Examples from Building Autoencoders in Keras.ipynb

examples-from-building-autoencoders-in-keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Examples from Building Autoencoders in Keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/gist/twolodzko/ab2011cda0c632bb0a4ebb442fdbef1d/examples-from-building-autoencoders-in-keras.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "5IZEIpkqWh0t",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Examples from \"Building Autoencoders  in Keras\"\n",
        "See the original [Keras blog post](https://blog.keras.io/building-autoencoders-in-keras.html) by François Chollet.\n",
        "\n",
        "## What are autoencoders?\n",
        "\n",
        "Quoting François Chollet:\n",
        "\n",
        "![alt text](https://blog.keras.io/img/ae/autoencoder_schema.jpg)\n",
        "\n",
        "\n",
        "    \"Autoencoding\" is a data compression algorithm where the compression and decompression functions are 1) data-specific, 2) lossy, and 3) learned automatically from examples rather than engineered by a human. Additionally, in almost all contexts where the term \"autoencoder\" is used, the compression and decompression functions are implemented with neural networks.\n",
        "\n",
        "    1) Autoencoders are data-specific, which means that they will only be able to compress data similar to what they have been trained on. This is different from, say, the MPEG-2 Audio Layer III (MP3) compression algorithm, which only holds assumptions about \"sound\" in general, but not about specific types of sounds. An autoencoder trained on pictures of faces would do a rather poor job of compressing pictures of trees, because the features it would learn would be face-specific.\n",
        "\n",
        "    2) Autoencoders are lossy, which means that the decompressed outputs will be degraded compared to the original inputs (similar to MP3 or JPEG compression). This differs from lossless arithmetic compression.\n",
        "\n",
        "    3) Autoencoders are learned automatically from data examples, which is a useful property: it means that it is easy to train specialized instances of the algorithm that will perform well on a specific type of input. It doesn't require any new engineering, just appropriate training data.\n",
        "\n",
        "    To build an autoencoder, you need three things: an encoding function, a decoding function, and a distance function between the amount of information loss between the compressed representation of your data and the decompressed representation (i.e. a \"loss\" function). The encoder and decoder will be chosen to be parametric functions (typically neural networks), and to be differentiable with respect to the distance function, so the parameters of the encoding/decoding functions can be optimize to minimize the reconstruction loss, using Stochastic Gradient Descent. It's simple! And you don't even need to understand any of these words to start using autoencoders in practice.\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "Eef7nuyyw4ac",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ]
    },
    {
      "metadata": {
        "id": "b9EsrdWrYRaG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "93d84b48-0ccb-4f7d-9cd1-4f2da26651a1"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from keras.layers import Lambda, Input, Dense\n",
        "from keras.models import Model\n",
        "from keras.datasets import mnist\n",
        "from keras import backend as K\n",
        "\n",
        "tf.test.gpu_device_name()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "Qz4_4ISKW2p3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Let's build the simplest possible autoencoder"
      ]
    },
    {
      "metadata": {
        "id": "glAPOkleWcet",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "d6b63567-0bbf-4892-a7d7-8604804abffe"
      },
      "cell_type": "code",
      "source": [
        "# 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)\n",
        "\n",
        "# this model maps an input to its encoded representation\n",
        "encoder = Model(input_img, encoded)\n",
        "\n",
        "# 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))\n",
        "\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_7 (InputLayer)         (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 784)               25872     \n",
            "=================================================================\n",
            "Total params: 50,992\n",
            "Trainable params: 50,992\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tG0GqMq0XNsC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9a20104a-8882-445f-c69f-362d881a8f48"
      },
      "cell_type": "code",
      "source": [
        "(x_train, _), (x_test, _) = mnist.load_data()\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",
        "\n",
        "x_train.shape, x_test.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 784), (10000, 784))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "XeVeCLIcXRbz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1771
        },
        "outputId": "194a4e83-c068-40a3-f592-38de4433041c"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "60000/60000 [==============================] - 2s 30us/step - loss: 0.3556 - val_loss: 0.2693\n",
            "Epoch 2/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.2606 - val_loss: 0.2487\n",
            "Epoch 3/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.2381 - val_loss: 0.2255\n",
