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ef216f4b · Saravanan G · 27e8a1bd · ef216f4b
Commit ef216f4b authored Apr 10, 2019 by Saravanan G
--- a/deep_auto_encoder.ipynb
+++ b/deep_auto_encoder.ipynb
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "deep_auto_encoder.ipynb",
+      "version": "0.3.2",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "metadata": {
+        "id": "kNQSuiScash4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from keras.datasets import mnist\n",
+        "from keras.layers import Input, Dense\n",
+        "from keras.models import Model"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "LVwNL6BFayAC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-zIjjlWHa2L5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "(X_train, _), (X_test, _) = mnist.load_data()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "Y-Cdl5rja23s",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "X_train = X_train.astype('float32')/255\n",
+        "X_test = X_test.astype('float32')/255"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "WD3WTHVna4-b",
+        "colab_type": "code",
+        "outputId": "09d9e53a-ead8-4114-b815-054d6b91edd8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "X_train = X_train.reshape(len(X_train), np.prod(X_train.shape[1:]))\n",
+        "X_test = X_test.reshape(len(X_test), np.prod(X_test.shape[1:]))\n",
+        "print(X_train.shape)\n",
+        "print(X_test.shape)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(60000, 784)\n",
+            "(10000, 784)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2Y2O2zJXa72j",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "input_img= Input(shape=(784,))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "nwsA5CURa9_V",
+        "colab_type": "code",
+        "outputId": "c06dff90-7772-4bc8-9a9c-e3d632d30dc7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 88
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "encoded = Dense(units=128, activation='relu')(input_img)\n",
+        "encoded = Dense(units=64, activation='relu')(encoded)\n",
+        "encoded = Dense(units=32, activation='relu')(encoded)\n",
+        "decoded = Dense(units=64, activation='relu')(encoded)\n",
+        "decoded = Dense(units=128, activation='relu')(decoded)\n",
+        "decoded = Dense(units=784, activation='sigmoid')(decoded)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Colocations handled automatically by placer.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ebMbt7g7bAOt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "autoencoder=Model(input_img, decoded)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "1tz9Z0V2bCbY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "encoder = Model(input_img, encoded)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VP6gANXEbEeu",
+        "colab_type": "code",
+        "outputId": "17fcfa91-01dc-4448-f165-952e1704aca4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 374
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "autoencoder.summary()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "input_1 (InputLayer)         (None, 784)               0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 128)               100480    \n",
+            "_________________________________________________________________\n",
+            "dense_2 (Dense)              (None, 64)                8256      \n",
+            "_________________________________________________________________\n",
+            "dense_3 (Dense)              (None, 32)                2080      \n",
+            "_________________________________________________________________\n",
+            "dense_4 (Dense)              (None, 64)                2112      \n",
+            "_________________________________________________________________\n",
+            "dense_5 (Dense)              (None, 128)               8320      \n",
+            "_________________________________________________________________\n",
+            "dense_6 (Dense)              (None, 784)               101136    \n",
+            "=================================================================\n",
+            "Total params: 222,384\n",
+            "Trainable params: 222,384\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rJQ7LmP-bGNn",
+        "colab_type": "code",
+        "outputId": "28fc7982-409f-4b75-f3fd-d2b6cc9f4ba5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "encoder.summary()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "input_1 (InputLayer)         (None, 784)               0         \n",
+            "_________________________________________________________________\n",
+            "dense_1 (Dense)              (None, 128)               100480    \n",
+            "_________________________________________________________________\n",
