Autoencoders_in_Keras_v1.ipynb

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  "metadata": {
    "colab": {
      "name": "Autoencoders_in_Keras_v1",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "G1uQuthDHQwF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# **Autoencoders in Keras using Fashion_MNIST on Google Colab**"
      ]
    },
    {
      "metadata": {
        "id": "6PSUA8wb4o6I",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Original code from this blog using MNIST:\n",
        "https://blog.keras.io/building-autoencoders-in-keras.html"
      ]
    },
    {
      "metadata": {
        "id": "wBUZAU6E8Wi_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "### USE THIS TO KILL THE VIRTUAL MACHINE BEFORE CLOSING THIS COLAB NOTEBOOK:\n",
        "\n",
        "!kill -9 -1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Yh0PhDC7Vude",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Step 1: Mount Google Drive:\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pMxlzfNXErpx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## Step 2: change directory to where your notebooks are:\n",
        "\n",
        "import os\n",
        "cwd = os.getcwd()\n",
        "os.chdir(\"drive/My Drive/Colab Notebooks\")\n",
        "print(cwd)\n",
        "!ls -ltr"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QnZQPrCaFKjR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8644f58a-f51a-407a-b783-fc5d0f21afd4"
      },
      "cell_type": "code",
      "source": [
        "# Import Numpy, TensorFlow, Keras and vectorized MNIST data\n",
        "import numpy as np\n",
        "from numpy import array\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD, Adam\n",
        "from keras.utils import to_categorical\n",
        "\n",
        "# test BatchNormalization\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "hO5Ffip7FSRm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "16bca97e-babb-4572-aa8d-08b23d8822ea"
      },
      "cell_type": "code",
      "source": [
        "# Test to see if we have access to GPU\n",
        "\n",
        "import tensorflow as tf\n",
        "tf.test.gpu_device_name()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "mDJjo86PHeTw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "B-ppceo_JObR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1. Lets Start with simple Feed-Forward NN as encoder/decoder"
      ]
    },
    {
      "metadata": {
        "id": "jBPdb4Sni2NW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model\n",
        "\n",
        "# this is the size of our encoded representations\n",
        "encoding_dim = 32  # 32 floats -> compression of factor 24.5, assuming the input is 784 floats\n",
        "\n",
        "# this is our input placeholder\n",
        "input_img = Input(shape=(784,))\n",
        "# \"encoded\" is the encoded representation of the input\n",
        "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
        "# \"decoded\" is the lossy reconstruction of the input\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "\n",
        "# this model maps an input to its reconstruction\n",
        "autoencoder = Model(input_img, decoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ERtTPpe2i2eu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this model maps an input to its encoded representation\n",
        "encoder = Model(input_img, encoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p9UtU9BRi2nc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# create a placeholder for an encoded (32-dimensional) input\n",
        "encoded_input = Input(shape=(encoding_dim,))\n",
        "# retrieve the last layer of the autoencoder model\n",
        "decoder_layer = autoencoder.layers[-1]\n",
        "# create the decoder model\n",
        "decoder = Model(encoded_input, decoder_layer(encoded_input))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XU0K9PPa3E3P",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Now let's train our autoencoder to reconstruct MNIST digits.\n",
        "# First, we'll configure our model to use a per-pixel binary crossentropy loss, \n",
        "# and the Adadelta optimizer:\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EQ7UskAl3FB5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "outputId": "45826bbd-b7b6-4fda-8bd9-72c91dc83111"
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.datasets import fashion_mnist\n",
        "import numpy as np\n",
        "#(x_train, _), (x_test, _) = mnist.load_data()\n",
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 1s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "BOayRGhS3FJL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a41bc0d2-9b9c-4c97-aeea-0ac7ca1d549f"
      },
      "cell_type": "code",
      "source": [
        "# We will normalize all values between 0 and 1 and we will flatten the 28x28 \n",
        "# images into vectors of size 784.\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\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:])))\n",
        "print (x_train.shape)\n",
        "print (x_test.shape)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SRllUnYt3FND",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1145
        },
        "outputId": "6d541f2e-6cc4-4ff0-ad01-763765ec930e"
      },
      "cell_type": "code",
      "source": [
        "autoencoder.fit(x_train, x_train, \n",
        "                epochs=30,\n",