            "Epoch 4/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.2173 - val_loss: 0.2074\n",
            "Epoch 5/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.2026 - val_loss: 0.1955\n",
            "Epoch 6/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1924 - val_loss: 0.1866\n",
            "Epoch 7/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1843 - val_loss: 0.1794\n",
            "Epoch 8/50\n",
            "29952/60000 [=============>................] - ETA: 0s - loss: 0.1792"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1776 - val_loss: 0.1730\n",
            "Epoch 9/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1717 - val_loss: 0.1676\n",
            "Epoch 10/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1664 - val_loss: 0.1626\n",
            "Epoch 11/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1617 - val_loss: 0.1581\n",
            "Epoch 12/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1574 - val_loss: 0.1539\n",
            "Epoch 13/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1535 - val_loss: 0.1502\n",
            "Epoch 14/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1499 - val_loss: 0.1468\n",
            "Epoch 15/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1466 - val_loss: 0.1437\n",
            "Epoch 16/50\n",
            " 2560/60000 [>.............................] - ETA: 1s - loss: 0.1459"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1437 - val_loss: 0.1408\n",
            "Epoch 17/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1410 - val_loss: 0.1382\n",
            "Epoch 18/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.1385 - val_loss: 0.1359\n",
            "Epoch 19/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1362 - val_loss: 0.1336\n",
            "Epoch 20/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1340 - val_loss: 0.1315\n",
            "Epoch 21/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1320 - val_loss: 0.1294\n",
            "Epoch 22/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1300 - val_loss: 0.1275\n",
            "Epoch 23/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1282 - val_loss: 0.1257\n",
            "Epoch 24/50\n",
            "  256/60000 [..............................] - ETA: 2s - loss: 0.1277"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1264 - val_loss: 0.1240\n",
            "Epoch 25/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1247 - val_loss: 0.1223\n",
            "Epoch 26/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1231 - val_loss: 0.1208\n",
            "Epoch 27/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1216 - val_loss: 0.1193\n",
            "Epoch 28/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1202 - val_loss: 0.1179\n",
            "Epoch 29/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1188 - val_loss: 0.1165\n",
            "Epoch 30/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1175 - val_loss: 0.1153\n",
            "Epoch 31/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1163 - val_loss: 0.1142\n",
            "Epoch 32/50\n",
            "  256/60000 [..............................] - ETA: 3s - loss: 0.1131"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1152 - val_loss: 0.1131\n",
            "Epoch 33/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1141 - val_loss: 0.1120\n",
            "Epoch 34/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1131 - val_loss: 0.1111\n",
            "Epoch 35/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1121 - val_loss: 0.1102\n",
            "Epoch 36/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1113 - val_loss: 0.1093\n",
            "Epoch 37/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1104 - val_loss: 0.1085\n",
            "Epoch 38/50\n",
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1097 - val_loss: 0.1078\n",
            "Epoch 39/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1090 - val_loss: 0.1071\n",
            "Epoch 40/50\n",
            " 2560/60000 [>.............................] - ETA: 1s - loss: 0.1082"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1083 - val_loss: 0.1064\n",
            "Epoch 41/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1076 - val_loss: 0.1058\n",
            "Epoch 42/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1070 - val_loss: 0.1052\n",
            "Epoch 43/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1065 - val_loss: 0.1047\n",
            "Epoch 44/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1059 - val_loss: 0.1041\n",
            "Epoch 45/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1054 - val_loss: 0.1037\n",
            "Epoch 46/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1049 - val_loss: 0.1032\n",
            "Epoch 47/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1045 - val_loss: 0.1028\n",
            "Epoch 48/50\n",
            " 2560/60000 [>.............................] - ETA: 1s - loss: 0.1044"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 25us/step - loss: 0.1040 - val_loss: 0.1023\n",
            "Epoch 49/50\n",
            "60000/60000 [==============================] - 1s 24us/step - loss: 0.1036 - val_loss: 0.1020\n",
            "Epoch 50/50\n",
