Feature-Encoder+Baseline.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xKAmeA8SaTTt"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9fFQi6mIabhD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import torchvision\n",
    "import keras\n",
    "from keras import layers\n",
    "from keras.models import Sequential, Model\n",
    "from keras.optimizers import SGD, RMSprop, Adam, Nadam\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.layers import Activation, Flatten, Input, BatchNormalization\n",
    "from keras.layers import Conv1D, MaxPooling1D \n",
    "from keras.layers import Conv2D, MaxPooling2D,Conv2DTranspose\n",
    "from keras.layers import GlobalAveragePooling2D, GlobalMaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X_train_mnist,y_train_mnist),(X_test_mnist,y_test_mnist) = keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainmnist = np.zeros((60000,32,32))\n",
    "testmnist = np.zeros((10000,32,32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(X_train_mnist)):\n",
    "    result = np.zeros((32,32))\n",
    "    result[:X_train_mnist[i].shape[0],:X_train_mnist[i].shape[1]] = X_train_mnist[i]\n",
    "    trainmnist[i] = result\n",
    "for i in range(len(X_test_mnist)):\n",
    "    result = np.zeros((32,32))\n",
    "    result[:X_test_mnist[i].shape[0],:X_test_mnist[i].shape[1]] = X_test_mnist[i]\n",
    "    testmnist[i] = result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "53j1G3wkaiU0"
   },
   "outputs": [],
   "source": [
    "# svhn_trainset = torchvision.datasets.SVHN('./train_32x32.mat', split='train', transform=None, target_transform=None, download=False)\n",
    "svhn_testset = torchvision.datasets.SVHN('./SVHN', split='test', transform=None, target_transform=None, download=False)\n",
    "svhn_extraset = torchvision.datasets.SVHN('./SVHN', split='extra', transform=None, target_transform=None, download=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "18nm3imDb2oa"
   },
   "outputs": [],
   "source": [
    "#svhn_extraset.data = svhn_extraset.data.reshape(np.shape(svhn_extraset.data)[0] , np.shape(svhn_extraset.data)[2], np.shape(svhn_extraset.data)[3], np.shape(svhn_extraset.data)[1])\n",
    "#svhn_trainset.data = svhn_trainset.data.reshape(np.shape(svhn_trainset.data)[0] , np.shape(svhn_trainset.data)[2], np.shape(svhn_trainset.data)[3], np.shape(svhn_trainset.data)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JrzTjiPRbJNA"
   },
   "outputs": [],
   "source": [
    "def plot_images(img, labels, nrows, ncols):\n",
    "    \"\"\" Plot nrows x ncols images\n",
    "    \"\"\"\n",
    "    fig, axes = plt.subplots(nrows, ncols)\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        #print(img[i].shape)\n",
    "        if img[i].shape == (32, 32, 3):\n",
    "            ax.imshow(img[i])\n",
    "        else:\n",
    "            ax.imshow(img[i,:,:,0])\n",
    "        ax.set_xticks([]); ax.set_yticks([])\n",
    "        ax.set_title(labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "jbxi2YeOq4oK",
    "outputId": "7ec5affa-b62f-4401-9cd1-3fff8e2f469a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(531131, 3, 32, 32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(svhn_extraset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "colab_type": "code",
    "id": "15N0BFL2blf4",
    "outputId": "eac12bca-250d-4ba9-feea-682557e65c63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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84r1roK2RTh91E1TasuWslv8jGvkvP8s7P/tJbCZUIoUfCJtSSfhsEVBKoTxgn+BtpU/w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\nHyFZ6o2aQz0HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(svhn_extraset.data, svhn_extraset.labels, 2, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "YKwqq07IcfYD",
    "outputId": "94e2bb19-6649-4523-992a-173eb557990c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(svhn_extraset.labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r6oeWbZQeKgT"
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(svhn_extraset.data,svhn_extraset.labels,test_size = 0.2,random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "colab_type": "code",
    "id": "nwCmF6KCriXB",
    "outputId": "0fc0b130-12b0-483e-b721-33a4025007e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n",
      "(32, 32, 3)\n"
     ]
    },
    {
     "data": {
      "image/png": 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3Ia43oo3suJfnI5qOHnPQ62d59KvLDI1cz21HPcbG3gRgTo5yzc4M8y8+BD/3Cxty3TSm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\n1mRNfory/wGO77Ds2Os6XwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(x_train, y_train, 2, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "5lP_XumweZS2",
