Deep inside - Autoencoders.ipynb

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  "metadata": {
    "colab": {
      "name": "Deep inside: Autoencoders.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JgFlNj-4qrv",
        "colab_type": "text"
      },
      "source": [
        "## Import the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aB6RU0ynjwrx",
        "colab_type": "code",
        "outputId": "86c6c152-b893-4922-849e-d266931603a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, add\n",
        "from keras.layers.core import Layer, Dense, Dropout\n",
        "from keras.layers.core import Activation, Flatten, Reshape\n",
        "from keras import regularizers\n",
        "from keras.regularizers import l2\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers.convolutional import UpSampling2D, ZeroPadding2D\n",
        "from keras.utils import np_utils"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "76tcHwdS5Zmh",
        "colab_type": "text"
      },
      "source": [
        "## Load the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qvO7xaNp5cQk",
        "colab_type": "text"
      },
      "source": [
        "We don't need the labels as the autoencoders are an unsupervised network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KG5MfWCW5TWE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, _), (X_test, _) = mnist.load_data()\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJefX70e68kR",
        "colab_type": "text"
      },
      "source": [
        "## Normalize the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ONfIJIn6-el",
        "colab_type": "text"
      },
      "source": [
        "We want the pixels values between 0 and 1 instead of between 0 and 255"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQN8rg5J661w",
        "colab_type": "code",
        "outputId": "a894066b-cb86-41bc-9a4e-2be9764134cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "X_train = X_train.astype(\"float32\") / 255.\n",
        "X_test = X_test.astype(\"float32\") / 255.\n",
        "\n",
        "print('X_train shape: ', X_train.shape)\n",
        "print(X_train.shape[0], ' train samples')\n",
        "print(X_test.shape[0], ' test samples')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape:  (60000, 28, 28, 1)\n",
            "60000  train samples\n",
            "10000  test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNTqK6767Zr_",
        "colab_type": "text"
      },
      "source": [
        "## Flatten the images for the Fully-Connected Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GyMp_q1W7XvM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape((len(X_train), np.prod(X_train.shape[1:])))\n",
        "\n",
        "X_test = X_test.reshape((len(X_test), np.prod(X_test.shape[1:])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mqxwmu8j72D9",
        "colab_type": "text"
      },
      "source": [
        "# Vanilla Autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DlBsmseI743q",
        "colab_type": "text"
      },
      "source": [
        "## Create the network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9Zgln_077B1",
        "colab_type": "text"
      },
      "source": [
        "The first network is the most simple autoencoder.\n",
        "It has 3 layers - Input - encoded - decoded"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOh-9WO_7uVa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_size = 784\n",
        "hidden_size = 64"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nGdcvW08Gvt",
        "colab_type": "code",
        "outputId": "d924ae52-fe28-48fe-81d9-6c2f9edfe1d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "x = Input(shape = (input_size, ))\n",
        "h = Dense(hidden_size, activation = 'relu')(x)\n",
        "r = Dense(input_size, activation = 'sigmoid')(h)\n",
        "\n",
        "autoencoder = Model(inputs = x, outputs = r)\n",
        "autoencoder.compile(optimizer = 'adam', loss = 'mse')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0807 20:06:33.377691 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "W0807 20:06:33.395208 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "W0807 20:06:33.400713 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "W0807 20:06:33.425886 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uaNLLzVC8b7w",
        "colab_type": "code",