+            "dense_2 (Dense)              (None, 64)                8256      \n",
+            "_________________________________________________________________\n",
+            "dense_3 (Dense)              (None, 32)                2080      \n",
+            "=================================================================\n",
+            "Total params: 110,816\n",
+            "Trainable params: 110,816\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_tIMUPQNbIQO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "autoencoder.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3rAbBOambKSh",
+        "colab_type": "code",
+        "outputId": "61f5fe40-d041-4059-ee04-6893747e776f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1822
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "autoencoder.fit(X_train, X_train,\n",
+        "                epochs=50,\n",
+        "                batch_size=256,\n",
+        "                shuffle=True,\n",
+        "                validation_data=(X_test, X_test))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Use tf.cast instead.\n",
+            "Train on 60000 samples, validate on 10000 samples\n",
+            "Epoch 1/50\n",
+            "60000/60000 [==============================] - 6s 108us/step - loss: 0.2495 - acc: 0.7839 - val_loss: 0.1668 - val_acc: 0.7990\n",
+            "Epoch 2/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.1506 - acc: 0.8048 - val_loss: 0.1371 - val_acc: 0.8067\n",
+            "Epoch 3/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.1316 - acc: 0.8086 - val_loss: 0.1251 - val_acc: 0.8100\n",
+            "Epoch 4/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.1217 - acc: 0.8102 - val_loss: 0.1169 - val_acc: 0.8102\n",
+            "Epoch 5/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.1157 - acc: 0.8111 - val_loss: 0.1118 - val_acc: 0.8109\n",
+            "Epoch 6/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.1115 - acc: 0.8117 - val_loss: 0.1077 - val_acc: 0.8112\n",
+            "Epoch 7/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.1077 - acc: 0.8123 - val_loss: 0.1050 - val_acc: 0.8118\n",
+            "Epoch 8/50\n",
+            "60000/60000 [==============================] - 7s 109us/step - loss: 0.1050 - acc: 0.8126 - val_loss: 0.1030 - val_acc: 0.8117\n",
+            "Epoch 9/50\n",
+            "60000/60000 [==============================] - 6s 107us/step - loss: 0.1031 - acc: 0.8128 - val_loss: 0.1010 - val_acc: 0.8122\n",
+            "Epoch 10/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.1015 - acc: 0.8130 - val_loss: 0.0993 - val_acc: 0.8123\n",
+            "Epoch 11/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0999 - acc: 0.8132 - val_loss: 0.0995 - val_acc: 0.8117\n",
+            "Epoch 12/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0989 - acc: 0.8133 - val_loss: 0.0986 - val_acc: 0.8128\n",
+            "Epoch 13/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0979 - acc: 0.8134 - val_loss: 0.0964 - val_acc: 0.8125\n",
+            "Epoch 14/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0970 - acc: 0.8135 - val_loss: 0.0955 - val_acc: 0.8126\n",
+            "Epoch 15/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0961 - acc: 0.8135 - val_loss: 0.0955 - val_acc: 0.8126\n",
+            "Epoch 16/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0953 - acc: 0.8136 - val_loss: 0.0940 - val_acc: 0.8127\n",
+            "Epoch 17/50\n",
+            "60000/60000 [==============================] - 7s 111us/step - loss: 0.0946 - acc: 0.8137 - val_loss: 0.0936 - val_acc: 0.8126\n",
+            "Epoch 18/50\n",
+            "60000/60000 [==============================] - 7s 113us/step - loss: 0.0941 - acc: 0.8137 - val_loss: 0.0929 - val_acc: 0.8130\n",
+            "Epoch 19/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0934 - acc: 0.8138 - val_loss: 0.0923 - val_acc: 0.8128\n",
+            "Epoch 20/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0929 - acc: 0.8139 - val_loss: 0.0924 - val_acc: 0.8127\n",
+            "Epoch 21/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0924 - acc: 0.8139 - val_loss: 0.0913 - val_acc: 0.8129\n",
+            "Epoch 22/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0918 - acc: 0.8139 - val_loss: 0.0908 - val_acc: 0.8131\n",
+            "Epoch 23/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0913 - acc: 0.8140 - val_loss: 0.0906 - val_acc: 0.8129\n",
+            "Epoch 24/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0909 - acc: 0.8140 - val_loss: 0.0900 - val_acc: 0.8130\n",
+            "Epoch 25/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0905 - acc: 0.8141 - val_loss: 0.0896 - val_acc: 0.8131\n",