            "60000/60000 [==============================] - 1s 25us/step - loss: 0.1033 - val_loss: 0.1016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "MtjtvxDyYmkP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "4fa4f405-ffc2-48b5-be4b-b32df2689544"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xl03PV97//nd/aRZrSMdsmWJcuL\nZHkBGxuDjYFgEiBkKU3BTYPTJaSUuCH5HdpS9xLn/vKDUMq9Tcm9v6Rplpu1mDoOcUtb05BAwHjD\nNl7kTfIiS9Y6WkYaabTMcv+QPdh4kWy+9ix6Pc6ZM5r56jt6z/t8fV7+fL6bEYvFYoiIiEjSsCS6\nABERETmfwllERCTJKJxFRESSjMJZREQkySicRUREkozCWUREJMnYEl3AWZ2d/aZ+Xm5uBj09g6Z+\n5mSlXppHvTSPemkO9dE8V9rLggLvJZel7cjZZrMmuoS0oV6aR700j3ppDvXRPGb2Mm3DWUREJFUp\nnEVERJKMwllERCTJKJxFRESSjMJZREQkySicRUREkozCWUREJMkonEVERJKMwllERCTJKJxFRESS\nTFqGc3NnkD1HOhJdhoiIyFVJy3B+6TcNfO3724lGY4kuRURE5IqlZTg7bFZGw1GCQ6OJLkVEROSK\npWU4ezPsAPQPKpxFRCT1TOh+zs888wx79+7FMAzWrl3L/Pnz48teeuklNmzYgMViobq6mnXr1rFj\nxw4ef/xxZs6cCcCsWbN46qmnrs03uIiz4RwcHAEyr9vfFRERMcO44bxjxw4aGxtZv349x44dY+3a\ntaxfvx6AUCjEK6+8wk9/+lPsdjurV69mz549ACxZsoQXXnjh2lZ/CV63A9DIWUREUtO409pbt25l\n5cqVAFRVVREIBAgGgwC43W5++MMfYrfbCYVCBINBCgoKrm3FE/DetPZIgisRERG5cuOOnP1+P7W1\ntfHXPp+Pzs5OPB5P/L3vfOc7/OhHP2L16tVMnTqVlpYWGhoaePTRRwkEAqxZs4Zly5Zd9u/k5mZg\ns1k/wFd5z5SSEABRi4WCAq8pnznZqY/mUS/No16aQ300j1m9nNA+53PFYheenvT5z3+e1atX88gj\nj7Bo0SIqKipYs2YN9957L01NTaxevZpXX30Vh8Nxyc/t6Rm80lIuKTISBqCtM0hnZ79pnztZFRR4\n1UeTqJfmUS/NoT6a50p7ebkgH3dau7CwEL/fH3/d0dERn7ru7e1l586dALhcLlasWMHu3bspKiri\nvvvuwzAMysvLyc/Pp729fcIFf1Ca1hYRkVQ2bjgvW7aMzZs3A1BXV0dhYWF8SjscDvPkk08yMDAA\nwP79+6msrGTTpk1873vfA6Czs5Ouri6Kioqu1Xe4gDdDB4SJiEjqGndae+HChdTW1rJq1SoMw2Dd\nunVs3LgRr9fL3XffzRe+8AVWr16NzWZj9uzZ3HXXXQwMDPDEE0/w2muvMTo6yle/+tXLTmmbzW6z\n4HbaFM4iIpKSjNjFdiIngNn7PNb+0zZCw2H+fs1yUz93MtI+KfOol+ZRL82hPprnuu5zTlXZmU6C\ng6MXPYBNREQkmaVtOGd5HESiMULDkUSXIiIickXSNpyzM50A9Id0xLaIiKSWtA3nrEwdsS0iIqkp\nbcM523M2nDVyFhGR1JK24Zx1dlpbI2cREUkxaRvOGjmLiEiqSuNwHhs5B0MaOYuISGpJ23DWAWEi\nIpKq0jacz46cFc4iIpJq0jacXQ4rNqtF+5xFRCTlpG04G4aBN8OukbOIiKSctA1nGLuvs64QJiIi\nqSbNw9nByGiU4VFdX1tERFJHmoezHYCgprZFRCSFpHc4u8+cTqWpbRERSSHpHc5nRs46KExERFJJ\nWoezJx7OGjmLiEjqSOtwjk9ra+QsIiIpJL3DWdPaIiKSgiZJOGtaW0REUkeah/PYtLbuTCUiIqkk\nrcM5w2XDYhia1hYRkZSS1uFsMQw8GXZNa4uISEpJ63AG8Lp18wsREUkt6R/OGXYGh8OEI9FElyIi\nIjIhaR/OHh0UJiIiKSbtw1nnOouISKpJ/3B2n70zlQ4KExGR1JD+4Zxx9s5UGjmLiEhqmAThrGlt\nERFJLekfzm5dwlNERFJL+odzhu5MJSIiqWUShLNGziIiklrSPpwz3drnLCIiqSXtw9lmtZDpsuki\nJCIikjLSPpxh7CphmtYWEZFUMSnC2ZthJxgKE43FEl2KiIjIuCZHOLvtRGMxBofCiS5FRERkXJMj\nnHXEtoiIpJBJEs4611lERFLH5AhnnU4lIiIpZHKEc/zmF5rWFhGR5DdJwlkjZxERSR22ifzSM888\nw969ezEMg7Vr1zJ//vz4spdeeokNGzZgsViorq5m3bp1GIZx2XWut/f2OWvkLCIiyW/ccN6xYweN\njY2sX7+eY8eOsXbtWtavXw9AKBTilVde4ac//Sl2u53Vq1ezZ88ewuHwJddJBM+Zfc5BjZxFRCQF\njDutvXXrVlauXAlAVVUVgUCAYDAIgNvt5oc//CF2u51QKEQwGKSgoOCy6ySCTqUSEZFUMm44+/1+\ncnNz4699Ph+dnZ3n/c53vvMd7r77bu655x6mTp06oXWuJ4fditNu1T5nERFJCRPa53yu2EUugfn5\nz3+e1atX88gjj7Bo0aIJrfN+ubkZ2GzWKy3nsgoKvPGfs71OBofD570nE6e+mUe9NI96aQ710Txm\n9XLccC4sLMTv98dfd3R0UFBQAEBvby/19fUsXrwYl8vFihUr2L1792XXuZSensGr/Q4XVVDgpbOz\nP/4602mlqWOIjo4+DMMw9W+lu/f3Uq6eemke9dIc6qN5rrSXlwvycae1ly1bxubNmwGoq6ujsLAQ\nj8cDQDgc5sknn2RgYACA/fv3U1lZedl1EsWb4SAciTI0EkloHSIiIuMZd+S8cOFCamtrWbVqFYZh\nsG7dOjZu3IjX6+Xuu+/mC1/4AqtXr8ZmszF79mzuuusuDMO4YJ1Ei18lLDSK23nFs/kiIiLXzYRS\n6oknnjjvdXV1dfznBx54gAceeGDcdRLNc84R24U57gRXIyIicmmT4gphoJtfiIhI6pg84ezWuc4i\nIpIaJk84nxk5B0MaOYuISHKbROGsm1+IiEhqmIThrGltERFJbpMonHVAmIiIpIZJE84uhxWb1VA4\ni4hI0ps04WwYBh63XdPaIiKS9CZNOMPY1Ha/jtYWEZEkN8nC2c7wSITRsK6vLSIiyWuShbMOChMR\nkeQ3ucLZrXOdRUQk+U2ucD57rnNIB4WJiEjymmThrGltERFJfpMqnD2a1hYRkRQwqcJZl/AUEZFU\nMMnCWXemEhGR5DfJwlnT2iIikvwmVThnuu0Yhqa1RUQkuU2qcLbEr6+tkbOIiCSvSRXOcOb62ho5\ni4hIEpt04exx2xkYChOJRhNdioiIyEVNunA+e1BYMBROcCUiIiIXNwnD+czpVJraFhGRJDX5wllX\nCRMRkSQ3+cI5fvMLhbOIiCSnSRjOZ29+oWltERFJTmkZzi8dfZkv/8d/JxKNXLBMVwkTEZFkl5bh\nbMHC6b426roOX7DsvTtTaeQsIiLJKS3D+eaSmwDY1rbrgmW6+YWIiCS7tAznqd5SpuVMYb//IP0j\nwfOWaVpbRESSXVqGM8AdFUuJxqK80/7uee/brBbcTpumtUVEJGmlbTgvn7YYi2Fhe+s7FyzzZujm\nFyIikrzSNpyzXVnMzauhKdhCc3/Lecu8GXaCoVFisViCqhMREbm0tA1ngKUliwDY1nb+6NnrdhCJ\nxhgc1vW1RUQk+aR1OM/Nq8Fjz2Rn2x7C0feC2KODwkREJImldThbLVYWF99IcHSAuq4j8fffO2Jb\nB4WJiEjySetwBlhaPHbO87kHhpX4MgGobw4kpCYREZHLSftwnuItZYqnlP1dh+LnPN8wMx+rxWDH\nwfYEVyciInKhtA9ngKUlN513zrPHbWdupY9THUFauwYSXJ2IiMj5JkU431R0AxbDwtbWnfH3lswp\nAmDHoY5ElSUiInJRkyKcvQ4P8/LncDrYStOZc55vmJGP3WZhx6F2ne8sIiJJZVKEM8DS4rFzns8e\nGOZ22lhQlUdr1yBNHcHLrSoiInJdTZpwrs2rHjvnuf29c56X1GhqW0REks+kCWerxcqS4oVnznke\nu8/z/Ko8XA6rprZFRCSpTCicn3nmGR566CFWrVrFvn37zlu2bds2HnzwQVatWsVf//VfE41G2b59\nO0uXLuXhhx/m4Ycf5mtf+9o1Kf5KLT17n+fWsfs8O+xWbpxZgD8wxPGWvkSWJiIiEmcb7xd27NhB\nY2Mj69ev59ixY6xdu5b169fHl3/lK1/hRz/6EcXFxXzxi1/kzTffxOVysWTJEl544YVrWvyVKvOU\nMNVTyoEz5zx7HR5unlPI1ro2th9qp6osO9ElioiIjD9y3rp1KytXrgSgqqqKQCBAMPjeAVQbN26k\nuLgYAJ/PR09PzzUq1RxLSxYTjUXZ2bYbgDkVPjJdNnYe7iAa1dS2iIgk3rgjZ7/fT21tbfy1z+ej\ns7MTj8cDEH/u6Ohgy5YtPP744xw9epSGhgYeffRRAoEAa9asYdmyZZf9O7m5Gdhs1g/yXS5QUOC9\n4L2PZC1n47F/Y2fnHh5ceB+GYbD8hjI2b2uko3+EeTPyTa0hXVysl3J11EvzqJfmUB/NY1Yvxw3n\n97vYgVNdXV08+uijrFu3jtzcXCoqKlizZg333nsvTU1NrF69mldffRWHw3HJz+3pGbzSUi6roMBL\nZ2f/RZfNy6vh3c4D7DlxhKneMuZX5LJ5WyOvbj1BcbbT1DrSweV6KVdGvTSPemkO9dE8V9rLywX5\nuNPahYWF+P3++OuOjg4KCgrir4PBII888ghf+tKXWL58OQBFRUXcd9/YqLS8vJz8/Hza25PnOtaL\nixcCsKt9LwCzy3PJynTwzpFOwpFoIksTEREZP5yXLVvG5s2bAairq6OwsDA+lQ3w7LPP8tnPfpYV\nK1bE39u0aRPf+973AOjs7KSrq4uioiKza79qc3yzcVod7OnYRywWw2IxWFxdSDA0yqHG5N5nLiIi\n6W/cae2FCxdSW1vLqlWrMAyDdevWsXHjRrxeL8uXL+fll1+msbGRDRs2AHD//ffz0Y9+lCeeeILX\nXnuN0dFRvvrVr152Svt6c1jtzMufwzvt79IUPE25dwo31xTx2q5mdhxsZ970vESXKCIik9iE9jk/\n8cQT572urq6O/3zgwIGLrvPtb3/7A5R17d1YOJ932t9ld/s+yr1TmF6WRV6Wk931nawOR7CbfHCa\niIjIRE2aK4S93wVT24bB4poiQsMR9h/vTnR5IiIyiU3acD47te0f6qap/zQAN8evtZ08B6+JiMjk\nM2nDGWBh4XwAdneMXZK0vMhDUa6bd+v9DI2EE1maiIhMYpM6nGveN7VtGAY3zyliJBzl3Qb/+B8g\nIiJyDUzqcL7Y1Pbis1PbB3UbSRERSYxJHc5w4dR2WX4mUwo87D/excDQaCJLExGRSWrSh/PZqe3d\nZ6a2AW6eU0gkGmP3kc4EVyciIpPRpA/ns1PbXedMbS85M7W9ta4tkaWJiMgkNenDGS6c2i7IcTNr\nSjaHT/XiD4QSWZqIiExCCmcuPrV967wSALYe0OhZRESuL4UzF5/aXlxdiMNm4e0DbRe9TaaIiMi1\nonA+4/1T226njYWzCmjvCXHsdF8iSxMRkUlG4XzGxae2iwHYcqA1kaWJiMgko3A+49yp7VP9zQDM\nmeYj1+tkx6F2RkYjCa5QREQmC4XzOc5Obe/p2A+AxWJwS20xoeEIe+p1OU8REbk+FM7nmHORqe1l\nmtoWEZHrTOF8DvtFprZL8jKpLMmi7kQ3Pf3DCa5QREQmA4Xz+7x/ahvGRs+xGGw7qHOeRUTk2lM4\nv897U9t741PbS2qKsFkN3t6vc55FROTaUzi/z3tT2z3xqW2P286CGfmc9g/Q2N6f4ApFRCTdKZwv\n4uzU9jvt78bfWzZ37HKeW/ZraltERK4thfNF1OZV43V42Nq6k6Hw2EFgc6f78GbY2X6wnXAkmuAK\nRUQknSmcL8JmsXFb6VJC4SF2tu8ee89q4ZbaYoKhUfY2dCW4QhERSWcK50tYXrYUq2Hl9aYt713O\nc+7YOc9v65xnERG5hhTOl5DtzGJh4XzaBjs40tMAQHmRl6mFHvYd66JvcCTBFYqISLpSOF/GHVOX\nAfB681vx95bNLSYSjbH9YHuiyhIRkTSncL6MiqxyKrLKOeA/TOfg2H7mm2uLsRhj5zyLiIhcCwrn\ncdwxZRkxYrxxegsA2ZkO5k330djeT3NnMMHViYhIOlI4j+PGwnlkObxsbXmHofAQAMvmjZ3zrNGz\niIhcCwrncdgsNm4rW8pQZIjtbWOnVS2YkU+my8ab+1oYHAonuEIREUk3CucJOHta1RvNW4jGotht\nFj6ypJyBoTCbd5xKdHkiIpJmFM4TkOXwsqhoAe2DnRzurgfg7pumkpXp4NWdTfQN6LQqERExj8J5\ngu6Ycva0qrEDw5wOKx+7tYLh0Qj/9vbJBFYmIiLpRuE8QdOyplKZNY26rsN0DHYCcPsNpeRnu3j9\n3dP4A6EEVygiIulC4XwFzl6U5I3mt4Gx621/8rZKwpEYv3zrRCJLExGRNKJwvgI3Fswj25HFttZ3\nCJ05rWrpnGLKCjJ5+0Abp/0DCa5QRETSgcL5ClgtVm4ru4WhyDDbW3cBYLEYPLBiOrEY/OK3xxNc\noYiIpAOF8xVaXnYztnNOqwK4YUY+VWVZ7D7ayfGWvgRXKCIiqU7hfIW8Dg83Fd1IR8jPoe6jABiG\nwadurwLg528cS2R5IiKSBhTOV+H2qbcC8KvGN+L3ep5dnsvcSh+HGnuoO9mdyPJERCTFKZyvQrl3\nCjW+WRztPcbO9j3x93/3zOh54xvH4qEtIiJypRTOV2nV7N/BYbGzoX4T/SNjd6eaVuzlpupCTrT2\ns/toZ4IrFBGRVKVwvkr57jw+VnUPA6OD/MvRX8bf/53bKrEYBht/e5xINJrACkVEJFUpnD+AO6Ys\nozKrnF0de9nXWQdASV4my+cX09o1yNsHdEtJERG5chMK52eeeYaHHnqIVatWsW/fvvOWbdu2jQcf\nfJBVq1bx13/910TPjBYvt066sBgW/qDm97AZVl488gtC4bFLeH58WSU2q4VNb51geCSS4CpFRCTV\njBvOO3bsoLGxkfXr1/P000/z9NNPn7f8K1/5Ci+88AIvvvgiAwMDvPnmm+Ouk05KMou4p+IuAiN9\n/KLh3wHwZbn48OKpdPUN85NXjyS4QhERSTXjhvPWrVtZuXIlAFVVVQQCAYLBYHz5xo0bKS4uBsDn\n89HT0zPuOunm7ml3UJpZzJaW7RztaQDgk7dVUlniZcuBNt7c15LgCkVEJJWMG85+v5/c3Nz4a5/P\nR2fne0ciezweADo6OtiyZQu33377uOukG5vFxmdqfg8Dg58e2sBIZASb1cKffWIuGU4bP331KM0d\n6fufExERMZftSle42Pm7XV1dPProo6xbt+68UL7cOu+Xm5uBzWa90nIuq6DAa+rnXf5vzeH+/rv4\n1yO/4rW211l9w+9SUODly59eyNM/2ME//utB/ueXVpDhsl+3msx0PXuZ7tRL86iX5lAfzWNWL8cN\n58LCQvx+f/x1R0cHBQUF8dfBYJBHHnmEL33pSyxfvnxC61xMT8/gFRd/OQUFXjo7+039zPF8qPgO\ntp3awytHXqPGW01FVjlVRR4+vHgqr+5s4u9/uotHPjYHwzCua10fVCJ6ma7US/Ool+ZQH81zpb28\nXJCPO629bNkyNm/eDEBdXR2FhYXxqWyAZ599ls9+9rOsWLFiwuukK4fVwaerP0WMGD89tIFwNAzA\np+6ooqo0i20H23ljr/Y/i4jI5Y07cl64cCG1tbWsWrUKwzBYt24dGzduxOv1snz5cl5++WUaGxvZ\nsGEDAPfffz8PPfTQBetMFrNyq1heejNvtWzn1cbfcF/l3disFh79xFy++oMd/Oy/6plekkV5kaaR\nRETk4oxYklwE2uxplURO1YTCIf6/7f+T/pEgf37DI8zMnQ7A3gY//7BhH4W5btb94WLczive5Z8Q\nmvYyj3ppHvXSHOqjea7rtLZcObfNzeqah4gR4zv7f0j7QAcAC2bkc+/N5XT0hPg//3FYN8cQEZGL\nUjhfI7N9M/j07N9lMBzi/9/7/fjNMX5nxXRmTMlm5+EOfrPndIKrFBGRZKRwvoZuKV3MvRV34R/q\n5tv7/k/8/OdHP16Lx23nxdfqaTgdSHSZIiKSZBTO19hHKz/MkuKFnOw7xf85+CLRWBRflovPf2wO\nkWiMb7y0l8Y27e8REZH3KJyvMcMw+IPqTzErp4q9nQf4RcMrAMydnsfn7p9DaDjM/1j/Ls2duoKY\niIiMUThfBzaLjUfmPUxxRiG/bnqT15u2AHBLbTGfvbeaYGiU5198l7Zucy/EIiIiqUnhfJ1k2DN4\nbMEf43V42FC/KX7/5xULSvmDu2fRNzDC3/3zHjp6QwmuVEREEk3hfB3luX382fw/wm6x8f26n9HY\n1wTAXYum8OCdM+jpH+b5f95Dd99QgisVEZFEUjhfZ9OypvLHc/+AcDTMt/b9gI7BsWuQ33NzOZ+8\nrRJ/YIjn/nkPvcHhBFcqIiKJonBOgHn5c/jUrI/TPxLk73d/i5ZgGwAfu7WCj94yjY6eEM+/+C59\ngyMJrlRERBJB4Zwgd0xZxqdmfpy+kX6+sfvbnOw7hWEYPLBiOnffNJUW/wD/48V3CYZGE12qiIhc\nZwrnBLpz6nI+U/Mgg+EQL+z5Dkd7GjAMg1V3zeCOG8to6gjy3M92ax+0iMgko3BOsFtKbuJzcz9D\nOBrhf+/9Pvv9BzEMg898eBYfWlhGc+cAT/94F00dOg9aRGSyUDgngRsK5/Fn8/8ICwbf2f8jdrbt\nwWIY/MHds+JHcX/9J7uoO9Gd6FJFROQ6UDgniZq8Wfz5jY/gtDr44cEX+W3zVgzD4J6by3n0E7WE\nI1G+8S97eXNfS6JLFRGRa0zhnESmZ1fw+I2P4rFnsv7oL3j15G8AWFJTxBOrbsTlsPKDfz/My28e\n1+0mRUTSmMI5yUz1lvLlRX9GrjOHXx7/D35e/69EY1FmTc1h7cOLyM92sWnLSb7/yiHCkWiiyxUR\nkWtA4ZyEijIK+H8W/RlFZ67F/a29P2BwNERJXiZ/s/omKku8bDnQxjf+ZS+DQ+FElysiIiZTOCcp\nnyuXv7jpC8zJm83B7iP83a5v0j7QQXamg7/8/YXcMCOfgyd7+PpPdtHaNZDockVExEQK5yTmtrn5\ns/l/xN3ld9Ax6Ofvdv0v6roO43RYWfPAPFYumsJp/wD/7w/fYceh9kSXKyIiJlE4JzmLYeGTM+7j\ns3NWMRoN8629P+BXp97AMODTd8/iTz9eCzH49i/r+Ol/HdV+aBGRNKBwThFLihfy5YWPkuXw8ouG\nV/jhwfWMRka5eU4RT332JkrzM3ltVzNf/8lu/AHddlJEJJUpnFNIRVY5f7n4z5mWNZWd7bv5+z3f\npnc4QGl+Jk+tvolbaos50drHf//BTvYd60p0uSIicpUUzikmx5nNl298lJuLF9HY18SzO/+BA/5D\nOB1WPnd/DZ+9ZzbDo2MXLPn5G8eIRDXNLSKSahTOKchutfNwzYN8aubHCY2G+Na+H/DPh3/OcGSE\n228o42/OnA/9ytZG/seL7+rGGSIiKUbhnKIMw+DOqcv5y8VfpDSzmLdatvPszm9wItDItGIvX/2j\nxdw4M5/Dp3p56ns72LK/VVcVExFJEdavfvWrX010EQCDgyOmfl5mptP0z0xGWQ4vt5QuJhwNU9d1\nmK2tO4nEotTkVXHznGJyvU72n+hm5+EOTrUHqS7PweWwXdHfmCy9vB7US/Ool+ZQH81zpb3MzHRe\ncplGzmnAbrHxOzM+yuM3fp5cVw7/efI1nt/1v2gf7OT2G8r42h8vobo8h3cb/Dz1vR06J1pEJMlp\n5JxG8tw+bilZTGC4j4PdR9jaugOX1UV1YQW3zivB47az/3gX2w910OIfoLo8B6fdOu7nTsZeXivq\npXnUS3Ooj+bRyFkuyW1zsXrOQ3xu7sM4rA7+pf6XvLDnO/hDXay8aSr//Y+XUFWWxc7DHTz13e3s\nOdqZ6JJFROR9NHJOUyWZRSwpXkhnqItD3Ud5u2UHDoud2qLp3DavFKfDyv7jXWw72E5r1wBVZdm4\nnRffFz3Ze2km9dI86qU51EfzmDlyVjinMZfNyaLCBRRnFnKkp4G9/joOdx9lek4FN1aWsXB2ISdb\n+6k70c1v97bgsFupKPZiMYzzPke9NI96aR710hzqo3kUzhOgDW6MYRiUeopZWnITPUO9HDwzijYM\nCwuKZ7BiQRk5HieHGnvYU+9nb4Of8iIvud73Nhr10jzqpXnUS3Ooj+ZROE+ANrjzOa0ObiyczxRP\nCUd6jrHff5AD/kNUZE9jwbQyls8voX9whAMnunlzbwuB4DAzpmTjsFnVSxOpl+ZRL82hPppH4TwB\n2uAurjizkFtLFtM/GuRg9xHebt3BUGSI2fkVLKkupbo8h+Ot/ew/3s1b+1rJynAwu8KnXppE26V5\n1EtzqI/mMTOcjViSXDaqs7Pf1M8rKPCa/pnp5mDXEf75yEa6h3rw2DP52PSPcGvpEqJR+K+dTfxy\nywlGRqPUVPj43RXTmV6aleiSU562S/Ool+ZQH81zpb0sKPBecpnCeZIbiYzy66Y32dz4a0YiI5R5\nSvjUzI8xK3cG/kCIF19rYPeZ062W1BTyqduryM9xJ7jq1KXt0jzqpTnUR/MonCdAG9yVCQz3senY\nf7Kt7R0AFuTX8jsz7qcgI4+2