    "outputId": "77d4f525-611c-49b9-89d0-aea2b21f7794"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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puxiYIWkrYBfbS/u1ERERbdKyoari1ZKuA7YDFgATba8u6x4DdgCmAT0N22xUbnu9pN5S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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Training Data Distribution SVHN\")\n",
    "plt.xlabel(\"Label\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.hist(y_train);\n",
    "plt.xticks(list(range(0,10)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "colab_type": "code",
    "id": "6YOz8zYYgJKf",
    "outputId": "04642fc2-1743-42f5-82e5-ed1bccedefa5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jYjfgJuCk3L4gIkaV3ZYDhwLzI2IX4DvA2XnZi4DjI2IkMFTSPrU6BjMzW1Uth6oWA/sC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\n/x+6cEmkaR+YgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Testing Data Distribution SVHN\")\n",
    "plt.xlabel(\"Label\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.hist(y_test);\n",
    "plt.xticks(list(range(0,10)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bsBOfUVve92Y"
   },
   "outputs": [],
   "source": [
    "#X_train, X_val, y_train, y_val = train_test_split(x_train, y_train, test_size=0.13, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "xUjnlqgkfa0z",
    "outputId": "edaaaa93-7bed-4c98-a878-62f4ac17fcd6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(424904, 3, 32, 32)"
      ]
     },
     "execution_count": 122,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QNEFsEVkntmK",
    "outputId": "4f77a06e-50f9-4a02-ba84-b62dee70fdb5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(424904, 10)"
      ]
     },
     "execution_count": 125,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y_train_oe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = svhn_extraset.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dORRTudggsIJ"
   },
   "outputs": [],
   "source": [
    "y_train_oe = keras.utils.to_categorical(svhn_extraset.labels,num_classes = 10)\n",
    "#y_test_oe = keras.utils.to_categorical(y_test,num_classes = 10)\n",
    "#y_val_oe = keras.utils.to_categorical(y_val,num_classes = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 646
    },
    "colab_type": "code",
    "id": "9vRkrNuhhWBA",
    "outputId": "a0a53901-d50a-4214-c71a-3679df555dc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 64, 16, 16)        1792      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 128, 8, 8)         73856     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 128, 8, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 256, 4, 4)         295168    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 256, 4, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 128, 1, 1)         524416    \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 128, 1, 1)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 896,522\n",
      "Trainable params: 896,522\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(64, kernel_size=(3,3), input_shape=(3,32,32),padding=\"same\",strides=(2,2), data_format=\"channels_first\"))\n",
    "#model.add(MaxPooling2D(pool_size=(2,2) , data_format=\"channels_first\"))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(128,kernel_size=(3,3), padding=\"same\", data_format=\"channels_first\",strides=(2,2)))\n",
    "#model.add(MaxPooling2D(pool_size=(2,2) , data_format=\"channels_first\"))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(256,kernel_size=(3,3), padding=\"same\", data_format=\"channels_first\",strides=(2,2)))\n",
    "#model.add(MaxPooling2D(pool_size=(4,2) , data_format=\"channels_last\"))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(128,kernel_size=(4,4), padding=\"same\",data_format=\"channels_first\",strides=(4,4)) )\n",
    "#model.add(MaxPooling2D(pool_size=(4,4) , data_format=\"channels_last\"))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer = 'adadelta', metrics = ['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/job:localhost/replica:0/task:0/device:GPU:0']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "K.tensorflow_backend._get_available_gpus()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "j8Jp9scsnYai",
    "outputId": "f48c45f4-6f09-4e53-ad87-62ebe53d4884",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "531131/531131 [==============================] - 168s 316us/step - loss: 0.8374 - acc: 0.8913\n",
      "Epoch 2/5\n",
      "531131/531131 [==============================] - 165s 310us/step - loss: 0.2004 - acc: 0.9495\n",
      "Epoch 3/5\n",
      "531131/531131 [==============================] - 165s 310us/step - loss: 0.1992 - acc: 0.9513\n",
      "Epoch 4/5\n",
      "531131/531131 [==============================] - 164s 310us/step - loss: 0.1967 - acc: 0.9522\n",
      "Epoch 5/5\n",
