initialized with bias_filler (defaults to 0).
+  optional bool bias_term = 4 [default = false];
+  optional FillerParameter bias_filler = 5;
+}
+
 message SigmoidParameter {
   enum Engine {
     DEFAULT = 0;
@@ -692,7 +1098,6 @@ message SigmoidParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
-// Message that stores parameters used by SliceLayer
 message SliceParameter {
   // The axis along which to slice -- may be negative to index from the end
   // (e.g., -1 for the last axis).
@@ -719,7 +1124,6 @@ message SoftmaxParameter {
   optional int32 axis = 2 [default = 1];
 }
 
-// Message that stores parameters used by TanHLayer
 message TanHParameter {
   enum Engine {
     DEFAULT = 0;
@@ -729,12 +1133,41 @@ message TanHParameter {
   optional Engine engine = 1 [default = DEFAULT];
 }
 
+// Message that stores parameters used by TileLayer
+message TileParameter {
+  // The index of the axis to tile.
+  optional int32 axis = 1 [default = 1];
+
+  // The number of copies (tiles) of the blob to output.
+  optional int32 tiles = 2;
+}
+
 // Message that stores parameters used by ThresholdLayer
 message ThresholdParameter {
   optional float threshold = 1 [default = 0]; // Strictly positive values
 }
 
-// Message that stores parameters used by WindowDataLayer
+// Message that stores parameters used by UnPoolingLayer
+message UnPoolingParameter {
+  enum UnPoolMethod {
+    FIXED = 0;  // Put in the middle of a kernel
+    DIV = 1;    // Divide equally through a kernel
+    REP = 2;    // Repeat through a kernel
+  }
+  optional UnPoolMethod unpool = 1 [default = FIXED]; // The unpooling method
+  // Pad, kernel size, and stride are all given as a single value for equal
+  // dimensions in height and width or as Y, X pairs.
+  optional uint32 out_pad = 4 [default = 0]; // The padding size (equal in Y, X)
+  optional uint32 out_pad_h = 9 [default = 0]; // The padding height
+  optional uint32 out_pad_w = 10 [default = 0]; // The padding width
+  optional uint32 out_kernel_size = 2; // The kernel size (square)
+  optional uint32 out_kernel_h = 5; // The kernel height
+  optional uint32 out_kernel_w = 6; // The kernel width
+  optional uint32 out_stride = 3 [default = 1]; // The stride (equal in Y, X)
+  optional uint32 out_stride_h = 7; // The stride height
+  optional uint32 out_stride_w = 8; // The stride width
+}
+
 message WindowDataParameter {
   // Specify the data source.
   optional string source = 1;
@@ -768,6 +1201,22 @@ message WindowDataParameter {
   optional string root_folder = 13 [default = ""];
 }
 
+message SPPParameter {
+  enum PoolMethod {
+    MAX = 0;
+    AVE = 1;
+    STOCHASTIC = 2;
+  }
+  optional uint32 pyramid_height = 1;
+  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
+  enum Engine {
+    DEFAULT = 0;
+    CAFFE = 1;
+    CUDNN = 2;
+  }
+  optional Engine engine = 6 [default = DEFAULT];
+}
+
 // DEPRECATED: use LayerParameter.
 message V1LayerParameter {
   repeated string bottom = 2;
@@ -955,7 +1404,6 @@ message V0LayerParameter {
   optional HDF5OutputParameter hdf5_output_param = 1001;
 }
 
-// Message that stores parameters used by PReLULayer
 message PReLUParameter {
   // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
   // Surpassing Human-Level Performance on ImageNet Classification, 2015.
diff --git a/src/caffe/solver.cpp b/src/caffe/solver.cpp
index 096980dd7af..b3d8afcc047 100644
--- a/src/caffe/solver.cpp
+++ b/src/caffe/solver.cpp
@@ -1,45 +1,64 @@
 #include <cstdio>
 
-#include <algorithm>
 #include <string>
 #include <vector>
 
-#include "caffe/net.hpp"
-#include "caffe/proto/caffe.pb.h"
 #include "caffe/solver.hpp"
+#include "caffe/util/format.hpp"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/util/math_functions.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 namespace caffe {
 
+template<typename Dtype>
+void Solver<Dtype>::SetActionFunction(ActionCallback func) {
+  action_request_function_ = func;
+}
+
+template<typename Dtype>
+SolverAction::Enum Solver<Dtype>::GetRequestedAction() {
+  if (action_request_function_) {
+    // If the external request function has been set, call it.
+    return action_request_function_();
+  }
+  return SolverAction::NONE;
+}
+
 template <typename Dtype>
-Solver<Dtype>::Solver(const SolverParameter& param)
-    : net_() {
+Solver<Dtype>::Solver(const SolverParameter& param, const Solver* root_solver)
+    : net_(), callbacks_(), root_solver_(root_solver),
+      requested_early_exit_(false) {
   Init(param);
 }
 
 template <typename Dtype>
-Solver<Dtype>::Solver(const string& param_file)
-    : net_() {
+Solver<Dtype>::Solver(const string& param_file, const Solver* root_solver)
+    : net_(), callbacks_(), root_solver_(root_solver),
+      requested_early_exit_(false) {
   SolverParameter param;
-  ReadProtoFromTextFileOrDie(param_file, &param);
+  ReadSolverParamsFromTextFileOrDie(param_file, &param);
   Init(param);
 }
 
 template <typename Dtype>
 void Solver<Dtype>::Init(const SolverParameter& param) {
-  LOG(INFO) << "Initializing solver from parameters: " << std::endl
-            << param.DebugString();
+  CHECK(Caffe::root_solver() || root_solver_)
+      << "root_solver_ needs to be set for all non-root solvers";
+  LOG_IF(INFO, Caffe::root_solver()) << "Initializing solver from parameters: "
+    << std::endl << param.DebugString();
   param_ = param;
   CHECK_GE(param_.average_loss(), 1) << "average_loss should be non-negative.";
-  if (param_.random_seed() >= 0) {
+  CheckSnapshotWritePermissions();
+  if (Caffe::root_solver() && param_.random_seed() >= 0) {
     Caffe::set_random_seed(param_.random_seed());
   }
   // Scaffolding code
   InitTrainNet();
-  InitTestNets();
-  LOG(INFO) << "Solver scaffolding done.";
+  if (Caffe::root_solver()) {
+    InitTestNets();
+    LOG(INFO) << "Solver scaffolding done.";
+  }
   iter_ = 0;
   current_step_ = 0;
 }
@@ -55,19 +74,22 @@ void Solver<Dtype>::InitTrainNet() {
       << "one of these fields specifying a train_net: " << field_names;
   NetParameter net_param;
   if (param_.has_train_net_param()) {
-    LOG(INFO) << "Creating training net specified in train_net_param.";
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net specified in train_net_param.";
     net_param.CopyFrom(param_.train_net_param());
   } else if (param_.has_train_net()) {
-    LOG(INFO) << "Creating training net from train_net file: "
-              << param_.train_net();
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net from train_net file: " << param_.train_net();
     ReadNetParamsFromTextFileOrDie(param_.train_net(), &net_param);
   }
   if (param_.has_net_param()) {
-    LOG(INFO) << "Creating training net specified in net_param.";
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net specified in net_param.";
     net_param.CopyFrom(param_.net_param());
   }
   if (param_.has_net()) {
-    LOG(INFO) << "Creating training net from net file: " << param_.net();
+    LOG_IF(INFO, Caffe::root_solver())
+        << "Creating training net from net file: " << param_.net();
     ReadNetParamsFromTextFileOrDie(param_.net(), &net_param);
   }
   // Set the correct NetState.  We start with the solver defaults (lowest
@@ -79,11 +101,16 @@ void Solver<Dtype>::InitTrainNet() {
   net_state.MergeFrom(net_param.state());
   net_state.MergeFrom(param_.train_state());
   net_param.mutable_state()->CopyFrom(net_state);
-  net_.reset(new Net<Dtype>(net_param));
+  if (Caffe::root_solver()) {
+    net_.reset(new Net<Dtype>(net_param));
+  } else {
+    net_.reset(new Net<Dtype>(net_param, root_solver_->net_.get()));
+  }
 }
 
 template <typename Dtype>
 void Solver<Dtype>::InitTestNets() {
+  CHECK(Caffe::root_solver());
   const bool has_net_param = param_.has_net_param();
   const bool has_net_file = param_.has_net();
   const int num_generic_nets = has_net_param + has_net_file;
@@ -153,40 +180,53 @@ void Solver<Dtype>::InitTestNets() {
     net_params[i].mutable_state()->CopyFrom(net_state);
     LOG(INFO)
         << "Creating test net (#" << i << ") specified by " << sources[i];
-    test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    if (Caffe::root_solver()) {
+      test_nets_[i].reset(new Net<Dtype>(net_params[i]));
+    } else {
+      test_nets_[i].reset(new Net<Dtype>(net_params[i],
+          root_solver_->test_nets_[i].get()));
+    }
     test_nets_[i]->set_debug_info(param_.debug_info());
   }
 }
 
 template <typename Dtype>
 void Solver<Dtype>::Step(int iters) {
-  vector<Blob<Dtype>*> bottom_vec;
   const int start_iter = iter_;
   const int stop_iter = iter_ + iters;
   int average_loss = this->param_.average_loss();
-  vector<Dtype> losses;
-  Dtype smoothed_loss = 0;
+  losses_.clear();
+  smoothed_loss_ = 0;
 
-  for (; iter_ < stop_iter; ++iter_) {
+  while (iter_ < stop_iter) {
+    // zero-init the params
+    net_->ClearParamDiffs();
     if (param_.test_interval() && iter_ % param_.test_interval() == 0
-        && (iter_ > 0 || param_.test_initialization())) {
+        && (iter_ > 0 || param_.test_initialization())
+        && Caffe::root_solver()) {
       TestAll();
+      if (requested_early_exit_) {
+        // Break out of the while loop because stop was requested while testing.
+        break;
+      }
     }
 
+    for (int i = 0; i < callbacks_.size(); ++i) {
+      callbacks_[i]->on_start();
+    }
     const bool display = param_.display() && iter_ % param_.display() == 0;
     net_->set_debug_info(display && param_.debug_info());
-    Dtype loss = net_->ForwardBackward(bottom_vec);
-    if (losses.size() < average_loss) {
-      losses.push_back(loss);
-      int size = losses.size();
-      smoothed_loss = (smoothed_loss * (size - 1) + loss) / size;
-    } else {
-      int idx = (iter_ - start_iter) % average_loss;
-      smoothed_loss += (loss - losses[idx]) / average_loss;
-      losses[idx] = loss;
+    // accumulate the loss and gradient
+    Dtype loss = 0;
+    for (int i = 0; i < param_.iter_size(); ++i) {
+      loss += net_->ForwardBackward();
     }
+    loss /= param_.iter_size();
+    // average the loss across iterations for smoothed reporting
+    UpdateSmoothedLoss(loss, start_iter, average_loss);
     if (display) {
-      LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss;
+      LOG_IF(INFO, Caffe::root_solver()) << "Iteration " << iter_
+          << ", loss = " << smoothed_loss_;
       const vector<Blob<Dtype>*>& result = net_->output_blobs();
       int score_index = 0;
       for (int j = 0; j < result.size(); ++j) {
@@ -201,27 +241,47 @@ void Solver<Dtype>::Step(int iters) {
             loss_msg_stream << " (* " << loss_weight
                             << " = " << loss_weight * result_vec[k] << " loss)";
           }
-          LOG(INFO) << "    Train net output #"
+          LOG_IF(INFO, Caffe::root_solver()) << "    Train net output #"
               << score_index++ << ": " << output_name << " = "
               << result_vec[k] << loss_msg_stream.str();
         }
       }
     }
-    ComputeUpdateValue();
-    net_->Update();
+    for (int i = 0; i < callbacks_.size(); ++i) {
+      callbacks_[i]->on_gradients_ready();
+    }
+    ApplyUpdate();
+
+    // Increment the internal iter_ counter -- its value should always indicate
+    // the number of times the weights have been updated.
+    ++iter_;
+
+    SolverAction::Enum request = GetRequestedAction();
 
     // Save a snapshot if needed.
-    if (param_.snapshot() && (iter_ + 1) % param_.snapshot() == 0) {
+    if ((param_.snapshot()
+         && iter_ % param_.snapshot() == 0
+         && Caffe::root_solver()) ||
+         (request == SolverAction::SNAPSHOT)) {
       Snapshot();
     }
+    if (SolverAction::STOP == request) {
+      requested_early_exit_ = true;
+      // Break out of training loop.
+      break;
+    }
   }
 }
 
 template <typename Dtype>
 void Solver<Dtype>::Solve(const char* resume_file) {
+  CHECK(Caffe::root_solver());
   LOG(INFO) << "Solving " << net_->name();
   LOG(INFO) << "Learning Rate Policy: " << param_.lr_policy();
 
+  // Initialize to false every time we start solving.
+  requested_early_exit_ = false;
+
   if (resume_file) {
     LOG(INFO) << "Restoring previous solver status from " << resume_file;
     Restore(resume_file);
@@ -229,6 +289,7 @@ void Solver<Dtype>::Solve(const char* resume_file) {
 
   // For a network that is trained by the solver, no bottom or top vecs
   // should be given, and we will just provide dummy vecs.
+  int start_iter = iter_;
   Step(param_.max_iter() - iter_);
   // If we haven't already, save a snapshot after optimization, unless
   // overridden by setting snapshot_after_train := false
@@ -236,6 +297,10 @@ void Solver<Dtype>::Solve(const char* resume_file) {
       && (!param_.snapshot() || iter_ % param_.snapshot() != 0)) {
     Snapshot();
   }
+  if (requested_early_exit_) {
+    LOG(INFO) << "Optimization stopped early.";
+    return;
+  }
   // After the optimization is done, run an additional train and test pass to
   // display the train and test loss/outputs if appropriate (based on the
   // display and test_interval settings, respectively).  Unlike in the rest of
@@ -243,9 +308,13 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   // updated the parameters "max_iter" times -- this final pass is only done to
   // display the loss, which is computed in the forward pass.
   if (param_.display() && iter_ % param_.display() == 0) {
+    int average_loss = this->param_.average_loss();
     Dtype loss;
-    net_->ForwardPrefilled(&loss);
-    LOG(INFO) << "Iteration " << iter_ << ", loss = " << loss;
+    net_->Forward(&loss);
+
+    UpdateSmoothedLoss(loss, start_iter, average_loss);
+
+    LOG(INFO) << "Iteration " << iter_ << ", loss = " << smoothed_loss_;
   }
   if (param_.test_interval() && iter_ % param_.test_interval() == 0) {
     TestAll();
@@ -253,29 +322,51 @@ void Solver<Dtype>::Solve(const char* resume_file) {
   LOG(INFO) << "Optimization Done.";
 }
 
-
 template <typename Dtype>
 void Solver<Dtype>::TestAll() {
-  for (int test_net_id = 0; test_net_id < test_nets_.size(); ++test_net_id) {
-    Test(test_net_id);
+  for (int test_net_id = 0;
+    test_net_id < test_nets_.size() && !requested_early_exit_;
+    ++test_net_id) {
+    if (param_.eval_type() == "classification") {
+      Test(test_net_id);
+    } else if (param_.eval_type() == "segmentation") {
+      TestSegmentation(test_net_id);
+    } else {
+    LOG(FATAL) << "Unknown evaluation type: " << param_.eval_type();
+    }
   }
 }
 
 template <typename Dtype>
 void Solver<Dtype>::Test(const int test_net_id) {
+  CHECK(Caffe::root_solver());
   LOG(INFO) << "Iteration " << iter_
             << ", Testing net (#" << test_net_id << ")";
   CHECK_NOTNULL(test_nets_[test_net_id].get())->
       ShareTrainedLayersWith(net_.get());
   vector<Dtype> test_score;
   vector<int> test_score_output_id;
-  vector<Blob<Dtype>*> bottom_vec;
   const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
   Dtype loss = 0;
   for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
+    SolverAction::Enum request = GetRequestedAction();
+    // Check to see if stoppage of testing/training has been requested.
+    while (request != SolverAction::NONE) {
+        if (SolverAction::SNAPSHOT == request) {
+          Snapshot();
+        } else if (SolverAction::STOP == request) {
+          requested_early_exit_ = true;
+        }
+        request = GetRequestedAction();
+    }
+    if (requested_early_exit_) {
+      // break out of test loop.
+      break;
+    }
+
     Dtype iter_loss;
     const vector<Blob<Dtype>*>& result =
-        test_net->Forward(bottom_vec, &iter_loss);
+        test_net->Forward(&iter_loss);
     if (param_.test_compute_loss()) {
       loss += iter_loss;
     }
@@ -297,6 +388,10 @@ void Solver<Dtype>::Test(const int test_net_id) {
       }
     }
   }
+  if (requested_early_exit_) {
+    LOG(INFO)     << "Test interrupted.";
+    return;
+  }
   if (param_.test_compute_loss()) {
     loss /= param_.test_iter(test_net_id);
     LOG(INFO) << "Test loss: " << loss;
@@ -313,515 +408,184 @@ void Solver<Dtype>::Test(const int test_net_id) {
                       << " = " << loss_weight * mean_score << " loss)";
     }
     LOG(INFO) << "    Test net output #" << i << ": " << output_name << " = "
-        << mean_score << loss_msg_stream.str();
+              << mean_score << loss_msg_stream.str();
   }
 }
 
-
-template <typename Dtype>
-void Solver<Dtype>::Snapshot() {
-  NetParameter net_param;
-  // For intermediate results, we will also dump the gradient values.
-  net_->ToProto(&net_param, param_.snapshot_diff());
-  string filename(param_.snapshot_prefix());
-  string model_filename, snapshot_filename;
-  const int kBufferSize = 20;
-  char iter_str_buffer[kBufferSize];
-  // Add one to iter_ to get the number of iterations that have completed.
-  snprintf(iter_str_buffer, kBufferSize, "_iter_%d", iter_ + 1);
-  filename += iter_str_buffer;
-  model_filename = filename + ".caffemodel";
-  LOG(INFO) << "Snapshotting to " << model_filename;
-  WriteProtoToBinaryFile(net_param, model_filename.c_str());
-  SolverState state;
-  SnapshotSolverState(&state);
-  state.set_iter(iter_ + 1);
-  state.set_learned_net(model_filename);
-  state.set_current_step(current_step_);
-  snapshot_filename = filename + ".solverstate";
-  LOG(INFO) << "Snapshotting solver state to " << snapshot_filename;
-  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
-}
-
-template <typename Dtype>
-void Solver<Dtype>::Restore(const char* state_file) {
-  SolverState state;
-  NetParameter net_param;
-  ReadProtoFromBinaryFile(state_file, &state);
-  if (state.has_learned_net()) {
-    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
-    net_->CopyTrainedLayersFrom(net_param);
-  }
-  iter_ = state.iter();
-  current_step_ = state.current_step();
-  RestoreSolverState(state);
-}
-
-
-// Return the current learning rate. The currently implemented learning rate
-// policies are as follows:
-//    - fixed: always return base_lr.
-//    - step: return base_lr * gamma ^ (floor(iter / step))
-//    - exp: return base_lr * gamma ^ iter
-//    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
-//    - multistep: similar to step but it allows non uniform steps defined by
-//      stepvalue
-//    - poly: the effective learning rate follows a polynomial decay, to be
-//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
-//    - sigmoid: the effective learning rate follows a sigmod decay
-//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
-//
-// where base_lr, max_iter, gamma, step, stepvalue and power are defined
-// in the solver parameter protocol buffer, and iter is the current iteration.
 template <typename Dtype>
-Dtype SGDSolver<Dtype>::GetLearningRate() {
-  Dtype rate;
-  const string& lr_policy = this->param_.lr_policy();
-  if (lr_policy == "fixed") {
-    rate = this->param_.base_lr();
-  } else if (lr_policy == "step") {
-    this->current_step_ = this->iter_ / this->param_.stepsize();
-    rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), this->current_step_);
-  } else if (lr_policy == "exp") {
-    rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
-  } else if (lr_policy == "inv") {
-    rate = this->param_.base_lr() *
-        pow(Dtype(1) + this->param_.gamma() * this->iter_,
-            - this->param_.power());
-  } else if (lr_policy == "multistep") {
-    if (this->current_step_ < this->param_.stepvalue_size() &&
-          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
-      this->current_step_++;
-      LOG(INFO) << "MultiStep Status: Iteration " <<
-      this->iter_ << ", step = " << this->current_step_;
+void Solver<Dtype>::TestSegmentation(const int test_net_id) {
+  LOG(INFO) << "Iteration " << iter_
+            << ", Testing net (#" << test_net_id << ")";
+  CHECK_NOTNULL(test_nets_[test_net_id].get())->
+      ShareTrainedLayersWith(net_.get());
+  vector<shared_ptr<Blob<Dtype> > > label_stats;
+  vector<Blob<Dtype>*> bottom_vec;
+  const shared_ptr<Net<Dtype> >& test_net = test_nets_[test_net_id];
+  Dtype loss = 0;
+  for (int i = 0; i < param_.test_iter(test_net_id); ++i) {
+    Dtype iter_loss;
+    const vector<Blob<Dtype>*>& result =
+        test_net->Forward(bottom_vec, &iter_loss);
+    if (param_.test_compute_loss()) {
+      loss += iter_loss;
+    }
+    if (result.size() == 0) {
+      continue;
+    }
+    if (i == 0) {
+      for (int j = 0; j < result.size(); ++j) {
+        shared_ptr<Blob<Dtype> > label_stat(new Blob<Dtype>());
+        label_stats.push_back(label_stat);
+        label_stat->Reshape(1, result[j]->channels(),
+                            result[j]->height(), result[j]->width());
+        // copy the result
+        caffe_copy(result[j]->count(), result[j]->cpu_data(),
+                   label_stat->mutable_cpu_data());
+      }
+    } else {
+      // add the result
+      for (int j = 0; j < result.size(); ++j) {
+        caffe_axpy(result[j]->count(), Dtype(1), result[j]->cpu_data(),
+                   label_stats[j]->mutable_cpu_data());
+      }
     }
-    rate = this->param_.base_lr() *
-        pow(this->param_.gamma(), this->current_step_);
-  } else if (lr_policy == "poly") {
-    rate = this->param_.base_lr() * pow(Dtype(1.) -
-        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
-        this->param_.power());
-  } else if (lr_policy == "sigmoid") {
-    rate = this->param_.base_lr() * (Dtype(1.) /
-        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
-          Dtype(this->param_.stepsize())))));
-  } else {
-    LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
   }
-  return rate;
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::PreSolve() {
-  // Initialize the history
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  history_.clear();
-  update_.clear();
-  temp_.clear();
-  for (int i = 0; i < net_params.size(); ++i) {
-    const vector<int>& shape = net_params[i]->shape();
-    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
-    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  if (param_.test_compute_loss()) {
+    loss /= param_.test_iter(test_net_id);
+    LOG(INFO) << "Test loss: " << loss;
   }
-}
-
-template <typename Dtype>
-void SGDSolver<Dtype>::ClipGradients() {
-  const Dtype clip_gradients = this->param_.clip_gradients();
-  if (clip_gradients < 0) { return; }
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  Dtype sumsq_diff = 0;
-  for (int i = 0; i < net_params.size(); ++i) {
-    if (this->net_->param_owners()[i] < 0) {
-      sumsq_diff += net_params[i]->sumsq_diff();
+  for (int i = 0; i < label_stats.size(); ++i) {
+    const int output_blob_index = test_net->output_blob_indices()[i];
+    const string& output_name = test_net->blob_names()[output_blob_index];
+    const Dtype* label_stat_data = label_stats[i]->cpu_data();
+    const int channels = label_stats[i]->channels();
+    // get sum infomation
+    Dtype sum_gtpred = 0;
+    Dtype sum_gt = 0;
+    for (int c = 0; c < channels; ++c) {
+      sum_gtpred += label_stat_data[c*3];
+      sum_gt += label_stat_data[c*3+1];
     }
-  }
-  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
-  if (l2norm_diff > clip_gradients) {
-    Dtype scale_factor = clip_gradients / l2norm_diff;
-    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
-        << l2norm_diff << " > " << clip_gradients << ") "
-        << "by scale factor " << scale_factor;
-    for (int i = 0; i < net_params.size(); ++i) {
-      if (this->net_->param_owners()[i] < 0) {
-        net_params[i]->scale_diff(scale_factor);
+    if (sum_gt > 0) {
+      // compute accuracy for segmentation
+      Dtype per_pixel_acc = sum_gtpred / sum_gt;
+      Dtype per_label_acc = 0;
+      Dtype iou, iou_acc = 0, weighted_iou_acc = 0;
+      int num_valid_labels = 0;
+      for (int c = 0; c < channels; ++c) {
+        if (label_stat_data[1] != 0) {
+          per_label_acc += label_stat_data[0] / label_stat_data[1];
+          ++num_valid_labels;
+        }
+        if (label_stat_data[1] + label_stat_data[2] != 0) {
+          iou = label_stat_data[0] / (label_stat_data[1] + label_stat_data[2]
+                                      - label_stat_data[0]);
+          iou_acc += iou;
+          weighted_iou_acc += iou * label_stat_data[1] / sum_gt;
+        }
+        label_stat_data += label_stats[i]->offset(0, 1);
       }
+      LOG(INFO) << "    Test net output #" << i << " " << output_name
+          << ": per_pixel_acc = " << per_pixel_acc;
+      LOG(INFO) << "    Test net output #" << i << " " << output_name
+          << ": per_label_acc = " << per_label_acc / num_valid_labels;
+      LOG(INFO) << "    Test net output #" << i << " " << output_name
+          << ": iou_acc = " << iou_acc / num_valid_labels;
+      LOG(INFO) << "    Test net output #" << i << " " << output_name
+          << ": weighted_iou_acc = " << weighted_iou_acc;
+    } else {
+      LOG(INFO) << "    Test net output #" << i << " " << output_name
+          << ": no valid labels!";
     }
   }
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = GetLearningRate();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  ClipGradients();
-  Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
-  switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                history_[param_id]->mutable_cpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-    }
+void Solver<Dtype>::Snapshot() {
+  CHECK(Caffe::root_solver());
+  string model_filename;
+  switch (param_.snapshot_format()) {
+  case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+    model_filename = SnapshotToBinaryProto();
     break;
-  case Caffe::GPU:
-#ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // Compute the value to history, and then copy them to the blob's diff.
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                history_[param_id]->mutable_gpu_data());
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          history_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-    }
-#else
-    NO_GPU;
-#endif
+  case caffe::SolverParameter_SnapshotFormat_HDF5:
+    model_filename = SnapshotToHDF5();
     break;
   default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+    LOG(FATAL) << "Unsupported snapshot format.";
   }
+
+  SnapshotSolverState(model_filename);
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::SnapshotSolverState(SolverState* state) {
-  state->clear_history();
-  for (int i = 0; i < history_.size(); ++i) {
-    // Add history
-    BlobProto* history_blob = state->add_history();
-    history_[i]->ToProto(history_blob);
+void Solver<Dtype>::CheckSnapshotWritePermissions() {
+  if (Caffe::root_solver() && param_.snapshot()) {
+    CHECK(param_.has_snapshot_prefix())
+        << "In solver params, snapshot is specified but snapshot_prefix is not";
+    string probe_filename = SnapshotFilename(".tempfile");
+    std::ofstream probe_ofs(probe_filename.c_str());
+    if (probe_ofs.good()) {
+      probe_ofs.close();
+      std::remove(probe_filename.c_str());
+    } else {
+      LOG(FATAL) << "Cannot write to snapshot prefix '"
+          << param_.snapshot_prefix() << "'.  Make sure "
+          << "that the directory exists and is writeable.";
+    }
   }
 }
 
 template <typename Dtype>
-void SGDSolver<Dtype>::RestoreSolverState(const SolverState& state) {
-  CHECK_EQ(state.history_size(), history_.size())
-      << "Incorrect length of history blobs.";
-  LOG(INFO) << "SGDSolver: restoring history";
-  for (int i = 0; i < history_.size(); ++i) {
-    history_[i]->FromProto(state.history(i));
-  }
+string Solver<Dtype>::SnapshotFilename(const string extension) {
+  return param_.snapshot_prefix() + "_iter_" + caffe::format_int(iter_)
+    + extension;
 }
 
 template <typename Dtype>
-void NesterovSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  SGDSolver<Dtype>::ClipGradients();
-  Dtype momentum = this->param_.momentum();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
-  switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->cpu_data(),
-          this->update_[param_id]->mutable_cpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->cpu_diff(), momentum,
-                this->history_[param_id]->mutable_cpu_data());
-
-      // compute udpate: step back then over step
-      caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->cpu_data(), -momentum,
-          this->update_[param_id]->mutable_cpu_data());
-
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          net_params[param_id]->mutable_cpu_diff());
-    }
-    break;
-  case Caffe::GPU:
-#ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      // save history momentum for stepping back
-      caffe_copy(net_params[param_id]->count(),
-          this->history_[param_id]->gpu_data(),
-          this->update_[param_id]->mutable_gpu_data());
-
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // update history
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-                net_params[param_id]->gpu_diff(), momentum,
-                this->history_[param_id]->mutable_gpu_data());
+string Solver<Dtype>::SnapshotToBinaryProto() {
+  string model_filename = SnapshotFilename(".caffemodel");
+  LOG(INFO) << "Snapshotting to binary proto file " << model_filename;
+  NetParameter net_param;
+  net_->ToProto(&net_param, param_.snapshot_diff());
+  WriteProtoToBinaryFile(net_param, model_filename);
+  return model_filename;
+}
 
-      // compute udpate: step back then over step
-      caffe_gpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
-          this->history_[param_id]->gpu_data(), -momentum,
-          this->update_[param_id]->mutable_gpu_data());
+template <typename Dtype>
+string Solver<Dtype>::SnapshotToHDF5() {
+  string model_filename = SnapshotFilename(".caffemodel.h5");
+  LOG(INFO) << "Snapshotting to HDF5 file " << model_filename;
+  net_->ToHDF5(model_filename, param_.snapshot_diff());
+  return model_filename;
+}
 
-      // copy
-      caffe_copy(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          net_params[param_id]->mutable_gpu_diff());
-    }
-#else
-    NO_GPU;
-#endif
-    break;
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+template <typename Dtype>
+void Solver<Dtype>::Restore(const char* state_file) {
+  CHECK(Caffe::root_solver());
+  string state_filename(state_file);
+  if (state_filename.size() >= 3 &&
+      state_filename.compare(state_filename.size() - 3, 3, ".h5") == 0) {
+    RestoreSolverStateFromHDF5(state_filename);
+  } else {
+    RestoreSolverStateFromBinaryProto(state_filename);
   }
 }
 
 template <typename Dtype>
-void AdaGradSolver<Dtype>::ComputeUpdateValue() {
-  const vector<shared_ptr<Blob<Dtype> > >& net_params = this->net_->params();
-  const vector<float>& net_params_lr = this->net_->params_lr();
-  const vector<float>& net_params_weight_decay =
-      this->net_->params_weight_decay();
-  // get the learning rate
-  Dtype rate = this->GetLearningRate();
-  Dtype delta = this->param_.delta();
-  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
-    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
-  }
-  SGDSolver<Dtype>::ClipGradients();
-  Dtype weight_decay = this->param_.weight_decay();
-  string regularization_type = this->param_.regularization_type();
-  switch (Caffe::mode()) {
-  case Caffe::CPU:
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_cpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->cpu_data(),
-              this->temp_[param_id]->mutable_cpu_data());
-          caffe_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->cpu_data(),
-              net_params[param_id]->mutable_cpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_powx(net_params[param_id]->count(),
-          net_params[param_id]->cpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_cpu_data());
-
-      // update history
-      caffe_add(net_params[param_id]->count(),
-          this->update_[param_id]->cpu_data(),
-          this->history_[param_id]->cpu_data(),
-          this->history_[param_id]->mutable_cpu_data());
-
-      // prepare update
-      caffe_powx(net_params[param_id]->count(),
-                this->history_[param_id]->cpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_cpu_data());
-
-      caffe_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_cpu_data());
-
-      caffe_div(net_params[param_id]->count(),
-                net_params[param_id]->cpu_diff(),
-                this->update_[param_id]->cpu_data(),
-                this->update_[param_id]->mutable_cpu_data());
-
-      // scale and copy
-      caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->cpu_data(), Dtype(0),
-          net_params[param_id]->mutable_cpu_diff());
-    }
-    break;
-  case Caffe::GPU:
-#ifndef CPU_ONLY
-    for (int param_id = 0; param_id < net_params.size(); ++param_id) {
-      Dtype local_rate = rate * net_params_lr[param_id];
-      Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
-
-      if (local_decay) {
-        if (regularization_type == "L2") {
-          // add weight decay
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              net_params[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else if (regularization_type == "L1") {
-          caffe_gpu_sign(net_params[param_id]->count(),
-              net_params[param_id]->gpu_data(),
-              this->temp_[param_id]->mutable_gpu_data());
-          caffe_gpu_axpy(net_params[param_id]->count(),
-              local_decay,
-              this->temp_[param_id]->gpu_data(),
-              net_params[param_id]->mutable_gpu_diff());
-        } else {
-          LOG(FATAL) << "Unknown regularization type: " << regularization_type;
-        }
-      }
-
-      // compute square of gradient in update
-      caffe_gpu_powx(net_params[param_id]->count(),
-          net_params[param_id]->gpu_diff(), Dtype(2),
-          this->update_[param_id]->mutable_gpu_data());
-
-      // update history
-      caffe_gpu_add(net_params[param_id]->count(),
-          this->update_[param_id]->gpu_data(),
-          this->history_[param_id]->gpu_data(),
-          this->history_[param_id]->mutable_gpu_data());
-
-      // prepare update
-      caffe_gpu_powx(net_params[param_id]->count(),
-                this->history_[param_id]->gpu_data(), Dtype(0.5),
-                this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_add_scalar(net_params[param_id]->count(),
-                delta, this->update_[param_id]->mutable_gpu_data());
-
-      caffe_gpu_div(net_params[param_id]->count(),
-                net_params[param_id]->gpu_diff(),
-                this->update_[param_id]->gpu_data(),
-                this->update_[param_id]->mutable_gpu_data());
-
-      // scale and copy
-      caffe_gpu_axpby(net_params[param_id]->count(), local_rate,
-          this->update_[param_id]->gpu_data(), Dtype(0),
-          net_params[param_id]->mutable_gpu_diff());
-    }
-#else
-    NO_GPU;
-#endif
-    break;
-  default:
-    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+void Solver<Dtype>::UpdateSmoothedLoss(Dtype loss, int start_iter,
+    int average_loss) {
+  if (losses_.size() < average_loss) {
+    losses_.push_back(loss);
+    int size = losses_.size();
+    smoothed_loss_ = (smoothed_loss_ * (size - 1) + loss) / size;
+  } else {
+    int idx = (iter_ - start_iter) % average_loss;
+    smoothed_loss_ += (loss - losses_[idx]) / average_loss;
+    losses_[idx] = loss;
   }
 }
 
 INSTANTIATE_CLASS(Solver);
-INSTANTIATE_CLASS(SGDSolver);
-INSTANTIATE_CLASS(NesterovSolver);
-INSTANTIATE_CLASS(AdaGradSolver);
 
 }  // namespace caffe
diff --git a/src/caffe/solvers/adadelta_solver.cpp b/src/caffe/solvers/adadelta_solver.cpp
new file mode 100644
index 00000000000..fd30f19acac
--- /dev/null
+++ b/src/caffe/solvers/adadelta_solver.cpp
@@ -0,0 +1,112 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::AdaDeltaPreSolve() {
+  // Add the extra history entries for AdaDelta after those from
+  // SGDSolver::PreSolve
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  for (int i = 0; i < net_params.size(); ++i) {
+        const vector<int>& shape = net_params[i]->shape();
+        this->history_.push_back(
+                shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+    Dtype delta, Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void AdaDeltaSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype delta = this->param_.delta();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  size_t update_history_offset = net_params.size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of gradients
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[param_id]->mutable_cpu_data());
+
+    // add delta to history to guard against dividing by zero later
+    caffe_set(net_params[param_id]->count(), delta,
+        this->temp_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[update_history_offset + param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add(net_params[param_id]->count(),
+        this->temp_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->temp_[param_id]->mutable_cpu_data());
+
+    // divide history of updates by history of gradients
+    caffe_div(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->temp_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // jointly compute the RMS of both for update and gradient history
+    caffe_powx(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // compute the update
+    caffe_mul(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+
+    // compute square of update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history of updates
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) - momentum,
+        this->update_[param_id]->cpu_data(), momentum,
+        this->history_[update_history_offset + param_id]->mutable_cpu_data());
+
+    // apply learning rate
+    caffe_cpu_scale(net_params[param_id]->count(), local_rate,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    adadelta_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        this->history_[update_history_offset + param_id]->mutable_gpu_data(),
+        momentum, delta, local_rate);
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdaDeltaSolver);
+REGISTER_SOLVER_CLASS(AdaDelta);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adadelta_solver.cu b/src/caffe/solvers/adadelta_solver.cu
new file mode 100644
index 00000000000..6c94585b89e
--- /dev/null
+++ b/src/caffe/solvers/adadelta_solver.cu
@@ -0,0 +1,30 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaDeltaUpdate(int N, Dtype* g, Dtype* h, Dtype* h2,
+    Dtype momentum, Dtype delta, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = momentum * h[i] + (1-momentum) * gi * gi;
+    gi = gi * sqrt((h2[i] + delta) / (hi + delta));
+    h2[i] = momentum * h2[i] + (1-momentum) * gi * gi;
+    g[i] = local_rate * gi;
+  }
+}
+template <typename Dtype>
+void adadelta_update_gpu(int N, Dtype* g, Dtype* h, Dtype* h2, Dtype momentum,
+    Dtype delta, Dtype local_rate) {
+  AdaDeltaUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, h2, momentum, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adadelta_update_gpu<float>(int , float*, float*, float*,
+    float, float, float);
+template void adadelta_update_gpu<double>(int, double*, double*, double*,
+    double, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adagrad_solver.cpp b/src/caffe/solvers/adagrad_solver.cpp
new file mode 100644
index 00000000000..e78eadca141
--- /dev/null
+++ b/src/caffe/solvers/adagrad_solver.cpp
@@ -0,0 +1,70 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void AdaGradSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype delta = this->param_.delta();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_add(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        this->history_[param_id]->cpu_data(),
+        this->history_[param_id]->mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+              this->history_[param_id]->cpu_data(), Dtype(0.5),
+              this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+              delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+              net_params[param_id]->cpu_diff(),
+              this->update_[param_id]->cpu_data(),
+              this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    adagrad_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(), delta, local_rate);
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdaGradSolver);
+REGISTER_SOLVER_CLASS(AdaGrad);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adagrad_solver.cu b/src/caffe/solvers/adagrad_solver.cu
new file mode 100644
index 00000000000..adefd554bbd
--- /dev/null
+++ b/src/caffe/solvers/adagrad_solver.cu
@@ -0,0 +1,26 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdaGradUpdate(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = h[i] + gi*gi;
+    g[i] = local_rate * gi / (sqrt(hi) + delta);
+  }
+}
+template <typename Dtype>
+void adagrad_update_gpu(int N, Dtype* g, Dtype* h, Dtype delta,
+    Dtype local_rate) {
+  AdaGradUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adagrad_update_gpu<float>(int, float*, float*, float, float);
+template void adagrad_update_gpu<double>(int, double*, double*, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adam_solver.cpp b/src/caffe/solvers/adam_solver.cpp
new file mode 100644
index 00000000000..4a91f00bd49
--- /dev/null
+++ b/src/caffe/solvers/adam_solver.cpp
@@ -0,0 +1,94 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+template <typename Dtype>
+void AdamSolver<Dtype>::AdamPreSolve() {
+  // Add the extra history entries for Adam after those from
+  // SGDSolver::PreSolve
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const vector<int>& shape = net_params[i]->shape();
+    this->history_.push_back(
+            shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+    Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate);
+#endif
+
+template <typename Dtype>
+void AdamSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  const Dtype beta1 = this->param_.momentum();
+  const Dtype beta2 = this->param_.momentum2();
+
+  // we create aliases for convenience
+  size_t update_history_offset = net_params.size();
+  Blob<Dtype>* val_m = this->history_[param_id].get();
+  Blob<Dtype>* val_v = this->history_[param_id + update_history_offset].get();
+  Blob<Dtype>* val_t = this->temp_[param_id].get();
+
+  const int t = this->iter_ + 1;
+  const Dtype correction = std::sqrt(Dtype(1) - pow(beta2, t)) /
+      (Dtype(1.) - pow(beta1, t));
+  const int N = net_params[param_id]->count();
+  const Dtype eps_hat = this->param_.delta();
+
+  switch (Caffe::mode()) {
+    case Caffe::CPU: {
+    // update m <- \beta_1 m_{t-1} + (1-\beta_1)g_t
+    caffe_cpu_axpby(N, Dtype(1)-beta1,
+        net_params[param_id]->cpu_diff(), beta1,
+        val_m->mutable_cpu_data());
+
+    // update v <- \beta_2 m_{t-1} + (1-\beta_2)g_t^2
+    caffe_mul(N,
+        net_params[param_id]->cpu_diff(),
+        net_params[param_id]->cpu_diff(),
+    val_t->mutable_cpu_data());
+    caffe_cpu_axpby(N, Dtype(1)-beta2,
+        val_t->cpu_data(), beta2,
+        val_v->mutable_cpu_data());
+
+    // set update
+    caffe_powx(N,
+        val_v->cpu_data(), Dtype(0.5),
+        val_t->mutable_cpu_data());
+    caffe_add_scalar(N, eps_hat, val_t->mutable_cpu_data());
+    caffe_div(N,
+        val_m->cpu_data(),
+        val_t->cpu_data(),
+        val_t->mutable_cpu_data());
+
+    caffe_cpu_scale(N, local_rate*correction,
+        val_t->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    adam_update_gpu(N, net_params[param_id]->mutable_gpu_diff(),
+        val_m->mutable_gpu_data(), val_v->mutable_gpu_data(), beta1, beta2,
+        eps_hat, local_rate*correction);
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(AdamSolver);
+REGISTER_SOLVER_CLASS(Adam);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/adam_solver.cu b/src/caffe/solvers/adam_solver.cu
new file mode 100644
index 00000000000..917ae100246
--- /dev/null
+++ b/src/caffe/solvers/adam_solver.cu
@@ -0,0 +1,29 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void AdamUpdate(int N, Dtype* g, Dtype* m, Dtype* v,
+    Dtype beta1, Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float mi = m[i] = m[i]*beta1 + gi*(1-beta1);
+    float vi = v[i] = v[i]*beta2 + gi*gi*(1-beta2);
+    g[i] = corrected_local_rate * mi / (sqrt(vi) + eps_hat);
+  }
+}
+template <typename Dtype>
+void adam_update_gpu(int N, Dtype* g, Dtype* m, Dtype* v, Dtype beta1,
+    Dtype beta2, Dtype eps_hat, Dtype corrected_local_rate) {
+  AdamUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, m, v, beta1, beta2, eps_hat, corrected_local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void adam_update_gpu<float>(int, float*, float*, float*,
+    float, float, float, float);
+template void adam_update_gpu<double>(int, double*, double*, double*,
+    double, double, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/nesterov_solver.cpp b/src/caffe/solvers/nesterov_solver.cpp
new file mode 100644
index 00000000000..23ab2d4369a
--- /dev/null
+++ b/src/caffe/solvers/nesterov_solver.cpp
@@ -0,0 +1,62 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void NesterovSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  CHECK(Caffe::root_solver());
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    // save history momentum for stepping back
+    caffe_copy(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              this->history_[param_id]->mutable_cpu_data());
+
+    // compute update: step back then over step
+    caffe_cpu_axpby(net_params[param_id]->count(), Dtype(1) + momentum,
+        this->history_[param_id]->cpu_data(), -momentum,
+        this->update_[param_id]->mutable_cpu_data());
+
+    // copy
+    caffe_copy(net_params[param_id]->count(),
+        this->update_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    nesterov_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        momentum, local_rate);
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(NesterovSolver);
+REGISTER_SOLVER_CLASS(Nesterov);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/nesterov_solver.cu b/src/caffe/solvers/nesterov_solver.cu
new file mode 100644
index 00000000000..57a456b8252
--- /dev/null
+++ b/src/caffe/solvers/nesterov_solver.cu
@@ -0,0 +1,27 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void NesterovUpdate(int N, Dtype* g, Dtype* h,
+    Dtype momentum, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float hi = h[i];
+    float hi_new = h[i] = momentum * hi + local_rate * g[i];
+    g[i] = (1+momentum) * hi_new - momentum * hi;
+  }
+}
+template <typename Dtype>
+void nesterov_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate) {
+  NesterovUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, momentum, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void nesterov_update_gpu<float>(int, float*, float*, float, float);
+template void nesterov_update_gpu<double>(int, double*, double*, double,
+    double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/rmsprop_solver.cpp b/src/caffe/solvers/rmsprop_solver.cpp
new file mode 100644
index 00000000000..3251ee423a7
--- /dev/null
+++ b/src/caffe/solvers/rmsprop_solver.cpp
@@ -0,0 +1,70 @@
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+
+namespace caffe {
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+    Dtype delta, Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void RMSPropSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+
+  // get the learning rate
+  Dtype delta = this->param_.delta();
+  Dtype rms_decay = this->param_.rms_decay();
+  Dtype local_rate = rate * net_params_lr[param_id];
+
+  switch (Caffe::mode()) {
+  case Caffe::CPU:
+    // compute square of gradient in update
+    caffe_powx(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), Dtype(2),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // update history
+    caffe_cpu_axpby(net_params[param_id] -> count(),
+        Dtype(1-rms_decay), this->update_[param_id]->cpu_data(),
+        rms_decay, this->history_[param_id]-> mutable_cpu_data());
+
+    // prepare update
+    caffe_powx(net_params[param_id]->count(),
+        this->history_[param_id]->cpu_data(), Dtype(0.5),
+        this->update_[param_id]->mutable_cpu_data());
+
+    caffe_add_scalar(net_params[param_id]->count(),
+        delta, this->update_[param_id]->mutable_cpu_data());
+
+    caffe_div(net_params[param_id]->count(),
+        net_params[param_id]->cpu_diff(), this->update_[param_id]->cpu_data(),
+        this->update_[param_id]->mutable_cpu_data());
+
+    // scale and copy
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+        this->update_[param_id]->cpu_data(), Dtype(0),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  case Caffe::GPU:
+#ifndef CPU_ONLY
+    rmsprop_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        this->history_[param_id]->mutable_gpu_data(),
+        rms_decay, delta, local_rate);
+#else
+    NO_GPU;
+#endif
+    break;
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+INSTANTIATE_CLASS(RMSPropSolver);
+REGISTER_SOLVER_CLASS(RMSProp);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/rmsprop_solver.cu b/src/caffe/solvers/rmsprop_solver.cu
new file mode 100644
index 00000000000..c5ffd329d77
--- /dev/null
+++ b/src/caffe/solvers/rmsprop_solver.cu
@@ -0,0 +1,28 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void RMSPropUpdate(int N, Dtype* g, Dtype* h,
+    Dtype rms_decay, Dtype delta, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    float gi = g[i];
+    float hi = h[i] = rms_decay*h[i] + (1-rms_decay)*gi*gi;
+    g[i] = local_rate * g[i] / (sqrt(hi) + delta);
+  }
+}
+template <typename Dtype>
+void rmsprop_update_gpu(int N, Dtype* g, Dtype* h, Dtype rms_decay,
+    Dtype delta, Dtype local_rate) {
+  RMSPropUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, rms_decay, delta, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void rmsprop_update_gpu<float>(int, float*, float*, float, float,
+    float);
+template void rmsprop_update_gpu<double>(int, double*, double*, double, double,
+    double);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/sgd_solver.cpp b/src/caffe/solvers/sgd_solver.cpp
new file mode 100644
index 00000000000..f30f316d1a0
--- /dev/null
+++ b/src/caffe/solvers/sgd_solver.cpp
@@ -0,0 +1,352 @@
+#include <string>
+#include <vector>
+
+#include "caffe/sgd_solvers.hpp"
+#include "caffe/util/hdf5.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+namespace caffe {
+
+// Return the current learning rate. The currently implemented learning rate
+// policies are as follows:
+//    - fixed: always return base_lr.
+//    - step: return base_lr * gamma ^ (floor(iter / step))
+//    - exp: return base_lr * gamma ^ iter
+//    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
+//    - multistep: similar to step but it allows non uniform steps defined by
+//      stepvalue
+//    - poly: the effective learning rate follows a polynomial decay, to be
+//      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
+//    - sigmoid: the effective learning rate follows a sigmod decay
+//      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
+//
+// where base_lr, max_iter, gamma, step, stepvalue and power are defined
+// in the solver parameter protocol buffer, and iter is the current iteration.
+template <typename Dtype>
+Dtype SGDSolver<Dtype>::GetLearningRate() {
+  Dtype rate;
+  const string& lr_policy = this->param_.lr_policy();
+  if (lr_policy == "fixed") {
+    rate = this->param_.base_lr();
+  } else if (lr_policy == "step") {
+    this->current_step_ = this->iter_ / this->param_.stepsize();
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "exp") {
+    rate = this->param_.base_lr() * pow(this->param_.gamma(), this->iter_);
+  } else if (lr_policy == "inv") {
+    rate = this->param_.base_lr() *
+        pow(Dtype(1) + this->param_.gamma() * this->iter_,
+            - this->param_.power());
+  } else if (lr_policy == "multistep") {
+    if (this->current_step_ < this->param_.stepvalue_size() &&
+          this->iter_ >= this->param_.stepvalue(this->current_step_)) {
+      this->current_step_++;
+      LOG(INFO) << "MultiStep Status: Iteration " <<
+      this->iter_ << ", step = " << this->current_step_;
+    }
+    rate = this->param_.base_lr() *
+        pow(this->param_.gamma(), this->current_step_);
+  } else if (lr_policy == "poly") {
+    rate = this->param_.base_lr() * pow(Dtype(1.) -
+        (Dtype(this->iter_) / Dtype(this->param_.max_iter())),
+        this->param_.power());
+  } else if (lr_policy == "sigmoid") {
+    rate = this->param_.base_lr() * (Dtype(1.) /
+        (Dtype(1.) + exp(-this->param_.gamma() * (Dtype(this->iter_) -
+          Dtype(this->param_.stepsize())))));
+  } else {
+    LOG(FATAL) << "Unknown learning rate policy: " << lr_policy;
+  }
+  return rate;
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::PreSolve() {
+  // Initialize the history
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  history_.clear();
+  update_.clear();
+  temp_.clear();
+  for (int i = 0; i < net_params.size(); ++i) {
+    const vector<int>& shape = net_params[i]->shape();
+    history_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    update_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+    temp_.push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ClipGradients() {
+  const Dtype clip_gradients = this->param_.clip_gradients();
+  if (clip_gradients < 0) { return; }
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  Dtype sumsq_diff = 0;
+  for (int i = 0; i < net_params.size(); ++i) {
+    sumsq_diff += net_params[i]->sumsq_diff();
+  }
+  const Dtype l2norm_diff = std::sqrt(sumsq_diff);
+  if (l2norm_diff > clip_gradients) {
+    Dtype scale_factor = clip_gradients / l2norm_diff;
+    LOG(INFO) << "Gradient clipping: scaling down gradients (L2 norm "
+        << l2norm_diff << " > " << clip_gradients << ") "
+        << "by scale factor " << scale_factor;
+    for (int i = 0; i < net_params.size(); ++i) {
+      net_params[i]->scale_diff(scale_factor);
+    }
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ApplyUpdate() {
+  CHECK(Caffe::root_solver());
+  Dtype rate = GetLearningRate();
+  if (this->param_.display() && this->iter_ % this->param_.display() == 0) {
+    LOG(INFO) << "Iteration " << this->iter_ << ", lr = " << rate;
+  }
+  ClipGradients();
+  for (int param_id = 0; param_id < this->net_->learnable_params().size();
+       ++param_id) {
+    Normalize(param_id);
+    Regularize(param_id);
+    ComputeUpdateValue(param_id, rate);
+  }
+  this->net_->Update();
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Normalize(int param_id) {
+  if (this->param_.iter_size() == 1) { return; }
+  // Scale gradient to counterbalance accumulation.
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const Dtype accum_normalization = Dtype(1.) / this->param_.iter_size();
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    caffe_gpu_scal(net_params[param_id]->count(), accum_normalization,
+        net_params[param_id]->mutable_gpu_diff());
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::Regularize(int param_id) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_weight_decay =
+      this->net_->params_weight_decay();
+  Dtype weight_decay = this->param_.weight_decay();
+  string regularization_type = this->param_.regularization_type();
+  Dtype local_decay = weight_decay * net_params_weight_decay[param_id];
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_cpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->cpu_data(),
+            temp_[param_id]->mutable_cpu_data());
+        caffe_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->cpu_data(),
+            net_params[param_id]->mutable_cpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+      }
+    }
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    if (local_decay) {
+      if (regularization_type == "L2") {
+        // add weight decay
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            net_params[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else if (regularization_type == "L1") {
+        caffe_gpu_sign(net_params[param_id]->count(),
+            net_params[param_id]->gpu_data(),
+            temp_[param_id]->mutable_gpu_data());
+        caffe_gpu_axpy(net_params[param_id]->count(),
+            local_decay,
+            temp_[param_id]->gpu_data(),
+            net_params[param_id]->mutable_gpu_diff());
+      } else {
+        LOG(FATAL) << "Unknown regularization type: " << regularization_type;
+      }
+    }
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+#ifndef CPU_ONLY
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate);
+#endif
+
+template <typename Dtype>
+void SGDSolver<Dtype>::ComputeUpdateValue(int param_id, Dtype rate) {
+  const vector<Blob<Dtype>*>& net_params = this->net_->learnable_params();
+  const vector<float>& net_params_lr = this->net_->params_lr();
+  Dtype momentum = this->param_.momentum();
+  Dtype local_rate = rate * net_params_lr[param_id];
+  // Compute the update to history, then copy it to the parameter diff.
+  switch (Caffe::mode()) {
+  case Caffe::CPU: {
+    caffe_cpu_axpby(net_params[param_id]->count(), local_rate,
+              net_params[param_id]->cpu_diff(), momentum,
+              history_[param_id]->mutable_cpu_data());
+    caffe_copy(net_params[param_id]->count(),
+        history_[param_id]->cpu_data(),
+        net_params[param_id]->mutable_cpu_diff());
+    break;
+  }
+  case Caffe::GPU: {
+#ifndef CPU_ONLY
+    sgd_update_gpu(net_params[param_id]->count(),
+        net_params[param_id]->mutable_gpu_diff(),
+        history_[param_id]->mutable_gpu_data(),
+        momentum, local_rate);
+#else
+    NO_GPU;
+#endif
+    break;
+  }
+  default:
+    LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverState(const string& model_filename) {
+  switch (this->param_.snapshot_format()) {
+    case caffe::SolverParameter_SnapshotFormat_BINARYPROTO:
+      SnapshotSolverStateToBinaryProto(model_filename);
+      break;
+    case caffe::SolverParameter_SnapshotFormat_HDF5:
+      SnapshotSolverStateToHDF5(model_filename);
+      break;
+    default:
+      LOG(FATAL) << "Unsupported snapshot format.";
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToBinaryProto(
+    const string& model_filename) {
+  SolverState state;
+  state.set_iter(this->iter_);
+  state.set_learned_net(model_filename);
+  state.set_current_step(this->current_step_);
+  state.clear_history();
+  for (int i = 0; i < history_.size(); ++i) {
+    // Add history
+    BlobProto* history_blob = state.add_history();
+    history_[i]->ToProto(history_blob);
+  }
+  string snapshot_filename = Solver<Dtype>::SnapshotFilename(".solverstate");
+  LOG(INFO)
+    << "Snapshotting solver state to binary proto file " << snapshot_filename;
+  WriteProtoToBinaryFile(state, snapshot_filename.c_str());
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::SnapshotSolverStateToHDF5(
+    const string& model_filename) {
+  string snapshot_filename =
+      Solver<Dtype>::SnapshotFilename(".solverstate.h5");
+  LOG(INFO) << "Snapshotting solver state to HDF5 file " << snapshot_filename;
+  hid_t file_hid = H5Fcreate(snapshot_filename.c_str(), H5F_ACC_TRUNC,
+      H5P_DEFAULT, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0)
+      << "Couldn't open " << snapshot_filename << " to save solver state.";
+  hdf5_save_int(file_hid, "iter", this->iter_);
+  hdf5_save_string(file_hid, "learned_net", model_filename);
+  hdf5_save_int(file_hid, "current_step", this->current_step_);
+  hid_t history_hid = H5Gcreate2(file_hid, "history", H5P_DEFAULT, H5P_DEFAULT,
+      H5P_DEFAULT);
+  CHECK_GE(history_hid, 0)
+      << "Error saving solver state to " << snapshot_filename << ".";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_save_nd_dataset<Dtype>(history_hid, oss.str(), *history_[i]);
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromBinaryProto(
+    const string& state_file) {
+  SolverState state;
+  ReadProtoFromBinaryFile(state_file, &state);
+  this->iter_ = state.iter();
+  if (state.has_learned_net()) {
+    NetParameter net_param;
+    ReadNetParamsFromBinaryFileOrDie(state.learned_net().c_str(), &net_param);
+    this->net_->CopyTrainedLayersFrom(net_param);
+  }
+  this->current_step_ = state.current_step();
+  CHECK_EQ(state.history_size(), history_.size())
+      << "Incorrect length of history blobs.";
+  LOG(INFO) << "SGDSolver: restoring history";
+  for (int i = 0; i < history_.size(); ++i) {
+    history_[i]->FromProto(state.history(i));
+  }
+}
+
+template <typename Dtype>
+void SGDSolver<Dtype>::RestoreSolverStateFromHDF5(const string& state_file) {
+  hid_t file_hid = H5Fopen(state_file.c_str(), H5F_ACC_RDONLY, H5P_DEFAULT);
+  CHECK_GE(file_hid, 0) << "Couldn't open solver state file " << state_file;
+  this->iter_ = hdf5_load_int(file_hid, "iter");
+  if (H5LTfind_dataset(file_hid, "learned_net")) {
+    string learned_net = hdf5_load_string(file_hid, "learned_net");
+    this->net_->CopyTrainedLayersFrom(learned_net);
+  }
+  this->current_step_ = hdf5_load_int(file_hid, "current_step");
+  hid_t history_hid = H5Gopen2(file_hid, "history", H5P_DEFAULT);
+  CHECK_GE(history_hid, 0) << "Error reading history from " << state_file;
+  int state_history_size = hdf5_get_num_links(history_hid);
+  CHECK_EQ(state_history_size, history_.size())
+      << "Incorrect length of history blobs.";
+  for (int i = 0; i < history_.size(); ++i) {
+    ostringstream oss;
+    oss << i;
+    hdf5_load_nd_dataset<Dtype>(history_hid, oss.str().c_str(), 0,
+                                kMaxBlobAxes, history_[i].get());
+  }
+  H5Gclose(history_hid);
+  H5Fclose(file_hid);
+}
+
+INSTANTIATE_CLASS(SGDSolver);
+REGISTER_SOLVER_CLASS(SGD);
+
+}  // namespace caffe
diff --git a/src/caffe/solvers/sgd_solver.cu b/src/caffe/solvers/sgd_solver.cu
new file mode 100644
index 00000000000..e5410352140
--- /dev/null
+++ b/src/caffe/solvers/sgd_solver.cu
@@ -0,0 +1,24 @@
+#include "caffe/util/math_functions.hpp"
+
+
+namespace caffe {
+
+template <typename Dtype>
+__global__ void SGDUpdate(int N, Dtype* g, Dtype* h,
+    Dtype momentum, Dtype local_rate) {
+  CUDA_KERNEL_LOOP(i, N) {
+    g[i] = h[i] = momentum*h[i] + local_rate*g[i];
+  }
+}
+template <typename Dtype>
+void sgd_update_gpu(int N, Dtype* g, Dtype* h, Dtype momentum,
+    Dtype local_rate) {
+  SGDUpdate<Dtype>  // NOLINT_NEXT_LINE(whitespace/operators)
+      <<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, g, h, momentum, local_rate);
+  CUDA_POST_KERNEL_CHECK;
+}
+template void sgd_update_gpu<float>(int, float*, float*, float, float);
+template void sgd_update_gpu<double>(int, double*, double*, double, double);
+
+}  // namespace caffe
diff --git a/src/caffe/syncedmem.cpp b/src/caffe/syncedmem.cpp
index 7617ccfb27f..4d3564172ab 100644
--- a/src/caffe/syncedmem.cpp
+++ b/src/caffe/syncedmem.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "caffe/common.hpp"
 #include "caffe/syncedmem.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -8,12 +6,18 @@ namespace caffe {
 
