diff --git a/scenes/chainer-caption/.gitignore b/scenes/chainer-caption/.gitignore
new file mode 100644
index 0000000..8e341b9
--- /dev/null
+++ b/scenes/chainer-caption/.gitignore
@@ -0,0 +1,2 @@
+data/*.model
+chainer_env/
\ No newline at end of file
diff --git a/scenes/chainer-caption/README.md b/scenes/chainer-caption/README.md
new file mode 100644
index 0000000..0e24cea
--- /dev/null
+++ b/scenes/chainer-caption/README.md
@@ -0,0 +1,83 @@
+# image caption generation by chainer on raspberrypi
+original repo https://github.com/apple2373/chainer-caption
+
+## descrtiption
+
+This repository contains an implementation of typical image caption generation based on neural network (i.e. CNN + RNN). The model first extracts the image feature by CNN and then generates captions by RNN. CNN is ResNet50 and RNN is a standard LSTM .
+This repo is an edited version of https://github.com/yoshihiroo/programming-workshop/tree/master/image_captioning_and_speech
+
+
+**What is new?**
+
+- upgrade to chainer V7.1
+- provide interface that accepts a `numpy array` and return the generated caption
+- edit chainer 7 global configurations to make code run faster
+
+## requirements
+- python 3.x
+- chainer V2 or higher  http://chainer.org
+  
+
+## citation:
+If you find this implementation useful, please consider to cite: 
+```
+@article{multilingual-caption-arxiv,
+title={{Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation}},
+author={Satoshi Tsutsui, David Crandall},
+journal={arXiv:1706.06275},
+year={2017}
+}
+
+@inproceedings{multilingual-caption,
+author={Satoshi Tsutsui, David Crandall},
+booktitle = {CVPR Language and Vision Workshop},
+title = {{Using Artificial Tokens to Control Languages for Multilingual Image Caption Generation}},
+year = {2017}
+}
+```
+
+Install Programs and Tools
+-------
+Install required programs and tools by commands below.
+```
+sudo apt-get install python3-pip
+sudo pip3 install chainer
+sudo pip3 install scipy
+sudo pip3 install h5py
+sudo apt-get install python-h5py
+sudo apt-get install libopenjp2-7-dev
+sudo apt-get install libtiff5
+sudo pip3 install Pillow
+sudo apt-get install espeak
+sudo apt-get install libatlas-base-dev
+```
+
+**Download CNN & RNN models run**
+```
+bash download.sh
+```
+
+
+**How to use** `image_captioning_interface.py`:
+
+just impot `image_captioning_interface` in your code and make an object of `image_captioning` class.
+
+This class `image_captioning` has 2 functions:
+1. The initialization function
+```python
+def __init__(self,
+    	rnn_model_place='./data/caption_en_model40.model',
+    	cnn_model_place='./data/ResNet50.model',
+    	dictonary_place='./data/MSCOCO/mscoco_caption_train2014_processed_dic.json',
+    	beamsize=3,
+    	depth_limit=50,
+    	gpu_id=-1,
+    	first_word="<sos>")
+```
+2. caption generation function
+```python
+def generate_from_img_nparray(self,image_array)
+```
+this function accepts `RGB` image in an nparry with size of `224 x 224`
+and returns the generated caption
+
diff --git a/scenes/chainer-caption/code/CaptionGenerator.py b/scenes/chainer-caption/code/CaptionGenerator.py
new file mode 100644
index 0000000..9e27d37
--- /dev/null
+++ b/scenes/chainer-caption/code/CaptionGenerator.py
@@ -0,0 +1,276 @@
+# -*- coding: utf-8 -*-
+#!/usr/bin/env python
+
+'''
+caption generation module by beam search
+currently you cannot use efficient generation by batch. Batch size is always one 
+
+beam search might have a small bug or not the most efficient, but seems to be ok.
+'''
+
+import os
+import chainer 
+import numpy as np
+import json
+import math
+from copy import deepcopy
+
+from chainer import cuda
+import chainer.functions as F
+from chainer import cuda, Function, gradient_check, Variable, optimizers
+from chainer import serializers
+
+from image_loader import Image_loader
+from ResNet50 import ResNet
+from Image2CaptionDecoder import Image2CaptionDecoder
+
+#priority queue
+#reference: http://www.bogotobogo.com/python/python_PriorityQueue_heapq_Data_Structure.php
+try:
+    import Queue as Q  # ver. < 3.0
+except ImportError:
+    import queue as Q
+
+import heapq
+
+class CaptionGenerator(object):
+    def __init__(self,rnn_model_place,cnn_model_place,dictonary_place,beamsize=3,depth_limit=50,gpu_id=-1,first_word="<sos>",hidden_dim=512,mean="imagenet"):
+        self.gpu_id=gpu_id
+        self.beamsize=beamsize
+        self.depth_limit=depth_limit
+        self.image_loader=Image_loader(mean)
+        self.index2token=self.parse_dic(dictonary_place)
+
+        self.cnn_model=ResNet()
+        serializers.load_hdf5(cnn_model_place, self.cnn_model)
+        self.cnn_model.train = False
+
+        self.rnn_model=Image2CaptionDecoder(len(self.token2index),hidden_dim=hidden_dim)
+        if len(rnn_model_place) > 0:
+            serializers.load_hdf5(rnn_model_place, self.rnn_model)
+        self.rnn_model.train = False
+        #added
+        chainer.global_config.train = False
+
+        self.first_word=first_word
+
+        #Gpu Setting
+        global xp
+        if self.gpu_id >= 0:
+            xp = cuda.cupy 
+            cuda.get_device(gpu_id).use()
+            self.cnn_model.to_gpu()
+            self.rnn_model.to_gpu()
+        else:
+            xp=np
+
+    def parse_dic(self,dictonary_place):
+        with open(dictonary_place, 'r') as f:
+            json_file = json.load(f)
+        if len(json_file) < 10:#this is ad-hock. I need to distinguish new format and old format...
+            self.token2index = { word['word']:word['idx'] for word in json_file["words"]}
+        else:
+            self.token2index = json_file
+
+        return {v:k for k,v in self.token2index.items()}
+
+    def successor(self,current_state):
+        '''
+        Args:
+            current_state: a stete, python tuple (hx,cx,path,cost)
+                hidden: hidden states of LSTM
+                cell: cell states LSTM
+                path: word indicies so far as a python list  e.g. initial is self.token2index["<sos>"]
+                cost: negative log likelihood
+
+        Returns:
+            k_best_next_states: a python list whose length is the beam size. possible_sentences[i] = {"indicies": list of word indices,"cost":negative log likelihood so far}
+
+        '''
+
+        word=[xp.array([current_state["path"][-1]],dtype=xp.int32)]
+        hx=current_state["hidden"]
+        cx=current_state["cell"]
+        hy, cy, next_words=self.rnn_model(hx,cx,word)
+
+        word_dist=F.softmax(next_words[0]).data[0]#possible next word distributions
+        k_best_next_sentences=[]
+        for i in range(self.beamsize):
+            next_word_idx=int(xp.argmax(word_dist))
+            k_best_next_sentences.append(\
+                {\
+                "hidden":hy,\
+                "cell":cy,\
+                "path":deepcopy(current_state["path"])+[next_word_idx],\
+                "cost":current_state["cost"]-xp.log(word_dist[next_word_idx])
+                }\
+                )
+            word_dist[next_word_idx]=0
+
+        return hy, cy, k_best_next_sentences
+
+    def beam_search(self,initial_state):
+        '''
+        Beam search is a graph search algorithm! So I use graph search abstraction
+
+        Args:
+            initial state: an initial stete, python tuple (hx,cx,path,cost)
+            each state has 
+                hx: hidden states
+                cx: cell states
+                path: word indicies so far as a python list  e.g. initial is self.token2index["<sos>"]
+                cost: negative log likelihood
+
+        Returns:
+            captions sorted by the cost (i.e. negative log llikelihood)
+        '''
+        found_paths=[]
+        top_k_states=[initial_state]
+        while (len(found_paths) < self.beamsize):
+            #forward one step for all top k states, then only select top k after that
+            new_top_k_states=[]
+            for state in top_k_states:
+                #examine to next five possible states
+                hy, cy, k_best_next_states = self.successor(state)
+                for next_state in k_best_next_states:
+                    new_top_k_states.append(next_state)
+            selected_top_k_states=heapq.nsmallest(self.beamsize, new_top_k_states, key=lambda x : x["cost"])
+
+            #within the selected states, let's check if it is terminal or not.
+            top_k_states=[]
+            for state in selected_top_k_states:
+                #is goal state? -> yes, then end the search
+                if state["path"][-1] == self.token2index["<eos>"] or len(state["path"])==self.depth_limit:
+                    found_paths.append(state)
+                else:
+                    top_k_states.append(state)
+
+        return sorted(found_paths, key=lambda x: x["cost"]) 
+
+    def beam_search0(self,initial_state):
+        #original one. This takes much memory
+        '''
+        Beam search is a graph search algorithm! So I use graph search abstraction
+        Args:
+            initial state: an initial stete, python tuple (hx,cx,path,cost)
+            each state has 
+                hx: hidden states
+                cx: cell states
+                path: word indicies so far as a python list  e.g. initial is self.token2index["<sos>"]
+                cost: negative log likelihood
+        Returns:
+            captions sorted by the cost (i.e. negative log llikelihood)
+        '''
+        found_paths=[]
+        q = Q.PriorityQueue()
+        q.put((0,initial_state))
+        while (len(found_paths) < self.beamsize):
+            i=0
+            # this is just a one step ahead? 