      "531131/531131 [==============================] - 165s 310us/step - loss: 0.1976 - acc: 0.9529\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fa70f75a668>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train_oe,epochs = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('feature_encoder_original.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://ufldl.stanford.edu/housenumbers/test_32x32.mat to ./SVHN/test_32x32.mat\n"
     ]
    }
   ],
   "source": [
    "# svhn_testset = torchvision.datasets.SVHN('./SVHN', split='test', transform=None, target_transform=None, download=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = svhn_testset.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = keras.utils.to_categorical(y_test,num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26032, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = svhn_testset.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26032/26032 [==============================] - 3s 109us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.48310300949835017, 0.8766133988936693]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test,y_test,verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O_G4C7jN3REY"
   },
   "outputs": [],
   "source": [
    "new_model = Model(model.inputs, model.layers[-5].output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ASijR-_P48qA"
   },
   "outputs": [],
   "source": [
    "new_model.set_weights(model.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "id": "C1-2onAR5I0A",
    "outputId": "1123c5f7-8e9c-41ec-ebc8-1c91c73c5762"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1_input (InputLayer)  (None, 3, 32, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 64, 16, 16)        1792      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 64, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 128, 8, 8)         73856     \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 128, 8, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 256, 4, 4)         295168    \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 256, 4, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 128, 1, 1)         524416    \n",
      "=================================================================\n",
      "Total params: 895,232\n",
      "Trainable params: 895,232\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "new_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model.save('7layerfeatureencoder.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "WZ7Kwequ5x1x",
    "outputId": "73bb0082-65a0-4c5c-ac36-8c821f30d928"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 128, 1, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(new_model.predict(x_test)[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Baseline Formulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_size     = 100     # latent sample size (i.e., 100 random numbers)\n",
    "g_hidden_size   = 128\n",
    "d_hidden_size   = 128\n",
    "leaky_alpha     = 0.01\n",
    "g_learning_rate = 0.001  # learning rate for the generator\n",
    "d_learning_rate = 0.001   # learning rate for the discriminator\n",
    "epochs          = 10\n",
    "batch_size      = 64      # train batch size\n",
    "eval_size       = 16      # evaluate size\n",
    "smooth          = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(x):    \n",
    "    x = x.reshape(-1, 784) # 784=28*28\n",
    "    x = np.float64(x)\n",
    "    x = (x / 255 - 0.5) * 2\n",
    "    x = np.clip(x, -1, 1)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deprocess(x):\n",
    "    x = (x / 2 + 1) * 255\n",
    "    x = np.clip(x, 0, 255)\n",
    "    x = np.uint8(x)\n",
    "    x = x.reshape(28, 28)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_trainable(model, trainable):\n",
    "    for layer in model.layers:\n",
    "        layer.trainable = trainable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_labels(size):\n",
    "    return np.ones([size, 1]), np.zeros([size,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train_real = preprocess(trainmnist)\n",
    "# X_test_real  = preprocess(testmnist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_real, y_train_fake = make_labels(batch_size)\n",
    "y_eval_real,  y_eval_fake  = make_labels(eval_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UCrfrOFt5-RL"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_transpose_1 (Conv2DTr (None, 256, 1, 1)         524544    \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 256, 1, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 128, 2, 2)         524416    \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 128, 2, 2)         8         \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 128, 2, 2)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 64, 8, 8)          131136    \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 64, 8, 8)          32        \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 64, 8, 8)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 1, 32, 32)         1025      \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 1, 32, 32)         0         \n",
      "=================================================================\n",
      "Total params: 1,181,161\n",
      "Trainable params: 1,181,141\n",
      "Non-trainable params: 20\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = Sequential()\n",
    "generator.add(Conv2DTranspose(256, (4,4), input_shape=(128,1,1), strides = (1,1),data_format=\"channels_first\", padding=\"same\"))\n",
    "generator.add(Activation('relu'))\n",
    "generator.add(Conv2DTranspose(128, (4,4), strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "generator.add(BatchNormalization())\n",
    "generator.add(Activation('relu'))\n",
    "generator.add(Conv2DTranspose(64, (4,4),strides = (4,4),data_format=\"channels_first\", padding=\"same\"))\n",
    "generator.add(BatchNormalization())\n",
    "generator.add(Activation('relu'))\n",
    "generator.add(Conv2DTranspose(1, (4,4), strides = (4,4),data_format=\"channels_first\", padding=\"same\"))\n",
    "generator.add(Activation('tanh'))\n",
    "\n",
    "\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_10 (Conv2D)           (None, 64, 16, 16)        1088      \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 64, 16, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 128, 8, 8)         131200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 128, 8, 8)         32        \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 128, 8, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 256, 4, 4)         524544    \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 256, 4, 4)         16        \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 256, 4, 4)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 128, 2, 2)         524416    \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 128, 2, 2)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 1, 1, 1)           129       \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 1)                 0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 2         \n",
      "_________________________________________________________________\n",
      "activation_18 (Activation)   (None, 1)                 0         \n",
      "=================================================================\n",
      "Total params: 1,181,427\n",
      "Trainable params: 1,181,403\n",
      "Non-trainable params: 24\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator = Sequential()\n",
    "discriminator.add(Conv2D(64, (4,4), input_shape=(1,32,32,), strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "discriminator.add(Activation('relu'))\n",
    "discriminator.add(Conv2D(128, (4,4), strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "discriminator.add(BatchNormalization())\n",
    "discriminator.add(Activation('relu'))\n",
    "discriminator.add(Conv2D(256, (4,4),strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "discriminator.add(BatchNormalization())\n",
    "discriminator.add(Activation('relu'))\n",
    "discriminator.add(Conv2D(128, (4,4), strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "discriminator.add(Activation('tanh'))\n",
    "discriminator.add(Conv2D(1, (1,1), strides = (2,2),data_format=\"channels_first\", padding=\"same\"))\n",
    "discriminator.add(Flatten())\n",
    "discriminator.add(Dense(1))\n",
    "discriminator.add(Activation('sigmoid'))\n",
    "\n",
    "\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "sequential_2 (Sequential)    (None, 1, 32, 32)         1181161   \n",
      "_________________________________________________________________\n",
      "sequential_4 (Sequential)    (None, 1)                 1181427   \n",
      "=================================================================\n",
      "Total params: 2,362,588\n",
      "Trainable params: 2,362,544\n",
      "Non-trainable params: 44\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "gan = Sequential([\n",
    "    generator,\n",
    "    discriminator\n",
    "])\n",
    "\n",
    "gan.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator.compile(loss='categorical_crossentropy', optimizer = 'adadelta', metrics = ['accuracy'])\n",
    "discriminator.compile(optimizer=Adam(lr=d_learning_rate), loss='binary_crossentropy')\n",
    "gan.compile(optimizer=Adam(lr=g_learning_rate), loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter_ = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_mnist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:   1/10 Discriminator Loss: 0.0103 Generator Loss: 8.3582\n",
      "Epoch:   2/10 Discriminator Loss: 0.0101 Generator Loss: 9.3626\n",
      "Epoch:   3/10 Discriminator Loss: 0.0101 Generator Loss: 10.3589\n",
      "Epoch:   4/10 Discriminator Loss: 0.0101 Generator Loss: 10.9210\n",
      "Epoch:   5/10 Discriminator Loss: 0.0101 Generator Loss: 10.8515\n",