 SyncedMemory::~SyncedMemory() {
   if (cpu_ptr_ && own_cpu_data_) {
-    CaffeFreeHost(cpu_ptr_);
+    CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
 
 #ifndef CPU_ONLY
-  if (gpu_ptr_) {
+  if (gpu_ptr_ && own_gpu_data_) {
+    int initial_device;
+    cudaGetDevice(&initial_device);
+    if (gpu_device_ != -1) {
+      CUDA_CHECK(cudaSetDevice(gpu_device_));
+    }
     CUDA_CHECK(cudaFree(gpu_ptr_));
+    cudaSetDevice(initial_device);
   }
 #endif  // CPU_ONLY
 }
@@ -21,7 +25,7 @@ SyncedMemory::~SyncedMemory() {
 inline void SyncedMemory::to_cpu() {
   switch (head_) {
   case UNINITIALIZED:
-    CaffeMallocHost(&cpu_ptr_, size_);
+    CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
     caffe_memset(size_, 0, cpu_ptr_);
     head_ = HEAD_AT_CPU;
     own_cpu_data_ = true;
@@ -29,7 +33,7 @@ inline void SyncedMemory::to_cpu() {
   case HEAD_AT_GPU:
 #ifndef CPU_ONLY
     if (cpu_ptr_ == NULL) {
-      CaffeMallocHost(&cpu_ptr_, size_);
+      CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
       own_cpu_data_ = true;
     }
     caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
@@ -48,13 +52,17 @@ inline void SyncedMemory::to_gpu() {
 #ifndef CPU_ONLY
   switch (head_) {
   case UNINITIALIZED:
+    CUDA_CHECK(cudaGetDevice(&gpu_device_));
     CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
     caffe_gpu_memset(size_, 0, gpu_ptr_);
     head_ = HEAD_AT_GPU;
+    own_gpu_data_ = true;
     break;
   case HEAD_AT_CPU:
     if (gpu_ptr_ == NULL) {
+      CUDA_CHECK(cudaGetDevice(&gpu_device_));
       CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+      own_gpu_data_ = true;
     }
     caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
     head_ = SYNCED;
@@ -76,7 +84,7 @@ const void* SyncedMemory::cpu_data() {
 void SyncedMemory::set_cpu_data(void* data) {
   CHECK(data);
   if (own_cpu_data_) {
-    CaffeFreeHost(cpu_ptr_);
+    CaffeFreeHost(cpu_ptr_, cpu_malloc_use_cuda_);
   }
   cpu_ptr_ = data;
   head_ = HEAD_AT_CPU;
@@ -87,6 +95,27 @@ const void* SyncedMemory::gpu_data() {
 #ifndef CPU_ONLY
   to_gpu();
   return (const void*)gpu_ptr_;
+#else
+  NO_GPU;
+  return NULL;
+#endif
+}
+
+void SyncedMemory::set_gpu_data(void* data) {
+#ifndef CPU_ONLY
+  CHECK(data);
+  if (own_gpu_data_) {
+    int initial_device;
+    cudaGetDevice(&initial_device);
+    if (gpu_device_ != -1) {
+      CUDA_CHECK(cudaSetDevice(gpu_device_));
+    }
+    CUDA_CHECK(cudaFree(gpu_ptr_));
+    cudaSetDevice(initial_device);
+  }
+  gpu_ptr_ = data;
+  head_ = HEAD_AT_GPU;
+  own_gpu_data_ = false;
 #else
   NO_GPU;
 #endif
@@ -105,9 +134,24 @@ void* SyncedMemory::mutable_gpu_data() {
   return gpu_ptr_;
 #else
   NO_GPU;
+  return NULL;
 #endif
 }
 
+#ifndef CPU_ONLY
+void SyncedMemory::async_gpu_push(const cudaStream_t& stream) {
+  CHECK(head_ == HEAD_AT_CPU);
+  if (gpu_ptr_ == NULL) {
+    CUDA_CHECK(cudaGetDevice(&gpu_device_));
+    CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
+    own_gpu_data_ = true;
+  }
+  const cudaMemcpyKind put = cudaMemcpyHostToDevice;
+  CUDA_CHECK(cudaMemcpyAsync(gpu_ptr_, cpu_ptr_, size_, put, stream));
+  // Assume caller will synchronize on the stream before use
+  head_ = SYNCED;
+}
+#endif
 
 }  // namespace caffe
 
diff --git a/src/caffe/test/test_accuracy_layer.cpp b/src/caffe/test/test_accuracy_layer.cpp
index 6cbf51df45e..6fe808bd5c5 100644
--- a/src/caffe/test/test_accuracy_layer.cpp
+++ b/src/caffe/test/test_accuracy_layer.cpp
@@ -1,6 +1,4 @@
 #include <cfloat>
-#include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,20 +6,21 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/accuracy_layer.hpp"
 #include "caffe/util/rng.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-class AccuracyLayerTest : public ::testing::Test {
+class AccuracyLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   AccuracyLayerTest()
       : blob_bottom_data_(new Blob<Dtype>()),
         blob_bottom_label_(new Blob<Dtype>()),
         blob_top_(new Blob<Dtype>()),
+        blob_top_per_class_(new Blob<Dtype>()),
         top_k_(3) {
     vector<int> shape(2);
     shape[0] = 100;
@@ -34,6 +33,8 @@ class AccuracyLayerTest : public ::testing::Test {
     blob_bottom_vec_.push_back(blob_bottom_data_);
     blob_bottom_vec_.push_back(blob_bottom_label_);
     blob_top_vec_.push_back(blob_top_);
+    blob_top_per_class_vec_.push_back(blob_top_);
+    blob_top_per_class_vec_.push_back(blob_top_per_class_);
   }
 
   virtual void FillBottoms() {
@@ -56,12 +57,15 @@ class AccuracyLayerTest : public ::testing::Test {
     delete blob_bottom_data_;
     delete blob_bottom_label_;
     delete blob_top_;
+    delete blob_top_per_class_;
   }
   Blob<Dtype>* const blob_bottom_data_;
   Blob<Dtype>* const blob_bottom_label_;
   Blob<Dtype>* const blob_top_;
+  Blob<Dtype>* const blob_top_per_class_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
+  vector<Blob<Dtype>*> blob_top_per_class_vec_;
   int top_k_;
 };
 
@@ -90,9 +94,22 @@ TYPED_TEST(AccuracyLayerTest, TestSetupTopK) {
   EXPECT_EQ(this->blob_top_->width(), 1);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestSetupOutputPerClass) {
+  LayerParameter layer_param;
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 1);
+  EXPECT_EQ(this->blob_top_->channels(), 1);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->num(), 10);
+  EXPECT_EQ(this->blob_top_per_class_->channels(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->height(), 1);
+  EXPECT_EQ(this->blob_top_per_class_->width(), 1);
+}
+
 TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   AccuracyLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -118,7 +135,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPU) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
-  Caffe::set_mode(Caffe::CPU);
   this->blob_bottom_data_->Reshape(2, 10, 4, 5);
   vector<int> label_shape(3);
   label_shape[0] = 2; label_shape[1] = 4; label_shape[2] = 5;
@@ -162,7 +178,6 @@ TYPED_TEST(AccuracyLayerTest, TestForwardWithSpatialAxes) {
 }
 
 TYPED_TEST(AccuracyLayerTest, TestForwardIgnoreLabel) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter layer_param;
   const TypeParam kIgnoreLabelValue = -1;
   layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
@@ -231,4 +246,91 @@ TYPED_TEST(AccuracyLayerTest, TestForwardCPUTopK) {
               num_correct_labels / 100.0, 1e-4);
 }
 
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClass) {
+  LayerParameter layer_param;
+  AccuracyLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  const int num_class = this->blob_top_per_class_->num();
+  vector<int> correct_per_class(num_class, 0);
+  vector<int> num_per_class(num_class, 0);
+  for (int i = 0; i < 100; ++i) {
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+      ++correct_per_class[max_id];
+    }
+  }
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / 100.0, 1e-4);
+  for (int i = 0; i < num_class; ++i) {
+    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
+    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                accuracy_per_class, 1e-4);
+  }
+}
+
+
+TYPED_TEST(AccuracyLayerTest, TestForwardCPUPerClassWithIgnoreLabel) {
+  LayerParameter layer_param;
+  const TypeParam kIgnoreLabelValue = -1;
+  layer_param.mutable_accuracy_param()->set_ignore_label(kIgnoreLabelValue);
+  AccuracyLayer<TypeParam> layer(layer_param);
+  // Manually set some labels to the ignore label value (-1).
+  this->blob_bottom_label_->mutable_cpu_data()[2] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[5] = kIgnoreLabelValue;
+  this->blob_bottom_label_->mutable_cpu_data()[32] = kIgnoreLabelValue;
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_per_class_vec_);
+
+  TypeParam max_value;
+  int max_id;
+  int num_correct_labels = 0;
+  const int num_class = this->blob_top_per_class_->num();
+  vector<int> correct_per_class(num_class, 0);
+  vector<int> num_per_class(num_class, 0);
+  int count = 0;
+  for (int i = 0; i < 100; ++i) {
+    if (kIgnoreLabelValue == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      continue;
+    }
+    ++count;
+    max_value = -FLT_MAX;
+    max_id = 0;
+    for (int j = 0; j < 10; ++j) {
+      if (this->blob_bottom_data_->data_at(i, j, 0, 0) > max_value) {
+        max_value = this->blob_bottom_data_->data_at(i, j, 0, 0);
+        max_id = j;
+      }
+    }
+    ++num_per_class[this->blob_bottom_label_->data_at(i, 0, 0, 0)];
+    if (max_id == this->blob_bottom_label_->data_at(i, 0, 0, 0)) {
+      ++num_correct_labels;
+      ++correct_per_class[max_id];
+    }
+  }
+  EXPECT_EQ(count, 97);
+  EXPECT_NEAR(this->blob_top_->data_at(0, 0, 0, 0),
+              num_correct_labels / TypeParam(count), 1e-4);
+  for (int i = 0; i < 10; ++i) {
+    TypeParam accuracy_per_class = (num_per_class[i] > 0 ?
+       static_cast<TypeParam>(correct_per_class[i]) / num_per_class[i] : 0);
+    EXPECT_NEAR(this->blob_top_per_class_->data_at(i, 0, 0, 0),
+                accuracy_per_class, 1e-4);
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_argmax_layer.cpp b/src/caffe/test/test_argmax_layer.cpp
index 3487d42f21e..472e6652239 100644
--- a/src/caffe/test/test_argmax_layer.cpp
+++ b/src/caffe/test/test_argmax_layer.cpp
@@ -6,20 +6,19 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/argmax_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-class ArgMaxLayerTest : public ::testing::Test {
+class ArgMaxLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   ArgMaxLayerTest()
-      : blob_bottom_(new Blob<Dtype>(10, 20, 1, 1)),
+      : blob_bottom_(new Blob<Dtype>(10, 10, 20, 20)),
         blob_top_(new Blob<Dtype>()),
         top_k_(5) {
-    Caffe::set_mode(Caffe::CPU);
     Caffe::set_random_seed(1701);
     // fill the values
     FillerParameter filler_param;
@@ -56,6 +55,43 @@ TYPED_TEST(ArgMaxLayerTest, TestSetupMaxVal) {
   EXPECT_EQ(this->blob_top_->channels(), 2);
 }
 
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxis) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(0);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(2), this->blob_bottom_->shape(2));
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisNegativeIndexing) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(-2);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+  EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestSetupAxisMaxVal) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(2);
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->shape(0), this->blob_bottom_->shape(0));
+  EXPECT_EQ(this->blob_top_->shape(1), this->blob_bottom_->shape(1));
+  EXPECT_EQ(this->blob_top_->shape(2), argmax_param->top_k());
+  EXPECT_EQ(this->blob_top_->shape(3), this->blob_bottom_->shape(3));
+}
+
 TYPED_TEST(ArgMaxLayerTest, TestCPU) {
   LayerParameter layer_param;
   ArgMaxLayer<TypeParam> layer(layer_param);
@@ -113,6 +149,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
@@ -122,10 +159,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUTopK) {
     EXPECT_LE(this->blob_top_->data_at(i, 0, 0, 0), dim);
     for (int j = 0; j < this->top_k_; ++j) {
       max_ind = this->blob_top_->data_at(i, 0, j, 0);
-      max_val = this->blob_bottom_->data_at(i, max_ind, 0, 0);
+      max_val = bottom_data[i * dim + max_ind];
       int count = 0;
       for (int k = 0; k < dim; ++k) {
-        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+        if (bottom_data[i * dim + k] > max_val) {
           ++count;
         }
       }
@@ -143,6 +180,7 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
   // Now, check values
+  const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   int max_ind;
   TypeParam max_val;
   int num = this->blob_bottom_->num();
@@ -153,10 +191,10 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
     for (int j = 0; j < this->top_k_; ++j) {
       max_ind = this->blob_top_->data_at(i, 0, j, 0);
       max_val = this->blob_top_->data_at(i, 1, j, 0);
-      EXPECT_EQ(this->blob_bottom_->data_at(i, max_ind, 0, 0), max_val);
+      EXPECT_EQ(bottom_data[i * dim + max_ind], max_val);
       int count = 0;
       for (int k = 0; k < dim; ++k) {
-        if (this->blob_bottom_->data_at(i, k, 0, 0) > max_val) {
+        if (bottom_data[i * dim + k] > max_val) {
           ++count;
         }
       }
@@ -165,5 +203,93 @@ TYPED_TEST(ArgMaxLayerTest, TestCPUMaxValTopK) {
   }
 }
 
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxis) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(0);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[1]; ++i) {
+    for (int j = 0; j < shape[2]; ++j) {
+      for (int k = 0; k < shape[3]; ++k) {
+        max_ind = this->blob_top_->data_at(0, i, j, k);
+        max_val = this->blob_bottom_->data_at(max_ind, i, j, k);
+        EXPECT_GE(max_ind, 0);
+        EXPECT_LE(max_ind, shape[0]);
+        for (int l = 0; l < shape[0]; ++l) {
+          EXPECT_LE(this->blob_bottom_->data_at(l, i, j, k), max_val);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(2);
+  argmax_param->set_top_k(this->top_k_);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  int max_ind;
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[0]; ++i) {
+    for (int j = 0; j < shape[1]; ++j) {
+      for (int k = 0; k < shape[3]; ++k) {
+        for (int m = 0; m < this->top_k_; ++m) {
+          max_ind = this->blob_top_->data_at(i, j, m, k);
+          max_val = this->blob_bottom_->data_at(i, j, max_ind, k);
+          EXPECT_GE(max_ind, 0);
+          EXPECT_LE(max_ind, shape[2]);
+          int count = 0;
+          for (int l = 0; l < shape[2]; ++l) {
+            if (this->blob_bottom_->data_at(i, j, l, k) > max_val) {
+              ++count;
+            }
+          }
+          EXPECT_EQ(m, count);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ArgMaxLayerTest, TestCPUAxisMaxValTopK) {
+  LayerParameter layer_param;
+  ArgMaxParameter* argmax_param = layer_param.mutable_argmax_param();
+  argmax_param->set_axis(-1);
+  argmax_param->set_top_k(this->top_k_);
+  argmax_param->set_out_max_val(true);
+  ArgMaxLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  TypeParam max_val;
+  std::vector<int> shape = this->blob_bottom_->shape();
+  for (int i = 0; i < shape[0]; ++i) {
+    for (int j = 0; j < shape[1]; ++j) {
+      for (int k = 0; k < shape[2]; ++k) {
+        for (int m = 0; m < this->top_k_; ++m) {
+          max_val = this->blob_top_->data_at(i, j, k, m);
+          int count = 0;
+          for (int l = 0; l < shape[3]; ++l) {
+            if (this->blob_bottom_->data_at(i, j, k, l) > max_val) {
+              ++count;
+            }
+          }
+          EXPECT_EQ(m, count);
+        }
+      }
+    }
+  }
+}
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_batch_norm_layer.cpp b/src/caffe/test/test_batch_norm_layer.cpp
new file mode 100644
index 00000000000..936b93a1756
--- /dev/null
+++ b/src/caffe/test/test_batch_norm_layer.cpp
@@ -0,0 +1,133 @@
+#include <algorithm>
+#include <cstring>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/batch_norm_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+#define BATCH_SIZE 2
+#define INPUT_DATA_SIZE 3
+
+namespace caffe {
+
+  template <typename TypeParam>
+  class BatchNormLayerTest : public MultiDeviceTest<TypeParam> {
+    typedef typename TypeParam::Dtype Dtype;
+   protected:
+    BatchNormLayerTest()
+        : blob_bottom_(new Blob<Dtype>(5, 2, 3, 4)),
+          blob_top_(new Blob<Dtype>()) {
+      // fill the values
+      FillerParameter filler_param;
+      GaussianFiller<Dtype> filler(filler_param);
+      filler.Fill(this->blob_bottom_);
+      blob_bottom_vec_.push_back(blob_bottom_);
+      blob_top_vec_.push_back(blob_top_);
+    }
+    virtual ~BatchNormLayerTest() { delete blob_bottom_; delete blob_top_; }
+    Blob<Dtype>* const blob_bottom_;
+    Blob<Dtype>* const blob_top_;
+    vector<Blob<Dtype>*> blob_bottom_vec_;
+    vector<Blob<Dtype>*> blob_top_vec_;
+  };
+
+  TYPED_TEST_CASE(BatchNormLayerTest, TestDtypesAndDevices);
+
+  TYPED_TEST(BatchNormLayerTest, TestForward) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+
+    // Test mean
+    int num = this->blob_bottom_->num();
+    int channels = this->blob_bottom_->channels();
+    int height = this->blob_bottom_->height();
+    int width = this->blob_bottom_->width();
+
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int i = 0; i < num; ++i) {
+        for ( int k = 0; k < height; ++k ) {
+          for ( int l = 0; l < width; ++l ) {
+            Dtype data = this->blob_top_->data_at(i, j, k, l);
+            sum += data;
+            var += data * data;
+          }
+        }
+      }
+      sum /= height * width * num;
+      var /= height * width * num;
+
+      const Dtype kErrorBound = 0.001;
+      // expect zero mean
+      EXPECT_NEAR(0, sum, kErrorBound);
+      // expect unit variance
+      EXPECT_NEAR(1, var, kErrorBound);
+    }
+  }
+
+  TYPED_TEST(BatchNormLayerTest, TestForwardInplace) {
+    typedef typename TypeParam::Dtype Dtype;
+    Blob<Dtype> blob_inplace(5, 2, 3, 4);
+    vector<Blob<Dtype>*> blob_bottom_vec;
+    vector<Blob<Dtype>*> blob_top_vec;
+    LayerParameter layer_param;
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(&blob_inplace);
+    blob_bottom_vec.push_back(&blob_inplace);
+    blob_top_vec.push_back(&blob_inplace);
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec, blob_top_vec);
+    layer.Forward(blob_bottom_vec, blob_top_vec);
+
+    // Test mean
+    int num = blob_inplace.num();
+    int channels = blob_inplace.channels();
+    int height = blob_inplace.height();
+    int width = blob_inplace.width();
+
+    for (int j = 0; j < channels; ++j) {
+      Dtype sum = 0, var = 0;
+      for (int i = 0; i < num; ++i) {
+        for ( int k = 0; k < height; ++k ) {
+          for ( int l = 0; l < width; ++l ) {
+            Dtype data = blob_inplace.data_at(i, j, k, l);
+            sum += data;
+            var += data * data;
+          }
+        }
+      }
+      sum /= height * width * num;
+      var /= height * width * num;
+
+      const Dtype kErrorBound = 0.001;
+      // expect zero mean
+      EXPECT_NEAR(0, sum, kErrorBound);
+      // expect unit variance
+      EXPECT_NEAR(1, var, kErrorBound);
+    }
+  }
+
+  TYPED_TEST(BatchNormLayerTest, TestGradient) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+
+    BatchNormLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-4);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_batch_reindex_layer.cpp b/src/caffe/test/test_batch_reindex_layer.cpp
new file mode 100644
index 00000000000..9ea1a2f6f47
--- /dev/null
+++ b/src/caffe/test/test_batch_reindex_layer.cpp
@@ -0,0 +1,118 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/batch_reindex_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template<typename TypeParam>
+class BatchReindexLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  BatchReindexLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_bottom_permute_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {
+  }
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    vector<int> sz;
+    sz.push_back(5);
+    sz.push_back(4);
+    sz.push_back(3);
+    sz.push_back(2);
+    blob_bottom_->Reshape(sz);
+    vector<int> permsz;
+    permsz.push_back(6);
+    blob_bottom_permute_->Reshape(permsz);
+
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    int perm[] = { 4, 0, 4, 0, 1, 2 };
+    for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+      blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+    }
+
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_permute_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~BatchReindexLayerTest() {
+    delete blob_bottom_permute_;
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_permute_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+
+  void TestForward() {
+    LayerParameter layer_param;
+
+    vector<int> sz;
+    sz.push_back(5);
+    sz.push_back(4);
+    sz.push_back(3);
+    sz.push_back(2);
+    blob_bottom_->Reshape(sz);
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      blob_bottom_->mutable_cpu_data()[i] = i;
+    }
+
+    vector<int> permsz;
+    permsz.push_back(6);
+    blob_bottom_permute_->Reshape(permsz);
+    int perm[] = { 4, 0, 4, 0, 1, 2 };
+    for (int i = 0; i < blob_bottom_permute_->count(); ++i) {
+      blob_bottom_permute_->mutable_cpu_data()[i] = perm[i];
+    }
+    BatchReindexLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    EXPECT_EQ(blob_top_->num(), blob_bottom_permute_->num());
+    EXPECT_EQ(blob_top_->channels(), blob_bottom_->channels());
+    EXPECT_EQ(blob_top_->height(), blob_bottom_->height());
+    EXPECT_EQ(blob_top_->width(), blob_bottom_->width());
+
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    int channels = blob_top_->channels();
+    int height = blob_top_->height();
+    int width = blob_top_->width();
+    for (int i = 0; i < blob_top_->count(); ++i) {
+      int n = i / (channels * width * height);
+      int inner_idx = (i % (channels * width * height));
+      EXPECT_EQ(
+          blob_top_->cpu_data()[i],
+          blob_bottom_->cpu_data()[perm[n] * channels * width * height
+              + inner_idx]);
+    }
+  }
+};
+
+TYPED_TEST_CASE(BatchReindexLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BatchReindexLayerTest, TestForward) {
+  this->TestForward();
+}
+
+TYPED_TEST(BatchReindexLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BatchReindexLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  }
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_benchmark.cpp b/src/caffe/test/test_benchmark.cpp
index 43aaa639b3c..b03fdf69a8a 100644
--- a/src/caffe/test/test_benchmark.cpp
+++ b/src/caffe/test/test_benchmark.cpp
@@ -1,4 +1,4 @@
-#include <unistd.h>  // for usleep
+#include <boost/thread.hpp>
 
 #include "gtest/gtest.h"
 
@@ -64,7 +64,7 @@ TYPED_TEST(BenchmarkTest, TestTimerMilliSeconds) {
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
-  usleep(300 * 1000);
+  boost::this_thread::sleep(boost::posix_time::milliseconds(300));
   EXPECT_GE(timer.MilliSeconds(), 300 - kMillisecondsThreshold);
   EXPECT_LE(timer.MilliSeconds(), 300 + kMillisecondsThreshold);
   EXPECT_TRUE(timer.initted());
@@ -79,7 +79,7 @@ TYPED_TEST(BenchmarkTest, TestTimerSeconds) {
   EXPECT_FALSE(timer.running());
   EXPECT_FALSE(timer.has_run_at_least_once());
   timer.Start();
-  usleep(300 * 1000);
+  boost::this_thread::sleep(boost::posix_time::milliseconds(300));
   EXPECT_GE(timer.Seconds(), 0.3 - kMillisecondsThreshold / 1000.);
   EXPECT_LE(timer.Seconds(), 0.3 + kMillisecondsThreshold / 1000.);
   EXPECT_TRUE(timer.initted());
diff --git a/src/caffe/test/test_bias_layer.cpp b/src/caffe/test/test_bias_layer.cpp
new file mode 100644
index 00000000000..3862e763e28
--- /dev/null
+++ b/src/caffe/test/test_bias_layer.cpp
@@ -0,0 +1,467 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/bias_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class BiasLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  BiasLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_eltwise_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_broadcast_0_(new Blob<Dtype>()),
+        blob_bottom_broadcast_1_(new Blob<Dtype>()),
+        blob_bottom_broadcast_2_(new Blob<Dtype>()),
+        blob_bottom_bias_(new Blob<Dtype>(vector<int>())),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    vector<int> broadcast_shape(2);
+    broadcast_shape[0] = 2; broadcast_shape[1] = 3;
+    this->blob_bottom_broadcast_0_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 3; broadcast_shape[1] = 4;
+    this->blob_bottom_broadcast_1_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 4; broadcast_shape[1] = 5;
+    this->blob_bottom_broadcast_2_->Reshape(broadcast_shape);
+    FillerParameter filler_param;
+    filler_param.set_min(1);
+    filler_param.set_max(10);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_eltwise_);
+    filler.Fill(this->blob_bottom_broadcast_0_);
+    filler.Fill(this->blob_bottom_broadcast_1_);
+    filler.Fill(this->blob_bottom_broadcast_2_);
+    filler.Fill(this->blob_bottom_bias_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~BiasLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_eltwise_;
+    delete blob_bottom_broadcast_0_;
+    delete blob_bottom_broadcast_1_;
+    delete blob_bottom_broadcast_2_;
+    delete blob_bottom_bias_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_eltwise_;
+  Blob<Dtype>* const blob_bottom_broadcast_0_;
+  Blob<Dtype>* const blob_bottom_broadcast_1_;
+  Blob<Dtype>* const blob_bottom_broadcast_2_;
+  Blob<Dtype>* const blob_bottom_bias_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(BiasLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_bottom_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  const Dtype* in_data_a = orig_bottom.cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestBackwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_bias_diff;
+  orig_bias_diff.CopyFrom(*this->blob_bottom_eltwise_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) {
+    EXPECT_NEAR(orig_bias_diff.cpu_diff()[i],
+                this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(0);
+  bias_param->set_num_axes(-1);
+  bias_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = layer->blobs()[0]->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w),
+                      orig_bottom.data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestBackwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_bias_diff;
+  orig_bias_diff.CopyFrom(*this->blob_bottom_broadcast_1_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) {
+    EXPECT_NEAR(orig_bias_diff.cpu_diff()[i],
+                this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(1);
+  bias_param->set_num_axes(2);
+  bias_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) +
+                      this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype bias = *this->blob_bottom_bias_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestForwardBiasAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  shared_ptr<BiasLayer<Dtype> > layer(new BiasLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype bias = *this->blob_bottom_bias_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] + bias, 1e-5);
+  }
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(0);
+  bias_param->set_num_axes(-1);
+  bias_param->mutable_filler()->set_type("gaussian");
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(0);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(1);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  BiasParameter* bias_param = layer_param.mutable_bias_param();
+  bias_param->set_axis(1);
+  bias_param->set_num_axes(2);
+  bias_param->mutable_filler()->set_type("gaussian");
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(BiasLayerTest, TestGradientBiasAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_bias_);
+  LayerParameter layer_param;
+  layer_param.mutable_bias_param()->set_axis(2);
+  BiasLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_blob.cpp b/src/caffe/test/test_blob.cpp
index 7da6423b67c..a9d7d519e45 100644
--- a/src/caffe/test/test_blob.cpp
+++ b/src/caffe/test/test_blob.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_caffe_main.cpp b/src/caffe/test/test_caffe_main.cpp
index c8caf5ac58e..fccf6f1613b 100644
--- a/src/caffe/test/test_caffe_main.cpp
+++ b/src/caffe/test/test_caffe_main.cpp
@@ -34,6 +34,7 @@ int main(int argc, char** argv) {
   cudaGetDevice(&device);
   cout << "Current device id: " << device << endl;
   cudaGetDeviceProperties(&CAFFE_TEST_CUDA_PROP, device);
+  cout << "Current device name: " << CAFFE_TEST_CUDA_PROP.name << endl;
 #endif
   // invoke the test.
   return RUN_ALL_TESTS();
diff --git a/src/caffe/test/test_common.cpp b/src/caffe/test/test_common.cpp
index b3a61b0fd25..58ae5c60a4f 100644
--- a/src/caffe/test/test_common.cpp
+++ b/src/caffe/test/test_common.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
diff --git a/src/caffe/test/test_concat_layer.cpp b/src/caffe/test/test_concat_layer.cpp
index 662a50fa23b..23c1e8c1d29 100644
--- a/src/caffe/test/test_concat_layer.cpp
+++ b/src/caffe/test/test_concat_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/concat_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -99,6 +98,19 @@ TYPED_TEST(ConcatLayerTest, TestSetupChannelsNegativeIndexing) {
   EXPECT_EQ(this->blob_top_->width(), this->blob_bottom_0_->width());
 }
 
+TYPED_TEST(ConcatLayerTest, TestForwardTrivial) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  this->blob_bottom_vec_0_.resize(1);
+  layer.SetUp(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_0_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_0_->count(); ++i) {
+    EXPECT_EQ(this->blob_bottom_0_->cpu_data()[i],
+              this->blob_top_->cpu_data()[i]);
+  }
+}
+
 TYPED_TEST(ConcatLayerTest, TestForwardNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -154,6 +166,16 @@ TYPED_TEST(ConcatLayerTest, TestForwardChannels) {
   }
 }
 
+TYPED_TEST(ConcatLayerTest, TestGradientTrivial) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  this->blob_bottom_vec_0_.resize(1);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_0_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(ConcatLayerTest, TestGradientNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -173,4 +195,13 @@ TYPED_TEST(ConcatLayerTest, TestGradientChannels) {
     this->blob_top_vec_);
 }
 
+TYPED_TEST(ConcatLayerTest, TestGradientChannelsBottomOneOnly) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConcatLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradient(&layer, this->blob_bottom_vec_0_,
+    this->blob_top_vec_, 1);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_contrastive_loss_layer.cpp b/src/caffe/test/test_contrastive_loss_layer.cpp
index d269fbc26f2..2fa055ee0de 100644
--- a/src/caffe/test/test_contrastive_loss_layer.cpp
+++ b/src/caffe/test/test_contrastive_loss_layer.cpp
@@ -1,7 +1,5 @@
 #include <algorithm>
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -9,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/contrastive_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -22,15 +20,15 @@ class ContrastiveLossLayerTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   ContrastiveLossLayerTest()
-      : blob_bottom_data_i_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_data_j_(new Blob<Dtype>(128, 10, 1, 1)),
-        blob_bottom_y_(new Blob<Dtype>(128, 1, 1, 1)),
+      : blob_bottom_data_i_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_data_j_(new Blob<Dtype>(512, 2, 1, 1)),
+        blob_bottom_y_(new Blob<Dtype>(512, 1, 1, 1)),
         blob_top_loss_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
-    filler_param.set_mean(0.0);
-    filler_param.set_std(0.3);  // distances~=1.0 to test both sides of margin
-    GaussianFiller<Dtype> filler(filler_param);
+    filler_param.set_min(-1.0);
+    filler_param.set_max(1.0);  // distances~=1.0 to test both sides of margin
+    UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_data_i_);
     blob_bottom_vec_.push_back(blob_bottom_data_i_);
     filler.Fill(this->blob_bottom_data_j_);
@@ -79,7 +77,8 @@ TYPED_TEST(ContrastiveLossLayerTest, TestForward) {
     if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
       loss += dist_sq;
     } else {
-      loss += std::max(margin-dist_sq, Dtype(0));
+      Dtype dist = std::max<Dtype>(margin - sqrt(dist_sq), 0.0);
+      loss += dist*dist;
     }
   }
   loss /= static_cast<Dtype>(num) * Dtype(2);
@@ -99,4 +98,47 @@ TYPED_TEST(ContrastiveLossLayerTest, TestGradient) {
       this->blob_top_vec_, 1);
 }
 