+            while not q.empty():
+                if i == self.beamsize:
+                    break
+                state=q.get()[1]
+                #is goal state? -> yes, then end the search
+                if state["path"][-1] == self.token2index["<eos>"] or len(state["path"])==self.depth_limit:
+                    found_paths.append(state)
+                    continue
+                #examine to next five possible states and add to priority queue 
+                hy, cy, k_best_next_states = self.successor(state)
+                for next_state in k_best_next_states:
+                    q.put((state["cost"],next_state))
+                i+=1
+
+        return sorted(found_paths, key=lambda x: x["cost"]) 
+
+    def generate(self,image_file_path):
+        '''
+        Args:
+            image_file_path: image_file_path
+        '''
+        img=self.image_loader.load(image_file_path)
+        return self.generate_from_img(img)
+
+
+    def generate_from_img_feature(self,image_feature):
+        if self.gpu_id >= 0:
+            image_feature=cuda.to_gpu(image_feature)
+
+        batch_size=1
+        hx=xp.zeros((self.rnn_model.n_layers, batch_size, self.rnn_model.hidden_dim), dtype=xp.float32)
+        cx=xp.zeros((self.rnn_model.n_layers, batch_size, self.rnn_model.hidden_dim), dtype=xp.float32)
+        
+        hy,cy = self.rnn_model.input_cnn_feature(hx,cx,image_feature)
+
+
+        initial_state={\
+                    "hidden":hy,\
+                    "cell":cy,\
+                    "path":[self.token2index[self.first_word]],\
+                    "cost":0,\
+                }\
+
+        captions=self.beam_search(initial_state)
+
+        caption_candidates=[]
+        for caption in captions:
+            sentence= [self.index2token[word_idx] for word_idx in caption["path"]]
+            log_likelihood = -float(caption["cost"])#cost is the negative log likelihood
+            caption_candidates.append({"sentence":sentence,"log_likelihood":log_likelihood})
+
+        return caption_candidates
+
+    def generate_from_img(self,image_array):
+        '''Generate Caption for an Numpy Image array
+        
+        Args:
+            image_array: numpy array of image
+
+        Returns:
+            list of generated captions, sorted by the cost (i.e. negative log llikelihood)
+
+            The structure is [caption,caption,caption,...]
+            Where caption = {"sentence": a generated sentence as a python list of word, "log_likelihood": The log llikelihood of the generated sentence} 
+
+        '''
+        if self.gpu_id >= 0:
+            image_array=cuda.to_gpu(image_array)
+        image_feature=self.cnn_model(image_array, "feature").data.reshape(1,1,2048)#次元が一つ多いのは、NstepLSTMはsequaenceとみなすから。(sequence size, batch size, feature dim)ということ
+        return self.generate_from_img_feature(image_feature)
+
+if __name__ == '__main__':
+    xp=np
+    #test code on cpu
+    caption_generator=CaptionGenerator(
+        rnn_model_place="../experiment1/caption_model1.model",\
+        cnn_model_place="../data/ResNet50.model",\
+        dictonary_place="../data/MSCOCO/mscoco_caption_train2014_processed_dic.json",\
+        beamsize=3,depth_limit=50,gpu_id=-1,)
+
+    # batch_size=1
+    # hx=xp.zeros((caption_generator.rnn_model.n_layers, batch_size, caption_generator.rnn_model.hidden_dim), dtype=xp.float32)
+    # cx=xp.zeros((caption_generator.rnn_model.n_layers, batch_size, caption_generator.rnn_model.hidden_dim), dtype=xp.float32)
+    # img=caption_generator.image_loader.load("../sample_imgs/COCO_val2014_000000185546.jpg")
+    # image_feature=caption_generator.cnn_model(img, "feature").data.reshape(1,1,2048)#次元が一つ多いのは、NstepLSTMはsequaenceとみなすから。(sequence size, batch size, feature dim)ということ
+    
+    # hy,cy = caption_generator.rnn_model.input_cnn_feature(hx,cx,image_feature)
+    # initial_state={\
+    #             "hidden":hy,\
+    #             "cell":cy,\
+    #             "path":[caption_generator.token2index["<sos>"]],\
+    #             "cost":0,\
+    #             }\
+
+    # #successor test
+    # next_states= caption_generator.successor(initial_state)
+    # print next_states
+
+    # print "beam search test"
+    # captions= caption_generator.beam_search(initial_state)
+    # print captions
+
+    captions = caption_generator.generate("../sample_imgs/COCO_val2014_000000185546.jpg")
+    for caption in captions:
+        print(caption["sentence"])
+        print(caption["log_likelihood"])
+
diff --git a/scenes/chainer-caption/code/Image2CaptionDecoder.py b/scenes/chainer-caption/code/Image2CaptionDecoder.py
new file mode 100644
index 0000000..0f65629
--- /dev/null
+++ b/scenes/chainer-caption/code/Image2CaptionDecoder.py
@@ -0,0 +1,99 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+#implementation of LSTM net for caption generation
+#inpired from https://github.com/dsanno/chainer-image-caption/blob/master/src/net.py
+#consulted on http://www.monthly-hack.com/entry/2016/10/24/200000
+#consulted on http://qiita.com/chantera/items/d8104012c80e3ea96df7
+
+import chainer
+import chainer.functions as F
+import chainer.links as L
+
+class Image2CaptionDecoder(chainer.Chain):
+    def __init__(self, vocaburary_size, img_feature_dim=2048, hidden_dim=512,dropout_ratio=0.5,train=True, n_layers=1):
+        super(Image2CaptionDecoder, self).__init__(
+            embed_word=  L.EmbedID(vocaburary_size, hidden_dim),
+            embed_image= L.Linear(img_feature_dim, hidden_dim),
+            lstm = L.NStepLSTM(n_layers=n_layers,in_size=hidden_dim,out_size=hidden_dim,dropout=dropout_ratio),
+            decode_word = L.Linear(hidden_dim, vocaburary_size),
+        )
+        self.dropout_ratio = dropout_ratio
+        self.train = train
+        #added
+        chainer.global_config.train = train
+        self.n_layers=n_layers
+        self.hidden_dim=hidden_dim
+
+    def input_cnn_feature(self,hx,cx,image_feature):
+        h = self.embed_image(image_feature)
+        h = [F.reshape(img_embedding,(1,self.hidden_dim)) for img_embedding in h]#一回　python list/tuple にしないとerrorが出る
+        hy, cy, ys  = self.lstm(hx, cx, h)
+        return hy,cy
+
+    def __call__(self, hx, cx, caption_batch):
+        #hx (~chainer.Variable): Initial hidden states.
+        #cx (~chainer.Variable): Initial cell states.
+        #xs (list of ~chianer.Variable): List of input sequences.Each element ``xs[i]`` is a :class:`chainer.Variable` holding a sequence.