vmG+s3EfJ9v6sVkNVt40lftvmUaGy57gqlOPtkvzqJfmUB/NY2Y4\na5+zAGOnXS0oqGVeXg1tA+0c6qnnrdPbGIoMc0f1PJZWF1Psy+B4Sx/7j3fz272t2G0Wyou8WCzG\n+H9AAG2XZlIvzaE+mkf7nCdA/xu8erFYjD2d+/lFwyt0D/XgdXr4SPmHuK1sKdGIwa92NfPK1pOE\nhiMU5br51B1VLJxVgGEopMf2N+G3AAAeDElEQVSj7dI86qU51EfzaFp7ArTBfXBn90f/6tTrhMJD\n5Ll8fHz6R1hYtIBgKMymt07w+p4WorEY04q8fOK2ShZU5SmkL0PbpXnUS3Ooj+ZROE+ANjjzOLMM\nfvrOL/nt6a1EYhHKvWV8ouo+qn0zae0a4JdvnWDnoQ5iQGWJl08sn8686T6F9EVouzSPemkO9dE8\nCucJ0AZnnrO99Ie6+Nfjm3mn/V0Aanyz+ETVfUz1ltLcGWTTWyd458jYkd1VpVl84rZKaisU0ufS\ndmke9dIc6qN5FM4ToA3OPO/v5an+Zn7Z8B8c7qkH4KaiG7i34i6KM4to6gjyy7dOxE+/mjElm99Z\nXkn1tFyFNNouzaRemkN9NI/CeQK0wZnnUr081HWUl4/9O83BFgwMFhTU8pFpH6I8awqNbf388q0T\nvNvgB8ZG0vfcPI0bZ+ZP6qO7tV2aR700h/poHjPD+eL3CHyfZ555hr1792IYBmvXrmX+/PnxZcPD\nw3zlK1+hvr6ejRs3ArB9+3Yef/xxZs6cCcCsWbN46qmnJlywpIaavFnM9s1gv/8g/3ny17zbeYB3\nOw9Q45vFPRV38cVPzedEax//9vZJ9tT7+d+/2E+RL4N7lkzl1rnF2G3WRH8FEZGkNG4479ixg8bG\nRtavX8+xY8dYu3Yt69evjy9/7rnnqKmpob6+/rz1lixZwgsvvGB+xZJULIaFBQVzmZ9fy+HuejY3\n/ppD3Uc51H2UquxK7qn4EGsemEdr1yCbd5zi7QNt/PA/j/DymydYedMU7ryxTBczERF5n3HDeevW\nraxcuRKAqqoqAoEAwWAQj8cDwJe//GV6e3vZtGnTta1UkpphGNTkzaImbxbHek+yufHX1HUd5n/v\n/R7l3jLuKr+d1ffM45O3TedX7zTx+run+fkbx/m3rY3ccUMpKxdNJS/bleivISKSFMYNZ7/fT21t\nbfy1z+ejs7MzHs4ej4fe3t4L1mtoaODRRx8lEAiwZs0ali1bdtm/k5ubgc3kac7LzefLlbmSXhYU\nzGPpzHmc6GniF4f+k+1Ne/hB3c/YlJHLvTPv4LOfXM5nPzaXzdtO8svfHmfzjib+a2cTS2qL+eiy\nShbMTO8Lmmi7NI96aQ710Txm9XJC+5zPNZHjxyoqKlizZg333nsvTU1NrF69mldffRWHw3HJdXp6\nBq+0lMvSQQ7mudpeesjh4Zmr+EjZSl5vfoutLTv5yd5f8NKBV7ilZDF3Tl/O0uqlbD/Yzmu7m9l2\noI1tB9ooycvgzhvLWDavBLfzijfRpKbt0jzqpTnUR/Nc1wPCCgsL8fv98dcdHR0UFBRcdp2ioiLu\nu+8+AMrLy8nPz6e9vZ2pU6dOtGZJI4UZ+Tw465PcX/lhtrTs4PXmLbzRvIXfNr/N/Pw5fKh8BU/N\nXcSJtn5+vauZnYc7+Nmv6vn5G8e5dW4xH1pYRlmBJ9FfQ0Tkuhk3nJctW8Y3v/lNVq1aRV1dHYWF\nhfEp7UvZtGkTnZ2d/Mmf/AmdnZ10dXVRVFRkWtGSmjLsGdw97Q4+NPU29nTs47WmN9nrr2Ovv44p\nnlKWly3l4Xtv4KEPzeTNfS38Zs/p+GPWlGxuW1DKTbMLcTp0lLeIpLcJnef8/PPP884772AYBuvW\nrePgwYN4vV7uvvtuvvjFL9LW1kZ9fT1z587lwQcf5M477+SJJ56gr6+P0dFR1qxZw+23337Zv6Hz\nnJPXteplLBbjWOAkv2l6i33+OqKxKC6rk8XFC7mtbCnFGUXsbeji17ubOXiyBwCnw8rNNYUsn1dK\nVVlWyu2b1nZpHvXSHOqjeXQRkgnQBmee69HL3uEAb7fsYEvLDnqHAwBUZk3jtrKl3Fg4n0B/mC37\nW9myv5WuvmEAin0Z3Da/hFvmFpPjufSt15KJtkvzqJfmUB/No3CeAG1w5rmevYxEI9R1HebNlm0c\n6jpKjBiZtgyWlCzk5uKbKPOUcKixh7f2tbLrSCfhSBSLYTBvuo+ba4u4cUZBUk97a7s0j3ppDvXR\nPArnCdAGZ55E9dIf6mJLyw7ebtlBcHQAgDJPCUuKF7K4aCG2mIvtB9t5a18rJ9vG6nPYLdw4s4Cb\na4qYO92HzWq57nVfjrZL86iX5lAfzaNwngBtcOZJdC/D0TB1XYfZ3rqLA12HicQiWAwLNb5Z3Fy8\nkPn5tXT0jLD9YDs7DrbT0RsCINNlY9HssaCeXZ6bFNf0TnQv04l6aQ710TzX/draIolks9hYUDCX\nBQVzCY4M8E7Hu2xv3UVd12Hqug7jtrlYWDifhXMX8PFli2nqGBwL6kPt/HZvK7/d20q2x8GiWQUs\nmlXArPIcrJbkGlGLiJxLI2cZV7L2snWgne2tu9jRtpvASB8AHnsmNxTOY1HhfKZnVdLQ3Mf2Q+28\nc7iDgaHw2O+47dwwM59FswqYU+HDbrt+QZ2svUxF6qU51EfzaFp7ArTBmSfZexmNRanvOc7uzn28\n27E/vn/aa/dwQ+E8FhbOp8I7jYbmPnYd7WT30U4CwREAXA4rC2aMBfXc6T5cjms7mZTsvUwl6qU5\n1EfzKJwnQBuceVKpl5FohIbeE+zu2Mu7nQfeC2qHh/n5c5iXP4eZOTNobgux62gHu4504g8MAWCz\nGlSX57JgRj4LZuSRn+02vb5U6mWyUy/NoT6aR+E8AdrgzJOqvYxEI9T3Hmd3xz72nhPUdoudat9M\n5ufPodZXTSBgsOtIJ3sb/JzqCMbXLyvI5IYZ+Syoymd6aZYpB5Slai+TkXppDvXRPArnCdAGZ550\n6GU0FuVE4BT7/QfZ7z9I22BHfFlFVjnz8muYm1eDO5rLvuPd7G3wc6ixh9FwFBjbTz1vuo950/OY\nU+kjK+PSN3G5nHToZbJQL82hPppH4TwB2uDMk4697BjsZL//EPv9BzkWOEk0NhbCOc5savNmU5tX\nQ6W3kuNNg+w95mdvg5/eM/upDWBasZe50/OYN93H9NKsCR/9nY69TBT10hzqo3kUzhOgDc486d7L\ngdHB+GlZh7qOMhAeu32pzbAyI2c6tfnVzPHNZiTo5sCJbg4c76K+OUAkOvZPx+20Macid2xUPS2X\n/JxL76tO915eT+qlOdRH8yicJ0AbnHkmUy+jsSgn+05R5z/Mga7DNAdb4svy3XnM8c2i2jeL8oxp\nnGwJceB4N/uPd8UPKgMozHEzpyKXmgof1eU5eM+ZAp9MvbzW1EtzqI/mUThPgDY480zmXvYOB8ZG\n1f7DHOlpYCgydtMNi2GhMqucGt8sqnNn4orkUXeih0ONPRw+1UtoeOycagOYWuRhToWPOdNyueWG\nKfT3hRL4jdLHZN4uzaQ+mkfhPAHa4MyjXo6JRCOc6DvF4e6jHOqup7GviRhj/3wybG5m586g2jeT\nmTlVBAMODp7s4dDJbhpOBwhHxn7PZjWoKMmiujyH6vJcqsqycdqT90YdyUzbpTnUR/MonCdAG5x5\n1MuLGxgd5EhPQzysu4d64svyXT6qfTOZ7ZtJhaeS1vZRDp7spqGlj4bmXs7+q7NaDKaXZlFdnkt1\neQ5VZdk4FNYTou3SHOqjeRTOE6ANzjzq5fhisRgdIT9Huus53F3P0d5jhMJj+6ENDKZ6y6j2zWRJ\nxTzcw7k0toQ4cqqXw6d6aGzvPy+sK0uymDU1h1lTs5lRlkOGS5fAvxhtl+ZQH82jcJ4AbXDmUS+v\nXCQa4VR/M4e7Gzjcc5QTgVNEYhFgbH/1VG8ZM3IqmZkznRLXFJrbRjjc2MPRpl5OtQeJnvlnaQBT\nCz3MnJrD7Kk5zJyaQ3bm1Z1jnW60XZpDfTSPwnkCtMGZR7384IbCwzT0Hqdl5DT7Wo7Q2N8UP7fa\nwKDUU8yMnEqqsiuZkjGVTn+Mo00Bjjb1crylj3AkGv+swhw3VWXZzJySzYwp2ZTmZ2IxEn87zOtN\n26U51EfzKJwnQBucedRL85zt5XBkhJOBUzT0Hqeh9wQn+hoZjYbjv5fv8lGVU8mMnEqmeaYxEHBQ\n3xygvjlAQ3OAweH3ftfttFFVlsWMsmxmlmVTUZKF25n+U+HaLs2hPppH93MWSXFOq4PZvhnM9s0A\nIBwN09jXzLHACY71nuBYoJHtbbvY3rYLGLvDVlVOBfOXVvLxrApsw9mcaAnS0Byg/nSAA8e7OXC8\nGwDDgLJ8D1VlWUwvzWJ6aTYleRmTcnQtkqoUziJJwGaxUZVTQVVOBUy7k2gsSutAO8d6T9DQe4Jj\ngZO823mAdzsPAGM375iWNYXpNRUsWTqNPFs1bR1hGpoDHG8JcLKtn+bOIG+8O3YRFbfTxvQSL9NL\ns6kszaKyJEv7rkWSmMJZJAlZDAtlnhLKPCWsmHIrsViM7qEejgVOcjzQyPHASY71nqSh90R8naKM\nAipLprFsdjkPZVYRC3k52RrkeEsfx1r6qDvZQ93J90738mU5qSzOoqLEy/SSLKYVZ+nIcJEkoX3O\nMi710jxm9jIUHuJk3ymOBxo5ceZx9gpmAHaLjSmeMqZlTWFa1lQKHSUEuu00tvVzvLWPk6199A2O\nnveZxb4MKkq8VBR5mVbspbzIm7T7r7VdmkN9NI/2OYsIbpuLGt8sanyzAOJT4Y19TZzsa+JUXxON\n/U2c6Gs8Zx0307xTqLixjNtWlJFlFNDbZeVkez8nWvo42dbPtrpBttW1x9cp8mUwrchDRXEW04o8\nTCv2kuGyX/fvKzKZKJxF0sS5U+G3li4BYCQyQnOwhca+Zk72naKxr4nDPfUc7qmPr5dhczM1u4xZ\nU6bwIW8pGdE8At02mtoHONnWR2N7kB2HOthx6L17YOdnu5ha6GFqoYfyIi/lhR7ysl0YOuhMxBQK\nZ5E05rA6mJ5dwfTsivh7g6MhmvpPc6q/Of58pKeBIz0N8d9xWZ2UZZdSMaWU5Z4SPOQzGHDT3D5I\nY1sfTR1B9tT72VPvj6/jdtrGwrrQw5RCD1MKPJTlZ+J06HKkIldK4SwyyWTY3eedxgUQCp8N7NM0\n9Z+mub9l7KCzwHsHnFkNKyWZRUxZUMpCTyk51lIiA17a/aM0dQQ51R6kvqmXo0298XUMoCDHTVlB\nJmUFHqYUZDKlwEORz43VYrmeX1skpSicRQS3zc2s3BnMyn0vsEciI5wOttIcbKGpv4Xm/hZaBlrP\nu8c1QK4zhymVJdw6r5RCVzGW4SyCvXZa/IM0dwZp7hy4