+TYPED_TEST(ContrastiveLossLayerTest, TestForwardLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // manually compute to compare
+  const Dtype margin = layer_param.contrastive_loss_param().margin();
+  const int num = this->blob_bottom_data_i_->num();
+  const int channels = this->blob_bottom_data_i_->channels();
+  Dtype loss(0);
+  for (int i = 0; i < num; ++i) {
+    Dtype dist_sq(0);
+    for (int j = 0; j < channels; ++j) {
+      Dtype diff = this->blob_bottom_data_i_->cpu_data()[i*channels+j] -
+          this->blob_bottom_data_j_->cpu_data()[i*channels+j];
+      dist_sq += diff*diff;
+    }
+    if (this->blob_bottom_y_->cpu_data()[i]) {  // similar pairs
+      loss += dist_sq;
+    } else {
+      loss += std::max(margin - dist_sq, Dtype(0.0));
+    }
+  }
+  loss /= static_cast<Dtype>(num) * Dtype(2);
+  EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);
+}
+
+TYPED_TEST(ContrastiveLossLayerTest, TestGradientLegacy) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_contrastive_loss_param()->set_legacy_version(true);
+  ContrastiveLossLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  GradientChecker<Dtype> checker(1e-2, 1e-2, 1701);
+  // check the gradient for the first two bottom layers
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 1);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_convolution_layer.cpp b/src/caffe/test/test_convolution_layer.cpp
index c1fe3b58c58..9bb19d13592 100644
--- a/src/caffe/test/test_convolution_layer.cpp
+++ b/src/caffe/test/test_convolution_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/conv_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_conv_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -19,54 +22,91 @@ template <typename Dtype>
 void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
     const vector<shared_ptr<Blob<Dtype> > >& weights,
     Blob<Dtype>* out) {
+  const bool has_depth = (out->num_axes() == 5);
+  if (!has_depth) { CHECK_EQ(4, out->num_axes()); }
   // Kernel size, stride, and pad
   int kernel_h, kernel_w;
-  if (conv_param->has_kernel_size()) {
-    kernel_h = kernel_w = conv_param->kernel_size();
-  } else {
+  if (conv_param->has_kernel_h() || conv_param->has_kernel_w()) {
     kernel_h = conv_param->kernel_h();
     kernel_w = conv_param->kernel_w();
+  } else {
+    kernel_h = kernel_w = conv_param->kernel_size(0);
   }
   int pad_h, pad_w;
-  if (!conv_param->has_pad_h()) {
-    pad_h = pad_w = conv_param->pad();
-  } else {
+  if (conv_param->has_pad_h() || conv_param->has_pad_w()) {
     pad_h = conv_param->pad_h();
     pad_w = conv_param->pad_w();
+  } else {
+    pad_h = pad_w = conv_param->pad_size() ? conv_param->pad(0) : 0;
   }
   int stride_h, stride_w;
-  if (!conv_param->has_stride_h()) {
-    stride_h = stride_w = conv_param->stride();
-  } else {
+  if (conv_param->has_stride_h() || conv_param->has_stride_w()) {
     stride_h = conv_param->stride_h();
     stride_w = conv_param->stride_w();
+  } else {
+    stride_h = stride_w = conv_param->stride_size() ? conv_param->stride(0) : 1;
+  }
+  int dilation_h, dilation_w;
+  dilation_h = dilation_w = conv_param->dilation_size() ?
+                            conv_param->dilation(0) : 1;
+  int kernel_d, pad_d, stride_d, dilation_d;
+  if (has_depth) {
+    kernel_d = kernel_h;
+    stride_d = stride_h;
+    pad_d = pad_h;
+    dilation_d = dilation_h;
+  } else {
+    kernel_d = stride_d = dilation_d = 1;
+    pad_d = 0;
   }
   // Groups
   int groups = conv_param->group();
-  int o_g = out->channels() / groups;
-  int k_g = in->channels() / groups;
+  int o_g = out->shape(1) / groups;
+  int k_g = in->shape(1) / groups;
   int o_head, k_head;
   // Convolution
-  const Dtype* in_data = in->cpu_data();
-  const Dtype* weight_data = weights[0]->cpu_data();
+  vector<int> weight_offset(4 + has_depth);
+  vector<int> in_offset(4 + has_depth);
+  vector<int> out_offset(4 + has_depth);
   Dtype* out_data = out->mutable_cpu_data();
-  for (int n = 0; n < out->num(); n++) {
+  for (int n = 0; n < out->shape(0); n++) {
     for (int g = 0; g < groups; g++) {
       o_head = o_g * g;
       k_head = k_g * g;
       for (int o = 0; o < o_g; o++) {
         for (int k = 0; k < k_g; k++) {
-          for (int y = 0; y < out->height(); y++) {
-            for (int x = 0; x < out->width(); x++) {
-              for (int p = 0; p < kernel_h; p++) {
-                for (int q = 0; q < kernel_w; q++) {
-                  int in_y = y * stride_h - pad_h + p;
-                  int in_x = x * stride_w - pad_w + q;
-                  if (in_y >= 0 && in_y < in->height()
-                    && in_x >= 0 && in_x < in->width()) {
-                    out_data[out->offset(n, o + o_head, y, x)] +=
-                        in_data[in->offset(n, k + k_head, in_y, in_x)]
-                        * weight_data[weights[0]->offset(o + o_head, k, p, q)];
+          for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+            for (int y = 0; y < out->shape(2 + has_depth); y++) {
+              for (int x = 0; x < out->shape(3 + has_depth); x++) {
+                for (int r = 0; r < kernel_d; r++) {
+                  for (int p = 0; p < kernel_h; p++) {
+                    for (int q = 0; q < kernel_w; q++) {
+                      int in_z = z * stride_d - pad_d + r * dilation_d;
+                      int in_y = y * stride_h - pad_h + p * dilation_h;
+                      int in_x = x * stride_w - pad_w + q * dilation_w;
+                      if (in_z >= 0 && in_z < (has_depth ? in->shape(2) : 1)
+                          && in_y >= 0 && in_y < in->shape(2 + has_depth)
+                          && in_x >= 0 && in_x < in->shape(3 + has_depth)) {
+                        weight_offset[0] = o + o_head;
+                        weight_offset[1] = k;
+                        if (has_depth) { weight_offset[2] = r; }
+                        weight_offset[2 + has_depth] = p;
+                        weight_offset[3 + has_depth] = q;
+                        in_offset[0] = n;
+                        in_offset[1] = k + k_head;
+                        if (has_depth) { in_offset[2] = in_z; }
+                        in_offset[2 + has_depth] = in_y;
+                        in_offset[3 + has_depth] = in_x;
+                        out_offset[0] = n;
+                        out_offset[1] = o + o_head;
+                        if (has_depth) { out_offset[2] = z; }
+                        out_offset[2 + has_depth] = y;
+                        out_offset[3 + has_depth] = x;
+                        out_data[out->offset(out_offset)] +=
+                            in->data_at(in_offset)
+                            * weights[0]->data_at(weight_offset);
+                      }
+                    }
                   }
                 }
               }
@@ -79,11 +119,18 @@ void caffe_conv(const Blob<Dtype>* in, ConvolutionParameter* conv_param,
   // Bias
   if (conv_param->bias_term()) {
     const Dtype* bias_data = weights[1]->cpu_data();
-    for (int n = 0; n < out->num(); n++) {
-      for (int o = 0; o < out->channels(); o++) {
-        for (int y = 0; y < out->height(); y++) {
-          for (int x = 0; x < out->width(); x++) {
-            out_data[out->offset(n, o, y, x)] += bias_data[o];
+    for (int n = 0; n < out->shape(0); n++) {
+      for (int o = 0; o < out->shape(1); o++) {
+        for (int z = 0; z < (has_depth ? out->shape(2) : 1); z++) {
+          for (int y = 0; y < out->shape(2 + has_depth); y++) {
+            for (int x = 0; x < out->shape(3 + has_depth); x++) {
+              out_offset[0] = n;
+              out_offset[1] = o;
+              if (has_depth) { out_offset[2] = z; }
+              out_offset[2 + has_depth] = y;
+              out_offset[3 + has_depth] = x;
+              out_data[out->offset(out_offset)] += bias_data[o];
+            }
           }
         }
       }
@@ -150,8 +197,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -188,8 +235,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -217,13 +264,189 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestDilatedConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(8);
+  bottom_shape.push_back(7);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("constant");
+  convolution_param->mutable_bias_filler()->set_value(0.1);
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
+TYPED_TEST(ConvolutionLayerTest, Test0DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  const int kNumOutput = 3;
+  convolution_param->set_num_output(kNumOutput);
+  convolution_param->set_axis(3);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  vector<int> top_shape = this->blob_bottom_->shape();
+  top_shape[3] = kNumOutput;
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(top_shape, this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  vector<int> weight_offset(2);
+  const Blob<Dtype>* weight = layer->blobs()[0].get();
+  const Blob<Dtype>* bias = layer->blobs()[1].get();
+  const int num = this->blob_top_->count(3);
+  const int dim = this->blob_top_->shape(3);
+  const int bottom_dim = this->blob_bottom_->shape(3);
+  for (int n = 0; n < num; ++n) {
+    for (int d = 0; d < dim; ++d) {
+      weight_offset[0] = d;
+      Dtype value = bias->cpu_data()[d];
+      for (int bottom_d = 0; bottom_d < bottom_dim; ++bottom_d) {
+        weight_offset[1] = bottom_d;
+        value += weight->data_at(weight_offset) *
+                 this->blob_bottom_->cpu_data()[n * bottom_dim + bottom_d];
+      }
+      EXPECT_NEAR(value, this->blob_top_->cpu_data()[n * dim + d], 1e-4);
+    }
+  }
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestSimple3DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 5;
+  bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+  bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+      this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestDilated3DConvolution) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
+  this->blob_top_vec_.push_back(this->blob_top_2_);
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 6;
+  bottom_shape[3] = 7;
+  bottom_shape[4] = 8;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(4);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<Layer<Dtype> > layer(
+      new ConvolutionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Check against reference convolution.
+  const Dtype* top_data;
+  const Dtype* ref_top_data;
+  caffe_conv(this->blob_bottom_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_));
+  top_data = this->blob_top_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+  caffe_conv(this->blob_bottom_2_, convolution_param, layer->blobs(),
+             this->MakeReferenceTop(this->blob_top_2_));
+  top_data = this->blob_top_2_->cpu_data();
+  ref_top_data = this->ref_blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    EXPECT_NEAR(top_data[i], ref_top_data[i], 1e-4);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, Test1x1Convolution) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(1);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(1);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -249,8 +472,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSimpleConvolutionGroup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -288,8 +511,8 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<Dtype> > layer(
@@ -350,14 +573,11 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   convolution_param->set_bias_term(false);
   layer.reset(new ConvolutionLayer<Dtype>(layer_param));
   layer->blobs().resize(1);
-  layer->blobs()[0].reset(new Blob<Dtype>(1, 3, 1, 3));
+  layer->blobs()[0].reset(new Blob<Dtype>(1, 1, 1, 3));
   Dtype* weights_2 = layer->blobs()[0]->mutable_cpu_data();
-  for (int c = 0; c < 3; ++c) {
-    int i = c * 3;  // 1 x 3 filter
-    weights_2[i +  0] = -1;
-    weights_2[i +  1] =  0;
-    weights_2[i +  2] =  1;
-  }
+  weights_2[0] = -1;
+  weights_2[1] =  0;
+  weights_2[2] =  1;
   layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
   layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
@@ -368,6 +588,124 @@ TYPED_TEST(ConvolutionLayerTest, TestSobelConvolution) {
   }
 }
 
+TYPED_TEST(ConvolutionLayerTest, TestNDAgainst2D) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kernel_h = 11;
+  const int kernel_w = 13;
+  vector<int> bottom_shape(4);
+  bottom_shape[0] = 15;
+  bottom_shape[1] = 18;
+  bottom_shape[2] = kernel_h * 2;
+  bottom_shape[3] = kernel_w * 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_num_output(12);
+  convolution_param->set_bias_term(false);
+  convolution_param->set_group(6);
+  convolution_param->set_kernel_h(kernel_h);
+  convolution_param->set_kernel_w(kernel_w);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  Blob<Dtype> weights;
+  Blob<Dtype> top_diff;
+  // Shape and fill weights and top_diff.
+  bool copy_diff;
+  bool reshape;
+  {
+    ConvolutionLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    top_diff.ReshapeLike(*this->blob_top_);
+    filler.Fill(&top_diff);
+    ASSERT_EQ(1, layer.blobs().size());
+    copy_diff = false; reshape = true;
+    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+  }
+  vector<bool> propagate_down(1, true);
+  Blob<Dtype> result_2d;
+  Blob<Dtype> backward_result_2d;
+  Blob<Dtype> backward_weight_result_2d;
+  // Test with 2D im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_2d.
+    convolution_param->set_force_nd_im2col(false);
+    ConvolutionLayer<Dtype> layer_2d(layer_param);
+    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_2d.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_2d.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_2d.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+  }
+  Blob<Dtype> result_nd;
+  Blob<Dtype> backward_result_nd;
+  Blob<Dtype> backward_weight_result_nd;
+  // Test with ND im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_nd.
+    convolution_param->set_force_nd_im2col(true);
+    ConvolutionLayer<Dtype> layer_nd(layer_param);
+    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_nd.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_nd.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_nd.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+  }
+  ASSERT_EQ(result_nd.count(), result_2d.count());
+  for (int i = 0; i < result_2d.count(); ++i)  {
+    EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+  }
+  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+  for (int i = 0; i < backward_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+              backward_result_nd.cpu_diff()[i]);
+  }
+  ASSERT_EQ(backward_weight_result_nd.count(),
+            backward_weight_result_2d.count());
+  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+              backward_weight_result_nd.cpu_diff()[i]);
+  }
+}
+
 TYPED_TEST(ConvolutionLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -375,8 +713,60 @@ TYPED_TEST(ConvolutionLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestDilatedGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(5);
+  bottom_shape.push_back(6);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+  }
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_dilation(2);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  ConvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
+
+TYPED_TEST(ConvolutionLayerTest, TestGradient3D) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 5;
+  bottom_shape[3] = this->blob_bottom_vec_[0]->shape(2);
+  bottom_shape[4] = this->blob_bottom_vec_[0]->shape(3);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -393,8 +783,8 @@ TYPED_TEST(ConvolutionLayerTest, Test1x1Gradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(1);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(1);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -409,8 +799,8 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -424,7 +814,7 @@ TYPED_TEST(ConvolutionLayerTest, TestGradientGroup) {
 #ifdef USE_CUDNN
 
 template <typename Dtype>
-class CuDNNConvolutionLayerTest : public ::testing::Test {
+class CuDNNConvolutionLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNConvolutionLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 4)),
@@ -467,14 +857,13 @@ class CuDNNConvolutionLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNConvolutionLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -505,14 +894,13 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("constant");
@@ -541,12 +929,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestSimpleConvolutionGroupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
@@ -572,7 +959,7 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   // Test separable convolution by computing the Sobel operator
   // as a single filter then comparing the result
   // as the convolution of two rectangular filters.
-  Caffe::set_mode(Caffe::GPU);
+
   // Fill bottoms with identical Gaussian noise.
   shared_ptr<GaussianFiller<TypeParam> > filler;
   FillerParameter filler_param;
@@ -584,8 +971,8 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(1);
   convolution_param->set_bias_term(false);
   shared_ptr<Layer<TypeParam> > layer(
@@ -646,14 +1033,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
   convolution_param->set_bias_term(false);
   layer.reset(new CuDNNConvolutionLayer<TypeParam>(layer_param));
   layer->blobs().resize(1);
-  layer->blobs()[0].reset(new Blob<TypeParam>(1, 3, 1, 3));
+  layer->blobs()[0].reset(new Blob<TypeParam>(1, 1, 1, 3));
   TypeParam* weights_2 = layer->blobs()[0]->mutable_cpu_data();
-  for (int c = 0; c < 3; ++c) {
-    int i = c * 3;  // 1 x 3 filter
-    weights_2[i +  0] = -1;
-    weights_2[i +  1] =  0;
-    weights_2[i +  2] =  1;
-  }
+  weights_2[0] = -1;
+  weights_2[1] =  0;
+  weights_2[2] =  1;
   layer->SetUp(sep_blob_bottom_vec, sep_blob_top_vec);
   layer->Forward(sep_blob_bottom_vec, sep_blob_top_vec);
   // Test equivalence of full and separable filters.
@@ -665,14 +1049,13 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestSobelConvolutionCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(2);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -683,12 +1066,11 @@ TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNConvolutionLayerTest, TestGradientGroupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(3);
   convolution_param->set_group(3);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
diff --git a/src/caffe/test/test_crop_layer.cpp b/src/caffe/test/test_crop_layer.cpp
new file mode 100644
index 00000000000..45f24e2ee8d
--- /dev/null
+++ b/src/caffe/test/test_crop_layer.cpp
@@ -0,0 +1,265 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/crop_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class CropLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  CropLayerTest()
+      : blob_bottom_0_(new Blob<Dtype>(2, 4, 5, 4)),
+        blob_bottom_1_(new Blob<Dtype>(2, 3, 4, 2)),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_0_);
+    filler.Fill(this->blob_bottom_1_);
+
+    blob_bottom_vec_.push_back(blob_bottom_0_);
+    blob_bottom_vec_.push_back(blob_bottom_1_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~CropLayerTest() {
+    delete blob_bottom_0_; delete blob_bottom_1_;
+    delete blob_top_;
+  }
+
+  Blob<Dtype>* const blob_bottom_0_;
+  Blob<Dtype>* const blob_bottom_1_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+
+TYPED_TEST_CASE(CropLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(CropLayerTest, TestSetupShapeAll) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop all dimensions
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeDefault) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last two dimensions, axis is 2 by default
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 2) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestSetupShapeNegativeIndexing) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  // Crop last dimension by negative indexing
+  layer_param.mutable_crop_param()->set_axis(-1);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->num_axes(); ++i) {
+    if (i < 3) {
+      EXPECT_EQ(this->blob_bottom_0_->shape(i), this->blob_top_->shape(i));
+    } else {
+      EXPECT_EQ(this->blob_bottom_1_->shape(i), this->blob_top_->shape(i));
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAll) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h, w));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllOffset) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  layer_param.mutable_crop_param()->add_offset(0);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if ( n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3) ) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c+1, h+1, w+2));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropHW) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_0_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_0_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_0_->width(); ++w) {
+          if (n < this->blob_top_->shape(0) &&
+              c < this->blob_top_->shape(1) &&
+              h < this->blob_top_->shape(2) &&
+              w < this->blob_top_->shape(3)) {
+            EXPECT_EQ(this->blob_top_->data_at(n, c, h, w),
+                this->blob_bottom_0_->data_at(n, c, h+1, w+2));
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5D) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_0_);
+  filler.Fill(this->blob_bottom_1_);
+  // Make layer
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
+  CropLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> bottom_idx = vector<int>(5, 0);
+  vector<int> top_idx = vector<int>(5, 0);
+  for (int n = 0; n < this->blob_bottom_0_->shape(0); ++n) {
+    for (int c = 0; c < this->blob_bottom_0_->shape(1); ++c) {
+      for (int z = 0; z < this->blob_bottom_0_->shape(2); ++z) {
+        for (int h = 0; h < this->blob_bottom_0_->shape(3); ++h) {
+          for (int w = 0; w < this->blob_bottom_0_->shape(4); ++w) {
+            if (n < this->blob_top_->shape(0) &&
+                c < this->blob_top_->shape(1) &&
+                z < this->blob_top_->shape(2) &&
+                h < this->blob_top_->shape(3) &&
+                w < this->blob_top_->shape(4)) {
+              bottom_idx[0] = top_idx[0] = n;
+              bottom_idx[1] = top_idx[1] = c;
+              bottom_idx[2] = z;
+              bottom_idx[3] = h;
+              bottom_idx[4] = top_idx[4] = w;
+              top_idx[2] = z+1;
+              top_idx[3] = h+2;
+              EXPECT_EQ(this->blob_top_->data_at(bottom_idx),
+                  this->blob_bottom_0_->data_at(top_idx));
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(CropLayerTest, TestCropAllGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(0);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCropHWGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  CropLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CropLayerTest, TestCrop5DGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_crop_param()->set_axis(2);
+  layer_param.mutable_crop_param()->add_offset(1);
+  layer_param.mutable_crop_param()->add_offset(2);
+  layer_param.mutable_crop_param()->add_offset(0);
+  CropLayer<Dtype> layer(layer_param);
+  // Add dimension to each bottom for >4D check
+  vector<int> bottom_0_shape = this->blob_bottom_0_->shape();
+  vector<int> bottom_1_shape = this->blob_bottom_1_->shape();
+  bottom_0_shape.push_back(2);
+  bottom_1_shape.push_back(1);
+  this->blob_bottom_0_->Reshape(bottom_0_shape);
+  this->blob_bottom_1_->Reshape(bottom_1_shape);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_data/generate_sample_data.py b/src/caffe/test/test_data/generate_sample_data.py
index e5dbc3406d8..2645073575f 100644
--- a/src/caffe/test/test_data/generate_sample_data.py
+++ b/src/caffe/test/test_data/generate_sample_data.py
@@ -1,10 +1,14 @@
 """
-Generate data used in the HDF5DataLayer test.
+Generate data used in the HDF5DataLayer and GradientBasedSolver tests.
 """
 import os
 import numpy as np
 import h5py
 
+script_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Generate HDF5DataLayer sample_data.h5
+
 num_cols = 8
 num_rows = 10
 height = 6
@@ -27,25 +31,51 @@
 print data
 print label
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data.h5', 'w') as f:
     f['data'] = data
     f['label'] = label
     f['label2'] = label2
 
-with h5py.File(os.path.dirname(__file__) + '/sample_data_2_gzip.h5', 'w') as f:
+with h5py.File(script_dir + '/sample_data_2_gzip.h5', 'w') as f:
     f.create_dataset(
         'data', data=data + total_size,
         compression='gzip', compression_opts=1
     )
     f.create_dataset(
         'label', data=label,
-        compression='gzip', compression_opts=1
+        compression='gzip', compression_opts=1,
+        dtype='uint8',
     )
     f.create_dataset(
         'label2', data=label2,
-        compression='gzip', compression_opts=1
+        compression='gzip', compression_opts=1,
+        dtype='uint8',
     )
 
-with open(os.path.dirname(__file__) + '/sample_data_list.txt', 'w') as f:
-    f.write(os.path.dirname(__file__) + '/sample_data.h5\n')
-    f.write(os.path.dirname(__file__) + '/sample_data_2_gzip.h5\n')
+with open(script_dir + '/sample_data_list.txt', 'w') as f:
+    f.write('src/caffe/test/test_data/sample_data.h5\n')
+    f.write('src/caffe/test/test_data/sample_data_2_gzip.h5\n')
+
+# Generate GradientBasedSolver solver_data.h5
+
+num_cols = 3
+num_rows = 8
+height = 10
+width = 10
+
+data = np.random.randn(num_rows, num_cols, height, width)
+data = data.reshape(num_rows, num_cols, height, width)
+data = data.astype('float32')
+
+targets = np.random.randn(num_rows, 1)
+targets = targets.astype('float32')
+
+print data
+print targets
+
+with h5py.File(script_dir + '/solver_data.h5', 'w') as f:
+    f['data'] = data
+    f['targets'] = targets
+
+with open(script_dir + '/solver_data_list.txt', 'w') as f:
+    f.write('src/caffe/test/test_data/solver_data.h5\n')
diff --git a/src/caffe/test/test_data/sample_data_2_gzip.h5 b/src/caffe/test/test_data/sample_data_2_gzip.h5
index a138e0367be..0cb9ef92241 100644
Binary files a/src/caffe/test/test_data/sample_data_2_gzip.h5 and b/src/caffe/test/test_data/sample_data_2_gzip.h5 differ
diff --git a/src/caffe/test/test_data/solver_data.h5 b/src/caffe/test/test_data/solver_data.h5
new file mode 100644
index 00000000000..7ee05ea7aac
Binary files /dev/null and b/src/caffe/test/test_data/solver_data.h5 differ
diff --git a/src/caffe/test/test_data/solver_data_list.txt b/src/caffe/test/test_data/solver_data_list.txt
new file mode 100644
index 00000000000..a6552f50073
--- /dev/null
+++ b/src/caffe/test/test_data/solver_data_list.txt
@@ -0,0 +1 @@
+src/caffe/test/test_data/solver_data.h5
diff --git a/src/caffe/test/test_data_layer.cpp b/src/caffe/test/test_data_layer.cpp
index afe2a40d227..3e8d113d918 100644
--- a/src/caffe/test/test_data_layer.cpp
+++ b/src/caffe/test/test_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <string>
 #include <vector>
 
@@ -6,8 +7,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
 #include "caffe/util/io.hpp"
@@ -348,6 +349,7 @@ class DataLayerTest : public MultiDeviceTest<TypeParam> {
 
 TYPED_TEST_CASE(DataLayerTest, TestDtypesAndDevices);
 
+#ifdef USE_LEVELDB
 TYPED_TEST(DataLayerTest, TestReadLevelDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
@@ -385,7 +387,9 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLevelDB) {
   this->Fill(unique_pixels, DataParameter_DB_LEVELDB);
   this->TestReadCrop(TEST);
 }
+#endif  // USE_LEVELDB
 
+#ifdef USE_LMDB
 TYPED_TEST(DataLayerTest, TestReadLMDB) {
   const bool unique_pixels = false;  // all pixels the same; images different
   this->Fill(unique_pixels, DataParameter_DB_LMDB);
@@ -424,4 +428,6 @@ TYPED_TEST(DataLayerTest, TestReadCropTestLMDB) {
   this->TestReadCrop(TEST);
 }
 
+#endif  // USE_LMDB
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_data_transformer.cpp b/src/caffe/test/test_data_transformer.cpp
index 16570e20356..31bf1c1fb14 100644
--- a/src/caffe/test/test_data_transformer.cpp
+++ b/src/caffe/test/test_data_transformer.cpp
@@ -1,8 +1,8 @@
+#ifdef USE_OPENCV
 #include <string>
 #include <vector>
 
 #include "gtest/gtest.h"
-#include "leveldb/db.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
@@ -39,23 +39,21 @@ class DataTransformTest : public ::testing::Test {
   int NumSequenceMatches(const TransformationParameter transform_param,
       const Datum& datum, Phase phase) {
     // Get crop sequence with Caffe seed 1701.
-    DataTransformer<Dtype>* transformer =
-        new DataTransformer<Dtype>(transform_param, phase);
+    DataTransformer<Dtype> transformer(transform_param, phase);
     const int crop_size = transform_param.crop_size();
     Caffe::set_random_seed(seed_);
-    transformer->InitRand();
-    Blob<Dtype>* blob =
-        new Blob<Dtype>(1, datum.channels(), datum.height(), datum.width());
+    transformer.InitRand();
+    Blob<Dtype> blob(1, datum.channels(), datum.height(), datum.width());
     if (transform_param.crop_size() > 0) {
-      blob->Reshape(1, datum.channels(), crop_size, crop_size);
+      blob.Reshape(1, datum.channels(), crop_size, crop_size);
     }
 
     vector<vector<Dtype> > crop_sequence;
     for (int iter = 0; iter < this->num_iter_; ++iter) {
       vector<Dtype> iter_crop_sequence;
-      transformer->Transform(datum, blob);
-      for (int j = 0; j < blob->count(); ++j) {
-        iter_crop_sequence.push_back(blob->cpu_data()[j]);
+      transformer.Transform(datum, &blob);
+      for (int j = 0; j < blob.count(); ++j) {
+        iter_crop_sequence.push_back(blob.cpu_data()[j]);
       }
       crop_sequence.push_back(iter_crop_sequence);
     }
@@ -63,17 +61,14 @@ class DataTransformTest : public ::testing::Test {
     int num_sequence_matches = 0;
     for (int iter = 0; iter < this->num_iter_; ++iter) {
       vector<Dtype> iter_crop_sequence = crop_sequence[iter];
-      transformer->Transform(datum, blob);
-      for (int j = 0; j < blob->count(); ++j) {
-        num_sequence_matches +=
-            (crop_sequence[iter][j] == blob->cpu_data()[j]);
+      transformer.Transform(datum, &blob);
+      for (int j = 0; j < blob.count(); ++j) {
+        num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]);
       }
     }
     return num_sequence_matches;
   }
 
-  virtual ~DataTransformTest() { }
-
   int seed_;
   int num_iter_;
 };
@@ -90,17 +85,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransform) {
 
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  EXPECT_EQ(blob->num(), 1);
-  EXPECT_EQ(blob->channels(), datum.channels());
-  EXPECT_EQ(blob->height(), datum.height());
-  EXPECT_EQ(blob->width(), datum.width());
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], label);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  EXPECT_EQ(blob.num(), 1);
+  EXPECT_EQ(blob.channels(), datum.channels());
+  EXPECT_EQ(blob.height(), datum.height());
+  EXPECT_EQ(blob.width(), datum.width());
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], label);
   }
 }
 
@@ -114,17 +108,16 @@ TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
 
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, 3, 4, 5);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  EXPECT_EQ(blob->num(), 1);
-  EXPECT_EQ(blob->channels(), datum.channels());
-  EXPECT_EQ(blob->height(), datum.height());
-  EXPECT_EQ(blob->width(), datum.width());
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], j);
+  Blob<TypeParam> blob(1, 3, 4, 5);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  EXPECT_EQ(blob.num(), 1);
+  EXPECT_EQ(blob.channels(), datum.channels());
+  EXPECT_EQ(blob.height(), datum.height());
+  EXPECT_EQ(blob.width(), datum.width());
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], j);
   }
 }
 
@@ -140,19 +133,17 @@ TYPED_TEST(DataTransformTest, TestCropSize) {
   transform_param.set_crop_size(crop_size);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  Blob<TypeParam>* blob =
-      new Blob<TypeParam>(1, channels, crop_size, crop_size);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  Blob<TypeParam> blob(1, channels, crop_size, crop_size);
   for (int iter = 0; iter < this->num_iter_; ++iter) {
-    transformer->Transform(datum, blob);
-    EXPECT_EQ(blob->num(), 1);
-    EXPECT_EQ(blob->channels(), datum.channels());
-    EXPECT_EQ(blob->height(), crop_size);
-    EXPECT_EQ(blob->width(), crop_size);
-    for (int j = 0; j < blob->count(); ++j) {
-      EXPECT_EQ(blob->cpu_data()[j], label);
+    transformer.Transform(datum, &blob);
+    EXPECT_EQ(blob.num(), 1);
+    EXPECT_EQ(blob.channels(), datum.channels());
+    EXPECT_EQ(blob.height(), crop_size);
+    EXPECT_EQ(blob.width(), crop_size);
+    for (int j = 0; j < blob.count(); ++j) {
+      EXPECT_EQ(blob.cpu_data()[j], label);
     }
   }
 }
@@ -279,13 +270,12 @@ TYPED_TEST(DataTransformTest, TestMeanValue) {
   transform_param.add_mean_value(mean_value);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  for (int j = 0; j < blob->count(); ++j) {
-    EXPECT_EQ(blob->cpu_data()[j], label - mean_value);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], label - mean_value);
   }
 }
 
@@ -302,14 +292,13 @@ TYPED_TEST(DataTransformTest, TestMeanValues) {
   transform_param.add_mean_value(2);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
   for (int c = 0; c < channels; ++c) {
     for (int j = 0; j < height * width; ++j) {
-      EXPECT_EQ(blob->cpu_data()[blob->offset(0, c) + j], label - c);
+      EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c);
     }
   }
 }
@@ -324,8 +313,8 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
   const int size = channels * height * width;
 
   // Create a mean file
-  string* mean_file = new string();
-  MakeTempFilename(mean_file);
+  string mean_file;
+  MakeTempFilename(&mean_file);
   BlobProto blob_mean;
   blob_mean.set_num(1);
   blob_mean.set_channels(channels);
@@ -336,20 +325,20 @@ TYPED_TEST(DataTransformTest, TestMeanFile) {
       blob_mean.add_data(j);
   }
 
-  LOG(INFO) << "Using temporary mean_file " << *mean_file;
-  WriteProtoToBinaryFile(blob_mean, *mean_file);
+  LOG(INFO) << "Using temporary mean_file " << mean_file;
+  WriteProtoToBinaryFile(blob_mean, mean_file);
 
-  transform_param.set_mean_file(*mean_file);
+  transform_param.set_mean_file(mean_file);
   Datum datum;
   FillDatum(label, channels, height, width, unique_pixels, &datum);
-  Blob<TypeParam>* blob = new Blob<TypeParam>(1, channels, height, width);
-  DataTransformer<TypeParam>* transformer =
-      new DataTransformer<TypeParam>(transform_param, TEST);
-  transformer->InitRand();
-  transformer->Transform(datum, blob);
-  for (int j = 0; j < blob->count(); ++j) {
-      EXPECT_EQ(blob->cpu_data()[j], 0);
+  Blob<TypeParam> blob(1, channels, height, width);
+  DataTransformer<TypeParam> transformer(transform_param, TEST);
+  transformer.InitRand();
+  transformer.Transform(datum, &blob);
+  for (int j = 0; j < blob.count(); ++j) {
+    EXPECT_EQ(blob.cpu_data()[j], 0);
   }
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_db.cpp b/src/caffe/test/test_db.cpp
index 5b2ac230a0b..1b487b14c58 100644
--- a/src/caffe/test/test_db.cpp
+++ b/src/caffe/test/test_db.cpp
@@ -1,3 +1,4 @@
+#if defined(USE_LEVELDB) && defined(USE_LMDB) && defined(USE_OPENCV)
 #include <string>
 
 #include "boost/scoped_ptr.hpp"
@@ -132,3 +133,4 @@ TYPED_TEST(DBTest, TestWrite) {
 }
 
 }  // namespace caffe
+#endif  // USE_LEVELDB, USE_LMDB and USE_OPENCV
diff --git a/src/caffe/test/test_deconvolution_layer.cpp b/src/caffe/test/test_deconvolution_layer.cpp
index fc63d5efbe3..c4b09ad555a 100644
--- a/src/caffe/test/test_deconvolution_layer.cpp
+++ b/src/caffe/test/test_deconvolution_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/deconv_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -58,8 +57,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
@@ -96,8 +95,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestSimpleDeconvolution) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   convolution_param->set_num_output(4);
   convolution_param->mutable_weight_filler()->set_type("constant");
   convolution_param->mutable_weight_filler()->set_value(1);
@@ -144,8 +143,8 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
       layer_param.mutable_convolution_param();
   this->blob_bottom_vec_.push_back(this->blob_bottom_2_);
   this->blob_top_vec_.push_back(this->blob_top_2_);
-  convolution_param->set_kernel_size(2);
-  convolution_param->set_stride(1);
+  convolution_param->add_kernel_size(2);
+  convolution_param->add_stride(1);
   convolution_param->set_num_output(1);
   convolution_param->mutable_weight_filler()->set_type("gaussian");
   convolution_param->mutable_bias_filler()->set_type("gaussian");
@@ -155,4 +154,151 @@ TYPED_TEST(DeconvolutionLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(DeconvolutionLayerTest, TestNDAgainst2D) {
+  typedef typename TypeParam::Dtype Dtype;
+  const int kernel_h = 11;
+  const int kernel_w = 13;
+  vector<int> bottom_shape(4);
+  bottom_shape[0] = 15;
+  bottom_shape[1] = 12;
+  bottom_shape[2] = kernel_h * 2;
+  bottom_shape[3] = kernel_w * 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->set_num_output(18);
+  convolution_param->set_bias_term(false);
+  convolution_param->set_group(6);
+  convolution_param->set_kernel_h(kernel_h);
+  convolution_param->set_kernel_w(kernel_w);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  Blob<Dtype> weights;
+  Blob<Dtype> top_diff;
+  // Shape and fill weights and top_diff.
+  bool copy_diff;
+  bool reshape;
+  {
+    DeconvolutionLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    top_diff.ReshapeLike(*this->blob_top_);
+    filler.Fill(&top_diff);
+    ASSERT_EQ(1, layer.blobs().size());
+    copy_diff = false; reshape = true;
+    weights.CopyFrom(*layer.blobs()[0], copy_diff, reshape);
+  }
+  vector<bool> propagate_down(1, true);
+  Blob<Dtype> result_2d;
+  Blob<Dtype> backward_result_2d;
+  Blob<Dtype> backward_weight_result_2d;
+  // Test with 2D im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_2d.
+    convolution_param->set_force_nd_im2col(false);
+    DeconvolutionLayer<Dtype> layer_2d(layer_param);
+    layer_2d.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_2d.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_2d.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_2d.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_2d.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_2d.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_2d.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_2d.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_2d.CopyFrom(weights, copy_diff, reshape);
+  }
+  Blob<Dtype> result_nd;
+  Blob<Dtype> backward_result_nd;
+  Blob<Dtype> backward_weight_result_nd;
+  // Test with ND im2col
+  {
+    caffe_set(this->blob_top_->count(), Dtype(0),
+              this->blob_top_->mutable_cpu_data());
+    caffe_set(this->blob_bottom_->count(), Dtype(0),
+              this->blob_bottom_->mutable_cpu_diff());
+    caffe_set(weights.count(), Dtype(0), weights.mutable_cpu_diff());
+    // Do SetUp and Forward; save Forward result in result_nd.
+    convolution_param->set_force_nd_im2col(true);
+    DeconvolutionLayer<Dtype> layer_nd(layer_param);
+    layer_nd.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(1, layer_nd.blobs().size());
+    copy_diff = false; reshape = false;
+    layer_nd.blobs()[0]->CopyFrom(weights, copy_diff, reshape);
+    layer_nd.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    copy_diff = false; reshape = true;
+    result_nd.CopyFrom(*this->blob_top_, copy_diff, reshape);
+    // Copy pre-generated top diff into actual top diff;
+    // do Backward and save result in backward_result_nd.
+    ASSERT_EQ(this->blob_top_->shape(), top_diff.shape());
+    caffe_copy(top_diff.count(), top_diff.cpu_data(),
+               this->blob_top_->mutable_cpu_diff());
+    layer_nd.Backward(this->blob_top_vec_, propagate_down,
+                      this->blob_bottom_vec_);
+    copy_diff = true; reshape = true;
+    backward_result_nd.CopyFrom(*this->blob_bottom_, copy_diff, reshape);
+    backward_weight_result_nd.CopyFrom(weights, copy_diff, reshape);
+  }
+  ASSERT_EQ(result_nd.count(), result_2d.count());
+  for (int i = 0; i < result_2d.count(); ++i)  {
+    EXPECT_EQ(result_2d.cpu_data()[i], result_nd.cpu_data()[i]);
+  }
+  ASSERT_EQ(backward_result_nd.count(), backward_result_2d.count());
+  for (int i = 0; i < backward_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_result_2d.cpu_diff()[i],
+              backward_result_nd.cpu_diff()[i]);
+  }
+  ASSERT_EQ(backward_weight_result_nd.count(),
+            backward_weight_result_2d.count());
+  for (int i = 0; i < backward_weight_result_2d.count(); ++i) {
+    EXPECT_EQ(backward_weight_result_2d.cpu_diff()[i],
+              backward_weight_result_nd.cpu_diff()[i]);
+  }
+}
+
+TYPED_TEST(DeconvolutionLayerTest, TestGradient3D) {
+  typedef typename TypeParam::Dtype Dtype;
+  vector<int> bottom_shape(5);
+  bottom_shape[0] = this->blob_bottom_vec_[0]->shape(0);
+  bottom_shape[1] = this->blob_bottom_vec_[0]->shape(1);
+  bottom_shape[2] = 2;
+  bottom_shape[3] = 3;
+  bottom_shape[4] = 2;
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  for (int i = 0; i < this->blob_bottom_vec_.size(); ++i) {
+    this->blob_bottom_vec_[i]->Reshape(bottom_shape);
+    filler.Fill(this->blob_bottom_vec_[i]);
+  }
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(2);
+  convolution_param->add_stride(2);
+  convolution_param->add_pad(1);
+  convolution_param->set_num_output(2);
+  convolution_param->mutable_weight_filler()->set_type("gaussian");
+  convolution_param->mutable_bias_filler()->set_type("gaussian");
+  DeconvolutionLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_dummy_data_layer.cpp b/src/caffe/test/test_dummy_data_layer.cpp
index 99548352746..1a01ca85f89 100644
--- a/src/caffe/test/test_dummy_data_layer.cpp
+++ b/src/caffe/test/test_dummy_data_layer.cpp
@@ -5,15 +5,15 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layers/dummy_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
 namespace caffe {
 
 template <typename Dtype>
-class DummyDataLayerTest : public ::testing::Test {
+class DummyDataLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   DummyDataLayerTest()
       : blob_top_a_(new Blob<Dtype>()),
@@ -44,7 +44,6 @@ class DummyDataLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(DummyDataLayerTest, TestDtypes);
 
 TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -74,7 +73,6 @@ TYPED_TEST(DummyDataLayerTest, TestOneTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
@@ -113,7 +111,6 @@ TYPED_TEST(DummyDataLayerTest, TestTwoTopConstant) {
 }
 
 TYPED_TEST(DummyDataLayerTest, TestThreeTopConstantGaussianConstant) {
-  Caffe::set_mode(Caffe::CPU);
   LayerParameter param;
   DummyDataParameter* dummy_data_param = param.mutable_dummy_data_param();
   dummy_data_param->add_num(5);
diff --git a/src/caffe/test/test_eltwise_layer.cpp b/src/caffe/test/test_eltwise_layer.cpp
index be0c1347709..c06e3baab15 100644
--- a/src/caffe/test/test_eltwise_layer.cpp
+++ b/src/caffe/test/test_eltwise_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/eltwise_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -80,7 +80,7 @@ TYPED_TEST(EltwiseLayerTest, TestProd) {
   const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
   const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i]);
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i] * in_data_c[i], 1e-4);
   }
 }
 
@@ -99,7 +99,7 @@ TYPED_TEST(EltwiseLayerTest, TestSum) {
   const Dtype* in_data_b = this->blob_bottom_b_->cpu_data();
   const Dtype* in_data_c = this->blob_bottom_c_->cpu_data();
   for (int i = 0; i < count; ++i) {
-    EXPECT_EQ(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i]);
+    EXPECT_NEAR(data[i], in_data_a[i] + in_data_b[i] + in_data_c[i], 1e-4);
   }
 }
 
diff --git a/src/caffe/test/test_embed_layer.cpp b/src/caffe/test/test_embed_layer.cpp
new file mode 100644
index 00000000000..dc7f5c4aa47
--- /dev/null
+++ b/src/caffe/test/test_embed_layer.cpp
@@ -0,0 +1,178 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/embed_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class EmbedLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  EmbedLayerTest()
+      : blob_bottom_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~EmbedLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(EmbedLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(EmbedLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 5);
+  EXPECT_EQ(this->blob_top_->shape(0), 4);
+  EXPECT_EQ(this->blob_top_->shape(1), 1);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 1);
+  EXPECT_EQ(this->blob_top_->shape(4), 10);
+}
+
+TYPED_TEST(EmbedLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  const int kNumOutput = 10;
+  const int kInputDim = 5;
+  embed_param->set_num_output(kNumOutput);
+  embed_param->set_input_dim(kInputDim);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->set_bias_term(false);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(1, layer->blobs().size());
+  vector<int> weight_shape(2);
+  weight_shape[0] = kInputDim;
+  weight_shape[1] = kNumOutput;
+  ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+  }
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> weight_offset(2, 0);
+  vector<int> top_offset(5, 0);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+    weight_offset[1] = 0;
+    top_offset[0] = i;
+    top_offset[4] = 0;
+    for (int j = 0; j < kNumOutput; ++j) {
+      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset),
+                this->blob_top_->data_at(top_offset));
+      ++top_offset[4];
+      ++weight_offset[1];
+    }
+  }
+}
+
+TYPED_TEST(EmbedLayerTest, TestForwardWithBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  const int kNumOutput = 10;
+  const int kInputDim = 5;
+  embed_param->set_num_output(kNumOutput);
+  embed_param->set_input_dim(kInputDim);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+  embed_param->set_bias_term(true);
+  shared_ptr<EmbedLayer<Dtype> > layer(new EmbedLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(2, layer->blobs().size());
+  vector<int> weight_shape(2);
+  weight_shape[0] = kInputDim;
+  weight_shape[1] = kNumOutput;
+  ASSERT_TRUE(weight_shape == layer->blobs()[0]->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    this->blob_bottom_->mutable_cpu_data()[i] = caffe_rng_rand() % kInputDim;
+  }
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<int> bias_offset(1, 0);
+  vector<int> weight_offset(2, 0);
+  vector<int> top_offset(5, 0);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    weight_offset[0] = static_cast<int>(this->blob_bottom_->cpu_data()[i]);
+    weight_offset[1] = 0;
+    top_offset[0] = i;
+    top_offset[4] = 0;
+    bias_offset[0] = 0;
+    for (int j = 0; j < kNumOutput; ++j) {
+      EXPECT_EQ(layer->blobs()[0]->data_at(weight_offset) +
+                layer->blobs()[1]->data_at(bias_offset),
+                this->blob_top_->data_at(top_offset));
+      ++top_offset[4];
+      ++weight_offset[1];
+      ++bias_offset[0];
+    }
+  }
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  embed_param->set_bias_term(false);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  EmbedLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_bottom_->mutable_cpu_data()[0] = 4;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 2;
+  this->blob_bottom_->mutable_cpu_data()[3] = 3;
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, -2);
+}
+
+TYPED_TEST(EmbedLayerTest, TestGradientWithBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  EmbedParameter* embed_param = layer_param.mutable_embed_param();
+  embed_param->set_num_output(10);
+  embed_param->set_input_dim(5);
+  embed_param->set_bias_term(true);
+  embed_param->mutable_weight_filler()->set_type("uniform");
+  embed_param->mutable_weight_filler()->set_min(-10);
+  embed_param->mutable_weight_filler()->set_max(10);
+  embed_param->mutable_bias_filler()->CopyFrom(embed_param->weight_filler());
+  EmbedLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_bottom_->mutable_cpu_data()[0] = 4;
+  this->blob_bottom_->mutable_cpu_data()[1] = 2;
+  this->blob_bottom_->mutable_cpu_data()[2] = 2;
+  this->blob_bottom_->mutable_cpu_data()[3] = 3;
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, -2);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_euclidean_loss_layer.cpp b/src/caffe/test/test_euclidean_loss_layer.cpp
index 1949742bbcb..f253f9fd393 100644
--- a/src/caffe/test/test_euclidean_loss_layer.cpp
+++ b/src/caffe/test/test_euclidean_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/euclidean_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_filler.cpp b/src/caffe/test/test_filler.cpp
index e04b0fd22af..26e9b217e35 100644
--- a/src/caffe/test/test_filler.cpp
+++ b/src/caffe/test/test_filler.cpp
@@ -1,5 +1,3 @@
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/filler.hpp"
@@ -142,4 +140,102 @@ TYPED_TEST(GaussianFillerTest, TestFill) {
   EXPECT_LE(var, target_var * 5.);
 }
 
+template <typename Dtype>
+class XavierFillerTest : public ::testing::Test {
+ protected:
+  XavierFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new XavierFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~XavierFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<XavierFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(XavierFillerTest, TestDtypes);
+
+TYPED_TEST(XavierFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(XavierFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
+template <typename Dtype>
+class MSRAFillerTest : public ::testing::Test {
+ protected:
+  MSRAFillerTest()
+      : blob_(new Blob<Dtype>(1000, 2, 4, 5)),
+        filler_param_() {
+  }
+  virtual void test_params(FillerParameter_VarianceNorm variance_norm,
+      Dtype n) {
+    this->filler_param_.set_variance_norm(variance_norm);
+    this->filler_.reset(new MSRAFiller<Dtype>(this->filler_param_));
+    this->filler_->Fill(blob_);
+    EXPECT_TRUE(this->blob_);
+    const int count = this->blob_->count();
+    const Dtype* data = this->blob_->cpu_data();
+    Dtype mean = 0.;
+    Dtype ex2 = 0.;
+    for (int i = 0; i < count; ++i) {
+      mean += data[i];
+      ex2 += data[i] * data[i];
+    }
+    mean /= count;
+    ex2 /= count;
+    Dtype std = sqrt(ex2 - mean*mean);
+    Dtype target_std = sqrt(2.0 / n);
+    EXPECT_NEAR(mean, 0.0, 0.1);
+    EXPECT_NEAR(std, target_std, 0.1);
+  }
+  virtual ~MSRAFillerTest() { delete blob_; }
+  Blob<Dtype>* const blob_;
+  FillerParameter filler_param_;
+  shared_ptr<MSRAFiller<Dtype> > filler_;
+};
+
+TYPED_TEST_CASE(MSRAFillerTest, TestDtypes);
+
+TYPED_TEST(MSRAFillerTest, TestFillFanIn) {
+  TypeParam n = 2*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_IN, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillFanOut) {
+  TypeParam n = 1000*4*5;
+  this->test_params(FillerParameter_VarianceNorm_FAN_OUT, n);
+}
+TYPED_TEST(MSRAFillerTest, TestFillAverage) {
+  TypeParam n = (2*4*5 + 1000*4*5) / 2.0;
+  this->test_params(FillerParameter_VarianceNorm_AVERAGE, n);
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_filter_layer.cpp b/src/caffe/test/test_filter_layer.cpp
new file mode 100644
index 00000000000..9ea2b8b2168
--- /dev/null
+++ b/src/caffe/test/test_filter_layer.cpp
@@ -0,0 +1,126 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/filter_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class FilterLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  FilterLayerTest()
+      : blob_bottom_data_(new Blob<Dtype>(4, 3, 6, 4)),
+        blob_bottom_labels_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_bottom_selector_(new Blob<Dtype>(4, 1, 1, 1)),
+        blob_top_data_(new Blob<Dtype>()),
+        blob_top_labels_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    // fill the values
+    Caffe::set_random_seed(1890);
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    // fill the selector blob
+    Dtype* bottom_data_selector_ = blob_bottom_selector_->mutable_cpu_data();
+    bottom_data_selector_[0] = 0;
+    bottom_data_selector_[1] = 1;
+    bottom_data_selector_[2] = 1;
+    bottom_data_selector_[3] = 0;
+    // fill the other bottom blobs
+    filler.Fill(blob_bottom_data_);
+    for (int i = 0; i < blob_bottom_labels_->count(); ++i) {
+      blob_bottom_labels_->mutable_cpu_data()[i] = caffe_rng_rand() % 5;
+    }
+    blob_bottom_vec_.push_back(blob_bottom_data_);
+    blob_bottom_vec_.push_back(blob_bottom_labels_);
+    blob_bottom_vec_.push_back(blob_bottom_selector_);
+    blob_top_vec_.push_back(blob_top_data_);
+    blob_top_vec_.push_back(blob_top_labels_);
+  }
+  virtual ~FilterLayerTest() {
+    delete blob_bottom_data_;
+    delete blob_bottom_labels_;
+    delete blob_bottom_selector_;
+    delete blob_top_data_;
+    delete blob_top_labels_;
+  }
+  Blob<Dtype>* const blob_bottom_data_;
+  Blob<Dtype>* const blob_bottom_labels_;
+  Blob<Dtype>* const blob_bottom_selector_;
+  // blobs for the top of FilterLayer
+  Blob<Dtype>* const blob_top_data_;
+  Blob<Dtype>* const blob_top_labels_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(FilterLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(FilterLayerTest, TestReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  // In the test first and last items should have been filtered
+  // so we just expect 2 remaining items
+  EXPECT_EQ(this->blob_top_data_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_labels_->shape(0), 2);
+  EXPECT_GT(this->blob_bottom_data_->shape(0),
+      this->blob_top_data_->shape(0));
+  EXPECT_GT(this->blob_bottom_labels_->shape(0),
+      this->blob_top_labels_->shape(0));
+  for (int i = 1; i < this->blob_bottom_labels_->num_axes(); i++) {
+    EXPECT_EQ(this->blob_bottom_labels_->shape(i),
+        this->blob_top_labels_->shape(i));
+  }
+}
+
+TYPED_TEST(FilterLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_labels_->data_at(0, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(1, 0, 0, 0));
+  EXPECT_EQ(this->blob_top_labels_->data_at(1, 0, 0, 0),
+      this->blob_bottom_labels_->data_at(2, 0, 0, 0));
+
+  int dim = this->blob_top_data_->count() /
+      this->blob_top_data_->shape(0);
+  const Dtype* top_data = this->blob_top_data_->cpu_data();
+  const Dtype* bottom_data = this->blob_bottom_data_->cpu_data();
+  // selector is 0 1 1 0, so we need to compare bottom(1,c,h,w)
+  // with top(0,c,h,w) and bottom(2,c,h,w) with top(1,c,h,w)
+  bottom_data += dim;  // bottom(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+
+  bottom_data += dim;  // bottom(2,c,h,w)
+  top_data += dim;  // top(1,c,h,w)
+  for (size_t n = 0; n < dim; n++)
+    EXPECT_EQ(top_data[n], bottom_data[n]);
+}
+
+TYPED_TEST(FilterLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  FilterLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  // check only input 0 (data) because labels and selector
+  // don't need backpropagation
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_flatten_layer.cpp b/src/caffe/test/test_flatten_layer.cpp
index 3042d293cf7..d929ac7a720 100644
--- a/src/caffe/test/test_flatten_layer.cpp
+++ b/src/caffe/test/test_flatten_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/flatten_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -42,13 +41,48 @@ TYPED_TEST(FlattenLayerTest, TestSetup) {
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
-  EXPECT_EQ(this->blob_top_->num(), 2);
-  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
-  EXPECT_EQ(this->blob_top_->height(), 1);
-  EXPECT_EQ(this->blob_top_->width(), 1);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6 * 5);
 }
 
-TYPED_TEST(FlattenLayerTest, Test) {
+TYPED_TEST(FlattenLayerTest, TestSetupWithAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6 * 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestSetupWithStartAndEndAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_flatten_param()->set_axis(0);
+  layer_param.mutable_flatten_param()->set_end_axis(-2);
+  FlattenLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2 * 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(1), 5);
+}
+
+TYPED_TEST(FlattenLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
   FlattenLayer<Dtype> layer(layer_param);
@@ -71,5 +105,4 @@ TYPED_TEST(FlattenLayerTest, TestGradient) {
       this->blob_top_vec_);
 }
 