+        xs = [self.embed_word(caption) for caption in caption_batch]
+        hy, cy, ys  = self.lstm(hx, cx, xs)
+        predicted_caption_batch = [self.decode_word(generated_caption) for generated_caption in ys]
+        if self.train:
+            loss=0
+            for y, t in zip(predicted_caption_batch, caption_batch):
+                loss+=F.softmax_cross_entropy(y[0:-1], t[1:])
+            return loss/len(predicted_caption_batch)
+        else:
+            return hy, cy, predicted_caption_batch
+
+class Image2CaptionDecoderOld(chainer.Chain):
+    def __init__(self, vocaburary_size, img_feature_dim=2048, hidden_dim=512,dropout_ratio=0.5,train=True):
+        self.dropout_ratio = dropout_ratio
+        super(Image2CaptionDecoderOld, self).__init__(
+            embed_word=  L.EmbedID(vocaburary_size, hidden_dim),
+            embed_image= L.Linear(img_feature_dim, hidden_dim),
+            lstm = L.LSTM(hidden_dim, hidden_dim),
+            decode_word = L.Linear(hidden_dim, vocaburary_size),
+        )
+        self.train = train
+        #added
+        chainer.global_config.train = train
+
+    def input_cnn_feature(self, image_feature):
+        self.lstm.reset_state()
+        h = self.embed_image(image_feature)
+        self.lstm(F.dropout(h, ratio=self.dropout_ratio))
+
+    def __call__(self, cur_word, next_word=None):
+        h = self.embed_word(cur_word)
+        h = self.lstm(F.dropout(h, ratio=self.dropout_ratio))
+        h = self.decode_word(F.dropout(h, ratio=self.dropout_ratio))
+        if self.train:
+            self.loss = F.softmax_cross_entropy(h, next_word)
+            return self.loss
+        else:
+            return h
+
+
+if __name__ == '__main__':
+    #test code on cpu
+    import numpy as np
+    image_feature=np.zeros([2,2048],dtype=np.float32)
+    x_batch=[[1,2,3,4,2,3,0,2],[1,2,3,3,1]]
+    x_batch = [np.array(x,dtype=np.int32) for x in x_batch]
+    model=Image2CaptionDecoder(5)
+    batch_size=len(x_batch)
+    hx=np.zeros((model.n_layers, batch_size, model.hidden_dim), dtype=np.float32)
+    cx=np.zeros((model.n_layers, batch_size, model.hidden_dim), dtype=np.float32)
+    hx,cx=model.input_cnn_feature(hx,cx,image_feature)
+    loss = model(hx, cx, x_batch)
+
+    #test old one
+    image_feature=np.zeros([1,2048],dtype=np.float32)
+    x_list=np.array([[1,2,3,4,2,3,0,2]],dtype=np.int32).T
+    model=Image2CaptionDecoderOld(5)
+    model.input_cnn_feature(image_feature)
+    loss = 0
+    for cur_word, next_word in zip(x_list, x_list[1:]):
+        loss += model(cur_word, next_word)
+
diff --git a/scenes/chainer-caption/code/ResNet50.py b/scenes/chainer-caption/code/ResNet50.py
new file mode 100644
index 0000000..cd50d63
--- /dev/null
+++ b/scenes/chainer-caption/code/ResNet50.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+
+#adapted from https://github.com/yasunorikudo/chainer-ResNet/blob/master/ResNet50.py
+
+import math
+import chainer
+import chainer.functions as F
+import chainer.links as L
+
+
+class BottleNeckA(chainer.Chain):
+    def __init__(self, in_size, ch, out_size, stride=2):
+        w = math.sqrt(2)
+        super(BottleNeckA, self).__init__(
+            conv1=L.Convolution2D(in_size, ch, 1, stride, 0, w),
+            bn1=L.BatchNormalization(ch),
+            conv2=L.Convolution2D(ch, ch, 3, 1, 1, w),
+            bn2=L.BatchNormalization(ch),
+            conv3=L.Convolution2D(ch, out_size, 1, 1, 0, w),
+            bn3=L.BatchNormalization(out_size),
+
+            conv4=L.Convolution2D(in_size, out_size, 1, stride, 0, w),
+            bn4=L.BatchNormalization(out_size),
+        )
+
+    def __call__(self, x, train):
+        h1 = F.relu(self.bn1(self.conv1(x)))
+        h1 = F.relu(self.bn2(self.conv2(h1)))
+        h1 = self.bn3(self.conv3(h1))
+        h2 = self.bn4(self.conv4(x))
+
+        return F.relu(h1 + h2)
+
+
+class BottleNeckB(chainer.Chain):
+    def __init__(self, in_size, ch):
+        w = math.sqrt(2)
+        super(BottleNeckB, self).__init__(
+            conv1=L.Convolution2D(in_size, ch, 1, 1, 0, w),
+            bn1=L.BatchNormalization(ch),
+            conv2=L.Convolution2D(ch, ch, 3, 1, 1, w),
+            bn2=L.BatchNormalization(ch),
+            conv3=L.Convolution2D(ch, in_size, 1, 1, 0, w),
+            bn3=L.BatchNormalization(in_size),
+        )
+
+    def __call__(self, x, train):
+        h = F.relu(self.bn1(self.conv1(x)))
+        h = F.relu(self.bn2(self.conv2(h)))
+        h = self.bn3(self.conv3(h))
+
+        return F.relu(h + x)
+
+
+class Block(chainer.Chain):
+    def __init__(self, layer, in_size, ch, out_size, stride=2):
+        super(Block, self).__init__()
+        links = [('a', BottleNeckA(in_size, ch, out_size, stride))]
+        for i in range(layer-1):
+            links += [('b{}'.format(i+1), BottleNeckB(out_size, ch))]
+
+        for l in links:
+            self.add_link(*l)
+        self.forward = links
+
+    def __call__(self, x, train):
+        for name, _ in self.forward:
+            f = getattr(self, name)
+            x = f(x, train)
+
+        return x
+
+
+class ResNet(chainer.Chain):
+
+    insize = 224
+
+    def __init__(self):
+        w = math.sqrt(2)
+        super(ResNet, self).__init__(
+            conv1=L.Convolution2D(3, 64, 7, 2, 3, w),
+            bn1=L.BatchNormalization(64),
+            res2=Block(3, 64, 64, 256, 1),
+            res3=Block(4, 256, 128, 512),
+            res4=Block(6, 512, 256, 1024),
+            res5=Block(3, 1024, 512, 2048),
+            fc=L.Linear(2048, 1000),
+        )
+        self.train = True
+        #added
+        chainer.global_config.train = True
+
+    def clear(self):
+        self.loss = None
+        self.accuracy = None
+
+    def __call__(self, x, t):
+        self.clear()
+        h = self.bn1(self.conv1(x))
+        h = F.max_pooling_2d(F.relu(h), 3, stride=2)
+        h = self.res2(h, self.train)
+        h = self.res3(h, self.train)
+        h = self.res4(h, self.train)
+        h = self.res5(h, self.train)
+        h = F.average_pooling_2d(h, 7, stride=1)
+        if t=="feature":
+            return h
+        h = self.fc(h)
+
+        if self.train:
+            self.loss = F.softmax_cross_entropy(h, t)
+            self.accuracy = F.accuracy(h, t)
+            return self.loss
+        else:
+            return h
diff --git a/scenes/chainer-caption/code/__pycache__/CaptionGenerator.cpython-35.pyc b/scenes/chainer-caption/code/__pycache__/CaptionGenerator.cpython-35.pyc
new file mode 100644
index 0000000..3a33a08
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diff --git a/scenes/chainer-caption/code/__pycache__/Image2CaptionDecoder.cpython-35.pyc b/scenes/chainer-caption/code/__pycache__/Image2CaptionDecoder.cpython-35.pyc
new file mode 100644
index 0000000..abf61cd
Binary files /dev/null and b/scenes/chainer-caption/code/__pycache__/Image2CaptionDecoder.cpython-35.pyc differ
diff --git a/scenes/chainer-caption/code/__pycache__/ResNet50.cpython-35.pyc b/scenes/chainer-caption/code/__pycache__/ResNet50.cpython-35.pyc
new file mode 100644
index 0000000..61a95f5
Binary files /dev/null and b/scenes/chainer-caption/code/__pycache__/ResNet50.cpython-35.pyc differ
diff --git a/scenes/chainer-caption/code/__pycache__/image_loader.cpython-35.pyc b/scenes/chainer-caption/code/__pycache__/image_loader.cpython-35.pyc
new file mode 100644
index 0000000..185f3b9
Binary files /dev/null and b/scenes/chainer-caption/code/__pycache__/image_loader.cpython-35.pyc differ
diff --git a/scenes/chainer-caption/code/image_loader.py b/scenes/chainer-caption/code/image_loader.py
new file mode 100644
index 0000000..9af401f
--- /dev/null
+++ b/scenes/chainer-caption/code/image_loader.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+'''
+The class to read and crop image and output as numpy array.
+
+The implementatin is partly taken from the following
+https://github.com/dsanno/chainer-recognize/blob/master/model.py#L32-L45
+http://qiita.com/dsanno/items/2c9ccfc53b5019475e0e#画像をnumpy配列に変換する
+'''
+import imageio
+import numpy as np
+from PIL import Image
+import scipy.misc
+
+
+class Image_loader(object):
+    def __init__(self,mean=None):
+
+        #define mean
+        #mean is a channelwise mean
+        if mean == None:
+            self.mean_image = np.zeros((3,1,1))
+        elif mean == "imagenet":
+            mean_image = np.ndarray((3, 224, 224), dtype=np.float32)
+            mean_image[0] = 103.939
+            mean_image[1] = 116.779
+            mean_image[2] = 123.68
+            self.mean_image = mean_image
+        else:
+            self.mean_image = mean#user specified mean
+ 
+    def load(self,image_path,image_w=224,image_h=224,expand_batch_dim=True):
+        '''
+        given path, load and resize.