+            "Epoch 26/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0900 - acc: 0.8141 - val_loss: 0.0893 - val_acc: 0.8132\n",
+            "Epoch 27/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0896 - acc: 0.8142 - val_loss: 0.0893 - val_acc: 0.8130\n",
+            "Epoch 28/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.0892 - acc: 0.8142 - val_loss: 0.0884 - val_acc: 0.8133\n",
+            "Epoch 29/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0889 - acc: 0.8142 - val_loss: 0.0881 - val_acc: 0.8132\n",
+            "Epoch 30/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0885 - acc: 0.8143 - val_loss: 0.0878 - val_acc: 0.8134\n",
+            "Epoch 31/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0882 - acc: 0.8143 - val_loss: 0.0877 - val_acc: 0.8134\n",
+            "Epoch 32/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.0879 - acc: 0.8143 - val_loss: 0.0871 - val_acc: 0.8134\n",
+            "Epoch 33/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.0876 - acc: 0.8143 - val_loss: 0.0872 - val_acc: 0.8135\n",
+            "Epoch 34/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0872 - acc: 0.8144 - val_loss: 0.0866 - val_acc: 0.8135\n",
+            "Epoch 35/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0869 - acc: 0.8144 - val_loss: 0.0860 - val_acc: 0.8135\n",
+            "Epoch 36/50\n",
+            "60000/60000 [==============================] - 6s 106us/step - loss: 0.0867 - acc: 0.8144 - val_loss: 0.0862 - val_acc: 0.8134\n",
+            "Epoch 37/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0866 - acc: 0.8144 - val_loss: 0.0862 - val_acc: 0.8133\n",
+            "Epoch 38/50\n",
+            "60000/60000 [==============================] - 6s 101us/step - loss: 0.0863 - acc: 0.8144 - val_loss: 0.0856 - val_acc: 0.8135\n",
+            "Epoch 39/50\n",
+            "60000/60000 [==============================] - 6s 104us/step - loss: 0.0861 - acc: 0.8145 - val_loss: 0.0856 - val_acc: 0.8134\n",
+            "Epoch 40/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0859 - acc: 0.8145 - val_loss: 0.0856 - val_acc: 0.8134\n",
+            "Epoch 41/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0858 - acc: 0.8145 - val_loss: 0.0854 - val_acc: 0.8136\n",
+            "Epoch 42/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0856 - acc: 0.8145 - val_loss: 0.0850 - val_acc: 0.8135\n",
+            "Epoch 43/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0854 - acc: 0.8145 - val_loss: 0.0848 - val_acc: 0.8135\n",
+            "Epoch 44/50\n",
+            "60000/60000 [==============================] - 6s 103us/step - loss: 0.0853 - acc: 0.8145 - val_loss: 0.0852 - val_acc: 0.8135\n",
+            "Epoch 45/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0852 - acc: 0.8145 - val_loss: 0.0852 - val_acc: 0.8137\n",
+            "Epoch 46/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0850 - acc: 0.8145 - val_loss: 0.0849 - val_acc: 0.8137\n",
+            "Epoch 47/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0849 - acc: 0.8146 - val_loss: 0.0844 - val_acc: 0.8135\n",
+            "Epoch 48/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0847 - acc: 0.8146 - val_loss: 0.0841 - val_acc: 0.8136\n",
+            "Epoch 49/50\n",
+            "60000/60000 [==============================] - 6s 102us/step - loss: 0.0846 - acc: 0.8146 - val_loss: 0.0845 - val_acc: 0.8135\n",
+            "Epoch 50/50\n",
+            "60000/60000 [==============================] - 6s 105us/step - loss: 0.0845 - acc: 0.8146 - val_loss: 0.0841 - val_acc: 0.8136\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7f08eaa0e160>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TYzVkp3WbMbf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "encoded_imgs = encoder.predict(X_test)\n",
+        "predicted = autoencoder.predict(X_test)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "y_btKd2obSMt",
+        "colab_type": "code",
+        "outputId": "2818c688-2101-4682-b625-8445cc81414d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        }
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(40, 4))\n",
+        "for i in range(10):\n",
+        "    # display original images\n",
+        "    ax = plt.subplot(3, 20, i + 1)\n",
+        "    plt.imshow(X_test[i].reshape(28, 28))\n",
+        "    plt.gray()\n",
+        "    ax.get_xaxis().set_visible(False)\n",
+        "    ax.get_yaxis().set_visible(False)\n",
+        "    \n",
+        "    # display encoded images\n",
+        "    ax = plt.subplot(3, 20, i + 1 + 20)\n",
+        "    plt.imshow(encoded_imgs[i].reshape(8,4))\n",
+        "    plt.gray()\n",
+        "    ax.get_xaxis().set_visible(False)\n",
+        "    ax.get_yaxis().set_visible(False)\n",
+        "    # display reconstructed images\n",
+        "    ax = plt.subplot(3, 20, 2*20 +i+ 1)\n",