-
 }  // namespace caffe
diff --git a/src/caffe/test/test_gradient_based_solver.cpp b/src/caffe/test/test_gradient_based_solver.cpp
index eb2569c04f2..975a8f0f88a 100644
--- a/src/caffe/test/test_gradient_based_solver.cpp
+++ b/src/caffe/test/test_gradient_based_solver.cpp
@@ -8,8 +8,10 @@
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
+#include "caffe/parallel.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/solver.hpp"
+#include "caffe/sgd_solvers.hpp"
+#include "caffe/util/io.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -23,22 +25,33 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
 
  protected:
   GradientBasedSolverTest() :
-      seed_(1701), num_(5), channels_(3), height_(10), width_(10) {}
+      seed_(1701), num_(4), channels_(3), height_(10), width_(10),
+      share_(false) {
+        input_file_ = new string(
+        CMAKE_SOURCE_DIR "caffe/test/test_data/solver_data_list.txt" CMAKE_EXT);
+      }
+  ~GradientBasedSolverTest() {
+    delete input_file_;
+  }
 
+  string snapshot_prefix_;
   shared_ptr<SGDSolver<Dtype> > solver_;
+  shared_ptr<P2PSync<Dtype> > sync_;
   int seed_;
+  // Dimensions are determined by generate_sample_data.py
+  // TODO this is brittle and the hdf5 file should be checked instead.
   int num_, channels_, height_, width_;
-  Dtype delta_;  // Stability constant for AdaGrad.
+  bool share_;
+  Dtype delta_;  // Stability constant for RMSProp, AdaGrad, AdaDelta and Adam
+
+  // Test data: check out generate_sample_data.py in the same directory.
+  string* input_file_;
 
-  virtual SolverParameter_SolverType solver_type() = 0;
   virtual void InitSolver(const SolverParameter& param) = 0;
 
   virtual void InitSolverFromProtoString(const string& proto) {
     SolverParameter param;
     CHECK(google::protobuf::TextFormat::ParseFromString(proto, &param));
-    // Disable saving a final snapshot so the tests don't pollute the user's
-    // working directory with useless snapshots.
-    param.set_snapshot_after_train(false);
     // Set the solver_mode according to current Caffe::mode.
     switch (Caffe::mode()) {
       case Caffe::CPU:
@@ -51,41 +64,58 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
         LOG(FATAL) << "Unknown Caffe mode: " << Caffe::mode();
     }
     InitSolver(param);
-    delta_ = (solver_type() == SolverParameter_SolverType_ADAGRAD) ?
-         param.delta() : 0;
+    delta_ = param.delta();
   }
 
-  void RunLeastSquaresSolver(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum, const int num_iters) {
+  string RunLeastSquaresSolver(const Dtype learning_rate,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
+      const int iter_size = 1, const int devices = 1,
+      const bool snapshot = false, const char* from_snapshot = NULL) {
     ostringstream proto;
+    int device_id = 0;
+#ifndef CPU_ONLY
+    if (Caffe::mode() == Caffe::GPU) {
+      CUDA_CHECK(cudaGetDevice(&device_id));
+    }
+#endif
     proto <<
+       "snapshot_after_train: " << snapshot << " "
        "max_iter: " << num_iters << " "
        "base_lr: " << learning_rate << " "
        "lr_policy: 'fixed' "
+       "iter_size: " << iter_size << " "
+       "device_id: " << device_id << " "
        "net_param { "
        "  name: 'TestNetwork' "
        "  layer { "
        "    name: 'data' "
-       "    type: 'DummyData' "
-       "    dummy_data_param { "
-       "      num: " << num_ << " "
-       "      channels: " << channels_ << " "
-       "      height: " << height_ << " "
-       "      width: " << width_ << " "
-       "      channels: 1 "
-       "      height: 1 "
-       "      width: 1 "
-       "      data_filler { "
-       "        type: 'gaussian' "
-       "        std: 1.0 "
-       "      } "
+       "    type: 'HDF5Data' "
+       "    hdf5_data_param { "
+       "      source: '" << *(this->input_file_) << "' "
+       "      batch_size: " << num_ / iter_size << " "
        "    } "
        "    top: 'data' "
        "    top: 'targets' "
-       "  } "
+       "  } ";
+    if (share_) {
+      proto <<
+         "  layer { "
+         "    name: 'slice' "
+         "    type: 'Slice' "
+         "    bottom: 'data' "
+         "    top: 'data1' "
+         "    top: 'data2' "
+         "    slice_param { "
+         "      axis: 0 "
+         "    } "
+         "  } ";
+    }
+    proto <<
        "  layer { "
        "    name: 'innerprod' "
        "    type: 'InnerProduct' "
+       "    param { name: 'weights' } "
+       "    param { name: 'bias' } "
        "    inner_product_param { "
        "      num_output: 1 "
        "      weight_filler { "
@@ -97,9 +127,42 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
        "        std: 1.0 "
        "      } "
        "    } "
-       "    bottom: 'data' "
-       "    top: 'innerprod' "
-       "  } "
+       "    bottom: '" << string(share_ ? "data1": "data") << "' "
+       "    top: '" << string(share_ ? "innerprod1": "innerprod") << "' "
+       "  } ";
+    if (share_) {
+      proto <<
+         "  layer { "
+         "    name: 'innerprod2' "
+         "    type: 'InnerProduct' "
+         "    param { name: 'weights' } "
+         "    param { name: 'bias' } "
+         "    inner_product_param { "
+         "      num_output: 1 "
+         "      weight_filler { "
+         "        type: 'gaussian' "
+         "        std: 1.0 "
+         "      } "
+         "      bias_filler { "
+         "        type: 'gaussian' "
+         "        std: 1.0 "
+         "      } "
+         "    } "
+         "    bottom: 'data2' "
+         "    top: 'innerprod2' "
+         "  } "
+         "  layer { "
+         "    name: 'concat' "
+         "    type: 'Concat' "
+         "    bottom: 'innerprod1' "
+         "    bottom: 'innerprod2' "
+         "    top: 'innerprod' "
+         "    concat_param { "
+         "      axis: 0 "
+         "    } "
+         "  } ";
+    }
+    proto <<
        "  layer { "
        "    name: 'loss' "
        "    type: 'EuclideanLoss' "
@@ -113,9 +176,45 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     if (momentum != 0) {
       proto << "momentum: " << momentum << " ";
     }
+    MakeTempDir(&snapshot_prefix_);
+    proto << "snapshot_prefix: '" << snapshot_prefix_ << "/' ";
+    if (snapshot) {
+      proto << "snapshot: " << num_iters << " ";
+    }
     Caffe::set_random_seed(this->seed_);
     this->InitSolverFromProtoString(proto.str());
-    this->solver_->Solve();
+    if (from_snapshot != NULL) {
+      this->solver_->Restore(from_snapshot);
+      for (int i = 0; i < this->solver_->iter(); ++i) {
+        this->solver_->net()->Forward();
+      }
+    }
+    if (devices == 1) {
+      this->solver_->Solve();
+    } else {
+      LOG(INFO) << "Multi-GPU test on " << devices << " devices";
+      vector<int> gpus;
+      // put current device at the beginning
+      int device_id = solver_->param().device_id();
+      gpus.push_back(device_id);
+      for (int i = 0; gpus.size() < devices; ++i) {
+        if (i != device_id)
+          gpus.push_back(i);
+      }
+      Caffe::set_solver_count(gpus.size());
+      this->sync_.reset(new P2PSync<Dtype>(
+          this->solver_, NULL, this->solver_->param()));
+      this->sync_->Run(gpus);
+      Caffe::set_solver_count(1);
+    }
+    if (snapshot) {
+      ostringstream resume_file;
+      resume_file << snapshot_prefix_ << "/_iter_" << num_iters
+                  << ".solverstate";
+      string resume_filename = resume_file.str();
+      return resume_filename;
+    }
+    return string();
   }
 
   // Compute an update value given the current state of the train net,
@@ -123,7 +222,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   // updated_params will store the updated weight and bias results,
   // using the blobs' diffs to hold the update values themselves.
   void ComputeLeastSquaresUpdate(const Dtype learning_rate,
-      const Dtype weight_decay, const Dtype momentum,
+      const Dtype weight_decay, const Dtype momentum, const int num_iters,
       vector<shared_ptr<Blob<Dtype> > >* updated_params) {
     const int N = num_;
     const int D = channels_ * height_ * width_;
@@ -131,8 +230,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     // Run a forward pass, and manually compute the update values from the
     // result.
     Net<Dtype>& net = *this->solver_->net();
-    vector<Blob<Dtype>*> empty_bottom_vec;
-    net.Forward(empty_bottom_vec);
+    net.Forward();
     ASSERT_TRUE(net.has_blob("data"));
     const Blob<Dtype>& data = *net.blob_by_name("data");
     ASSERT_TRUE(net.has_blob("targets"));
@@ -189,25 +287,53 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
           ((i == D) ? bias.cpu_data()[0] : weights.cpu_data()[i]);
       // Finally, compute update.
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
-      ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      if (solver_->type() != string("AdaDelta")
+          && solver_->type() != string("Adam")) {
+        ASSERT_EQ(2, history.size());  // 1 blob for weights, 1 for bias
+      } else {
+        ASSERT_EQ(4, history.size());  // additional blobs for update history
+      }
       Dtype update_value = learning_rate * grad;
       const Dtype history_value = (i == D) ?
             history[1]->cpu_data()[0] : history[0]->cpu_data()[i];
       const Dtype temp = momentum * history_value;
-      switch (solver_type()) {
-      case SolverParameter_SolverType_SGD:
+      if (solver_->type() == string("SGD")) {
         update_value += temp;
-        break;
-      case SolverParameter_SolverType_NESTEROV:
+      } else if (solver_->type() == string("Nesterov")) {
         update_value += temp;
         // step back then over-step
         update_value = (1 + momentum) * update_value - temp;
-        break;
-      case SolverParameter_SolverType_ADAGRAD:
+      } else if (solver_->type() == string("AdaGrad")) {
         update_value /= std::sqrt(history_value + grad * grad) + delta_;
-        break;
-      default:
-        LOG(FATAL) << "Unknown solver type: " << solver_type();
+      } else if (solver_->type() == string("RMSProp")) {
+        const Dtype rms_decay = 0.95;
+        update_value /= std::sqrt(rms_decay*history_value
+            + grad * grad * (1 - rms_decay)) + delta_;
+      } else if (solver_->type() == string("AdaDelta")) {
+        const Dtype update_history_value = (i == D) ?
+            history[1 + num_param_blobs]->cpu_data()[0] :
+            history[0 + num_param_blobs]->cpu_data()[i];
+        const Dtype weighted_gradient_average =
+            momentum * history_value + (1 - momentum) * (grad * grad);
+        update_value = grad * std::sqrt((update_history_value + delta_) /
+            (weighted_gradient_average + delta_)) * learning_rate;
+        // not actually needed, just here for illustrative purposes
+        // const Dtype weighted_update_average =
+        //   momentum * update_history_value + (1 - momentum) * (update_value);
+      } else if (solver_->type() == string("Adam")) {
+        const Dtype momentum2 = 0.999;
+        const Dtype m = history_value;
+        const Dtype v = (i == D) ?
+            history[1 + num_param_blobs]->cpu_data()[0] :
+            history[0 + num_param_blobs]->cpu_data()[i];
+        const Dtype val_m = (1 - momentum) * grad + momentum * m;
+        const Dtype val_v = (1 - momentum2) * grad * grad + momentum2 * v;
+        Dtype alpha_t = learning_rate *
+            std::sqrt(Dtype(1) - pow(momentum2, num_iters)) /
+            (Dtype(1.) - pow(momentum, num_iters));
+        update_value = alpha_t * val_m / (std::sqrt(val_v) + delta_);
+      } else {
+        LOG(FATAL) << "Unknown solver type: " << solver_->type();
       }
       if (i == D) {
         updated_bias.mutable_cpu_diff()[0] = update_value;
@@ -252,7 +378,7 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     EXPECT_NEAR(expected_updated_bias, solver_updated_bias, error_margin);
 
     // Check the solver's history -- should contain the previous update value.
-    if (solver_type() == SolverParameter_SolverType_SGD) {
+    if (solver_->type() == string("SGD")) {
       const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
       ASSERT_EQ(2, history.size());
       for (int i = 0; i < D; ++i) {
@@ -270,6 +396,45 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
     }
   }
 
+  void CheckAccumulation(const Dtype kLearningRate, const Dtype kWeightDecay,
+      const Dtype kMomentum, const int kNumIters, const int kIterSize) {
+    const double kPrecision = 1e-2;
+    const double kMinPrecision = 1e-7;
+    // Solve without accumulation and save parameters.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters);
+    // Save parameters for comparison.
+    Net<Dtype>& net = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& param_blobs =
+        net.layer_by_name("innerprod")->blobs();
+    vector<shared_ptr<Blob<Dtype> > > noaccum_params(param_blobs.size());
+    for (int i = 0; i < param_blobs.size(); ++i) {
+      noaccum_params[i].reset(new Blob<Dtype>());
+      noaccum_params[i]->CopyFrom(*param_blobs[i], false, true);
+    }
+    // Solve by equivalent accumulation of gradients over divided batches.
+    this->RunLeastSquaresSolver(kLearningRate, kWeightDecay, kMomentum,
+        kNumIters, kIterSize);
+    Net<Dtype>& net_accum = *this->solver_->net();
+    const vector<shared_ptr<Blob<Dtype> > >& accum_params =
+        net_accum.layer_by_name("innerprod")->blobs();
+    // Compare accumulated parameters against no accumulation standard.
+    const int D = this->channels_ * this->height_ * this->width_;
+    for (int i = 0; i < D; ++i) {
+      const Dtype expected_param = noaccum_params[0]->cpu_data()[i];
+      const Dtype accum_param = accum_params[0]->cpu_data()[i];
+      const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+          std::min(fabs(expected_param), fabs(accum_param)));
+      EXPECT_NEAR(expected_param, accum_param, error_margin);
+    }
+    ASSERT_EQ(1, accum_params[1]->count());
+    const Dtype expected_bias = noaccum_params[1]->cpu_data()[0];
+    const Dtype accum_bias = accum_params[1]->cpu_data()[0];
+    const Dtype error_margin = std::max(kMinPrecision, kPrecision *
+        std::min(fabs(expected_bias), fabs(accum_bias)));
+    EXPECT_NEAR(expected_bias, accum_bias, error_margin);
+  }
+
   // Test that the correct update is computed for a regularized least squares
   // problem:
   //
@@ -288,20 +453,108 @@ class GradientBasedSolverTest : public MultiDeviceTest<TypeParam> {
   void TestLeastSquaresUpdate(const Dtype learning_rate = 1.0,
       const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
       const int iter_to_check = 0) {
-    // Initialize the solver and run K (= iter_to_check) solver iterations.
-    RunLeastSquaresSolver(learning_rate, weight_decay, momentum, iter_to_check);
+    const int kNum = num_;
+    const int kIterSize = 1;
+    // Test over all numbers of devices.
+    int available_devices = 1;
+#ifndef CPU_ONLY
+    if (Caffe::mode() == Caffe::GPU) {
+      CUDA_CHECK(cudaGetDeviceCount(&available_devices));
+    }
+#endif
+    for (int devices = 1; devices <= available_devices; ++devices) {
+      // Configure batch size for single / multi device equivalence.
+      // Constant data is needed for multi device as for accumulation.
+      num_ = kNum * devices;
+
+      // Initialize the solver and run K (= iter_to_check) solver iterations
+      // (on single device).
+      RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+                            iter_to_check, kIterSize, 1);
+
+      // Compute the (K+1)th update using the analytic least squares gradient.
+      vector<shared_ptr<Blob<Dtype> > > updated_params;
+      ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
+          iter_to_check + 1, &updated_params);
 
-    // Compute the (K+1)th update using the analytic least squares gradient.
-    vector<shared_ptr<Blob<Dtype> > > updated_params;
-    ComputeLeastSquaresUpdate(learning_rate, weight_decay, momentum,
-                              &updated_params);
+      // Reinitialize the solver and run K+1 solver iterations.
+      num_ = kNum;
+      RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+          iter_to_check + 1, kIterSize, devices);
+
+      // Check that the solver's solution matches ours.
+      CheckLeastSquaresUpdate(updated_params);
+    }
+  }
 
-    // Reinitialize the solver and run K+1 solver iterations.
+  void TestSnapshot(const Dtype learning_rate = 1.0,
+      const Dtype weight_decay = 0.0, const Dtype momentum = 0.0,
+      const int num_iters = 1) {
+    // Run the solver for num_iters * 2 iterations.
+    const int total_num_iters = num_iters * 2;
+    bool snapshot = false;
+    const int kIterSize = 1;
+    const int kDevices = 1;
+    RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
+        total_num_iters, kIterSize, kDevices, snapshot);
+
+    // Save the resulting param values.
+    vector<shared_ptr<Blob<Dtype> > > param_copies;
+    const vector<Blob<Dtype>*>& orig_params =
+        solver_->net()->learnable_params();
+    param_copies.resize(orig_params.size());
+    for (int i = 0; i < orig_params.size(); ++i) {
+      param_copies[i].reset(new Blob<Dtype>());
+      const bool kReshape = true;
+      for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+        param_copies[i]->CopyFrom(*orig_params[i], copy_diff, kReshape);
+      }
+    }
+
+    // Save the solver history
+    vector<shared_ptr<Blob<Dtype> > > history_copies;
+    const vector<shared_ptr<Blob<Dtype> > >& orig_history = solver_->history();
+    history_copies.resize(orig_history.size());
+    for (int i = 0; i < orig_history.size(); ++i) {
+      history_copies[i].reset(new Blob<Dtype>());
+      const bool kReshape = true;
+      for (int copy_diff = false; copy_diff <= true; ++copy_diff) {
+        history_copies[i]->CopyFrom(*orig_history[i], copy_diff, kReshape);
+      }
+    }
+
+    // Run the solver for num_iters iterations and snapshot.
+    snapshot = true;
+    string snapshot_name = RunLeastSquaresSolver(learning_rate, weight_decay,
+        momentum, num_iters, kIterSize, kDevices, snapshot);
+
+    // Reinitialize the solver and run for num_iters more iterations.
+    snapshot = false;
     RunLeastSquaresSolver(learning_rate, weight_decay, momentum,
-                          iter_to_check + 1);
+        total_num_iters, kIterSize, kDevices,
+        snapshot, snapshot_name.c_str());
 
-    // Check that the solver's solution matches ours.
-    CheckLeastSquaresUpdate(updated_params);
+    // Check that params now match.
+    const vector<Blob<Dtype>*>& params = solver_->net()->learnable_params();
+    for (int i = 0; i < params.size(); ++i) {
+      for (int j = 0; j < params[i]->count(); ++j) {
+        EXPECT_EQ(param_copies[i]->cpu_data()[j], params[i]->cpu_data()[j])
+            << "param " << i << " data differed at dim " << j;
+        EXPECT_EQ(param_copies[i]->cpu_diff()[j], params[i]->cpu_diff()[j])
+            << "param " << i << " diff differed at dim " << j;
+      }
+    }
+
+    // Check that history now matches.
+    const vector<shared_ptr<Blob<Dtype> > >& history = solver_->history();
+    for (int i = 0; i < history.size(); ++i) {
+      for (int j = 0; j < history[i]->count(); ++j) {
+        EXPECT_EQ(history_copies[i]->cpu_data()[j], history[i]->cpu_data()[j])
+            << "history blob " << i << " data differed at dim " << j;
+        EXPECT_EQ(history_copies[i]->cpu_diff()[j], history[i]->cpu_diff()[j])
+            << "history blob " << i << " diff differed at dim " << j;
+      }
+    }
   }
 };
 
@@ -314,10 +567,6 @@ class SGDSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new SGDSolver<Dtype>(param));
   }
-
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_SGD;
-  }
 };
 
 TYPED_TEST_CASE(SGDSolverTest, TestDtypesAndDevices);
@@ -326,23 +575,38 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
-  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+  const Dtype kMomentum = 0;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithWeightDecayMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
 }
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -352,8 +616,8 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentum) {
 
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -364,14 +628,72 @@ TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
 TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.9;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(SGDSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(SGDSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(SGDSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 
 template <typename TypeParam>
 class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -381,9 +703,6 @@ class AdaGradSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new AdaGradSolver<Dtype>(param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_ADAGRAD;
-  }
 };
 
 TYPED_TEST_CASE(AdaGradSolverTest, TestDtypesAndDevices);
@@ -392,15 +711,15 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
 }
@@ -408,14 +727,73 @@ TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithWeightDecay) {
 TYPED_TEST(AdaGradSolverTest, TestAdaGradLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
-  const Dtype kWeightDecay = 0.1;
-  const Dtype kMomentum = 0.0;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaGradSolverTest,
+      TestAdaGradLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
   const int kNumIters = 4;
+  this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaGradSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaGradSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 
 template <typename TypeParam>
 class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
@@ -425,9 +803,6 @@ class NesterovSolverTest : public GradientBasedSolverTest<TypeParam> {
   virtual void InitSolver(const SolverParameter& param) {
     this->solver_.reset(new NesterovSolver<Dtype>(param));
   }
-  virtual SolverParameter_SolverType solver_type() {
-    return SolverParameter_SolverType_NESTEROV;
-  }
 };
 
 TYPED_TEST_CASE(NesterovSolverTest, TestDtypesAndDevices);
@@ -436,23 +811,35 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdate) {
   this->TestLeastSquaresUpdate();
 }
 
-TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneTenth) {
+TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateLROneHundredth) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 0.1;
+  const Dtype kLearningRate = 0.01;
   this->TestLeastSquaresUpdate(kLearningRate);
 }
 
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithWeightDecay) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
+  const Dtype kLearningRate = 0.01;
   const Dtype kWeightDecay = 0.5;
   this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
 }
 
+TYPED_TEST(NesterovSolverTest,
+           TestNesterovLeastSquaresUpdateWithWeightDecayMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 1;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -462,8 +849,8 @@ TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithMomentum) {
 
 TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
   typedef typename TypeParam::Dtype Dtype;
-  const Dtype kLearningRate = 1.0;
-  const Dtype kWeightDecay = 0.0;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
   const Dtype kMomentum = 0.5;
   const int kNumIters = 4;
   for (int i = 0; i <= kNumIters; ++i) {
@@ -474,12 +861,405 @@ TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
 TYPED_TEST(NesterovSolverTest, TestNesterovLeastSquaresUpdateWithEverything) {
   typedef typename TypeParam::Dtype Dtype;
   const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest,
+           TestNesterovLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(NesterovSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(NesterovSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(NesterovSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+template <typename TypeParam>
+class AdaDeltaSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    this->solver_.reset(new AdaDeltaSolver<Dtype>(param));
+  }
+};
+
+TYPED_TEST_CASE(AdaDeltaSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  this->TestLeastSquaresUpdate(kLearningRate);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.95;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithHalfMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.5;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithMomentum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 1;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithMomentumMultiIter) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestAdaDeltaLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest,
+           TestAdaDeltaLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
+  const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdaDeltaSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.1;
   const Dtype kWeightDecay = 0.1;
+  const Dtype kMomentum = 0.95;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+template <typename TypeParam>
+class AdamSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    SolverParameter new_param = param;
+    const Dtype momentum = 0.9;
+    new_param.set_momentum(momentum);
+    const Dtype momentum2 = 0.999;
+    new_param.set_momentum2(momentum2);
+    this->solver_.reset(new AdamSolver<Dtype>(new_param));
+  }
+};
+
+TYPED_TEST_CASE(AdamSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdate) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0;
+  const Dtype kMomentum = 0.9;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum);
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestAdamLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
   const Dtype kMomentum = 0.9;
   const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(AdamSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.9;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+template <typename TypeParam>
+class RMSPropSolverTest : public GradientBasedSolverTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  virtual void InitSolver(const SolverParameter& param) {
+    const Dtype rms_decay = 0.95;
+    SolverParameter new_param = param;
+    new_param.set_rms_decay(rms_decay);
+    this->solver_.reset(new RMSPropSolver<Dtype>(new_param));
+  }
+};
+
+TYPED_TEST_CASE(RMSPropSolverTest, TestDtypesAndDevices);
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithWeightDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 1.0;
+  const Dtype kWeightDecay = 0.5;
+  this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithRmsDecay) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.0;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestRMSPropLeastSquaresUpdateWithEverything) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  for (int i = 0; i <= kNumIters; ++i) {
+    this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest,
+      TestRMSPropLeastSquaresUpdateWithEverythingShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  this->share_ = true;
   for (int i = 0; i <= kNumIters; ++i) {
     this->TestLeastSquaresUpdate(kLearningRate, kWeightDecay, kMomentum, i);
   }
 }
 
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccum) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestLeastSquaresUpdateWithEverythingAccumShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0.0;
+  const int kNumIters = 4;
+  const int kIterSize = 2;
+  this->share_ = true;
+  this->CheckAccumulation(kLearningRate, kWeightDecay, kMomentum, kNumIters,
+      kIterSize);
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshot) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
+TYPED_TEST(RMSPropSolverTest, TestSnapshotShare) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kLearningRate = 0.01;
+  const Dtype kWeightDecay = 0.5;
+  const Dtype kMomentum = 0;
+  const int kNumIters = 4;
+  this->share_ = true;
+  for (int i = 1; i <= kNumIters; ++i) {
+    this->TestSnapshot(kLearningRate, kWeightDecay, kMomentum, i);
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_hdf5_output_layer.cpp b/src/caffe/test/test_hdf5_output_layer.cpp
index a23034f284a..3833ebff78e 100644
--- a/src/caffe/test/test_hdf5_output_layer.cpp
+++ b/src/caffe/test/test_hdf5_output_layer.cpp
@@ -5,9 +5,10 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
+#include "caffe/layers/hdf5_output_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/util/hdf5.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_hdf5data_layer.cpp b/src/caffe/test/test_hdf5data_layer.cpp
index c9b027f88cf..8884ce95a23 100644
--- a/src/caffe/test/test_hdf5data_layer.cpp
+++ b/src/caffe/test/test_hdf5data_layer.cpp
@@ -1,13 +1,14 @@
 #include <string>
 #include <vector>
 
+#include "hdf5.h"
+
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/filler.hpp"
+#include "caffe/layers/hdf5_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_hinge_loss_layer.cpp b/src/caffe/test/test_hinge_loss_layer.cpp
index b6a99022905..8bf89fa6387 100644
--- a/src/caffe/test/test_hinge_loss_layer.cpp
+++ b/src/caffe/test/test_hinge_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/hinge_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_im2col_kernel.cu b/src/caffe/test/test_im2col_kernel.cu
index ee684c00255..e3a9791bcca 100644
--- a/src/caffe/test/test_im2col_kernel.cu
+++ b/src/caffe/test/test_im2col_kernel.cu
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,8 +5,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 #include "caffe/util/im2col.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -19,39 +18,71 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_col);
 
-extern cudaDeviceProp CAFFE_TEST_CUDA_PROP;
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col);
 
 template <typename Dtype>
-class Im2colKernelTest : public ::testing::Test {
+class Im2colKernelTest : public GPUDeviceTest<Dtype> {
  protected:
   Im2colKernelTest()
         // big so launches > 1024 threads
-      : blob_bottom_(new Blob<Dtype>(5, 500, 10, 10)),
+      : blob_bottom_(new Blob<Dtype>(5, 500, 15, 15)),
+        blob_kernel_shape_(new Blob<int>()),
+        blob_stride_(new Blob<int>()),
+        blob_pad_(new Blob<int>()),
+        blob_dilation_(new Blob<int>()),
         blob_top_(new Blob<Dtype>()),
         blob_top_cpu_(new Blob<Dtype>()) {
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
+    vector<int> dim_blob_shape(1, 2);
+    blob_kernel_shape_->Reshape(dim_blob_shape);
+    blob_stride_->Reshape(dim_blob_shape);
+    blob_pad_->Reshape(dim_blob_shape);
+    blob_dilation_->Reshape(dim_blob_shape);
 
     height_ = blob_bottom_->height();
     width_ = blob_bottom_->width();
     channels_ = blob_bottom_->channels();
     pad_ = 0;
     stride_ = 2;
+    dilation_ = 3;
     kernel_size_ = 3;
-    height_col_ = (height_ + 2 * pad_ - kernel_size_) / stride_ + 1;
-    width_col_ = (width_ + 2 * pad_ - kernel_size_) / stride_ + 1;
+    height_col_ = (height_ + 2 * pad_ -
+        (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
+    width_col_ = (width_ + 2 * pad_ -
+        (dilation_ * (kernel_size_ - 1) + 1)) / stride_ + 1;
+
+    for (int i = 0; i < 2; ++i) {
+      blob_kernel_shape_->mutable_cpu_data()[i] = kernel_size_;
+      blob_stride_->mutable_cpu_data()[i] = stride_;
+      blob_pad_->mutable_cpu_data()[i] = pad_;
+      blob_dilation_->mutable_cpu_data()[i] = dilation_;
+    }
   }
 
   virtual ~Im2colKernelTest() {
-      delete blob_bottom_;
-      delete blob_top_;
-      delete blob_top_cpu_;
+    delete blob_bottom_;
+    delete blob_top_;
+    delete blob_top_cpu_;
+    delete blob_kernel_shape_;
+    delete blob_stride_;
+    delete blob_pad_;
+    delete blob_dilation_;
   }
 
+  Blob<int>* const blob_kernel_shape_;
+  Blob<int>* const blob_stride_;
+  Blob<int>* const blob_pad_;
+  Blob<int>* const blob_dilation_;
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
   Blob<Dtype>* const blob_top_cpu_;
@@ -60,6 +91,7 @@ class Im2colKernelTest : public ::testing::Test {
   int channels_;
   int pad_;
   int stride_;
+  int dilation_;
   int kernel_size_;
   int height_col_;
   int width_col_;
@@ -67,9 +99,7 @@ class Im2colKernelTest : public ::testing::Test {
 
 TYPED_TEST_CASE(Im2colKernelTest, TestDtypes);
 
-TYPED_TEST(Im2colKernelTest, TestGPU) {
-  Caffe::set_mode(Caffe::GPU);
-
+TYPED_TEST(Im2colKernelTest, Test2D) {
   // Reshape the blobs to correct size for im2col output
   this->blob_top_->Reshape(this->blob_bottom_->num(),
           this->channels_ * this->kernel_size_ * this->kernel_size_,
@@ -90,7 +120,7 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
     im2col_cpu(this->blob_bottom_->cpu_data() + this->blob_bottom_->offset(n),
       this->channels_, this->height_, this->width_,
       this->kernel_size_, this->kernel_size_, this->pad_, this->pad_,
-      this->stride_, this->stride_,
+      this->stride_, this->stride_, this->dilation_, this->dilation_,
       cpu_data + this->blob_top_cpu_->offset(n));
   }
 
@@ -107,6 +137,7 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
         num_kernels, bottom_data + this->blob_bottom_->offset(n),
         this->height_, this->width_, this->kernel_size_, this->kernel_size_,
         this->pad_, this->pad_, this->stride_, this->stride_,
+        this->dilation_, this->dilation_,
         this->height_col_, this->width_col_,
         top_data + this->blob_top_->offset(n));
       CUDA_POST_KERNEL_CHECK;
@@ -124,4 +155,59 @@ TYPED_TEST(Im2colKernelTest, TestGPU) {
   }
 }
 
+TYPED_TEST(Im2colKernelTest, TestND) {
+  // Reshape the blobs to correct size for im2col output
+  this->blob_top_->Reshape(this->blob_bottom_->num(),
+      this->channels_ * this->kernel_size_ * this->kernel_size_,
+      this->height_col_,
+      this->width_col_);
+
+  this->blob_top_cpu_->ReshapeLike(*this->blob_top_);
+
+  const TypeParam* bottom_data_cpu = this->blob_bottom_->cpu_data();
+  TypeParam* top_data_cpu = this->blob_top_cpu_->mutable_cpu_data();
+
+  // CPU Version
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    im2col_nd_cpu(bottom_data_cpu + this->blob_bottom_->offset(n), 2,
+        this->blob_bottom_->shape().data() + 1,
+        this->blob_top_cpu_->shape().data() + 1,
+        this->blob_kernel_shape_->cpu_data(),
+        this->blob_pad_->cpu_data(), this->blob_stride_->cpu_data(),
+        this->blob_dilation_->cpu_data(),
+        top_data_cpu + this->blob_top_cpu_->offset(n));
+  }
+
+  // GPU version
+  int num_kernels = this->channels_ * this->height_col_ * this->width_col_;
+  int default_grid_dim = CAFFE_GET_BLOCKS(num_kernels);
+  const TypeParam* bottom_data_gpu = this->blob_bottom_->gpu_data();
+
+  // Launch with different grid sizes
+  for (int grid_div = 2; grid_div <= 8; grid_div++) {
+    for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+      const int grid_dim = default_grid_dim / grid_div;
+      TypeParam* top_data_gpu = this->blob_top_->mutable_gpu_data();
+      // NOLINT_NEXT_LINE(whitespace/operators)
+      im2col_nd_gpu_kernel<TypeParam, 2><<<grid_dim, CAFFE_CUDA_NUM_THREADS>>>(
+          num_kernels, bottom_data_gpu + this->blob_bottom_->offset(n),
+          this->blob_bottom_->gpu_shape() + 1, this->blob_top_->gpu_shape() + 1,
+          this->blob_kernel_shape_->gpu_data(), this->blob_pad_->gpu_data(),
+          this->blob_stride_->gpu_data(), this->blob_dilation_->gpu_data(),
+          top_data_gpu + this->blob_top_->offset(n));
+      CUDA_POST_KERNEL_CHECK;
+    }
+
+    // Compare results against CPU version
+    for (int i = 0; i < this->blob_top_->count(); ++i) {
+      TypeParam cpuval = top_data_cpu[i];
+      TypeParam gpuval = this->blob_top_->cpu_data()[i];
+      EXPECT_EQ(cpuval, gpuval);
+      if (cpuval != gpuval) {
+        break;
+      }
+    }
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_im2col_layer.cpp b/src/caffe/test/test_im2col_layer.cpp
index f50abe103f8..a7faf18f972 100644
--- a/src/caffe/test/test_im2col_layer.cpp
+++ b/src/caffe/test/test_im2col_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/im2col_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -21,6 +20,7 @@ class Im2colLayerTest : public MultiDeviceTest<TypeParam> {
       : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
+    Caffe::set_random_seed(1701);
     FillerParameter filler_param;
     GaussianFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
@@ -41,14 +41,21 @@ TYPED_TEST(Im2colLayerTest, TestSetup) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(11);
+  this->blob_bottom_->Reshape(bottom_shape);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   EXPECT_EQ(this->blob_top_->num(), 2);
   EXPECT_EQ(this->blob_top_->channels(), 27);
   EXPECT_EQ(this->blob_top_->height(), 2);
-  EXPECT_EQ(this->blob_top_->width(), 2);
+  EXPECT_EQ(this->blob_top_->width(), 3);
 }
 
 TYPED_TEST(Im2colLayerTest, TestForward) {
@@ -56,8 +63,8 @@ TYPED_TEST(Im2colLayerTest, TestForward) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -73,14 +80,68 @@ TYPED_TEST(Im2colLayerTest, TestGradient) {
   LayerParameter layer_param;
   ConvolutionParameter* convolution_param =
       layer_param.mutable_convolution_param();
-  convolution_param->set_kernel_size(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
       this->blob_top_vec_);
 }
 
+TYPED_TEST(Im2colLayerTest, TestDilatedGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(9);
+  this->blob_bottom_->Reshape(bottom_shape);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
+
+TYPED_TEST(Im2colLayerTest, TestGradientForceND) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->set_force_nd_im2col(true);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(Im2colLayerTest, TestDilatedGradientForceND) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ConvolutionParameter* convolution_param =
+      layer_param.mutable_convolution_param();
+  vector<int> bottom_shape;
+  bottom_shape.push_back(2);
+  bottom_shape.push_back(3);
+  bottom_shape.push_back(10);
+  bottom_shape.push_back(9);
+  this->blob_bottom_->Reshape(bottom_shape);
+  convolution_param->add_kernel_size(3);
+  convolution_param->add_stride(2);
+  convolution_param->add_dilation(3);
+  convolution_param->set_force_nd_im2col(true);
+  Im2colLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+                                  this->blob_top_vec_);
+}
 
 TYPED_TEST(Im2colLayerTest, TestRect) {
   typedef typename TypeParam::Dtype Dtype;
@@ -89,7 +150,7 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -100,7 +161,6 @@ TYPED_TEST(Im2colLayerTest, TestRect) {
   }
 }
 
-
 TYPED_TEST(Im2colLayerTest, TestRectGradient) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -108,7 +168,7 @@ TYPED_TEST(Im2colLayerTest, TestRectGradient) {
       layer_param.mutable_convolution_param();
   convolution_param->set_kernel_h(5);
   convolution_param->set_kernel_w(3);
-  convolution_param->set_stride(2);
+  convolution_param->add_stride(2);
   Im2colLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-2);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/test/test_image_data_layer.cpp b/src/caffe/test/test_image_data_layer.cpp
index 931a5ebf137..a4080ccd145 100644
--- a/src/caffe/test/test_image_data_layer.cpp
+++ b/src/caffe/test/test_image_data_layer.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <map>
 #include <string>
 #include <vector>
@@ -7,9 +8,9 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/image_data_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -177,3 +178,4 @@ TYPED_TEST(ImageDataLayerTest, TestShuffle) {
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_infogain_loss_layer.cpp b/src/caffe/test/test_infogain_loss_layer.cpp
index 7ec2f8073c1..a24ac683dc5 100644
--- a/src/caffe/test/test_infogain_loss_layer.cpp
+++ b/src/caffe/test/test_infogain_loss_layer.cpp
@@ -1,6 +1,3 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/loss_layers.hpp"
+#include "caffe/layers/infogain_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_inner_product_layer.cpp b/src/caffe/test/test_inner_product_layer.cpp
index c03df17383a..f1ec2333fae 100644
--- a/src/caffe/test/test_inner_product_layer.cpp
+++ b/src/caffe/test/test_inner_product_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -23,16 +22,21 @@ class InnerProductLayerTest : public MultiDeviceTest<TypeParam> {
  protected:
   InnerProductLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_nobatch_(new Blob<Dtype>(1, 2, 3, 4)),
         blob_top_(new Blob<Dtype>()) {
     // fill the values
     FillerParameter filler_param;
     UniformFiller<Dtype> filler(filler_param);
     filler.Fill(this->blob_bottom_);
-    blob_bottom_vec_.push_back(blob_bottom_);
     blob_top_vec_.push_back(blob_top_);
   }
-  virtual ~InnerProductLayerTest() { delete blob_bottom_; delete blob_top_; }
+  virtual ~InnerProductLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_nobatch_;
+    delete blob_top_;
+  }
   Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_nobatch_;
   Blob<Dtype>* const blob_top_;
   vector<Blob<Dtype>*> blob_bottom_vec_;
   vector<Blob<Dtype>*> blob_top_vec_;
@@ -42,6 +46,7 @@ TYPED_TEST_CASE(InnerProductLayerTest, TestDtypesAndDevices);
 
 TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
   LayerParameter layer_param;
   InnerProductParameter* inner_product_param =
       layer_param.mutable_inner_product_param();
@@ -55,8 +60,157 @@ TYPED_TEST(InnerProductLayerTest, TestSetUp) {
   EXPECT_EQ(this->blob_top_->channels(), 10);
 }
 
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeFalse) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(false);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(10, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(60, layer->blobs()[0]->shape(1));
+}
+
+/** @brief TestSetUp while toggling tranpose flag
+ */
+TYPED_TEST(InnerProductLayerTest, TestSetUpTranposeTrue) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  LayerParameter layer_param;
+  InnerProductParameter* inner_product_param =
+      layer_param.mutable_inner_product_param();
+  inner_product_param->set_num_output(10);
+  inner_product_param->set_transpose(true);
+  shared_ptr<InnerProductLayer<Dtype> > layer(
+      new InnerProductLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(2, this->blob_top_->num());
+  EXPECT_EQ(1, this->blob_top_->height());
+  EXPECT_EQ(1, this->blob_top_->width());
+  EXPECT_EQ(10, this->blob_top_->channels());
+  EXPECT_EQ(2, layer->blobs()[0]->num_axes());
+  EXPECT_EQ(60, layer->blobs()[0]->shape(0));
+  EXPECT_EQ(10, layer->blobs()[0]->shape(1));
+}
+
 TYPED_TEST(InnerProductLayerTest, TestForward) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* data = this->blob_top_->cpu_data();
+    const int count = this->blob_top_->count();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_GE(data[i], 1.);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+/**
+ * @brief Init. an IP layer without transpose + random weights,
+ * run Forward, save the result.
+ * Init. another IP layer with transpose.
+ * manually copy and transpose the weights from the first IP layer,
+ * then run Forward on the same input and check that the result is the same
+ */
+TYPED_TEST(InnerProductLayerTest, TestForwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count = this->blob_top_->count();
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->ReshapeLike(*this->blob_top_);
+    caffe_copy(count, this->blob_top_->cpu_data(), top->mutable_cpu_data());
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    const int count_w = layer->blobs()[0]->count();
+    EXPECT_EQ(count_w, ip_t->blobs()[0]->count());
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    const Dtype* w = layer->blobs()[0]->cpu_data();
+    Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+    const int width = layer->blobs()[0]->shape(1);
+    const int width_t = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < count_w; ++i) {
+      int r = i / width;
+      int c = i % width;
+      w_t[c*width_t+r] = w[r*width+c];  // copy while transposing
+    }
+    // copy bias from 1st IP layer to 2nd IP layer
+    ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+    caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+        ip_t->blobs()[1]->mutable_cpu_data());
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    EXPECT_EQ(count, this->blob_top_->count())
+        << "Invalid count for top blob for IP with transpose.";
+    Blob<Dtype>* const top_t = new Blob<Dtype>();\
+    top_t->ReshapeLike(*this->blob_top_vec_[0]);
+    caffe_copy(count,
+      this->blob_top_vec_[0]->cpu_data(),
+      top_t->mutable_cpu_data());
+    const Dtype* data = top->cpu_data();
+    const Dtype* data_t = top_t->cpu_data();
+    for (int i = 0; i < count; ++i) {
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestForwardNoBatch) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_nobatch_);
   bool IS_VALID_CUDA = false;
 #ifndef CPU_ONLY
   IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
@@ -87,6 +241,7 @@ TYPED_TEST(InnerProductLayerTest, TestForward) {
 
 TYPED_TEST(InnerProductLayerTest, TestGradient) {
   typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
   bool IS_VALID_CUDA = false;
 #ifndef CPU_ONLY
   IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
@@ -110,4 +265,127 @@ TYPED_TEST(InnerProductLayerTest, TestGradient) {
   }
 }
 
+TYPED_TEST(InnerProductLayerTest, TestGradientTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(11);
+    inner_product_param->mutable_weight_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_type("gaussian");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(true);
+    InnerProductLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
+TYPED_TEST(InnerProductLayerTest, TestBackwardTranspose) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_);
+  bool IS_VALID_CUDA = false;
+#ifndef CPU_ONLY
+  IS_VALID_CUDA = CAFFE_TEST_CUDA_PROP.major >= 2;
+#endif
+  if (Caffe::mode() == Caffe::CPU ||
+      sizeof(Dtype) == 4 || IS_VALID_CUDA) {
+    LayerParameter layer_param;
+    InnerProductParameter* inner_product_param =
+        layer_param.mutable_inner_product_param();
+    inner_product_param->set_num_output(10);
+    inner_product_param->mutable_weight_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_type("uniform");
+    inner_product_param->mutable_bias_filler()->set_min(1);
+    inner_product_param->mutable_bias_filler()->set_max(2);
+    inner_product_param->set_transpose(false);
+    shared_ptr<InnerProductLayer<Dtype> > layer(
+        new InnerProductLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    // copy top blob
+    Blob<Dtype>* const top = new Blob<Dtype>();
+    top->CopyFrom(*this->blob_top_, false, true);
+    // fake top diff
+    Blob<Dtype>* const diff = new Blob<Dtype>();
+    diff->ReshapeLike(*this->blob_top_);
+    {
+      FillerParameter filler_param;
+      UniformFiller<Dtype> filler(filler_param);
+      filler.Fill(diff);
+    }
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    vector<bool> propagate_down(1, true);
+    layer->Backward(this->blob_top_vec_,
+        propagate_down,
+        this->blob_bottom_vec_);
+    // copy first ip's weights and their diffs
+    Blob<Dtype>* const w = new Blob<Dtype>();
+    w->CopyFrom(*layer->blobs()[0], false, true);
+    w->CopyFrom(*layer->blobs()[0], true, true);
+    // copy bottom diffs
+    Blob<Dtype>* const bottom_diff = new Blob<Dtype>();
+    bottom_diff->CopyFrom(*this->blob_bottom_vec_[0], true, true);
+    // repeat original top with tranposed ip
+    this->blob_top_vec_.clear();
+    this->blob_top_vec_.push_back(new Blob<Dtype>());
+    inner_product_param->set_transpose(true);
+    shared_ptr<InnerProductLayer<Dtype> > ip_t(
+        new InnerProductLayer<Dtype>(layer_param));
+    ip_t->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    // manually copy and transpose the weights from 1st IP layer into 2nd
+    {
+      const Dtype* w_src = w->cpu_data();
+      Dtype* w_t = ip_t->blobs()[0]->mutable_cpu_data();
+      const int width = layer->blobs()[0]->shape(1);
+      const int width_t = ip_t->blobs()[0]->shape(1);
+      for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+        int r = i / width;
+        int c = i % width;
+        w_t[c*width_t+r] = w_src[r*width+c];  // copy while transposing
+      }
+      // copy bias from 1st IP layer to 2nd IP layer
+      ASSERT_EQ(layer->blobs()[1]->count(), ip_t->blobs()[1]->count());
+      caffe_copy(layer->blobs()[1]->count(), layer->blobs()[1]->cpu_data(),
+          ip_t->blobs()[1]->mutable_cpu_data());
+    }
+    ip_t->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    caffe_copy(this->blob_top_vec_[0]->count(),
+      diff->cpu_data(),
+      this->blob_top_vec_[0]->mutable_cpu_diff());
+    ip_t->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+    const Dtype* data = w->cpu_diff();
+    const Dtype* data_t = ip_t->blobs()[0]->cpu_diff();
+    const int WIDTH = layer->blobs()[0]->shape(1);
+    const int WIDTH_T = ip_t->blobs()[0]->shape(1);
+    for (int i = 0; i < layer->blobs()[0]->count(); ++i) {
+      int r = i / WIDTH;
+      int c = i % WIDTH;
+      EXPECT_NE(Dtype(0.), data[r*WIDTH+c]);
+      EXPECT_FLOAT_EQ(data[r*WIDTH+c], data_t[c*WIDTH_T+r]);
+    }
+    data = bottom_diff->cpu_diff();
+    data_t = this->blob_bottom_vec_[0]->cpu_diff();
+    for (int i = 0; i < this->blob_bottom_vec_[0]->count(); ++i) {
+      EXPECT_NE(Dtype(0.), data[i]);
+      EXPECT_FLOAT_EQ(data[i], data_t[i]);
+    }
+  } else {
+    LOG(ERROR) << "Skipping test due to old architecture.";
+  }
+}
+
 }  // namespace caffe
diff --git a/src/caffe/test/test_internal_thread.cpp b/src/caffe/test/test_internal_thread.cpp
index 31882b6db1d..93f1cc541cd 100644
--- a/src/caffe/test/test_internal_thread.cpp
+++ b/src/caffe/test/test_internal_thread.cpp
@@ -2,6 +2,7 @@
 #include "gtest/gtest.h"
 
 #include "caffe/internal_thread.hpp"
+#include "caffe/util/math_functions.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -13,11 +14,40 @@ class InternalThreadTest : public ::testing::Test {};
 TEST_F(InternalThreadTest, TestStartAndExit) {
   InternalThread thread;
   EXPECT_FALSE(thread.is_started());
-  EXPECT_TRUE(thread.StartInternalThread());
+  thread.StartInternalThread();
   EXPECT_TRUE(thread.is_started());
-  EXPECT_TRUE(thread.WaitForInternalThreadToExit());
+  thread.StopInternalThread();
   EXPECT_FALSE(thread.is_started());
 }
 
+class TestThreadA : public InternalThread {
+  void InternalThreadEntry() {
+    EXPECT_EQ(4244559767, caffe_rng_rand());
+  }
+};
+
+class TestThreadB : public InternalThread {
+  void InternalThreadEntry() {
+    EXPECT_EQ(1726478280, caffe_rng_rand());
+  }
+};
+
+TEST_F(InternalThreadTest, TestRandomSeed) {
+  TestThreadA t1;
+  Caffe::set_random_seed(9658361);
+  t1.StartInternalThread();
+  t1.StopInternalThread();
+
+  TestThreadA t2;
+  Caffe::set_random_seed(9658361);
+  t2.StartInternalThread();
+  t2.StopInternalThread();
+
+  TestThreadB t3;
+  Caffe::set_random_seed(3435563);
+  t3.StartInternalThread();
+  t3.StopInternalThread();
+}
+
 }  // namespace caffe
 
diff --git a/src/caffe/test/test_io.cpp b/src/caffe/test/test_io.cpp
index 4ab96311bbc..c2c919e90dc 100644
--- a/src/caffe/test/test_io.cpp
+++ b/src/caffe/test/test_io.cpp
@@ -1,3 +1,4 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
@@ -420,3 +421,4 @@ TEST_F(IOTest, TestDecodeDatumToCVMatContentNative) {
 }
 