        "outputId": "2bdd991d-d643-4736-828a-b9b44ba30def",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(autoencoder).create(prog = 'dot', format = 'svg'))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"191pt\" viewBox=\"0.00 0.00 141.00 191.00\" width=\"141pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 187)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-187 137,-187 137,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139990240464064 -->\n<g class=\"node\" id=\"node1\">\n<title>139990240464064</title>\n<polygon fill=\"none\" points=\"0,-146.5 0,-182.5 133,-182.5 133,-146.5 0,-146.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-160.8\">input_1: InputLayer</text>\n</g>\n<!-- 139990240464344 -->\n<g class=\"node\" id=\"node2\">\n<title>139990240464344</title>\n<polygon fill=\"none\" points=\"13,-73.5 13,-109.5 120,-109.5 120,-73.5 13,-73.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-87.8\">dense_1: Dense</text>\n</g>\n<!-- 139990240464064&#45;&gt;139990240464344 -->\n<g class=\"edge\" id=\"edge1\">\n<title>139990240464064-&gt;139990240464344</title>\n<path d=\"M66.5,-146.4551C66.5,-138.3828 66.5,-128.6764 66.5,-119.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-119.5903 66.5,-109.5904 63.0001,-119.5904 70.0001,-119.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139990240587848 -->\n<g class=\"node\" id=\"node3\">\n<title>139990240587848</title>\n<polygon fill=\"none\" points=\"13,-.5 13,-36.5 120,-36.5 120,-.5 13,-.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-14.8\">dense_2: Dense</text>\n</g>\n<!-- 139990240464344&#45;&gt;139990240587848 -->\n<g class=\"edge\" id=\"edge2\">\n<title>139990240464344-&gt;139990240587848</title>\n<path d=\"M66.5,-73.4551C66.5,-65.3828 66.5,-55.6764 66.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-46.5903 66.5,-36.5904 63.0001,-46.5904 70.0001,-46.5903\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ccsKwQv58r7e",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8_sGhmG8ons",
        "colab_type": "code",
        "outputId": "1548d6fb-47bd-4151-ff41-79740689e30f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "epochs = 5\n",
        "batch_size = 128\n",
        "\n",
        "history = autoencoder.fit(X_train, X_train,\n",
        "                          batch_size = batch_size,\n",
        "                          epochs = epochs,\n",
        "                          verbose = 1,\n",
        "                          validation_data = (X_test, X_test))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0807 20:06:33.621917 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "W0807 20:06:33.687054 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 3s 44us/step - loss: 0.0445 - val_loss: 0.0223\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.0173 - val_loss: 0.0129\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.0109 - val_loss: 0.0087\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.0078 - val_loss: 0.0066\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.0062 - val_loss: 0.0055\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdXNxXZ49DZa",
        "colab_type": "code",
        "outputId": "93f0941d-971d-4de0-88ca-aac86fc16471",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "conv_encoder = Model(x, h)\n",
        "encoded_imgs = conv_encoder.predict(X_test)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize = (20, 8))\n",
        "for i in range(n):\n",
        "  ax = plt.subplot(1, n, i + 1)\n",
        "  plt.imshow(encoded_imgs[i].reshape(4, 16).T)\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x576 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mzDtd7XK9yIm",
        "colab_type": "text"
      },
      "source": [
        "## Predict on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KDDWK3D9kK_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs = autoencoder.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mCSATy7g-AW5",
        "colab_type": "text"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3HWnLen99L4",
        "colab_type": "code",
        "outputId": "211f325a-0cb2-427e-ac43-2d2cb888c9a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize = (20, 6))\n",
        "\n",
        "for i in range(n):\n",
        "  # display original images\n",
        "  ax = plt.subplot(3, n, 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 reconstructed images\n",
        "  ax = plt.subplot(3, n, i + n + 1)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x432 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTYwfnfK-ozR",
        "colab_type": "text"
      },
      "source": [
        "## Plot the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSxKgTgO-ltE",
        "colab_type": "code",
        "outputId": "e147eac6-d841-45f9-f6e0-8631ff7034f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'validation'], loc = 'upper right')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'loss'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W5xZsJPdgUe0bGGOaLQuSuhCBPlfAHYth0E9g4TPwl+GwYa7XlQXE1HG9OFJc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\nJMYYY+rl/wP/0T8VEktkjwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dBzX1QFFd5XS",
        "colab_type": "text"
      },
      "source": [
        "# Multilayer Autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WDUDgJ4Td_Ks",
        "colab_type": "text"
      },
      "source": [
        "## Create the network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmK1ZomceBwJ",
        "colab_type": "text"
      },
      "source": [
