diff --git a/recognition/MySolution/s47539934-GCN/Accuracy.png b/recognition/MySolution/s47539934-GCN/Accuracy.png
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Binary files /dev/null and b/recognition/MySolution/s47539934-GCN/Accuracy.png differ
diff --git a/recognition/MySolution/s47539934-GCN/Data.py b/recognition/MySolution/s47539934-GCN/Data.py
new file mode 100644
index 0000000000..302d946232
--- /dev/null
+++ b/recognition/MySolution/s47539934-GCN/Data.py
@@ -0,0 +1,51 @@
+'''
+Author name: Arsh Upadhyaya, s47539934
+To preprocess dataset facebook.npz
+'''
+
+import numpy as np
+from sklearn import preprocessing
+import torch
+import scipy.sparse
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import scipy.sparse as sp
+import torch.optim as optim
+
+
+def load_data(file_path):
+    '''
+    parameters: 
+    takes file path
+    returns:
+    Adjacency matrix: normalized coo matrix
+    features and labels(targets) as tensors
+    '''
+
+    data = np.load("/content/drive/MyDrive/facebook.npz")
+    edges = data['edges']
+    features = data['features']
+    labels = data['target']
+
+    features = sp.csr_matrix(features)
+
+    adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))
+
+    #normalize
+    colsum = np.array(adj.sum(0))
+    D = np.power(colsum, -1)[0]
+    D[np.isinf(D)] = 0
+    D_inv = sp.diags(D)
+    adj_trans = D_inv.dot(adj)
+
+    #transform data type
+    indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))
+    values = torch.FloatTensor(adj_trans.data)
+    shape = adj_trans.shape
+
+    adj_trans = torch.sparse_coo_tensor(indices, values, shape)
+    features = torch.FloatTensor(np.array(features.todense()))
+    labels = torch.LongTensor(labels)
+
+    return adj_trans, features, labels
+   
diff --git a/recognition/MySolution/s47539934-GCN/GCN_final.ipynb b/recognition/MySolution/s47539934-GCN/GCN_final.ipynb
new file mode 100644
index 0000000000..1bfab0bd5e
--- /dev/null
+++ b/recognition/MySolution/s47539934-GCN/GCN_final.ipynb
@@ -0,0 +1,648 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOdKktAKTsIX4iuqCL4y12Z",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mr-popo123/PatternFlow/blob/master/GCN_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        },
+        "id": "4Tpyn_NtoRSE",
+        "outputId": "927e952a-8a55-4448-b8f2-fbd1a0fb6b4d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nAuthor name: Arsh Upadhyaya\\nRoll no. s4753993\\nCode for 2 layer GCN\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ],
+      "source": [
+        "'''\n",
+        "Author name: Arsh Upadhyaya\n",
+        "Roll no. s4753993\n",
+        "Code for 2 layer GCN\n",
+        "'''"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive"
+      ],
+      "metadata": {
+        "id": "jsqqEDjjqjZi"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "p_dNPCQKrVqa",
+        "outputId": "b49f52ea-d538-45b0-9ebf-de7ab4d024fe"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "import torch.nn.init as init\n",
+        "import numpy as np\n",
+        "import scipy.sparse as sp\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "from torch.nn.parameter import Parameter\n",
+        "from torch.nn.modules.module import Module\n",
+        "import torch.optim as optim\n",
+        "from random import sample\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "V2CFruE7r4yV"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_data(file_path):\n",
+        "    \n",
+        "    data = np.load(\"/content/drive/MyDrive/facebook.npz\")#path of file through google drive\n",
+        "    edges = data['edges']\n",
+        "    features = data['features']\n",
+        "    labels = data['target']\n",
+        "\n",
+        "    features = sp.csr_matrix(features)\n",
+        "\n",
+        "    adj= sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]))\n",
+        "\n",
+        "    #normalize\n",
+        "    colsum = np.array(adj.sum(0))\n",
+        "    D = np.power(colsum, -1)[0]\n",
+        "    D[np.isinf(D)] = 0\n",
+        "    D_inv = sp.diags(D)\n",
+        "    adj_trans = D_inv.dot(adj)\n",
+        "\n",
+        "    #transform data type\n",
+        "    indices = torch.LongTensor(np.vstack((adj_trans.tocoo().row, adj_trans.tocoo().col)))\n",
+        "    values = torch.FloatTensor(adj_trans.data)\n",
+        "    shape = adj_trans.shape\n",
+        "\n",
+        "    adj_trans = torch.sparse_coo_tensor(indices, values, shape)\n",
+        "    features = torch.FloatTensor(np.array(features.todense()))\n",
+        "    labels = torch.LongTensor(labels)\n",
+        "\n",
+        "    return adj_trans, features, labels"
+      ],
+      "metadata": {
+        "id": "KDdp_bTnru0h"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "\n",
+        "\n",
+        "class GraphConvolution(Module):\n",
+        "    '''\n",
+        "    Starting of graph convolutional layer.\n",
+        "    Parameters:\n",
+        "    input_features: dimensions of input layer\n",
+        "    output_features: dimenstions of output layer\n",
+        "    use_bias: optional but good practice\n",
+        "    '''\n",
+        "\n",
+        "    def __init__(self, in_features, out_features, use_bias=True):\n",
+        "        super(GraphConvolution, self).__init__()\n",
+        "        self.in_features = in_features\n",
+        "        self.out_features = out_features\n",
+        "        self.use_bias=use_bias\n",
+        "        self.weight = Parameter(torch.FloatTensor(in_features, out_features))\n",
+        "        if self.use_bias:\n",
+        "            self.bias = Parameter(torch.FloatTensor(out_features))\n",
+        "        else:\n",
+        "            self.register_parameter('bias', None)\n",
+        "        self.reset_parameters()\n",
+        "     #initialize parameters \n",
+        "    def reset_parameters(self):\n",
+        "        self.weight = nn.init.kaiming_uniform_(self.weight)\n",
+        "        if self.use_bias:\n",
+        "            init.zeros_(self.bias)\n",
+        "\n",
+        "# parameters:\n",
+        "# in_feature: an n-dimenstional vector\n",
+        "# adj_matrix: an adjacency matrix in tensor format\n",
+        "\n",
+        "    def forward(self, input, adj):\n",
+        "\n",
+        "       support = torch.mm(input, self.weight) \n",
+        "       output = torch.sparse.mm(adj, support)\n",
+        "\n",
+        "       return output\n",
+        "\n",
+        "\n",
+        "class GCN(nn.Module):\n",
+        "\n",
+        "#  A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function\n",
+        "#  parameters:\n",
+        "#  in_feature:n dimensional vector, which is input\n",
+        "#  out_class: n dimensional vector, final output\n",
+        "#  in this case model goes 128->32->4\n",
+        "#  since in_feature=128(known from dataset)\n",
+        "#  out_class=4(since finally 4 classes)\n",
+        "\n",
+        "  def __init__(self, in_feature,  out_class, dropout):\n",
+        "        super(GCN, self).__init__()\n",
+        "\n",
+        "        self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model\n",
+        "        self.gcn_conv_2 = GraphConvolution(32, out_class)\n",
+        "        self.dropout = dropout\n",
+        "\n",
+        "  def forward(self, x, adj):\n",
+        "        x = F.relu(self.gcn_conv_1(x, adj))\n",
+        "        x = F.dropout(x, self.dropout, training=self.training)\n",