 }  // namespace caffe
+#endif  // USE_OPENCV
diff --git a/src/caffe/test/test_layer_factory.cpp b/src/caffe/test/test_layer_factory.cpp
index efb1b37ac42..7d5d39d8b91 100644
--- a/src/caffe/test/test_layer_factory.cpp
+++ b/src/caffe/test/test_layer_factory.cpp
@@ -1,11 +1,14 @@
 #include <map>
 #include <string>
 
+#include "boost/scoped_ptr.hpp"
 #include "gtest/gtest.h"
 
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
 #include "caffe/layer_factory.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -21,11 +24,24 @@ TYPED_TEST(LayerFactoryTest, TestCreateLayer) {
   typename LayerRegistry<Dtype>::CreatorRegistry& registry =
       LayerRegistry<Dtype>::Registry();
   shared_ptr<Layer<Dtype> > layer;
-  LayerParameter layer_param;
   for (typename LayerRegistry<Dtype>::CreatorRegistry::iterator iter =
        registry.begin(); iter != registry.end(); ++iter) {
     // Special case: PythonLayer is checked by pytest
     if (iter->first == "Python") { continue; }
+    LayerParameter layer_param;
+    // Data layers expect a DB
+    if (iter->first == "Data") {
+#ifdef USE_LEVELDB
+      string tmp;
+      MakeTempDir(&tmp);
+      boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+      db->Open(tmp, db::NEW);
+      db->Close();
+      layer_param.mutable_data_param()->set_source(tmp);
+#else
+      continue;
+#endif  // USE_LEVELDB
+    }
     layer_param.set_type(iter->first);
     layer = LayerRegistry<Dtype>::CreateLayer(layer_param);
     EXPECT_EQ(iter->first, layer->type());
diff --git a/src/caffe/test/test_lrn_layer.cpp b/src/caffe/test/test_lrn_layer.cpp
index c4e2f8ea7f2..4c97b1ae07b 100644
--- a/src/caffe/test/test_lrn_layer.cpp
+++ b/src/caffe/test/test_lrn_layer.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -7,7 +6,12 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/lrn_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_lcn_layer.hpp"
+#include "caffe/layers/cudnn_lrn_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -246,5 +250,201 @@ TYPED_TEST(LRNLayerTest, TestGradientWithinChannel) {
       this->blob_top_vec_);
 }
 
+#ifdef USE_CUDNN
+template <typename Dtype>
+class CuDNNLRNLayerTest : public GPUDeviceTest<Dtype> {
+ protected:
+  CuDNNLRNLayerTest()
+      : epsilon_(Dtype(1e-5)),
+        blob_bottom_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 7, 3, 3);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~CuDNNLRNLayerTest() { delete blob_bottom_; delete blob_top_; }
+  void ReferenceLRNForward(const Blob<Dtype>& blob_bottom,
+      const LayerParameter& layer_param, Blob<Dtype>* blob_top);
+
+  Dtype epsilon_;
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+template <typename TypeParam>
+void CuDNNLRNLayerTest<TypeParam>::ReferenceLRNForward(
+    const Blob<TypeParam>& blob_bottom, const LayerParameter& layer_param,
+    Blob<TypeParam>* blob_top) {
+  typedef TypeParam Dtype;
+  blob_top->Reshape(blob_bottom.num(), blob_bottom.channels(),
+      blob_bottom.height(), blob_bottom.width());
+  Dtype* top_data = blob_top->mutable_cpu_data();
+  LRNParameter lrn_param = layer_param.lrn_param();
+  Dtype alpha = lrn_param.alpha();
+  Dtype beta = lrn_param.beta();
+  int size = lrn_param.local_size();
+  switch (lrn_param.norm_region()) {
+  case LRNParameter_NormRegion_ACROSS_CHANNELS:
+    for (int n = 0; n < blob_bottom.num(); ++n) {
+      for (int c = 0; c < blob_bottom.channels(); ++c) {
+        for (int h = 0; h < blob_bottom.height(); ++h) {
+          for (int w = 0; w < blob_bottom.width(); ++w) {
+            int c_start = c - (size - 1) / 2;
+            int c_end = min(c_start + size, blob_bottom.channels());
+            c_start = max(c_start, 0);
+            Dtype scale = 1.;
+            for (int i = c_start; i < c_end; ++i) {
+              Dtype value = blob_bottom.data_at(n, i, h, w);
+              scale += value * value * alpha / size;
+            }
+            *(top_data + blob_top->offset(n, c, h, w)) =
+              blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+          }
+        }
+      }
+    }
+    break;
+  case LRNParameter_NormRegion_WITHIN_CHANNEL:
+    for (int n = 0; n < blob_bottom.num(); ++n) {
+      for (int c = 0; c < blob_bottom.channels(); ++c) {
+        for (int h = 0; h < blob_bottom.height(); ++h) {
+          int h_start = h - (size - 1) / 2;
+          int h_end = min(h_start + size, blob_bottom.height());
+          h_start = max(h_start, 0);
+          for (int w = 0; w < blob_bottom.width(); ++w) {
+            Dtype scale = 1.;
+            int w_start = w - (size - 1) / 2;
+            int w_end = min(w_start + size, blob_bottom.width());
+            w_start = max(w_start, 0);
+            for (int nh = h_start; nh < h_end; ++nh) {
+              for (int nw = w_start; nw < w_end; ++nw) {
+                Dtype value = blob_bottom.data_at(n, c, nh, nw);
+                scale += value * value * alpha / (size * size);
+              }
+            }
+            *(top_data + blob_top->offset(n, c, h, w)) =
+              blob_bottom.data_at(n, c, h, w) / pow(scale, beta);
+          }
+        }
+      }
+    }
+    break;
+  default:
+    LOG(FATAL) << "Unknown normalization region.";
+  }
+}
+
+TYPED_TEST_CASE(CuDNNLRNLayerTest, TestDtypes);
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsCuDNN) {
+  // typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CuDNNLRNLayer<TypeParam> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<TypeParam> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardAcrossChannelsLargeRegionCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestForwardWithinChannel) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_norm_region(
+      LRNParameter_NormRegion_WITHIN_CHANNEL);
+  layer_param.mutable_lrn_param()->set_local_size(3);
+  CuDNNLCNLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  Blob<Dtype> top_reference;
+  this->ReferenceLRNForward(*(this->blob_bottom_), layer_param,
+      &top_reference);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(this->blob_top_->cpu_data()[i], top_reference.cpu_data()[i],
+                this->epsilon_);
+  }
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientWithinChannel) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_norm_region(
+      LRNParameter_NormRegion_WITHIN_CHANNEL);
+  layer_param.mutable_lrn_param()->set_local_size(3);
+  CuDNNLCNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(CuDNNLRNLayerTest, TestGradientAcrossChannelsLargeRegionCuDNN) {
+  typedef TypeParam Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_lrn_param()->set_local_size(15);
+  CuDNNLRNLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_top_->count(); ++i) {
+    this->blob_top_->mutable_cpu_diff()[i] = 1.;
+  }
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+#endif
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_math_functions.cpp b/src/caffe/test/test_math_functions.cpp
index 667f744bdd7..efc5a2784eb 100644
--- a/src/caffe/test/test_math_functions.cpp
+++ b/src/caffe/test/test_math_functions.cpp
@@ -1,8 +1,6 @@
 #include <stdint.h>  // for uint32_t & uint64_t
 #include <time.h>
-#include <climits>
 #include <cmath>  // for std::fabs
-#include <cstdlib>  // for rand_r
 
 #include "gtest/gtest.h"
 
@@ -15,8 +13,10 @@
 
 namespace caffe {
 
-template<typename Dtype>
-class MathFunctionsTest : public ::testing::Test {
+template <typename TypeParam>
+class MathFunctionsTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   MathFunctionsTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -39,47 +39,23 @@ class MathFunctionsTest : public ::testing::Test {
     delete blob_top_;
   }
 
-  // http://en.wikipedia.org/wiki/Hamming_distance
-  int ReferenceHammingDistance(const int n, const Dtype* x, const Dtype* y) {
-    int dist = 0;
-    uint64_t val;
-    for (int i = 0; i < n; ++i) {
-      if (sizeof(Dtype) == 8) {
-        val = static_cast<uint64_t>(x[i]) ^ static_cast<uint64_t>(y[i]);
-      } else if (sizeof(Dtype) == 4) {
-        val = static_cast<uint32_t>(x[i]) ^ static_cast<uint32_t>(y[i]);
-      } else {
-        LOG(FATAL) << "Unrecognized Dtype size: " << sizeof(Dtype);
-      }
-      // Count the number of set bits
-      while (val) {
-        ++dist;
-        val &= val - 1;
-      }
-    }
-    return dist;
-  }
-
   Blob<Dtype>* const blob_bottom_;
   Blob<Dtype>* const blob_top_;
 };
 
-TYPED_TEST_CASE(MathFunctionsTest, TestDtypes);
+template <typename Dtype>
+class CPUMathFunctionsTest
+  : public MathFunctionsTest<CPUDevice<Dtype> > {
+};
 
-TYPED_TEST(MathFunctionsTest, TestNothing) {
+TYPED_TEST_CASE(CPUMathFunctionsTest, TestDtypes);
+
+TYPED_TEST(CPUMathFunctionsTest, TestNothing) {
   // The first test case of a test suite takes the longest time
   //   due to the set up overhead.
 }
 
-TYPED_TEST(MathFunctionsTest, TestHammingDistanceCPU) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  EXPECT_EQ(this->ReferenceHammingDistance(n, x, y),
-            caffe_cpu_hamming_distance<TypeParam>(n, x, y));
-}
-
-TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -90,7 +66,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumCPU) {
   EXPECT_LT((cpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sign<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -100,7 +76,7 @@ TYPED_TEST(MathFunctionsTest, TestSignCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_cpu_sgnbit<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -110,7 +86,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   caffe_abs<TypeParam>(n, x, this->blob_bottom_->mutable_cpu_diff());
@@ -120,7 +96,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -133,11 +109,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleCPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
+TYPED_TEST(CPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->cpu_data();
   TypeParam* top_data = this->blob_top_->mutable_cpu_data();
-  Caffe::set_mode(Caffe::CPU);
   caffe_copy(n, bottom_data, top_data);
   for (int i = 0; i < n; ++i) {
     EXPECT_EQ(bottom_data[i], top_data[i]);
@@ -146,19 +121,13 @@ TYPED_TEST(MathFunctionsTest, TestCopyCPU) {
 
 #ifndef CPU_ONLY
 
-// TODO: Fix caffe_gpu_hamming_distance and re-enable this test.
-TYPED_TEST(MathFunctionsTest, DISABLED_TestHammingDistanceGPU) {
-  int n = this->blob_bottom_->count();
-  const TypeParam* x = this->blob_bottom_->cpu_data();
-  const TypeParam* y = this->blob_top_->cpu_data();
-  int reference_distance = this->ReferenceHammingDistance(n, x, y);
-  x = this->blob_bottom_->gpu_data();
-  y = this->blob_top_->gpu_data();
-  int computed_distance = caffe_gpu_hamming_distance<TypeParam>(n, x, y);
-  EXPECT_EQ(reference_distance, computed_distance);
-}
+template <typename Dtype>
+class GPUMathFunctionsTest : public MathFunctionsTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUMathFunctionsTest, TestDtypes);
 
-TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestAsum) {
   int n = this->blob_bottom_->count();
   const TypeParam* x = this->blob_bottom_->cpu_data();
   TypeParam std_asum = 0;
@@ -170,7 +139,7 @@ TYPED_TEST(MathFunctionsTest, TestAsumGPU) {
   EXPECT_LT((gpu_asum - std_asum) / std_asum, 1e-2);
 }
 
-TYPED_TEST(MathFunctionsTest, TestSignGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSign) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sign<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -181,7 +150,7 @@ TYPED_TEST(MathFunctionsTest, TestSignGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestSgnbit) {
   int n = this->blob_bottom_->count();
   caffe_gpu_sgnbit<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -192,7 +161,7 @@ TYPED_TEST(MathFunctionsTest, TestSgnbitGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestFabs) {
   int n = this->blob_bottom_->count();
   caffe_gpu_abs<TypeParam>(n, this->blob_bottom_->gpu_data(),
                             this->blob_bottom_->mutable_gpu_diff());
@@ -203,7 +172,7 @@ TYPED_TEST(MathFunctionsTest, TestFabsGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestScale) {
   int n = this->blob_bottom_->count();
   TypeParam alpha = this->blob_bottom_->cpu_diff()[caffe_rng_rand() %
                                                    this->blob_bottom_->count()];
@@ -216,11 +185,10 @@ TYPED_TEST(MathFunctionsTest, TestScaleGPU) {
   }
 }
 
-TYPED_TEST(MathFunctionsTest, TestCopyGPU) {
+TYPED_TEST(GPUMathFunctionsTest, TestCopy) {
   const int n = this->blob_bottom_->count();
   const TypeParam* bottom_data = this->blob_bottom_->gpu_data();
   TypeParam* top_data = this->blob_top_->mutable_gpu_data();
-  Caffe::set_mode(Caffe::GPU);
   caffe_copy(n, bottom_data, top_data);
   bottom_data = this->blob_bottom_->cpu_data();
   top_data = this->blob_top_->mutable_cpu_data();
diff --git a/src/caffe/test/test_maxpool_dropout_layers.cpp b/src/caffe/test/test_maxpool_dropout_layers.cpp
index 611d9790863..4f0e20ac3a7 100644
--- a/src/caffe/test/test_maxpool_dropout_layers.cpp
+++ b/src/caffe/test/test_maxpool_dropout_layers.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,8 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_memory_data_layer.cpp b/src/caffe/test/test_memory_data_layer.cpp
index a79033f59f1..7998bc18262 100644
--- a/src/caffe/test/test_memory_data_layer.cpp
+++ b/src/caffe/test/test_memory_data_layer.cpp
@@ -1,10 +1,12 @@
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
+#endif  // USE_OPENCV
 
 #include <string>
 #include <vector>
 
-#include "caffe/data_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/memory_data_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
@@ -113,6 +115,7 @@ TYPED_TEST(MemoryDataLayerTest, TestForward) {
   }
 }
 
+#ifdef USE_OPENCV
 TYPED_TEST(MemoryDataLayerTest, AddDatumVectorDefaultTransform) {
   typedef typename TypeParam::Dtype Dtype;
 
@@ -292,5 +295,5 @@ TYPED_TEST(MemoryDataLayerTest, TestSetBatchSize) {
     }
   }
 }
-
+#endif  // USE_OPENCV
 }  // namespace caffe
diff --git a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
index 9038017e3e2..8cc21022305 100644
--- a/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
+++ b/src/caffe/test/test_multinomial_logistic_loss_layer.cpp
@@ -1,6 +1,3 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/multinomial_logistic_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -16,7 +13,7 @@
 namespace caffe {
 
 template <typename Dtype>
-class MultinomialLogisticLossLayerTest : public ::testing::Test {
+class MultinomialLogisticLossLayerTest : public CPUDeviceTest<Dtype> {
  protected:
   MultinomialLogisticLossLayerTest()
       : blob_bottom_data_(new Blob<Dtype>(10, 5, 1, 1)),
@@ -51,7 +48,6 @@ TYPED_TEST_CASE(MultinomialLogisticLossLayerTest, TestDtypes);
 
 TYPED_TEST(MultinomialLogisticLossLayerTest, TestGradientCPU) {
   LayerParameter layer_param;
-  Caffe::set_mode(Caffe::CPU);
   MultinomialLogisticLossLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   GradientChecker<TypeParam> checker(1e-2, 2*1e-2, 1701, 0, 0.05);
diff --git a/src/caffe/test/test_mvn_layer.cpp b/src/caffe/test/test_mvn_layer.cpp
index 933b4326417..28a762d2741 100644
--- a/src/caffe/test/test_mvn_layer.cpp
+++ b/src/caffe/test/test_mvn_layer.cpp
@@ -1,11 +1,10 @@
-#include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/mvn_layer.hpp"
+#include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -73,7 +72,8 @@ TYPED_TEST(MVNLayerTest, TestForward) {
 TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{normalize_variance: false}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -105,7 +105,8 @@ TYPED_TEST(MVNLayerTest, TestForwardMeanOnly) {
 TYPED_TEST(MVNLayerTest, TestForwardAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{across_channels: true}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
   layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -149,7 +150,8 @@ TYPED_TEST(MVNLayerTest, TestGradient) {
 TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{normalize_variance: false}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{normalize_variance: false}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
@@ -159,7 +161,8 @@ TYPED_TEST(MVNLayerTest, TestGradientMeanOnly) {
 TYPED_TEST(MVNLayerTest, TestGradientAcrossChannels) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
-  layer_param.ParseFromString("mvn_param{across_channels: true}");
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "mvn_param{across_channels: true}", &layer_param));
   MVNLayer<Dtype> layer(layer_param);
   GradientChecker<Dtype> checker(1e-2, 1e-3);
   checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
diff --git a/src/caffe/test/test_net.cpp b/src/caffe/test/test_net.cpp
index 08106e79274..1e0788ec127 100644
--- a/src/caffe/test/test_net.cpp
+++ b/src/caffe/test/test_net.cpp
@@ -288,6 +288,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
       const bool force_backward = false, const bool bias_term = false,
       const Dtype blobs_lr_w1 = 1, const Dtype blobs_lr_b1 = 2,
       const Dtype blobs_lr_w2 = 1, const Dtype blobs_lr_b2 = 2) {
+    string bias_str = bias_term ? "true ":"false ";
     ostringstream proto;
     proto << "name: 'UnsharedWeightsNetwork' ";
     if (force_backward) {
@@ -314,7 +315,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
@@ -340,7 +341,7 @@ class NetTest : public MultiDeviceTest<TypeParam> {
         "  type: 'InnerProduct' "
         "  inner_product_param { "
         "    num_output: 10 "
-        "    bias_term: " << bias_term <<
+        "    bias_term: " << bias_str <<
         "    weight_filler { "
         "      type: 'gaussian' "
         "      std: 10 "
@@ -554,11 +555,14 @@ class NetTest : public MultiDeviceTest<TypeParam> {
   virtual void InitReshapableNet() {
     const string& proto =
         "name: 'ReshapableNetwork' "
-        "input: 'data' "
-        "input_dim: 1 "
-        "input_dim: 3 "
-        "input_dim: 100 "
-        "input_dim: 100 "
+        "layer { "
+        "  name: 'data' "
+        "  type: 'Input' "
+        "  top: 'data' "
+        "  input_param { "
+        "  shape: { dim: 1 dim: 3 dim: 100 dim: 100 } "
+        "  } "
+        "} "
         "layer { "
         "  name: 'conv1' "
         "  type: 'Convolution' "
@@ -613,6 +617,105 @@ class NetTest : public MultiDeviceTest<TypeParam> {
     InitNetFromProtoString(proto);
   }
 
+  virtual void InitSkipPropNet(bool test_skip_true) {
+    string proto =
+      "name: 'SkipPropTestNetwork' "
+      "layer { "
+      "  name: 'data' "
+      "  type: 'DummyData' "
+      "  dummy_data_param { "
+      "    shape { "
+      "      dim: 5 "
+      "      dim: 2 "
+      "      dim: 3 "
+      "      dim: 4 "
+      "    } "
+      "    data_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    shape { "
+      "      dim: 5 "
+      "    } "
+      "    data_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  top: 'data' "
+      "  top: 'label' "
+      "} "
+      "layer { "
+      "  name: 'silence' "
+      "  bottom: 'label' "
+      "  type: 'Silence' "
+      "} "
+      "layer { "
+      "  name: 'innerproduct' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  param { "
+      "    lr_mult: 1 "
+      "    decay_mult: 1 "
+      "  } "
+      "  param { "
+      "    lr_mult: 2 "
+      "    decay_mult: 0 "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'innerproduct' "
+      "} "
+      "layer { "
+      "  name: 'ip_fake_labels' "
+      "  type: 'InnerProduct' "
+      "  inner_product_param { "
+      "    num_output: 1 "
+      "    weight_filler { "
+      "      type: 'gaussian' "
+      "      std: 0.01 "
+      "    } "
+      "    bias_filler { "
+      "      type: 'constant' "
+      "      value: 0 "
+      "    } "
+      "  } "
+      "  bottom: 'data' "
+      "  top: 'fake_labels' "
+      "} "
+      "layer { "
+      "  name: 'argmax' "
+      "  bottom: 'fake_labels' "
+      "  top: 'label_argmax' "
+      "  type: 'ArgMax' "
+      "} "
+      "layer { "
+      "  name: 'loss' "
+      "  bottom: 'innerproduct' "
+      "  bottom: 'label_argmax' ";
+    if (test_skip_true)
+      proto += "  propagate_down: true "
+               "  propagate_down: false ";
+    else
+      proto += "  propagate_down: true "
+               "  propagate_down: true ";
+    proto +=
+      "  top: 'cross_entropy_loss' "
+      "  type: 'SigmoidCrossEntropyLoss' "
+      "  loss_weight: 0.1 "
+      "} ";
+    InitNetFromProtoString(proto);
+  }
+
   int seed_;
   shared_ptr<Net<Dtype> > net_;
 };
@@ -721,7 +824,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   this->InitUnsharedWeightsNet(NULL, NULL, kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -736,7 +839,7 @@ TYPED_TEST(NetTest, TestLossWeight) {
   for (int i = 0; i < kNumLossWeights; ++i) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&kLossWeights[i], NULL, kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -765,14 +868,13 @@ TYPED_TEST(NetTest, TestLossWeight) {
 
 TYPED_TEST(NetTest, TestLossWeightMidNet) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Caffe::set_random_seed(this->seed_);
   const bool kForceBackward = true;
   Dtype loss_weight = 0;
   Dtype midnet_loss_weight = 1;
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   const bool kReshape = true;
   Blob<Dtype> data_grad;
@@ -787,7 +889,7 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
     Caffe::set_random_seed(this->seed_);
     this->InitUnsharedWeightsNet(&loss_weight, &kLossWeights[i],
                                  kForceBackward);
-    const Dtype weighted_loss = this->net_->ForwardBackward(bottom);
+    const Dtype weighted_loss = this->net_->ForwardBackward();
     const Dtype error_margin = kErrorMargin * fabs(kLossWeights[i]);
     EXPECT_NEAR(loss * kLossWeights[i], weighted_loss, error_margin)
         << "loss weight = " << kLossWeights[i];
@@ -803,7 +905,6 @@ TYPED_TEST(NetTest, TestLossWeightMidNet) {
 
 TYPED_TEST(NetTest, TestComboLossWeight) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   Dtype loss_weight;
   Dtype midnet_loss_weight;
   const bool kForceBackward = true;
@@ -816,7 +917,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss = this->net_->ForwardBackward(bottom);
+  const Dtype loss = this->net_->ForwardBackward();
   const bool kCopyDiff = true;
   vector<shared_ptr<Blob<Dtype> > > blob_grads;
   this->CopyNetBlobs(kCopyDiff, &blob_grads);
@@ -828,7 +929,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_2 = this->net_->ForwardBackward();
   vector<shared_ptr<Blob<Dtype> > > blob_grads_loss_2;
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   vector<shared_ptr<Blob<Dtype> > > param_grads_loss_2;
@@ -839,7 +940,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_main_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_main_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_loss_3.size());
@@ -874,7 +975,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_2 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_2 = this->net_->ForwardBackward();
   this->CopyNetBlobs(kCopyDiff, &blob_grads_loss_2);
   this->CopyNetParams(kCopyDiff, &param_grads_loss_2);
 
@@ -883,7 +984,7 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
   Caffe::set_random_seed(this->seed_);
   this->InitUnsharedWeightsNet(&loss_weight, &midnet_loss_weight,
                                kForceBackward);
-  const Dtype loss_midnet_3 = this->net_->ForwardBackward(bottom);
+  const Dtype loss_midnet_3 = this->net_->ForwardBackward();
   const vector<shared_ptr<Blob<Dtype> > >& blob_grads_midnet_loss_3 =
       this->net_->blobs();
   ASSERT_EQ(blob_grads.size(), blob_grads_midnet_loss_3.size());
@@ -932,40 +1033,35 @@ TYPED_TEST(NetTest, TestComboLossWeight) {
 }
 
 TYPED_TEST(NetTest, TestBackwardWithAccuracyLayer) {
-  typedef typename TypeParam::Dtype Dtype;
   const bool kForceBackward = false;
   const bool kAccuracyLayer = true;
   this->InitTinyNet(kForceBackward, kAccuracyLayer);
   EXPECT_TRUE(this->net_->has_blob("accuracy"));
-  vector<Blob<Dtype>*> bottom;
   // Test that we can do Backward even though we have an 'Accuracy' layer.
-  this->net_->ForwardBackward(bottom);
+  this->net_->ForwardBackward();
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_GT(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestSharedWeightsDataNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Dtype loss;
-  this->net_->Forward(bottom, &loss);
+  this->net_->Forward(&loss);
   EXPECT_FLOAT_EQ(loss, 0);
 }
 
 TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitUnsharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
-  net->Forward(bottom);
+  net->Forward();
   net->Backward();
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
   Layer<Dtype>* ip2_layer = net->layer_by_name("innerproduct2").get();
@@ -981,10 +1077,9 @@ TYPED_TEST(NetTest, TestUnsharedWeightsDiffNet) {
 TYPED_TEST(NetTest, TestSharedWeightsDiffNet) {
   typedef typename TypeParam::Dtype Dtype;
   this->InitSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   Net<Dtype>* net = this->net_.get();
   Dtype loss;
-  net->Forward(bottom, &loss);
+  net->Forward(&loss);
   net->Backward();
   EXPECT_FLOAT_EQ(loss, 0);
   Layer<Dtype>* ip1_layer = net->layer_by_name("innerproduct1").get();
@@ -1002,17 +1097,15 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   typedef typename TypeParam::Dtype Dtype;
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
   Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update as the data minus the two diffs.
   Blob<Dtype> shared_params;
@@ -1024,11 +1117,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // Make sure the diffs are non-trivial.
   for (int i = 0; i < count; ++i) {
     EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
-    EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
-    EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
   }
-  caffe_axpy(count, Dtype(1), ip2_weights->cpu_diff(),
-             shared_params.mutable_cpu_diff());
   caffe_axpy(count, Dtype(-1), shared_params.cpu_diff(),
              shared_params.mutable_cpu_data());
   const Dtype* expected_updated_params = shared_params.cpu_data();
@@ -1051,7 +1140,7 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
   // locations in memory.
   EXPECT_NE(ip1_weights->cpu_data(), ip2_weights->cpu_data());
   EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   // Compute the expected update.
   Blob<Dtype> unshared_params1;
@@ -1065,8 +1154,8 @@ TYPED_TEST(NetTest, TestSharedWeightsUpdate) {
     EXPECT_NE(0, ip1_weights->cpu_diff()[i]);
     EXPECT_NE(0, ip2_weights->cpu_diff()[i]);
     EXPECT_NE(ip1_weights->cpu_diff()[i], ip2_weights->cpu_diff()[i]);
-    EXPECT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
-              shared_params.cpu_diff()[i]);
+    EXPECT_FLOAT_EQ(ip1_weights->cpu_diff()[i] + ip2_weights->cpu_diff()[i],
+                    shared_params.cpu_diff()[i]);
   }
   caffe_axpy(count, Dtype(-1), ip1_weights->cpu_diff(),
              unshared_params1.mutable_cpu_data());
@@ -1091,17 +1180,15 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   // Create a net with weight sharing; Update it once.
   Caffe::set_random_seed(this->seed_);
   this->InitDiffDataSharedWeightsNet();
-  vector<Blob<Dtype>*> bottom;
   EXPECT_EQ(this->net_->layer_names()[1], "innerproduct1");
   EXPECT_EQ(this->net_->layer_names()[2], "innerproduct2");
   Blob<Dtype>* ip1_weights = this->net_->layers()[1]->blobs()[0].get();
   Blob<Dtype>* ip2_weights = this->net_->layers()[2]->blobs()[0].get();
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
-  this->net_->ForwardBackward(bottom);
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
+  this->net_->ForwardBackward();
   this->net_->Update();
   Blob<Dtype> shared_params;
   const bool kReshape = true;
@@ -1123,19 +1210,17 @@ TYPED_TEST(NetTest, TestSharedWeightsResume) {
   ASSERT_FALSE(NULL == ip1_weights);
   ASSERT_FALSE(NULL == ip2_weights);
   EXPECT_NE(ip1_weights, ip2_weights);
-  // Check that data blobs of shared weights share the same location in memory.
+  // Check that data and diff blobs of shared weights share the same memory
+  // locations.
   EXPECT_EQ(ip1_weights->cpu_data(), ip2_weights->cpu_data());
+  EXPECT_EQ(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
   for (int i = 0; i < count; ++i) {
     EXPECT_FLOAT_EQ(shared_params.cpu_data()[i], ip1_weights->cpu_data()[i]);
   }
-  // Check that diff blobs of shared weights are at different locations in
-  // memory.  (The diffs should be accumulated at update time.)
-  EXPECT_NE(ip1_weights->cpu_diff(), ip2_weights->cpu_diff());
 }
 
 TYPED_TEST(NetTest, TestParamPropagateDown) {
   typedef typename TypeParam::Dtype Dtype;
-  vector<Blob<Dtype>*> bottom;
   const bool kBiasTerm = true, kForceBackward = false;
   const Dtype* kLossWeight1 = NULL;
   const Dtype* kLossWeight2 = NULL;
@@ -1145,7 +1230,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   Dtype blobs_lr_w1 = 1, blobs_lr_w2 = 1, blobs_lr_b1 = 2, blobs_lr_b2 = 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params = this->net_->params();
   const int num_params = params.size();
@@ -1165,7 +1250,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 *= 2, blobs_lr_w2 *= 2, blobs_lr_b1 *= 2, blobs_lr_b2 *= 2;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params2 = this->net_->params();
   ASSERT_EQ(num_params, params2.size());
@@ -1181,7 +1266,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 1, blobs_lr_w2 = 0, blobs_lr_b1 = 0, blobs_lr_b2 = 1;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params3 = this->net_->params();
   ASSERT_EQ(num_params, params3.size());
@@ -1200,7 +1285,7 @@ TYPED_TEST(NetTest, TestParamPropagateDown) {
   blobs_lr_w1 = 0, blobs_lr_w2 = 1, blobs_lr_b1 = 1, blobs_lr_b2 = 0;
   this->InitUnsharedWeightsNet(kLossWeight1, kLossWeight2, kForceBackward,
       kBiasTerm, blobs_lr_w1, blobs_lr_w2, blobs_lr_b1, blobs_lr_b2);
-  this->net_->Forward(bottom);
+  this->net_->Forward();
   this->net_->Backward();
   const vector<shared_ptr<Blob<Dtype> > >& params4 = this->net_->params();
   ASSERT_EQ(num_params, params4.size());
@@ -1222,7 +1307,7 @@ TYPED_TEST(NetTest, TestFromTo) {
   // Run Forward and Backward, recording the data diff and loss.
   Blob<Dtype> data;
   data.ReshapeLike(*this->net_->blob_by_name("data"));
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   data.CopyFrom(*this->net_->blob_by_name("data"), true, true);
   const Dtype *loss_ptr = this->net_->output_blobs()[0]->cpu_data();
@@ -2169,25 +2254,27 @@ TEST_F(FilterNetTest, TestFilterInOutByExcludeMultiRule) {
 TYPED_TEST(NetTest, TestReshape) {
   typedef typename TypeParam::Dtype Dtype;
   // We set up bottom blobs of two different sizes, switch between
-  // them, and check that forward and backward both run and the results
-  // are the same.
+  // them, check that forward and backward both run and the results
+  // are the same, and check that the output shapes change.
   Caffe::set_random_seed(this->seed_);
   Caffe::set_mode(Caffe::CPU);
   FillerParameter filler_param;
   filler_param.set_std(1);
   GaussianFiller<Dtype> filler(filler_param);
-  Blob<Dtype> blob1(4, 3, 9, 11);
-  Blob<Dtype> blob2(2, 3, 12, 10);
+  // Check smaller shape first as larger first could hide realloc failures.
+  Blob<Dtype> blob1(2, 3, 12, 10);
+  Blob<Dtype> blob2(4, 3, 9, 11);
+  ASSERT_LT(blob1.count(), blob2.count());
   filler.Fill(&blob1);
   filler.Fill(&blob2);
 
   this->InitReshapableNet();
-  Blob<Dtype>* input_blob = this->net_->input_blobs()[0];
+  shared_ptr<Blob<Dtype> > input_blob = this->net_->blob_by_name("data");
   Blob<Dtype>* output_blob = this->net_->output_blobs()[0];
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   // call backward just to make sure it runs
   this->net_->Backward();
   Blob<Dtype> output1(output_blob->num(), output_blob->channels(),
@@ -2198,7 +2285,7 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   Blob<Dtype> output2(output_blob->num(), output_blob->channels(),
       output_blob->height(), output_blob->width());
@@ -2208,19 +2295,79 @@ TYPED_TEST(NetTest, TestReshape) {
   input_blob->Reshape(blob1.num(), blob1.channels(), blob1.height(),
       blob1.width());
   caffe_copy(blob1.count(), blob1.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output1.count(); ++i) {
-    CHECK_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
+    EXPECT_FLOAT_EQ(*(output1.cpu_data() + i), *(output_blob->cpu_data() + i));
   }
 
   input_blob->Reshape(blob2.num(), blob2.channels(), blob2.height(),
       blob2.width());
   caffe_copy(blob2.count(), blob2.cpu_data(), input_blob->mutable_cpu_data());
-  this->net_->ForwardPrefilled();
+  this->net_->Forward();
   this->net_->Backward();
   for (int i = 0; i < output2.count(); ++i) {
-    CHECK_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+    EXPECT_FLOAT_EQ(*(output2.cpu_data() + i), *(output_blob->cpu_data() + i));
+  }
+
+  EXPECT_EQ(output1.num(), blob1.num());
+  EXPECT_EQ(output2.num(), blob2.num());
+  bool same_spatial_shape = true;
+  const int kFirstSpatialAxis = 2;
+  for (int i = kFirstSpatialAxis; i < output1.num_axes(); ++i) {
+    if (output1.shape(i) != output2.shape(i)) {
+      same_spatial_shape = false;
+      break;
+    }
+  }
+  EXPECT_FALSE(same_spatial_shape);
+}
+
+TYPED_TEST(NetTest, TestSkipPropagateDown) {
+  // check bottom_need_backward if propagate_down is true
+  this->InitSkipPropNet(false);
+  vector<bool> vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is true, the loss layer will try to
+      // backpropagate on labels
+      EXPECT_TRUE(need_back) << "bottom_need_backward should be True";
+    }
+    // layer_need_backward should be True except for data and silence layers
+    if (layer_name.find("data") != std::string::npos ||
+          layer_name == "silence") {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    } else {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    }
+  }
+  // check bottom_need_backward if propagat_down is false
+  this->InitSkipPropNet(true);
+  vec_layer_need_backward.clear();
+  vec_layer_need_backward = this->net_->layer_need_backward();
+  for (int layer_id = 0; layer_id < this->net_->layers().size(); ++layer_id) {
+    string layer_name = this->net_->layer_names()[layer_id];
+    if (layer_name == "loss") {
+      // access to bottom_need_backward coresponding to label's blob
+      bool need_back = this->net_->bottom_need_backward()[layer_id][1];
+      // if propagate_down is false, the loss layer will not try to
+      // backpropagate on labels
+      EXPECT_FALSE(need_back) << "bottom_need_backward should be False";
+    }
+    // layer_need_backward should be False except for innerproduct and
+    // loss layers
+    if (layer_name == "innerproduct" || layer_name == "loss") {
+      EXPECT_TRUE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be True";
+    } else {
+      EXPECT_FALSE(vec_layer_need_backward[layer_id])
+          << "layer_need_backward for " << layer_name << " should be False";
+    }
   }
 }
 
diff --git a/src/caffe/test/test_neuron_layer.cpp b/src/caffe/test/test_neuron_layer.cpp
index c9d52f247a6..dd591f7d204 100644
--- a/src/caffe/test/test_neuron_layer.cpp
+++ b/src/caffe/test/test_neuron_layer.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "google/protobuf/text_format.h"
@@ -8,7 +7,26 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+
+#include "caffe/layers/absval_layer.hpp"
+#include "caffe/layers/bnll_layer.hpp"
+#include "caffe/layers/dropout_layer.hpp"
+#include "caffe/layers/elu_layer.hpp"
+#include "caffe/layers/exp_layer.hpp"
+#include "caffe/layers/inner_product_layer.hpp"
+#include "caffe/layers/log_layer.hpp"
+#include "caffe/layers/power_layer.hpp"
+#include "caffe/layers/prelu_layer.hpp"
+#include "caffe/layers/relu_layer.hpp"
+#include "caffe/layers/sigmoid_layer.hpp"
+#include "caffe/layers/tanh_layer.hpp"
+#include "caffe/layers/threshold_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_relu_layer.hpp"
+#include "caffe/layers/cudnn_sigmoid_layer.hpp"
+#include "caffe/layers/cudnn_tanh_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -117,6 +135,49 @@ class NeuronLayerTest : public MultiDeviceTest<TypeParam> {
           + slope_data[c] * std::min(bottom_data[i], (Dtype)(0)));
     }
   }
+
+  void LogBottomInit() {
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    Dtype* bottom_data = this->blob_bottom_->mutable_cpu_data();
+    caffe_exp(this->blob_bottom_->count(), bottom_data, bottom_data);
+  }
+
+  void TestLogForward(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    layer.SetUp(blob_bottom_vec_, blob_top_vec_);
+    layer.Forward(blob_bottom_vec_, blob_top_vec_);
+    const Dtype kDelta = 2e-4;
+    const Dtype* bottom_data = blob_bottom_->cpu_data();
+    const Dtype* top_data = blob_top_->cpu_data();
+    for (int i = 0; i < blob_bottom_->count(); ++i) {
+      const Dtype bottom_val = bottom_data[i];
+      const Dtype top_val = top_data[i];
+      if (base == -1) {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val), kDelta);
+      } else {
+        EXPECT_NEAR(top_val, log(shift + scale * bottom_val) / log(base),
+                    kDelta);
+      }
+    }
+  }
+
+  void TestLogGradient(const float base, const float scale, const float shift) {
+    LogBottomInit();
+    LayerParameter layer_param;
+    layer_param.mutable_log_param()->set_base(base);
+    layer_param.mutable_log_param()->set_scale(scale);
+    layer_param.mutable_log_param()->set_shift(shift);
+    LogLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 1e-2);
+    checker.CheckGradientEltwise(&layer, blob_bottom_vec_, blob_top_vec_);
+  }
 };
 
 TYPED_TEST_CASE(NeuronLayerTest, TestDtypesAndDevices);
@@ -199,6 +260,64 @@ TYPED_TEST(NeuronLayerTest, TestReLUGradientWithNegativeSlope) {
       this->blob_top_vec_);
 }
 
+TYPED_TEST(NeuronLayerTest, TestELU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0.5 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype kDelta = 2e-4;
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    if (bottom_data[i] > 0) {
+      EXPECT_FLOAT_EQ(top_data[i], bottom_data[i]);
+    } else {
+      EXPECT_NEAR(top_data[i], 0.5 * (exp(bottom_data[i]) - 1), kDelta);
+    }
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLU) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Now, check values
+  const Dtype* bottom_data = this->blob_bottom_->cpu_data();
+  const Dtype* top_data = this->blob_top_->cpu_data();
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_GE(top_data[i], 0.);
+    EXPECT_TRUE(top_data[i] == 0 || top_data[i] == bottom_data[i]);
+  }
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ELULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NeuronLayerTest, TestELUasReLUGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  CHECK(google::protobuf::TextFormat::ParseFromString(
+      "elu_param { alpha: 0 }", &layer_param));
+  ELULayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3, 1701, 0., 0.01);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
 TYPED_TEST(NeuronLayerTest, TestSigmoid) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -339,6 +458,88 @@ TYPED_TEST(NeuronLayerTest, TestExpGradientBase2Shift1Scale3) {
   this->TestExpGradient(kBase, kScale, kShift);
 }
 
+TYPED_TEST(NeuronLayerTest, TestLogLayer) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  // Test default base of "-1" -- should actually set base := e.
+  const Dtype kBase = -1;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 1;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 0;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogLayerBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogForward(kBase, kScale, kShift);
+}
+
+TYPED_TEST(NeuronLayerTest, TestLogGradientBase2Shift1Scale3) {
+  typedef typename TypeParam::Dtype Dtype;
+  const Dtype kBase = 2;
+  const Dtype kScale = 3;
+  const Dtype kShift = 1;
+  this->TestLogGradient(kBase, kScale, kShift);
+}
+
 TYPED_TEST(NeuronLayerTest, TestDropoutHalf) {
   const float kDropoutRatio = 0.5;
   this->TestDropoutForward(kDropoutRatio);
@@ -541,14 +742,10 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
   caffe_copy(ip2.blobs()[0]->count(), ip.blobs()[0]->cpu_data(),
       ip2.blobs()[0]->mutable_cpu_data());
   // Forward in-place
-  ip.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
   ip.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
-  prelu.Reshape(this->blob_top_vec_, this->blob_top_vec_);
   prelu.Forward(this->blob_top_vec_, this->blob_top_vec_);
   // Forward non-in-place
-  ip2.Reshape(blob_bottom_vec_2, blob_middle_vec_2);
   ip2.Forward(blob_bottom_vec_2, blob_middle_vec_2);
-  prelu2.Reshape(blob_middle_vec_2, blob_top_vec_2);
   prelu2.Forward(blob_middle_vec_2, blob_top_vec_2);
   // Check numbers
   for (int s = 0; s < blob_top_2->count(); ++s) {
@@ -590,7 +787,7 @@ TYPED_TEST(NeuronLayerTest, TestPReLUInPlace) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNNeuronLayerTest : public ::testing::Test {
+class CuDNNNeuronLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNNeuronLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
@@ -613,7 +810,6 @@ class CuDNNNeuronLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNNeuronLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -628,7 +824,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNReLULayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
@@ -637,7 +832,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CHECK(google::protobuf::TextFormat::ParseFromString(
       "relu_param { negative_slope: 0.01 }", &layer_param));
@@ -657,7 +851,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUWithNegativeSlopeCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CHECK(google::protobuf::TextFormat::ParseFromString(
       "relu_param { negative_slope: 0.01 }", &layer_param));
@@ -668,7 +861,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestReLUGradientWithNegativeSlopeCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -685,7 +877,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSigmoidLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3, 1701, 0., 0.01);
@@ -694,7 +885,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestSigmoidGradientCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -717,7 +907,6 @@ TYPED_TEST(CuDNNNeuronLayerTest, TestTanHCuDNN) {
 }
 
 TYPED_TEST(CuDNNNeuronLayerTest, TestTanHGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNTanHLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
diff --git a/src/caffe/test/test_normalize_layer.cpp b/src/caffe/test/test_normalize_layer.cpp
new file mode 100644
index 00000000000..96b9338966a
--- /dev/null
+++ b/src/caffe/test/test_normalize_layer.cpp
@@ -0,0 +1,300 @@
+#include <cmath>
+#include <cstring>
+#include <vector>
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/normalize_layer.hpp"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class NormalizeLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  NormalizeLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 2, 3)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    // GaussianFiller<Dtype> filler(filler_param);
+    filler_param.set_value(1);
+    ConstantFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~NormalizeLayerTest() { delete blob_bottom_; delete blob_top_; }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(NormalizeLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(NormalizeLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    Dtype norm = 0;
+    for (int j = 0; j < channels; ++j) {
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+      }
+    }
+    const Dtype kErrorBound = 1e-5;
+    // expect unit norm
+    EXPECT_NEAR(1, sqrt(norm), kErrorBound);
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestForwardScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(10);
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    Dtype norm = 0;
+    for (int j = 0; j < channels; ++j) {
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+      }
+    }
+    const Dtype kErrorBound = 1e-5;
+    // expect unit norm
+    EXPECT_NEAR(10, sqrt(norm), kErrorBound);
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestForwardScaleChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_channel_shared(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(10);
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    Dtype norm = 0;
+    for (int j = 0; j < channels; ++j) {
+      for (int k = 0; k < height; ++k) {
+        for (int l = 0; l < width; ++l) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+      }
+    }
+    const Dtype kErrorBound = 1e-5;
+    // expect unit norm
+    EXPECT_NEAR(10, sqrt(norm), kErrorBound);
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestForwardEltWise) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    for (int k = 0; k < height; ++k) {
+      for (int l = 0; l < width; ++l) {
+        Dtype norm = 0;
+        for (int j = 0; j < channels; ++j) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+        const Dtype kErrorBound = 1e-5;
+        // expect unit norm
+        EXPECT_NEAR(1, sqrt(norm), kErrorBound);
+      }
+    }
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestForwardEltWiseScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(10);
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    for (int k = 0; k < height; ++k) {
+      for (int l = 0; l < width; ++l) {
+        Dtype norm = 0;
+        for (int j = 0; j < channels; ++j) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+        const Dtype kErrorBound = 1e-5;
+        // expect unit norm
+        EXPECT_NEAR(10, sqrt(norm), kErrorBound);
+      }
+    }
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestForwardEltWiseScaleChannel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  norm_param->set_channel_shared(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(10);
+  NormalizeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // Test norm
+  int num = this->blob_bottom_->num();
+  int channels = this->blob_bottom_->channels();
+  int height = this->blob_bottom_->height();
+  int width = this->blob_bottom_->width();
+
+  for (int i = 0; i < num; ++i) {
+    for (int k = 0; k < height; ++k) {
+      for (int l = 0; l < width; ++l) {
+        Dtype norm = 0;
+        for (int j = 0; j < channels; ++j) {
+          Dtype data = this->blob_top_->data_at(i, j, k, l);
+          norm += data * data;
+        }
+        const Dtype kErrorBound = 1e-5;
+        // expect unit norm
+        EXPECT_NEAR(10, sqrt(norm), kErrorBound);
+      }
+    }
+  }
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_, 0);
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradientScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(3);
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradientScaleChannel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_channel_shared(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(3);
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradientEltWise) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-3, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradientEltWiseScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(3);
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-3, 2e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(NormalizeLayerTest, TestGradientEltWiseScaleChannel) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  NormalizeParameter* norm_param = layer_param.mutable_norm_param();
+  norm_param->set_across_spatial(false);
+  norm_param->set_channel_shared(false);
+  norm_param->mutable_scale_filler()->set_type("constant");
+  norm_param->mutable_scale_filler()->set_value(3);
+  NormalizeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-3, 2e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_pooling_layer.cpp b/src/caffe/test/test_pooling_layer.cpp
index e9964e7f0b7..bb95cae032d 100644
--- a/src/caffe/test/test_pooling_layer.cpp
+++ b/src/caffe/test/test_pooling_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_pooling_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -608,7 +611,7 @@ TYPED_TEST(PoolingLayerTest, TestGradientAvePadded) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNPoolingLayerTest : public ::testing::Test {
+class CuDNNPoolingLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -963,7 +966,6 @@ class CuDNNPoolingLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNPoolingLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -977,7 +979,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -994,7 +995,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestSetupPaddedCuDNN) {
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   layer_param.set_kernelsize(3);
   layer_param.set_stride(2);
@@ -1020,7 +1020,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, PrintBackwardCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
   this->TestForwardRectWide();
@@ -1030,7 +1029,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxCuDNN) {
 // the corresponding backward test.
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   this->blob_top_vec_.push_back(this->blob_top_mask_);
   this->TestForwardSquare();
   this->TestForwardRectHigh();
@@ -1039,7 +1037,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxTopMaskCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1059,7 +1056,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -1105,7 +1101,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardMaxPaddedCuDNN) {
 
 /*
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1126,7 +1121,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientMaxTopMaskCuDNN) {
 */
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -1152,7 +1146,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestForwardAveCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
@@ -1170,7 +1163,6 @@ TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAveCuDNN) {
 }
 
 TYPED_TEST(CuDNNPoolingLayerTest, TestGradientAvePaddedCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   for (int kernel_h = 3; kernel_h <= 4; kernel_h++) {
     for (int kernel_w = 3; kernel_w <= 4; kernel_w++) {
       LayerParameter layer_param;
diff --git a/src/caffe/test/test_power_layer.cpp b/src/caffe/test/test_power_layer.cpp
index 76c9e857f36..1aa587ac97a 100644
--- a/src/caffe/test/test_power_layer.cpp
+++ b/src/caffe/test/test_power_layer.cpp
@@ -6,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/power_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_random_number_generator.cpp b/src/caffe/test/test_random_number_generator.cpp
index 98424c06bfc..833b0047b5d 100644
--- a/src/caffe/test/test_random_number_generator.cpp
+++ b/src/caffe/test/test_random_number_generator.cpp
@@ -1,5 +1,4 @@
 #include <cmath>
-#include <cstring>
 