+        the output will be croped image whose size is image_w x image_h
+        '''
+        image = Image.open(image_path).convert('RGB')
+        return self.resise(image,image_w,image_h,expand_batch_dim)
+
+    def resise(self,image,image_w=224,image_h=224,expand_batch_dim=True):
+        '''
+        given image numpy array, resize
+        the output will be croped image whose size is image_w x image_h
+        '''
+        w, h = image.size
+        if w > h:
+            shape = (image_w * w // h, image_h)
+        else:
+            shape = (image_w, image_h * h // w)
+        x = (shape[0] - image_w) // 2
+        y = (shape[1] - image_h) // 2
+        pixels = np.asarray(image.resize(shape).crop((x, y, x + image_w, y + image_h))).astype(np.float32)
+        pixels = pixels[:,:,::-1].transpose(2,0,1)
+        pixels -= self.mean_image
+        if expand_batch_dim:
+            return pixels.reshape((1,) + pixels.shape)
+        else:
+            return pixels
+    #not working
+    def save(self,np_image,file_path):
+        '''
+        given numpy array (that is generated by self.resise() ), save it into file
+
+        '''
+        np_image=np_image.transpose(1,2,0)[:,:,::-1]#reverse rgb channel and tensor shape 
+        scipy.misc.imsave(file_path, np_image)
+
+    def save2(self,np_image,file_path):
+        imageio.imwrite(file_path, np_image)
+        
\ No newline at end of file
diff --git a/scenes/chainer-caption/data/.gitkeep b/scenes/chainer-caption/data/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/scenes/chainer-caption/data/MSCOCO/mscoco_caption_train2014_processed_dic.json b/scenes/chainer-caption/data/MSCOCO/mscoco_caption_train2014_processed_dic.json
new file mode 100644
index 0000000..94b8791
--- /dev/null
+++ b/scenes/chainer-caption/data/MSCOCO/mscoco_caption_train2014_processed_dic.json
@@ -0,0 +1,8852 @@
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+    "zoo": 215, 
+    "zookeeper": 645, 
+    "zoomed": 4941, 
+    "zooms": 8253, 
+    "zucchini": 5815
+}
\ No newline at end of file
diff --git a/scenes/chainer-caption/data/env.yml b/scenes/chainer-caption/data/env.yml
new file mode 100644
index 0000000..8301f52
--- /dev/null
+++ b/scenes/chainer-caption/data/env.yml
@@ -0,0 +1,78 @@
+name: null
+channels:
+  - defaults
+dependencies:
+  - backports=1.0=py27_1
+  - backports.functools_lru_cache=1.5=py27_1
+  - backports_abc=0.5=py27_0
+  - blas=1.0=mkl
+  - ca-certificates=2019.1.23=0
+  - certifi=2019.3.9=py27_0
+  - cycler=0.10.0=py27_0
+  - dbus=1.13.6=h746ee38_0
+  - expat=2.2.6=he6710b0_0
+  - fontconfig=2.13.0=h9420a91_0
+  - freetype=2.9.1=h8a8886c_1
+  - functools32=3.2.3.2=py27_1
+  - futures=3.2.0=py27_0
+  - glib=2.56.2=hd408876_0
+  - gst-plugins-base=1.14.0=hbbd80ab_1
+  - gstreamer=1.14.0=hb453b48_1
+  - icu=58.2=h9c2bf20_1
+  - intel-openmp=2019.3=199
+  - jpeg=9b=h024ee3a_2
+  - kiwisolver=1.0.1=py27hf484d3e_0
+  - libedit=3.1.20181209=hc058e9b_0
+  - libffi=3.2.1=hd88cf55_4
+  - libgcc-ng=8.2.0=hdf63c60_1
+  - libgfortran-ng=7.3.0=hdf63c60_0
+  - libpng=1.6.36=hbc83047_0
+  - libstdcxx-ng=8.2.0=hdf63c60_1
+  - libtiff=4.0.10=h2733197_2
+  - libuuid=1.0.3=h1bed415_2
+  - libxcb=1.13=h1bed415_1
+  - libxml2=2.9.9=he19cac6_0
+  - matplotlib=2.2.3=py27hb69df0a_0
+  - mkl=2019.3=199
+  - mkl_fft=1.0.10=py27ha843d7b_0
+  - mkl_random=1.0.2=py27hd81dba3_0
+  - ncurses=6.1=he6710b0_1
+  - numpy=1.16.2=py27h7e9f1db_0
+  - numpy-base=1.16.2=py27hde5b4d6_0
+  - olefile=0.46=py27_0
+  - openssl=1.1.1b=h7b6447c_1
+  - pandas=0.24.2=py27he6710b0_0
+  - pcre=8.43=he6710b0_0
+  - pillow=5.4.1=py27h34e0f95_0
+  - pip=19.0.3=py27_0
+  - pyparsing=2.3.1=py27_0
+  - pyqt=5.9.2=py27h05f1152_2
+  - python=2.7.16=h9bab390_0
+  - python-dateutil=2.8.0=py27_0
+  - pytz=2018.9=py27_0
+  - qt=5.9.7=h5867ecd_1
+  - readline=7.0=h7b6447c_5
+  - scipy=1.2.1=py27h7c811a0_0
+  - setuptools=40.8.0=py27_0
+  - singledispatch=3.4.0.3=py27_0
+  - sip=4.19.8=py27hf484d3e_0
+  - six=1.12.0=py27_0
+  - sqlite=3.27.2=h7b6447c_0
+  - subprocess32=3.5.3=py27h7b6447c_0
+  - tk=8.6.8=hbc83047_0
+  - tornado=5.1.1=py27h7b6447c_0
+  - wheel=0.33.1=py27_0
+  - xz=5.2.4=h14c3975_4
+  - zlib=1.2.11=h7b6447c_3
+  - zstd=1.3.7=h0b5b093_0
+  - pip:
+    - chainer==1.24.0
+    - cupy==2.0.0
+    - fastrlock==0.4
+    - filelock==3.0.10
+    - h5py==2.9.0
+    - nltk==3.4
+    - nose==1.3.7
+    - protobuf==3.7.1
+prefix: /u/stsutsui/gpu/public-repos/chainer-caption-github/conda
+
diff --git a/scenes/chainer-caption/data/synset_words.txt b/scenes/chainer-caption/data/synset_words.txt
new file mode 100644
index 0000000..a9e8c7f
--- /dev/null
+++ b/scenes/chainer-caption/data/synset_words.txt
@@ -0,0 +1,1000 @@
+n01440764 tench, Tinca tinca
+n01443537 goldfish, Carassius auratus
+n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+n01491361 tiger shark, Galeocerdo cuvieri
+n01494475 hammerhead, hammerhead shark
+n01496331 electric ray, crampfish, numbfish, torpedo
+n01498041 stingray
+n01514668 cock
+n01514859 hen
+n01518878 ostrich, Struthio camelus
+n01530575 brambling, Fringilla montifringilla
+n01531178 goldfinch, Carduelis carduelis
+n01532829 house finch, linnet, Carpodacus mexicanus
+n01534433 junco, snowbird
+n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea
+n01558993 robin, American robin, Turdus migratorius
+n01560419 bulbul
+n01580077 jay
+n01582220 magpie
+n01592084 chickadee
+n01601694 water ouzel, dipper
+n01608432 kite
+n01614925 bald eagle, American eagle, Haliaeetus leucocephalus
+n01616318 vulture
+n01622779 great grey owl, great gray owl, Strix nebulosa
+n01629819 European fire salamander, Salamandra salamandra
+n01630670 common newt, Triturus vulgaris
+n01631663 eft
+n01632458 spotted salamander, Ambystoma maculatum
+n01632777 axolotl, mud puppy, Ambystoma mexicanum
+n01641577 bullfrog, Rana catesbeiana
+n01644373 tree frog, tree-frog
+n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+n01664065 loggerhead, loggerhead turtle, Caretta caretta
+n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+n01667114 mud turtle
+n01667778 terrapin
+n01669191 box turtle, box tortoise
+n01675722 banded gecko
+n01677366 common iguana, iguana, Iguana iguana
+n01682714 American chameleon, anole, Anolis carolinensis
+n01685808 whiptail, whiptail lizard
+n01687978 agama
+n01688243 frilled lizard, Chlamydosaurus kingi
+n01689811 alligator lizard
+n01692333 Gila monster, Heloderma suspectum
+n01693334 green lizard, Lacerta viridis
+n01694178 African chameleon, Chamaeleo chamaeleon
+n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+n01697457 African crocodile, Nile crocodile, Crocodylus niloticus
+n01698640 American alligator, Alligator mississipiensis
+n01704323 triceratops
+n01728572 thunder snake, worm snake, Carphophis amoenus
+n01728920 ringneck snake, ring-necked snake, ring snake
+n01729322 hognose snake, puff adder, sand viper
+n01729977 green snake, grass snake
+n01734418 king snake, kingsnake
+n01735189 garter snake, grass snake
+n01737021 water snake
+n01739381 vine snake
+n01740131 night snake, Hypsiglena torquata
+n01742172 boa constrictor, Constrictor constrictor
+n01744401 rock python, rock snake, Python sebae
+n01748264 Indian cobra, Naja naja
+n01749939 green mamba
+n01751748 sea snake
+n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus
+n01756291 sidewinder, horned rattlesnake, Crotalus cerastes
+n01768244 trilobite
+n01770081 harvestman, daddy longlegs, Phalangium opilio
+n01770393 scorpion
+n01773157 black and gold garden spider, Argiope aurantia
+n01773549 barn spider, Araneus cavaticus
+n01773797 garden spider, Aranea diademata
+n01774384 black widow, Latrodectus mactans
+n01774750 tarantula
+n01775062 wolf spider, hunting spider
+n01776313 tick
+n01784675 centipede
+n01795545 black grouse
+n01796340 ptarmigan
+n01797886 ruffed grouse, partridge, Bonasa umbellus
+n01798484 prairie chicken, prairie grouse, prairie fowl
+n01806143 peacock
+n01806567 quail
+n01807496 partridge
+n01817953 African grey, African gray, Psittacus erithacus
+n01818515 macaw
+n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+n01820546 lorikeet
+n01824575 coucal
+n01828970 bee eater