        "We extend the idea of the first network to more layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asOXpJDi_QL-",
        "colab_type": "code",
        "outputId": "91dc9375-8c3c-4af2-db45-c44a23b52cba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "input_size = 784\n",
        "hidden_size = 128\n",
        "code_size = 64\n",
        "\n",
        "x = Input(shape = (input_size, ))\n",
        "hidden_1 = Dense(hidden_size, activation = 'relu')(x)\n",
        "code = Dense(code_size, activation = 'relu')(hidden_1)\n",
        "hidden_2 = Dense(hidden_size, activation = 'relu')(code)\n",
        "r = Dense(input_size, activation = 'sigmoid')(hidden_2)\n",
        "\n",
        "autoencoder = Model(inputs = x, outputs = r)\n",
        "autoencoder.compile(optimizer = 'adam', \n",
        "                    loss = 'binary_crossentropy')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0807 20:06:43.951799 139991491549056 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjnw5BfNezBh",
        "colab_type": "code",
        "outputId": "d5b31f24-1912-4795-8587-645e8b70fb4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        }
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(autoencoder).create(prog = 'dot', format = 'svg'))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"337pt\" viewBox=\"0.00 0.00 141.00 337.00\" width=\"141pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 333)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-333 137,-333 137,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139990231194368 -->\n<g class=\"node\" id=\"node1\">\n<title>139990231194368</title>\n<polygon fill=\"none\" points=\"0,-292.5 0,-328.5 133,-328.5 133,-292.5 0,-292.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-306.8\">input_2: InputLayer</text>\n</g>\n<!-- 139990240463784 -->\n<g class=\"node\" id=\"node2\">\n<title>139990240463784</title>\n<polygon fill=\"none\" points=\"13,-219.5 13,-255.5 120,-255.5 120,-219.5 13,-219.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-233.8\">dense_3: Dense</text>\n</g>\n<!-- 139990231194368&#45;&gt;139990240463784 -->\n<g class=\"edge\" id=\"edge1\">\n<title>139990231194368-&gt;139990240463784</title>\n<path d=\"M66.5,-292.4551C66.5,-284.3828 66.5,-274.6764 66.5,-265.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-265.5903 66.5,-255.5904 63.0001,-265.5904 70.0001,-265.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139989998052632 -->\n<g class=\"node\" id=\"node3\">\n<title>139989998052632</title>\n<polygon fill=\"none\" points=\"13,-146.5 13,-182.5 120,-182.5 120,-146.5 13,-146.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-160.8\">dense_4: Dense</text>\n</g>\n<!-- 139990240463784&#45;&gt;139989998052632 -->\n<g class=\"edge\" id=\"edge2\">\n<title>139990240463784-&gt;139989998052632</title>\n<path d=\"M66.5,-219.4551C66.5,-211.3828 66.5,-201.6764 66.5,-192.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-192.5903 66.5,-182.5904 63.0001,-192.5904 70.0001,-192.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139989998052856 -->\n<g class=\"node\" id=\"node4\">\n<title>139989998052856</title>\n<polygon fill=\"none\" points=\"13,-73.5 13,-109.5 120,-109.5 120,-73.5 13,-73.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-87.8\">dense_5: Dense</text>\n</g>\n<!-- 139989998052632&#45;&gt;139989998052856 -->\n<g class=\"edge\" id=\"edge3\">\n<title>139989998052632-&gt;139989998052856</title>\n<path d=\"M66.5,-146.4551C66.5,-138.3828 66.5,-128.6764 66.5,-119.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-119.5903 66.5,-109.5904 63.0001,-119.5904 70.0001,-119.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139989997798680 -->\n<g class=\"node\" id=\"node5\">\n<title>139989997798680</title>\n<polygon fill=\"none\" points=\"13,-.5 13,-36.5 120,-36.5 120,-.5 13,-.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-14.8\">dense_6: Dense</text>\n</g>\n<!-- 139989998052856&#45;&gt;139989997798680 -->\n<g class=\"edge\" id=\"edge4\">\n<title>139989998052856-&gt;139989997798680</title>\n<path d=\"M66.5,-73.4551C66.5,-65.3828 66.5,-55.6764 66.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-46.5903 66.5,-36.5904 63.0001,-46.5904 70.0001,-46.5903\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h9swyjFXfIGa",
        "colab_type": "text"
      },
      "source": [
        "## Train the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIvuLlpWfEPJ",
        "colab_type": "code",
        "outputId": "8216bcf0-6b8e-45a8-d9a2-d2763055e5c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "epochs = 5\n",
        "batch_size = 128\n",
        "\n",
        "history = autoencoder.fit(X_train, X_train,\n",
        "                          batch_size = batch_size,\n",
        "                          epochs = epochs,\n",
        "                          verbose = 1,\n",