 #include "gtest/gtest.h"
 
diff --git a/src/caffe/test/test_reduction_layer.cpp b/src/caffe/test/test_reduction_layer.cpp
new file mode 100644
index 00000000000..6ed7cda6adc
--- /dev/null
+++ b/src/caffe/test/test_reduction_layer.cpp
@@ -0,0 +1,296 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/reduction_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReductionLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ReductionLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    Caffe::set_random_seed(1701);
+    FillerParameter filler_param;
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ReductionLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+
+  void TestForward(ReductionParameter_ReductionOp op,
+                   float coeff = 1, int axis = 0) {
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    if (coeff != 1.0) { reduction_param->set_coeff(coeff); }
+    if (axis != 0) { reduction_param->set_axis(axis); }
+    shared_ptr<ReductionLayer<Dtype> > layer(
+        new ReductionLayer<Dtype>(layer_param));
+    layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+    const Dtype* in_data = this->blob_bottom_->cpu_data();
+    const int num = this->blob_bottom_->count(0, axis);
+    const int dim = this->blob_bottom_->count(axis);
+    for (int n = 0; n < num; ++n) {
+      Dtype expected_result = 0;
+      for (int d = 0; d < dim; ++d) {
+        switch (op) {
+          case ReductionParameter_ReductionOp_SUM:
+            expected_result += *in_data;
+            break;
+          case ReductionParameter_ReductionOp_MEAN:
+            expected_result += *in_data / dim;
+            break;
+          case ReductionParameter_ReductionOp_ASUM:
+            expected_result += fabs(*in_data);
+            break;
+          case ReductionParameter_ReductionOp_SUMSQ:
+            expected_result += (*in_data) * (*in_data);
+            break;
+          default:
+            LOG(FATAL) << "Unknown reduction op: "
+                << ReductionParameter_ReductionOp_Name(op);
+        }
+        ++in_data;
+      }
+      expected_result *= coeff;
+      const Dtype computed_result = this->blob_top_->cpu_data()[n];
+      EXPECT_FLOAT_EQ(expected_result, computed_result)
+          << "Incorrect result computed with op "
+          << ReductionParameter_ReductionOp_Name(op) << ", coeff " << coeff;
+    }
+  }
+
+  void TestGradient(ReductionParameter_ReductionOp op,
+                    float coeff = 1, int axis = 0) {
+    typedef typename TypeParam::Dtype Dtype;
+    LayerParameter layer_param;
+    ReductionParameter* reduction_param = layer_param.mutable_reduction_param();
+    reduction_param->set_operation(op);
+    reduction_param->set_coeff(coeff);
+    reduction_param->set_axis(axis);
+    ReductionLayer<Dtype> layer(layer_param);
+    GradientChecker<Dtype> checker(1e-2, 2e-3);
+    checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+        this->blob_top_vec_);
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReductionLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReductionLayerTest, TestSetUp) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 0);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis1) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(1);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 1);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSetUpWithAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reduction_param()->set_axis(2);
+  shared_ptr<ReductionLayer<Dtype> > layer(
+      new ReductionLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_top_->num_axes(), 2);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSum) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeff) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp = ReductionParameter_ReductionOp_SUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMean) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestMeanCoeffGradientAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_MEAN;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSum) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestAbsSumCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_ASUM;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquares) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestForward(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeff) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestForward(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestForward(kOp, kCoeff, kAxis);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  this->TestGradient(kOp);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffGradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  this->TestGradient(kOp, kCoeff);
+}
+
+TYPED_TEST(ReductionLayerTest, TestSumOfSquaresCoeffAxis1Gradient) {
+  const ReductionParameter_ReductionOp kOp =
+      ReductionParameter_ReductionOp_SUMSQ;
+  const float kCoeff = 2.3;
+  const int kAxis = 1;
+  this->TestGradient(kOp, kCoeff, kAxis);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_reshape_layer.cpp b/src/caffe/test/test_reshape_layer.cpp
new file mode 100644
index 00000000000..4f2613868d4
--- /dev/null
+++ b/src/caffe/test/test_reshape_layer.cpp
@@ -0,0 +1,279 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/reshape_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ReshapeLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+ protected:
+  ReshapeLayerTest()
+    : blob_bottom_(new Blob<Dtype>(2, 3, 6, 5)),
+      blob_top_(new Blob<Dtype>()) {
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+
+  virtual ~ReshapeLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ReshapeLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenOutputSizes) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3 * 6 * 5);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenValues) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(-1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int c = 0; c < 3 * 6 * 5; ++c) {
+    EXPECT_EQ(this->blob_top_->data_at(0, c, 0, 0),
+        this->blob_bottom_->data_at(0, c / (6 * 5), (c / 5) % 6, c % 5));
+    EXPECT_EQ(this->blob_top_->data_at(1, c, 0, 0),
+        this->blob_bottom_->data_at(1, c / (6 * 5), (c / 5) % 6, c % 5));
+  }
+}
+
+// Test whether setting output dimensions to 0 either explicitly or implicitly
+// copies the respective dimension of the input layer.
+TYPED_TEST(ReshapeLayerTest, TestCopyDimensions) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(0);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 6);
+  EXPECT_EQ(this->blob_top_->width(), 5);
+}
+
+// When a dimension is set to -1, we should infer its value from the other
+// dimensions (including those that get copied from below).
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecified) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(0);
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
+
+  // Count is 180, thus height should be 180 / (2*3*10) = 3.
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInferenceOfUnspecifiedWithStartAxis) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(3);
+  blob_shape->add_dim(10);
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 4);
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 3);
+  EXPECT_EQ(this->blob_top_->height(), 10);
+  EXPECT_EQ(this->blob_top_->width(), 3);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesStart) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(0);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 1);
+  EXPECT_EQ(this->blob_top_->shape(1), 1);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 2);
+  EXPECT_EQ(this->blob_top_->shape(4), 3);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(2);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 1);
+  EXPECT_EQ(this->blob_top_->shape(3), 1);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 6);
+  EXPECT_EQ(this->blob_top_->shape(6), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestInsertSingletonAxesEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(-1);
+  layer_param.mutable_reshape_param()->set_num_axes(0);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+  blob_shape->add_dim(1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 7);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3);
+  EXPECT_EQ(this->blob_top_->shape(2), 6);
+  EXPECT_EQ(this->blob_top_->shape(3), 5);
+  EXPECT_EQ(this->blob_top_->shape(4), 1);
+  EXPECT_EQ(this->blob_top_->shape(5), 1);
+  EXPECT_EQ(this->blob_top_->shape(6), 1);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestFlattenMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_reshape_param()->set_axis(1);
+  layer_param.mutable_reshape_param()->set_num_axes(2);
+  BlobShape* blob_shape = layer_param.mutable_reshape_param()->mutable_shape();
+  blob_shape->add_dim(-1);
+
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+
+  ASSERT_EQ(this->blob_top_->num_axes(), 3);
+  EXPECT_EQ(this->blob_top_->shape(0), 2);
+  EXPECT_EQ(this->blob_top_->shape(1), 3 * 6);
+  EXPECT_EQ(this->blob_top_->shape(2), 5);
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestForwardAfterReshape) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  // We know the above produced the correct result from TestForward.
+  // Reshape the bottom and call layer.Reshape, then try again.
+  vector<int> new_bottom_shape(1, 2 * 3 * 6 * 5);
+  this->blob_bottom_->Reshape(new_bottom_shape);
+  layer.Reshape(this->blob_bottom_vec_, this->blob_top_vec_);
+  FillerParameter filler_param;
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(this->blob_bottom_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_top_->cpu_data()[i],
+              this->blob_bottom_->cpu_data()[i]);
+  }
+}
+
+TYPED_TEST(ReshapeLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  BlobShape* shape = layer_param.mutable_reshape_param()->mutable_shape();
+  shape->add_dim(6);
+  shape->add_dim(2);
+  shape->add_dim(3);
+  shape->add_dim(5);
+  ReshapeLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_scale_layer.cpp b/src/caffe/test/test_scale_layer.cpp
new file mode 100644
index 00000000000..ad116795f44
--- /dev/null
+++ b/src/caffe/test/test_scale_layer.cpp
@@ -0,0 +1,507 @@
+#include <algorithm>
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/scale_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class ScaleLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  ScaleLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_eltwise_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_bottom_broadcast_0_(new Blob<Dtype>()),
+        blob_bottom_broadcast_1_(new Blob<Dtype>()),
+        blob_bottom_broadcast_2_(new Blob<Dtype>()),
+        blob_bottom_scale_(new Blob<Dtype>(vector<int>())),
+        blob_top_(new Blob<Dtype>()) {
+    Caffe::set_random_seed(1701);
+    vector<int> broadcast_shape(2);
+    broadcast_shape[0] = 2; broadcast_shape[1] = 3;
+    this->blob_bottom_broadcast_0_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 3; broadcast_shape[1] = 4;
+    this->blob_bottom_broadcast_1_->Reshape(broadcast_shape);
+    broadcast_shape[0] = 4; broadcast_shape[1] = 5;
+    this->blob_bottom_broadcast_2_->Reshape(broadcast_shape);
+    FillerParameter filler_param;
+    filler_param.set_min(1);
+    filler_param.set_max(10);
+    UniformFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    filler.Fill(this->blob_bottom_eltwise_);
+    filler.Fill(this->blob_bottom_broadcast_0_);
+    filler.Fill(this->blob_bottom_broadcast_1_);
+    filler.Fill(this->blob_bottom_broadcast_2_);
+    filler.Fill(this->blob_bottom_scale_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~ScaleLayerTest() {
+    delete blob_bottom_;
+    delete blob_bottom_eltwise_;
+    delete blob_bottom_broadcast_0_;
+    delete blob_bottom_broadcast_1_;
+    delete blob_bottom_broadcast_2_;
+    delete blob_bottom_scale_;
+    delete blob_top_;
+  }
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_eltwise_;
+  Blob<Dtype>* const blob_bottom_broadcast_0_;
+  Blob<Dtype>* const blob_bottom_broadcast_1_;
+  Blob<Dtype>* const blob_bottom_broadcast_2_;
+  Blob<Dtype>* const blob_bottom_scale_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(ScaleLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_bottom_->cpu_data();
+  const int count = this->blob_bottom_->count();
+  const Dtype* in_data_a = orig_bottom.cpu_data();
+  const Dtype* in_data_b = this->blob_bottom_eltwise_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardEltwiseInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_eltwise_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_eltwise_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_eltwise_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data_a = this->blob_bottom_->cpu_data();
+  const Dtype* in_data_b = layer->blobs()[0]->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data_a[i] * in_data_b[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_0_->data_at(n, c, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_bottom_->data_at(n, c, h, w),
+                      orig_bottom.data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_1_->data_at(c, h, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestBackwardBroadcastMiddleInPlace) {
+  typedef typename TypeParam::Dtype Dtype;
+  Blob<Dtype> orig_bottom(this->blob_bottom_->shape());
+  orig_bottom.CopyFrom(*this->blob_bottom_);
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  Blob<Dtype> top_diff(this->blob_bottom_->shape());
+  FillerParameter filler_param;
+  filler_param.set_type("gaussian");
+  filler_param.set_std(1);
+  GaussianFiller<Dtype> filler(filler_param);
+  filler.Fill(&top_diff);
+  vector<bool> propagate_down(2, true);
+  // Run forward + backward without in-place computation;
+  // save resulting bottom diffs.
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_top_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  const bool kReshape = true;
+  const bool kCopyDiff = true;
+  Blob<Dtype> orig_bottom_diff;
+  orig_bottom_diff.CopyFrom(*this->blob_bottom_, kCopyDiff, kReshape);
+  Blob<Dtype> orig_scale_diff;
+  orig_scale_diff.CopyFrom(*this->blob_bottom_broadcast_1_,
+                            kCopyDiff, kReshape);
+  // Rerun forward + backward with in-place computation;
+  // check that resulting bottom diffs are the same.
+  this->blob_top_vec_[0] = this->blob_bottom_;  // in-place computation
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  caffe_copy(top_diff.count(), top_diff.cpu_data(),
+             this->blob_bottom_->mutable_cpu_diff());
+  layer->Backward(this->blob_top_vec_, propagate_down, this->blob_bottom_vec_);
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_NEAR(orig_bottom_diff.cpu_diff()[i],
+                this->blob_bottom_->cpu_diff()[i], 1e-5);
+  }
+  for (int i = 0; i < this->blob_bottom_broadcast_1_->count(); ++i) {
+    EXPECT_NEAR(orig_scale_diff.cpu_diff()[i],
+                this->blob_bottom_broadcast_1_->cpu_diff()[i], 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastMiddleWithParamAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      layer->blobs()[0]->data_at(c, h, 0, 0) +
+                      layer->blobs()[1]->data_at(c, h, 0, 0), 1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_bottom_->num(); ++n) {
+    for (int c = 0; c < this->blob_bottom_->channels(); ++c) {
+      for (int h = 0; h < this->blob_bottom_->height(); ++h) {
+        for (int w = 0; w < this->blob_bottom_->width(); ++w) {
+          EXPECT_NEAR(this->blob_top_->data_at(n, c, h, w),
+                      this->blob_bottom_->data_at(n, c, h, w) *
+                      this->blob_bottom_broadcast_2_->data_at(h, w, 0, 0),
+                      1e-5);
+        }
+      }
+    }
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestForwardScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  shared_ptr<ScaleLayer<Dtype> > layer(new ScaleLayer<Dtype>(layer_param));
+  layer->SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_->shape());
+  layer->Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  const Dtype* data = this->blob_top_->cpu_data();
+  const int count = this->blob_top_->count();
+  const Dtype* in_data = this->blob_bottom_->cpu_data();
+  const Dtype scale = *this->blob_bottom_scale_->cpu_data();
+  for (int i = 0; i < count; ++i) {
+    EXPECT_NEAR(data[i], in_data[i] * scale, 1e-5);
+  }
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwise) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_eltwise_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientEltwiseWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(0);
+  scale_param->set_num_axes(-1);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastBegin) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_0_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(0);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddle) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(1);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastMiddleWithParam) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_1_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_axis(1);
+  scale_param->set_num_axes(2);
+  scale_param->mutable_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientBroadcastEnd) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_broadcast_2_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScale) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAndBias) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  ScaleParameter* scale_param = layer_param.mutable_scale_param();
+  scale_param->set_bias_term(true);
+  scale_param->mutable_bias_filler()->set_type("gaussian");
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(ScaleLayerTest, TestGradientScaleAxis2) {
+  typedef typename TypeParam::Dtype Dtype;
+  this->blob_bottom_vec_.push_back(this->blob_bottom_scale_);
+  LayerParameter layer_param;
+  layer_param.mutable_scale_param()->set_axis(2);
+  ScaleLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
index e5737e43f6e..5dfd7656db2 100644
--- a/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
+++ b/src/caffe/test/test_sigmoid_cross_entropy_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -8,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_slice_layer.cpp b/src/caffe/test/test_slice_layer.cpp
index ccd03646d19..c2b231e1ef4 100644
--- a/src/caffe/test/test_slice_layer.cpp
+++ b/src/caffe/test/test_slice_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -6,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/slice_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -88,6 +87,21 @@ TYPED_TEST(SliceLayerTest, TestSetupChannels) {
   EXPECT_EQ(this->blob_bottom_->width(), this->blob_top_0_->width());
 }
 
+TYPED_TEST(SliceLayerTest, TestTrivialSlice) {
+  // Test the trivial (single output) "slice" operation --
+  // should be the identity.
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SliceLayer<Dtype> layer(layer_param);
+  this->blob_top_vec_0_.resize(1);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_0_);
+  ASSERT_EQ(this->blob_bottom_->shape(), this->blob_top_0_->shape());
+  for (int i = 0; i < this->blob_bottom_->count(); ++i) {
+    EXPECT_EQ(this->blob_bottom_->cpu_data()[i],
+              this->blob_top_0_->cpu_data()[i]);
+  }
+}
+
 TYPED_TEST(SliceLayerTest, TestSliceAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   LayerParameter layer_param;
@@ -161,6 +175,18 @@ TYPED_TEST(SliceLayerTest, TestSliceAcrossChannels) {
   }
 }
 
+TYPED_TEST(SliceLayerTest, TestGradientTrivial) {
+  // Test the trivial (single output) "slice" operation --
+  // should be the identity.
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SliceLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-3);
+  this->blob_top_vec_0_.resize(1);
+  checker.CheckGradientEltwise(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_0_);
+}
+
 TYPED_TEST(SliceLayerTest, TestGradientAcrossNum) {
   typedef typename TypeParam::Dtype Dtype;
   // Gradient checks are slow; reduce blob size.
diff --git a/src/caffe/test/test_softmax_layer.cpp b/src/caffe/test/test_softmax_layer.cpp
index f6674422e56..94443576724 100644
--- a/src/caffe/test/test_softmax_layer.cpp
+++ b/src/caffe/test/test_softmax_layer.cpp
@@ -1,5 +1,4 @@
 #include <cmath>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -7,7 +6,11 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/softmax_layer.hpp"
+
+#ifdef USE_CUDNN
+#include "caffe/layers/cudnn_softmax_layer.hpp"
+#endif
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -82,7 +85,7 @@ TYPED_TEST(SoftmaxLayerTest, TestGradient) {
 
 #ifdef USE_CUDNN
 template <typename Dtype>
-class CuDNNSoftmaxLayerTest : public ::testing::Test {
+class CuDNNSoftmaxLayerTest : public GPUDeviceTest<Dtype> {
  protected:
   CuDNNSoftmaxLayerTest()
       : blob_bottom_(new Blob<Dtype>(2, 10, 2, 3)),
@@ -104,7 +107,6 @@ class CuDNNSoftmaxLayerTest : public ::testing::Test {
 TYPED_TEST_CASE(CuDNNSoftmaxLayerTest, TestDtypes);
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
@@ -138,7 +140,6 @@ TYPED_TEST(CuDNNSoftmaxLayerTest, TestForwardCuDNN) {
 }
 
 TYPED_TEST(CuDNNSoftmaxLayerTest, TestGradientCuDNN) {
-  Caffe::set_mode(Caffe::GPU);
   LayerParameter layer_param;
   CuDNNSoftmaxLayer<TypeParam> layer(layer_param);
   GradientChecker<TypeParam> checker(1e-2, 1e-3);
diff --git a/src/caffe/test/test_softmax_with_loss_layer.cpp b/src/caffe/test/test_softmax_with_loss_layer.cpp
index 1498d5c5ce1..c67f3e0d907 100644
--- a/src/caffe/test/test_softmax_with_loss_layer.cpp
+++ b/src/caffe/test/test_softmax_with_loss_layer.cpp
@@ -1,6 +1,4 @@
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 #include <vector>
 
 #include "boost/scoped_ptr.hpp"
@@ -9,7 +7,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/softmax_loss_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_solver.cpp b/src/caffe/test/test_solver.cpp
index ceabc9cdd2c..b181642681c 100644
--- a/src/caffe/test/test_solver.cpp
+++ b/src/caffe/test/test_solver.cpp
@@ -7,6 +7,7 @@
 
 #include "caffe/common.hpp"
 #include "caffe/proto/caffe.pb.h"
+#include "caffe/sgd_solvers.hpp"
 #include "caffe/solver.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
diff --git a/src/caffe/test/test_solver_factory.cpp b/src/caffe/test/test_solver_factory.cpp
new file mode 100644
index 00000000000..eef5290fe2e
--- /dev/null
+++ b/src/caffe/test/test_solver_factory.cpp
@@ -0,0 +1,50 @@
+#include <map>
+#include <string>
+
+#include "boost/scoped_ptr.hpp"
+#include "google/protobuf/text_format.h"
+#include "gtest/gtest.h"
+
+#include "caffe/common.hpp"
+#include "caffe/solver.hpp"
+#include "caffe/solver_factory.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SolverFactoryTest : public MultiDeviceTest<TypeParam> {
+ protected:
+  SolverParameter simple_solver_param() {
+    const string solver_proto =
+        "train_net_param { "
+        "  layer { "
+        "    name: 'data' type: 'DummyData' top: 'data' "
+        "    dummy_data_param { shape { dim: 1 } } "
+        "  } "
+        "} ";
+    SolverParameter solver_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        solver_proto, &solver_param));
+    return solver_param;
+  }
+};
+
+TYPED_TEST_CASE(SolverFactoryTest, TestDtypesAndDevices);
+
+TYPED_TEST(SolverFactoryTest, TestCreateSolver) {
+  typedef typename TypeParam::Dtype Dtype;
+  typename SolverRegistry<Dtype>::CreatorRegistry& registry =
+      SolverRegistry<Dtype>::Registry();
+  shared_ptr<Solver<Dtype> > solver;
+  SolverParameter solver_param = this->simple_solver_param();
+  for (typename SolverRegistry<Dtype>::CreatorRegistry::iterator iter =
+       registry.begin(); iter != registry.end(); ++iter) {
+    solver_param.set_type(iter->first);
+    solver.reset(SolverRegistry<Dtype>::CreateSolver(solver_param));
+    EXPECT_EQ(iter->first, solver->type());
+  }
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_split_layer.cpp b/src/caffe/test/test_split_layer.cpp
index be5204bfc3e..007142126ea 100644
--- a/src/caffe/test/test_split_layer.cpp
+++ b/src/caffe/test/test_split_layer.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <string>
 #include <vector>
 
@@ -8,9 +7,9 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/split_layer.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/insert_splits.hpp"
-#include "caffe/vision_layers.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -887,67 +886,6 @@ TEST_F(SplitLayerInsertionTest, TestInsertionTwoTop) {
   this->RunInsertionTest(input_proto, expected_output_proto);
 }
 
-TEST_F(SplitLayerInsertionTest, TestInputInsertion) {
-  const string& input_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  const string& expected_output_proto =
-      "name: 'TestNetwork' "
-      "input: 'data' "
-      "input_dim: 10 "
-      "input_dim: 3 "
-      "input_dim: 227 "
-      "input_dim: 227 "
-      "layer { "
-      "  name: 'data_input_0_split' "
-      "  type: 'Split' "
-      "  bottom: 'data' "
-      "  top: 'data_input_0_split_0' "
-      "  top: 'data_input_0_split_1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod1' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_0' "
-      "  top: 'innerprod1' "
-      "} "
-      "layer { "
-      "  name: 'innerprod2' "
-      "  type: 'InnerProduct' "
-      "  bottom: 'data_input_0_split_1' "
-      "  top: 'innerprod2' "
-      "} "
-      "layer { "
-      "  name: 'loss' "
-      "  type: 'EuclideanLoss' "
-      "  bottom: 'innerprod1' "
-      "  bottom: 'innerprod2' "
-      "} ";
-  this->RunInsertionTest(input_proto, expected_output_proto);
-}
-
 TEST_F(SplitLayerInsertionTest, TestWithInPlace) {
   const string& input_proto =
       "name: 'TestNetwork' "
diff --git a/src/caffe/test/test_spp_layer.cpp b/src/caffe/test/test_spp_layer.cpp
new file mode 100644
index 00000000000..59a3af2aec1
--- /dev/null
+++ b/src/caffe/test/test_spp_layer.cpp
@@ -0,0 +1,134 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/concat_layer.hpp"
+#include "caffe/layers/flatten_layer.hpp"
+#include "caffe/layers/pooling_layer.hpp"
+#include "caffe/layers/split_layer.hpp"
+#include "caffe/layers/spp_layer.hpp"
+
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class SPPLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  SPPLayerTest()
+      : blob_bottom_(new Blob<Dtype>()),
+        blob_bottom_2_(new Blob<Dtype>()),
+        blob_bottom_3_(new Blob<Dtype>()),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    Caffe::set_random_seed(1701);
+    blob_bottom_->Reshape(2, 3, 9, 8);
+    blob_bottom_2_->Reshape(4, 3, 1024, 765);
+    blob_bottom_3_->Reshape(10, 3, 7, 7);
+    // fill the values
+    FillerParameter filler_param;
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(this->blob_bottom_);
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_bottom_vec_2_.push_back(blob_bottom_2_);
+    blob_bottom_vec_3_.push_back(blob_bottom_3_);
+    blob_top_vec_.push_back(blob_top_);
+  }
+  virtual ~SPPLayerTest() { delete blob_bottom_; delete blob_top_; }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_bottom_2_;
+  Blob<Dtype>* const blob_bottom_3_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_bottom_vec_2_;
+  vector<Blob<Dtype>*> blob_bottom_vec_3_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(SPPLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(SPPLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 2);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(5);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_2_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 5)/(1 - 4) = 341
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 341 = 1023)
+  EXPECT_EQ(this->blob_top_->num(), 4);
+  EXPECT_EQ(this->blob_top_->channels(), 1023);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestEqualOutputDims2) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_3_, this->blob_top_vec_);
+  // expected number of pool results is geometric sum
+  // (1 - r ** n)/(1 - r) where r = 4 and n = pyramid_height
+  // (1 - 4 ** 3)/(1 - 4) = 21
+  // multiply bottom num_channels * expected_pool_results
+  // to get expected num_channels (3 * 21 = 63)
+  EXPECT_EQ(this->blob_top_->num(), 10);
+  EXPECT_EQ(this->blob_top_->channels(), 63);
+  EXPECT_EQ(this->blob_top_->height(), 1);
+  EXPECT_EQ(this->blob_top_->width(), 1);
+}
+
+TYPED_TEST(SPPLayerTest, TestForwardBackward) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  layer_param.mutable_spp_param()->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  vector<bool> propagate_down(this->blob_bottom_vec_.size(), true);
+  layer.Backward(this->blob_top_vec_, propagate_down,
+                 this->blob_bottom_vec_);
+}
+
+TYPED_TEST(SPPLayerTest, TestGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  SPPParameter* spp_param = layer_param.mutable_spp_param();
+  spp_param->set_pyramid_height(3);
+  SPPLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-4, 1e-2);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_stochastic_pooling.cpp b/src/caffe/test/test_stochastic_pooling.cpp
index 12962c65d85..cd5db8383ab 100644
--- a/src/caffe/test/test_stochastic_pooling.cpp
+++ b/src/caffe/test/test_stochastic_pooling.cpp
@@ -1,5 +1,4 @@
 #include <algorithm>
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
@@ -7,7 +6,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/pooling_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
@@ -16,8 +15,10 @@ using std::min;
 
 namespace caffe {
 
-template <typename Dtype>
-class StochasticPoolingLayerTest : public ::testing::Test {
+template <typename TypeParam>
+class StochasticPoolingLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
  protected:
   StochasticPoolingLayerTest()
       : blob_bottom_(new Blob<Dtype>()),
@@ -45,9 +46,14 @@ class StochasticPoolingLayerTest : public ::testing::Test {
   vector<Blob<Dtype>*> blob_top_vec_;
 };
 
-TYPED_TEST_CASE(StochasticPoolingLayerTest, TestDtypes);
+template <typename Dtype>
+class CPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<CPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(CPUStochasticPoolingLayerTest, TestDtypes);
 
-TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
+TYPED_TEST(CPUStochasticPoolingLayerTest, TestSetup) {
   LayerParameter layer_param;
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
   pooling_param->set_kernel_size(3);
@@ -60,8 +66,16 @@ TYPED_TEST(StochasticPoolingLayerTest, TestSetup) {
   EXPECT_EQ(this->blob_top_->width(), 2);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
-  Caffe::set_mode(Caffe::GPU);
+#ifndef CPU_ONLY
+
+template <typename Dtype>
+class GPUStochasticPoolingLayerTest
+  : public StochasticPoolingLayerTest<GPUDevice<Dtype> > {
+};
+
+TYPED_TEST_CASE(GPUStochasticPoolingLayerTest, TestDtypes);
+
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochastic) {
   LayerParameter layer_param;
   layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -104,8 +118,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPU) {
   EXPECT_GE(total / this->blob_top_->count(), 0.55);
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestStochasticTestPhase) {
   LayerParameter layer_param;
   layer_param.set_phase(TEST);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -142,8 +155,7 @@ TYPED_TEST(StochasticPoolingLayerTest, TestStochasticGPUTestPhase) {
   }
 }
 
-TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
-  Caffe::set_mode(Caffe::GPU);
+TYPED_TEST(GPUStochasticPoolingLayerTest, TestGradient) {
   LayerParameter layer_param;
   layer_param.set_phase(TRAIN);
   PoolingParameter* pooling_param = layer_param.mutable_pooling_param();
@@ -158,6 +170,6 @@ TYPED_TEST(StochasticPoolingLayerTest, TestGradientGPU) {
       this->blob_top_vec_);
 }
 
-
+#endif
 
 }  // namespace caffe
diff --git a/src/caffe/test/test_syncedmem.cpp b/src/caffe/test/test_syncedmem.cpp
index b946233d07c..16dfb58230f 100644
--- a/src/caffe/test/test_syncedmem.cpp
+++ b/src/caffe/test/test_syncedmem.cpp
@@ -1,4 +1,3 @@
-#include <cstring>
 #include <vector>
 
 #include "gtest/gtest.h"
diff --git a/src/caffe/test/test_tanh_layer.cpp b/src/caffe/test/test_tanh_layer.cpp
index 5dc92832fc8..bb8699a8e91 100644
--- a/src/caffe/test/test_tanh_layer.cpp
+++ b/src/caffe/test/test_tanh_layer.cpp
@@ -5,8 +5,8 @@
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
-#include "caffe/common_layers.hpp"
 #include "caffe/filler.hpp"
+#include "caffe/layers/tanh_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 #include "caffe/test/test_gradient_check_util.hpp"
diff --git a/src/caffe/test/test_threshold_layer.cpp b/src/caffe/test/test_threshold_layer.cpp
index 05ce82120e6..1e84cc5ab84 100644
--- a/src/caffe/test/test_threshold_layer.cpp
+++ b/src/caffe/test/test_threshold_layer.cpp
@@ -5,7 +5,7 @@
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/filler.hpp"
-#include "caffe/vision_layers.hpp"
+#include "caffe/layers/threshold_layer.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
 
diff --git a/src/caffe/test/test_tile_layer.cpp b/src/caffe/test/test_tile_layer.cpp
new file mode 100644
index 00000000000..7ff75520e8e
--- /dev/null
+++ b/src/caffe/test/test_tile_layer.cpp
@@ -0,0 +1,161 @@
+#include <vector>
+
+#include "gtest/gtest.h"
+
+#include "caffe/blob.hpp"
+#include "caffe/common.hpp"
+#include "caffe/filler.hpp"
+#include "caffe/layers/tile_layer.hpp"
+
+#include "caffe/test/test_caffe_main.hpp"
+#include "caffe/test/test_gradient_check_util.hpp"
+
+namespace caffe {
+
+template <typename TypeParam>
+class TileLayerTest : public MultiDeviceTest<TypeParam> {
+  typedef typename TypeParam::Dtype Dtype;
+
+ protected:
+  TileLayerTest()
+      : blob_bottom_(new Blob<Dtype>(2, 3, 4, 5)),
+        blob_top_(new Blob<Dtype>()) {}
+  virtual void SetUp() {
+    blob_bottom_vec_.push_back(blob_bottom_);
+    blob_top_vec_.push_back(blob_top_);
+    FillerParameter filler_param;
+    filler_param.set_mean(0.0);
+    filler_param.set_std(1.0);
+    GaussianFiller<Dtype> filler(filler_param);
+    filler.Fill(blob_bottom_);
+  }
+
+  virtual ~TileLayerTest() {
+    delete blob_bottom_;
+    delete blob_top_;
+  }
+
+  Blob<Dtype>* const blob_bottom_;
+  Blob<Dtype>* const blob_top_;
+  vector<Blob<Dtype>*> blob_bottom_vec_;
+  vector<Blob<Dtype>*> blob_top_vec_;
+};
+
+TYPED_TEST_CASE(TileLayerTest, TestDtypesAndDevices);
+
+TYPED_TEST(TileLayerTest, TestTrivialSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 1;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+    layer_param.mutable_tile_param()->set_axis(i);
+    TileLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+    for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+      EXPECT_EQ(this->blob_top_->shape(j), this->blob_bottom_->shape(j));
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestSetup) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  for (int i = 0; i < this->blob_bottom_->num_axes(); ++i) {
+    layer_param.mutable_tile_param()->set_axis(i);
+    TileLayer<Dtype> layer(layer_param);
+    layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+    ASSERT_EQ(this->blob_top_->num_axes(), this->blob_bottom_->num_axes());
+    for (int j = 0; j < this->blob_bottom_->num_axes(); ++j) {
+      const int top_dim =
+          ((i == j) ? kNumTiles : 1) * this->blob_bottom_->shape(j);
+      EXPECT_EQ(top_dim, this->blob_top_->shape(j));
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 0;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+       for (int h = 0; h < this->blob_top_->height(); ++h) {
+         for (int w = 0; w < this->blob_top_->width(); ++w) {
+           const int bottom_n = n % this->blob_bottom_->num();
+           EXPECT_EQ(this->blob_bottom_->data_at(bottom_n, c, h, w),
+                     this->blob_top_->data_at(n, c, h, w));
+         }
+       }
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestForwardChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_);
+  layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_);
+  for (int n = 0; n < this->blob_top_->num(); ++n) {
+    for (int c = 0; c < this->blob_top_->channels(); ++c) {
+       for (int h = 0; h < this->blob_top_->height(); ++h) {
+         for (int w = 0; w < this->blob_top_->width(); ++w) {
+           const int bottom_c = c % this->blob_bottom_->channels();
+           EXPECT_EQ(this->blob_bottom_->data_at(n, bottom_c, h, w),
+                     this->blob_top_->data_at(n, c, h, w));
+         }
+       }
+    }
+  }
+}
+
+TYPED_TEST(TileLayerTest, TestTrivialGradient) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kNumTiles = 1;
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientNum) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 0;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+TYPED_TEST(TileLayerTest, TestGradientChannels) {
+  typedef typename TypeParam::Dtype Dtype;
+  LayerParameter layer_param;
+  const int kTileAxis = 1;
+  const int kNumTiles = 3;
+  layer_param.mutable_tile_param()->set_axis(kTileAxis);
+  layer_param.mutable_tile_param()->set_tiles(kNumTiles);
+  TileLayer<Dtype> layer(layer_param);
+  GradientChecker<Dtype> checker(1e-2, 1e-2);
+  checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_,
+      this->blob_top_vec_);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/test/test_upgrade_proto.cpp b/src/caffe/test/test_upgrade_proto.cpp
index eec627656ef..9dcc2aa55ec 100644
--- a/src/caffe/test/test_upgrade_proto.cpp
+++ b/src/caffe/test/test_upgrade_proto.cpp
@@ -1,13 +1,15 @@
-#include <cstring>
 #include <string>
 #include <vector>
 
+#include "boost/scoped_ptr.hpp"
 #include "google/protobuf/text_format.h"
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer.hpp"
+#include "caffe/util/db.hpp"
+#include "caffe/util/io.hpp"
 #include "caffe/util/upgrade_proto.hpp"
 
 #include "caffe/test/test_caffe_main.hpp"
@@ -2901,9 +2903,87 @@ TEST_F(NetUpgradeTest, TestUpgradeV1LayerType) {
       continue;  // Empty string isn't actually a valid layer type.
     }
     layer_param.set_type(v2_layer_type);
+    // Data layers expect a DB
+    if (v2_layer_type == "Data") {
+      #ifdef USE_LEVELDB
+      string tmp;
+      MakeTempDir(&tmp);
+      boost::scoped_ptr<db::DB> db(db::GetDB(DataParameter_DB_LEVELDB));
+      db->Open(tmp, db::NEW);
+      db->Close();
+      layer_param.mutable_data_param()->set_source(tmp);
+      #else
+      continue;
+      #endif  // USE_LEVELDB
+    }
+    #ifndef USE_OPENCV
+    if (v2_layer_type == "ImageData" || v2_layer_type == "WindowData") {
+     continue;
+    }
+    #endif  // !USE_OPENCV
     layer = LayerRegistry<float>::CreateLayer(layer_param);
     EXPECT_EQ(v2_layer_type, layer->type());
   }
 }
 
+class SolverTypeUpgradeTest : public ::testing::Test {
+ protected:
+  void RunSolverTypeUpgradeTest(
+      const string& input_param_string, const string& output_param_string) {
+    // Test upgrading old solver_type field (enum) to new type field (string)
+    SolverParameter input_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        input_param_string, &input_param));
+    SolverParameter expected_output_param;
+    CHECK(google::protobuf::TextFormat::ParseFromString(
+        output_param_string, &expected_output_param));
+    SolverParameter actual_output_param = input_param;
+    UpgradeSolverType(&actual_output_param);
+    EXPECT_EQ(expected_output_param.DebugString(),
+        actual_output_param.DebugString());
+  }
+};
+
+TEST_F(SolverTypeUpgradeTest, TestSimple) {
+  const char* old_type_vec[6] = { "SGD", "ADAGRAD", "NESTEROV", "RMSPROP",
+      "ADADELTA", "ADAM" };
+  const char* new_type_vec[6] = { "SGD", "AdaGrad", "Nesterov", "RMSProp",
+      "AdaDelta", "Adam" };
+  for (int i = 0; i < 6; ++i) {
+    const string& input_proto =
+        "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "test_iter: 100 "
+        "test_interval: 500 "
+        "base_lr: 0.01 "
+        "momentum: 0.0 "
+        "weight_decay: 0.0005 "
+        "lr_policy: 'inv' "
+        "gamma: 0.0001 "
+        "power: 0.75 "
+        "display: 100 "
+        "max_iter: 10000 "
+        "snapshot: 5000 "
+        "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+        "solver_mode: GPU "
+        "solver_type: " + std::string(old_type_vec[i]) + " ";
+    const string& expected_output_proto =
+        "net: 'examples/mnist/lenet_train_test.prototxt' "
+        "test_iter: 100 "
+        "test_interval: 500 "
+        "base_lr: 0.01 "
+        "momentum: 0.0 "
+        "weight_decay: 0.0005 "
+        "lr_policy: 'inv' "
+        "gamma: 0.0001 "
+        "power: 0.75 "
+        "display: 100 "
+        "max_iter: 10000 "
+        "snapshot: 5000 "
+        "snapshot_prefix: 'examples/mnist/lenet_rmsprop' "
+        "solver_mode: GPU "
+        "type: '" + std::string(new_type_vec[i]) + "' ";
+    this->RunSolverTypeUpgradeTest(input_proto, expected_output_proto);
+  }
+}
+
 }  // NOLINT(readability/fn_size)  // namespace caffe
diff --git a/src/caffe/test/test_util_blas.cpp b/src/caffe/test/test_util_blas.cpp
index 8770f309951..9ee8818ff1d 100644
--- a/src/caffe/test/test_util_blas.cpp
+++ b/src/caffe/test/test_util_blas.cpp
@@ -1,7 +1,5 @@
 #ifndef CPU_ONLY  // CPU-GPU test
 
-#include <cstring>
-
 #include "gtest/gtest.h"
 
 #include "caffe/blob.hpp"
diff --git a/src/caffe/util/blocking_queue.cpp b/src/caffe/util/blocking_queue.cpp
new file mode 100644
index 00000000000..058668fe28c
--- /dev/null
+++ b/src/caffe/util/blocking_queue.cpp
@@ -0,0 +1,96 @@
+#include <boost/thread.hpp>
+#include <string>
+
+#include "caffe/data_reader.hpp"
+#include "caffe/layers/base_data_layer.hpp"
+#include "caffe/parallel.hpp"
+#include "caffe/util/blocking_queue.hpp"
+
+namespace caffe {
+
+template<typename T>
+class BlockingQueue<T>::sync {
+ public:
+  mutable boost::mutex mutex_;
+  boost::condition_variable condition_;
+};
+
+template<typename T>
+BlockingQueue<T>::BlockingQueue()
+    : sync_(new sync()) {
+}
+
+template<typename T>
+void BlockingQueue<T>::push(const T& t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+  queue_.push(t);
+  lock.unlock();
+  sync_->condition_.notify_one();
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_pop(T* t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  if (queue_.empty()) {
+    return false;
+  }
+
+  *t = queue_.front();
+  queue_.pop();
+  return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::pop(const string& log_on_wait) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  while (queue_.empty()) {
+    if (!log_on_wait.empty()) {
+      LOG_EVERY_N(INFO, 1000)<< log_on_wait;
+    }
+    sync_->condition_.wait(lock);
+  }
+
+  T t = queue_.front();
+  queue_.pop();
+  return t;
+}
+
+template<typename T>
+bool BlockingQueue<T>::try_peek(T* t) {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  if (queue_.empty()) {
+    return false;
+  }
+
+  *t = queue_.front();
+  return true;
+}
+
+template<typename T>
+T BlockingQueue<T>::peek() {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+
+  while (queue_.empty()) {
+    sync_->condition_.wait(lock);
+  }
+
+  return queue_.front();
+}
+
+template<typename T>
+size_t BlockingQueue<T>::size() const {
+  boost::mutex::scoped_lock lock(sync_->mutex_);
+  return queue_.size();
+}
+
+template class BlockingQueue<Batch<float>*>;
+template class BlockingQueue<Batch<double>*>;
+template class BlockingQueue<Datum*>;
+template class BlockingQueue<shared_ptr<DataReader::QueuePair> >;
+template class BlockingQueue<P2PSync<float>*>;
+template class BlockingQueue<P2PSync<double>*>;
+
+}  // namespace caffe
diff --git a/src/caffe/util/db.cpp b/src/caffe/util/db.cpp
index 7f7018107ec..7f22509b56e 100644
--- a/src/caffe/util/db.cpp
+++ b/src/caffe/util/db.cpp
@@ -1,83 +1,40 @@
 #include "caffe/util/db.hpp"
+#include "caffe/util/db_leveldb.hpp"
+#include "caffe/util/db_lmdb.hpp"
 
-#include <sys/stat.h>
 #include <string>
 
 namespace caffe { namespace db {
 
-const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
-
-void LevelDB::Open(const string& source, Mode mode) {
-  leveldb::Options options;
-  options.block_size = 65536;
-  options.write_buffer_size = 268435456;
-  options.max_open_files = 100;
-  options.error_if_exists = mode == NEW;
-  options.create_if_missing = mode != READ;
-  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
-  CHECK(status.ok()) << "Failed to open leveldb " << source
-                     << std::endl << status.ToString();
-  LOG(INFO) << "Opened leveldb " << source;
-}
-
-void LMDB::Open(const string& source, Mode mode) {
-  MDB_CHECK(mdb_env_create(&mdb_env_));
-  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
-  if (mode == NEW) {
-    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
-  }
-  int flags = 0;
-  if (mode == READ) {
-    flags = MDB_RDONLY | MDB_NOTLS;
-  }
-  MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
-  LOG(INFO) << "Opened lmdb " << source;
-}
-
-LMDBCursor* LMDB::NewCursor() {
-  MDB_txn* mdb_txn;
-  MDB_cursor* mdb_cursor;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
-  return new LMDBCursor(mdb_txn, mdb_cursor);
-}
-
-LMDBTransaction* LMDB::NewTransaction() {
-  MDB_txn* mdb_txn;
-  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
-  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
-  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
-}
-
-void LMDBTransaction::Put(const string& key, const string& value) {
-  MDB_val mdb_key, mdb_value;
-  mdb_key.mv_data = const_cast<char*>(key.data());
-  mdb_key.mv_size = key.size();
-  mdb_value.mv_data = const_cast<char*>(value.data());
-  mdb_value.mv_size = value.size();
-  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
-}
-
 DB* GetDB(DataParameter::DB backend) {
   switch (backend) {
+#ifdef USE_LEVELDB
   case DataParameter_DB_LEVELDB:
     return new LevelDB();
+#endif  // USE_LEVELDB
+#ifdef USE_LMDB
   case DataParameter_DB_LMDB:
     return new LMDB();
+#endif  // USE_LMDB
   default:
     LOG(FATAL) << "Unknown database backend";
+    return NULL;
   }
 }
 
 DB* GetDB(const string& backend) {
+#ifdef USE_LEVELDB
   if (backend == "leveldb") {
     return new LevelDB();
-  } else if (backend == "lmdb") {
+  }
+#endif  // USE_LEVELDB
+#ifdef USE_LMDB
+  if (backend == "lmdb") {
     return new LMDB();
-  } else {
-    LOG(FATAL) << "Unknown database backend";
   }
+#endif  // USE_LMDB
+  LOG(FATAL) << "Unknown database backend";
+  return NULL;
 }
 