+        "        x = self.gcn_conv_2(x, adj)\n",
+        "\n",
+        "        return F.log_softmax(x, dim=1)"
+      ],
+      "metadata": {
+        "id": "dZwEJTIVt9-C"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def accuracy(output, labels):\n",
+        "    '''\n",
+        "    calculate accuracy\n",
+        "    parameters: \n",
+        "    output:result of running an instance of the model\n",
+        "    labels: the true value\n",
+        "    function compares ratio of two values, giving result<1, \n",
+        "    as predicted probability always less than true value\n",
+        "    '''\n",
+        "    predict = output.argmax(1)\n",
+        "    acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0])\n",
+        "    return acc_"
+      ],
+      "metadata": {
+        "id": "qsMqOqxTwjd9"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def loss(output,labels):\n",
+        "\n",
+        "    prab = output.gather(1, labels.view(-1,1))\n",
+        "    loss = -torch.mean(prab)\n",
+        "    return loss"
+      ],
+      "metadata": {
+        "id": "WUessmBVwpQb"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def train_model(n_epochs):\n",
+        "  '''\n",
+        "  parameter: number of epochs\n",
+        "  trains model over the range of the epoch and at each train, \n",
+        "  calculates accuracy and losses\n",
+        "  '''\n",
+        "  train_losses=[]\n",
+        "  validation_losses=[]\n",
+        "  train_accuracies=[]\n",
+        "  validation_accuracies=[]\n",
+        "  for epoch in range(n_epochs):\n",
+        "    model.train()\n",
+        "    optimizer.zero_grad()\n",
+        "    output=model(features,adj)\n",
+        "    train_loss=loss(output[train_set],labels[train_set])\n",
+        "    train_losses.append(train_loss.item())\n",
+        "\n",
+        "    train_accuracy=accuracy(output[train_set],labels[train_set])\n",
+        "    train_accuracies.append(train_accuracy.item())\n",
+        "    train_loss.backward()\n",
+        "    optimizer.step()\n",
+        "    output=model(features,adj)\n",
+        "    validation_loss=loss(output[val_set],labels[val_set])\n",
+        "    validation_losses.append(validation_loss.item())\n",
+        "    validation_accuracy=accuracy(output[val_set],labels[val_set])\n",
+        "    validation_accuracies.append(validation_accuracy.item())\n",
+        "    print('Epoch: {:04d}'.format(epoch + 1),\n",
+        "              'Train loss: {:.4f}'.format(train_loss.item()),\n",
+        "              'Train accuracy: {:.4f}'.format(train_accuracy.item()),\n",
+        "              'Validation loss: {:.4f}'.format(validation_loss.item()),\n",
+        "              'Validation accuracy: {:.4f}'.format(validation_accuracy.item()))\n",
+        "    \n",
+        "    np.save('train_losses', train_losses)\n",
+        "    np.save('train_accuracies', train_accuracies)\n",
+        "    np.save('validation_losses', validation_losses)\n",
+        "    np.save('validation_accuracies', validation_accuracies)\n"
+      ],
+      "metadata": {
+        "id": "1tJrfwlHw2bC"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def test_model():\n",
+        "  output=model(features,adj)\n",
+        "  test_loss=loss(output[test_set],labels[test_set])\n",
+        "  test_accuracy=accuracy(output[test_set],labels[test_set])\n",
+        "  print('Test set results:',\n",
+        "        'Test loss: {:.4f}'.format(test_loss.item()),\n",
+        "        'Test accuracy: {:.4f}'.format(test_accuracy.item()))"
+      ],
+      "metadata": {
+        "id": "dnk6qMppxA1h"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "    adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels\n",
+        "    features.shape[0]\n",
+        "    num_nodes=features.shape[0]\n",
+        "    #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train<n_test)\n",
+        "    train_set = torch.LongTensor(range(int(num_nodes*0.2)))\n",
+        "    val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4)))\n",
+        "    test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes))\n",
+        "\n",
+        "    model = GCN(in_feature=features.shape[1],\n",
+        "                out_class=len(np.unique(labels)), dropout=0.5)\n",
+        "\n",
+        "    optimizer = optim.Adam([model.gcn_conv_1.weight,model.gcn_conv_2.weight], lr=0.01)\n",
+        "    train_model(200)\n",
+        "    test_model()\n",
+        "    train_accuracies = np.load('train_accuracies.npy')\n",
+        "    train_losses = np.load('train_losses.npy')\n",
+        "    validation_accuracies = np.load('validation_accuracies.npy')\n",
+        "    validation_losses = np.load('validation_losses.npy')\n",
+        "    plt.plot(range(200),train_accuracies,'b')\n",
+        "    plt.plot(range(200),validation_accuracies,'r')\n",
+        "    plt.legend(['train_accuracy', 'validation_accuracy'])\n",
+        "    plt.show()\n",
+        "    plt.plot(range(200),train_losses,'b')\n",
+        "    plt.plot(range(200),validation_losses,'r')\n",
+        "    plt.legend(['train_loss', 'validation_loss'])\n",
+        "    plt.show()\n",
+        "    outputs = model.gcn_conv_1(features, adj).detach().numpy()\n",
+        "    plot_tsne(labels, outputs )\n",
+        "   "
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "R-FCTjjoxbqp",
+        "outputId": "9f5610b8-abce-4aeb-bdb6-5d4025e7c826"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch: 0001 Train loss: 4.3900 Train accuracy: 0.3240 Validation loss: 3.4887 Validation accuracy: 0.3954\n",
+            "Epoch: 0002 Train loss: 3.5393 Train accuracy: 0.3576 Validation loss: 2.6608 Validation accuracy: 0.4551\n",
+            "Epoch: 0003 Train loss: 2.8554 Train accuracy: 0.4306 Validation loss: 2.1850 Validation accuracy: 0.5011\n",
+            "Epoch: 0004 Train loss: 2.2749 Train accuracy: 0.5056 Validation loss: 1.8538 Validation accuracy: 0.5565\n",
+            "Epoch: 0005 Train loss: 1.9135 Train accuracy: 0.5525 Validation loss: 1.5360 Validation accuracy: 0.6153\n",
+            "Epoch: 0006 Train loss: 1.6013 Train accuracy: 0.6061 Validation loss: 1.3416 Validation accuracy: 0.6513\n",
+            "Epoch: 0007 Train loss: 1.4299 Train accuracy: 0.6413 Validation loss: 1.2913 Validation accuracy: 0.6609\n",
+            "Epoch: 0008 Train loss: 1.1754 Train accuracy: 0.6905 Validation loss: 1.0890 Validation accuracy: 0.7132\n",
+            "Epoch: 0009 Train loss: 1.1588 Train accuracy: 0.6994 Validation loss: 0.9336 Validation accuracy: 0.7425\n",
+            "Epoch: 0010 Train loss: 0.9862 Train accuracy: 0.7241 Validation loss: 0.8839 Validation accuracy: 0.7617\n",
+            "Epoch: 0011 Train loss: 0.9158 Train accuracy: 0.7441 Validation loss: 0.8608 Validation accuracy: 0.7679\n",
+            "Epoch: 0012 Train loss: 0.8643 Train accuracy: 0.7583 Validation loss: 0.8197 Validation accuracy: 0.7690\n",
+            "Epoch: 0013 Train loss: 0.8375 Train accuracy: 0.7657 Validation loss: 0.7565 Validation accuracy: 0.7928\n",
+            "Epoch: 0014 Train loss: 0.7775 Train accuracy: 0.7850 Validation loss: 0.7599 Validation accuracy: 0.7944\n",
+            "Epoch: 0015 Train loss: 0.7378 Train accuracy: 0.7944 Validation loss: 0.7366 Validation accuracy: 0.8069\n",
+            "Epoch: 0016 Train loss: 0.6868 Train accuracy: 0.8069 Validation loss: 0.6670 Validation accuracy: 0.8140\n",