      "Epoch:   6/10 Discriminator Loss: 0.0101 Generator Loss: 10.7975\n",
      "Epoch:   7/10 Discriminator Loss: 0.0101 Generator Loss: 10.4821\n",
      "Epoch:   8/10 Discriminator Loss: 0.0101 Generator Loss: 10.7396\n",
      "Epoch:   9/10 Discriminator Loss: 0.0101 Generator Loss: 10.6604\n",
      "Epoch:  10/10 Discriminator Loss: 0.0101 Generator Loss: 10.6024\n"
     ]
    }
   ],
   "source": [
    "losses = []\n",
    "\n",
    "for e in range(epochs):\n",
    "    for i in range(len(trainmnist)//batch_size):\n",
    "        X_batch_real = trainmnist[i*batch_size:(i+1)*batch_size]\n",
    "        feature_encoder = new_model.predict(x_train[i*batch_size:(i+1)*batch_size])\n",
    "        X_batch_fake = generator.predict_on_batch(feature_encoder)\n",
    "        iter_+=1\n",
    "        #print(X_batch_real.shape)\n",
    "#         try:\n",
    "#         print(len(X_batch_fake),len(y_train_fake))\n",
    "        tmp = X_batch_real.reshape((64,1,32,32))\n",
    "        make_trainable(discriminator, True)\n",
    "        discriminator.train_on_batch(tmp, y_train_real * (1 - smooth))\n",
    "        discriminator.train_on_batch(X_batch_fake, y_train_fake)\n",
    "    \n",
    "        make_trainable(discriminator, False)\n",
    "        gan.train_on_batch(feature_encoder, y_train_real)\n",
    "  \n",
    "  \n",
    "    X_eval_real = testmnist[np.random.choice(len(X_test_mnist), eval_size, replace=False)]\n",
    "    tmp2 = X_eval_real.reshape((eval_size,1,32,32))\n",
    "    feature_encoder = new_model.predict(x_train[:eval_size])\n",
    "    X_eval_fake = generator.predict_on_batch(feature_encoder)\n",
    "  \n",
    "    d_loss  = discriminator.test_on_batch(tmp2, y_eval_real)\n",
    "    d_loss += discriminator.test_on_batch(X_eval_fake, y_eval_fake)\n",
    "    g_loss  = gan.test_on_batch(feature_encoder, y_eval_real) # we want the fake to be realistic!\n",
    "    \n",
    "    losses.append((d_loss, g_loss))\n",
    "    print(\"Epoch: {:>3}/{} Discriminator Loss: {:>6.4f} Generator Loss: {:>6.4f}\".format(e+1, epochs, d_loss, g_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses = np.array(losses)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(losses.T[0], label='Discriminator')\n",
    "plt.plot(losses.T[1], label='Generator')\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_samples = new_model.predict(x_train[:20])\n",
    "generated_digits = generator.predict(latent_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 1, 32, 32)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_digits.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_image = []\n",
    "for i in range(len(generated_digits)):\n",
    "    new_image.append(generated_digits[i].reshape((32,32)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAG4BJREFUeJztnXtwlVfVxp8FBMKtBAj3W8BGhNJCMaXYKrZUFLBCUerYqZU/bOkw4Hw6fjpMO/2kY2e0TC+ig61YGLFasLVV8ROBgqXQmU65lWvzcS2XcEmggYRwJ6zvj3OYSeO7Vk7eJO9J3c9vhknYz1ln77PPWXnP2eustURVQQgJjxbZXgAhJDvQ+QkJFDo/IYFC5yckUOj8hAQKnZ+QQKHzExIodH5CAoXOT0igtGqIsYiMBzAPQEsAL6nqz73bd+zYUfPz8yO1nJwc0666ujpyPDc317S5cuWKqbVs2dLULly4YGpt27aNHL906ZJp06KF/ff14sWLptahQwdTO336tKlZa6yqqqq3DQBcvny5Ue1at25t2sRFROpt4z0u7/48zXuur169Gus+62tTXl6Oc+fOZXSHsZ1fRFoCmA9gHIASABtFZJmqfmDZ5OfnY86cOZFar169zLkqKioixwcPHmzalJaWmprnWMXFxaY2ZMiQyPH9+/ebNu3atTM1z+722283tb/+9a+mNnTo0Mjx9evXmzbDhw83tYMHD5razTffbGpHjhyJHO/Xr59p4zmB9wc7jrMePXrUtGnVynYLbx3ec/3RRx/Fmq++NvPmzcv4Phrytn8UgH2qekBVLwNYCmByA+6PEJIgDXH+PgBq/nkvSY8RQj4BNMT5o95P/VuKoIhMF5FNIrLp7NmzDZiOENKYNMT5SwDU/ADXF8Cx2jdS1QWqWqSqRR07dmzAdISQxqQhzr8RQKGIDBSR1gC+BWBZ4yyLENLUxD7tV9WrIjILwEqkQn2LVHWXZ9O2bVvcdNNNkdoTTzxh2n3961+PHF+9erVps2TJElPbtm2bqT3zzDOmtnjx4sjxz33uc6bN888/b2pf+MIXTG3t2rWmNmjQIFPbsmVL5LgVqQD8CEefPvYxzrlz50xt4MCBpmbhRWisEDEAeAVprDBs3FN7by5vPzzOnDkTOd6+fXvT5vz585HjVlg8igbF+VV1OYDlDbkPQkh24Df8CAkUOj8hgULnJyRQ6PyEBAqdn5BAadBpf325cuUKTpw4Eal5oSgrAeZXv/qVaTN16lRTGzt2rKl5GXpWMsjWrVtNm7y8PFOzQjwA8Pbbb5vaF7/4RVOz9tdLLPEy7TzNy4C0QmleEk6XLl1MzQthderUydSsbDrvefFCdl4maVx69OgROe5lfVrPi5dZ+G+3zfiWhJD/KOj8hAQKnZ+QQKHzExIodH5CAiXR0/42bdqYSSkrV6407axyUZMmTTJt7rvvPlObO3euqT388MOmdv/990eOjxw50rSZP3++qRUUFJjagAEDTM0rhWWV3bp27Zpp45WRuuGGG0zNS/qxTvW9Wnbe6baXDu6dzg8bNixy3Evu8ubyEnu8hCBv/60ydW3atDFtLK0+9QB55SckUOj8hAQKnZ+QQKHzExIodH5CAoXOT0igJBrqU1Uz1DN9+nTTzkqmuOOOO0wbK3wCAH/4wx9M7eTJk6bWs2fPyPEVK1aYNrfddpupeSE2L2TjlUC3kkS8xBhvLs/OC7FZ7bD69u1r2nhhwOPHj5ua14HJSp7y2nV5z4v3mL0QoRfGtMKH3n5YSVVeKLI2vPITEih0fkIChc5PSKDQ+QkJFDo/IYFC5yckUBoU6hORgwDOAqgGcFVVi7zbV1dXo6qqKlLzWhNZmWpW6y8gVS/QIm49O+8+LbysOI+cnBxT27lzp6n17t07ctwLD3bt2tXUvMwyLyRmhaK8UKoXVoxbC9G6Ty/LrrKy0tS8Gn5e+DCOFqdtWH1CfY0R579bVU81wv0QQhKEb/sJCZSGOr8CWCUim0XE/ooeIaTZ0dC3/Xeq6jER6Q7gTRH5P1VdV/MG6T8K0wH7q6eEkORp0JVfVY+lf5YB+AuAURG3WaCqRapa5B3aEEKSJbbzi0h7Eel4/XcAXwZgH0MTQpoVDXnb3wPAX9IZYa0AvKKqdnobUmGST3/605Gal1lmha8OHTpk2njvMvr3729qXvaYtUYvVNa9e3dT88JeXsjGCzdZ83lZZV7