YJRtsxoU+zIozc+kLD+T0jOPwlyFtggo\nnEXkEhxWB5XZ06jMnhZ/LxKN0D7Yyelgazy4m4Mt7PcfYr//0HvrWuyU5BRTMqWIRZnFZFuLiA16\n6eqOcdo/QIt/4Ex4D5z3N88N7ZK8TEryMij2jT10ty6ZTBTOIjJhVouVUk8xpZ5iFnNj/P2+kX5O\n94+F9elgKy0DbWMHovU3nbd+pj2D0pJiZs0oYnlGIZmxfCKhDHp6DFr9g7R0XTy0DSAv20VxXgYl\nvrHQnj09D5fFIMfj0IFoknYUziLygWU5vGTleanJmxV/LxKN0BHy0xJso3WgjZZgGy0DbTT0nqC+\n9/h567ttLoqKC6mYXsjNGQVkGj6iQxkM9jlo7x6mrWuA1q7B8y5TepbTbqUw102RL4Nin5ui3AyK\nfBkU5brxuO0KbklJCmcRuSaslrEDyEoyi4AF8feHIyO0D3bQNtBB+0AHbWd+PtU/drrXuQwMfN5c\nCovyWerOJ8eeiyXsITzgJhbO5OTpftq7B2nvGaSpI3hBDW6nlYIcN4U5bgpzMyjMdcdf52Y5db1x\nSVoKZxG5rpxWB+XeKZR7p5z3fiQaoTPURdtgBx0DnXSE/HQMjj0f6j7KIY6e9/sWw4KvKJfCinxq\n3Xl4rDlYRjyEQ26CARudPcN09IZo6xrkVPuFwW2zGuRluSjIcZOf46Yg2zX2nOMiP9tNpsumUbck\njMJZRJKC1WKlOLOQ4sxCKDh/WSg8RGfIT+egn47BLjpDfnpHezjd187B7iMXfJbFsOArzaWoKo85\nLh8eSzbWSCbhkJuhfgfdvRE6e0N09g7RfqL7gvVhbNSdl+Uae2SfeWSNBXdetousDE2Zy7WjcBaR\npOe2uS4YbZ+9jnEoHKLzTGB3xJ/9+Ie6ONR99KKf5/Flkl+Wxw2uXLz2bJxRD4y6GR10MdhvpycQ\npjMQwh8YuuDgtLPsNgu5Xic+rxNflgtflhOfd+w598xzhlOjb7k6CmcRSWlum5vyrCmUZ025YNlQ\neJiuoW78oS46Q110hbrpDHXhD3XR1H/6gn3cAFjBU5SJb1ou5a4cvLZsHLFMjFE34SEXQ0EHgUCM\nrr5hevqGONwTumRtTruVHK+TXI+DXO9YaOd6neR4nPiyxp6zMu06t1suoHAWkbTlsjnj1xt/v2gs\nSt9IP12hHrqGuuke6qF7qIeu0NhzS7CVU/3NF36oAQ6fndzSXKqc2WQ5snCSiS2SQWzUyWjISWjA\nTl9fjED/CN39w7R3D16yRsOArAwH2R4HOR4nOR4H2ZljzzkeJ1keB9mZYw+7Ted6TxYKZxGZlCyG\nhRxnNjnObKqouGB5NBalf2SAnuEeuod66R7qoWeol+6hXnqGxt5rH+y48IMB7GAvsJFdlkWFMwuv\nIwuXkYEtmgGjLiLDDkZCdgb7rfQHowSCo7R1X/zAtXNlOG1knwnrrMyxEM/KtJOVMfY6K9Nx5me7\ngjzFKZxFRC7CYljIdnrJdnqpyCq/6O+MREboHe6jdzhw/mMoQM9wgMBwH8cDjcSIXbiyAWSBPcdO\ntsPLTIeXTLsHB25ssQyMUSfREQejw3aGB60MBK30D0QIBEdo7br0SPwst9NKVoYDb6YDr9uON8OB\nN+O956wzzxaHjdFwFLtNU+vJROEsInKVHFYHhRn5FGbkX/J3xkbgQQLDfQRG+ugd7hv7+czrvpF+\n+ob7OdnfRDQWvfiHZIw9XFYXPkcm5XYPbksGdtzYYi4IO4mNOggP2xkO2QgNWAgGDfoHR+noDRC7\nyP8N3s/psOJ12/G47Xgy7PFAzzz7ntuOx2U777UuqXrtKJxFRK6hsRF4FtnOrMv+XjQWZWB0MB7W\nfSP9BEb66B8JvvcYDdI30o8/1H3p0fiZILcUWvDaMyl0eHBbM3AYbuwxF5aoAyJOoiN2wsM2iLro\nC0QJBS0EQxGaOwcIRy7xn4T3sdsseNx2Ml12PG4bmS47mW4bGS47mWeD3GUnwzW2bOzZhstp0wVg\nxqFwFhFJAhbDgtfhwevwXPQAtnOdDfJzQ7t/JEgw/vPAmWX9+ENdDEdaL/5B1jOP/LGHy+qi2J5B\nhi0Dp8WFw3BhjTqxRB3EInaio3YiIzZGh60MD1kYGrQwMABdfUM0d4Yn/F0NY2z/eYbrvSCPv3ba\ncTutZLjsZDhtuM8uc9pwn3m4nNa0D3eFs4hIijk3yCdiNDJKcHSA4OggwdEgAyMD9I8OEBwdIGId\nwd8fYGBkgIHwIAOjg7QOthGOXiJsrcRH5/jG3nLb3OTZ3LitbpwWFzZc2HBgiTghYicWthMetRIZ\nsTIybGV4yGBo0MJgKELAP8JIeGIj9bMMwOW0xsPa7Tgb3Nb4a5fTet7z2VB3OWy4HVZcjrGfLZbk\nDHmFs4hImrNb7eRac8h15Vyw7OzFXM4Vi8UYiY4ycCbAB0dDDIZDDIwOMjg6yEB4cOy9c34eGB2k\nbaT90qFuAVxnHtljbxkYeGxOXFYXLqsLu+HEbjixYscac2BE7MSiNmKjdqJhK+FRC+FhKyMjFoaH\nYDgUpqc/TMtwZEL71S/GYbfgctjOhLX1fT+PvXbarbicVuZPz6OsYGL/IfqgJhTOzzzzDHv37sUw\nDNauXcv8+fPjy4aHh/nKV75CfX09GzdunNA6IiKSvAzDwGl14LQ68Llyr2jdkchYqL8/zEPhoTOP\nUPx5cHSIocgQg6MheoZ7GYoMXfqDz07Bu95XKwa5ZwLeYXFgN5zYDMfYyD1mxxKzQ9QGYSvRiI1o\n2EIkbCE8YmF0xMLISIyR4ShDQwaBARgeuXTKHznVy5d+b8Ell5tp3HDesWMHjY2NrF+/nmPHjrF2\n7VrWr18fX/7cc89RU1NDfX39hNcREZH05LDacVhzyOXCUfp4orEoQ+HhseAODzF05jkUDjEUHmYo\nMhbwQ+EhhiLDDJ0J/KHIMKHwEP2j/QxF/Jc+6h3GUs/GBSEPY4N7r8WGw+IYC3qLHZvhGBvJY2NR\nafYVf6erNW44b926lZUrVwJQVVVFIBAgGAzi8YwN7b/85S/T29vLpk2bJryOiIjI+1kMCxl2Nxl2\nN3lX+RmxWIzR6Oh5IX725+HIyJnH8HnPI5ERhiLDjJyzfCg8zGB4gOFIN+FYBICCoIfbWGTeF76M\nccPZ7/dTW1sbf+3z+ejs7IwHrcfjobe394rWERERuRYMw8BhdeCwOsY9fW2iwtEwI5ER3Da3KZ83\nEVd8QFjsKva6T2Sd3NwMbCZfbq6gwGvq501m6qV51EvzqJfmUB/NY1Yvxw3nwsJC/H5//HVHRwcF\nBQWXWePq1unpGf9ydFfiYkcgytVRL82jXppHvTSH+mieK+3l5YJ83IupLlu2jM2bNwNQV1dHYWHh\nuNPTV7OOiIiIjBl35Lxw4UJqa2tZtWoVhmGwbt06Nm7ciNfr5e677+aLX/wibW1tnDhxgocffpgH\nH3yQj33sYxesIyIiIhNjxK5mJ/I1YPa0iqZqzKNemke9NI96aQ710TzXdVpbREREri+Fs4iISJJR\nOIuIiCQZhbOIiEiSUTiLiIgkGYWziIhIklE4i4iIJJmkOc9ZRERExmjkLCIikmQUziIiIklG4Swi\nIpJkFM4iIiJJRuEsIiKSZBTOIiIiSWbc+zmnomeeeYa9e/diGAZr165l/vz5iS4ppRw9epTHHnuM\nP/zDP+Qzn/kMra2t/OVf/iWRSISCggL+7u/+DofDkegyU8Jzzz3Hrl27CIfD/Omf/inz5s1TL69Q\nKBTiySefpKuri+HhYR577DGqq6vVxw9gaGiI+++/n8cee4xbbrlFvbwK27dv5/HHH2fmzJkAzJo1\ni8997nOm9TLtRs47duygsbGR9evX8/TTT/P0008nuqSUMjg4yNe+9jVuueWW+HsvvPACn/70p/nZ\nz37GtGnT2LBhQwIrTB3btm2jvr6e9evX893vfpdnnnlGvbwKv/nNb5g7dy4/+clP+MY3vsGzzz6r\nPn5A3/rWt8jOzgb07/uDWLJkCT/+8Y/58Y9/zFNPPWVqL9MunLdu3crKlSsBqKqqIhAIEAwGE1xV\n6nA4HPzTP/0ThYWF8fe2b9/OXXfdBcCdd97J1q1bE1VeSlm8eDH/8A//AEBWVhahUEi9vAr33Xcf\njzzyCACtra0UFRWpjx/AsWPHaGho4I477gD079tMZvYy7cLZ7/eTm5sbf+3z+ejs7ExgRanFZrPh\ncrnOey8UCsWnZvLy8tTPCbJarWRkZACwYcMGVqxYoV5+AKtWreKJJ55g7dq16uMH8Ld/+7c8+eST\n8dfq5dVraGjg0Ucf5fd///fZsmWLqb1My33O59LVSc2lfl65X/3qV2zYsIHvf//7fPjDH46/r15e\nmRdffJFDhw7xF3/xF+f1Tn2cuJdffpkbbriBqVOnXnS5ejlxFRUVrFmzhnvvvZempiZWr15NJBKJ\nL/+gvUy7cC4sLMTv98dfd3R0UFBQkMCKUl9GRgZDQ0O4XC7a29vPm/KWy3vzzTf59re/zXe/+128\nXq96eRUOHDhAXl4eJSUl1NTUEIlEyMzMVB+vwuuvv05TUxOvv/46bW1tOBwObZNXqaioiPvuuw+A\n8vJy8vPz2b9/v2m9TLtp7WXLlrF582YA6urqKCwsxOPxJLiq1HbrrbfGe/rqq69y2223Jbii1NDf\n389zzz3HP/7jP5KTkwOol1fjnXfe4fvf/z4wtttqcHBQfbxK3/jGN/j5z3/OSy+9xO/93u/x2GOP\nqZdXadOmTXzve98DoLOzk66uLh544AHTepmWd6V6/vnneeeddzAMg3Xr1lFdXZ3oklLGgQMH+Nu/\n/VtOnz6NzWajqKiI559/nieffJLh4WFKS0v5+te/jt1uT3SpSW/9+vV885vfpLKyMv7es88+y3/7\nb/9NvbwCQ0ND/M3f/A2tra0MDQ2xZs0a5s6dy1/91V+pjx/AN7/5TcrKyli+fLl6eRWCwSBPPPEE\nfX19jI6OsmbNGmpqakzrZVqGs4iISCpLu2ltERGRVKdwFhERSTIKZxERkSSjcBYREUkyCmcREZEk\no3AWERFJMgpnERGRJKNwFhERSTL/F4hKM2Ejor5wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f07497fc048>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "43nSojeLXeaJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "f3ac490b-116c-40d3-9941-13588f8f029f"
      },