+n01829413 hornbill
+n01833805 hummingbird
+n01843065 jacamar
+n01843383 toucan
+n01847000 drake
+n01855032 red-breasted merganser, Mergus serrator
+n01855672 goose
+n01860187 black swan, Cygnus atratus
+n01871265 tusker
+n01872401 echidna, spiny anteater, anteater
+n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+n01877812 wallaby, brush kangaroo
+n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+n01883070 wombat
+n01910747 jellyfish
+n01914609 sea anemone, anemone
+n01917289 brain coral
+n01924916 flatworm, platyhelminth
+n01930112 nematode, nematode worm, roundworm
+n01943899 conch
+n01944390 snail
+n01945685 slug
+n01950731 sea slug, nudibranch
+n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore
+n01968897 chambered nautilus, pearly nautilus, nautilus
+n01978287 Dungeness crab, Cancer magister
+n01978455 rock crab, Cancer irroratus
+n01980166 fiddler crab
+n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus
+n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+n01985128 crayfish, crawfish, crawdad, crawdaddy
+n01986214 hermit crab
+n01990800 isopod
+n02002556 white stork, Ciconia ciconia
+n02002724 black stork, Ciconia nigra
+n02006656 spoonbill
+n02007558 flamingo
+n02009229 little blue heron, Egretta caerulea
+n02009912 American egret, great white heron, Egretta albus
+n02011460 bittern
+n02012849 crane
+n02013706 limpkin, Aramus pictus
+n02017213 European gallinule, Porphyrio porphyrio
+n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana
+n02018795 bustard
+n02025239 ruddy turnstone, Arenaria interpres
+n02027492 red-backed sandpiper, dunlin, Erolia alpina
+n02028035 redshank, Tringa totanus
+n02033041 dowitcher
+n02037110 oystercatcher, oyster catcher
+n02051845 pelican
+n02056570 king penguin, Aptenodytes patagonica
+n02058221 albatross, mollymawk
+n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+n02074367 dugong, Dugong dugon
+n02077923 sea lion
+n02085620 Chihuahua
+n02085782 Japanese spaniel
+n02085936 Maltese dog, Maltese terrier, Maltese
+n02086079 Pekinese, Pekingese, Peke
+n02086240 Shih-Tzu
+n02086646 Blenheim spaniel
+n02086910 papillon
+n02087046 toy terrier
+n02087394 Rhodesian ridgeback
+n02088094 Afghan hound, Afghan
+n02088238 basset, basset hound
+n02088364 beagle
+n02088466 bloodhound, sleuthhound
+n02088632 bluetick
+n02089078 black-and-tan coonhound
+n02089867 Walker hound, Walker foxhound
+n02089973 English foxhound
+n02090379 redbone
+n02090622 borzoi, Russian wolfhound
+n02090721 Irish wolfhound
+n02091032 Italian greyhound
+n02091134 whippet
+n02091244 Ibizan hound, Ibizan Podenco
+n02091467 Norwegian elkhound, elkhound
+n02091635 otterhound, otter hound
+n02091831 Saluki, gazelle hound
+n02092002 Scottish deerhound, deerhound
+n02092339 Weimaraner
+n02093256 Staffordshire bullterrier, Staffordshire bull terrier
+n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+n02093647 Bedlington terrier
+n02093754 Border terrier
+n02093859 Kerry blue terrier
+n02093991 Irish terrier
+n02094114 Norfolk terrier
+n02094258 Norwich terrier
+n02094433 Yorkshire terrier
+n02095314 wire-haired fox terrier
+n02095570 Lakeland terrier
+n02095889 Sealyham terrier, Sealyham
+n02096051 Airedale, Airedale terrier
+n02096177 cairn, cairn terrier
+n02096294 Australian terrier
+n02096437 Dandie Dinmont, Dandie Dinmont terrier
+n02096585 Boston bull, Boston terrier
+n02097047 miniature schnauzer
+n02097130 giant schnauzer
+n02097209 standard schnauzer
+n02097298 Scotch terrier, Scottish terrier, Scottie
+n02097474 Tibetan terrier, chrysanthemum dog
+n02097658 silky terrier, Sydney silky
+n02098105 soft-coated wheaten terrier
+n02098286 West Highland white terrier
+n02098413 Lhasa, Lhasa apso
+n02099267 flat-coated retriever
+n02099429 curly-coated retriever
+n02099601 golden retriever
+n02099712 Labrador retriever
+n02099849 Chesapeake Bay retriever
+n02100236 German short-haired pointer
+n02100583 vizsla, Hungarian pointer
+n02100735 English setter
+n02100877 Irish setter, red setter
+n02101006 Gordon setter
+n02101388 Brittany spaniel
+n02101556 clumber, clumber spaniel
+n02102040 English springer, English springer spaniel
+n02102177 Welsh springer spaniel
+n02102318 cocker spaniel, English cocker spaniel, cocker
+n02102480 Sussex spaniel
+n02102973 Irish water spaniel
+n02104029 kuvasz
+n02104365 schipperke
+n02105056 groenendael
+n02105162 malinois
+n02105251 briard
+n02105412 kelpie
+n02105505 komondor
+n02105641 Old English sheepdog, bobtail
+n02105855 Shetland sheepdog, Shetland sheep dog, Shetland
+n02106030 collie
+n02106166 Border collie
+n02106382 Bouvier des Flandres, Bouviers des Flandres
+n02106550 Rottweiler
+n02106662 German shepherd, German shepherd dog, German police dog, alsatian
+n02107142 Doberman, Doberman pinscher
+n02107312 miniature pinscher
+n02107574 Greater Swiss Mountain dog
+n02107683 Bernese mountain dog
+n02107908 Appenzeller
+n02108000 EntleBucher
+n02108089 boxer
+n02108422 bull mastiff
+n02108551 Tibetan mastiff
+n02108915 French bulldog
+n02109047 Great Dane
+n02109525 Saint Bernard, St Bernard
+n02109961 Eskimo dog, husky
+n02110063 malamute, malemute, Alaskan malamute
+n02110185 Siberian husky
+n02110341 dalmatian, coach dog, carriage dog
+n02110627 affenpinscher, monkey pinscher, monkey dog
+n02110806 basenji
+n02110958 pug, pug-dog
+n02111129 Leonberg
+n02111277 Newfoundland, Newfoundland dog
+n02111500 Great Pyrenees
+n02111889 Samoyed, Samoyede
+n02112018 Pomeranian
+n02112137 chow, chow chow
+n02112350 keeshond
+n02112706 Brabancon griffon
+n02113023 Pembroke, Pembroke Welsh corgi
+n02113186 Cardigan, Cardigan Welsh corgi
+n02113624 toy poodle
+n02113712 miniature poodle
+n02113799 standard poodle
+n02113978 Mexican hairless
+n02114367 timber wolf, grey wolf, gray wolf, Canis lupus
+n02114548 white wolf, Arctic wolf, Canis lupus tundrarum
+n02114712 red wolf, maned wolf, Canis rufus, Canis niger
+n02114855 coyote, prairie wolf, brush wolf, Canis latrans
+n02115641 dingo, warrigal, warragal, Canis dingo
+n02115913 dhole, Cuon alpinus
+n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+n02117135 hyena, hyaena
+n02119022 red fox, Vulpes vulpes
+n02119789 kit fox, Vulpes macrotis
+n02120079 Arctic fox, white fox, Alopex lagopus
+n02120505 grey fox, gray fox, Urocyon cinereoargenteus
+n02123045 tabby, tabby cat
+n02123159 tiger cat
+n02123394 Persian cat
+n02123597 Siamese cat, Siamese
+n02124075 Egyptian cat
+n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+n02127052 lynx, catamount
+n02128385 leopard, Panthera pardus
+n02128757 snow leopard, ounce, Panthera uncia
+n02128925 jaguar, panther, Panthera onca, Felis onca
+n02129165 lion, king of beasts, Panthera leo
+n02129604 tiger, Panthera tigris
+n02130308 cheetah, chetah, Acinonyx jubatus
+n02132136 brown bear, bruin, Ursus arctos
+n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus
+n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+n02134418 sloth bear, Melursus ursinus, Ursus ursinus
+n02137549 mongoose
+n02138441 meerkat, mierkat
+n02165105 tiger beetle
+n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+n02167151 ground beetle, carabid beetle
+n02168699 long-horned beetle, longicorn, longicorn beetle
+n02169497 leaf beetle, chrysomelid
+n02172182 dung beetle
+n02174001 rhinoceros beetle
+n02177972 weevil
+n02190166 fly
+n02206856 bee
+n02219486 ant, emmet, pismire
+n02226429 grasshopper, hopper
+n02229544 cricket
+n02231487 walking stick, walkingstick, stick insect
+n02233338 cockroach, roach
+n02236044 mantis, mantid
+n02256656 cicada, cicala
+n02259212 leafhopper
+n02264363 lacewing, lacewing fly
+n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+n02268853 damselfly
+n02276258 admiral
+n02277742 ringlet, ringlet butterfly
+n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+n02280649 cabbage butterfly
+n02281406 sulphur butterfly, sulfur butterfly
+n02281787 lycaenid, lycaenid butterfly
+n02317335 starfish, sea star
+n02319095 sea urchin
+n02321529 sea cucumber, holothurian
+n02325366 wood rabbit, cottontail, cottontail rabbit
+n02326432 hare
+n02328150 Angora, Angora rabbit
+n02342885 hamster
+n02346627 porcupine, hedgehog