        "                          validation_data = (X_test, X_test))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1814 - val_loss: 0.1170\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.1078 - val_loss: 0.0997\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.0970 - val_loss: 0.0925\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.0911 - val_loss: 0.0880\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 2s 33us/step - loss: 0.0875 - val_loss: 0.0850\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUCqeBXJfttU",
        "colab_type": "text"
      },
      "source": [
        "## Predict on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Hudb5fgffv7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs = autoencoder.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lciPzHUMf3_1",
        "colab_type": "text"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_wgTzLLf2Vs",
        "colab_type": "code",
        "outputId": "d7efaae9-384d-42f9-ec2e-ce0b7c79fc2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize = (20, 6))\n",
        "\n",
        "for i in range(n):\n",
        "  # Displaying original images\n",
        "  ax = plt.subplot(3, n, 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",
        "  # Displaying reconstructed images\n",
        "  ax = plt.subplot(3, n, i + n + 1)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  \n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x432 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EjcDn1CThjQ-",
        "colab_type": "text"
      },
      "source": [
        "## Plot the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuuzDTeaheaR",
        "colab_type": "code",
        "outputId": "4a2dd5f6-db3e-4a6d-a8f1-62d2eb6d9d71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train, validation'], loc = 'upper right')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'loss'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TE961ikhoKCw6U8pAOPen3rTbeT+CLz6B334Nnvyyd9xGmE67jYr0ZkZt3FPM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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZqlpWp1lT8o",
        "colab_type": "text"
      },
      "source": [
        "# Convolutional Autoencoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XkyeOymh6v6",
        "colab_type": "code",
        "outputId": "2097ab18-3e66-4036-82c4-9cdbe2712b25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "num_classes = 10\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
        "X_train = X_train.astype(\"float32\") / 255.\n",
        "X_test = X_test.astype(\"float32\") /255.\n",
        "\n",
        "print('X_train shape: ', X_train.shape)\n",
        "print(X_train.shape[0], 'train samples')\n",
        "print(X_test.shape[0], 'test samples')\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train, num_classes)\n",
        "y_test = np_utils.to_categorical(y_test, num_classes)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape:  (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8xBDTgEmLM_",
        "colab_type": "text"
      },
      "source": [
        "## Create the network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0gpB7yoQmONA",
        "colab_type": "text"
      },
      "source": [
        "This network does not take flattened vectors as an input, but 3D images instead"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "advx4QXRmIrR",
        "colab_type": "code",
        "outputId": "34227717-d19c-49f7-8d33-5d9a26bda15d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "x = Input(shape = (28, 28, 1))\n",
        "\n",
        "# Encoder\n",
        "conv1_1 = Conv2D(16, (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "pool1 = MaxPooling2D((2, 2), padding = 'same')(conv1_1)\n",
        "conv1_2 = Conv2D(8, (3, 3), activation = 'relu', padding = 'same')(pool1)\n",
        "pool2 = MaxPooling2D((2, 2), padding = 'same')(conv1_2)\n",
        "conv1_3 = Conv2D(8, (3, 3), activation = 'relu', padding = 'same')(pool2)\n",
        "h = MaxPooling2D((2, 2), padding = 'same')(conv1_3)\n",
        "\n",
        "# Decoder\n",
        "conv2_1 = Conv2D(8, (3, 3), activation = 'relu', padding = 'same')(h)\n",
        "up1 = UpSampling2D((2, 2))(conv2_1)\n",
        "conv2_2 = Conv2D(8, (3, 3), activation = 'relu', padding = 'same')(up1)\n",
        "up2 = UpSampling2D((2, 2))(conv2_2)\n",
        "conv2_3 = Conv2D(16, (3, 3), activation = 'relu')(up2)\n",
        "up3 = UpSampling2D((2, 2))(conv2_3)\n",
        "r = Conv2D(1, (3, 3), activation = 'sigmoid', padding = 'same')(up3)\n",
        "\n",
        "autoencoder = Model(inputs = x, outputs = r)\n",
        "autoencoder.compile(optimizer = 'adadelta', \n",
        "                    loss = 'binary_crossentropy')"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0807 20:06:56.263381 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "W0807 20:06:56.307431 139991491549056 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2018: The name tf.image.resize_nearest_neighbor is deprecated. Please use tf.compat.v1.image.resize_nearest_neighbor instead.\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Atw30DYoVRz",