+            "Epoch: 0017 Train loss: 0.6686 Train accuracy: 0.8133 Validation loss: 0.6862 Validation accuracy: 0.8178\n",
+            "Epoch: 0018 Train loss: 0.6493 Train accuracy: 0.8160 Validation loss: 0.6588 Validation accuracy: 0.8227\n",
+            "Epoch: 0019 Train loss: 0.6240 Train accuracy: 0.8200 Validation loss: 0.6322 Validation accuracy: 0.8231\n",
+            "Epoch: 0020 Train loss: 0.5855 Train accuracy: 0.8342 Validation loss: 0.6391 Validation accuracy: 0.8267\n",
+            "Epoch: 0021 Train loss: 0.6050 Train accuracy: 0.8264 Validation loss: 0.6529 Validation accuracy: 0.8233\n",
+            "Epoch: 0022 Train loss: 0.5898 Train accuracy: 0.8336 Validation loss: 0.6133 Validation accuracy: 0.8351\n",
+            "Epoch: 0023 Train loss: 0.5672 Train accuracy: 0.8385 Validation loss: 0.5549 Validation accuracy: 0.8427\n",
+            "Epoch: 0024 Train loss: 0.5425 Train accuracy: 0.8456 Validation loss: 0.5471 Validation accuracy: 0.8456\n",
+            "Epoch: 0025 Train loss: 0.5270 Train accuracy: 0.8391 Validation loss: 0.6032 Validation accuracy: 0.8402\n",
+            "Epoch: 0026 Train loss: 0.5505 Train accuracy: 0.8418 Validation loss: 0.5323 Validation accuracy: 0.8520\n",
+            "Epoch: 0027 Train loss: 0.5287 Train accuracy: 0.8436 Validation loss: 0.5333 Validation accuracy: 0.8489\n",
+            "Epoch: 0028 Train loss: 0.4763 Train accuracy: 0.8525 Validation loss: 0.5155 Validation accuracy: 0.8571\n",
+            "Epoch: 0029 Train loss: 0.4986 Train accuracy: 0.8556 Validation loss: 0.5572 Validation accuracy: 0.8507\n",
+            "Epoch: 0030 Train loss: 0.5033 Train accuracy: 0.8543 Validation loss: 0.5330 Validation accuracy: 0.8567\n",
+            "Epoch: 0031 Train loss: 0.4924 Train accuracy: 0.8565 Validation loss: 0.5375 Validation accuracy: 0.8538\n",
+            "Epoch: 0032 Train loss: 0.4729 Train accuracy: 0.8545 Validation loss: 0.4987 Validation accuracy: 0.8594\n",
+            "Epoch: 0033 Train loss: 0.4674 Train accuracy: 0.8587 Validation loss: 0.5351 Validation accuracy: 0.8534\n",
+            "Epoch: 0034 Train loss: 0.4613 Train accuracy: 0.8707 Validation loss: 0.4877 Validation accuracy: 0.8703\n",
+            "Epoch: 0035 Train loss: 0.4648 Train accuracy: 0.8643 Validation loss: 0.5190 Validation accuracy: 0.8567\n",
+            "Epoch: 0036 Train loss: 0.4521 Train accuracy: 0.8643 Validation loss: 0.4910 Validation accuracy: 0.8587\n",
+            "Epoch: 0037 Train loss: 0.4204 Train accuracy: 0.8752 Validation loss: 0.4906 Validation accuracy: 0.8649\n",
+            "Epoch: 0038 Train loss: 0.4273 Train accuracy: 0.8694 Validation loss: 0.4968 Validation accuracy: 0.8649\n",
+            "Epoch: 0039 Train loss: 0.4203 Train accuracy: 0.8787 Validation loss: 0.4721 Validation accuracy: 0.8696\n",
+            "Epoch: 0040 Train loss: 0.4012 Train accuracy: 0.8798 Validation loss: 0.4694 Validation accuracy: 0.8705\n",
+            "Epoch: 0041 Train loss: 0.3982 Train accuracy: 0.8745 Validation loss: 0.4833 Validation accuracy: 0.8678\n",
+            "Epoch: 0042 Train loss: 0.3973 Train accuracy: 0.8816 Validation loss: 0.4795 Validation accuracy: 0.8729\n",
+            "Epoch: 0043 Train loss: 0.4204 Train accuracy: 0.8796 Validation loss: 0.4687 Validation accuracy: 0.8687\n",
+            "Epoch: 0044 Train loss: 0.4084 Train accuracy: 0.8785 Validation loss: 0.4870 Validation accuracy: 0.8689\n",
+            "Epoch: 0045 Train loss: 0.3875 Train accuracy: 0.8785 Validation loss: 0.4462 Validation accuracy: 0.8738\n",
+            "Epoch: 0046 Train loss: 0.3925 Train accuracy: 0.8858 Validation loss: 0.4632 Validation accuracy: 0.8712\n",
+            "Epoch: 0047 Train loss: 0.3917 Train accuracy: 0.8805 Validation loss: 0.4578 Validation accuracy: 0.8725\n",
+            "Epoch: 0048 Train loss: 0.3831 Train accuracy: 0.8787 Validation loss: 0.4646 Validation accuracy: 0.8692\n",
+            "Epoch: 0049 Train loss: 0.3836 Train accuracy: 0.8805 Validation loss: 0.4597 Validation accuracy: 0.8761\n",
+            "Epoch: 0050 Train loss: 0.3688 Train accuracy: 0.8861 Validation loss: 0.4474 Validation accuracy: 0.8774\n",
+            "Epoch: 0051 Train loss: 0.3558 Train accuracy: 0.8845 Validation loss: 0.4439 Validation accuracy: 0.8781\n",
+            "Epoch: 0052 Train loss: 0.3557 Train accuracy: 0.8916 Validation loss: 0.4506 Validation accuracy: 0.8776\n",
+            "Epoch: 0053 Train loss: 0.3592 Train accuracy: 0.8896 Validation loss: 0.4526 Validation accuracy: 0.8761\n",
+            "Epoch: 0054 Train loss: 0.3707 Train accuracy: 0.8836 Validation loss: 0.4313 Validation accuracy: 0.8810\n",
+            "Epoch: 0055 Train loss: 0.3525 Train accuracy: 0.8892 Validation loss: 0.4342 Validation accuracy: 0.8727\n",
+            "Epoch: 0056 Train loss: 0.3584 Train accuracy: 0.8883 Validation loss: 0.4513 Validation accuracy: 0.8805\n",
+            "Epoch: 0057 Train loss: 0.3423 Train accuracy: 0.8930 Validation loss: 0.4256 Validation accuracy: 0.8807\n",
+            "Epoch: 0058 Train loss: 0.3471 Train accuracy: 0.8921 Validation loss: 0.4312 Validation accuracy: 0.8807\n",
+            "Epoch: 0059 Train loss: 0.3641 Train accuracy: 0.8876 Validation loss: 0.4347 Validation accuracy: 0.8749\n",
+            "Epoch: 0060 Train loss: 0.3414 Train accuracy: 0.8963 Validation loss: 0.4126 Validation accuracy: 0.8870\n",
+            "Epoch: 0061 Train loss: 0.3350 Train accuracy: 0.8934 Validation loss: 0.4251 Validation accuracy: 0.8834\n",
+            "Epoch: 0062 Train loss: 0.3310 Train accuracy: 0.8965 Validation loss: 0.4313 Validation accuracy: 0.8792\n",
+            "Epoch: 0063 Train loss: 0.3320 Train accuracy: 0.9003 Validation loss: 0.4241 Validation accuracy: 0.8832\n",
+            "Epoch: 0064 Train loss: 0.3269 Train accuracy: 0.8979 Validation loss: 0.4126 Validation accuracy: 0.8861\n",
+            "Epoch: 0065 Train loss: 0.3269 Train accuracy: 0.8959 Validation loss: 0.4320 Validation accuracy: 0.8783\n",
+            "Epoch: 0066 Train loss: 0.3368 Train accuracy: 0.8968 Validation loss: 0.4203 Validation accuracy: 0.8763\n",
+            "Epoch: 0067 Train loss: 0.3193 Train accuracy: 0.8934 Validation loss: 0.4086 Validation accuracy: 0.8823\n",
+            "Epoch: 0068 Train loss: 0.3212 Train accuracy: 0.8976 Validation loss: 0.4023 Validation accuracy: 0.8838\n",
+            "Epoch: 0069 Train loss: 0.3257 Train accuracy: 0.8976 Validation loss: 0.4120 Validation accuracy: 0.8832\n",
+            "Epoch: 0070 Train loss: 0.3185 Train accuracy: 0.8945 Validation loss: 0.4238 Validation accuracy: 0.8832\n",
+            "Epoch: 0071 Train loss: 0.3080 Train accuracy: 0.8972 Validation loss: 0.4324 Validation accuracy: 0.8743\n",
+            "Epoch: 0072 Train loss: 0.2978 Train accuracy: 0.9032 Validation loss: 0.4261 Validation accuracy: 0.8783\n",
+            "Epoch: 0073 Train loss: 0.3136 Train accuracy: 0.8972 Validation loss: 0.4067 Validation accuracy: 0.8810\n",
+            "Epoch: 0074 Train loss: 0.2973 Train accuracy: 0.9054 Validation loss: 0.4071 Validation accuracy: 0.8836\n",
+            "Epoch: 0075 Train loss: 0.3117 Train accuracy: 0.8996 Validation loss: 0.4051 Validation accuracy: 0.8843\n",
+            "Epoch: 0076 Train loss: 0.3011 Train accuracy: 0.8994 Validation loss: 0.4112 Validation accuracy: 0.8841\n",