+n02356798 fox squirrel, eastern fox squirrel, Sciurus niger
+n02361337 marmot
+n02363005 beaver
+n02364673 guinea pig, Cavia cobaya
+n02389026 sorrel
+n02391049 zebra
+n02395406 hog, pig, grunter, squealer, Sus scrofa
+n02396427 wild boar, boar, Sus scrofa
+n02397096 warthog
+n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius
+n02403003 ox
+n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+n02410509 bison
+n02412080 ram, tup
+n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+n02417914 ibex, Capra ibex
+n02422106 hartebeest
+n02422699 impala, Aepyceros melampus
+n02423022 gazelle
+n02437312 Arabian camel, dromedary, Camelus dromedarius
+n02437616 llama
+n02441942 weasel
+n02442845 mink
+n02443114 polecat, fitch, foulmart, foumart, Mustela putorius
+n02443484 black-footed ferret, ferret, Mustela nigripes
+n02444819 otter
+n02445715 skunk, polecat, wood pussy
+n02447366 badger
+n02454379 armadillo
+n02457408 three-toed sloth, ai, Bradypus tridactylus
+n02480495 orangutan, orang, orangutang, Pongo pygmaeus
+n02480855 gorilla, Gorilla gorilla
+n02481823 chimpanzee, chimp, Pan troglodytes
+n02483362 gibbon, Hylobates lar
+n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus
+n02484975 guenon, guenon monkey
+n02486261 patas, hussar monkey, Erythrocebus patas
+n02486410 baboon
+n02487347 macaque
+n02488291 langur
+n02488702 colobus, colobus monkey
+n02489166 proboscis monkey, Nasalis larvatus
+n02490219 marmoset
+n02492035 capuchin, ringtail, Cebus capucinus
+n02492660 howler monkey, howler
+n02493509 titi, titi monkey
+n02493793 spider monkey, Ateles geoffroyi
+n02494079 squirrel monkey, Saimiri sciureus
+n02497673 Madagascar cat, ring-tailed lemur, Lemur catta
+n02500267 indri, indris, Indri indri, Indri brevicaudatus
+n02504013 Indian elephant, Elephas maximus
+n02504458 African elephant, Loxodonta africana
+n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+n02514041 barracouta, snoek
+n02526121 eel
+n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+n02606052 rock beauty, Holocanthus tricolor
+n02607072 anemone fish
+n02640242 sturgeon
+n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus
+n02643566 lionfish
+n02655020 puffer, pufferfish, blowfish, globefish
+n02666196 abacus
+n02667093 abaya
+n02669723 academic gown, academic robe, judge's robe
+n02672831 accordion, piano accordion, squeeze box
+n02676566 acoustic guitar
+n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier
+n02690373 airliner
+n02692877 airship, dirigible
+n02699494 altar
+n02701002 ambulance
+n02704792 amphibian, amphibious vehicle
+n02708093 analog clock
+n02727426 apiary, bee house
+n02730930 apron
+n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+n02749479 assault rifle, assault gun
+n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack
+n02776631 bakery, bakeshop, bakehouse
+n02777292 balance beam, beam
+n02782093 balloon
+n02783161 ballpoint, ballpoint pen, ballpen, Biro
+n02786058 Band Aid
+n02787622 banjo
+n02788148 bannister, banister, balustrade, balusters, handrail
+n02790996 barbell
+n02791124 barber chair
+n02791270 barbershop
+n02793495 barn
+n02794156 barometer
+n02795169 barrel, cask
+n02797295 barrow, garden cart, lawn cart, wheelbarrow
+n02799071 baseball
+n02802426 basketball
+n02804414 bassinet
+n02804610 bassoon
+n02807133 bathing cap, swimming cap
+n02808304 bath towel
+n02808440 bathtub, bathing tub, bath, tub
+n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+n02814860 beacon, lighthouse, beacon light, pharos
+n02815834 beaker
+n02817516 bearskin, busby, shako
+n02823428 beer bottle
+n02823750 beer glass
+n02825657 bell cote, bell cot
+n02834397 bib
+n02835271 bicycle-built-for-two, tandem bicycle, tandem
+n02837789 bikini, two-piece
+n02840245 binder, ring-binder
+n02841315 binoculars, field glasses, opera glasses
+n02843684 birdhouse
+n02859443 boathouse
+n02860847 bobsled, bobsleigh, bob
+n02865351 bolo tie, bolo, bola tie, bola
+n02869837 bonnet, poke bonnet
+n02870880 bookcase
+n02871525 bookshop, bookstore, bookstall
+n02877765 bottlecap
+n02879718 bow
+n02883205 bow tie, bow-tie, bowtie
+n02892201 brass, memorial tablet, plaque
+n02892767 brassiere, bra, bandeau
+n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty
+n02895154 breastplate, aegis, egis
+n02906734 broom
+n02909870 bucket, pail
+n02910353 buckle
+n02916936 bulletproof vest
+n02917067 bullet train, bullet
+n02927161 butcher shop, meat market
+n02930766 cab, hack, taxi, taxicab
+n02939185 caldron, cauldron
+n02948072 candle, taper, wax light
+n02950826 cannon
+n02951358 canoe
+n02951585 can opener, tin opener
+n02963159 cardigan
+n02965783 car mirror
+n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig
+n02966687 carpenter's kit, tool kit
+n02971356 carton
+n02974003 car wheel
+n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+n02978881 cassette
+n02979186 cassette player
+n02980441 castle
+n02981792 catamaran
+n02988304 CD player
+n02992211 cello, violoncello
+n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone
+n02999410 chain
+n03000134 chainlink fence
+n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+n03000684 chain saw, chainsaw
+n03014705 chest
+n03016953 chiffonier, commode
+n03017168 chime, bell, gong
+n03018349 china cabinet, china closet
+n03026506 Christmas stocking
+n03028079 church, church building
+n03032252 cinema, movie theater, movie theatre, movie house, picture palace
+n03041632 cleaver, meat cleaver, chopper
+n03042490 cliff dwelling
+n03045698 cloak
+n03047690 clog, geta, patten, sabot
+n03062245 cocktail shaker
+n03063599 coffee mug
+n03063689 coffeepot
+n03065424 coil, spiral, volute, whorl, helix
+n03075370 combination lock
+n03085013 computer keyboard, keypad
+n03089624 confectionery, confectionary, candy store
+n03095699 container ship, containership, container vessel
+n03100240 convertible
+n03109150 corkscrew, bottle screw
+n03110669 cornet, horn, trumpet, trump
+n03124043 cowboy boot
+n03124170 cowboy hat, ten-gallon hat
+n03125729 cradle
+n03126707 crane
+n03127747 crash helmet
+n03127925 crate
+n03131574 crib, cot
+n03133878 Crock Pot
+n03134739 croquet ball
+n03141823 crutch
+n03146219 cuirass
+n03160309 dam, dike, dyke
+n03179701 desk
+n03180011 desktop computer
+n03187595 dial telephone, dial phone
+n03188531 diaper, nappy, napkin
+n03196217 digital clock
+n03197337 digital watch
+n03201208 dining table, board
+n03207743 dishrag, dishcloth
+n03207941 dishwasher, dish washer, dishwashing machine
+n03208938 disk brake, disc brake
+n03216828 dock, dockage, docking facility
+n03218198 dogsled, dog sled, dog sleigh
+n03220513 dome
+n03223299 doormat, welcome mat
+n03240683 drilling platform, offshore rig
+n03249569 drum, membranophone, tympan
+n03250847 drumstick
+n03255030 dumbbell
+n03259280 Dutch oven
+n03271574 electric fan, blower
+n03272010 electric guitar
+n03272562 electric locomotive
+n03290653 entertainment center
+n03291819 envelope
+n03297495 espresso maker
+n03314780 face powder
+n03325584 feather boa, boa
+n03337140 file, file cabinet, filing cabinet
+n03344393 fireboat
+n03345487 fire engine, fire truck
+n03347037 fire screen, fireguard
+n03355925 flagpole, flagstaff
+n03372029 flute, transverse flute
+n03376595 folding chair
+n03379051 football helmet
+n03384352 forklift
+n03388043 fountain
+n03388183 fountain pen
+n03388549 four-poster
+n03393912 freight car
+n03394916 French horn, horn
+n03400231 frying pan, frypan, skillet
+n03404251 fur coat
+n03417042 garbage truck, dustcart
+n03424325 gasmask, respirator, gas helmet
+n03425413 gas pump, gasoline pump, petrol pump, island dispenser
+n03443371 goblet
+n03444034 go-kart
+n03445777 golf ball
+n03445924 golfcart, golf cart
+n03447447 gondola
+n03447721 gong, tam-tam
+n03450230 gown
+n03452741 grand piano, grand
+n03457902 greenhouse, nursery, glasshouse
+n03459775 grille, radiator grille
+n03461385 grocery store, grocery, food market, market
+n03467068 guillotine
+n03476684 hair slide
+n03476991 hair spray
+n03478589 half track
+n03481172 hammer
+n03482405 hamper
+n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier
+n03485407 hand-held computer, hand-held microcomputer
+n03485794 handkerchief, hankie, hanky, hankey