        "                batch_size=256,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, x_test))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.3238 - val_loss: 0.3242\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3210 - val_loss: 0.3216\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3185 - val_loss: 0.3193\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3164 - val_loss: 0.3173\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3145 - val_loss: 0.3156\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3129 - val_loss: 0.3141\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3114 - val_loss: 0.3127\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3101 - val_loss: 0.3115\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3090 - val_loss: 0.3104\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3080 - val_loss: 0.3095\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3070 - val_loss: 0.3086\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3062 - val_loss: 0.3078\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3054 - val_loss: 0.3070\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3047 - val_loss: 0.3063\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3040 - val_loss: 0.3058\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3034 - val_loss: 0.3051\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3028 - val_loss: 0.3046\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3022 - val_loss: 0.3040\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3017 - val_loss: 0.3035\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3012 - val_loss: 0.3030\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.3007 - val_loss: 0.3025\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3002 - val_loss: 0.3021\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.2998 - val_loss: 0.3016\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2993 - val_loss: 0.3012\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2989 - val_loss: 0.3008\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2985 - val_loss: 0.3004\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2981 - val_loss: 0.3000\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2977 - val_loss: 0.2996\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.2974 - val_loss: 0.2993\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.2970 - val_loss: 0.2989\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa2f5f8c160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "obujnIg_8YBS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# encode and decode some digits\n",
        "# note that we take them from the *test* set\n",
        "encoded_imgs = encoder.predict(x_test)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QT-Rl1lx8YIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "d6373629-b51b-4a01-9480-74d21a337e9c"
      },
      "cell_type": "code",
      "source": [
        "# use Matplotlib (don't ask)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\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",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\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",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T8073rdzGGGOMMcYYY4wxHYpf1BhjjDHGGGOMMca0hKr0ie616q7TVLZUk7TQ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\njDHGGNMS/h+Jsl6F4cLmawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa27baa8da0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "X9dGh7Gz8YLu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "342635b8-0838-4dc2-e862-de6905638bfb"
      },
      "cell_type": "code",
      "source": [
        "decoder.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 784)               25872     \n",
            "=================================================================\n",
            "Total params: 25,872\n",
            "Trainable params: 25,872\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L5KruWDPJIIE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Adding a sparsity constraint on the encoded representations. Limit the number of neurons firing by regularizing the activity:"
      ]
    },
    {
      "metadata": {
        "id": "A1c0JVPJ8YO3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras import regularizers\n",
        "\n",
        "encoding_dim = 32\n",
        "\n",
        "input_img = Input(shape=(784,))\n",
        "# add a Dense layer with a L1 activity regularizer\n",
        "encoded = Dense(encoding_dim, activation='relu', activity_regularizer=regularizers.l1(10e-5))(input_img)\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3GV9ISrWH2qj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Now let's train our autoencoder to reconstruct MNIST digits.\n",
        "# First, we'll configure our model to use a per-pixel binary crossentropy loss, \n",
        "# and the Adadelta optimizer:\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L89Wb02B8YRq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1145
        },
        "outputId": "c093e62e-ed73-4084-c2ba-1eb177d5c9a7"
      },
      "cell_type": "code",
      "source": [
        "autoencoder.fit(x_train, x_train, \n",
        "                epochs=30,\n",
        "                batch_size=256,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, x_test))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.6839 - val_loss: 0.6704\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.6601 - val_loss: 0.6504\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.6415 - val_loss: 0.6331\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.6254 - val_loss: 0.6182\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.6114 - val_loss: 0.6053\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5993 - val_loss: 0.5941\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5888 - val_loss: 0.5843\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.5796 - val_loss: 0.5757\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5715 - val_loss: 0.5682\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5644 - val_loss: 0.5616\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5582 - val_loss: 0.5557\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5526 - val_loss: 0.5505\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5477 - val_loss: 0.5459\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5433 - val_loss: 0.5418\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5394 - val_loss: 0.5381\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5359 - val_loss: 0.5348\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5327 - val_loss: 0.5318\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.5298 - val_loss: 0.5291\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.5273 - val_loss: 0.5266\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5249 - val_loss: 0.5244\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5228 - val_loss: 0.5224\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5209 - val_loss: 0.5206\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5191 - val_loss: 0.5189\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5175 - val_loss: 0.5174\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5160 - val_loss: 0.5160\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5146 - val_loss: 0.5147\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5134 - val_loss: 0.5135\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5122 - val_loss: 0.5124\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5111 - val_loss: 0.5113\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5101 - val_loss: 0.5104\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa27fbffa58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "kz6oVoaY8YUd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# encode and decode some digits\n",