+            "Epoch: 0077 Train loss: 0.3050 Train accuracy: 0.9034 Validation loss: 0.3888 Validation accuracy: 0.8863\n",
+            "Epoch: 0078 Train loss: 0.2980 Train accuracy: 0.8972 Validation loss: 0.4027 Validation accuracy: 0.8883\n",
+            "Epoch: 0079 Train loss: 0.3056 Train accuracy: 0.9008 Validation loss: 0.4039 Validation accuracy: 0.8852\n",
+            "Epoch: 0080 Train loss: 0.3011 Train accuracy: 0.9070 Validation loss: 0.4078 Validation accuracy: 0.8814\n",
+            "Epoch: 0081 Train loss: 0.3007 Train accuracy: 0.9054 Validation loss: 0.4115 Validation accuracy: 0.8785\n",
+            "Epoch: 0082 Train loss: 0.2993 Train accuracy: 0.9012 Validation loss: 0.4114 Validation accuracy: 0.8830\n",
+            "Epoch: 0083 Train loss: 0.2954 Train accuracy: 0.9030 Validation loss: 0.3962 Validation accuracy: 0.8872\n",
+            "Epoch: 0084 Train loss: 0.2963 Train accuracy: 0.9050 Validation loss: 0.3913 Validation accuracy: 0.8867\n",
+            "Epoch: 0085 Train loss: 0.2991 Train accuracy: 0.9045 Validation loss: 0.4021 Validation accuracy: 0.8843\n",
+            "Epoch: 0086 Train loss: 0.2806 Train accuracy: 0.9070 Validation loss: 0.4208 Validation accuracy: 0.8798\n",
+            "Epoch: 0087 Train loss: 0.2864 Train accuracy: 0.9072 Validation loss: 0.3814 Validation accuracy: 0.8892\n",
+            "Epoch: 0088 Train loss: 0.2831 Train accuracy: 0.9043 Validation loss: 0.3878 Validation accuracy: 0.8850\n",
+            "Epoch: 0089 Train loss: 0.2794 Train accuracy: 0.9068 Validation loss: 0.3692 Validation accuracy: 0.8856\n",
+            "Epoch: 0090 Train loss: 0.2795 Train accuracy: 0.9099 Validation loss: 0.3932 Validation accuracy: 0.8830\n",
+            "Epoch: 0091 Train loss: 0.2743 Train accuracy: 0.9121 Validation loss: 0.4001 Validation accuracy: 0.8850\n",
+            "Epoch: 0092 Train loss: 0.2731 Train accuracy: 0.9094 Validation loss: 0.3834 Validation accuracy: 0.8863\n",
+            "Epoch: 0093 Train loss: 0.2735 Train accuracy: 0.9103 Validation loss: 0.3819 Validation accuracy: 0.8914\n",
+            "Epoch: 0094 Train loss: 0.2735 Train accuracy: 0.9092 Validation loss: 0.3869 Validation accuracy: 0.8876\n",
+            "Epoch: 0095 Train loss: 0.2665 Train accuracy: 0.9099 Validation loss: 0.3953 Validation accuracy: 0.8874\n",
+            "Epoch: 0096 Train loss: 0.2615 Train accuracy: 0.9117 Validation loss: 0.3739 Validation accuracy: 0.8899\n",
+            "Epoch: 0097 Train loss: 0.2735 Train accuracy: 0.9105 Validation loss: 0.3973 Validation accuracy: 0.8823\n",
+            "Epoch: 0098 Train loss: 0.2727 Train accuracy: 0.9105 Validation loss: 0.3818 Validation accuracy: 0.8899\n",
+            "Epoch: 0099 Train loss: 0.2704 Train accuracy: 0.9081 Validation loss: 0.3867 Validation accuracy: 0.8894\n",
+            "Epoch: 0100 Train loss: 0.2666 Train accuracy: 0.9134 Validation loss: 0.4053 Validation accuracy: 0.8856\n",
+            "Epoch: 0101 Train loss: 0.2713 Train accuracy: 0.9085 Validation loss: 0.3910 Validation accuracy: 0.8905\n",
+            "Epoch: 0102 Train loss: 0.2704 Train accuracy: 0.9141 Validation loss: 0.3779 Validation accuracy: 0.8916\n",
+            "Epoch: 0103 Train loss: 0.2828 Train accuracy: 0.9074 Validation loss: 0.3753 Validation accuracy: 0.8910\n",
+            "Epoch: 0104 Train loss: 0.2757 Train accuracy: 0.9068 Validation loss: 0.3863 Validation accuracy: 0.8883\n",
+            "Epoch: 0105 Train loss: 0.2667 Train accuracy: 0.9105 Validation loss: 0.3693 Validation accuracy: 0.8941\n",
+            "Epoch: 0106 Train loss: 0.2746 Train accuracy: 0.9061 Validation loss: 0.3991 Validation accuracy: 0.8872\n",
+            "Epoch: 0107 Train loss: 0.2622 Train accuracy: 0.9161 Validation loss: 0.3815 Validation accuracy: 0.8838\n",
+            "Epoch: 0108 Train loss: 0.2657 Train accuracy: 0.9126 Validation loss: 0.3894 Validation accuracy: 0.8912\n",
+            "Epoch: 0109 Train loss: 0.2543 Train accuracy: 0.9148 Validation loss: 0.3655 Validation accuracy: 0.8903\n",
+            "Epoch: 0110 Train loss: 0.2562 Train accuracy: 0.9148 Validation loss: 0.3808 Validation accuracy: 0.8890\n",
+            "Epoch: 0111 Train loss: 0.2676 Train accuracy: 0.9134 Validation loss: 0.3741 Validation accuracy: 0.8914\n",
+            "Epoch: 0112 Train loss: 0.2524 Train accuracy: 0.9152 Validation loss: 0.3852 Validation accuracy: 0.8956\n",
+            "Epoch: 0113 Train loss: 0.2643 Train accuracy: 0.9143 Validation loss: 0.3880 Validation accuracy: 0.8930\n",
+            "Epoch: 0114 Train loss: 0.2602 Train accuracy: 0.9112 Validation loss: 0.3843 Validation accuracy: 0.8923\n",
+            "Epoch: 0115 Train loss: 0.2589 Train accuracy: 0.9206 Validation loss: 0.3860 Validation accuracy: 0.8899\n",
+            "Epoch: 0116 Train loss: 0.2623 Train accuracy: 0.9152 Validation loss: 0.3620 Validation accuracy: 0.8947\n",
+            "Epoch: 0117 Train loss: 0.2520 Train accuracy: 0.9166 Validation loss: 0.3657 Validation accuracy: 0.8896\n",
+            "Epoch: 0118 Train loss: 0.2511 Train accuracy: 0.9128 Validation loss: 0.3706 Validation accuracy: 0.8943\n",
+            "Epoch: 0119 Train loss: 0.2458 Train accuracy: 0.9197 Validation loss: 0.3884 Validation accuracy: 0.8934\n",
+            "Epoch: 0120 Train loss: 0.2474 Train accuracy: 0.9137 Validation loss: 0.3792 Validation accuracy: 0.8961\n",
+            "Epoch: 0121 Train loss: 0.2493 Train accuracy: 0.9194 Validation loss: 0.3964 Validation accuracy: 0.8921\n",
+            "Epoch: 0122 Train loss: 0.2468 Train accuracy: 0.9166 Validation loss: 0.3584 Validation accuracy: 0.8972\n",
+            "Epoch: 0123 Train loss: 0.2542 Train accuracy: 0.9128 Validation loss: 0.3737 Validation accuracy: 0.8954\n",
+            "Epoch: 0124 Train loss: 0.2515 Train accuracy: 0.9199 Validation loss: 0.3854 Validation accuracy: 0.8919\n",
+            "Epoch: 0125 Train loss: 0.2568 Train accuracy: 0.9166 Validation loss: 0.3775 Validation accuracy: 0.8945\n",
+            "Epoch: 0126 Train loss: 0.2502 Train accuracy: 0.9174 Validation loss: 0.3639 Validation accuracy: 0.8941\n",
+            "Epoch: 0127 Train loss: 0.2517 Train accuracy: 0.9181 Validation loss: 0.3844 Validation accuracy: 0.8941\n",
+            "Epoch: 0128 Train loss: 0.2492 Train accuracy: 0.9186 Validation loss: 0.3765 Validation accuracy: 0.8956\n",
+            "Epoch: 0129 Train loss: 0.2531 Train accuracy: 0.9121 Validation loss: 0.3869 Validation accuracy: 0.8932\n",
+            "Epoch: 0130 Train loss: 0.2533 Train accuracy: 0.9123 Validation loss: 0.3742 Validation accuracy: 0.8887\n",
+            "Epoch: 0131 Train loss: 0.2422 Train accuracy: 0.9161 Validation loss: 0.3780 Validation accuracy: 0.8903\n",
+            "Epoch: 0132 Train loss: 0.2359 Train accuracy: 0.9230 Validation loss: 0.3769 Validation accuracy: 0.8919\n",
+            "Epoch: 0133 Train loss: 0.2451 Train accuracy: 0.9190 Validation loss: 0.3751 Validation accuracy: 0.8916\n",
+            "Epoch: 0134 Train loss: 0.2480 Train accuracy: 0.9181 Validation loss: 0.3746 Validation accuracy: 0.8959\n",
+            "Epoch: 0135 Train loss: 0.2453 Train accuracy: 0.9154 Validation loss: 0.3701 Validation accuracy: 0.8932\n",
+            "Epoch: 0136 Train loss: 0.2501 Train accuracy: 0.9148 Validation loss: 0.3707 Validation accuracy: 0.8923\n",
+            "Epoch: 0137 Train loss: 0.2447 Train accuracy: 0.9190 Validation loss: 0.3763 Validation accuracy: 0.8981\n",