        "colab_type": "code",
        "outputId": "cbff2584-ce3d-4f3a-92c3-9ffd17ba6628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(autoencoder).create(prog = 'dot', format = 'svg'))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xyrDIgxorb_",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msVB0uRvojoW",
        "colab_type": "code",
        "outputId": "4470bcff-662f-40c7-900c-4caa7d7c9e58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "epochs = 5\n",
        "batch_size = 128\n",
        "\n",
        "history = autoencoder.fit(X_train, X_train,\n",
        "                          batch_size = batch_size,\n",
        "                          epochs = epochs,\n",
        "                          verbose = 1,\n",
        "                          validation_data = (X_test, X_test))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 5s 91us/step - loss: 0.2135 - val_loss: 0.1648\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1558 - val_loss: 0.1423\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1415 - val_loss: 0.1360\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1343 - val_loss: 0.1328\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 3s 53us/step - loss: 0.1300 - val_loss: 0.1269\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgXi6yKBo-5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs = autoencoder.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wd7Bdj17pXWi",
        "colab_type": "text"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9o2aTVRpU0h",
        "colab_type": "code",
        "outputId": "1a15bcb2-ac46-4bbd-8185-fe57863a9191",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize = (20, 6))\n",
        "\n",
        "for i in range(n):\n",
        "  # Displaying original images\n",
        "  ax = plt.subplot(3, n, 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",
        "  # Displaying reconstructed images\n",
        "  ax = plt.subplot(3, n, i + n + 1)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  \n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 1440x432 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qriB8n3-qJxT",
        "colab_type": "text"
      },
      "source": [
        "## Plot the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTFf0bv7qFQb",
        "colab_type": "code",
        "outputId": "6d58e9a1-61aa-4c63-c178-18fa03cc0915",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'validation'], loc = 'upper right')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'loss'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kYdFe1VTD4iet5UICI6zAyJzidFWtsruwzBqi+m4Xh0ur6JMYzg1junNpVhIh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\n/x8BA8rpvIUlyQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfud9ue45xc0",
        "colab_type": "text"
      },
      "source": [
        "# Sparse Autoencoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grPPcQmD572J",
        "colab_type": "text"
      },
      "source": [
        "## Create the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWakF75WqiX4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_size = 784\n",
        "hidden_size = 32"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7-VzH9q8yh0",
        "colab_type": "text"
      },
      "source": [
        "## Normalizing the data, flattening the images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9Wmm9827r4q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
        "\n",
        "X_train = X_train.astype(\"float32\") / 255.\n",
        "X_test = X_test.astype(\"float32\") / 255.\n",
        "\n",
        "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:])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfIYtyiF6Ipf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = Input(shape = (input_size, ))\n",
        "h = Dense(hidden_size, activation = 'relu',\n",
        "          activity_regularizer = regularizers.l1(10e-6))(x)\n",
        "r = Dense(input_size, activation = 'sigmoid')(h)\n",
        "\n",
        "autoencoder = Model(inputs = x, outputs = r)\n",
        "autoencoder.compile(optimizer = 'Adam', \n",
        "                    loss = 'binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gb0lKPBk6myU",
        "colab_type": "code",