+n03492542 hard disc, hard disk, fixed disk
+n03494278 harmonica, mouth organ, harp, mouth harp
+n03495258 harp
+n03496892 harvester, reaper
+n03498962 hatchet
+n03527444 holster
+n03529860 home theater, home theatre
+n03530642 honeycomb
+n03532672 hook, claw
+n03534580 hoopskirt, crinoline
+n03535780 horizontal bar, high bar
+n03538406 horse cart, horse-cart
+n03544143 hourglass
+n03584254 iPod
+n03584829 iron, smoothing iron
+n03590841 jack-o'-lantern
+n03594734 jean, blue jean, denim
+n03594945 jeep, landrover
+n03595614 jersey, T-shirt, tee shirt
+n03598930 jigsaw puzzle
+n03599486 jinrikisha, ricksha, rickshaw
+n03602883 joystick
+n03617480 kimono
+n03623198 knee pad
+n03627232 knot
+n03630383 lab coat, laboratory coat
+n03633091 ladle
+n03637318 lampshade, lamp shade
+n03642806 laptop, laptop computer
+n03649909 lawn mower, mower
+n03657121 lens cap, lens cover
+n03658185 letter opener, paper knife, paperknife
+n03661043 library
+n03662601 lifeboat
+n03666591 lighter, light, igniter, ignitor
+n03670208 limousine, limo
+n03673027 liner, ocean liner
+n03676483 lipstick, lip rouge
+n03680355 Loafer
+n03690938 lotion
+n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+n03692522 loupe, jeweler's loupe
+n03697007 lumbermill, sawmill
+n03706229 magnetic compass
+n03709823 mailbag, postbag
+n03710193 mailbox, letter box
+n03710637 maillot
+n03710721 maillot, tank suit
+n03717622 manhole cover
+n03720891 maraca
+n03721384 marimba, xylophone
+n03724870 mask
+n03729826 matchstick
+n03733131 maypole
+n03733281 maze, labyrinth
+n03733805 measuring cup
+n03742115 medicine chest, medicine cabinet
+n03743016 megalith, megalithic structure
+n03759954 microphone, mike
+n03761084 microwave, microwave oven
+n03763968 military uniform
+n03764736 milk can
+n03769881 minibus
+n03770439 miniskirt, mini
+n03770679 minivan
+n03773504 missile
+n03775071 mitten
+n03775546 mixing bowl
+n03776460 mobile home, manufactured home
+n03777568 Model T
+n03777754 modem
+n03781244 monastery
+n03782006 monitor
+n03785016 moped
+n03786901 mortar
+n03787032 mortarboard
+n03788195 mosque
+n03788365 mosquito net
+n03791053 motor scooter, scooter
+n03792782 mountain bike, all-terrain bike, off-roader
+n03792972 mountain tent
+n03793489 mouse, computer mouse
+n03794056 mousetrap
+n03796401 moving van
+n03803284 muzzle
+n03804744 nail
+n03814639 neck brace
+n03814906 necklace
+n03825788 nipple
+n03832673 notebook, notebook computer
+n03837869 obelisk
+n03838899 oboe, hautboy, hautbois
+n03840681 ocarina, sweet potato
+n03841143 odometer, hodometer, mileometer, milometer
+n03843555 oil filter
+n03854065 organ, pipe organ
+n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO
+n03866082 overskirt
+n03868242 oxcart
+n03868863 oxygen mask
+n03871628 packet
+n03873416 paddle, boat paddle
+n03874293 paddlewheel, paddle wheel
+n03874599 padlock
+n03876231 paintbrush
+n03877472 pajama, pyjama, pj's, jammies
+n03877845 palace
+n03884397 panpipe, pandean pipe, syrinx
+n03887697 paper towel
+n03888257 parachute, chute
+n03888605 parallel bars, bars
+n03891251 park bench
+n03891332 parking meter
+n03895866 passenger car, coach, carriage
+n03899768 patio, terrace
+n03902125 pay-phone, pay-station
+n03903868 pedestal, plinth, footstall
+n03908618 pencil box, pencil case
+n03908714 pencil sharpener
+n03916031 perfume, essence
+n03920288 Petri dish
+n03924679 photocopier
+n03929660 pick, plectrum, plectron
+n03929855 pickelhaube
+n03930313 picket fence, paling
+n03930630 pickup, pickup truck
+n03933933 pier
+n03935335 piggy bank, penny bank
+n03937543 pill bottle
+n03938244 pillow
+n03942813 ping-pong ball
+n03944341 pinwheel
+n03947888 pirate, pirate ship
+n03950228 pitcher, ewer
+n03954731 plane, carpenter's plane, woodworking plane
+n03956157 planetarium
+n03958227 plastic bag
+n03961711 plate rack
+n03967562 plow, plough
+n03970156 plunger, plumber's helper
+n03976467 Polaroid camera, Polaroid Land camera
+n03976657 pole
+n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+n03980874 poncho
+n03982430 pool table, billiard table, snooker table
+n03983396 pop bottle, soda bottle
+n03991062 pot, flowerpot
+n03992509 potter's wheel
+n03995372 power drill
+n03998194 prayer rug, prayer mat
+n04004767 printer
+n04005630 prison, prison house
+n04008634 projectile, missile
+n04009552 projector
+n04019541 puck, hockey puck
+n04023962 punching bag, punch bag, punching ball, punchball
+n04026417 purse
+n04033901 quill, quill pen
+n04033995 quilt, comforter, comfort, puff
+n04037443 racer, race car, racing car
+n04039381 racket, racquet
+n04040759 radiator
+n04041544 radio, wireless
+n04044716 radio telescope, radio reflector
+n04049303 rain barrel
+n04065272 recreational vehicle, RV, R.V.
+n04067472 reel
+n04069434 reflex camera
+n04070727 refrigerator, icebox
+n04074963 remote control, remote
+n04081281 restaurant, eating house, eating place, eatery
+n04086273 revolver, six-gun, six-shooter
+n04090263 rifle
+n04099969 rocking chair, rocker
+n04111531 rotisserie
+n04116512 rubber eraser, rubber, pencil eraser
+n04118538 rugby ball
+n04118776 rule, ruler
+n04120489 running shoe
+n04125021 safe
+n04127249 safety pin
+n04131690 saltshaker, salt shaker
+n04133789 sandal
+n04136333 sarong
+n04141076 sax, saxophone
+n04141327 scabbard
+n04141975 scale, weighing machine
+n04146614 school bus
+n04147183 schooner
+n04149813 scoreboard
+n04152593 screen, CRT screen
+n04153751 screw
+n04154565 screwdriver
+n04162706 seat belt, seatbelt
+n04179913 sewing machine
+n04192698 shield, buckler
+n04200800 shoe shop, shoe-shop, shoe store
+n04201297 shoji
+n04204238 shopping basket
+n04204347 shopping cart
+n04208210 shovel
+n04209133 shower cap
+n04209239 shower curtain
+n04228054 ski
+n04229816 ski mask
+n04235860 sleeping bag
+n04238763 slide rule, slipstick
+n04239074 sliding door
+n04243546 slot, one-armed bandit
+n04251144 snorkel
+n04252077 snowmobile
+n04252225 snowplow, snowplough
+n04254120 soap dispenser
+n04254680 soccer ball
+n04254777 sock
+n04258138 solar dish, solar collector, solar furnace
+n04259630 sombrero
+n04263257 soup bowl
+n04264628 space bar
+n04265275 space heater
+n04266014 space shuttle
+n04270147 spatula
+n04273569 speedboat
+n04275548 spider web, spider's web
+n04277352 spindle
+n04285008 sports car, sport car
+n04286575 spotlight, spot
+n04296562 stage
+n04310018 steam locomotive
+n04311004 steel arch bridge
+n04311174 steel drum
+n04317175 stethoscope
+n04325704 stole
+n04326547 stone wall
+n04328186 stopwatch, stop watch
+n04330267 stove
+n04332243 strainer
+n04335435 streetcar, tram, tramcar, trolley, trolley car
+n04336792 stretcher
+n04344873 studio couch, day bed
+n04346328 stupa, tope
+n04347754 submarine, pigboat, sub, U-boat
+n04350905 suit, suit of clothes
+n04355338 sundial
+n04355933 sunglass
+n04356056 sunglasses, dark glasses, shades
+n04357314 sunscreen, sunblock, sun blocker
+n04366367 suspension bridge
+n04367480 swab, swob, mop
+n04370456 sweatshirt
+n04371430 swimming trunks, bathing trunks
+n04371774 swing
+n04372370 switch, electric switch, electrical switch
+n04376876 syringe
+n04380533 table lamp
+n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle
+n04392985 tape player
+n04398044 teapot
+n04399382 teddy, teddy bear
+n04404412 television, television system
+n04409515 tennis ball
+n04417672 thatch, thatched roof
+n04418357 theater curtain, theatre curtain
+n04423845 thimble
+n04428191 thresher, thrasher, threshing machine
+n04429376 throne
+n04435653 tile roof
+n04442312 toaster
+n04443257 tobacco shop, tobacconist shop, tobacconist
+n04447861 toilet seat
+n04456115 torch
+n04458633 totem pole
+n04461696 tow truck, tow car, wrecker
+n04462240 toyshop
+n04465501 tractor
+n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+n04476259 tray
+n04479046 trench coat
+n04482393 tricycle, trike, velocipede
+n04483307 trimaran
+n04485082 tripod
+n04486054 triumphal arch
+n04487081 trolleybus, trolley coach, trackless trolley
+n04487394 trombone
+n04493381 tub, vat
+n04501370 turnstile
+n04505470 typewriter keyboard
+n04507155 umbrella
+n04509417 unicycle, monocycle
+n04515003 upright, upright piano
+n04517823 vacuum, vacuum cleaner