        "# note that we take them from the *test* set\n",
        "encoded_imgs = encoder.predict(x_test)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CFnTez_HIaey",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "5b7b400a-d6b5-4185-d617-a722fb767bff"
      },
      "cell_type": "code",
      "source": [
        "# use Matplotlib (don't ask)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\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",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\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",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T8073rdzGGGOMMcYYY4wxHYpf1BhjjDHGGGOMMca0hKr0ie616q7TVLZUk7TQ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\njDHGGNMS/h+Jsl6F4cLmawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa27c29fcc0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "X90O8XXYJXk4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Deep autoencoders; many layer fully-connected NN"
      ]
    },
    {
      "metadata": {
        "id": "M7PqK8xEJWUF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Lets increase the number of hidden layers:\n",
        "\n",
        "input_img = Input(shape=(784,))\n",
        "encoded = Dense(128, activation='relu')(input_img)\n",
        "encoded = Dense(64, activation='relu')(encoded)\n",
        "encoded = Dense(32, activation='relu')(encoded)\n",
        "\n",
        "decoded = Dense(64, activation='relu')(encoded)\n",
        "decoded = Dense(128, activation='relu')(decoded)\n",
        "decoded = Dense(784, activation='sigmoid')(decoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_65xnQFjJWpL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1145
        },
        "outputId": "f8ce1213-37be-479c-a1a3-2e8092a54b31"
      },
      "cell_type": "code",
      "source": [
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.fit(x_train, x_train,\n",
        "                epochs=30,\n",
        "                batch_size=256,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, x_test))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 3s 46us/step - loss: 0.3193 - val_loss: 0.3207\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3181 - val_loss: 0.3175\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3165 - val_loss: 0.3190\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3154 - val_loss: 0.3174\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3143 - val_loss: 0.3138\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3130 - val_loss: 0.3185\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3122 - val_loss: 0.3122\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3111 - val_loss: 0.3162\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3103 - val_loss: 0.3118\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3094 - val_loss: 0.3112\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3086 - val_loss: 0.3103\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3080 - val_loss: 0.3082\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3071 - val_loss: 0.3093\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3066 - val_loss: 0.3067\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3058 - val_loss: 0.3094\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 2s 40us/step - loss: 0.3053 - val_loss: 0.3076\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3048 - val_loss: 0.3057\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3043 - val_loss: 0.3047\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 2s 40us/step - loss: 0.3036 - val_loss: 0.3060\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3034 - val_loss: 0.3044\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3027 - val_loss: 0.3057\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 2s 40us/step - loss: 0.3024 - val_loss: 0.3034\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3018 - val_loss: 0.3025\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3014 - val_loss: 0.3033\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3010 - val_loss: 0.3022\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.3004 - val_loss: 0.3011\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 2s 40us/step - loss: 0.2998 - val_loss: 0.3049\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2996 - val_loss: 0.3012\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2992 - val_loss: 0.3000\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2989 - val_loss: 0.3008\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa279955e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "yfT2AXjFJWs2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# encode and decode some digits\n",
        "# note that we take them from the *test* set\n",
        "encoded_imgs = encoder.predict(x_test)\n",
        "decoded_imgs = decoder.predict(encoded_imgs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PwLJrtEEJWv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "aab4aaab-1785-47c7-e708-d01c3a57b6aa"
      },
      "cell_type": "code",
      "source": [
        "# use Matplotlib (don't ask)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n = 10  # how many digits we will display\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\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",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\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",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T8073rdzGGGOMMcYYY4wxHYpf1BhjjDHGGGOMMca0hKr0ie616q7TVLZUk7TQ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\njDHGGNMS/h+Jsl6F4cLmawAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa276d0b278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "v941iTZBNaHt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Convolutional Autoencoders"