 }  // namespace db
diff --git a/src/caffe/util/db_leveldb.cpp b/src/caffe/util/db_leveldb.cpp
new file mode 100644
index 00000000000..f5c4d8a660d
--- /dev/null
+++ b/src/caffe/util/db_leveldb.cpp
@@ -0,0 +1,23 @@
+#ifdef USE_LEVELDB
+#include "caffe/util/db_leveldb.hpp"
+
+#include <string>
+
+namespace caffe { namespace db {
+
+void LevelDB::Open(const string& source, Mode mode) {
+  leveldb::Options options;
+  options.block_size = 65536;
+  options.write_buffer_size = 268435456;
+  options.max_open_files = 100;
+  options.error_if_exists = mode == NEW;
+  options.create_if_missing = mode != READ;
+  leveldb::Status status = leveldb::DB::Open(options, source, &db_);
+  CHECK(status.ok()) << "Failed to open leveldb " << source
+                     << std::endl << status.ToString();
+  LOG(INFO) << "Opened leveldb " << source;
+}
+
+}  // namespace db
+}  // namespace caffe
+#endif  // USE_LEVELDB
diff --git a/src/caffe/util/db_lmdb.cpp b/src/caffe/util/db_lmdb.cpp
new file mode 100644
index 00000000000..0bc82b53e2b
--- /dev/null
+++ b/src/caffe/util/db_lmdb.cpp
@@ -0,0 +1,68 @@
+#ifdef USE_LMDB
+#include "caffe/util/db_lmdb.hpp"
+
+#include <sys/stat.h>
+
+#include <string>
+
+namespace caffe { namespace db {
+
+const size_t LMDB_MAP_SIZE = 1099511627776;  // 1 TB
+
+void LMDB::Open(const string& source, Mode mode) {
+  MDB_CHECK(mdb_env_create(&mdb_env_));
+  MDB_CHECK(mdb_env_set_mapsize(mdb_env_, LMDB_MAP_SIZE));
+  if (mode == NEW) {
+    CHECK_EQ(mkdir(source.c_str(), 0744), 0) << "mkdir " << source << "failed";
+  }
+  int flags = 0;
+  if (mode == READ) {
+    flags = MDB_RDONLY | MDB_NOTLS;
+  }
+  int rc = mdb_env_open(mdb_env_, source.c_str(), flags, 0664);
+#ifndef ALLOW_LMDB_NOLOCK
+  MDB_CHECK(rc);
+#else
+  if (rc == EACCES) {
+    LOG(WARNING) << "Permission denied. Trying with MDB_NOLOCK ...";
+    // Close and re-open environment handle
+    mdb_env_close(mdb_env_);
+    MDB_CHECK(mdb_env_create(&mdb_env_));
+    // Try again with MDB_NOLOCK
+    flags |= MDB_NOLOCK;
+    MDB_CHECK(mdb_env_open(mdb_env_, source.c_str(), flags, 0664));
+  } else {
+    MDB_CHECK(rc);
+  }
+#endif
+  LOG(INFO) << "Opened lmdb " << source;
+}
+
+LMDBCursor* LMDB::NewCursor() {
+  MDB_txn* mdb_txn;
+  MDB_cursor* mdb_cursor;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, MDB_RDONLY, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  MDB_CHECK(mdb_cursor_open(mdb_txn, mdb_dbi_, &mdb_cursor));
+  return new LMDBCursor(mdb_txn, mdb_cursor);
+}
+
+LMDBTransaction* LMDB::NewTransaction() {
+  MDB_txn* mdb_txn;
+  MDB_CHECK(mdb_txn_begin(mdb_env_, NULL, 0, &mdb_txn));
+  MDB_CHECK(mdb_dbi_open(mdb_txn, NULL, 0, &mdb_dbi_));
+  return new LMDBTransaction(&mdb_dbi_, mdb_txn);
+}
+
+void LMDBTransaction::Put(const string& key, const string& value) {
+  MDB_val mdb_key, mdb_value;
+  mdb_key.mv_data = const_cast<char*>(key.data());
+  mdb_key.mv_size = key.size();
+  mdb_value.mv_data = const_cast<char*>(value.data());
+  mdb_value.mv_size = value.size();
+  MDB_CHECK(mdb_put(mdb_txn_, *mdb_dbi_, &mdb_key, &mdb_value, 0));
+}
+
+}  // namespace db
+}  // namespace caffe
+#endif  // USE_LMDB
diff --git a/src/caffe/util/hdf5.cpp b/src/caffe/util/hdf5.cpp
new file mode 100644
index 00000000000..7730e76ab87
--- /dev/null
+++ b/src/caffe/util/hdf5.cpp
@@ -0,0 +1,187 @@
+#include "caffe/util/hdf5.hpp"
+
+#include <string>
+#include <vector>
+
+namespace caffe {
+
+// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
+template <typename Dtype>
+void hdf5_load_nd_dataset_helper(
+    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
+    Blob<Dtype>* blob) {
+  // Verify that the dataset exists.
+  CHECK(H5LTfind_dataset(file_id, dataset_name_))
+      << "Failed to find HDF5 dataset " << dataset_name_;
+  // Verify that the number of dimensions is in the accepted range.
+  herr_t status;
+  int ndims;
+  status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
+  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
+  CHECK_GE(ndims, min_dim);
+  CHECK_LE(ndims, max_dim);
+
+  // Verify that the data format is what we expect: float or double.
+  std::vector<hsize_t> dims(ndims);
+  H5T_class_t class_;
+  status = H5LTget_dataset_info(
+      file_id, dataset_name_, dims.data(), &class_, NULL);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
+  switch (class_) {
+  case H5T_FLOAT:
+    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";
+    break;
+  case H5T_INTEGER:
+    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";
+    break;
+  case H5T_TIME:
+    LOG(FATAL) << "Unsupported datatype class: H5T_TIME";
+  case H5T_STRING:
+    LOG(FATAL) << "Unsupported datatype class: H5T_STRING";
+  case H5T_BITFIELD:
+    LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD";
+  case H5T_OPAQUE:
+    LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE";
+  case H5T_COMPOUND:
+    LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND";
+  case H5T_REFERENCE:
+    LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE";
+  case H5T_ENUM:
+    LOG(FATAL) << "Unsupported datatype class: H5T_ENUM";
+  case H5T_VLEN:
+    LOG(FATAL) << "Unsupported datatype class: H5T_VLEN";
+  case H5T_ARRAY:
+    LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY";
+  default:
+    LOG(FATAL) << "Datatype class unknown";
+  }
+
+  vector<int> blob_dims(dims.size());
+  for (int i = 0; i < dims.size(); ++i) {
+    blob_dims[i] = dims[i];
+  }
+  blob->Reshape(blob_dims);
+}
+
+template <>
+void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
+        int min_dim, int max_dim, Blob<float>* blob) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+  herr_t status = H5LTread_dataset_float(
+    file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
+        int min_dim, int max_dim, Blob<double>* blob) {
+  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
+  herr_t status = H5LTread_dataset_double(
+    file_id, dataset_name_, blob->mutable_cpu_data());
+  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
+}
+
+template <>
+void hdf5_save_nd_dataset<float>(
+    const hid_t file_id, const string& dataset_name, const Blob<float>& blob,
+    bool write_diff) {
+  int num_axes = blob.num_axes();
+  hsize_t *dims = new hsize_t[num_axes];
+  for (int i = 0; i < num_axes; ++i) {
+    dims[i] = blob.shape(i);
+  }
+  const float* data;
+  if (write_diff) {
+    data = blob.cpu_diff();
+  } else {
+    data = blob.cpu_data();
+  }
+  herr_t status = H5LTmake_dataset_float(
+      file_id, dataset_name.c_str(), num_axes, dims, data);
+  CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
+  delete[] dims;
+}
+
+template <>
+void hdf5_save_nd_dataset<double>(
+    hid_t file_id, const string& dataset_name, const Blob<double>& blob,
+    bool write_diff) {
+  int num_axes = blob.num_axes();
+  hsize_t *dims = new hsize_t[num_axes];
+  for (int i = 0; i < num_axes; ++i) {
+    dims[i] = blob.shape(i);
+  }
+  const double* data;
+  if (write_diff) {
+    data = blob.cpu_diff();
+  } else {
+    data = blob.cpu_data();
+  }
+  herr_t status = H5LTmake_dataset_double(
+      file_id, dataset_name.c_str(), num_axes, dims, data);
+  CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
+  delete[] dims;
+}
+
+string hdf5_load_string(hid_t loc_id, const string& dataset_name) {
+  // Get size of dataset
+  size_t size;
+  H5T_class_t class_;
+  herr_t status = \
+    H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size);
+  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name;
+  char *buf = new char[size];
+  status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf);
+  CHECK_GE(status, 0)
+    << "Failed to load int dataset with name " << dataset_name;
+  string val(buf);
+  delete[] buf;
+  return val;
+}
+
+void hdf5_save_string(hid_t loc_id, const string& dataset_name,
+                      const string& s) {
+  herr_t status = \
+    H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str());
+  CHECK_GE(status, 0)
+    << "Failed to save string dataset with name " << dataset_name;
+}
+
+int hdf5_load_int(hid_t loc_id, const string& dataset_name) {
+  int val;
+  herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val);
+  CHECK_GE(status, 0)
+    << "Failed to load int dataset with name " << dataset_name;
+  return val;
+}
+
+void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) {
+  hsize_t one = 1;
+  herr_t status = \
+    H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i);
+  CHECK_GE(status, 0)
+    << "Failed to save int dataset with name " << dataset_name;
+}
+
+int hdf5_get_num_links(hid_t loc_id) {
+  H5G_info_t info;
+  herr_t status = H5Gget_info(loc_id, &info);
+  CHECK_GE(status, 0) << "Error while counting HDF5 links.";
+  return info.nlinks;
+}
+
+string hdf5_get_name_by_idx(hid_t loc_id, int idx) {
+  ssize_t str_size = H5Lget_name_by_idx(
+      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT);
+  CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx;
+  char *c_str = new char[str_size+1];
+  ssize_t status = H5Lget_name_by_idx(
+      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1,
+      H5P_DEFAULT);
+  CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx;
+  string result(c_str);
+  delete[] c_str;
+  return result;
+}
+
+}  // namespace caffe
diff --git a/src/caffe/util/im2col.cpp b/src/caffe/util/im2col.cpp
index c48f31f35d4..114a86cb81e 100644
--- a/src/caffe/util/im2col.cpp
+++ b/src/caffe/util/im2col.cpp
@@ -1,34 +1,54 @@
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
+#include <vector>
 
 #include "caffe/util/im2col.hpp"
 #include "caffe/util/math_functions.hpp"
 
 namespace caffe {
 
+// Function uses casting from int to unsigned to compare if value of
+// parameter a is greater or equal to zero and lower than value of
+// parameter b. The b parameter is of type signed and is always positive,
+// therefore its value is always lower than 0x800... where casting
+// negative value of a parameter converts it to value higher than 0x800...
+// The casting allows to use one condition instead of two.
+inline bool is_a_ge_zero_and_a_lt_b(int a, int b) {
+  return static_cast<unsigned>(a) < static_cast<unsigned>(b);
+}
+
 template <typename Dtype>
 void im2col_cpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_col) {
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
-  int channels_col = channels * kernel_h * kernel_w;
-  for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % kernel_w;
-    int h_offset = (c / kernel_w) % kernel_h;
-    int c_im = c / kernel_h / kernel_w;
-    for (int h = 0; h < height_col; ++h) {
-      for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride_h - pad_h + h_offset;
-        int w_pad = w * stride_w - pad_w + w_offset;
-        if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
-          data_col[(c * height_col + h) * width_col + w] =
-            data_im[(c_im * height + h_pad) * width + w_pad];
-        else
-          data_col[(c * height_col + h) * width_col + w] = 0;
+  const int output_h = (height + 2 * pad_h -
+    (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int output_w = (width + 2 * pad_w -
+    (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+  const int channel_size = height * width;
+  for (int channel = channels; channel--; data_im += channel_size) {
+    for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+      for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+        int input_row = -pad_h + kernel_row * dilation_h;
+        for (int output_rows = output_h; output_rows; output_rows--) {
+          if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+            for (int output_cols = output_w; output_cols; output_cols--) {
+              *(data_col++) = 0;
+            }
+          } else {
+            int input_col = -pad_w + kernel_col * dilation_w;
+            for (int output_col = output_w; output_col; output_col--) {
+              if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+                *(data_col++) = data_im[input_row * width + input_col];
+              } else {
+                *(data_col++) = 0;
+              }
+              input_col += stride_w;
+            }
+          }
+          input_row += stride_h;
+        }
       }
     }
   }
@@ -38,33 +58,139 @@ void im2col_cpu(const Dtype* data_im, const int channels,
 template void im2col_cpu<float>(const float* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_col);
 template void im2col_cpu<double>(const double* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_col);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_col);
+
+template <typename Dtype>
+inline void im2col_nd_core_cpu(const Dtype* data_input, const bool im2col,
+    const int num_spatial_axes, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_output) {
+  if (!im2col) {
+    int im_size = im_shape[0];
+    for (int i = 0; i < num_spatial_axes; ++i) {
+      im_size *= im_shape[1 + i];
+    }
+    caffe_set(im_size, Dtype(0), data_output);
+  }
+  int kernel_size = 1;
+  for (int i = 0; i < num_spatial_axes; ++i) {
+    kernel_size *= kernel_shape[i];
+  }
+  const int channels_col = col_shape[0];
+  vector<int> d_offset(num_spatial_axes, 0);
+  vector<int> d_iter(num_spatial_axes, 0);
+  for (int c_col = 0; c_col < channels_col; ++c_col) {
+    // Loop over spatial axes in reverse order to compute a per-axis offset.
+    int offset = c_col;
+    for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+      if (d_i < num_spatial_axes - 1) {
+        offset /= kernel_shape[d_i + 1];
+      }
+      d_offset[d_i] = offset % kernel_shape[d_i];
+    }
+    for (bool incremented = true; incremented; ) {
+      // Loop over spatial axes in forward order to compute the indices in the
+      // image and column, and whether the index lies in the padding.
+      int index_col = c_col;
+      int index_im = c_col / kernel_size;
+      bool is_padding = false;
+      for (int d_i = 0; d_i < num_spatial_axes; ++d_i) {
+        const int d = d_iter[d_i];
+        const int d_im = d * stride[d_i] - pad[d_i] +
+            d_offset[d_i] * dilation[d_i];
+        is_padding |= d_im < 0 || d_im >= im_shape[d_i + 1];
+        index_col *= col_shape[d_i + 1];
+        index_col += d;
+        index_im *= im_shape[d_i + 1];
+        index_im += d_im;
+      }
+      if (im2col) {
+        if (is_padding) {
+          data_output[index_col] = 0;
+        } else {
+          data_output[index_col] = data_input[index_im];
+        }
+      } else if (!is_padding) {  // col2im
+        data_output[index_im] += data_input[index_col];
+      }
+      // Loop over spatial axes in reverse order to choose an index,
+      // like counting.
+      incremented = false;
+      for (int d_i = num_spatial_axes - 1; d_i >= 0; --d_i) {
+        const int d_max = col_shape[d_i + 1];
+        DCHECK_LT(d_iter[d_i], d_max);
+        if (d_iter[d_i] == d_max - 1) {
+          d_iter[d_i] = 0;
+        } else {  // d_iter[d_i] < d_max - 1
+          ++d_iter[d_i];
+          incremented = true;
+          break;
+        }
+      }
+    }  // while(incremented) {
+  }  // for (int c = 0; c < channels_col; ++c) {
+}
+
+template <typename Dtype>
+void im2col_nd_cpu(const Dtype* data_im, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col) {
+  const bool kIm2Col = true;
+  im2col_nd_core_cpu(data_im, kIm2Col, num_spatial_axes, im_shape, col_shape,
+                  kernel_shape, pad, stride, dilation, data_col);
+}
+
+// Explicit instantiation
+template void im2col_nd_cpu<float>(const float* data_im,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, float* data_col);
+template void im2col_nd_cpu<double>(const double* data_im,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, double* data_col);
 
 template <typename Dtype>
 void col2im_cpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_im) {
   caffe_set(height * width * channels, Dtype(0), data_im);
-  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
-  int channels_col = channels * patch_h * patch_w;
-  for (int c = 0; c < channels_col; ++c) {
-    int w_offset = c % patch_w;
-    int h_offset = (c / patch_w) % patch_h;
-    int c_im = c / patch_h / patch_w;
-    for (int h = 0; h < height_col; ++h) {
-      for (int w = 0; w < width_col; ++w) {
-        int h_pad = h * stride_h - pad_h + h_offset;
-        int w_pad = w * stride_w - pad_w + w_offset;
-        if (h_pad >= 0 && h_pad < height && w_pad >= 0 && w_pad < width)
-          data_im[(c_im * height + h_pad) * width + w_pad] +=
-              data_col[(c * height_col + h) * width_col + w];
+  const int output_h = (height + 2 * pad_h -
+    (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  const int output_w = (width + 2 * pad_w -
+    (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
+  const int channel_size = height * width;
+  for (int channel = channels; channel--; data_im += channel_size) {
+    for (int kernel_row = 0; kernel_row < kernel_h; kernel_row++) {
+      for (int kernel_col = 0; kernel_col < kernel_w; kernel_col++) {
+        int input_row = -pad_h + kernel_row * dilation_h;
+        for (int output_rows = output_h; output_rows; output_rows--) {
+          if (!is_a_ge_zero_and_a_lt_b(input_row, height)) {
+            data_col += output_w;
+          } else {
+            int input_col = -pad_w + kernel_col * dilation_w;
+            for (int output_col = output_w; output_col; output_col--) {
+              if (is_a_ge_zero_and_a_lt_b(input_col, width)) {
+                data_im[input_row * width + input_col] += *data_col;
+              }
+              data_col++;
+              input_col += stride_w;
+            }
+          }
+          input_row += stride_h;
+        }
       }
     }
   }
@@ -72,12 +198,37 @@ void col2im_cpu(const Dtype* data_col, const int channels,
 
 // Explicit instantiation
 template void col2im_cpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_im);
 template void col2im_cpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_im);
+
+template <typename Dtype>
+void col2im_nd_cpu(const Dtype* data_col, const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_im) {
+  const bool kIm2Col = false;
+  im2col_nd_core_cpu(data_col, kIm2Col, num_spatial_axes, im_shape, col_shape,
+                     kernel_shape, pad, stride, dilation, data_im);
+}
+
+// Explicit instantiation
+template void col2im_nd_cpu<float>(const float* data_col,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, float* data_im);
+template void col2im_nd_cpu<double>(const double* data_col,
+    const int num_spatial_axes,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, double* data_im);
+
 
 }  // namespace caffe
diff --git a/src/caffe/util/im2col.cu b/src/caffe/util/im2col.cu
index c90f93eb67b..a8f30a02484 100644
--- a/src/caffe/util/im2col.cu
+++ b/src/caffe/util/im2col.cu
@@ -1,7 +1,4 @@
 #include <algorithm>
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
 
 #include "caffe/common.hpp"
 #include "caffe/util/im2col.hpp"
@@ -13,26 +10,28 @@ __global__ void im2col_gpu_kernel(const int n, const Dtype* data_im,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_col) {
   CUDA_KERNEL_LOOP(index, n) {
-    int w_out = index % width_col;
-    int h_index = index / width_col;
-    int h_out = h_index % height_col;
-    int channel_in = h_index / height_col;
-    int channel_out = channel_in * kernel_h * kernel_w;
-    int h_in = h_out * stride_h - pad_h;
-    int w_in = w_out * stride_w - pad_w;
+    const int h_index = index / width_col;
+    const int h_col = h_index % height_col;
+    const int w_col = index % width_col;
+    const int c_im = h_index / height_col;
+    const int c_col = c_im * kernel_h * kernel_w;
+    const int h_offset = h_col * stride_h - pad_h;
+    const int w_offset = w_col * stride_w - pad_w;
     Dtype* data_col_ptr = data_col;
-    data_col_ptr += (channel_out * height_col + h_out) * width_col + w_out;
+    data_col_ptr += (c_col * height_col + h_col) * width_col + w_col;
     const Dtype* data_im_ptr = data_im;
-    data_im_ptr += (channel_in * height + h_in) * width + w_in;
+    data_im_ptr += (c_im * height + h_offset) * width + w_offset;
     for (int i = 0; i < kernel_h; ++i) {
       for (int j = 0; j < kernel_w; ++j) {
-        int h = h_in + i;
-        int w = w_in + j;
-        *data_col_ptr = (h >= 0 && w >= 0 && h < height && w < width) ?
-            data_im_ptr[i * width + j] : 0;
+        int h_im = h_offset + i * dilation_h;
+        int w_im = w_offset + j * dilation_w;
+        *data_col_ptr =
+            (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) ?
+            data_im_ptr[i * dilation_h * width + j * dilation_w] : 0;
         data_col_ptr += height_col * width_col;
       }
     }
@@ -44,68 +43,241 @@ void im2col_gpu(const Dtype* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     Dtype* data_col) {
   // We are going to launch channels * height_col * width_col kernels, each
   // kernel responsible for copying a single-channel grid.
-  int height_col = (height + 2 * pad_h - kernel_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - kernel_w) / stride_w + 1;
+  int height_col = (height + 2 * pad_h -
+      (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1;
+  int width_col = (width + 2 * pad_w -
+      (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1;
   int num_kernels = channels * height_col * width_col;
   // NOLINT_NEXT_LINE(whitespace/operators)
   im2col_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
       num_kernels, data_im, height, width, kernel_h, kernel_w, pad_h,
-      pad_w, stride_h, stride_w, height_col,
+      pad_w, stride_h, stride_w, dilation_h, dilation_w, height_col,
       width_col, data_col);
   CUDA_POST_KERNEL_CHECK;
 }
 
-
 // Explicit instantiation
 template void im2col_gpu<float>(const float* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    float* data_col);
+    const int dilation_h, const int dilation_w, float* data_col);
 template void im2col_gpu<double>(const double* data_im, const int channels,
     const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h, const int stride_w,
-    double* data_col);
+    const int dilation_h, const int dilation_w, double* data_col);
+
+template <typename Dtype, int num_axes>
+__global__ void im2col_nd_gpu_kernel(const int n, const Dtype* data_im,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col) {
+  int d_temp[num_axes];  // NOLINT(runtime/arrays)
+  int d_iter[num_axes];  // NOLINT(runtime/arrays)
+
+  __shared__ int shared_dilation[num_axes];
+  __shared__ int shared_kernel_shape[num_axes];
+  __shared__ int shared_pad[num_axes];
+  __shared__ int shared_stride[num_axes];
+  __shared__ int shared_col_shape[num_axes + 1];
+  __shared__ int shared_im_shape[num_axes + 1];
+
+  if (threadIdx.x < num_axes) {
+    shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+    shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+    shared_pad[threadIdx.x] = pad[threadIdx.x];
+    shared_stride[threadIdx.x] = stride[threadIdx.x];
+  }
+  if (threadIdx.x < num_axes + 1) {
+    shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+    shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+  }
+  __syncthreads();
+
+  int i;
+  CUDA_KERNEL_LOOP(index, n) {
+    // Initialize channel_in, computed in the loop below, with intermediate
+    // computations used to compute the spatial indices.
+    int channel_in = index;
+    int channel_out = 1;
+    for (i = num_axes - 1; i >= 0; --i) {
+      d_temp[i] = channel_in % shared_col_shape[i + 1];
+      channel_in /= shared_col_shape[i + 1];
+      channel_out *= shared_kernel_shape[i];
+    }
+    channel_out *= channel_in;
+    int data_col_inc = 1;
+    for (i = 0; i < num_axes; ++i) {
+      channel_out *= shared_col_shape[i + 1];
+      channel_out += d_temp[i];
+      d_temp[i] = d_temp[i] * shared_stride[i] - shared_pad[i];
+      channel_in *= shared_im_shape[i + 1];
+      channel_in += d_temp[i];
+      data_col_inc *= shared_col_shape[i + 1];
+      d_iter[i] = 0;
+    }
+    Dtype* data_col_ptr = data_col + channel_out;
+    const Dtype* data_im_ptr = data_im + channel_in;
+    bool incremented;
+    do {
+      bool in_range = true;
+      for (i = 0; i < num_axes; ++i) {
+        const int d_iter_im = d_iter[i] * shared_dilation[i] + d_temp[i];
+        in_range &= d_iter_im >= 0 && d_iter_im < shared_im_shape[i + 1];
+        if (!in_range) { break; }
+      }
+      if (in_range) {
+        int data_im_offset = d_iter[0] * shared_dilation[0];
+        for (i = 1; i < num_axes; ++i) {
+          data_im_offset *= shared_im_shape[i + 1];
+          data_im_offset += d_iter[i] * shared_dilation[i];
+        }
+        *data_col_ptr = data_im_ptr[data_im_offset];
+      } else {
+        *data_col_ptr = 0;
+      }
+      data_col_ptr += data_col_inc;
+      incremented = false;
+      for (i = num_axes - 1; i >= 0; --i) {
+        const int d_max = shared_kernel_shape[i];
+        if (d_iter[i] == d_max - 1) {
+          d_iter[i] = 0;
+        } else {  // d_iter[i] < d_max - 1
+          ++d_iter[i];
+          incremented = true;
+          break;
+        }
+      }  // for (int i = num_axes - 1; i >= 0; --i)
+    } while (incremented);  // do
+  }  // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void im2col_nd_gpu(const Dtype* data_im, const int num_spatial_axes,
+    const int num_kernels, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_col) {
+  // num_axes should be smaller than block size
+  DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
+  switch (num_spatial_axes) {
+  case 1:
+    im2col_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 2:
+    im2col_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 3:
+    im2col_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 4:
+    im2col_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 5:
+    im2col_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 6:
+    im2col_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 7:
+    im2col_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 8:
+    im2col_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 9:
+    im2col_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  case 10:
+    im2col_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
+        <<<CAFFE_GET_BLOCKS(num_kernels), CAFFE_CUDA_NUM_THREADS>>>(
+        num_kernels, data_im, im_shape, col_shape,
+        kernel_shape, pad, stride, dilation, data_col);
+    break;
+  default:
+    LOG(FATAL) << "im2col_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void im2col_nd_gpu<float>(const float* data_im,
+    const int num_spatial_axes, const int col_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, float* data_col);
+template void im2col_nd_gpu<double>(const double* data_im,
+    const int num_spatial_axes, const int col_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, double* data_col);
 
 template <typename Dtype>
 __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
     const int height, const int width, const int channels,
-    const int patch_h, const int patch_w,
+    const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w,
     const int stride_h, const int stride_w,
+    const int dilation_h, const int dilation_w,
     const int height_col, const int width_col,
     Dtype* data_im) {
   CUDA_KERNEL_LOOP(index, n) {
     Dtype val = 0;
-    int w = index % width + pad_w;
-    int h = (index / width) % height + pad_h;
-    int c = index / (width * height);
+    const int w_im = index % width + pad_w;
+    const int h_im = (index / width) % height + pad_h;
+    const int c_im = index / (width * height);
+    int kernel_extent_w = (kernel_w - 1) * dilation_w + 1;
+    int kernel_extent_h = (kernel_h - 1) * dilation_h + 1;
     // compute the start and end of the output
-    int w_col_start = (w < patch_w) ? 0 : (w - patch_w) / stride_w + 1;
-    int w_col_end = min(w / stride_w + 1, width_col);
-    int h_col_start = (h < patch_h) ? 0 : (h - patch_h) / stride_h + 1;
-    int h_col_end = min(h / stride_h + 1, height_col);
-    /*
-    for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
-      for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
-        // the col location: [c * width * height + h_out, w_out]
-        int c_col = c * patch_h * patch_w + (h - h_col * stride_h) * ksize
-            + (w - w_col * stride_w);
-        val += data_col[(c_col * height_col + h_col) * width_col + w_col];
-      }
-    }
-    */
-    // equivalent implementation
-    int offset =
-        (c * patch_h * patch_w + h * patch_w + w) * height_col * width_col;
-    int coeff_h_col = (1 - stride_h * patch_w * height_col) * width_col;
-    int coeff_w_col = (1 - stride_w * height_col * width_col);
-    for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
-      for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
-        val += data_col[offset + h_col * coeff_h_col + w_col * coeff_w_col];
+    const int w_col_start =
+        (w_im < kernel_extent_w) ? 0 : (w_im - kernel_extent_w) / stride_w + 1;
+    const int w_col_end = min(w_im / stride_w + 1, width_col);
+    const int h_col_start =
+        (h_im < kernel_extent_h) ? 0 : (h_im - kernel_extent_h) / stride_h + 1;
+    const int h_col_end = min(h_im / stride_h + 1, height_col);
+    // TODO: use LCM of stride and dilation to avoid unnecessary loops
+    for (int h_col = h_col_start; h_col < h_col_end; h_col += 1) {
+      for (int w_col = w_col_start; w_col < w_col_end; w_col += 1) {
+        int h_k = (h_im - h_col * stride_h);
+        int w_k = (w_im - w_col * stride_w);
+        if (h_k % dilation_h == 0 && w_k % dilation_w == 0) {
+          h_k /= dilation_h;
+          w_k /= dilation_w;
+          int data_col_index = (((c_im * kernel_h + h_k) * kernel_w + w_k) *
+                                height_col + h_col) * width_col + w_col;
+          val += data_col[data_col_index];
+        }
       }
     }
     data_im[index] = val;
@@ -114,31 +286,227 @@ __global__ void col2im_gpu_kernel(const int n, const Dtype* data_col,
 
 template <typename Dtype>
 void col2im_gpu(const Dtype* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, Dtype* data_im) {
-  int height_col = (height + 2 * pad_h - patch_h) / stride_h + 1;
-  int width_col = (width + 2 * pad_w - patch_w) / stride_w + 1;
+    const int stride_w, const int dilation_h, const int dilation_w,
+    Dtype* data_im) {
+  int height_col = (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) /
+      stride_h + 1;
+  int width_col = (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) /
+      stride_w + 1;
   int num_kernels = channels * height * width;
   // To avoid involving atomic operations, we will launch one kernel per
   // bottom dimension, and then in the kernel add up the top dimensions.
   // NOLINT_NEXT_LINE(whitespace/operators)
   col2im_gpu_kernel<Dtype><<<CAFFE_GET_BLOCKS(num_kernels),
                              CAFFE_CUDA_NUM_THREADS>>>(
-      num_kernels, data_col, height, width, channels, patch_h, patch_w,
-      pad_h, pad_w, stride_h, stride_w,
+      num_kernels, data_col, height, width, channels, kernel_h, kernel_w,
+      pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w,
       height_col, width_col, data_im);
   CUDA_POST_KERNEL_CHECK;
 }
 
 // Explicit instantiation
 template void col2im_gpu<float>(const float* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, float* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    float* data_im);
 template void col2im_gpu<double>(const double* data_col, const int channels,
-    const int height, const int width, const int patch_h, const int patch_w,
+    const int height, const int width, const int kernel_h, const int kernel_w,
     const int pad_h, const int pad_w, const int stride_h,
-    const int stride_w, double* data_im);
+    const int stride_w, const int dilation_h, const int dilation_w,
+    double* data_im);
+
+template <typename Dtype, int num_axes>
+__global__ void col2im_nd_gpu_kernel(const int n, const Dtype* data_col,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_im) {
+  int d_im[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_iter[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_start[num_axes];  // NOLINT(runtime/arrays)
+  int d_col_end[num_axes];  // NOLINT(runtime/arrays)
+
+  __shared__ int shared_dilation[num_axes];
+  __shared__ int shared_kernel_shape[num_axes];
+  __shared__ int shared_pad[num_axes];
+  __shared__ int shared_stride[num_axes];
+  __shared__ int shared_col_shape[num_axes + 1];
+  __shared__ int shared_im_shape[num_axes + 1];
+
+  if (threadIdx.x < num_axes) {
+    shared_dilation[threadIdx.x] = dilation[threadIdx.x];
+    shared_kernel_shape[threadIdx.x] = kernel_shape[threadIdx.x];
+    shared_pad[threadIdx.x] = pad[threadIdx.x];
+    shared_stride[threadIdx.x] = stride[threadIdx.x];
+  }
+  if (threadIdx.x < num_axes + 1) {
+    shared_col_shape[threadIdx.x] = col_shape[threadIdx.x];
+    shared_im_shape[threadIdx.x] = im_shape[threadIdx.x];
+  }
+  __syncthreads();
+
+  CUDA_KERNEL_LOOP(index, n) {
+    // Initialize channel_in, computed in the loop below, with intermediate
+    // computations used to compute the spatial indices.
+    int c_im = index;
+    // Calculate d_im (image dimensions).
+    for (int i = num_axes - 1; i >= 0; --i) {
+      d_im[i] = c_im % shared_im_shape[i + 1] + shared_pad[i];
+      c_im /= shared_im_shape[i + 1];
+    }
+    // Calculate col start/end indices.
+    bool done = false;
+    for (int i = 0; i < num_axes; ++i) {
+      const int kernel_extent =
+          shared_dilation[i] * (shared_kernel_shape[i] - 1) + 1;
+      d_col_start[i] = d_col_iter[i] =
+          (d_im[i] < kernel_extent) ? 0 :
+          (d_im[i] - kernel_extent) / shared_stride[i] + 1;
+      d_col_end[i] =
+          min(d_im[i] / shared_stride[i] + 1, shared_col_shape[i + 1]);
+      if (d_col_start[i] >= d_col_end[i]) {
+        // Skip computation if the dimension is 0 at any spatial axis --
+        // final val will be 0.
+        data_im[index] = 0;
+        done = true;
+        break;  // for (int i = 0; i < num_axes; ++i)
+      }
+    }
+    if (done) {
+      continue;  // CUDA_KERNEL_LOOP(index, n)
+    }
+    // Loop over the col to compute the output val.
+    Dtype val = 0;
+    bool incremented = true;
+    bool skip = false;
+    do {
+      // Compute the final offset.
+      int final_offset = 0;
+      int kernel_shape_prod = 1;
+      int kernel_index;
+      for (int i = num_axes - 1; i >= 0; --i) {
+        kernel_index = d_im[i] - d_col_iter[i] * shared_stride[i];
+        if (kernel_index % shared_dilation[i]) {
+          skip = true;
+          break;
+        } else {
+          kernel_index /= shared_dilation[i];
+          final_offset += kernel_index * kernel_shape_prod;
+          kernel_shape_prod *= shared_kernel_shape[i];
+        }
+      }
+      if (!skip) {
+        final_offset += kernel_shape_prod * c_im;
+        for (int i = 0; i < num_axes; ++i) {
+          final_offset *= shared_col_shape[i + 1];
+          final_offset += d_col_iter[i];
+        }
+        val += data_col[final_offset];
+      }
+      skip = false;
+      incremented = false;
+      for (int i = num_axes - 1; i >= 0; --i) {
+        const int d_max = d_col_end[i];
+        if (d_col_iter[i] == d_max - 1) {
+          d_col_iter[i] = d_col_start[i];
+        } else {  // d_col_iter[i] < d_max - 1
+          ++d_col_iter[i];
+          incremented = true;
+          break;  // for (int i = num_axes - 1; i >= 0; --i)
+        }
+      }  // for (int i = num_axes - 1; i >= 0; --i)
+    }  while (incremented);
+    data_im[index] = val;
+  }  // CUDA_KERNEL_LOOP(index, n)
+}
+
+template <typename Dtype>
+void col2im_nd_gpu(const Dtype* data_col, const int num_spatial_axes,
+    const int im_size, const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, Dtype* data_im) {
+  // num_axes should be smaller than block size
+  DCHECK_LT(num_spatial_axes, CAFFE_CUDA_NUM_THREADS);
+  switch (num_spatial_axes) {
+  case 1:
+    col2im_nd_gpu_kernel<Dtype, 1>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 2:
+    col2im_nd_gpu_kernel<Dtype, 2>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 3:
+    col2im_nd_gpu_kernel<Dtype, 3>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 4:
+    col2im_nd_gpu_kernel<Dtype, 4>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 5:
+    col2im_nd_gpu_kernel<Dtype, 5>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 6:
+    col2im_nd_gpu_kernel<Dtype, 6>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 7:
+    col2im_nd_gpu_kernel<Dtype, 7>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 8:
+    col2im_nd_gpu_kernel<Dtype, 8>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 9:
+    col2im_nd_gpu_kernel<Dtype, 9>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  case 10:
+    col2im_nd_gpu_kernel<Dtype, 10>  // NOLINT_NEXT_LINE(whitespace/operators)
+          <<<CAFFE_GET_BLOCKS(im_size), CAFFE_CUDA_NUM_THREADS>>>(
+          im_size, data_col, im_shape, col_shape,
+          kernel_shape, pad, stride, dilation, data_im);
+    break;
+  default:
+    LOG(FATAL) << "col2im_nd_gpu does not support computation with "
+               << num_spatial_axes << " spatial axes";
+  }
+  CUDA_POST_KERNEL_CHECK;
+}
+
+// Explicit instantiation
+template void col2im_nd_gpu<float>(const float* data_col,
+    const int num_spatial_axes, const int im_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, float* data_im);
+template void col2im_nd_gpu<double>(const double* data_col,
+    const int num_spatial_axes, const int im_size,
+    const int* im_shape, const int* col_shape,
+    const int* kernel_shape, const int* pad, const int* stride,
+    const int* dilation, double* data_im);
 
 }  // namespace caffe
diff --git a/src/caffe/util/insert_splits.cpp b/src/caffe/util/insert_splits.cpp
index 416f80ab3c2..7a899c69787 100644
--- a/src/caffe/util/insert_splits.cpp
+++ b/src/caffe/util/insert_splits.cpp
@@ -19,12 +19,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
   map<pair<int, int>, float> top_idx_to_loss_weight;
   map<pair<int, int>, int> top_idx_to_bottom_split_idx;
   map<int, string> layer_idx_to_layer_name;
-  layer_idx_to_layer_name[-1] = "input";
-  // Determine the number of times each blob is used as an input (bottom) blob.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const string& blob_name = param.input(i);
-    blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     const LayerParameter& layer_param = param.layer(i);
     layer_idx_to_layer_name[i] = layer_param.name();
@@ -32,7 +26,8 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       const string& blob_name = layer_param.bottom(j);
       if (blob_name_to_last_top_idx.find(blob_name) ==
           blob_name_to_last_top_idx.end()) {
-        LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j;
+        LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
+                   << layer_param.name() << "', bottom index " << j << ")";
       }
       const pair<int, int>& bottom_idx = make_pair(i, j);
       const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
@@ -44,7 +39,7 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
     }
     // A use of a top blob as a loss should be handled similarly to the use of
-    // a top blob as an input (bottom) blob to another layer.
+    // a top blob as a bottom blob to another layer.
     const int last_loss =
         std::min(layer_param.loss_weight_size(), layer_param.top_size());
     for (int j = 0; j < last_loss; ++j) {
@@ -56,19 +51,6 @@ void InsertSplits(const NetParameter& param, NetParameter* param_split) {
       }
     }
   }
-  // Create split layer for any input blobs used by other layer as bottom
-  // blobs more than once.
-  for (int i = 0; i < param.input_size(); ++i) {
-    const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
-    if (split_count > 1) {
-      const string& layer_name = layer_idx_to_layer_name[-1];
-      const string& blob_name = param.input(i);
-      LayerParameter* split_layer_param = param_split->add_layer();
-      const float kZeroLossWeight = 0;
-      ConfigureSplitLayer(layer_name, blob_name, i, split_count,
-          kZeroLossWeight, split_layer_param);
-    }
-  }
   for (int i = 0; i < param.layer_size(); ++i) {
     LayerParameter* layer_param = param_split->add_layer();
     layer_param->CopyFrom(param.layer(i));
diff --git a/src/caffe/util/io.cpp b/src/caffe/util/io.cpp
index 77ef7f257f4..835d2d4e4ff 100644
--- a/src/caffe/util/io.cpp
+++ b/src/caffe/util/io.cpp
@@ -2,10 +2,12 @@
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
+#ifdef USE_OPENCV
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/highgui/highgui_c.h>
 #include <opencv2/imgproc/imgproc.hpp>
+#endif  // USE_OPENCV
 #include <stdint.h>
 
 #include <algorithm>
@@ -67,6 +69,7 @@ void WriteProtoToBinaryFile(const Message& proto, const char* filename) {
   CHECK(proto.SerializeToOstream(&output));
 }
 
+#ifdef USE_OPENCV
 cv::Mat ReadImageToCVMat(const string& filename,
     const int height, const int width, const bool is_color) {
   cv::Mat cv_img;
@@ -98,6 +101,7 @@ cv::Mat ReadImageToCVMat(const string& filename,
 cv::Mat ReadImageToCVMat(const string& filename) {
   return ReadImageToCVMat(filename, 0, 0, true);
 }
+
 // Do the file extension and encoding match?
 static bool matchExt(const std::string & fn,
                      std::string en) {
@@ -111,6 +115,7 @@ static bool matchExt(const std::string & fn,
     return true;
   return false;
 }
+
 bool ReadImageToDatum(const string& filename, const int label,
     const int height, const int width, const bool is_color,
     const std::string & encoding, Datum* datum) {
@@ -135,6 +140,7 @@ bool ReadImageToDatum(const string& filename, const int label,
     return false;
   }
 }
+#endif  // USE_OPENCV
 
 bool ReadFileToDatum(const string& filename, const int label,
     Datum* datum) {
@@ -156,6 +162,7 @@ bool ReadFileToDatum(const string& filename, const int label,
   }
 }
 
+#ifdef USE_OPENCV
 cv::Mat DecodeDatumToCVMatNative(const Datum& datum) {
   cv::Mat cv_img;
   CHECK(datum.encoded()) << "Datum not encoded";
@@ -227,80 +234,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum) {
   }
   datum->set_data(buffer);
 }
-
-// Verifies format of data stored in HDF5 file and reshapes blob accordingly.
-template <typename Dtype>
-void hdf5_load_nd_dataset_helper(
-    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,
-    Blob<Dtype>* blob) {
-  // Verify that the dataset exists.
-  CHECK(H5LTfind_dataset(file_id, dataset_name_))
-      << "Failed to find HDF5 dataset " << dataset_name_;
-  // Verify that the number of dimensions is in the accepted range.
-  herr_t status;
-  int ndims;
-  status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);
-  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;
-  CHECK_GE(ndims, min_dim);
-  CHECK_LE(ndims, max_dim);
-
-  // Verify that the data format is what we expect: float or double.
-  std::vector<hsize_t> dims(ndims);
-  H5T_class_t class_;
-  status = H5LTget_dataset_info(
-      file_id, dataset_name_, dims.data(), &class_, NULL);
-  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;
-  CHECK_EQ(class_, H5T_FLOAT) << "Expected float or double data";
-
-  vector<int> blob_dims(dims.size());
-  for (int i = 0; i < dims.size(); ++i) {
-    blob_dims[i] = dims[i];
-  }
-  blob->Reshape(blob_dims);
-}
-
-template <>
-void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<float>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
-  herr_t status = H5LTread_dataset_float(
-    file_id, dataset_name_, blob->mutable_cpu_data());
-  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,
-        int min_dim, int max_dim, Blob<double>* blob) {
-  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);
-  herr_t status = H5LTread_dataset_double(
-    file_id, dataset_name_, blob->mutable_cpu_data());
-  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;
-}
-
-template <>
-void hdf5_save_nd_dataset<float>(
-    const hid_t file_id, const string& dataset_name, const Blob<float>& blob) {
-  hsize_t dims[HDF5_NUM_DIMS];
-  dims[0] = blob.num();
-  dims[1] = blob.channels();
-  dims[2] = blob.height();
-  dims[3] = blob.width();
-  herr_t status = H5LTmake_dataset_float(
-      file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
-  CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;
-}
-
-template <>
-void hdf5_save_nd_dataset<double>(
-    const hid_t file_id, const string& dataset_name, const Blob<double>& blob) {
-  hsize_t dims[HDF5_NUM_DIMS];
-  dims[0] = blob.num();
-  dims[1] = blob.channels();
-  dims[2] = blob.height();
-  dims[3] = blob.width();
-  herr_t status = H5LTmake_dataset_double(
-      file_id, dataset_name.c_str(), HDF5_NUM_DIMS, dims, blob.cpu_data());
-  CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;
-}
-
+#endif  // USE_OPENCV
 }  // namespace caffe
diff --git a/src/caffe/util/math_functions.cpp b/src/caffe/util/math_functions.cpp
index 13e17be582b..71c02274a75 100644
--- a/src/caffe/util/math_functions.cpp
+++ b/src/caffe/util/math_functions.cpp
@@ -206,6 +206,16 @@ void caffe_exp<double>(const int n, const double* a, double* y) {
   vdExp(n, a, y);
 }
 
+template <>
+void caffe_log<float>(const int n, const float* a, float* y) {
+  vsLn(n, a, y);
+}
+
+template <>
+void caffe_log<double>(const int n, const double* a, double* y) {
+  vdLn(n, a, y);
+}
+
 template <>
 void caffe_abs<float>(const int n, const float* a, float* y) {
     vsAbs(n, a, y);
@@ -338,28 +348,6 @@ float caffe_cpu_dot<float>(const int n, const float* x, const float* y);
 template
 double caffe_cpu_dot<double>(const int n, const double* x, const double* y);
 
-template <>
-int caffe_cpu_hamming_distance<float>(const int n, const float* x,
-                                  const float* y) {
-  int dist = 0;
-  for (int i = 0; i < n; ++i) {
-    dist += __builtin_popcount(static_cast<uint32_t>(x[i]) ^
-                               static_cast<uint32_t>(y[i]));
-  }
-  return dist;
-}
-
-template <>
-int caffe_cpu_hamming_distance<double>(const int n, const double* x,
-                                   const double* y) {
-  int dist = 0;
-  for (int i = 0; i < n; ++i) {
-    dist += __builtin_popcountl(static_cast<uint64_t>(x[i]) ^
-                                static_cast<uint64_t>(y[i]));
-  }
-  return dist;
-}
-
 template <>
 float caffe_cpu_asum<float>(const int n, const float* x) {
   return cblas_sasum(n, x, 1);
diff --git a/src/caffe/util/math_functions.cu b/src/caffe/util/math_functions.cu
index 43e65eb9a69..4c587537435 100644
--- a/src/caffe/util/math_functions.cu
+++ b/src/caffe/util/math_functions.cu
@@ -4,8 +4,6 @@
 #include <thrust/reduce.h>
 
 #include <cmath>
-#include <cstdlib>
-#include <cstring>
 
 #include "caffe/common.hpp"
 #include "caffe/util/math_functions.hpp"
@@ -324,6 +322,27 @@ void caffe_gpu_exp<double>(const int N, const double* a, double* y) {
       N, a, y);
 }
 
+template <typename Dtype>
+__global__ void log_kernel(const int n, const Dtype* a, Dtype* y) {
+  CUDA_KERNEL_LOOP(index, n) {
+    y[index] = log(a[index]);
+  }
+}
+
+template <>
+void caffe_gpu_log<float>(const int N, const float* a, float* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<float><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
+template <>
+void caffe_gpu_log<double>(const int N, const double* a, double* y) {
+  // NOLINT_NEXT_LINE(whitespace/operators)
+  log_kernel<double><<<CAFFE_GET_BLOCKS(N), CAFFE_CUDA_NUM_THREADS>>>(
+      N, a, y);
+}
+
 template <typename Dtype>
 __global__ void powx_kernel(const int n, const Dtype* a,
     const Dtype alpha, Dtype* y) {
@@ -352,51 +371,6 @@ DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sign, y[index] = (Dtype(0) < x[index])
                                       - (x[index] < Dtype(0)));
 DEFINE_AND_INSTANTIATE_GPU_UNARY_FUNC(sgnbit, y[index] = signbit(x[index]));
 
-__global__ void popc_kernel(const int n, const float* a,
-    const float* b, uint8_t* y) {
-  CUDA_KERNEL_LOOP(index, n) {
-    y[index] = __popc(static_cast<uint32_t>(a[index]) ^
-                      static_cast<uint32_t>(b[index]));
-  }
-}
-
-__global__ void popcll_kernel(const int n, const double* a,
-    const double* b, uint8_t* y) {
-  CUDA_KERNEL_LOOP(index, n) {
-    y[index] = __popcll(static_cast<uint64_t>(a[index]) ^
-                      static_cast<uint64_t>(b[index]));
-  }
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<float>(const int n, const float* x,
-                                  const float* y) {
-  // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
-  // TestHammingDistanceGPU in test_math_functions.cpp).
-  NOT_IMPLEMENTED;
-  thrust::device_vector<uint8_t> popcounts(n);
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  popc_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
-      n, x, y, thrust::raw_pointer_cast(popcounts.data()));
-  return thrust::reduce(popcounts.begin(), popcounts.end(),
-                        (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
-template <>
-uint32_t caffe_gpu_hamming_distance<double>(const int n, const double* x,
-                                   const double* y) {
-  // TODO: Fix caffe_gpu_hamming_distance (see failing unit test
-  // TestHammingDistanceGPU in test_math_functions.cpp).
-  NOT_IMPLEMENTED;
-  thrust::device_vector<uint8_t> popcounts(n);
-  // NOLINT_NEXT_LINE(whitespace/operators)
-  popcll_kernel<<<CAFFE_GET_BLOCKS(n), CAFFE_CUDA_NUM_THREADS>>>(
-      n, x, y, thrust::raw_pointer_cast(popcounts.data()));
-  return thrust::reduce(popcounts.begin(), popcounts.end(),
-                        /* NOLINT_NEXT_LINE(build/include_what_you_use) */
-                        (uint32_t) 0, thrust::plus<uint32_t>());
-}
-
 void caffe_gpu_rng_uniform(const int n, unsigned int* r) {
   CURAND_CHECK(curandGenerate(Caffe::curand_generator(), r, n));
 }
diff --git a/src/caffe/util/signal_handler.cpp b/src/caffe/util/signal_handler.cpp
new file mode 100644
index 00000000000..5d764ec524f
--- /dev/null
+++ b/src/caffe/util/signal_handler.cpp
@@ -0,0 +1,115 @@
+#include <boost/bind.hpp>
+#include <glog/logging.h>
+
+#include <signal.h>
+#include <csignal>
+
+#include "caffe/util/signal_handler.h"
+
+namespace {
+  static volatile sig_atomic_t got_sigint = false;
+  static volatile sig_atomic_t got_sighup = false;
+  static bool already_hooked_up = false;
+
+  void handle_signal(int signal) {
+    switch (signal) {
+    case SIGHUP:
+      got_sighup = true;
+      break;
+    case SIGINT:
+      got_sigint = true;
+      break;
+    }
+  }
+
+  void HookupHandler() {
+    if (already_hooked_up) {
+      LOG(FATAL) << "Tried to hookup signal handlers more than once.";
+    }
+    already_hooked_up = true;
+
+    struct sigaction sa;
+    // Setup the handler
+    sa.sa_handler = &handle_signal;
+    // Restart the system call, if at all possible
+    sa.sa_flags = SA_RESTART;
+    // Block every signal during the handler
+    sigfillset(&sa.sa_mask);
+    // Intercept SIGHUP and SIGINT
+    if (sigaction(SIGHUP, &sa, NULL) == -1) {
+      LOG(FATAL) << "Cannot install SIGHUP handler.";
+    }
+    if (sigaction(SIGINT, &sa, NULL) == -1) {
+      LOG(FATAL) << "Cannot install SIGINT handler.";
+    }
+  }
+
+  // Set the signal handlers to the default.
+  void UnhookHandler() {
+    if (already_hooked_up) {
+      struct sigaction sa;
+      // Setup the sighub handler
+      sa.sa_handler = SIG_DFL;
+      // Restart the system call, if at all possible
+      sa.sa_flags = SA_RESTART;
+      // Block every signal during the handler
+      sigfillset(&sa.sa_mask);
+      // Intercept SIGHUP and SIGINT
+      if (sigaction(SIGHUP, &sa, NULL) == -1) {
+        LOG(FATAL) << "Cannot uninstall SIGHUP handler.";
+      }
+      if (sigaction(SIGINT, &sa, NULL) == -1) {
+        LOG(FATAL) << "Cannot uninstall SIGINT handler.";
+      }
+
+      already_hooked_up = false;
+    }
+  }
+
+  // Return true iff a SIGINT has been received since the last time this
+  // function was called.
+  bool GotSIGINT() {
+    bool result = got_sigint;
+    got_sigint = false;
+    return result;
+  }
+
+  // Return true iff a SIGHUP has been received since the last time this
+  // function was called.
+  bool GotSIGHUP() {
+    bool result = got_sighup;
+    got_sighup = false;
+    return result;
+  }
+}  // namespace
+
+namespace caffe {
+
+SignalHandler::SignalHandler(SolverAction::Enum SIGINT_action,
+                             SolverAction::Enum SIGHUP_action):
+  SIGINT_action_(SIGINT_action),
+  SIGHUP_action_(SIGHUP_action) {
+  HookupHandler();
+}
+
+SignalHandler::~SignalHandler() {
+  UnhookHandler();
+}
+
+SolverAction::Enum SignalHandler::CheckForSignals() const {
+  if (GotSIGHUP()) {
+    return SIGHUP_action_;
+  }
+  if (GotSIGINT()) {
+    return SIGINT_action_;
+  }
+  return SolverAction::NONE;
+}
+
+// Return the function that the solver can use to find out if a snapshot or
+// early exit is being requested.
+ActionCallback SignalHandler::GetActionFunction() {
+  return boost::bind(&SignalHandler::CheckForSignals, this);
+}
+
+}  // namespace caffe
diff --git a/src/caffe/util/upgrade_proto.cpp b/src/caffe/util/upgrade_proto.cpp
index 38a06026adf..9e186915b43 100644
--- a/src/caffe/util/upgrade_proto.cpp
+++ b/src/caffe/util/upgrade_proto.cpp
@@ -13,7 +13,79 @@
 namespace caffe {
 
 bool NetNeedsUpgrade(const NetParameter& net_param) {
-  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
+  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param)
+      || NetNeedsDataUpgrade(net_param) || NetNeedsInputUpgrade(net_param);
+}
+
+bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
+  bool success = true;
+  if (NetNeedsV0ToV1Upgrade(*param)) {
+    // NetParameter was specified using the old style (V0LayerParameter); try to
+    // upgrade it.
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V0LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV0Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+          << "V0NetParameter to NetParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V0LayerParameter";
+    }
+    LOG(WARNING) << "Note that future Caffe releases will not support "
+        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
+        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
+        << "weights upgrade this and any other net protos to the new format.";
+  }
+  // NetParameter uses old style data transformation fields; try to upgrade it.
+  if (NetNeedsDataUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "transformation parameters: " << param_file;
+    UpgradeNetDataTransformation(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "data transformation parameters.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "transform_param messages for transformation fields.";
+  }
+  if (NetNeedsV1ToV2Upgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "V1LayerParameter: " << param_file;
+    NetParameter original_param(*param);
+    if (!UpgradeV1Net(original_param, param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+                 << "V1LayerParameter (see above); continuing anyway.";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "V1LayerParameter";
+    }
+  }
+  // NetParameter uses old style input fields; try to upgrade it.
+  if (NetNeedsInputUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "input fields: " << param_file;
+    UpgradeNetInput(param);
+    LOG(INFO) << "Successfully upgraded file specified using deprecated "
+              << "input fields.";
+    LOG(WARNING) << "Note that future Caffe releases will only support "
+                 << "input layers and not input fields.";
+  }
+  return success;
+}
+
+void ReadNetParamsFromTextFileOrDie(const string& param_file,
+                                    NetParameter* param) {
+  CHECK(ReadProtoFromTextFile(param_file, param))
+      << "Failed to parse NetParameter file: " << param_file;
+  UpgradeNetAsNeeded(param_file, param);
+}
+
+void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
+                                      NetParameter* param) {
+  CHECK(ReadProtoFromBinaryFile(param_file, param))
+      << "Failed to parse NetParameter file: " << param_file;
+  UpgradeNetAsNeeded(param_file, param);
 }
 
 bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
@@ -193,7 +265,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_pad()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_pad(v0_layer_param.pad());
+        layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
       } else {
@@ -203,7 +275,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_kernelsize()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_kernel_size(
+        layer_param->mutable_convolution_param()->add_kernel_size(
             v0_layer_param.kernelsize());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_kernel_size(
@@ -224,7 +296,7 @@ bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
     }
     if (v0_layer_param.has_stride()) {
       if (type == "conv") {
-        layer_param->mutable_convolution_param()->set_stride(
+        layer_param->mutable_convolution_param()->add_stride(
             v0_layer_param.stride());
       } else if (type == "pool") {
         layer_param->mutable_pooling_param()->set_stride(
@@ -583,60 +655,15 @@ void UpgradeNetDataTransformation(NetParameter* net_param) {
   }
 }
 
-bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
-  bool success = true;
-  if (NetNeedsV0ToV1Upgrade(*param)) {
-    // NetParameter was specified using the old style (V0LayerParameter); try to
-    // upgrade it.
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "V0LayerParameter: " << param_file;
-    NetParameter original_param(*param);
-    if (!UpgradeV0Net(original_param, param)) {
-      success = false;
-      LOG(ERROR) << "Warning: had one or more problems upgrading "
-          << "V0NetParameter to NetParameter (see above); continuing anyway.";
-    } else {
-      LOG(INFO) << "Successfully upgraded file specified using deprecated "
-                << "V0LayerParameter";
-    }
-    LOG(ERROR) << "Note that future Caffe releases will not support "
-        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
-        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
-        << "weights upgrade this and any other net protos to the new format.";
-  }
-  // NetParameter uses old style data transformation fields; try to upgrade it.
-  if (NetNeedsDataUpgrade(*param)) {
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "transformation parameters: " << param_file;
-    UpgradeNetDataTransformation(param);
-    LOG(INFO) << "Successfully upgraded file specified using deprecated "
-              << "data transformation parameters.";
-    LOG(ERROR) << "Note that future Caffe releases will only support "
-               << "transform_param messages for transformation fields.";
-  }
-  if (NetNeedsV1ToV2Upgrade(*param)) {
-    LOG(ERROR) << "Attempting to upgrade input file specified using deprecated "
-               << "V1LayerParameter: " << param_file;
-    NetParameter original_param(*param);
-    if (!UpgradeV1Net(original_param, param)) {
-      success = false;
-      LOG(ERROR) << "Warning: had one or more problems upgrading "
-          << "V1LayerParameter (see above); continuing anyway.";
-    } else {
-      LOG(INFO) << "Successfully upgraded file specified using deprecated "
-                << "V1LayerParameter";
-    }
-  }
-  return success;
-}
-
 bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
-  bool is_fully_compatible = true;
   if (v1_net_param.layer_size() > 0) {
-    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
-               << "fields; these will be ignored for the upgrade.";
-    is_fully_compatible = false;
+    LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; "
+        << "the definition includes both 'layer' and 'layers' fields. "
+        << "The current format defines 'layer' fields with string type like "
+        << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. "
+        << "Manually switch the definition to 'layer' format to continue.";
   }
+  bool is_fully_compatible = true;
   net_param->CopyFrom(v1_net_param);
   net_param->clear_layers();
   net_param->clear_layer();
@@ -923,18 +950,119 @@ const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
   }
 }
 
-void ReadNetParamsFromTextFileOrDie(const string& param_file,
-                                    NetParameter* param) {
-  CHECK(ReadProtoFromTextFile(param_file, param))
-      << "Failed to parse NetParameter file: " << param_file;
-  UpgradeNetAsNeeded(param_file, param);
+bool NetNeedsInputUpgrade(const NetParameter& net_param) {
+  return net_param.input_size() > 0;
 }
 
-void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
-                                      NetParameter* param) {
-  CHECK(ReadProtoFromBinaryFile(param_file, param))
-      << "Failed to parse NetParameter file: " << param_file;
-  UpgradeNetAsNeeded(param_file, param);
+void UpgradeNetInput(NetParameter* net_param) {
+  // Collect inputs and convert to Input layer definitions.
+  // If the NetParameter holds an input alone, without shape/dim, then
+  // it's a legacy caffemodel and simply stripping the input field is enough.
+  bool has_shape = net_param->input_shape_size() > 0;
+  bool has_dim = net_param->input_dim_size() > 0;
+  if (has_shape || has_dim) {
+    LayerParameter* layer_param = net_param->add_layer();
+    layer_param->set_name("input");
+    layer_param->set_type("Input");
+    InputParameter* input_param = layer_param->mutable_input_param();
+    // Convert input fields into a layer.
+    for (int i = 0; i < net_param->input_size(); ++i) {
+      layer_param->add_top(net_param->input(i));
+      if (has_shape) {
+        input_param->add_shape()->CopyFrom(net_param->input_shape(i));
+      } else {
+        // Turn legacy input dimensions into shape.
+        BlobShape* shape = input_param->add_shape();
+        int first_dim = i*4;
+        int last_dim = first_dim + 4;
+        for (int j = first_dim; j < last_dim; j++) {
+          shape->add_dim(net_param->input_dim(j));
+        }
+      }
+    }
+    // Swap input layer to beginning of net to satisfy layer dependencies.
+    for (int i = net_param->layer_size() - 1; i > 0; --i) {
+      net_param->mutable_layer(i-1)->Swap(net_param->mutable_layer(i));
+    }
+  }
+  // Clear inputs.
+  net_param->clear_input();
+  net_param->clear_input_shape();
+  net_param->clear_input_dim();
+}
+
+// Return true iff the solver contains any old solver_type specified as enums
+bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
+  if (solver_param.has_solver_type()) {
+    return true;
+  }
+  return false;
+}
+
+bool UpgradeSolverType(SolverParameter* solver_param) {
+  CHECK(!solver_param->has_solver_type() || !solver_param->has_type())
+      << "Failed to upgrade solver: old solver_type field (enum) and new type "
+      << "field (string) cannot be both specified in solver proto text.";
+  if (solver_param->has_solver_type()) {
+    string type;
+    switch (solver_param->solver_type()) {
+    case SolverParameter_SolverType_SGD:
+      type = "SGD";
+      break;
+    case SolverParameter_SolverType_NESTEROV:
+      type = "Nesterov";
+      break;
+    case SolverParameter_SolverType_ADAGRAD:
+      type = "AdaGrad";
+      break;
+    case SolverParameter_SolverType_RMSPROP:
+      type = "RMSProp";
+      break;
+    case SolverParameter_SolverType_ADADELTA:
+      type = "AdaDelta";
+      break;
+    case SolverParameter_SolverType_ADAM:
+      type = "Adam";
+      break;
+    default:
+      LOG(FATAL) << "Unknown SolverParameter solver_type: " << type;
+    }
+    solver_param->set_type(type);
+    solver_param->clear_solver_type();
+  } else {
+    LOG(ERROR) << "Warning: solver type already up to date. ";
+    return false;
+  }
+  return true;
+}
+
+// Check for deprecations and upgrade the SolverParameter as needed.
+bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) {
+  bool success = true;
+  // Try to upgrade old style solver_type enum fields into new string type
+  if (SolverNeedsTypeUpgrade(*param)) {
+    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
+              << "'solver_type' field (enum)': " << param_file;
+    if (!UpgradeSolverType(param)) {
+      success = false;
+      LOG(ERROR) << "Warning: had one or more problems upgrading "
+                 << "SolverType (see above).";
+    } else {
+      LOG(INFO) << "Successfully upgraded file specified using deprecated "
+                << "'solver_type' field (enum) to 'type' field (string).";
+      LOG(WARNING) << "Note that future Caffe releases will only support "
+                   << "'type' field (string) for a solver's type.";
+    }
+  }
+  return success;
+}
+
+// Read parameters from a file into a SolverParameter proto message.
+void ReadSolverParamsFromTextFileOrDie(const string& param_file,
+                                       SolverParameter* param) {
+  CHECK(ReadProtoFromTextFile(param_file, param))
+      << "Failed to parse SolverParameter file: " << param_file;
+  UpgradeSolverAsNeeded(param_file, param);
 }
 
 }  // namespace caffe
diff --git a/tools/caffe.cpp b/tools/caffe.cpp
index eb9e97f5e27..5d9331f0c22 100644
--- a/tools/caffe.cpp
+++ b/tools/caffe.cpp
@@ -1,3 +1,9 @@
+#ifdef WITH_PYTHON_LAYER
+#include "boost/python.hpp"
+namespace bp = boost::python;
+#endif
+
+#include <gflags/gflags.h>
 #include <glog/logging.h>
 
 #include <cstring>
@@ -7,18 +13,23 @@
 
 #include "boost/algorithm/string.hpp"
 #include "caffe/caffe.hpp"
+#include "caffe/util/signal_handler.h"
 
 using caffe::Blob;
 using caffe::Caffe;
 using caffe::Net;
 using caffe::Layer;
+using caffe::Solver;
 using caffe::shared_ptr;
+using caffe::string;
 using caffe::Timer;
 using caffe::vector;
+using std::ostringstream;
 
-
-DEFINE_int32(gpu, -1,
-    "Run in GPU mode on given device ID.");
+DEFINE_string(gpu, "",
+    "Optional; run in GPU mode on given device IDs separated by ','."
+    "Use '-gpu all' to run on all available GPUs. The effective training "
+    "batch size is multiplied by the number of devices.");
 DEFINE_string(solver, "",
     "The solver definition protocol buffer text file.");
 DEFINE_string(model, "",
@@ -26,10 +37,16 @@ DEFINE_string(model, "",
 DEFINE_string(snapshot, "",
     "Optional; the snapshot solver state to resume training.");
 DEFINE_string(weights, "",
-    "Optional; the pretrained weights to initialize finetuning. "
-    "Cannot be set simultaneously with snapshot.");
+    "Optional; the pretrained weights to initialize finetuning, "
+    "separated by ','. Cannot be set simultaneously with snapshot.");
 DEFINE_int32(iterations, 50,
     "The number of iterations to run.");
+DEFINE_string(sigint_effect, "stop",
+             "Optional; action to take when a SIGINT signal is received: "
+              "snapshot, stop or none.");
+DEFINE_string(sighup_effect, "snapshot",
+             "Optional; action to take when a SIGHUP signal is received: "
+             "snapshot, stop or none.");
 
 // A simple registry for caffe commands.
 typedef int (*BrewFunction)();
@@ -61,6 +78,29 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
   }
 }
 
+// Parse GPU ids or use all available devices
+static void get_gpus(vector<int>* gpus) {
+  if (FLAGS_gpu == "all") {
+    int count = 0;
+#ifndef CPU_ONLY
+    CUDA_CHECK(cudaGetDeviceCount(&count));
+#else
+    NO_GPU;
+#endif
+    for (int i = 0; i < count; ++i) {
+      gpus->push_back(i);
+    }
+  } else if (FLAGS_gpu.size()) {
+    vector<string> strings;
+    boost::split(strings, FLAGS_gpu, boost::is_any_of(","));
+    for (int i = 0; i < strings.size(); ++i) {
+      gpus->push_back(boost::lexical_cast<int>(strings[i]));
+    }
+  } else {
+    CHECK_EQ(gpus->size(), 0);
+  }
+}
+
 // caffe commands to call by
 //     caffe <command> <args>
 //
@@ -69,10 +109,13 @@ static BrewFunction GetBrewFunction(const caffe::string& name) {
 
 // Device Query: show diagnostic information for a GPU device.
 int device_query() {
-  CHECK_GT(FLAGS_gpu, -1) << "Need a device ID to query.";
-  LOG(INFO) << "Querying device ID = " << FLAGS_gpu;
-  caffe::Caffe::SetDevice(FLAGS_gpu);
-  caffe::Caffe::DeviceQuery();
+  LOG(INFO) << "Querying GPUs " << FLAGS_gpu;
+  vector<int> gpus;
+  get_gpus(&gpus);
+  for (int i = 0; i < gpus.size(); ++i) {
+    caffe::Caffe::SetDevice(gpus[i]);
+    caffe::Caffe::DeviceQuery();
+  }
   return 0;
 }
 RegisterBrewFunction(device_query);
@@ -91,6 +134,22 @@ void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {
   }
 }
 
+// Translate the signal effect the user specified on the command-line to the
+// corresponding enumeration.
+caffe::SolverAction::Enum GetRequestedAction(
+    const std::string& flag_value) {
+  if (flag_value == "stop") {
+    return caffe::SolverAction::STOP;
+  }
+  if (flag_value == "snapshot") {
+    return caffe::SolverAction::SNAPSHOT;
+  }
+  if (flag_value == "none") {
+    return caffe::SolverAction::NONE;
+  }
+  LOG(FATAL) << "Invalid signal effect \""<< flag_value << "\" was specified";
+}
+
 // Train / Finetune a model.
 int train() {
   CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";
@@ -99,36 +158,65 @@ int train() {
       "but not both.";
 
   caffe::SolverParameter solver_param;
-  caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);
+  caffe::ReadSolverParamsFromTextFileOrDie(FLAGS_solver, &solver_param);
 
-  // If the gpu flag is not provided, allow the mode and device to be set
+  // If the gpus flag is not provided, allow the mode and device to be set
   // in the solver prototxt.
-  if (FLAGS_gpu < 0
+  if (FLAGS_gpu.size() == 0
       && solver_param.solver_mode() == caffe::SolverParameter_SolverMode_GPU) {
-    FLAGS_gpu = solver_param.device_id();
+      if (solver_param.has_device_id()) {
+          FLAGS_gpu = "" +
+              boost::lexical_cast<string>(solver_param.device_id());
+      } else {  // Set default GPU if unspecified
+          FLAGS_gpu = "" + boost::lexical_cast<string>(0);
+      }
   }
 
-  // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
-    Caffe::set_mode(Caffe::GPU);
-  } else {
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() == 0) {
     LOG(INFO) << "Use CPU.";
     Caffe::set_mode(Caffe::CPU);
+  } else {
+    ostringstream s;
+    for (int i = 0; i < gpus.size(); ++i) {
+      s << (i ? ", " : "") << gpus[i];
+    }
+    LOG(INFO) << "Using GPUs " << s.str();
+#ifndef CPU_ONLY
+    cudaDeviceProp device_prop;
+    for (int i = 0; i < gpus.size(); ++i) {
+      cudaGetDeviceProperties(&device_prop, gpus[i]);
+      LOG(INFO) << "GPU " << gpus[i] << ": " << device_prop.name;
+    }
+#endif
+    solver_param.set_device_id(gpus[0]);
+    Caffe::SetDevice(gpus[0]);
+    Caffe::set_mode(Caffe::GPU);
+    Caffe::set_solver_count(gpus.size());
   }
 
-  LOG(INFO) << "Starting Optimization";
+  caffe::SignalHandler signal_handler(
+        GetRequestedAction(FLAGS_sigint_effect),
+        GetRequestedAction(FLAGS_sighup_effect));
+
   shared_ptr<caffe::Solver<float> >
-    solver(caffe::GetSolver<float>(solver_param));
+      solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
+
+  solver->SetActionFunction(signal_handler.GetActionFunction());
 
   if (FLAGS_snapshot.size()) {
     LOG(INFO) << "Resuming from " << FLAGS_snapshot;
-    solver->Solve(FLAGS_snapshot);
+    solver->Restore(FLAGS_snapshot.c_str());
   } else if (FLAGS_weights.size()) {
-    CopyLayers(&*solver, FLAGS_weights);
-    solver->Solve();
+    CopyLayers(solver.get(), FLAGS_weights);
+  }
+
+  if (gpus.size() > 1) {
+    caffe::P2PSync<float> sync(solver, NULL, solver->param());
+    sync.Run(gpus);
   } else {
+    LOG(INFO) << "Starting Optimization";
     solver->Solve();
   }
   LOG(INFO) << "Optimization Done.";
@@ -143,9 +231,16 @@ int test() {
   CHECK_GT(FLAGS_weights.size(), 0) << "Need model weights to score.";
 
   // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() != 0) {
+    LOG(INFO) << "Use GPU with device ID " << gpus[0];
+#ifndef CPU_ONLY
+    cudaDeviceProp device_prop;
+    cudaGetDeviceProperties(&device_prop, gpus[0]);
+    LOG(INFO) << "GPU device name: " << device_prop.name;
+#endif
+    Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";
@@ -156,14 +251,13 @@ int test() {
   caffe_net.CopyTrainedLayersFrom(FLAGS_weights);
   LOG(INFO) << "Running for " << FLAGS_iterations << " iterations.";
 
-  vector<Blob<float>* > bottom_vec;
   vector<int> test_score_output_id;
   vector<float> test_score;
   float loss = 0;
   for (int i = 0; i < FLAGS_iterations; ++i) {
     float iter_loss;
     const vector<Blob<float>*>& result =
-        caffe_net.Forward(bottom_vec, &iter_loss);
+        caffe_net.Forward(&iter_loss);
     loss += iter_loss;
     int idx = 0;
     for (int j = 0; j < result.size(); ++j) {
@@ -187,8 +281,8 @@ int test() {
   for (int i = 0; i < test_score.size(); ++i) {
     const std::string& output_name = caffe_net.blob_names()[
         caffe_net.output_blob_indices()[test_score_output_id[i]]];
-    const float loss_weight =
-        caffe_net.blob_loss_weights()[caffe_net.output_blob_indices()[i]];
+    const float loss_weight = caffe_net.blob_loss_weights()[
+        caffe_net.output_blob_indices()[test_score_output_id[i]]];
     std::ostringstream loss_msg_stream;
     const float mean_score = test_score[i] / FLAGS_iterations;
     if (loss_weight) {
@@ -208,9 +302,11 @@ int time() {
   CHECK_GT(FLAGS_model.size(), 0) << "Need a model definition to time.";
 
   // Set device id and mode
-  if (FLAGS_gpu >= 0) {
-    LOG(INFO) << "Use GPU with device ID " << FLAGS_gpu;
-    Caffe::SetDevice(FLAGS_gpu);
+  vector<int> gpus;
+  get_gpus(&gpus);
+  if (gpus.size() != 0) {
+    LOG(INFO) << "Use GPU with device ID " << gpus[0];
+    Caffe::SetDevice(gpus[0]);
     Caffe::set_mode(Caffe::GPU);
   } else {
     LOG(INFO) << "Use CPU.";
@@ -225,7 +321,7 @@ int time() {
   // Note that for the speed benchmark, we will assume that the network does
   // not take any input blobs.
   float initial_loss;
-  caffe_net.Forward(vector<Blob<float>*>(), &initial_loss);
+  caffe_net.Forward(&initial_loss);
   LOG(INFO) << "Initial loss: " << initial_loss;
   LOG(INFO) << "Performing Backward";
   caffe_net.Backward();
@@ -252,9 +348,6 @@ int time() {
     forward_timer.Start();
     for (int i = 0; i < layers.size(); ++i) {
       timer.Start();
-      // Although Reshape should be essentially free, we include it here
-      // so that we will notice Reshape performance bugs.
-      layers[i]->Reshape(bottom_vecs[i], top_vecs[i]);
       layers[i]->Forward(bottom_vecs[i], top_vecs[i]);
       forward_time_per_layer[i] += timer.MicroSeconds();
     }
@@ -296,6 +389,8 @@ RegisterBrewFunction(time);
 int main(int argc, char** argv) {
   // Print output to stderr (while still logging).
   FLAGS_alsologtostderr = 1;
+  // Set version
+  gflags::SetVersionString(AS_STRING(CAFFE_VERSION));
   // Usage message.
   gflags::SetUsageMessage("command line brew\n"
       "usage: caffe <command> <args>\n\n"
@@ -307,7 +402,16 @@ int main(int argc, char** argv) {
   // Run tool or show usage.
   caffe::GlobalInit(&argc, &argv);
   if (argc == 2) {
-    return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+    try {
+#endif
+      return GetBrewFunction(caffe::string(argv[1]))();
+#ifdef WITH_PYTHON_LAYER
+    } catch (bp::error_already_set) {
+      PyErr_Print();
+      return 1;
+    }
+#endif
   } else {
     gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");
   }
diff --git a/tools/compute_image_mean.cpp b/tools/compute_image_mean.cpp
index b1fc7cae38f..2035d515195 100644
--- a/tools/compute_image_mean.cpp
+++ b/tools/compute_image_mean.cpp
@@ -24,6 +24,7 @@ DEFINE_string(backend, "lmdb",
 int main(int argc, char** argv) {
   ::google::InitGoogleLogging(argv[0]);
 
+#ifdef USE_OPENCV
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
 #endif
@@ -115,5 +116,8 @@ int main(int argc, char** argv) {
     }
     LOG(INFO) << "mean_value channel [" << c << "]:" << mean_values[c] / dim;
   }
+#else
+  LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   return 0;
 }
diff --git a/tools/convert_imageset.cpp b/tools/convert_imageset.cpp
index 816a91f971b..9c52bfa0ef8 100644
--- a/tools/convert_imageset.cpp
+++ b/tools/convert_imageset.cpp
@@ -20,6 +20,7 @@
 
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 #include "caffe/util/io.hpp"
 #include "caffe/util/rng.hpp"
 
@@ -43,7 +44,10 @@ DEFINE_string(encode_type, "",
     "Optional: What type should we encode the image as ('png','jpg',...).");
 
 int main(int argc, char** argv) {
+#ifdef USE_OPENCV
   ::google::InitGoogleLogging(argv[0]);
+  // Print output to stderr (while still logging)
+  FLAGS_alsologtostderr = 1;
 
 #ifndef GFLAGS_GFLAGS_H_
   namespace gflags = google;
@@ -96,8 +100,6 @@ int main(int argc, char** argv) {
   std::string root_folder(argv[1]);
   Datum datum;
   int count = 0;
-  const int kMaxKeyLength = 256;
-  char key_cstr[kMaxKeyLength];
   int data_size = 0;
   bool data_size_initialized = false;
 
@@ -128,25 +130,27 @@ int main(int argc, char** argv) {
       }
     }
     // sequential
-    int length = snprintf(key_cstr, kMaxKeyLength, "%08d_%s", line_id,
-        lines[line_id].first.c_str());
+    string key_str = caffe::format_int(line_id, 8) + "_" + lines[line_id].first;
 
     // Put in db
     string out;
     CHECK(datum.SerializeToString(&out));
-    txn->Put(string(key_cstr, length), out);
+    txn->Put(key_str, out);
 
     if (++count % 1000 == 0) {
       // Commit db
       txn->Commit();
       txn.reset(db->NewTransaction());
-      LOG(ERROR) << "Processed " << count << " files.";
+      LOG(INFO) << "Processed " << count << " files.";
     }
   }
   // write the last batch
   if (count % 1000 != 0) {
     txn->Commit();
-    LOG(ERROR) << "Processed " << count << " files.";
+    LOG(INFO) << "Processed " << count << " files.";
   }
+#else
+  LOG(FATAL) << "This tool requires OpenCV; compile with USE_OPENCV.";
+#endif  // USE_OPENCV
   return 0;
 }
diff --git a/tools/extra/parse_log.py b/tools/extra/parse_log.py
index 16ba077aee6..48f9bee0b49 100755
--- a/tools/extra/parse_log.py
+++ b/tools/extra/parse_log.py
@@ -3,7 +3,7 @@
 """
 Parse training log
 
-Competitor to parse_log.sh
+Evolved from parse_log.sh
 """
 
 import os
@@ -11,18 +11,7 @@
 import extract_seconds
 import argparse
 import csv
-
-
-def get_line_type(line):
-    """Return either 'test' or 'train' depending on line type
-    """
-
-    line_type = None
-    if line.find('Train') != -1:
-        line_type = 'train'
-    elif line.find('Test') != -1:
-        line_type = 'test'
-    return line_type
+from collections import OrderedDict
 
 
 def parse_log(path_to_log):
@@ -36,70 +25,112 @@ def parse_log(path_to_log):
     for the two dict_lists
     """
 
-    re_iteration = re.compile('Iteration (\d+)')
-    re_accuracy = re.compile('output #\d+: accuracy = ([\.\d]+)')
-    re_train_loss = re.compile('Iteration \d+, loss = ([\.\d]+)')
-    re_output_loss = re.compile('output #\d+: loss = ([\.\d]+)')
-    re_lr = re.compile('lr = ([\.\d]+)')
+    regex_iteration = re.compile('Iteration (\d+)')
+    regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
+    regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
 
     # Pick out lines of interest
     iteration = -1
-    test_accuracy = -1
     learning_rate = float('NaN')
     train_dict_list = []
     test_dict_list = []
-    train_dict_names = ('NumIters', 'Seconds', 'TrainingLoss', 'LearningRate')
-    test_dict_names = ('NumIters', 'Seconds', 'TestAccuracy', 'TestLoss')
+    train_row = None
+    test_row = None
 
     logfile_year = extract_seconds.get_log_created_year(path_to_log)
     with open(path_to_log) as f:
         start_time = extract_seconds.get_start_time(f, logfile_year)
 
         for line in f:
-            iteration_match = re_iteration.search(line)
+            iteration_match = regex_iteration.search(line)
             if iteration_match:
                 iteration = float(iteration_match.group(1))
             if iteration == -1:
-                # Only look for other stuff if we've found the first iteration
+                # Only start parsing for other stuff if we've found the first
+                # iteration
                 continue
 
             time = extract_seconds.extract_datetime_from_line(line,
                                                               logfile_year)
             seconds = (time - start_time).total_seconds()
 
-            lr_match = re_lr.search(line)
-            if lr_match:
-                learning_rate = float(lr_match.group(1))
-
-            accuracy_match = re_accuracy.search(line)
-            if accuracy_match and get_line_type(line) == 'test':
-                test_accuracy = float(accuracy_match.group(1))
-
-            train_loss_match = re_train_loss.search(line)
-            if train_loss_match:
-                train_loss = float(train_loss_match.group(1))
-                train_dict_list.append({'NumIters': iteration,
-                                        'Seconds': seconds,
-                                        'TrainingLoss': train_loss,
-                                        'LearningRate': learning_rate})
-
-            output_loss_match = re_output_loss.search(line)
-            if output_loss_match and get_line_type(line) == 'test':
-                test_loss = float(output_loss_match.group(1))
-                # NOTE: we assume that (1) accuracy always comes right before
-                # loss for test data so the test_accuracy variable is already
-                # correctly populated and (2) there's one and only one output
-                # named "accuracy" for the test net
-                test_dict_list.append({'NumIters': iteration,
-                                       'Seconds': seconds,
-                                       'TestAccuracy': test_accuracy,
-                                       'TestLoss': test_loss})
-
-    return train_dict_list, train_dict_names, test_dict_list, test_dict_names
-
-
-def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
-                   test_dict_list, test_dict_names, verbose=False):
+            learning_rate_match = regex_learning_rate.search(line)
+            if learning_rate_match:
+                learning_rate = float(learning_rate_match.group(1))
+
+            train_dict_list, train_row = parse_line_for_net_output(
+                regex_train_output, train_row, train_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+            test_dict_list, test_row = parse_line_for_net_output(
+                regex_test_output, test_row, test_dict_list,
+                line, iteration, seconds, learning_rate
+            )
+
+    fix_initial_nan_learning_rate(train_dict_list)
+    fix_initial_nan_learning_rate(test_dict_list)
+
+    return train_dict_list, test_dict_list
+
+
+def parse_line_for_net_output(regex_obj, row, row_dict_list,
+                              line, iteration, seconds, learning_rate):
+    """Parse a single line for training or test output
+
+    Returns a a tuple with (row_dict_list, row)
+    row: may be either a new row or an augmented version of the current row
+    row_dict_list: may be either the current row_dict_list or an augmented
+    version of the current row_dict_list
+    """
+
+    output_match = regex_obj.search(line)
+    if output_match:
+        if not row or row['NumIters'] != iteration:
+            # Push the last row and start a new one
+            if row:
+                # If we're on a new iteration, push the last row
+                # This will probably only happen for the first row; otherwise
+                # the full row checking logic below will push and clear full
+                # rows
+                row_dict_list.append(row)
+
+            row = OrderedDict([
+                ('NumIters', iteration),
+                ('Seconds', seconds),
+                ('LearningRate', learning_rate)
+            ])
+
+        # output_num is not used; may be used in the future
+        # output_num = output_match.group(1)
+        output_name = output_match.group(2)
+        output_val = output_match.group(3)
+        row[output_name] = float(output_val)
+
+    if row and len(row_dict_list) >= 1 and len(row) == len(row_dict_list[0]):
+        # The row is full, based on the fact that it has the same number of
+        # columns as the first row; append it to the list
+        row_dict_list.append(row)
+        row = None
+
+    return row_dict_list, row
+
+
+def fix_initial_nan_learning_rate(dict_list):
+    """Correct initial value of learning rate
+
+    Learning rate is normally not printed until after the initial test and
+    training step, which means the initial testing and training rows have
+    LearningRate = NaN. Fix this by copying over the LearningRate from the
+    second row, if it exists.
+    """
+
+    if len(dict_list) > 1:
+        dict_list[0]['LearningRate'] = dict_list[1]['LearningRate']
+
+
+def save_csv_files(logfile_path, output_dir, train_dict_list, test_dict_list,
+                   delimiter=',', verbose=False):
     """Save CSV files to output_dir
 
     If the input log file is, e.g., caffe.INFO, the names will be
@@ -108,18 +139,22 @@ def save_csv_files(logfile_path, output_dir, train_dict_list, train_dict_names,
 
     log_basename = os.path.basename(logfile_path)
     train_filename = os.path.join(output_dir, log_basename + '.train')
-    write_csv(train_filename, train_dict_list, train_dict_names, verbose)
+    write_csv(train_filename, train_dict_list, delimiter, verbose)
 
     test_filename = os.path.join(output_dir, log_basename + '.test')
-    write_csv(test_filename, test_dict_list, test_dict_names, verbose)
+    write_csv(test_filename, test_dict_list, delimiter, verbose)
 
 
-def write_csv(output_filename, dict_list, header_names, verbose=False):
+def write_csv(output_filename, dict_list, delimiter, verbose=False):
     """Write a CSV file
     """
 
+    dialect = csv.excel
+    dialect.delimiter = delimiter
+
     with open(output_filename, 'w') as f:
-        dict_writer = csv.DictWriter(f, header_names)
+        dict_writer = csv.DictWriter(f, fieldnames=dict_list[0].keys(),
+                                     dialect=dialect)
         dict_writer.writeheader()
         dict_writer.writerows(dict_list)
     if verbose:
@@ -141,16 +176,20 @@ def parse_args():
                         action='store_true',
                         help='Print some extra info (e.g., output filenames)')
 
+    parser.add_argument('--delimiter',
+                        default=',',
+                        help=('Column delimiter in output files '
+                              '(default: \'%(default)s\')'))
+
     args = parser.parse_args()
     return args
 
 
 def main():
     args = parse_args()
-    train_dict_list, train_dict_names, test_dict_list, test_dict_names = \
-        parse_log(args.logfile_path)
+    train_dict_list, test_dict_list = parse_log(args.logfile_path)
     save_csv_files(args.logfile_path, args.output_dir, train_dict_list,
-                   train_dict_names, test_dict_list, test_dict_names)
+                   test_dict_list, delimiter=args.delimiter)
 
 
 if __name__ == '__main__':
diff --git a/tools/extra/parse_log.sh b/tools/extra/parse_log.sh
index 98ef0a05002..9892c897682 100755
--- a/tools/extra/parse_log.sh
+++ b/tools/extra/parse_log.sh
@@ -14,7 +14,12 @@ echo "Usage parse_log.sh /path/to/your.log"
 exit
 fi
 LOG=`basename $1`
-grep -B 1 'Test ' $1 > aux.txt
+sed -n '/Iteration .* Testing net/,/Iteration *. loss/p' $1 > aux.txt
+sed -i '/Waiting for data/d' aux.txt
+sed -i '/prefetch queue empty/d' aux.txt
+sed -i '/Iteration .* loss/d' aux.txt
+sed -i '/Iteration .* lr/d' aux.txt
+sed -i '/Train net/d' aux.txt
 grep 'Iteration ' aux.txt | sed  's/.*Iteration \([[:digit:]]*\).*/\1/g' > aux0.txt
 grep 'Test net output #0' aux.txt | awk '{print $11}' > aux1.txt
 grep 'Test net output #1' aux.txt | awk '{print $11}' > aux2.txt
diff --git a/tools/extra/plot_log.gnuplot.example b/tools/extra/plot_log.gnuplot.example
index 334ff1f2858..748b96e6925 100644
--- a/tools/extra/plot_log.gnuplot.example
+++ b/tools/extra/plot_log.gnuplot.example
@@ -39,8 +39,8 @@ set key right
 
 # Training loss vs. training iterations
 set title "Training loss vs. training iterations"
-set xlabel "Training loss"
-set ylabel "Training iterations"
+set xlabel "Training iterations"
+set ylabel "Training loss"
 plot "mnist.log.train" using 1:3 title "mnist"
 
 # Training loss vs. training time
diff --git a/tools/extra/plot_training_log.py.example b/tools/extra/plot_training_log.py.example
index b6fda54e01c..4d3ed0d15a9 100755
--- a/tools/extra/plot_training_log.py.example
+++ b/tools/extra/plot_training_log.py.example
@@ -150,7 +150,7 @@ Be warned that the fields in the training log may change in the future.
 You had better check the data files and change the mapping from field name to
  field index in create_field_index before designing your own plots.
 Usage:
-    ./plot_log.sh chart_type[0-%s] /where/to/save.png /path/to/first.log ...
+    ./plot_training_log.py chart_type[0-%s] /where/to/save.png /path/to/first.log ...
 Notes:
     1. Supporting multiple logs.
     2. Log file name must end with the lower-cased "%s".
diff --git a/tools/extra/summarize.py b/tools/extra/summarize.py
new file mode 100755
index 00000000000..7e2d22fd364
--- /dev/null
+++ b/tools/extra/summarize.py
@@ -0,0 +1,140 @@
+#!/usr/bin/env python
+
+"""Net summarization tool.
+
+This tool summarizes the structure of a net in a concise but comprehensive
+tabular listing, taking a prototxt file as input.
+
+Use this tool to check at a glance that the computation you've specified is the
+computation you expect.
+"""
+
+from caffe.proto import caffe_pb2
+from google import protobuf
+import re
+import argparse
+
+# ANSI codes for coloring blobs (used cyclically)
+COLORS = ['92', '93', '94', '95', '97', '96', '42', '43;30', '100',
+          '444', '103;30', '107;30']
+DISCONNECTED_COLOR = '41'
+
+def read_net(filename):
+    net = caffe_pb2.NetParameter()
+    with open(filename) as f:
+        protobuf.text_format.Parse(f.read(), net)
+    return net
+
+def format_param(param):
+    out = []
+    if len(param.name) > 0:
+        out.append(param.name)
+    if param.lr_mult != 1:
+        out.append('x{}'.format(param.lr_mult))
+    if param.decay_mult != 1:
+        out.append('Dx{}'.format(param.decay_mult))
+    return ' '.join(out)
+
+def printed_len(s):
+    return len(re.sub(r'\033\[[\d;]+m', '', s))
+
+def print_table(table, max_width):
+    """Print a simple nicely-aligned table.
+
+    table must be a list of (equal-length) lists. Columns are space-separated,
+    and as narrow as possible, but no wider than max_width. Text may overflow
+    columns; note that unlike string.format, this will not affect subsequent
+    columns, if possible."""
+
+    max_widths = [max_width] * len(table[0])
+    column_widths = [max(printed_len(row[j]) + 1 for row in table)
+                     for j in range(len(table[0]))]
+    column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)]
+
+    for row in table:
+        row_str = ''
+        right_col = 0
+        for cell, width in zip(row, column_widths):
+            right_col += width
+            row_str += cell + ' '
+            row_str += ' ' * max(right_col - printed_len(row_str), 0)
+        print row_str
+
+def summarize_net(net):
+    disconnected_tops = set()
+    for lr in net.layer:
+        disconnected_tops |= set(lr.top)
+        disconnected_tops -= set(lr.bottom)
+
+    table = []
+    colors = {}
+    for lr in net.layer:
+        tops = []
+        for ind, top in enumerate(lr.top):
+            color = colors.setdefault(top, COLORS[len(colors) % len(COLORS)])
+            if top in disconnected_tops:
+                top = '\033[1;4m' + top
+            if len(lr.loss_weight) > 0:
+                top = '{} * {}'.format(lr.loss_weight[ind], top)
+            tops.append('\033[{}m{}\033[0m'.format(color, top))
+        top_str = ', '.join(tops)
+
+        bottoms = []
+        for bottom in lr.bottom:
+            color = colors.get(bottom, DISCONNECTED_COLOR)
+            bottoms.append('\033[{}m{}\033[0m'.format(color, bottom))
+        bottom_str = ', '.join(bottoms)
+
+        if lr.type == 'Python':
+            type_str = lr.python_param.module + '.' + lr.python_param.layer
+        else:
+            type_str = lr.type
+
+        # Summarize conv/pool parameters.
+        # TODO support rectangular/ND parameters
+        conv_param = lr.convolution_param
+        if (lr.type in ['Convolution', 'Deconvolution']
+                and len(conv_param.kernel_size) == 1):
+            arg_str = str(conv_param.kernel_size[0])
+            if len(conv_param.stride) > 0 and conv_param.stride[0] != 1:
+                arg_str += '/' + str(conv_param.stride[0])
+            if len(conv_param.pad) > 0 and conv_param.pad[0] != 0:
+                arg_str += '+' + str(conv_param.pad[0])
+            arg_str += ' ' + str(conv_param.num_output)
+            if conv_param.group != 1:
+                arg_str += '/' + str(conv_param.group)
+        elif lr.type == 'Pooling':
+            arg_str = str(lr.pooling_param.kernel_size)
+            if lr.pooling_param.stride != 1:
+                arg_str += '/' + str(lr.pooling_param.stride)
+            if lr.pooling_param.pad != 0:
+                arg_str += '+' + str(lr.pooling_param.pad)
+        else:
+            arg_str = ''
+
+        if len(lr.param) > 0:
+            param_strs = map(format_param, lr.param)
+            if max(map(len, param_strs)) > 0:
+                param_str = '({})'.format(', '.join(param_strs))
+            else:
+                param_str = ''
+        else:
+            param_str = ''
+
+        table.append([lr.name, type_str, param_str, bottom_str, '->', top_str,
+                      arg_str])
+    return table
+
+def main():
+    parser = argparse.ArgumentParser(description="Print a concise summary of net computation.")
+    parser.add_argument('filename', help='net prototxt file to summarize')
+    parser.add_argument('-w', '--max-width', help='maximum field width',
+            type=int, default=30)
+    args = parser.parse_args()
+
+    net = read_net(args.filename)
+    table = summarize_net(net)
+    print_table(table, max_width=args.max_width)
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/extract_features.cpp b/tools/extract_features.cpp
index 364c436dfd8..704467250a6 100644
--- a/tools/extract_features.cpp
+++ b/tools/extract_features.cpp
@@ -1,4 +1,3 @@
-#include <stdio.h>  // for snprintf
 #include <string>
 #include <vector>
 
@@ -10,14 +9,13 @@
 #include "caffe/net.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/db.hpp"
+#include "caffe/util/format.hpp"
 #include "caffe/util/io.hpp"
-#include "caffe/vision_layers.hpp"
 
 using caffe::Blob;
 using caffe::Caffe;
 using caffe::Datum;
 using caffe::Net;
-using boost::shared_ptr;
 using std::string;
 namespace db = caffe::db;
 
@@ -42,7 +40,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
     "  save_feature_dataset_name1[,name2,...]  num_mini_batches  db_type"
     "  [CPU/GPU] [DEVICE_ID=0]\n"
     "Note: you can extract multiple features in one pass by specifying"
-    " multiple feature blob names and dataset names seperated by ','."
+    " multiple feature blob names and dataset names separated by ','."
     " The names cannot contain white space characters and the number of blobs"
     " and datasets must be equal.";
     return 1;
@@ -52,7 +50,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
   arg_pos = num_required_args;
   if (argc > arg_pos && strcmp(argv[arg_pos], "GPU") == 0) {
     LOG(ERROR)<< "Using GPU";
-    uint device_id = 0;
+    int device_id = 0;
     if (argc > arg_pos + 1) {
       device_id = atoi(argv[arg_pos + 1]);
       CHECK_GE(device_id, 0);
@@ -96,7 +94,7 @@ int feature_extraction_pipeline(int argc, char** argv) {
    }
    */
   std::string feature_extraction_proto(argv[++arg_pos]);
-  shared_ptr<Net<Dtype> > feature_extraction_net(
+  boost::shared_ptr<Net<Dtype> > feature_extraction_net(
       new Net<Dtype>(feature_extraction_proto, caffe::TEST));
   feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto);
 
@@ -120,29 +118,27 @@ int feature_extraction_pipeline(int argc, char** argv) {
 
   int num_mini_batches = atoi(argv[++arg_pos]);
 
-  std::vector<shared_ptr<db::DB> > feature_dbs;
-  std::vector<shared_ptr<db::Transaction> > txns;
+  std::vector<boost::shared_ptr<db::DB> > feature_dbs;
+  std::vector<boost::shared_ptr<db::Transaction> > txns;
+  const char* db_type = argv[++arg_pos];
   for (size_t i = 0; i < num_features; ++i) {
     LOG(INFO)<< "Opening dataset " << dataset_names[i];
-    shared_ptr<db::DB> db(db::GetDB(argv[++arg_pos]));
+    boost::shared_ptr<db::DB> db(db::GetDB(db_type));
     db->Open(dataset_names.at(i), db::NEW);
     feature_dbs.push_back(db);
-    shared_ptr<db::Transaction> txn(db->NewTransaction());
+    boost::shared_ptr<db::Transaction> txn(db->NewTransaction());
     txns.push_back(txn);
   }
 
   LOG(ERROR)<< "Extacting Features";
 
   Datum datum;
-  const int kMaxKeyStrLength = 100;
-  char key_str[kMaxKeyStrLength];
-  std::vector<Blob<float>*> input_vec;
   std::vector<int> image_indices(num_features, 0);
   for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) {
-    feature_extraction_net->Forward(input_vec);
+    feature_extraction_net->Forward();
     for (int i = 0; i < num_features; ++i) {
-      const shared_ptr<Blob<Dtype> > feature_blob = feature_extraction_net
-          ->blob_by_name(blob_names[i]);
+      const boost::shared_ptr<Blob<Dtype> > feature_blob =
+        feature_extraction_net->blob_by_name(blob_names[i]);
       int batch_size = feature_blob->num();
       int dim_features = feature_blob->count() / batch_size;
       const Dtype* feature_blob_data;
@@ -157,11 +153,11 @@ int feature_extraction_pipeline(int argc, char** argv) {
         for (int d = 0; d < dim_features; ++d) {
           datum.add_float_data(feature_blob_data[d]);
         }
-        int length = snprintf(key_str, kMaxKeyStrLength, "%d",
-            image_indices[i]);
+        string key_str = caffe::format_int(image_indices[i], 10);
+
         string out;
         CHECK(datum.SerializeToString(&out));
-        txns.at(i)->Put(std::string(key_str, length), out);
+        txns.at(i)->Put(key_str, out);
         ++image_indices[i];
         if (image_indices[i] % 1000 == 0) {
           txns.at(i)->Commit();
@@ -185,4 +181,3 @@ int feature_extraction_pipeline(int argc, char** argv) {
   LOG(ERROR)<< "Successfully extracted the features!";
   return 0;
 }
-
diff --git a/tools/upgrade_net_proto_binary.cpp b/tools/upgrade_net_proto_binary.cpp
index 8a0dd7af743..ede07ecc2ff 100644
--- a/tools/upgrade_net_proto_binary.cpp
+++ b/tools/upgrade_net_proto_binary.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -39,11 +40,11 @@ int main(int argc, char** argv) {
                  << "see details above.";
     }
   } else {
-    LOG(ERROR) << "File already in V1 proto format: " << argv[1];
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
   WriteProtoToBinaryFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter binary proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter binary proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_net_proto_text.cpp b/tools/upgrade_net_proto_text.cpp
index 9200431bc27..617b48dc918 100644
--- a/tools/upgrade_net_proto_text.cpp
+++ b/tools/upgrade_net_proto_text.cpp
@@ -16,6 +16,7 @@ using std::ofstream;
 using namespace caffe;  // NOLINT(build/namespaces)
 
 int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
   ::google::InitGoogleLogging(argv[0]);
   if (argc != 3) {
     LOG(ERROR) << "Usage: "
@@ -31,7 +32,6 @@ int main(int argc, char** argv) {
     return 2;
   }
   bool need_upgrade = NetNeedsUpgrade(net_param);
-  bool need_data_upgrade = NetNeedsDataUpgrade(net_param);
   bool success = true;
   if (need_upgrade) {
     success = UpgradeNetAsNeeded(input_filename, &net_param);
@@ -43,13 +43,9 @@ int main(int argc, char** argv) {
     LOG(ERROR) << "File already in latest proto format: " << input_filename;
   }
 
-  if (need_data_upgrade) {
-    UpgradeNetDataTransformation(&net_param);
-  }
-
   // Save new format prototxt.
   WriteProtoToTextFile(net_param, argv[2]);
 
-  LOG(ERROR) << "Wrote upgraded NetParameter text proto to " << argv[2];
+  LOG(INFO) << "Wrote upgraded NetParameter text proto to " << argv[2];
   return !success;
 }
diff --git a/tools/upgrade_solver_proto_text.cpp b/tools/upgrade_solver_proto_text.cpp
new file mode 100644
index 00000000000..ddff1ce6bad
--- /dev/null
+++ b/tools/upgrade_solver_proto_text.cpp
@@ -0,0 +1,51 @@
+// This is a script to upgrade old solver prototxts to the new format.
+// Usage:
+//    upgrade_solver_proto_text old_solver_proto_file_in solver_proto_file_out
+
+#include <cstring>
+#include <fstream>  // NOLINT(readability/streams)
+#include <iostream>  // NOLINT(readability/streams)
+#include <string>
+
+#include "caffe/caffe.hpp"
+#include "caffe/util/io.hpp"
+#include "caffe/util/upgrade_proto.hpp"
+
+using std::ofstream;
+
+using namespace caffe;  // NOLINT(build/namespaces)
+
+int main(int argc, char** argv) {
+  FLAGS_alsologtostderr = 1;  // Print output to stderr (while still logging)
+  ::google::InitGoogleLogging(argv[0]);
+  if (argc != 3) {
+    LOG(ERROR) << "Usage: upgrade_solver_proto_text "
+        << "old_solver_proto_file_in solver_proto_file_out";
+    return 1;
+  }
+
+  SolverParameter solver_param;
+  string input_filename(argv[1]);
+  if (!ReadProtoFromTextFile(input_filename, &solver_param)) {
+    LOG(ERROR) << "Failed to parse input text file as SolverParameter: "
+               << input_filename;
+    return 2;
+  }
+  bool need_upgrade = SolverNeedsTypeUpgrade(solver_param);
+  bool success = true;
+  if (need_upgrade) {
+    success = UpgradeSolverAsNeeded(input_filename, &solver_param);
+    if (!success) {
+      LOG(ERROR) << "Encountered error(s) while upgrading prototxt; "
+                 << "see details above.";
+    }
+  } else {
+    LOG(ERROR) << "File already in latest proto format: " << input_filename;
+  }
+
+  // Save new format prototxt.
+  WriteProtoToTextFile(solver_param, argv[2]);
+
+  LOG(INFO) << "Wrote upgraded SolverParameter text proto to " << argv[2];
+  return !success;
+}