+n04522168 vase
+n04523525 vault
+n04525038 velvet
+n04525305 vending machine
+n04532106 vestment
+n04532670 viaduct
+n04536866 violin, fiddle
+n04540053 volleyball
+n04542943 waffle iron
+n04548280 wall clock
+n04548362 wallet, billfold, notecase, pocketbook
+n04550184 wardrobe, closet, press
+n04552348 warplane, military plane
+n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin
+n04554684 washer, automatic washer, washing machine
+n04557648 water bottle
+n04560804 water jug
+n04562935 water tower
+n04579145 whiskey jug
+n04579432 whistle
+n04584207 wig
+n04589890 window screen
+n04590129 window shade
+n04591157 Windsor tie
+n04591713 wine bottle
+n04592741 wing
+n04596742 wok
+n04597913 wooden spoon
+n04599235 wool, woolen, woollen
+n04604644 worm fence, snake fence, snake-rail fence, Virginia fence
+n04606251 wreck
+n04612504 yawl
+n04613696 yurt
+n06359193 web site, website, internet site, site
+n06596364 comic book
+n06785654 crossword puzzle, crossword
+n06794110 street sign
+n06874185 traffic light, traffic signal, stoplight
+n07248320 book jacket, dust cover, dust jacket, dust wrapper
+n07565083 menu
+n07579787 plate
+n07583066 guacamole
+n07584110 consomme
+n07590611 hot pot, hotpot
+n07613480 trifle
+n07614500 ice cream, icecream
+n07615774 ice lolly, lolly, lollipop, popsicle
+n07684084 French loaf
+n07693725 bagel, beigel
+n07695742 pretzel
+n07697313 cheeseburger
+n07697537 hotdog, hot dog, red hot
+n07711569 mashed potato
+n07714571 head cabbage
+n07714990 broccoli
+n07715103 cauliflower
+n07716358 zucchini, courgette
+n07716906 spaghetti squash
+n07717410 acorn squash
+n07717556 butternut squash
+n07718472 cucumber, cuke
+n07718747 artichoke, globe artichoke
+n07720875 bell pepper
+n07730033 cardoon
+n07734744 mushroom
+n07742313 Granny Smith
+n07745940 strawberry
+n07747607 orange
+n07749582 lemon
+n07753113 fig
+n07753275 pineapple, ananas
+n07753592 banana
+n07754684 jackfruit, jak, jack
+n07760859 custard apple
+n07768694 pomegranate
+n07802026 hay
+n07831146 carbonara
+n07836838 chocolate sauce, chocolate syrup
+n07860988 dough
+n07871810 meat loaf, meatloaf
+n07873807 pizza, pizza pie
+n07875152 potpie
+n07880968 burrito
+n07892512 red wine
+n07920052 espresso
+n07930864 cup
+n07932039 eggnog
+n09193705 alp
+n09229709 bubble
+n09246464 cliff, drop, drop-off
+n09256479 coral reef
+n09288635 geyser
+n09332890 lakeside, lakeshore
+n09399592 promontory, headland, head, foreland
+n09421951 sandbar, sand bar
+n09428293 seashore, coast, seacoast, sea-coast
+n09468604 valley, vale
+n09472597 volcano
+n09835506 ballplayer, baseball player
+n10148035 groom, bridegroom
+n10565667 scuba diver
+n11879895 rapeseed
+n11939491 daisy
+n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+n12144580 corn
+n12267677 acorn
+n12620546 hip, rose hip, rosehip
+n12768682 buckeye, horse chestnut, conker
+n12985857 coral fungus
+n12998815 agaric
+n13037406 gyromitra
+n13040303 stinkhorn, carrion fungus
+n13044778 earthstar
+n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+n13054560 bolete
+n13133613 ear, spike, capitulum
+n15075141 toilet tissue, toilet paper, bathroom tissue
diff --git a/scenes/chainer-caption/download.sh b/scenes/chainer-caption/download.sh
new file mode 100644
index 0000000..bc890a2
--- /dev/null
+++ b/scenes/chainer-caption/download.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+cd data
+if [ ! -f ResNet50.model ]; then
+  wget https://www.dropbox.com/s/eqdmml7kj3545sv/ResNet50.model 
+fi
+if [ ! -f caption_en_model40.model ]; then
+  wget https://www.dropbox.com/s/sy4ayrvush0bqle/caption_en_model40.model
+fi
+
+cd MSCOCO
+if [ "$1" == "train" ]; then
+  if [ ! -f train2014_ResNet50_features.zip ]; then
+    wget  https://www.dropbox.com/s/v3tewruak581uf3/train2014_ResNet50_features.zip
+    unzip train2014_ResNet50_features.zip
+  fi 
+  if [ ! -f val2014_ResNet50_features.zip ]; then
+    wget https://www.dropbox.com/s/qi3s55vzgmkiqkh/val2014_ResNet50_features.zip
+    unzip val2014_ResNet50_features.zip
+  fi 
+  if [ ! -f captions_train2014_cn_translation.json ]; then
+    wget https://github.com/apple2373/mt-mscoco/raw/master/captions_train2014_cn_translation.json.zip
+    unzip captions_train2014_cn_translation.json.zip
+    rm captions_train2014_cn_translation.json.zip
+  fi 
+  if [ ! -f captions_train2014_jp_translation.json.zip ]; then
+    wget https://github.com/apple2373/mt-mscoco/raw/master/captions_train2014_jp_translation.json.zip
+    unzip captions_train2014_jp_translation.json.zip
+    rm captions_train2014_jp_translation.json.zip
+  fi 
+  if [ ! -f yjcaptions26k_clean.json ]; then
+    wget https://github.com/yahoojapan/YJCaptions/raw/master/yjcaptions26k.zip
+    unzip yjcaptions26k.zip
+    rm yjcaptions26k.zip
+  fi 
+  if [ ! -f captions_train2014.json ]; then
+    wget http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip
+    unzip captions_train-val2014.zip
+    rm captions_train-val2014.zip
+  fi 
+fi
+	
diff --git a/scenes/chainer-caption/image_captioning_interface.py b/scenes/chainer-caption/image_captioning_interface.py
new file mode 100644
index 0000000..d67a38f
--- /dev/null
+++ b/scenes/chainer-caption/image_captioning_interface.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+'''
+Sample code to generate caption using beam search
+'''
+import sys
+import json
+import os
+import cv2
+# comment out the below if you want to do type check. Remeber this have to be done BEFORE import chainer
+#os.environ["CHAINER_TYPE_CHECK"] = "0"
+import chainer 
+from PIL import Image
+import argparse
+import numpy as np
+import math
+from chainer import cuda
+import chainer.functions as F
+from chainer import cuda, Function, gradient_check, Variable, optimizers
+from chainer import serializers
+
+sys.path.append('./code')
+from CaptionGenerator import CaptionGenerator
+
+chainer.global_config.autotune = True
+chainer.global_config.enable_backprop = False
+chainer.global_config.type_check = False
+
+class image_captioning ():
+    def __init__(self,
+    	rnn_model_place='./data/caption_en_model40.model',
+    	cnn_model_place='./data/ResNet50.model',
+    	dictonary_place='./data/MSCOCO/mscoco_caption_train2014_processed_dic.json',
+    	beamsize=3,
+    	depth_limit=50,
+    	gpu_id=-1,
+    	first_word="<sos>"):
+
+    	self.caption_generator=CaptionGenerator(
+	    	rnn_model_place=rnn_model_place,
+	    	cnn_model_place=cnn_model_place,
+	    	dictonary_place=dictonary_place,
+	    	beamsize=beamsize,
+	    	depth_limit=depth_limit,
+	    	gpu_id=gpu_id,
+	    	first_word= first_word,
+    	)
+
+    def generate_from_img_nparray(self,image_array):
+        image_feature=self.caption_generator.cnn_model(image_array, "feature").data.reshape(1,1,2048)
+        return self.caption_generator.generate_from_img_feature(image_feature)
+
+    def resize (self,image_array):
+        image_array = cv2.resize(image_array, dsize=(224, 224))
+        image_array = cv2.cvtColor(image_array, cv2.COLOR_BGR2RGB)
+        pixels = np.asarray(image_array).astype(np.float32)
+        pixels = pixels[:,:,::-1].transpose(2,0,1)
+        pixels -= self.caption_generator.image_loader.mean_image
+        return pixels.reshape((1,) + pixels.shape)
+
+
+    def resize_img_and_generate(self,image_array):
+        return self.generate_from_img_nparray(self.resize(image_array))
+
+#run this file for test purpose
+if __name__ == '__main__':
+    test_image_path = "test3.jpg"
+    image_captioning=image_captioning()
+    #test generate_from_img_nparray
+    # np_image = image_captioning.caption_generator.image_loader.load(test_image_path)
+    # captions = image_captioning.generate_from_img_nparray(np_image)
+    # for caption in captions:
+    #     print (" ".join(caption["sentence"]))
+    #     print (caption["log_likelihood"])
+
+    #test resize_img_and_generate
+    image = cv2.imread(test_image_path)
+    captions = image_captioning.resize_img_and_generate(image)
+    for caption in captions:
+        print (" ".join(caption["sentence"]))
+        print (caption["log_likelihood"])
\ No newline at end of file
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