        "outputId": "4569ce16-6899-4331-976b-4ea1786f0054",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(autoencoder).create(prog = 'dot', format = 'svg'))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"191pt\" viewBox=\"0.00 0.00 141.00 191.00\" width=\"141pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 187)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-187 137,-187 137,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139988702464656 -->\n<g class=\"node\" id=\"node1\">\n<title>139988702464656</title>\n<polygon fill=\"none\" points=\"0,-146.5 0,-182.5 133,-182.5 133,-146.5 0,-146.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-160.8\">input_4: InputLayer</text>\n</g>\n<!-- 139988702464544 -->\n<g class=\"node\" id=\"node2\">\n<title>139988702464544</title>\n<polygon fill=\"none\" points=\"13,-73.5 13,-109.5 120,-109.5 120,-73.5 13,-73.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-87.8\">dense_7: Dense</text>\n</g>\n<!-- 139988702464656&#45;&gt;139988702464544 -->\n<g class=\"edge\" id=\"edge1\">\n<title>139988702464656-&gt;139988702464544</title>\n<path d=\"M66.5,-146.4551C66.5,-138.3828 66.5,-128.6764 66.5,-119.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-119.5903 66.5,-109.5904 63.0001,-119.5904 70.0001,-119.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139988708902336 -->\n<g class=\"node\" id=\"node3\">\n<title>139988708902336</title>\n<polygon fill=\"none\" points=\"13,-.5 13,-36.5 120,-36.5 120,-.5 13,-.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"66.5\" y=\"-14.8\">dense_8: Dense</text>\n</g>\n<!-- 139988702464544&#45;&gt;139988708902336 -->\n<g class=\"edge\" id=\"edge2\">\n<title>139988702464544-&gt;139988708902336</title>\n<path d=\"M66.5,-73.4551C66.5,-65.3828 66.5,-55.6764 66.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"70.0001,-46.5903 66.5,-36.5904 63.0001,-46.5904 70.0001,-46.5903\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TCBmemdu67mO",
        "colab_type": "code",
        "outputId": "a36a1e3d-43d2-48ad-a5a2-de2fb58a074a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "source": [
        "epochs = 15\n",
        "batch_size = 128\n",
        "\n",
        "history = autoencoder.fit(X_train, X_train,\n",
        "                          batch_size = batch_size,\n",
        "                          epochs = epochs,\n",
        "                          verbose = 1,\n",
        "                          validation_data = (X_test, X_test))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/15\n",
            "60000/60000 [==============================] - 2s 35us/step - loss: 0.5888 - val_loss: 0.4959\n",
            "Epoch 2/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.4239 - val_loss: 0.3600\n",
            "Epoch 3/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.3104 - val_loss: 0.2663\n",
            "Epoch 4/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.2318 - val_loss: 0.2009\n",
            "Epoch 5/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.1763 - val_loss: 0.1541\n",
            "Epoch 6/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.1362 - val_loss: 0.1200\n",
            "Epoch 7/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.1067 - val_loss: 0.0946\n",
            "Epoch 8/15\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.0846 - val_loss: 0.0754\n",
            "Epoch 9/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.0677 - val_loss: 0.0606\n",
            "Epoch 10/15\n",
            "60000/60000 [==============================] - 2s 28us/step - loss: 0.0547 - val_loss: 0.0492\n",
            "Epoch 11/15\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.0445 - val_loss: 0.0401\n",
            "Epoch 12/15\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.0364 - val_loss: 0.0330\n",
            "Epoch 13/15\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.0300 - val_loss: 0.0273\n",
            "Epoch 14/15\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.0249 - val_loss: 0.0227\n",
            "Epoch 15/15\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.0208 - val_loss: 0.0191\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGXEEr5n8ifY",
        "colab_type": "text"
      },
      "source": [
        "## Predict on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWxn1y8y7dl5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs = autoencoder.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDV9q8xZ9Ym3",
        "colab_type": "text"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yitjYIVz8pzU",
        "colab_type": "code",
        "outputId": "3da5a793-f3b4-484c-85c8-e8ecba1c5fe3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize = (20, 6))\n",
        "\n",
        "for i in range(n):\n",
        "  # Displaying original images\n",
        "  ax = plt.subplot(3, n, 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",
        "  # Displaying reconstructed images\n",
        "  ax = plt.subplot(3, n, i + n + 1)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  \n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x432 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opy1IVgdM-Dm",
        "colab_type": "text"
      },
      "source": [
        "I don't know what the fuck is wrong, if anybody figures it out, please help me out."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kM9kqm_zNJGN",
        "colab_type": "text"
      },
      "source": [
        "## Plot the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRLLzjidAVWN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "734e8e76-38d7-4423-d0b5-f9c50bd1a0d9"
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'validation'], loc = 'upper right')\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'loss'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1dd7r0xjTblkotDfpZ8CVbzhHI71xOVSWNFnar0s0f502jOW7Cvnff6/1YpPG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\nxhhj6lkoGGOMqff/AWJ+TzU4KI2+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHvsvzRoQvba",
        "colab_type": "text"
      },
      "source": [
        "# Denoising Autoencoder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEjtc7pkNfOM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(X_train, _), (X_test, _) = mnist.load_data()\n",
        "\n",
        "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
        "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
        "\n",
        "X_train = X_train.astype(\"float32\") / 255.\n",
        "X_test = X_test.astype(\"float32\") / 255."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufcUnY5oi0hb",
        "colab_type": "text"
      },
      "source": [
        "## Create noisy data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24E60Sl6V5oI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "1df08121-78a6-4d7d-ad91-ca47d91eef6d"
      },
      "source": [
        "noise_factor = 0.5\n",
        "\n",
        "X_train_noisy = X_train + noise_factor * np.random.normal(size = X_train.shape)\n",
        "X_test_noisy = X_test + noise_factor * np.random.normal(size = X_test.shape)\n",
        "\n",
        "X_train_noisy = np.clip(X_train_noisy, 0.0, 1.0)\n",
        "X_test_noisy = np.clip(X_test_noisy, 0.0, 1.0)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize = (10, 4.5))\n",
        "for i in range(n):\n",
        "  # Plotting original images\n",
        "  ax = plt.subplot(2, n, 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",
        "  if i == n/2:\n",
        "    ax.set_title('Original Images')\n",
        "    \n",
        "  # Plotting noisy images\n",
        "  ax = plt.subplot(2, n, i + 1 + n)\n",
        "  plt.imshow(X_test_noisy[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  if i == n/2:\n",
        "    ax.set_title('Noisy Input')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lIiYmRhR9Vmc/cOAAnn/+eQCRo/JzrmP8Ta9evaRYIp8FVLzHjBmTqPLPIHXGQj7++OPi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\n2YoVK+LgwYPitmAgpwsnP7qGf/jhh5ATiJdJkyYBgOxFw/Y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            "text/plain": [
              "<Figure size 720x324 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xuPIwwNGkiDk",
        "colab_type": "text"
      },
      "source": [
        "## Create the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0fiXqRojXEi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = Input(shape = (28, 28, 1))\n",
        "\n",
        "# Encoder\n",
        "conv1_1 = Conv2D(32, (3, 3), activation = 'relu', padding = 'same')(x)\n",
        "pool1 = MaxPooling2D((2, 2), padding = 'same')(conv1_1)\n",
        "conv1_2 = Conv2D(32, (3, 3), activation = 'relu', padding = 'same')(pool1)\n",
        "h = MaxPooling2D((2, 2), padding = 'same')(conv1_2)\n",
        "\n",
        "# Decoder\n",
        "conv2_1 = Conv2D(32, (3, 3), activation = 'relu', padding = 'same')(h)\n",
        "up1 = UpSampling2D((2, 2))(conv2_1)\n",
        "conv2_2 = Conv2D(32, (3, 3), activation = 'relu', padding = 'same')(up1)\n",
        "up2 = UpSampling2D((2, 2))(conv2_2)\n",
        "r = Conv2D(1, (3, 3), activation = 'sigmoid', padding = 'same')(up2)\n",
        "\n",
        "autoencoder = Model(inputs = x, outputs = r)\n",
        "autoencoder.compile(optimizer = 'adadelta', \n",
        "                    loss = 'binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rf8BFFYmAAh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "outputId": "98006f8d-01d5-423d-f805-53620e1715d8"
      },
      "source": [
        "from IPython.display import SVG\n",
        "from keras.utils.vis_utils import model_to_dot\n",
        "\n",
        "SVG(model_to_dot(autoencoder).create(prog = 'dot', format = 'svg'))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.SVG object>"
            ],
            "image/svg+xml": "<svg height=\"702pt\" viewBox=\"0.00 0.00 229.00 702.00\" width=\"229pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 698)\">\n<title>G</title>\n<polygon fill=\"#ffffff\" points=\"-4,4 -4,-698 225,-698 225,4 -4,4\" stroke=\"transparent\"/>\n<!-- 139988694194944 -->\n<g class=\"node\" id=\"node1\">\n<title>139988694194944</title>\n<polygon fill=\"none\" points=\"44,-657.5 44,-693.5 177,-693.5 177,-657.5 44,-657.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-671.8\">input_5: InputLayer</text>\n</g>\n<!-- 139988694195952 -->\n<g class=\"node\" id=\"node2\">\n<title>139988694195952</title>\n<polygon fill=\"none\" points=\"44,-584.5 44,-620.5 177,-620.5 177,-584.5 44,-584.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-598.8\">conv2d_8: Conv2D</text>\n</g>\n<!