+        "    plt.imshow(predicted[i].reshape(28, 28))\n",
+        "    plt.gray()\n",
+        "    ax.get_xaxis().set_visible(False)\n",
+        "    ax.get_yaxis().set_visible(False)\n",
+        "  \n",
+        "    \n",
+        "plt.show()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TC9t33nnnwvbevXsXts8yyyzt3ie13SuvvFLYfs899xS2H3PMMYXtq6++ent3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\n62WXXVaL3VYnijhuvvnm2XM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+            "text/plain": [
+              "<Figure size 2880x288 with 30 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "59PJ-PbxbYE7",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
+%% Cell type:code id: tags:
+
+``` 
+from keras.datasets import mnist
+from keras.layers import Input, Dense
+from keras.models import Model
+```
+
+%% Cell type:code id: tags:
+
+``` 
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+%matplotlib inline
+```
+
+%% Cell type:code id: tags:
+
+``` 
+(X_train, _), (X_test, _) = mnist.load_data()
+```
+
+%% Cell type:code id: tags:
+
+``` 
+X_train = X_train.astype('float32')/255
+X_test = X_test.astype('float32')/255
+```
+
+%% Cell type:code id: tags:
+
+``` 
+X_train = X_train.reshape(len(X_train), np.prod(X_train.shape[1:]))
+X_test = X_test.reshape(len(X_test), np.prod(X_test.shape[1:]))
+print(X_train.shape)
+print(X_test.shape)
+```
+
+%% Output
+
+    (60000, 784)
+    (10000, 784)
+
+%% Cell type:code id: tags:
+
+``` 
+input_img= Input(shape=(784,))
+```
+
+%% Cell type:code id: tags:
+
+``` 
+encoded = Dense(units=128, activation='relu')(input_img)
+encoded = Dense(units=64, activation='relu')(encoded)
+encoded = Dense(units=32, activation='relu')(encoded)
+decoded = Dense(units=64, activation='relu')(encoded)
+decoded = Dense(units=128, activation='relu')(decoded)
+decoded = Dense(units=784, activation='sigmoid')(decoded)
+```
+
+%% Output
+
+    WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
+    Instructions for updating:
+    Colocations handled automatically by placer.
+
+%% Cell type:code id: tags:
+
+``` 
+autoencoder=Model(input_img, decoded)
+```
+
+%% Cell type:code id: tags:
+
+``` 
+encoder = Model(input_img, encoded)
+```
+
+%% Cell type:code id: tags:
+
+``` 
+autoencoder.summary()
+```
+
+%% Output
+
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    input_1 (InputLayer)         (None, 784)               0
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 128)               100480
+    _________________________________________________________________
+    dense_2 (Dense)              (None, 64)                8256
+    _________________________________________________________________
+    dense_3 (Dense)              (None, 32)                2080
+    _________________________________________________________________
+    dense_4 (Dense)              (None, 64)                2112
+    _________________________________________________________________
+    dense_5 (Dense)              (None, 128)               8320
+    _________________________________________________________________
+    dense_6 (Dense)              (None, 784)               101136
+    =================================================================
+    Total params: 222,384
+    Trainable params: 222,384
+    Non-trainable params: 0
+    _________________________________________________________________
+
+%% Cell type:code id: tags:
+
+``` 
+encoder.summary()
+```
+
+%% Output
+
+    _________________________________________________________________
+    Layer (type)                 Output Shape              Param #
+    =================================================================
+    input_1 (InputLayer)         (None, 784)               0
+    _________________________________________________________________
+    dense_1 (Dense)              (None, 128)               100480
+    _________________________________________________________________
+    dense_2 (Dense)              (None, 64)                8256
+    _________________________________________________________________
+    dense_3 (Dense)              (None, 32)                2080
+    =================================================================
+    Total params: 110,816
+    Trainable params: 110,816
+    Non-trainable params: 0
+    _________________________________________________________________
+
+%% Cell type:code id: tags:
+
+``` 
+autoencoder.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+```
+
+%% Cell type:code id: tags:
+
+``` 
+autoencoder.fit(X_train, X_train,
+                epochs=50,
+                batch_size=256,
+                shuffle=True,
+                validation_data=(X_test, X_test))
+```
+
+%% Output
+
+    WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
+    Instructions for updating:
+    Use tf.cast instead.