bLS9jztvHkpKSyPHDhw/Huj+vlVfbtm1Nzcru9MKUXvsvK1QN+KFKr42WFQ72WsedP3++3muoTWznV9UDAIbHtSeEZBeG+ggJFDo/IYFC5yckUOj8hAQKnZ+QQEm0gGdlZaVZ7HLMmDGm3caNGyPHx40bZ9ps2bLF1Dp37mxqXuHPW265JXJ81apVpo0XcvQy7QYOHGhq3jclrRCWFwLywpFeuMnTrOzIoiI78dPK3gTs4qkAcOLECVMbMmRI5Pjq1atNm6NHj5qal2nnhRw9OyuL0MsItZ5P9uojhNQJnZ+QQKHzExIodH5CAoXOT0igSH1qfjWU4cOHq3UyfuDAAdPOSgbatWuXaTNgwABT82q+eSes1imwV/evrKzM1LzkEu8k3Xvclp13Wu7VrOvTp4+pebUQ4yQYxW135T1n1nPzwQcfmDZevT3vNN17HXgJQZbWsmVL08aKHrzwwgs4evRoRkf+vPITEih0fkIChc5PSKDQ+QkJFDo/IYFC5yckUBJN7Ll48SKKi4sjtYKCAtPuww8/jBzv27evaXPkyBFTs5I9AKC8vNzUBg8eHDm+du1a06ZXr16m5tWz85KPvHCTlVziha+8pJ+4YS+rtZkXvvISnbyQ9NWrV+uteWHF0tJSU/NqCXrr8JJ+rFCrN9fp06cjx702ZLXhlZ+QQKHzExIodH5CAoXOT0ig0PkJCRQ6PyGBUmeoT0QWAbgXQJmqDkuPdQHwJwAFAA4C+KaqRsceatChQweMHj06UvPCPFaGmJedN2LEiLqWE4lXH88KowwaNMi0adOmjal5oRyvhdbQoUPrfZ9eGzIvHOmF86yWUZ6d1/7Lw9srKxQM2K3evJBuYWGhqXl23hq9+oQ33nhj5LiXNWmFuXNyckyb2mRy5f8dgPG1xmYDWKOqhQDWpP9PCPkEUafzq+o6ALX/3E0GsDj9+2IA9zXyugghTUzcz/w9VPU4AKR/2rWfCSHNkiY/8BOR6SKySUQ2eS2pCSHJEtf5S0WkFwCkf5q1qlR1gaoWqWpRt27dYk5HCGls4jr/MgDT0r9PA/C3xlkOISQpMgn1LQFwF4B8ESkB8BMAPwfwqoh8F8BhAPdnMll5eTmWLl0aqX3jG98w7ayQhxc22rlzp6l5LaO8QqJW5uHevXtNm7gtxfr162dqu3fvNjUrW83KAgP8kJK3x15YqUWL6OvKuXPnTBsrExAA9uzZY2reXlmP21uHF2b1Cqtajxnw98rKWPQy9Kw11ierr07nV9UHDOmejGchhDQ7+A0/QgKFzk9IoND5CQkUOj8hgULnJyRQEi3gmZeXhylTpkRqXmaWVcxy+/btpo3V3w8Ajh07ZmpehpsVNvLCYatXrzY1r5/gqVOnTM0r7mmFgLw1eoVQvcw9r7intUYv5OgV6bQyOwE/VGmF2Lws0vz8fFOrqKio91yAXxTUwnsNeNmnmcIrPyGBQucnJFDo/IQECp2fkECh8xMSKHR+QgIl0VDf5cuXzR56Xvhq//79keMDBw40bbwwYM+ePU2trMwsTWDaderUybTxCphcvHjR1HJzc03NCwG9++67keMTJkwwbf75z3+aWv/+/U3NK07avn37yHHvMXv987wsTa9wpsUNN9xgat7+engZdV441XvNWVh9Ab1waW145SckUOj8hAQKnZ+QQKHzExIodH5CAkXqczrYUEaOHKnr1q2L1Lx1WK2fqqurTRsvycLTrFNUT6uqqjJtvMdVWVlpal7ixsqVK03NOp3ftm2baWO1UAOAXbt21XsuwE6A8U7ZvWSbVq3swJRnZ9XVO3TokGnTtm1bU/NO5lu2bBnLzqpq7SVBWTbz589HSUmJ3WOtBrzyExIodH5CAoXOT0ig0PkJCRQ6PyGBQucnJFAyade1CMC9AMpUdVh6bA6ARwBcz1p5TFWX13VfV65cwYkTJyI1L0mnuLg4crywsNC0OXr0qKl57Z28enBWfT8r8QjwQzzt2rUzNS/k6Nk999xzkeOTJk0ybRYtWmRqo0aNMjVvj612WF5ij/eYvbqLXjjVCkd6e+g9Z15LMS/By6tBaCUS5eXlmTZWTUavZdi/3TaD2/wOwPiI8edVdUT6X52OTwhpXtTp/Kq6DkB5AmshhCRIQz7zzxKR7SKySETsZHxCSLMkrvO/AOBTAEYAOA7gWeuGIjJdRDaJyKbycr6BIKS5EMv5VbVUVatV9RqA3wIwT4VUdYGqFqlqUZyKK4SQpiGW84tIzWPvKQDsGkuEkGZJJqG+JQDuApAvIiUAfgLgLhEZAUABHATwaCaTVVRUYMWKFZGa967AyrLavXu3abN3715TGzZsmKl54cO33347crykpMS08UI8XhaYFRIFgA0bNpjaiBEjIse9Gnj33nuvqf3617+OZfeb3/wmcvzxxx83bbww1ciRI03ttddeM7Vx48ZFjt92222mzdy5c03txRdfNLWbb77Z1IYMGWJq1loWLFhQ77m8UGRt6nR+VX0gYnhhxjMQQpol/IYfIYFC5yckUOj8hAQKnZ+QQKHzExIoibbrEhEznONl01kZTF5Ybs2aNaZ26dIlU/OKN1rtqbxWY4cPHzY1K0sQsNtdAX7hz82bN0eOP/jgg6bNvHnzTO3hhx82NS/U+qMf/Shy3Fu7tx8zZswwtV/84hemtmrVqshx7zFv3LjR1KZMmVLvuQB//1966aXIce81YIWyvdd2bXjlJyRQ6PyEBAqdn5BAofMTEih0fkIChc5PSKAk2qvvpptu0ldeeSVS80IU165dixy3evgBfjFFL6Tk9UezQnpeBp6Xqeb1+Ltw4YKprV+/3tTuvPPOyPFly5aZNvn5+abmFdUsKioytTfeeCNy3Cv6edddd5najTfeaGp///vfTe173/te5LiVdQgAs2bNMjWrQCoATJgwwdRef/11U7vjjjsix//xj3+YNoMHD44cX7JkCUpLS9mrjxBiQ+cnJFDo/IQECp2fkECh8xMSKIkm9ly+fNk87bWSZgC7/txnP/tZ02bp0qWmNnbsWFM7ePCgqY0fH9W4yG8lZbViAvxohRd18JKPrFP9MWPGmDbPPmtWXsfkyZNNzYtyWKfRXq3Ge+65x9SeeOIJU/vxj39sakeOHIkc99qG7du3z9SsNmSAn8T1ne98x9TeeeedyPHWrVubNn369Ikcz8nJMW1qwys/IYFC5yckUOj8hAQKnZ+QQKHzExIodH5CAiWTdl39APweQE8A1wAsUNV5ItIFwJ8AFCDVsuubqmrHp5AKhcycOTNS++lPf2ra5eXlRY57YTkv/ObV9zt06JCp3XrrrZHjXmuwHTt2mNqZM2dM7Wc/+5mpfe1rXzO1r3zlK5HjjzzyiGnjtYWaNGmSqXmPzQrbPfXUU6aNl8zkhbC8VmRf/epXI8e919u0adNMbfny5aZmtUoD/D226iR6r28r0Sk3N9e0qU0mV/6rAH6oqkMAjAYwU0SGApgNYI2qFgJYk/4/IeQTQp3Or6rHVXVL+vezAIoB9AEwGcDi9M0WA7ivqRZJCGl86vWZX0QKANwK4D0APVT1OJD6AwHAbkdLCGl2ZPz1XhHpAOB1AN9X1Urvq6m17KYDmA74LakJIcmS0ZVfRHKQcvw/qur1Ei2lItIrrfcCUBZlq6oLVLVIVYvo/IQ0H+p0fkld4hcCKFbVmjWMlgG4fiw6DcDfGn95hJCmos4afiLyeQDrAexAKtQHAI8h9bn/VQD9ARwGcL+qlnv3NWDAAJ09Ozoo8NBDD5l2ViZgu3btTBuv1ZEVOgSA8nL7IVjznTx50rSxsq8AP3PPa1/m1dV74IEHIsfnz59v2njhza5du5qaF6q0at29/PLLpk3Hjh1NrVUr+xOqp/3rX/+KHD9//rxpU1BQYGrea+eXv/ylqf3gBz8wtZKSksjxlStXmjZWCHPmzJnYs2dPRp/J6/zMr6rvALDuzM7BJIQ0a/gNP0IChc5PSKDQ+QkJFDo/IYFC5yckUBIt4NmiRQszXOZlbVnhMi+DyQtheq3BvFCO9a3G3r17mzZnz541Na+lmLeOxYsXm1qHDh0ixx999FHTxvvylVewcsaMGaZmZfX17dvXtPEKVi5cuNDUHnzwwXrf54cffmjarFq1ytSGDx9uanfffbepvfvuu6a2Z8+eyHGvjZpVZNR7bdeGV35CAoXOT0ig0PkJCRQ6PyGBQucnJFDo/IQESp1ZfY1J79691Sok+eSTT5p2VgaW19/Py1QbNGiQqV2+fNnUrHDk+++/b9p4oSGvYKX32Hbv3m1qxcXFkeOFhYWmzYoVK0zt29/+tql169bN1Ky+dV6W47Vr10zNCwN6WM+n12ewc+fOplZRUWFqXral11/R0qxsVsDOtpw4cSK2b9+eUVYfr/yEBAqdn5BAofMTEih0fkIChc5PSKAketrfvXt3nTp1aqT29NNPm3ZWAo9XPtyr6+adKrdoUf+/h94eNvZcdXHlypXIcS95x9tHL/rh4SVqNfY64kQCvOfMO7X3Xlce3n1aUR8vQmC9dkaPHo3NmzfztJ8QYkPnJyRQ6PyEBAqdn5BAofMTEih0fkICpc64hYj0A/B7AD2Rate1QFXnicgcAI8AuN6r6jFVXe7dV05OjlnDzWvVZIWvvNBQ3BBbdXW1qVnhMm8uL8RmPS7AD5V5a7TCXl5tNy9U5oWorHqBgF270Guj5j0ur15jnGQs7znzkqouXrwYy857rVq1HL36j9Y+Zto9G8isgOdVAD9U1S0i0hHAZhF5M609r6rPZDwbIaTZkEmvvuMAjqd/PysixQDsvExCyCeCen3mF5ECALci1aEXAGaJyHYRWSQidhI0IaTZkbHzi0gHAK8D+L6qVgJ4AcCnAIxA6p3Bs4bddBHZJCKbvBrwhJBkycj5RSQHKcf/o6q+AQCqWqqq1ap6DcBvAYyKslXVBapapKpF3mEPISRZ6nR+SR0fLgRQrKrP1RjvVeNmUwDsbPzlEUKaijqz+kTk8wDWA9iBVKgPAB4D8ABSb/kVwEEAj6YPB02Kiop0w4YNkVpjZ9PVJ+Txn4S1J95+xN3HOOHUuCHYuGuMsx9x1+jZefNZIU4vTGzZ3H777Rln9WVy2v8OgKg7c2P6hJDmDb/hR0ig0PkJCRQ6PyGBQucnJFDo/IQESrxqhDHZtWuX2b5qx44dpl2S4Zo4JB2+ipN5GMcGAKqqqkzNy+qzHltjZ1QC8fY/bpHOuM+1l1VpZQN6a7T2oz4hbl75CQkUOj8hgULnJyRQ6PyEBAqdn5BAofMTEiiJhvo+85nP4K233orUvMKIcYoweqGhOCEUb764fd/ihq88zQrNeUU6vRCbF76qqKgwNavPXNy54hTprGs+i/Pnz8eayyvI6r1GTp8+HTnuFQS1XgPea6M2vPITEih0fkIChc5PSKDQ+QkJFDo/IYFC5yckUBIN9VVVVWH9+vWR2sSJE127KLyssrg91bz+aFY/QW8uL0PswoULptauXTtT8zLtrPnKyspMm27dupnaRx99ZGpdu3Y1tVOnTtV7Lm8/rNAhYIfKAKBz5+heMnH7JHq9J7zS9F5Wn2VXXl5u2nTp0iVynFl9hJA6ofMTEih0fkIChc5PSKDQ+QkJlEzadeUCWAegDVLRgT+r6k9EZCCApQC6ANgC4CFVtbMv4Lfr8hIwrBNsb+1ekkjcZJs4bZXitnCq63mxsJJtvJPoyspKU7NOywE/AcaKVnjPs3fKHvc5s+y8RCcvEhDn9QH4UR8rguBFfKzIyJgxY7Bly5aMjvwzufJfAjBWVYcj1ZtvvIiMBvA0gOdVtRDAaQDfzWRCQkjzoE7n1xTXA8s56X8KYCyAP6fHFwO4r0lWSAhpEjL6zC8iLUVkK4AyAG8C2A/gjKpef09VAqBP0yyRENIUZOT8qlqtqiMA9AUwCsCQqJtF2YrIdBHZJCKbTp48GX+lhJBGpV6n/ap6BsBaAKMB5InI9VOMvgCOGTYLVLVIVYu8r3YSQpKlTucXkW4ikpf+vS2ALwEoBvAWgKnpm00D8LemWiQhpPHJJLGnF4DFItISqT8Wr6rq/4rIBwCWishTAN4HsLCuO6qurjbDSnl5eaadFa7xwideuMYLsXkJGLm5uZHjXkKKFxry6tJ5oTkvkcWaL07yC+AnLXnJNlb4yntcXujQm8vbR+s5a4pkLG+NXr1DK0HtzJkzpk2nTp0ix+uT2FOn86vqdgC3RowfQOrzPyHkEwi/4UdIoND5CQkUOj8hgULnJyRQ6PyEBEqdWX2NOpnISQCH0v/NBxBd6C1ZuI6Pw3V8nE/aOgaoakbfpkvU+T82scgmVS3KyuRcB9fBdfBtPyGhQucnJFCy6fwLsjh3TbiOj8N1fJz/2HVk7TM/ISS78G0/IYGSFecXkfEisltE9onI7GysIb2OgyKyQ0S2isimBOddJCJlIrKzxlgXEXlTRPamf9qpdk27jjkicjS9J1tFxO6j1njr6Ccib4lIsYjsEpH/So8nuifOOhLdExHJFZENIrItvY4n0+MDReS99H78SUTsKqSZoKqJ/gPQEqkyYIMAtAawDcDQpNeRXstBAPlZmHcMgJEAdtYYmwtgdvr32QCeztI65gD474T3oxeAkenfOwLYA2Bo0nvirCPRPQEgADqkf88B8B5SBXReBfCt9PiLAGY0ZJ5sXPlHAdinqgc0Vep7KYDJWVhH1lDVdQBqd2GcjFQhVCChgqjGOhJHVY+r6pb072eRKhbTBwnvibOORNEUTV40NxvO3wfAkRr/z2bxTwWwSkQ2i8j0LK3hOj1U9TiQehEC6J7FtcwSke3pjwVN/vGjJiJSgFT9iPeQxT2ptQ4g4T1JomhuNpw/qtRItkIOd6rqSAATAMwUkTFZWkdz4gUAn0KqR8NxAM8mNbGIdADwOoDvq6rdSST5dSS+J9qAormZkg3nLwHQr8b/zeKfTY2qHkv/LAPwF2S3MlGpiPQCgPTPsmwsQlVL0y+8awB+i4T2RERykHK4P6rqG+nhxPckah3Z2pP03PUumpsp2XD+jQAK0yeXrQF8C8CypBchIu1FpOP13wF8GcBO36pJWYZUIVQgiwVRrztbmilIYE8kVXhuIYBiVX2uhpTonljrSHpPEiuam9QJZq3TzIlInaTuB/B4ltYwCKlIwzYAu5JcB4AlSL19vILUO6HvAugKYA2AvemfXbK0jpcB7ACwHSnn65XAOj6P1FvY7QC2pv9NTHpPnHUkuicAbkGqKO52pP7Q/E+N1+wGAPsAvAagTUPm4Tf8CAkUfsOPkECh8xMSKHR+QgKFzk9IoND5CQkUOj8hgULnJyRQ6PyEBMr/A3E9oHDEOmWMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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URPfqKysr002bNsXavMaZ1n5rHl6qzGsWeuLECdM2dOjQ2HEv9eZVqp06dcq0ef+zt3+e1XDzL3/5i+ljVU0CdiUjAFxyySWmzWok6lUkfuUrXzFtM2bMMG0PPvigaVu1alXs+L333mv6fOc73zFtd999t2n78pe/bNq++93vmjarOvJrX/ua6XPllVfGjq9ZswaNjY3cq48QYkPxExIoFD8hgULxExIoFD8hgZJoYU9HR4e57ZK3dZVVpDN//nzT59FHHzVtVs80wN46CQAuvPDC2PHW1lbTx+vF563oexkEK+sAANu2bYsdP++880wfb5X95ptvNm3e9lrWXHnFWF7xi1d8ZBUzAYDVLt57vXnPWW1trWnziris7AcAvPzyy7HjI0eONH2sfoFeIVZPeOYnJFAofkICheInJFAofkICheInJFAofkICJZ3tuiYD+DWAEgBdANaq6hoRKQbwWwBTkdqya4Wq2jkvAPv378fSpUtjbQ8//LDpZxXpZLpdl1e84/X+mzNnTuz43r17TZ8tW7aYNs/v/vvvN21eSuzWW2+NHfe2u9q9e7dpGzNmjGlra2szbVdddVXs+B//+EfTJ9MCqfr6etM2b9682PGNGzeaPl6BjtU7DwC++c1vmraVK1eatjvvvDN23HvOpk+fHjtu9ZmMI50zfweA76nqLAALAdwoIrMBrASwSVVnAtgU/U0IOUPoVfyq2qyqr0a32wDUAZgIYDmADdHdNgCIf6snhPRL+nTNLyJTAcwHsBXAeFVtBlJvEADOyXVwhJD8kfbXe0VkGIAnAXxbVY9612A9/CoBVAL+VyoJIcmS1plfRAYhJfxHVPX0bgwtIlIa2UsBxO7GoKprVbVcVcspfkL6D72KX1Kn+HUA6lT1nm6mZwBURLcrANjLoISQfkevPfxE5BMAXgSwE6lUHwCsQuq6/3EA5wJ4HcDnVdUuhwIwY8YMveeee2Jty5YtM/2OHj0aO+6lNQYNGmTaBg60r3a8SjXrk0tnZ2dGcXh+Xhxe/BdddFHs+LPPPmv6tLS0mDavgtDrQXj99dfHjnu9BIuKikyb9zr1LkFfe+212HHvtWNtDwf4z+e6detMW2VlpWmzqkK99LfV3++iiy7C9u3b07om7/WaX1VfAmA92KXpHIQQ0v/gN/wICRSKn5BAofgJCRSKn5BAofgJCZREG3ieddZZZorFS9eMGDHCfLxM8NJGXirHwovDa9LpfenJs+3Zs6fPsXipVK+azmtm+ctf/tK0WdWFQ4YMMX08XnrpJdO2aNEi02alU48fP276PPHEE6bN26JsyZIlpu3NN980bVaF4QUXXGD6bN++PXbc+796wjM/IYFC8RMSKBQ/IYFC8RMSKBQ/IYFC8RMSKL1W9eWSKVOm6C233BJr+8Y3vmH6WTF66cFjx46ZNqshaKZ4jSyHDx9u2jKtVPNSc1YFpPc/e41EZ82aZdoyqY7MJJWaD7yKSi/N6vl5z6f3mNZz7VVNWvN43nnnoaqqKq2qPp75CQkUip+QQKH4CQkUip+QQKH4CQmURAt72tvbzQIHrwAmkwKeXK/oe3gr+h7ptj/vibfK7vWfs/jYxz6WURwe/WVV3yLTTtL56EBtZQm8noa5gGd+QgKF4ickUCh+QgKF4ickUCh+QgKF4ickUHpN9YnIZAC/BlCC1HZda1V1jYjcBuAGAEeiu65S1ee8xxo8eDCmTZsWa8u0Hx8hZzqZpnyzJZ08fweA76nqqyIyHMArIvJ8ZPu5qv40f+ERQvJFOnv1NQNojm63iUgdgIn5DowQkl/69FlbRKYCmI/UDr0A8C0RqRGR9SIyOsexEULySNriF5FhAJ4E8G1VPQrgPgAfBFCG1CeDnxl+lSJSJSJVXtMLQkiypCV+ERmElPAfUdWnAEBVW1S1U1W7ADwA4Pw4X1Vdq6rlqlqe6XfgCSG5p1fxS2opch2AOlW9p9t4abe7fRbArtyHRwjJF+ms9i8C8CUAO0VkRzS2CsB1IlIGQAE0APh6bw80ZswYVFRUZBgqISSXpLPa/xKAuESkm9MnhPRv+M0aQgKF4ickUCh+QgKF4ickUCh+QgIl0QaetbW1mDdvXqyturo6yVAICR6e+QkJFIqfkECh+AkJFIqfkECh+AkJFIqfkEBJNNU3e/ZsbN26NdZm7VcGFK7BIfHhc3ZmwzM/IYFC8RMSKBQ/IYFC8RMSKBQ/IYFC8RMSKImm+k6cOIG6urpY25w5c0y/zs7O2PEBAwaYPpmmoaxj9Xa8TMg0xkz8Mv2/cu2X6zkEgK6uLtN2Ju8Bme9U6pk7M4SQrKD4CQkUip+QQKH4CQkUip+QQOl1tV9EhgB4AcDg6P5PqOoPRWQagMcAFAN4FcCXVPWU91hDhw51V/UtMlkhznQ1NB+r0RaZxpiJX6aZkUznI8l5PJNX9D3yXRyVzqydBHCJqs5DajvuJSKyEMBdAH6uqjMBtAL4av7CJITkml7FrymORX8Oin4UwCUAnojGNwC4Ki8REkLyQlqfl0RkQLRD72EAzwN4DcC7qtoR3aURwMT8hEgIyQdpiV9VO1W1DMAkAOcDmBV3tzhfEakUkSoRqTpy5EjmkRJCckqfVkpU9V0AmwEsBDBKRE4vGE4C0GT4rFXVclUtHzduXDaxEkJySK/iF5FxIjIquj0UwGUA6gD8GcA10d0qADydryAJIbknncKeUgAbRGQAUm8Wj6vqsyJSC+AxEflvANsBrEvngEkWfGRCkn3pkjzW+7X4hWROr+JX1RoA82PG65G6/ieEnIHwLZ+QQKH4CQkUip+QQKH4CQkUip+QQBEv3ZTzg4kcAXAw+nMsgLcSO7gN43gvjOO9nGlxTFHVtL5Nl6j433NgkSpVLS/IwRkH42Ac/NhPSKhQ/IQESiHFv7aAx+4O43gvjOO9vG/jKNg1PyGksPBjPyGBUhDxi8gSEdkrIgdEZGUhYojiaBCRnSKyQ0SqEjzuehE5LCK7uo0Vi8jzIrI/+j26QHHcJiJvRHOyQ0SuSCCOySLyZxGpE5HdIvKf0Xiic+LEkeiciMgQEfmbiFRHcdwejU8Tka3RfPxWRIqyOpCqJvoDYABSbcCmAygCUA1gdtJxRLE0ABhbgOMuBrAAwK5uY3cDWBndXgngrgLFcRuAmxOej1IAC6LbwwHsAzA76Tlx4kh0TgAIgGHR7UEAtiLVQOdxAF+Ixu8H8B/ZHKcQZ/7zARxQ1XpNtfp+DMDyAsRRMFT1BQDv9BhejlQjVCChhqhGHImjqs2q+mp0uw2pZjETkfCcOHEkiqbIe9PcQoh/IoBD3f4uZPNPBbBRRF4RkcoCxXCa8araDKRehADOKWAs3xKRmuiyIO+XH90RkalI9Y/YigLOSY84gITnJImmuYUQf1yLmkKlHBap6gIASwHcKCKLCxRHf+I+AB9Eao+GZgA/S+rAIjIMwJMAvq2qR5M6bhpxJD4nmkXT3HQphPgbAUzu9rfZ/DPfqGpT9PswgN+hsJ2JWkSkFACi34cLEYSqtkQvvC4ADyChORGRQUgJ7hFVfSoaTnxO4uIo1JxEx+5z09x0KYT4twGYGa1cFgH4AoBnkg5CRM4WkeGnbwP4NIBdvldeeQapRqhAARuinhZbxGeRwJxIqmHhOgB1qnpPN1Oic2LFkfScJNY0N6kVzB6rmVcgtZL6GoBbCxTDdKQyDdUAdicZB4BHkfr42I7UJ6GvAhgDYBOA/dHv4gLF8TCAnQBqkBJfaQJxfAKpj7A1AHZEP1ckPSdOHInOCYC5SDXFrUHqjea/ur1m/wbgAID/BTA4m+PwG36EBAq/4UdIoFD8hAQKxU9IoFD8hAQKxU9IoFD8hAQKxU9IoFD8hATK/wND02H0HPBFbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ANar6ioj0AbAAwCBkt+y6V1UPeI9VV1dnlnPWr7e3++vSpUts+65du8w+b7/9thmz1pcDgKVLl5qxDz74ILbdW6dv+fLlZmz2bHvjI28NQm97rU8++SS23dt2y9v+yyvBeiUxq5TmbW3m8SZqHTt2zIxZJc6pU6eafbxz703s8Tz//PNm7Jlnnoltt9bpA+yc8Law+8l9c7jPKQC/VdUrAFwH4DciciWAqQCWqepQAMui74noHNFq8qtqg6qujm4fAVAPoArAHQDmRnebCyD+Ux1E1C616W9+ERkEYASAFQAqVLUByP4HAcBeZ5iI2p2cP94rIt0ALAIwRVUP57qwhYhMAjApup1kjERUBDm984tIR2QT/01VfSdq3i0ilVG8EkDsh+JVtUZVM6qaYfITtR+tJr9kM3Y2gHpVfalZaDGAh6LbDwF4r/DDI6JiaXUNPxH5GYD/BbAO2VIfAExD9u/+hQAGAvgGwC9Vdb/3WFdeeaW+/vrrsbFMJmP2s9as82a+hWrcuHGx7R999JHZJ8n6iYBf6rviiiti2zdv3pzoWElZ23J5pcOkv6GuXr3ajI0cOdKMWefR2moMsNdPzGQyqK2tzekHaPVvflX9MwDrwexVCYmoXeMn/IgCxeQnChSTnyhQTH6iQDH5iQKV6gKeANChQ9sPyZLe2bztuqwZhrfffrvZx5tpZy2eCgBfffWVGfv888/NWBLezMny8nIzZpX6vNeh93MNGTLEjA0bNsyMeXbs2BHb7pUHre3XvMVkW+I7P1GgmPxEgWLyEwWKyU8UKCY/UaCY/ESBSrXUd/LkSWzcuDE2Nnz48IIey5txdq6vK+CVPr/88ss2P55XHmrLgpDF5JXzPN4ehZbLLrss0bGSqq6ubnMf6+cq9AKeRPQ3iMlPFCgmP1GgmPxEgWLyEwUq9av9W7duTeVY5/oV/TS1lyv6lC4+60SBYvITBYrJTxQoJj9RoJj8RIFi8hMFqtVSn4hUA3gdQH9kt+uqUdVXRGQ6gF8B2BvddZqqfug9VteuXTFixIj8RkxEBZFLnf8UgN+q6moR6Q5glYic2fjtZVX9t+INj4iKJZe9+hoANES3j4hIPYCqYg+MiIqrTX/zi8ggACOQ3aEXAB4XkbUiMkdEehd4bERURDknv4h0A7AIwBRVPQxgJoAhAIYj+5vBi0a/SSJSKyK1hw4dKsCQiagQckp+EemIbOK/qarvAICq7lbV06raBGAWgNgNw1W1RlUzqprp2bNnocZNRHlqNfklO0NmNoB6VX2pWXtls7vdBWB94YdHRMWSy9X+GwA8AGCdiKyJ2qYBuF9EhgNQANsA/Lq1B+rRowcmTJiQcKhEVEi5XO3/M4C4+bFuTZ+I2jd+wo8oUEx+okAx+YkCxeQnChSTnyhQqS7gWVdXh/79+8fGvvvuuzSHQhQ8vvMTBYrJTxQoJj9RoJj8RIFi8hMFislPFKhUS33Dhg1DbW1tmockIgPf+YkCxeQnChSTnyhQTH6iQDH5iQLF5CcKVKqlvtOnT+PgwYOxsV69eqU5FKLg8Z2fKFBMfqJAMfmJAsXkJwoUk58oUK1e7ReRLgA+A9A5uv/bqvqsiAwGMB9AHwCrATygqj96j1VWVsar+kTtRC7v/CcB3KKqw5DdjnuCiFwH4PcAXlbVoQAOAHiseMMkokJrNfk162j0bcfonwK4BcDbUftcAHcWZYREVBQ5/c0vImXRDr17AHwEYAuAg6p6KrrLDgBVxRkiERVDTsmvqqdVdTiAAQBGAbgi7m5xfUVkkojUikjt3r17k4+UiAqqTVf7VfUggP8BcB2AXiJy5oLhAAC7jD41qppR1Uy/fv3yGSsRFVCryS8i/USkV3T7fAD/AKAewKcA7onu9hCA94o1SCIqvFwm9lQCmCsiZcj+Z7FQVZeIyF8AzBeR5wF8CWB2EcdJRAXWavKr6loAI2LatyL79z8RnYP4CT+iQDH5iQLF5CcKFJOfKFBMfqJAiWrsB/OKczCRvQC2R9+WA9iX2sFtHMfZOI6znWvjuFhVc/o0XarJf9aBRWpVNVOSg3McHAfHwV/7iULF5CcKVCmTv6aEx26O4zgbx3G2v9lxlOxvfiIqLf7aTxSokiS/iEwQkY0isllEppZiDNE4tonIOhFZIyK1KR53jojsEZH1zdr6iMhHIrIp+tq7ROOYLiI7o3OyRkQmpjCOahH5VETqRWSDiEyO2lM9J844Uj0nItJFRL4QkbpoHM9F7YNFZEV0PhaISKe8DqSqqf4DUIbsMmCXAOgEoA7AlWmPIxrLNgDlJTjuzwGMBLC+WdsLAKZGt6cC+H2JxjEdwJMpn49KACOj290BfAXgyrTPiTOOVM8JAAHQLbrdEcAKZBfQWQjgvqj9jwD+KZ/jlOKdfxSAzaq6VbNLfc8HcEcJxlEyqvoZgP0tmu9AdiFUIKUFUY1xpE5VG1R1dXT7CLKLxVQh5XPijCNVmlX0RXNLkfxVAL5t9n0pF/9UAH8SkVUiMqlEYzijQlUbgOyLEMCFJRzL4yKyNvqzoOh/fjQnIoOQXT9iBUp4TlqMA0j5nKSxaG4pkl9i2kpVcrhBVUcC+EcAvxGRn5doHO3JTABDkN2joQHAi2kdWES6AVgEYIqqHk7ruDmMI/VzonksmpurUiT/DgDVzb43F/8sNlXdFX3dA+BdlHZlot0iUgkA0dc9pRiEqu6OXnhNAGYhpXMiIh2RTbg3VfWdqDn1cxI3jlKdk+jYbV40N1elSP6VAIZGVy47AbgPwOK0ByEiF4hI9zO3AdwKYL3fq6gWI7sQKlDCBVHPJFvkLqRwTkREkF0Dsl5VX2oWSvWcWONI+5yktmhuWlcwW1zNnIjsldQtAP65RGO4BNlKQx2ADWmOA8A8ZH99bET2N6HHAPQFsAzApuhrnxKN478ArAOwFtnkq0xhHD9D9lfYtQDWRP8mpn1OnHGkek4A/B2yi+KuRfY/mn9p9pr9AsBmAP8NoHM+x+En/IgCxU/4EQWKyU8UKCY/UaCY/ESBYvITBYrJTxQoJj9RoJj8RIH6f6jfbJ8vHGURAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in new_image:\n",
    "    plt.imshow(i,cmap = 'gray')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "svhn_Sammed.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}