-- 139988694194944&#45;&gt;139988694195952 -->\n<g class=\"edge\" id=\"edge1\">\n<title>139988694194944-&gt;139988694195952</title>\n<path d=\"M110.5,-657.4551C110.5,-649.3828 110.5,-639.6764 110.5,-630.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-630.5903 110.5,-620.5904 107.0001,-630.5904 114.0001,-630.5903\" stroke=\"#000000\"/>\n</g>\n<!-- 139988694194664 -->\n<g class=\"node\" id=\"node3\">\n<title>139988694194664</title>\n<polygon fill=\"none\" points=\"0,-511.5 0,-547.5 221,-547.5 221,-511.5 0,-511.5\" stroke=\"#000000\"/>\n<text fill=\"#000000\" font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-525.8\">max_pooling2d_4: MaxPooling2D</text>\n</g>\n<!-- 139988694195952&#45;&gt;139988694194664 -->\n<g class=\"edge\" id=\"edge2\">\n<title>139988694195952-&gt;139988694194664</title>\n<path 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font-size=\"14.00\" text-anchor=\"middle\" x=\"110.5\" y=\"-14.8\">conv2d_12: Conv2D</text>\n</g>\n<!-- 139989897447296&#45;&gt;139989897514344 -->\n<g class=\"edge\" id=\"edge9\">\n<title>139989897447296-&gt;139989897514344</title>\n<path d=\"M110.5,-73.4551C110.5,-65.3828 110.5,-55.6764 110.5,-46.6817\" fill=\"none\" stroke=\"#000000\"/>\n<polygon fill=\"#000000\" points=\"114.0001,-46.5903 110.5,-36.5904 107.0001,-46.5904 114.0001,-46.5903\" stroke=\"#000000\"/>\n</g>\n</g>\n</svg>"
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpihNbmymQQ9",
        "colab_type": "text"
      },
      "source": [
        "## Train the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aj7lzpffmLNb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "d65206a2-8b0b-4cdd-bf8e-568c74f93061"
      },
      "source": [
        "epochs = 5\n",
        "batch_size = 128\n",
        "\n",
        "history = autoencoder.fit(X_train_noisy, X_train,\n",
        "                          batch_size = batch_size,\n",
        "                          epochs = epochs,\n",
        "                          verbose = 1,\n",
        "                          validation_data = (X_test_noisy, X_test))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 73us/step - loss: 0.1827 - val_loss: 0.1274\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 60us/step - loss: 0.1210 - val_loss: 0.1129\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.1127 - val_loss: 0.1099\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 61us/step - loss: 0.1090 - val_loss: 0.1104\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 60us/step - loss: 0.1065 - val_loss: 0.1067\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJYFu4xWmh0V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs = autoencoder.predict(X_test_noisy)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hz0Avtetmzzi",
        "colab_type": "text"
      },
      "source": [
        "## Plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9hcf9Yaamxoy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e2c200d1-45e1-4767-dd6a-199fb0160064"
      },
      "source": [
        "n = 10\n",
        "plt.figure(figsize = (10, 7))\n",
        "\n",
        "for i in range(n):\n",
        "  # Plotting original images\n",
        "  ax = plt.subplot(3, n, 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",
        "  if i == n/2:\n",
        "    ax.set_title('Original Images')\n",
        "    \n",
        "  # Plotting noisy images\n",
        "  ax = plt.subplot(3, n, i + n + 1)\n",
        "  plt.imshow(X_test_noisy[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  if i == n/2:\n",
        "    ax.set_title('Noisy Input')\n",
        "    \n",
        "  # Plotting reconstructed images\n",
        "  ax = plt.subplot(3, n, i + 2*n + 1)\n",
        "  plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "  plt.gray()\n",
        "  ax.get_xaxis().set_visible(False)\n",
        "  ax.get_yaxis().set_visible(False)\n",
        "  if i == n/2:\n",
        "    ax.set_title('Reconstructed Images')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SS0VMTIwo+qzOvn//fjz33HMAIkfl51zH+JtevXpJsUQ+C6h4jxkzJknln0HqjIUcOXKk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\ne/4f8OtEcQG/JN4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JFBn_zWoRZc",
        "colab_type": "text"
      },
      "source": [
        "## Plot the losses"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFZDtH5koL4n",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "outputId": "d27bfe2d-63d8-4b91-fb0c-6edccdcbf5d2"
      },
      "source": [
        "print(history.history.keys())\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.xlabel('epochs')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'validation'], loc = 'upper right')\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'loss'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K5RGbetUYcwoh+y92Vd2rqhOAkcA3RGRgF9ssUdVCVS3Mzs7u/SL7ibjoSO6Y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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}