+            "Epoch: 0138 Train loss: 0.2424 Train accuracy: 0.9217 Validation loss: 0.3777 Validation accuracy: 0.8952\n",
+            "Epoch: 0139 Train loss: 0.2370 Train accuracy: 0.9252 Validation loss: 0.3852 Validation accuracy: 0.8939\n",
+            "Epoch: 0140 Train loss: 0.2355 Train accuracy: 0.9226 Validation loss: 0.3915 Validation accuracy: 0.8925\n",
+            "Epoch: 0141 Train loss: 0.2444 Train accuracy: 0.9177 Validation loss: 0.3515 Validation accuracy: 0.8996\n",
+            "Epoch: 0142 Train loss: 0.2441 Train accuracy: 0.9174 Validation loss: 0.3866 Validation accuracy: 0.8927\n",
+            "Epoch: 0143 Train loss: 0.2331 Train accuracy: 0.9259 Validation loss: 0.3848 Validation accuracy: 0.8892\n",
+            "Epoch: 0144 Train loss: 0.2392 Train accuracy: 0.9197 Validation loss: 0.3639 Validation accuracy: 0.8952\n",
+            "Epoch: 0145 Train loss: 0.2399 Train accuracy: 0.9186 Validation loss: 0.3797 Validation accuracy: 0.8947\n",
+            "Epoch: 0146 Train loss: 0.2233 Train accuracy: 0.9257 Validation loss: 0.3860 Validation accuracy: 0.8947\n",
+            "Epoch: 0147 Train loss: 0.2353 Train accuracy: 0.9188 Validation loss: 0.3675 Validation accuracy: 0.8994\n",
+            "Epoch: 0148 Train loss: 0.2305 Train accuracy: 0.9223 Validation loss: 0.3645 Validation accuracy: 0.8956\n",
+            "Epoch: 0149 Train loss: 0.2265 Train accuracy: 0.9212 Validation loss: 0.3564 Validation accuracy: 0.8968\n",
+            "Epoch: 0150 Train loss: 0.2375 Train accuracy: 0.9199 Validation loss: 0.3674 Validation accuracy: 0.8972\n",
+            "Epoch: 0151 Train loss: 0.2319 Train accuracy: 0.9215 Validation loss: 0.3816 Validation accuracy: 0.8952\n",
+            "Epoch: 0152 Train loss: 0.2314 Train accuracy: 0.9203 Validation loss: 0.3726 Validation accuracy: 0.8979\n",
+            "Epoch: 0153 Train loss: 0.2260 Train accuracy: 0.9263 Validation loss: 0.3674 Validation accuracy: 0.8996\n",
+            "Epoch: 0154 Train loss: 0.2357 Train accuracy: 0.9197 Validation loss: 0.3735 Validation accuracy: 0.8965\n",
+            "Epoch: 0155 Train loss: 0.2206 Train accuracy: 0.9223 Validation loss: 0.3705 Validation accuracy: 0.8959\n",
+            "Epoch: 0156 Train loss: 0.2323 Train accuracy: 0.9179 Validation loss: 0.3876 Validation accuracy: 0.8923\n",
+            "Epoch: 0157 Train loss: 0.2290 Train accuracy: 0.9201 Validation loss: 0.3802 Validation accuracy: 0.8872\n",
+            "Epoch: 0158 Train loss: 0.2328 Train accuracy: 0.9252 Validation loss: 0.3720 Validation accuracy: 0.8923\n",
+            "Epoch: 0159 Train loss: 0.2239 Train accuracy: 0.9232 Validation loss: 0.3854 Validation accuracy: 0.8988\n",
+            "Epoch: 0160 Train loss: 0.2254 Train accuracy: 0.9277 Validation loss: 0.3594 Validation accuracy: 0.8979\n",
+            "Epoch: 0161 Train loss: 0.2278 Train accuracy: 0.9306 Validation loss: 0.3826 Validation accuracy: 0.8974\n",
+            "Epoch: 0162 Train loss: 0.2242 Train accuracy: 0.9288 Validation loss: 0.3734 Validation accuracy: 0.8976\n",
+            "Epoch: 0163 Train loss: 0.2240 Train accuracy: 0.9275 Validation loss: 0.3672 Validation accuracy: 0.8970\n",
+            "Epoch: 0164 Train loss: 0.2224 Train accuracy: 0.9248 Validation loss: 0.3748 Validation accuracy: 0.9001\n",
+            "Epoch: 0165 Train loss: 0.2253 Train accuracy: 0.9230 Validation loss: 0.3584 Validation accuracy: 0.8974\n",
+            "Epoch: 0166 Train loss: 0.2265 Train accuracy: 0.9246 Validation loss: 0.3568 Validation accuracy: 0.8952\n",
+            "Epoch: 0167 Train loss: 0.2286 Train accuracy: 0.9190 Validation loss: 0.3855 Validation accuracy: 0.8972\n",
+            "Epoch: 0168 Train loss: 0.2223 Train accuracy: 0.9255 Validation loss: 0.3659 Validation accuracy: 0.8983\n",
+            "Epoch: 0169 Train loss: 0.2231 Train accuracy: 0.9250 Validation loss: 0.3513 Validation accuracy: 0.8999\n",
+            "Epoch: 0170 Train loss: 0.2142 Train accuracy: 0.9270 Validation loss: 0.3557 Validation accuracy: 0.9043\n",
+            "Epoch: 0171 Train loss: 0.2231 Train accuracy: 0.9230 Validation loss: 0.3734 Validation accuracy: 0.8999\n",
+            "Epoch: 0172 Train loss: 0.2160 Train accuracy: 0.9268 Validation loss: 0.3735 Validation accuracy: 0.9008\n",
+            "Epoch: 0173 Train loss: 0.2217 Train accuracy: 0.9243 Validation loss: 0.3569 Validation accuracy: 0.8981\n",
+            "Epoch: 0174 Train loss: 0.2209 Train accuracy: 0.9243 Validation loss: 0.3751 Validation accuracy: 0.8972\n",
+            "Epoch: 0175 Train loss: 0.2157 Train accuracy: 0.9266 Validation loss: 0.3789 Validation accuracy: 0.8968\n",
+            "Epoch: 0176 Train loss: 0.2188 Train accuracy: 0.9277 Validation loss: 0.3782 Validation accuracy: 0.8985\n",
+            "Epoch: 0177 Train loss: 0.2162 Train accuracy: 0.9266 Validation loss: 0.3794 Validation accuracy: 0.8941\n",
+            "Epoch: 0178 Train loss: 0.2164 Train accuracy: 0.9241 Validation loss: 0.3822 Validation accuracy: 0.8985\n",
+            "Epoch: 0179 Train loss: 0.2180 Train accuracy: 0.9290 Validation loss: 0.3800 Validation accuracy: 0.8965\n",
+            "Epoch: 0180 Train loss: 0.2144 Train accuracy: 0.9281 Validation loss: 0.3875 Validation accuracy: 0.9003\n",
+            "Epoch: 0181 Train loss: 0.2072 Train accuracy: 0.9286 Validation loss: 0.3540 Validation accuracy: 0.9045\n",
+            "Epoch: 0182 Train loss: 0.2197 Train accuracy: 0.9286 Validation loss: 0.3723 Validation accuracy: 0.8952\n",
+            "Epoch: 0183 Train loss: 0.2175 Train accuracy: 0.9270 Validation loss: 0.3723 Validation accuracy: 0.8994\n",
+            "Epoch: 0184 Train loss: 0.2140 Train accuracy: 0.9261 Validation loss: 0.3686 Validation accuracy: 0.8999\n",
+            "Epoch: 0185 Train loss: 0.2124 Train accuracy: 0.9317 Validation loss: 0.3637 Validation accuracy: 0.9041\n",
+            "Epoch: 0186 Train loss: 0.2154 Train accuracy: 0.9286 Validation loss: 0.3621 Validation accuracy: 0.8981\n",
+            "Epoch: 0187 Train loss: 0.2132 Train accuracy: 0.9292 Validation loss: 0.3644 Validation accuracy: 0.9008\n",
+            "Epoch: 0188 Train loss: 0.2082 Train accuracy: 0.9286 Validation loss: 0.3781 Validation accuracy: 0.8992\n",
+            "Epoch: 0189 Train loss: 0.2212 Train accuracy: 0.9239 Validation loss: 0.3671 Validation accuracy: 0.8990\n",
+            "Epoch: 0190 Train loss: 0.2111 Train accuracy: 0.9272 Validation loss: 0.3727 Validation accuracy: 0.9036\n",
+            "Epoch: 0191 Train loss: 0.2158 Train accuracy: 0.9317 Validation loss: 0.3767 Validation accuracy: 0.8981\n",
+            "Epoch: 0192 Train loss: 0.2113 Train accuracy: 0.9272 Validation loss: 0.3783 Validation accuracy: 0.8999\n",
+            "Epoch: 0193 Train loss: 0.2044 Train accuracy: 0.9288 Validation loss: 0.3763 Validation accuracy: 0.8996\n",
+            "Epoch: 0194 Train loss: 0.2130 Train accuracy: 0.9286 Validation loss: 0.3706 Validation accuracy: 0.9014\n",
+            "Epoch: 0195 Train loss: 0.2074 Train accuracy: 0.9339 Validation loss: 0.3619 Validation accuracy: 0.8988\n",
+            "Epoch: 0196 Train loss: 0.2030 Train accuracy: 0.9312 Validation loss: 0.3659 Validation accuracy: 0.9003\n",
+            "Epoch: 0197 Train loss: 0.2146 Train accuracy: 0.9277 Validation loss: 0.3672 Validation accuracy: 0.8959\n",