      ]
    },
    {
      "metadata": {
        "id": "Pdfry149JWy2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "\n",
        "input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format\n",
        "\n",
        "x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\n",
        "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\n",
        "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
        "\n",
        "# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
        "\n",
        "x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "x = Conv2D(16, (3, 3), activation='relu')(x)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JoKdmEJYESf6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# this model maps an input to its encoded representation\n",
        "encoder = Model(input_img, encoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BojqTKW1JW1j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.datasets import mnist\n",
        "from keras.datasets import fashion_mnist\n",
        "import numpy as np\n",
        "#(x_train, _), (x_test, _) = mnist.load_data()\n",
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
        "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pVa0itv5JW4z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1145
        },
        "outputId": "cd5cb28c-f61d-4d09-f837-5966e8255a71"
      },
      "cell_type": "code",
      "source": [
        "from keras.callbacks import TensorBoard\n",
        "\n",
        "autoencoder.fit(x_train, x_train,\n",
        "                epochs=30,\n",
        "                batch_size=128,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test, x_test))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/30\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.2937 - val_loss: 0.2939\n",
            "Epoch 2/30\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.2928 - val_loss: 0.2980\n",
            "Epoch 3/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2920 - val_loss: 0.2928\n",
            "Epoch 4/30\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.2915 - val_loss: 0.2908\n",
            "Epoch 5/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2909 - val_loss: 0.2927\n",
            "Epoch 6/30\n",
            "60000/60000 [==============================] - 7s 120us/step - loss: 0.2906 - val_loss: 0.2916\n",
            "Epoch 7/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2903 - val_loss: 0.2930\n",
            "Epoch 8/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2896 - val_loss: 0.2914\n",
            "Epoch 9/30\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.2892 - val_loss: 0.2876\n",
            "Epoch 10/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2889 - val_loss: 0.2911\n",
            "Epoch 11/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2881 - val_loss: 0.2906\n",
            "Epoch 12/30\n",
            "60000/60000 [==============================] - 7s 120us/step - loss: 0.2880 - val_loss: 0.2907\n",
            "Epoch 13/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2876 - val_loss: 0.2929\n",
            "Epoch 14/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2874 - val_loss: 0.2924\n",
            "Epoch 15/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2874 - val_loss: 0.2889\n",
            "Epoch 16/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2871 - val_loss: 0.2896\n",
            "Epoch 17/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2868 - val_loss: 0.2876\n",
            "Epoch 18/30\n",
            "60000/60000 [==============================] - 7s 123us/step - loss: 0.2860 - val_loss: 0.2859\n",
            "Epoch 19/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2862 - val_loss: 0.2890\n",
            "Epoch 20/30\n",
            "60000/60000 [==============================] - 7s 120us/step - loss: 0.2862 - val_loss: 0.2873\n",
            "Epoch 21/30\n",
            "60000/60000 [==============================] - 7s 120us/step - loss: 0.2861 - val_loss: 0.2910\n",
            "Epoch 22/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2859 - val_loss: 0.2890\n",
            "Epoch 23/30\n",
            "60000/60000 [==============================] - 7s 120us/step - loss: 0.2860 - val_loss: 0.2868\n",
            "Epoch 24/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2857 - val_loss: 0.2862\n",
            "Epoch 25/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2855 - val_loss: 0.2896\n",
            "Epoch 26/30\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.2853 - val_loss: 0.2863\n",
            "Epoch 27/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2852 - val_loss: 0.2861\n",
            "Epoch 28/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2854 - val_loss: 0.2855\n",
            "Epoch 29/30\n",
            "60000/60000 [==============================] - 7s 122us/step - loss: 0.2850 - val_loss: 0.2874\n",
            "Epoch 30/30\n",
            "60000/60000 [==============================] - 7s 121us/step - loss: 0.2846 - val_loss: 0.2856\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa276b70358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "MZD5_uvDx6I0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "outputId": "e86d0da5-094f-46bb-833c-0c89468599fd"
      },
      "cell_type": "code",
      "source": [
        "decoded_imgs = autoencoder.predict(x_test)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(1,n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i)\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 reconstruction\n",
        "    ax = plt.subplot(2, n, i + n)\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",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa2774d5e10>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "bbfzOf-CyS2_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can also have a look at the 128-dimensional encoded representations. These representations are 8x4x4, so we reshape them to 4x32 in order to be able to display them as grayscale images."