+    Train on 60000 samples, validate on 10000 samples
+    Epoch 1/50
+    60000/60000 [==============================] - 6s 108us/step - loss: 0.2495 - acc: 0.7839 - val_loss: 0.1668 - val_acc: 0.7990
+    Epoch 2/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.1506 - acc: 0.8048 - val_loss: 0.1371 - val_acc: 0.8067
+    Epoch 3/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.1316 - acc: 0.8086 - val_loss: 0.1251 - val_acc: 0.8100
+    Epoch 4/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.1217 - acc: 0.8102 - val_loss: 0.1169 - val_acc: 0.8102
+    Epoch 5/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.1157 - acc: 0.8111 - val_loss: 0.1118 - val_acc: 0.8109
+    Epoch 6/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.1115 - acc: 0.8117 - val_loss: 0.1077 - val_acc: 0.8112
+    Epoch 7/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.1077 - acc: 0.8123 - val_loss: 0.1050 - val_acc: 0.8118
+    Epoch 8/50
+    60000/60000 [==============================] - 7s 109us/step - loss: 0.1050 - acc: 0.8126 - val_loss: 0.1030 - val_acc: 0.8117
+    Epoch 9/50
+    60000/60000 [==============================] - 6s 107us/step - loss: 0.1031 - acc: 0.8128 - val_loss: 0.1010 - val_acc: 0.8122
+    Epoch 10/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.1015 - acc: 0.8130 - val_loss: 0.0993 - val_acc: 0.8123
+    Epoch 11/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0999 - acc: 0.8132 - val_loss: 0.0995 - val_acc: 0.8117
+    Epoch 12/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0989 - acc: 0.8133 - val_loss: 0.0986 - val_acc: 0.8128
+    Epoch 13/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0979 - acc: 0.8134 - val_loss: 0.0964 - val_acc: 0.8125
+    Epoch 14/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0970 - acc: 0.8135 - val_loss: 0.0955 - val_acc: 0.8126
+    Epoch 15/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0961 - acc: 0.8135 - val_loss: 0.0955 - val_acc: 0.8126
+    Epoch 16/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0953 - acc: 0.8136 - val_loss: 0.0940 - val_acc: 0.8127
+    Epoch 17/50
+    60000/60000 [==============================] - 7s 111us/step - loss: 0.0946 - acc: 0.8137 - val_loss: 0.0936 - val_acc: 0.8126
+    Epoch 18/50
+    60000/60000 [==============================] - 7s 113us/step - loss: 0.0941 - acc: 0.8137 - val_loss: 0.0929 - val_acc: 0.8130
+    Epoch 19/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0934 - acc: 0.8138 - val_loss: 0.0923 - val_acc: 0.8128
+    Epoch 20/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0929 - acc: 0.8139 - val_loss: 0.0924 - val_acc: 0.8127
+    Epoch 21/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0924 - acc: 0.8139 - val_loss: 0.0913 - val_acc: 0.8129
+    Epoch 22/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0918 - acc: 0.8139 - val_loss: 0.0908 - val_acc: 0.8131
+    Epoch 23/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0913 - acc: 0.8140 - val_loss: 0.0906 - val_acc: 0.8129
+    Epoch 24/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0909 - acc: 0.8140 - val_loss: 0.0900 - val_acc: 0.8130
+    Epoch 25/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0905 - acc: 0.8141 - val_loss: 0.0896 - val_acc: 0.8131
+    Epoch 26/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0900 - acc: 0.8141 - val_loss: 0.0893 - val_acc: 0.8132
+    Epoch 27/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0896 - acc: 0.8142 - val_loss: 0.0893 - val_acc: 0.8130
+    Epoch 28/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.0892 - acc: 0.8142 - val_loss: 0.0884 - val_acc: 0.8133
+    Epoch 29/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0889 - acc: 0.8142 - val_loss: 0.0881 - val_acc: 0.8132