+            "Epoch: 0198 Train loss: 0.2108 Train accuracy: 0.9268 Validation loss: 0.3587 Validation accuracy: 0.8994\n",
+            "Epoch: 0199 Train loss: 0.2156 Train accuracy: 0.9286 Validation loss: 0.3550 Validation accuracy: 0.9036\n",
+            "Epoch: 0200 Train loss: 0.2084 Train accuracy: 0.9295 Validation loss: 0.3740 Validation accuracy: 0.8988\n",
+            "Test set results: Test loss: 0.3741 Test accuracy: 0.8958\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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DD2u3Xmys/p/7jt+AHveJjNRPqNdck9tHnZmpLfmnBn+qK150UW4/yIkT2rI7/XRt0eSVk6NhsHatfkTNzCw8DFJTtRX78svaLzpmjAZiRISGY7t2GpQ+Ho+2kPMG5vXX65vAK6/kLuvaVQPy1lu1X2jKFF0G+hyvv57bVdCihb4R1Kmjfa9t2ugIBN/56o0a5Q/VTZu08//ttzUA27TRM4rattXrmZna7fHsszpjU2FdMh6PHvX13ff110W/eZVVRoYegHvzzbIdsS7Kvn36xlLSO3lZbNminy4uuKAafGwLbhUO9Mq4WKCX3YIF+ok1bw74GomgQ/h83SkzZsjJ41fNm+soEBHNlVvGHJY+rJJ//MO7kcREbUkuWaJh6PFoX2mjRhp6rVppC9l3ymP9+tqa9YXHvHmnni4J2k/pc+iQHjzzjUf0XRo21NMxt23TGjp21Heav/1N30B83RS3366t4kce0TBt1Uov/fqJzJqlfbAXX6xjF0FbgkOGaFeAb6f4PkpkZ2tL1Rfw8+fr/QcP6ptHcZOMiGiLc8gQfey//13+P2i4OXy48ElWTJlYoAeh3bu1Ydm6tZ7OnZiY291av74eOL/3Xu36bNMmt+UdHZ3bbTJ8SJacWLJcW27vvntyMHjOoMG6wqJFuR//817uv19bzA8+mDuWEDQs33xTl9WooV0dX32lfbg9e2p3xZw5GqLDhmkwb9miszX5tnHDDTryISVFPxYUHGaWkaEHsnwjGUA/auRt7a9fn3vfokX5H79li74ZJCeXvJM//FDHQJZHTo7/z5E3phQs0IPEwYMi772nWTFunOanL9saNNDjXx9/rMNYzz5bgz3mNM/J8yIWLtTjV4POPSI/xl0rHl9rNe8lKkqb9M2ba3dDgwbarbJwobbQr702d11fv+3WrRreX36ZW+zkybnrtWhx6vCUlBR9Lt+kJH375u9KKY7Ho10XnTrpm0NhkpK0C6Sy56M1ppqxQK+mCmbRpEn6F7n1Vm0gT5qky3XoqkdWj/qTnjzhGwK3cKF2WYwdq+8EDz2kHeW+8cJ3360HEn/4QQ+uvfOOyOjRGoS+M35q1cp/wC0rS8frDh9ecvFffqnjl4vqovANXr/zTjsIZoyfFBfoNjlXgBw97GHl6VeTdsU4Rr07koMHoXVrbfJmZUFcrU2svPAhojatRTp35lCbWBq+OV0ffO21+n1YU6fq5CS7d+tkJaAzTWVn6yxPL71UdAEiOotVjx5w662n3peTozNaVUR2NqxbB3362EQjxvhJhSfnqgzhHugL/7SWoQ/3ZgX9yPrPCr7/Xmfs+3rONj7+2zYmrxlFvVrZ+uWKixfr9Htjx0L37jqlngjExen0gkePwvbt+mWG99yjs0ktWQL16gX61zTG+FlxgW5fEh0gO15bCMB5rKTz5clsdR34e6up9L3+cfoCtGgBSz6Hs86CzEyd83XECG2B33qrBnqTJrlzobZpoz9nzw7I72OMCTwL9Kr2zTdsiYqlc9IiDjZsTcODKTx/3my2N+vFbR89rvO6Xn01DB6cOzdr48Y68bVPs2YBKd0YU71ZoFeGI0fgmWfw3Dyeo01bUveDf/J99ACOfracns/dzKEmg+nPlxy96bew9nOGbfgzHD6s/dkzZ+rk1sYYU0YW6JXhz3+GqVP59k8L+OToJTwiT9KOKCLIYQsd6bl/KQCR110G53XTb1H41a+0b9zC3BhTThbo/vLjjzBsGFx/PZ7npvEDZ9PzyEp6spKl0aOIjnG0OL6N/4v9lEePPky7tR/C+efrV9/ceGOgqzfGhAAb5eIvkybBdB1WeLxGbfo2+J4VN71IncQ18PHH+r1pPiJw/Hjud6sZY0wpFTfKpUZVFxMycnLY8fBLbGp4Lkn//Eq/PXjMGL79v0+4xvM+N/2+PXVe+Av873/5wxx0TLaFuTHGz6zLpTxE8Fw5nDYLF3CMWtT45WCQE0zZ/iuenjOIFq1h7p2BLtIYE26shV4eM2dSY+ECHuRppt+8hiypxybO5tVNA7n/fli+3I5tGmOqnrXQS8HjgdRUPdeH9HSOTPwtq+nPvvG/5c9v1GBmp/Xs3Qubn3Q0bhzoao0x4coCvRReew3uvBO++goaTnuJs3/O4F+DX+HlV/UDzs2/bxvgCo0xxrpcSmXWLJ2rasIdOdT/12t81eBSnlnYjcjIQFdmjDG5rIVegt27oeHnn7Cw6Sw+XX8urdlB9mPP2SAVY0y1Y4FelD17YOZMDr6/gY/5J+yDy5jN4YYxtL97eKCrM8aYU1igF2bPHhg4EH76iVYRDZnVZCI3JtwLU6ZQ9+KLsb4WY0x1ZIFe0JYtcNVVyK5dPHzBcp764gKmPw50BN59N9DVGWNMkSzQ81qxAq64gmPHYUztf/PRVxcwfTr85jeBLswYY0pmge5z6BCMGcPPtZvQff9nxHTvyFfT4NxzA12YMcaUTqmGLTrnhjrnfnDObXbOTS7k/rbOuSXOubXOuW+dc8P8X2ol+93vkO3bue7w2zTq2ZGlSy3MjTHBpcRAd85FAC8ClwNdgTHOua4FVnsEmCMivYDRQDHfTlwNJSXByy+zOPZulh4/n9mzbe4sY0zwKU0LvS+wWUSSROQ4MBsYUWAdARp6rzcCdvmvxCrw5puIc/xm6/1cf71+jacxxgSb0gR6K2BHntsp3mV5TQXGOudSgAVA8BxG9Hhg5kx2d7uETVltGD8+0AUZY0z5+OvU/zHAmyLSGhgGvO2cO2Xbzrk7nHMJzrmEtLQ0Pz11BS1cCNu384ZnPB066PBzY4wJRqUJ9J1Amzy3W3uX5XUrMAdARFYAUUDzghsSkRkiEi8i8dHR0eWr2F+OHdOvjLviCrKbncYfN17N+PFQw2a3McYEqdLE1yqgs3Oug3OuFnrQc36BdbYDQwCcc13QQK8mTfAirF4Nn34KEydy93kJ1KwfxZ32pRTGmCBWYqCLSDYwEVgEbEJHs2x0zj3hnPNNanI/cLtzbj3wLjBOAvVlpaW1ejUAm0ZM5pV/t2HSJGjWLMA1GWNMBZTqxCIRWYAe7My77NE81xOB/v4trZKtXg0xMUyf25J69eC++wJdkDHGVEz49hivWQO9e7NkqWPwYGjaNNAFGWNMxYRnoB85AomJZJ3dhx9+sJEtxpjQEJ6Bvn495OSwPqIPAIMGBbgeY4zxg/AM9DVrAFiY1od69aBXrwDXY4wxfhBesy3u2we33w6ffQbR0cxf05rzz7fvqzDGhIbwaaF7PHDzzfDxxzB6NLun/YvvNjjrPzfGhIzwaaFPnw6ffAIvvIDcNZHxl0O9eprxxhgTCsIn0F9/HS64AO66i3fegUWL4IUXoE2bkh9qjDHBIDy6XNLTYcMGuPxycI5p06BHD5gwIdCFGWOM/4RHoC9frj8HDSI5GRISYOxYiIgIbFnGGONP4RHoS5dCnTpwzjnMnauLrrsuoBUZY4zfhUegL1sG550HtWrx3nsQHw8dOgS6KGOM8a/QD/T9++Hbb2HQIHbsgFWrrHVujAlNoR/oq1aBCPTvz7JluujyywNbkjHGVIbQD/RNm/Rnt2588QU0agSxsYEtyRhjKkN4BHqTJnDaaSxfDuefb6NbjDGhKTwCvUsXMvY5EhP13CJjjAlFYRPoX32lNwcMCGw5xhhTWUI70NPTIS0NunZl+XKoVQvOOSfQRRljTOUI7UD3HRDt0oWVK6F3b4iKCmxJxhhTWcIi0D1ndWHdOg10Y4wJVaEf6HXrkpTdlkOH7JuJjDGhLbQD/fvv4ayzWLtef82ePQNcjzHGVKLQDvSUFGjblrVrdex5t26BLsgYYypPqQLdOTfUOfeDc26zc25yIfc/55xb57386JzL9H+p5ZCaCjExrFsHXbvaAVFjTGgr8RuLnHMRwIvAJUAKsMo5N19EEn3riMi9edb/DRD43ursbB22GBPD2vlw6aWBLsgYYypXaVrofYHNIpIkIseB2cCIYtYfA7zrj+IqJD0dRDhQ93T27LEDosaY0FeaQG8F7MhzO8W77BTOuXZAB2BxEfff4ZxLcM4lpKWllbXWsklNBWBpYgxgp/wbY0Kfvw+KjgbmikhOYXeKyAwRiReR+OjoaD8/dQHeQH/1wxiGDtUvtTDGmFBWYh86sBNok+d2a++ywowG7qpoUX7hDfSfDsUw5/8CXIsxxlSB0rTQVwGdnXMdnHO10NCeX3Al59zZQBNghX9LLCdvoJ95QYz1nxtjwkKJgS4i2cBEYBGwCZgjIhudc08454bnWXU0MFtEpHJKLaM9ezhKFKd1ahDoSowxpkqUpssFEVkALCiw7NECt6f6r6yKkz2p7CGGFi1doEsxxpgqEbJnip7YmUoqMZx+eqArMcaYqhGygZ6zSwO9RYtAV2KMMVUjZAO9Rpq10I0x4SU0Az0nh8jMNGuhG2PCSmgGekYGNcRjLXRjTFgJzUD3jkE/UDuG+vUDXIsxxlSR0Az0LVsAyI625rkxJnyEZqBPm0Z6rRbsbdc30JUYY0yVKdWJRUFl2TJYtoxXT5tGs1b2jRbGmPARei30v/wFYmKYfuQOOyBqjAkroRfoCQlkD72SvYfq2JBFY0xYCa1A//lnSE3lYPOOANZCN8aEldAK9K1bAUir3wHAWujGmLASWoGelATAtpraQm9V6BflGWNMaAqtQE9OBmDjz9pC79gxkMUYY0zVCq1AT0qCevXYkBpNixZQt26gCzLGmKoTWoGenAwdOpCU7Kx1bowJO6EV6ElJ0LEjW7ZYd4sxJvyETqCLQHIy2e06kpICnToFuiBjjKlaoRPoaWnw889kNOiAiLXQjTHhJ3QC3TvCZUekJrkFujEm3IROoHtPKvrpRHvAAt0YE35CJ9D37gUgMSOGOnXstH9jTPgpVaA754Y6535wzm12zk0uYp3rnXOJzrmNzrl3/FtmKezdC86xcXdTOnYE56q8AmOMCagS50N3zkUALwKXACnAKufcfBFJzLNOZ+AhoL+I7HfOnVZZBRcpLQ2aNydpWwQdOlT5sxtjTMCVpoXeF9gsIkkichyYDYwosM7twIsish9ARPb6t8xSSEuD6Gj27LFJuYwx4ak0gd4K2JHndop3WV5nAmc65750zq10zg0tbEPOuTuccwnOuYS0tLTyVVyUtDQkOpq0NDit6j8fGGNMwPnroGhNoDMwGBgD/N0517jgSiIyQ0TiRSQ+OjraT0/tlZbG8YbReDwQE+PfTRtjTDAoTaDvBNrkud3auyyvFGC+iJwQkWTgRzTgq05aGj/X1TcJa6EbY8JRaQJ9FdDZOdfBOVcLGA3ML7DOh2jrHOdcc7QLJsmPdRYvOxsyMjhQSwPdWujGmHBUYqCLSDYwEVgEbALmiMhG59wTzrnh3tUWARnOuURgCfCAiGRUVtGnyNCnyqipTXNroRtjwlGJwxYBRGQBsKDAskfzXBfgPu+l6nkPsO71WAvdGBO+QuNMUW+g7zoRTc2a0KRJgOsxxpgACKlA334kmuhoqBEav5UxxpRJaESfN9CTs6Ktu8UYE7ZCKtC3ZDazA6LGmLAVGoG+dy80a8auvTWthW6MCVuhEeje0/737rUhi8aY8BUygZ7TNJojR2zIojEmfIVMoB9tYGPQjTHhLWQCPSvK5nExxoS34A/0nBzIyCAz0gLdGBPegj/Q9+0Dj4c075ck2ZdbGGPCVfAHuncM+u7saGrUsBa6MSZ8hUygbz8SzemnQ0REgOsxxpgACZlATzoUbd0txpiwFjKB/uP+aFq2DHAtxhgTQCET6Il7m1ugG2PCWvAH+t69SJMm7E6PtC4XY0xYC/5AT0sju4mOQbcWujEmnIVEoB+tb4FujDEhEeiHoizQjTEmJAJ9f00NdOtDN8aEs+AOdI8H0tNJc6cREQHR0YEuyBhjAie4A33/fsjJYdcJO0vUGGOCO9DznPZv3S3GmHBXqkB3zg11zv3gnNvsnJtcyP3jnHNpzrl13stt/i+1EHlO+7cDosaYcFezpBWccxHAi8AlQAqwyjk3X0QSC6z6LxGZWAk1Fi1PoHewWRaNMWGuNC30vsBmEUkSkePAbGBE5ZZVSnv3ApCcFU2TJgGuxRhjAqw0gd4K2JHndo6bw+kAABJVSURBVIp3WUHXOue+dc7Ndc61KWxDzrk7nHMJzrmENG/rukJ270Zq1GDbsRgaN6745owxJpj566Dox0B7EekBfAbMLGwlEZkhIvEiEh/tjzGGO3fiiY4hh5oW6MaYsFeaQN8J5G1xt/YuO0lEMkTkmPfma0Af/5RXgl27ON5cj4Zal4sxJtyVJtBXAZ2dcx2cc7WA0cD8vCs45/IOGhwObPJficXYtYsjTTTQrYVujAl3JY5yEZFs59xEYBEQAbwhIhudc08ACSIyH7jbOTccyAb2AeMqseZcu3aRFd8PsEA3xpgSAx1ARBYACwosezTP9YeAh/xbWgmOHYO0NDLrWZeLMcZAMJ8pumcPABm1dcCNtdCNMeEueAN91y4AUiOsD90YYyAEAn23a0lUFERFBbgeY4wJsOAN9J06cnJHTktrnRtjDMEc6Lt2QWQkKUeaWaAbYwzBHugtW7L/QA0b4WKMMYRAoGdm2gFRY4yBYA70nTu1hb7fAt0YYyCYA333bmjRgsxMO6nIGGMgWAP9xAk4cABpHm1dLsYY4xWcgZ6eDsCxhtHk5FigG2MMBHmgZ0U1B6zLxRhjIMgD/WAtDXRroRtjTJAH+r4I/dYjC3RjjAnyQM9w1kI3xhif4Ax07xdM781pBlgfujHGQLAGeno6NGrEvkORgLXQjTEGgjnQo3UMOkCjRoEtxxhjqoPgDfTmzcnMhAYNoGapvkjPGGNCW3AGeloaNG9u87gYY0wewRnoebpcLNCNMUYFX2eFSG6XS5KNcDHB7cSJE6SkpHD06NFAl2KqmaioKFq3bk1kZGSpHxN8gX74MBw9erLLpX37QBdkTPmlpKTQoEED2rdvj3Mu0OWYakJEyMjIICUlhQ4dOpT6caXqcnHODXXO/eCc2+ycm1zMetc658Q5F1/qCsrKe1KR76CodbmYYHb06FGaNWtmYW7ycc7RrFmzMn9yKzHQnXMRwIvA5UBXYIxzrmsh6zUAJgFfl6mCsvKeVOTrQ7cuFxPsLMxNYcrzuihNC70vsFlEkkTkODAbGFHIek8CTwOV2xnobaHnNGnOwYPWQjfGGJ/SBHorYEee2yneZSc553oDbUTk38VtyDl3h3MuwTmXkOZraZeVN9AP1bZ5XIwxJq8KD1t0ztUAngXuL2ldEZkhIvEiEh8dHV2+J/QGemakPt66XIypmMzMTF566aUyP27YsGFk+k7XNtVCaUa57ATa5Lnd2rvMpwHQDVjq7fM5HZjvnBsuIgn+KvSkPn3gwQfJyNbz/a2FbkLFPffAunX+3WbPnjBtWvHr+AL9zjvvzLc8OzubmsWchr1gwQJ/lFhpSqo/FJWmhb4K6Oyc6+CcqwWMBub77hSRAyLSXETai0h7YCVQOWEOMGAAPP00mQf0gIEFujEVM3nyZLZs2ULPnj0555xzGDBgAMOHD6drVx37cPXVV9OnTx9iY2OZMWPGyce1b9+e9PR0tm7dSpcuXbj99tuJjY3l0ksv5ciRI0U+39///nfOOecc4uLiuPbaazl8+DAAqampXHPNNcTFxREXF8dXX30FwFtvvUWPHj2Ii4vjpptuAmDcuHHMnTv35Dbr168PwNKlS0td/8KFC+nduzdxcXEMGTIEj8dD586d8XUHezwezjjjDMrdPRwIIlLiBRgG/AhsAaZ4lz2BBnfBdZcC8SVts0+fPlIRc+eKgMj69RXajDEBlZiYGOgSJDk5WWJjY0VEZMmSJVK3bl1JSko6eX9GRoaIiBw+fFhiY2MlPT1dRETatWsnaWlpkpycLBEREbJ27VoRERk1apS8/fbbRT6f7/EiIlOmTJHp06eLiMj1118vzz33nIiIZGdnS2ZmpmzYsEE6d+4saWlp+Wq5+eab5b333ju5nXr16pWp/r1790rr1q1PrudbZ+rUqSdrWLRokYwcObK0u7FSFPb6ABKkiFwt1ecREVkALCiw7NEi1h1crneWMtq/X39aC90Y/+rbt2++k1mmT5/OvHnzANixYwc//fQTzZo1y/eYDh060LNnTwD69OnD1q1bi9z+hg0beOSRR8jMzCQrK4vLLrsMgMWLF/PWW28BEBERQaNGjXjrrbcYNWoUzZvrIIimTZv6pf60tDQGDhx4cj3fdm+55RZGjBjBPffcwxtvvMH48eNLfL7qJGg7mHzHYizQjfGvevXqnby+dOlS/vvf/7JixQrq1q3L4MGDCz3ZpXbt2ievR0REFNvlMm7cOD788EPi4uJ48803Wbp0aZlrrFmzJh6PB9CukePHj1eofp82bdoQExPD4sWL+eabb5g1a1aZawuk4JycCw30GjV0+lxjTPk1aNCAQ4cOFXrfgQMHaNKkCXXr1uX7779n5cqVFX6+Q4cO0aJFC06cOJEvMIcMGcLLL78MQE5ODgcOHOCiiy7ivffeIyMjA4B9+/YB2n+/evVqAObPn8+JEyfKVH+/fv34/PPPSU5OzrddgNtuu42xY8cyatQoIiIiKvz7VqWgDXTf1Ll2kp0xFdOsWTP69+9Pt27deOCBB/LdN3ToULKzs+nSpQuTJ0+mX79+FX6+J598knPPPZf+/ftz9tlnn1z+/PPPs2TJErp3706fPn1ITEwkNjaWKVOmMGjQIOLi4rjvvvsAuP3221m2bBlxcXGsWLEiX6u8NPVHR0czY8YMRo4cSVxcHDfccMPJxwwfPpysrKyg624BcNrHXvXi4+MlIaH8A2FuvBFWroQtW/xYlDFVbNOmTXTp0iXQZZg8EhISuPfee1m+fHmgSyn09eGcWy0ihc6XFdR96HZSkTHGn5566ilefvnloOs79wn6LhdjTPV011130bNnz3yXf/zjH4Euq1iTJ09m27ZtXHDBBYEupVyCuoXesmWgqzDGFOXFF18MdAlhJ2hb6NblYowx+QVloB8/DqmpEBMT6EqMMab6CMpA37YNPB7o3DnQlRhjTPURlIG+ebP+POOMwNZhjDHViQW6MaZMfDMb7tq1i+uuu67QdQYPHkxJ55lMmzbt5EyLYPOr+0NQjnLZvBnq14fTTgt0Jcb4UaAmRC+nli1b5pvCtqymTZvG2LFjqVu3LlD951cvSnWadz1oW+hnnGGn/RvjD5MnT843xHDq1Kn84Q9/YMiQIfTu3Zvu3bvz0UcfnfK4rVu30q1bNwCOHDnC6NGj6dKlC9dcc02+ybkmTJhAfHw8sbGxPPbYY4DOgLhr1y4uvPBCLrzwQiB3fnWAZ599lm7dutGtWzemed+QbN71UihqXt3KvlRkPvQzzxS57rpyP9yYaqM6zIe+Zs0aGThw4MnbXbp0ke3bt8uBAwdERCQtLU06deokHo9HRHLnHs87j/pf//pXGT9+vIiIrF+/XiIiImTVqlUikjvXeHZ2tgwaNEjWe7/EwDefuo/vdkJCgnTr1k2ysrLk0KFD0rVrV1mzZk1Yzrte1vnQg66Fnp0NycnWf26Mv/Tq1Yu9e/eya9cu1q9fT5MmTTj99NN5+OGH6dGjBxdffDE7d+4kNTW1yG18/vnnjB07FoAePXrQo0ePk/fNmTOH3r1706tXLzZu3EhiYmKx9XzxxRdcc8011KtXj/r16zNy5MiT86qUdd71AQMG0L17d2bNmsXGjRsBnXd9woQJQO6864sXL/bLvOtxcXH069fv5LzrK1euLHLedd/c7/6cd716dPyUwY4dcOKEBbox/jRq1Cjmzp3Lnj17uOGGG5g1axZpaWmsXr2ayMhI2rdvX+w84kVJTk7mmWeeYdWqVTRp0oRx48aVazs+Nu968YKuhW4jXIzxvxtuuIHZs2czd+5cRo0axYEDBzjttNOIjIxkyZIlbNu2rdjHDxw4kHfeeQfQlvG3334LwMGDB6lXrx6NGjUiNTWVTz/99ORjipqHfcCAAXz44YccPnyYn3/+mXnz5jFgwIAy/07hOO+6BboxhtjYWA4dOkSrVq1o0aIFN954IwkJCXTv3p233nor37zlhZkwYQJZWVl06dKFRx99lD59+gAQFxdHr169OPvss/nFL35B//79Tz7mjjvuYOjQoScPivr07t2bcePG0bdvX84991xuu+02evXqVebfKRznXQ+6+dA/+gjefBPef1+/sciYYGbzoYev0sy7HvLzoY8YoRdjjAlWlTXvurVxjTFBzeZdzxV0LXRjQo2I4OwsuXIL1XnXy9Mdbi10YwIoKiqKjIyMcv3zmtAlImRkZBAVFVWmx5Wqhe6cGwo8D0QAr4nIUwXu/zVwF5ADZAF3iEjxZw8YY2jdujUpKSn+Oe3bhJSoqChat25dpseUGOjOuQjgReASIAVY5ZybXyCw3xGRV7zrDweeBYaWqRJjwlBkZGS+sw2NqYjSdLn0BTaLSJKIHAdmA/nGmYjIwTw36wH2+dEYY6pYabpcWgE78txOAc4tuJJz7i7gPqAWcFFhG3LO3QHcAdC2bduy1mqMMaYYfjsoKiIvikgn4HfAI0WsM0NE4kUkPjo62l9PbYwxhtK10HcCbfLcbu1dVpTZwMslbXT16tXpzrniJ4goWnMgvZyPrWzVtTarq2ysrrKrrrWFWl3tirqjNIG+CujsnOuABvlo4Bd5V3DOdRaRn7w3rwB+ogQiUu4munMuoahTXwOtutZmdZWN1VV21bW2cKqrxEAXkWzn3ERgETps8Q0R2eicewKdaH0+MNE5dzFwAtgP3OzPIo0xxpSsVOPQRWQBsKDAskfzXJ/k57qMMcaUUbCeKTqj5FUCprrWZnWVjdVVdtW1trCpK2DT5xpjjPGvYG2hG2OMKcAC3RhjQkTQBbpzbqhz7gfn3Gbn3OQA1tHGObfEOZfonNvonJvkXT7VObfTObfOexkWgNq2Oue+8z5/gndZU+fcZ865n7w/m1RxTWfl2SfrnHMHnXP3BGp/OefecM7tdc5tyLOs0H3k1HTva+5b51zvKq7rL865773PPc8519i7vL1z7kieffdKFddV5N/OOfeQd3/94Jy7rLLqKqa2f+Wpa6tzbp13eZXss2LyoXJfYyISNBd02OQWoCM6xcB6oGuAamkB9PZebwD8CHQFpgK/DfB+2go0L7Dsz8Bk7/XJwNMB/jvuQU+QCMj+AgYCvYENJe0jYBjwKeCAfsDXVVzXpUBN7/Wn89TVPu96Adhfhf7tvP8H64HaQAfv/2xEVdZW4P6/Ao9W5T4rJh8q9TUWbC30EicKqyoisltE1nivHwI2ofPeVFcjgJne6zOBqwNYyxBgi4iU90zhChORz4F9BRYXtY9GAG+JWgk0ds61qKq6ROQ/IpLtvbkSPVu7ShWxv4oyApgtIsdEJBnYjP7vVnltzjkHXA+8W1nPX0RNReVDpb7Ggi3QC5soLOAh6pxrD/QCvvYumuj92PRGVXdteAnwH+fcaqcTogHEiMhu7/U9QEwA6vIZTf5/sEDvL5+i9lF1et3dgrbkfDo459Y655Y55wYEoJ7C/nbVaX8NAFIl90x2qOJ9ViAfKvU1FmyBXu045+oD7wP3iE4j/DLQCegJ7EY/7lW1C0SkN3A5cJdzbmDeO0U/4wVkvKpzrhYwHHjPu6g67K9TBHIfFcU5NwXIBnzfLLwbaCsivdCZTt9xzjWswpKq5d+ugDHkbzxU6T4rJB9OqozXWLAFelknCqtUzrlI9I81S0Q+ABCRVBHJEREP8Hcq8aNmUURkp/fnXmCet4ZU30c478+9VV2X1+XAGhFJ9dYY8P2VR1H7KOCvO+fcOOBK4EZvEODt0sjwXl+N9lWfWVU1FfO3C/j+AnDO1QRGAv/yLavKfVZYPlDJr7FgC/STE4V5W3qjgfmBKMTbN/c6sElEns2zPG+/1zXAhoKPreS66jnnGviuowfUNqD7yTfHzs3AR1VZVx75WkyB3l8FFLWP5gO/9I5E6AccyPOxudI5/QrIB4HhInI4z/Jop98ohnOuI9AZSKrCuor6280HRjvnajud1K8z8E1V1ZXHxcD3IpLiW1BV+6yofKCyX2OVfbTX3xf0aPCP6DvrlADWcQH6celbYJ33Mgx4G/jOu3w+0KKK6+qIjjBYD2z07SOgGfA/dCbM/wJNA7DP6gEZQKM8ywKyv9A3ld3ohHIpwK1F7SN05MGL3tfcd0B8Fde1Ge1f9b3OXvGue633b7wOWANcVcV1Ffm3A6Z499cPwOVV/bf0Ln8T+HWBdatknxWTD5X6GrNT/40xJkQEW5eLMcaYIligG2NMiLBAN8aYEGGBbowxIcIC3RhjQoQFujHGhAgLdGOMCRH/DxRzKJ0hFbdkAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning: The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
+            "  FutureWarning,\n",
+            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning: The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
+            "  FutureWarning,\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.manifold import TSNE\n",
+        "def plot_tsne(labels,output):\n",
+        "  tsne=TSNE().fit_transform(outputs)\n",
+        "  plt.title('tsne result')\n",
+        "  plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels)\n",
+        "  plt.savefig(\"GCS_tsne.png\")"
+      ],
+      "metadata": {
+        "id": "GCA-iK6PIxtg"
+      },
+      "execution_count": 28,
+      "outputs": []
+    }
+  ]
+}
diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_100.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_100.png
new file mode 100644
index 0000000000..c2903796eb
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diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_200.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_200.png
new file mode 100644
index 0000000000..c793327c14
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diff --git a/recognition/MySolution/s47539934-GCN/GCN_tsne_50.png b/recognition/MySolution/s47539934-GCN/GCN_tsne_50.png
new file mode 100644
index 0000000000..491855f840
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diff --git a/recognition/MySolution/s47539934-GCN/Model.py b/recognition/MySolution/s47539934-GCN/Model.py
new file mode 100644
index 0000000000..c4827c3f22
--- /dev/null
+++ b/recognition/MySolution/s47539934-GCN/Model.py
@@ -0,0 +1,81 @@
+"""
+Author: Arsh Upadhyaya, 47539934
+Code for 2 layer GCN model
+"""
+import math
+import torch.nn.init as init
+import numpy as np
+import scipy.sparse as sp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+from torch.nn.modules.module import Module
+import torch.optim as optim
+from random import sample
+import matplotlib.pyplot as plt
+
+
+class GraphConvolution(Module):
+    '''
+    Starting of graph convolutional layer.