      ]
    },
    {
      "metadata": {
        "id": "1auINATJyTmj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "ed22e235-566f-41c7-d85d-d573e933bc91"
      },
      "cell_type": "code",
      "source": [
        "encoded_imgs = encoder.predict(x_test)\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 8))\n",
        "for i in range(1,n):\n",
        "    ax = plt.subplot(1, n, i)\n",
        "    plt.imshow(encoded_imgs[i].reshape(4, 4 * 8).T)\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa265c66588>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "V7zhBNBxFdH2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## **Application to image denoising:**"
      ]
    },
    {
      "metadata": {
        "id": "0EXNyo2LFaKo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Synthetic noise added to the images\n",
        "\n",
        "from keras.datasets import mnist\n",
        "from keras.datasets import fashion_mnist\n",
        "import numpy as np\n",
        "#(x_train, _), (x_test, _) = mnist.load_data()\n",
        "(x_train, _), (x_test, _) = fashion_mnist.load_data()\n",
        "\n",
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
        "x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format\n",
        "\n",
        "noise_factor = 0.5\n",
        "x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape) \n",
        "x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape) \n",
        "\n",
        "x_train_noisy = np.clip(x_train_noisy, 0., 1.)\n",
        "x_test_noisy = np.clip(x_test_noisy, 0., 1.)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QcqWp3KcFaUa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "ebf6a11e-c715-40d8-d2b5-ffc12210462b"
      },
      "cell_type": "code",
      "source": [
        "# Plot the noisy images:\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 2))\n",
        "for i in range(1,n+1):\n",
        "    ax = plt.subplot(1, n, i)\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",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4Jmr7Ft5Paq39/PPPl1hLRrAUx9xzz13i73znO+k4vmetVAzXZL0XOG/xs1rF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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fa23210add8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "N6Y4pJlrFaZV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input_img = Input(shape=(28, 28, 1))  # adapt this if using `channels_first` image data format\n",
        "\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)\n",
        "x = MaxPooling2D((2, 2), padding='same')(x)\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
        "encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
        "\n",
        "# at this point the representation is (7, 7, 32)\n",
        "\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)\n",
        "x = UpSampling2D((2, 2))(x)\n",
        "decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iuniIwFwFads",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "38f71fa6-23a4-4de1-a8f3-e4a842968e89"
      },
      "cell_type": "code",
      "source": [
        "autoencoder.fit(x_train_noisy, x_train,\n",
        "                epochs=20,\n",
        "                batch_size=128,\n",
        "                shuffle=True,\n",
        "                validation_data=(x_test_noisy, x_test))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 9s 145us/step - loss: 0.3606 - val_loss: 0.3258\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.3145 - val_loss: 0.3128\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.3079 - val_loss: 0.3097\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.3049 - val_loss: 0.3036\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.3030 - val_loss: 0.3024\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.3015 - val_loss: 0.3040\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.3002 - val_loss: 0.3014\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2994 - val_loss: 0.3001\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2985 - val_loss: 0.3007\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2977 - val_loss: 0.2987\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2970 - val_loss: 0.2996\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2965 - val_loss: 0.2991\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2960 - val_loss: 0.2977\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2956 - val_loss: 0.2976\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2952 - val_loss: 0.2978\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2948 - val_loss: 0.2972\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2947 - val_loss: 0.2963\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 8s 131us/step - loss: 0.2943 - val_loss: 0.2958\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2941 - val_loss: 0.2955\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.2937 - val_loss: 0.2949\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa21b79efd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "nnqvwWKSFatH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uGeM6nxhFak1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}