+    Epoch 30/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0885 - acc: 0.8143 - val_loss: 0.0878 - val_acc: 0.8134
+    Epoch 31/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0882 - acc: 0.8143 - val_loss: 0.0877 - val_acc: 0.8134
+    Epoch 32/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.0879 - acc: 0.8143 - val_loss: 0.0871 - val_acc: 0.8134
+    Epoch 33/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.0876 - acc: 0.8143 - val_loss: 0.0872 - val_acc: 0.8135
+    Epoch 34/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0872 - acc: 0.8144 - val_loss: 0.0866 - val_acc: 0.8135
+    Epoch 35/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0869 - acc: 0.8144 - val_loss: 0.0860 - val_acc: 0.8135
+    Epoch 36/50
+    60000/60000 [==============================] - 6s 106us/step - loss: 0.0867 - acc: 0.8144 - val_loss: 0.0862 - val_acc: 0.8134
+    Epoch 37/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0866 - acc: 0.8144 - val_loss: 0.0862 - val_acc: 0.8133
+    Epoch 38/50
+    60000/60000 [==============================] - 6s 101us/step - loss: 0.0863 - acc: 0.8144 - val_loss: 0.0856 - val_acc: 0.8135
+    Epoch 39/50
+    60000/60000 [==============================] - 6s 104us/step - loss: 0.0861 - acc: 0.8145 - val_loss: 0.0856 - val_acc: 0.8134
+    Epoch 40/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0859 - acc: 0.8145 - val_loss: 0.0856 - val_acc: 0.8134
+    Epoch 41/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0858 - acc: 0.8145 - val_loss: 0.0854 - val_acc: 0.8136
+    Epoch 42/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0856 - acc: 0.8145 - val_loss: 0.0850 - val_acc: 0.8135
+    Epoch 43/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0854 - acc: 0.8145 - val_loss: 0.0848 - val_acc: 0.8135
+    Epoch 44/50
+    60000/60000 [==============================] - 6s 103us/step - loss: 0.0853 - acc: 0.8145 - val_loss: 0.0852 - val_acc: 0.8135
+    Epoch 45/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0852 - acc: 0.8145 - val_loss: 0.0852 - val_acc: 0.8137
+    Epoch 46/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0850 - acc: 0.8145 - val_loss: 0.0849 - val_acc: 0.8137
+    Epoch 47/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0849 - acc: 0.8146 - val_loss: 0.0844 - val_acc: 0.8135
+    Epoch 48/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0847 - acc: 0.8146 - val_loss: 0.0841 - val_acc: 0.8136
+    Epoch 49/50
+    60000/60000 [==============================] - 6s 102us/step - loss: 0.0846 - acc: 0.8146 - val_loss: 0.0845 - val_acc: 0.8135
+    Epoch 50/50
+    60000/60000 [==============================] - 6s 105us/step - loss: 0.0845 - acc: 0.8146 - val_loss: 0.0841 - val_acc: 0.8136
+
+    <keras.callbacks.History at 0x7f08eaa0e160>
+
+%% Cell type:code id: tags:
+
+``` 
+encoded_imgs = encoder.predict(X_test)
+predicted = autoencoder.predict(X_test)
+```
+
+%% Cell type:code id: tags:
+
+``` 
+plt.figure(figsize=(40, 4))
+for i in range(10):
+    # display original images
+    ax = plt.subplot(3, 20, i + 1)
+    plt.imshow(X_test[i].reshape(28, 28))
+    plt.gray()
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+
+    # display encoded images
+    ax = plt.subplot(3, 20, i + 1 + 20)
+    plt.imshow(encoded_imgs[i].reshape(8,4))
+    plt.gray()
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+    # display reconstructed images
+    ax = plt.subplot(3, 20, 2*20 +i+ 1)
+    plt.imshow(predicted[i].reshape(28, 28))
+    plt.gray()
+    ax.get_xaxis().set_visible(False)
+    ax.get_yaxis().set_visible(False)
+
+
+plt.show()
+```
+
+%% Output
+
+
+
+%% Cell type:code id: tags:
+
+``` 
+
+```