+    Parameters:
+    input_features: dimensions of input layer
+    output_features: dimenstions of output layer
+    use_bias: optional but good practice
+    '''
+    
+    def __init__(self, in_features, out_features, use_bias=True):
+        super(GraphConvolution, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.use_bias=use_bias
+        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
+        if self.use_bias:
+            self.bias = Parameter(torch.FloatTensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+     #initialize parameters 
+    def reset_parameters(self):
+        self.weight = nn.init.kaiming_uniform_(self.weight)
+        if self.use_bias:
+            init.zeros_(self.bias)
+
+'''
+parameters:
+in_feature: an n-dimenstional vector
+adj_matrix: an adjacency matrix in tensor format
+'''
+    def forward(self, input, adj):
+     
+        support = torch.mm(input, self.weight) 
+        output = torch.sparse.mm(adj, support)
+       
+        return output
+
+
+class GCN(nn.Module):
+ '''
+ A model that contains 2 layers of GCN , by creating 2 instances from GraphConvolution function
+ parameters:
+ in_feature:n dimensional vector, which is input
+ out_class: n dimensional vector, final output
+ in this case model goes 128->32->4
+ since in_feature=128(known from dataset)
+ out_class=4(since finally 4 classes)
+ 
+ '''
+    def __init__(self, in_feature,  out_class, dropout):
+        super(GCN, self).__init__()
+
+        self.gcn_conv_1 = GraphConvolution(in_feature, 32)#32 is like the hidden layer for the overall model
+        self.gcn_conv_2 = GraphConvolution(32, out_class)
+        self.dropout = dropout
+
+    def forward(self, x, adj):
+        x = F.relu(self.gcn_conv_1(x, adj))
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.gcn_conv_2(x, adj)
+
+        return F.log_softmax(x, dim=1)
diff --git a/recognition/MySolution/s47539934-GCN/README.md b/recognition/MySolution/s47539934-GCN/README.md
new file mode 100644
index 0000000000..dd92300640
--- /dev/null
+++ b/recognition/MySolution/s47539934-GCN/README.md
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+
+# GCN on Facebook.npz Dataset
+### By: Arsh Upadhyaya
+### roll_no: s4753993
+## Goal 
+
+The objective is to accurately classify nodes into there respective categories(total 4)
+as per the facebook.npz dataset
+## Why GCN
+
+GCN and a conventional CNN are similar in there objectives, they both take some features 
+from existing/given data and perform some operation on it, be it classification, recognition or even 
+creation(like in GANs). However the difference lies in there flexibility. A CNN requires data in some 
+clear format(like in images). However in a lot of real world applications, this is not the case. In fact, 
+the internet itself is a graph with the websites themselves being the nodes and the edges being hyperlinks.
+Such is the case with the facebook dataset, and we have to classify the websites to certain classes.
+
+## Working of GCN
+
+GCN use graphs as objects, and to create these, they multiply features by features by weights. They use adjacency matrix to store data. This matrix is used in forward pass(function used in model) and thus forms a graph, in which adjacent nodes have information about each other. For a simple 2 layer convolutional network, like the one used in the model, each node has information of nodes within 2 edges. Hence one can Imagine just how strong a deep GCN can be, as unlike the neurons in CNN, the nodes in GCN are not bound by euclidean geometry. Especially on a bigger dataset(internet), it could be assumed there would be a lot of hyperbolic and elliptical non-euclidean connections over space, thus increasing the interconnectivity of the model.
+
+## Program Flow
+
+#load_data()
+this function is responsible for first extracting features,edges and targets, then create an adjacency matrix, and normalize that matrix.
+Further it returns features as a tensor.
+
+#GCN_model()
+Since the given data has input of first layer as 128 dim vectors, a hidden layer of 32 was used and then directly finished with out_class=4, which is the number of required categories. Alternatives, a third layer could have also been added, however the accuracy was already pretty good.
+Maybe a dropout was not necessary in hindsight as the dataset seems to small to need it.
+
+train_model()
+considered 200 epochs, and at each epoch, found difference between ouput of model and the target itself. 
+Similarly for losses as well.
+Done for both training and validation set.
+
+
+## results
+### accuracy plot
+<img width="493" alt="Accuracy" src="https://user-images.githubusercontent.com/116279628/197387353-7d547ee3-5bdb-40e9-87af-0ed3f9322dc8.png">
+
+we can see that training_accuracy>validation_accuracy, but only by a little bit which is a good sign.
+Reached accuracy of 0.923 on training set after 200 epochs, went as high as 0.9446 after 400 epochs.
+
+### loss plot
+<img width="506" alt="loss" src="https://user-images.githubusercontent.com/116279628/197387403-62f1ecaf-7bff-4197-b324-64ffcaec5c64.png">
+
+### test_set 
+<img width="641" alt="test" src="https://user-images.githubusercontent.com/116279628/197387288-5faaf529-5733-4e6c-907a-de1bc56c757f.png">
+
+The test_accuracy and test_loss is pretty similar to training and validation accuracy and losses as expected
+
+### tsne
+![GCN_tsne_200](https://user-images.githubusercontent.com/116279628/197387517-f2536959-50be-4cbb-b4a1-f1d5fb506dc5.png)
+
+After 200 epochs there is a significant accuracy, and the 4 different classification categories are very rarely overlapping
+
+## Dependancies
+1.numpy
+
+2.matplotlib
+
+3.pytorch
+
+4.python
+
+5.sklearn
+
+6.scipy
+
+## reference
+[1] https://arxiv.org/abs/1609.02907
+
+[2]  https://github.com/tkipf/pygcn/tree/1600b5b748b3976413d1e307540ccc62605b4d6d/pygcn
+especially helpful in training and testing functions
diff --git a/recognition/MySolution/s47539934-GCN/loss.png b/recognition/MySolution/s47539934-GCN/loss.png
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diff --git a/recognition/MySolution/s47539934-GCN/predict.py b/recognition/MySolution/s47539934-GCN/predict.py
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+    adj, features, labels = load_data('facebook.npz')#returns normalized adjacency matrix, tensor features and labels
+    features.shape[0]
+    num_nodes=features.shape[0]
+    #split data in semi supervised quatity, i.e train:set:test=20:20:60(since n_train<n_test)
+    train_set = torch.LongTensor(range(int(num_nodes*0.2)))
+    val_set = torch.LongTensor(range(int(num_nodes*0.2),int(num_nodes*0.4)))
+    test_set = torch.LongTensor(range(int(num_nodes*0.4),num_nodes))
+
+    model = GCN(in_feature=features.shape[1],
+                out_class=len(np.unique(labels)), dropout=0.5)
+
+    optimizer = optim.Adam([model.gcn_conv_1.weight,model.gcn_conv_2.weight], lr=0.01)
+    train_model(200)
+    test_model()
+    train_accuracies = np.load('train_accuracies.npy')
+    train_losses = np.load('train_losses.npy')
+    validation_accuracies = np.load('validation_accuracies.npy')
+    validation_losses = np.load('validation_losses.npy')
+    plt.plot(range(200),train_accuracies,'b')
+    plt.plot(range(200),validation_accuracies,'r')
+    plt.legend(['train_accuracy', 'validation_accuracy'])
+    plt.show()
+    plt.plot(range(200),train_losses,'b')
+    plt.plot(range(200),validation_losses,'r')
+    plt.legend(['train_loss', 'validation_loss'])
+    plt.show()
+    #for plotting tsne map
+    outputs = model.gcn_conv_1(features, adj).detach().numpy()
+    plot_tsne(labels, outputs )
diff --git a/recognition/MySolution/s47539934-GCN/test.png b/recognition/MySolution/s47539934-GCN/test.png
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diff --git a/recognition/MySolution/s47539934-GCN/train.py b/recognition/MySolution/s47539934-GCN/train.py
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+++ b/recognition/MySolution/s47539934-GCN/train.py
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+'''
+Author_name: Arsh Upadhyaya
+roll_no.4753993
+assigning accuracy and losses to all 3 datasets, by training over n_epoch
+'''
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.optim as optim
+from sklearn import preprocessing
+def accuracy(output, labels):
+    '''
+    calculate accuracy
+    parameters: 
+    output:result of running an instance of the model
+    labels: the true value
+    function compares ratio of two values, giving result<1, 
+    as predicted probability always less than true value
+    '''
+    predict = output.argmax(1)
+    acc_ = torch.div(predict.eq(labels).sum(), labels.shape[0])
+    return acc_
+def loss(output,labels):
+
+    prab = output.gather(1, labels.view(-1,1))
+    loss = -torch.mean(prab)
+    return loss
+def train_model(n_epochs):
+  '''
+  parameter: number of epochs
+  trains model over the range of the epoch and at each train, 
+  calculates accuracy and losses
+  '''
+  train_losses=[]
+  validation_losses=[]
+  train_accuracies=[]
+  validation_accuracies=[]
+  for epoch in range(n_epochs):
+    model.train()
+    optimizer.zero_grad()
+    output=model(features,adj)
+    train_loss=loss(output[train_set],labels[train_set])
+    train_losses.append(train_loss.item())
+   
+    train_accuracy=accuracy(output[train_set],labels[train_set])
+    train_accuracies.append(train_accuracy.item())
+    train_loss.backward()
+    optimizer.step()
+    output=model(features,adj)
+    validation_loss=loss(output[val_set],labels[val_set])
+    validation_losses.append(validation_loss.item())
+    validation_accuracy=accuracy(output[val_set],labels[val_set])
+    validation_accuracies.append(validation_accuracy.item())
+    print('Epoch: {:04d}'.format(epoch + 1),
+              'Train loss: {:.4f}'.format(train_loss.item()),
+              'Train accuracy: {:.4f}'.format(train_accuracy.item()),
+              'Validation loss: {:.4f}'.format(validation_loss.item()),
+              'Validation accuracy: {:.4f}'.format(validation_accuracy.item()))
+    torch.save(model.state_dict(),'train_model.pth')#this random file just used as buffer
+    np.save('train_losses', train_losses)
+    np.save('train_accuracies', train_accuracies)
+    np.save('validation_losses', validation_losses)
+    np.save('validation_accuracies', validation_accuracies)
+  
+#test
+def test_mode():
+  model.load_state_dict(torch.load('train_mode.pth'))
+  output=model(features,adj)
+  test_loss=loss(output[test_set],labels[test_set])
+  test_accuracy=accuracy(output[test_set],labels[test_set])
+  print('Test set results:',
+        'Test loss: {:.4f}'.format(test_loss.item()),
+        'Test accuracy: {:.4f}'.format(test_accuracy.item()))
diff --git a/recognition/MySolution/s47539934-GCN/tsne.py b/recognition/MySolution/s47539934-GCN/tsne.py
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index 0000000000..2e18831296
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+++ b/recognition/MySolution/s47539934-GCN/tsne.py
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+'''
+Author_name: Arsh Upadhyaya
+roll no. s4753993
+to plot classes of dataset facebook.npz and see how same classes come together in a cluster
+'''
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+def plot_tsne(labels,output):
+  tsne=TSNE().fit_transform(outputs)
+  plt.title('tsne result')
+  plt.scatter(tsne[:,0],tsne[:,1],marker='o',c=labels)
+  plt.savefig("GCS_tsne.png")