      "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)\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": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAADjCAYAAADdR/IFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3Xm8XfP1//GlZkIQiTmDxBgZSCSm\nkFS+hhBDDVVpqyhVtKqKGtoaWv1+aSmqlP4oqTHmIVIzMYSGSJpIQqIJIYPMDTHn90cflvdn9e7t\n3JNz7t3n3Nfzr7V9Pvecnb3PZ599ts/6rBWWLVu2zAAAAAAAANDsvtbcOwAAAAAAAID/4EENAAAA\nAABAQfCgBgAAAAAAoCB4UAMAAAAAAFAQPKgBAAAAAAAoCB7UAAAAAAAAFMRKeY0rrLBCU+0HgkpW\nTec8Np9KnUfOYfNhLNYHxmLtYyzWB8Zi7WMs1gfGYu1jLNaHrPPIjBoAAAAAAICC4EENAAAAAABA\nQfCgBgAAAAAAoCB4UAMAAAAAAFAQPKgBAAAAAAAoCB7UAAAAAAAAFAQPagAAAAAAAAqCBzUAAAAA\nAAAFsVJz7wBajp/97Gcer7766klb9+7dPT700EMzX+Pqq6/2+IUXXkjahg4dury7CAAAAABAs2JG\nDQAAAAAAQEHwoAYAAAAAAKAgeFADAAAAAABQECssW7ZsWWbjCis05b5A5JyWRmvO83j77bd7nLf2\nTDmmTp2abA8cONDjt956q6LvVa5Kncd6HYtbbrllsj1p0iSPTznlFI+vvPLKJtunqF7GYqnWXHNN\njy+55BKPf/CDHyT9Xn75ZY8PO+ywpG369OlV2rvyMRZrX0sbi/WKsVj7GIv1gbHYOOuuu67H7du3\nL+lv4v3Qqaee6vH48eM9fv3115N+Y8eOLen1GYv1Ies8MqMGAAAAAACgIHhQAwAAAAAAUBCU50ZF\naaqTWenpTpry8ve//93jzTffPOk3ePBgjzt37py0DRkyxOPf/va3Jb0vmtf222+fbH/++ecez5gx\no6l3B2a20UYbeXzcccd5rOfGzKxXr14e77///knbVVddVaW9wxd22GEHj+++++6krWPHjlV73732\n2ivZnjhxosdvv/121d4XpdHvSDOz+++/3+OTTz7Z42uuuSbp99lnn1V3x+pMu3btPL7jjjs8fv75\n55N+1157rcfTpk2r+n59oXXr1sn27rvv7vGIESM8/uSTT5psn4BasN9++3l8wAEHJG39+/f3uEuX\nLiW9Xkxp6tChg8errrpq5t+tuOKKJb0+6hszagAAAAAAAAqCBzUAAAAAAAAFQeoTllvv3r09Pvjg\ngzP7TZgwweM4nXDu3LkeL1myxONVVlkl6Tdq1CiPe/TokbS1adOmxD1GUfTs2TPZfv/99z2+5557\nmnp3WqS2bdsm2zfeeGMz7QkaY++99/Y4b/p0pcXUmmOOOcbjI444osn2A1/S774//elPmf3++Mc/\nenz99dcnbUuXLq38jtURrfZilt7PaJrR7Nmzk37Nle6kVfnM0uu8pq1OmTKl+jtWg9Zee+1kW9Pp\nt9tuO4+12qgZqWRFpsslnHTSSR5rireZ2eqrr+5xJaogxeqmQGMwowYAAAAAAKAgeFADAAAAAABQ\nEDyoAQAAAAAAKIgmXaMmlmrWvMB33303afvwww89vvnmmz2eNWtW0o/82uan5XxjPqfmceuaCjNn\nzizptU877bRke9ttt83s+9BDD5X0mmhemt+t5WLNzIYOHdrUu9Mi/fjHP/b4oIMOStr69OnT6NfT\n0q9mZl/72pf/D2Ds2LEeP/PMM41+bXxppZW+/MoeNGhQs+xDXPvipz/9qcdrrrlm0qZrTqF6dPxt\nuummmf1uvfVWj/UeCw1bf/31Pb799tuTtvXWW89jXRfoRz/6UfV3LMO5557rcadOnZK2H/zgBx5z\n39ywIUOGePyb3/wmadtss80a/Ju4ls28efMqv2OoCL02nnLKKVV9r0mTJnmsv4NQWVoiXa/XZuma\nqVpW3czs888/9/iaa67x+Lnnnkv6FeFayYwaAAAAAACAguBBDQAAAAAAQEE0aerTxRdfnGx37Nix\npL/TKZv//ve/k7amnFI2Y8YMj+O/ZfTo0U22H0XzwAMPeKzT0MzS8zV//vxGv3Ys97ryyis3+jVQ\nLFtvvbXHMVUiTi9HdVx22WUe6xTQcn3jG9/I3J4+fbrH3/zmN5N+MY0G+QYMGODxzjvv7HH8Pqqm\nWKZY01HXWGONpI3Up+qI5djPOeeckv5OU0uXLVtW0X2qRzvssIPHceq8uuCCC5pgb/5b165dk21N\nFb/nnnuSNr5bG6bpMH/4wx881pL3Ztnj5corr0y2NZ27nHtefLWY4qJpTJq6MmLEiKTfRx995PGi\nRYs8jt9Tel/6yCOPJG3jx4/3+MUXX/R4zJgxSb+lS5dmvj4aR5dLMEvHmN5rxs9Fqfr27evxp59+\nmrRNnjzZ42effTZp08/dxx9/XNZ7l4IZNQAAAAAAAAXBgxoAAAAAAICC4EENAAAAAABAQTTpGjVa\njtvMrHv37h5PnDgxadtmm208zssT3mmnnTx+++23Pc4qpdcQzUl77733PNay09Fbb72VbLfkNWqU\nrkdRrtNPP93jLbfcMrOf5oc2tI1iOuOMMzyOnxfGUfUMHz7cYy2fXS4tQ7pkyZKkrUOHDh5rmdiX\nXnop6bfiiisu937Us5ibreWVp06d6vFFF13UZPt04IEHNtl7oWHdunVLtnv16pXZV+9vHn744art\nUz1o165dsn3IIYdk9j322GM91vvGatN1aR577LHMfnGNmri+I/7jZz/7mcdacr1Ucd21ffbZx+NY\n4lvXs6nmmhb1KG/dmB49enisJZmjUaNGeay/K6dNm5b0a9++vce6NqlZZdb0Q8P0mcBJJ53kcRxj\na6+9doN//8477yTbI0eO9Phf//pX0qa/Q3StxD59+iT99JowaNCgpG3s2LEea4nvSmNGDQAAAAAA\nQEHwoAYAAAAAAKAgmjT16fHHH8/dVrGs2hdiadCePXt6rNOXdtxxx5L368MPP/T49ddf9zimY+kU\nKJ12juW3//77e6ylLldZZZWk35w5czw+66yzkrYPPvigSnuH5dGxY8dku3fv3h7reDOjjGEl7bHH\nHsn2Vltt5bFO3y11Km+c2qnTj7XUpZnZ17/+dY/zSgf/8Ic/9Pjqq68uaT9aknPPPTfZ1unfOsU+\npp5Vmn73xc8VU8GbXl5KThTTBJDt97//fbL97W9/22O9vzQzGzZsWJPsU9SvXz+PN9hgg6Ttr3/9\nq8d/+9vfmmqXaoqm5ZqZHX300Q32GzduXLI9e/ZsjwcOHJj5+q1bt/ZY06rMzG6++WaPZ82a9dU7\n24LFe/9bbrnFY011MktTf/PSAVVMd1JxaQtUx5///OdkW9PW8kpt67ODf/7znx6fffbZST/9bR/t\nsssuHut96PXXX5/002cMeg0wM7vqqqs8vuuuuzyudCosM2oAAAAAAAAKggc1AAAAAAAABdGkqU+V\nsGDBgmT7ySefbLBfXlpVHp1SHNOsdIrV7bffXtbro2GaDhOnPCo97k8//XRV9wmVEVMlVFNWy2gJ\nNM3stttuS9ryppIqrcSl0znPP//8pF9eqqG+xvHHH+9x27Ztk34XX3yxx6uttlrS9sc//tHjTz75\n5Kt2u24ceuihHscqA1OmTPG4KSukafpaTHV66qmnPF64cGFT7VKLtvvuu2e2xWoyeamHSC1btizZ\n1s/6u+++m7RVs2rP6quvnmzrlP4TTzzR47i/xxxzTNX2qV5oKoOZ2VprreWxVomJ9y36/fStb33L\n45hu0blzZ4833HDDpO2+++7zeN999/V4/vz5Je17vWvVqpXHcWkDXR5h7ty5Sdvvfvc7j1kCoVji\nfZ1WW/r+97+ftK2wwgoe62+DmBZ/ySWXeFzucglt2rTxWKuPnnfeeUk/XYYlpk02FWbUAAAAAAAA\nFAQPagAAAAAAAAqCBzUAAAAAAAAFUXNr1FRDu3btPP7Tn/7k8de+lj7H0rLR5JQun3vvvTfZ3muv\nvRrsd9NNNyXbsVwtiq9bt26ZbbpGCZbfSit9eUkvdU2auNbTEUcc4XHMBS+VrlHz29/+1uNLL700\n6bfGGmt4HD8L999/v8dTp04taz9q0WGHHeaxHh+z9Pup2nS9oyFDhnj82WefJf1+/etfe9yS1hJq\nalpOVOMo5uy/+uqrVdunlmS//fZLtrXsua7NFNdTKJWuidK/f/+kbaeddmrwb+68886y3qslW3XV\nVZNtXefnsssuy/w7LfV7ww03eKzXazOzzTffPPM1dP2Uaq5xVKsOOuggj3/+858nbVoyW0vUm5kt\nWrSoujuGssVr2emnn+6xrkljZvbOO+94rOvFvvTSS2W9t649s9lmmyVt+tty+PDhHse1aVXc36FD\nh3pczfX5mFEDAAAAAABQEDyoAQAAAAAAKAhSn8zspJNO8ljLx8ZS4JMnT26yfapHG220kcdx6rZO\nR9V0C51Wb2a2ZMmSKu0dKkmnah999NFJ25gxYzx+9NFHm2yf8CUt7RxLupab7pRFU5g0hcbMbMcd\nd6zoe9Wi1q1bJ9tZaQ5m5adVlEPLqmsa3cSJE5N+Tz75ZJPtU0tW6lhpys9Ivbn88suT7QEDBni8\n8cYbJ21aIl2nxB9wwAFlvbe+Riy7rd58802PY2lofDUtrR1peltMz8/Su3fvkt971KhRHnMv+9/y\nUjr1vnHGjBlNsTuoAE0/Mvvv1Gn16aefety3b1+PDz300KTf1ltv3eDfL126NNneZpttGozN0vvc\nDTbYIHOf1OzZs5Ptpkr7ZkYNAAAAAABAQfCgBgAAAAAAoCBaZOrTrrvummzH1cW/oCuQm5mNHz++\navvUEtx1110et2nTJrPf3/72N49bUrWXejJw4ECP11tvvaRtxIgRHmslBVRWrFqndFpptemU/rhP\neft43nnnefyd73yn4vtVFLEKySabbOLxrbfe2tS74zp37tzgf+d7sHnkpVhUouoQzF5++eVku3v3\n7h737Nkzadtnn3081kom7733XtLvxhtvLOm9tYLI2LFjM/s9//zzHnN/1HjxmqqpappeGNMrtHrl\nwQcf7HGsEqNjMbYdd9xxHuv5fu2110ra93oXU1yUjrdf/epXSdt9993nMVXuiuWJJ55ItjVVWn8n\nmJm1b9/e4yuuuMLjvFRQTaWKaVZ5stKdPv/882T7nnvu8fjHP/5x0jZz5syS3295MKMGAAAAAACg\nIHhQAwAAAAAAUBA8qAEAAAAAACiIFZblJH/p2gL15De/+U2yfdZZZ3n8+OOPezxo0KCkXzXLb0V5\nOXmN1ZznUfN/77jjDo9XXnnlpN9TTz3l8YEHHuhxrZcwrNR5rLWxOGzYMI8POeSQpE23Nf+zqGpp\nLP7ud7/z+JRTTsnsF8dfNf3oRz/y+NJLL03adI2amBusawRUYi2Goo7F1VdfPdkeOXKkx/E8abng\n+fPnV3Q/2rVrl2xn5V/HPO2rrrqqovuRp5bGYiXstttuHj/99NMex7Wdpk+f7nHHjh2rvl/Lq6hj\nsTltvvnmHk+ZMiVp03U39t57b4/jejhNqVbHYlwzT49169atM/cp69/72GOPJdsnnXSSxw8++GDS\ntsUWW3h83XXXeXzCCSd81W5XTZHGou5LvB/Io32vueYaj7Uculm6Boqe9wkTJmS+dteuXZPtF154\nweOilAmv1bG4zjrrJNu6XqyuJTtv3ryk31tvveWxrvHXo0ePpF+fPn0avU/6+TEzO/vssz3W9aeq\nIes8MqMGAAAAAACgIHhQAwAAAAAAUBAtpjy3Ti/XMm9mZh9//LHHWvatKVOd6kUsu63TxvLSLXRq\nb62nO7VUG264ocf9+vXzePLkyUm/Wkh3qlWDBw9ulvdt27Ztsr3tttt6rNeAPHEaf0u5/i5dujTZ\n1jSvmDb40EMPeRzTyEqx3XbbJduabhFTZrKm4TZmSjqWj36f5pWyf/TRR5tid1BFv/zlLz2OY+/M\nM8/0uDnTnepBTBk9/PDDPb7zzjs91jSo6Morr/RYz42Z2Ycffujx3XffnbRpaoemsHXu3Dnp11LL\nrmvq9k9/+tOS/06vjSeeeGKDcaXo+NMlG4444oiKv1e9i6lEOj7KcdNNNyXbealP//73vz3Wz9pf\n//rXpJ+W/24uzKgBAAAAAAAoCB7UAAAAAAAAFAQPagAAAAAAAAqixaxRc/rpp3u8/fbbJ20jRozw\n+Pnnn2+yfapHp512WrK94447Ntjv3nvvTbZ1bSDUpu9973sea6nfhx9+uBn2Bk3pnHPOSba1RGme\nadOmeXzUUUclbVqCsSXRa2Eslbnffvt5fOuttzb6tefOnZts61oY66+/fkmvEXO4UT2HHnpog/89\n5vb/+c9/bordQQUddthhyfZ3v/tdj3X9BLP/Lk+LytHy2jrejjzyyKSfjjldT0jXpIkuvPDCZHub\nbbbx+IADDmjw9cz++7uwpdA1Sm6//fak7ZZbbvF4pZXSn66bbbaZx3lreVWCrsenn5dzzz036ffr\nX/+6qvuB/zjjjDM8bsw6QSeccILH5dxLNSVm1AAAAAAAABQED2oAAAAAAAAKom5Tn3SKuJnZL37x\nC48XL16ctF1wwQVNsk8tQakl9U4++eRkm5Lcta9Dhw4N/vcFCxY08Z6gKQwfPtzjrbbaqqzXeO21\n1zx+9tlnl3uf6sGkSZM81tKxZmY9e/b0uEuXLo1+bS0/G914443J9pAhQxrsF8uJo3I23XTTZDum\nX3xhxowZyfbo0aOrtk+ojn333Tez7cEHH0y2X3nllWrvDixNg9K4XPFaqek8mvo0YMCApN96663n\ncSwnXs+0FHK8pm255ZaZf7fnnnt6vPLKK3t83nnnJf2ylmIol6Ym9+rVq6KvjWzf//73PdaUs5gS\npyZMmJBs33333ZXfsSphRg0AAAAAAEBB8KAGAAAAAACgIOoq9alNmzYeX3HFFUnbiiuu6LFO2Tcz\nGzVqVHV3DP9Fp3aamX3yySeNfo1FixZlvoZOf2zdunXma6yzzjrJdqmpWzpF88wzz0zaPvjgg5Je\no97sv//+Df73Bx54oIn3pOXSqbh51Q/ypt1fe+21Hm+88caZ/fT1P//881J3MTF48OCy/q6levXV\nVxuMK+HNN98sqd92222XbI8fP76i+9GS7bLLLsl21hiOVRNRe+I1+P333/f497//fVPvDprAHXfc\n4bGmPn3zm99M+unSACzN8NUef/zxBv+7pgqbpalPn376qcc33HBD0u+6667z+Cc/+UnSlpWOiurp\n06dPsq3Xx1atWmX+nS6poVWezMw++uijCu1d9TGjBgAAAAAAoCB4UAMAAAAAAFAQPKgBAAAAAAAo\niJpfo0bXnhkxYoTHnTp1SvpNnTrVYy3VjeYxbty45X6NYcOGJdszZ870eIMNNvA45v9W2qxZs5Lt\n3/zmN1V9v6LYbbfdku0NN9ywmfYEX7j66qs9vvjiizP7afnXvPVlSl17ptR+11xzTUn90PR0faOG\ntr/AmjTVo+vsRXPnzvX48ssvb4rdQYXpOgl6j2JmNmfOHI8px12f9HtSv58PPPDApN+vfvUrj2+7\n7bak7fXXX6/S3tWfRx55JNnWe3Mt5Xzccccl/bp06eJx//79S3qvGTNmlLGHKEVcy3CttdZqsJ+u\n82WWrgP13HPPVX7HmggzagAAAAAAAAqCBzUAAAAAAAAFUfOpT507d/a4V69emf207LKmQaGyYunz\nOKWzkg477LCy/k7L8uWlbNx///0ejx49OrPfyJEjy9qPWnfwwQcn25qGOGbMGI+feeaZJtunlu7u\nu+/2+PTTT0/a2rZtW7X3fe+995LtiRMnenz88cd7rOmJKJZly5blbqP69t5778y2t956y+NFixY1\nxe6gwjT1KY6vhx56KPPvdKr/uuuu67F+JlBbXn31VY9/+ctfJm2XXHKJxxdddFHS9p3vfMfjpUuX\nVmnv6oPeh5il5dEPP/zwzL8bMGBAZttnn33msY7Zn//85+XsIjLoNe+MM84o6W9uvvnmZPupp56q\n5C41G2bUAAAAAAAAFAQPagAAAAAAAAqCBzUAAAAAAAAFUXNr1HTo0CHZjuXXvhDXZ9BytKieb3zj\nG8m25hauvPLKJb1G165dPW5Mae3rr7/e42nTpmX2u+uuuzyeNGlSya8PszXWWMPjQYMGZfa78847\nPdacXlTX9OnTPT7iiCOStoMOOsjjU045paLvG0vSX3XVVRV9fVTfaqutltnGWgjVo9+LuuZe9OGH\nH3r8ySefVHWf0PT0e3LIkCFJ26mnnurxhAkTPD7qqKOqv2OouptuuinZ/sEPfuBxvKe+4IILPB43\nblx1d6zGxe+tn/zkJx63atXK4969eyf92rVr53H8LTF06FCPzzvvvArsJb6g5+S1117zOO+3o44B\nPb/1hBk1AAAAAAAABcGDGgAAAAAAgIJYYVlODc4VVlihKfelJHGK/VlnndVgvz59+iTbeeWVi6iS\npVGLeB5bikqdx6KcQ52C+PTTTydtc+bM8fjII4/0+IMPPqj+jlVRPY7FffbZx2Mtn21mNnjwYI+1\nRP21116b9NN/i05TNStm2dh6G4uVNmvWrGR7pZW+zIy+8MILPb788subbJ+iehyLK664osd/+ctf\nkrbvfe97Hmt6RK2nvLTUsaglmbt165a06b8lHp//9//+n8c6Ft9+++1K72LJ6nEsFkX79u09jqk3\nt956q8cxRa4cLXUsKi15bma20047eXz++ecnbXqfWxT1MhYPOOAAj++77z6P8/59e+65p8dPPvlk\ndXasiWT9O5lRAwAAAAAAUBA8qAEAAAAAACiImkh92m233TwePnx40qarRCtSn75UlPPYEjGttPYx\nFusDYzHfAw88kGxfeumlHhdlSnG9j8WNN9442f71r3/t8csvv+xxrVdVa6ljUe9ltXqPmdkzzzzj\n8dVXX520LViwwOOPP/64SnvXOPU+FosiVrbdeeedPe7bt6/HMf24VC11LNaTehmLY8eO9TimhqpL\nLrnE4zPPPLOq+9SUSH0CAAAAAAAoOB7UAAAAAAAAFAQPagAAAAAAAApipa/u0vz69evncdaaNGZm\nU6dO9XjJkiVV3ScAAOqFlmVH83j33XeT7WOOOaaZ9gTV8Oyzz3r89a9/vRn3BLXi0EMPTbZ1HY8u\nXbp4XO4aNUBRrLfeeh7rWjmxJPof/vCHJtunImBGDQAAAAAAQEHwoAYAAAAAAKAgaiL1KY9OA9xz\nzz09nj9/fnPsDgAAAAAsl8WLFyfbnTp1aqY9Aarr0ksvbTC+8MILk34zZ85ssn0qAmbUAAAAAAAA\nFAQPagAAAAAAAAqCBzUAAAAAAAAFscKyZcuWZTZKeSw0rZzT0micx+ZTqfPIOWw+jMX6wFisfYzF\n+sBYrH2MxfrAWKx9jMX6kHUemVEDAAAAAABQEDyoAQAAAAAAKIjc1CcAAAAAAAA0HWbUAAAAAAAA\nFAQPagAAAAAAAAqCBzUAAAAAAAAFwYMaAAAAAACAguBBDQAAAAAAQEHwoAYAAAAAAKAgeFADAAAA\nAABQEDyoAQAAAAAAKAge1AAAAAAAABQED2oAAAAAAAAKggc1AAAAAAAABcGDGgAAAAAAgILgQQ0A\nAAAAAEBB8KAGAAAAAACgIHhQAwAAAAAAUBA8qAEAAAAAACgIHtQAAAAAAAAUBA9qAAAAAAAACoIH\nNQAAAAAAAAXBgxoAAAAAAICC4EENAAAAAABAQfCgBgAAAAAAoCB4UAMAAAAAAFAQK+U1rrDCCk21\nHwiWLVtWsdfiPDafSp1HzmHzYSzWB8Zi7WMs1gfGYu1jLNYHxmLtYyzWh6zzyIwaAAAAAACAguBB\nDQAAAAAAQEHkpj4BwPL62tfS58Gff/55M+0JgCxxynMlp1MDAACgcZhRAwAAAAAAUBA8qAEAAAAA\nACgIHtQAAAAAAAAUBGvUoKqySr2ttFL60dP1ELLiqNw1FFZZZRWPP/7444q8Zj1b3nJ9eWvS5K2L\noWvb5J2XuAbOZ5991thdBAppxRVXTLZ1LGlbqes+xXFU6rUWAPAf5d4TcY1tmVj/DcuDGTUAAAAA\nAAAFwYMaAAAAAACAgiD1CctttdVW87hv375JW9u2bT0eMGBA5mt8+OGHHn/wwQceT5s2Len34osv\netyqVaukbcGCBR4vXLjQ45gKs2TJEo9jaoGm0XzyySce1/tUxVLTjPR4xemcmn6hrxH76XZ8r5VX\nXrnB943nMO81sv4t9X4Om1re9G+Odfn0uOal8ZWa4pc3VqqtOd8bAKqBa1nLEe9z9PeO3g/H3yP6\nOyOmJi9dutRjPkv4KsyoAQAAAAAAKAge1AAAAAAAABQED2oAAAAAAAAKgjVqUBJdO6RXr15J2/nn\nn99gPzOzzTff3GNdrybmfeq6Irr2guZympnNnDnT4yeeeCJpe/LJJz0eOXKkx4sXL0766evH/cha\n96Ee1uPQfNq8fY7/1nL+TuNVV1016de6dWuPN9xww6RN83znzZvn8bvvvpv008/Fp59+mrRlrY9T\nK+epKehxieXN27Rp4/EJJ5zgcb9+/ZJ+ixYt8vimm25K2h577DGPdc0pNE7eWNRceY3N0uvY+++/\n77Guu2VW3njOKzVabhnSljY2s75P8o5f3nFeaaUvb+Xi9VY/G3r+43erXkfzyrjjP7KuofF6mrV2\nW1y3otLifjAWa0c511vGbOXo9bRnz55J29lnn+1xjx49PNZ1Mc3M7r33Xo8ffvjhpO3NN99s8O+q\nfU1AbWJGDQAAAAAAQEHwoAYAAAAAAKAgaiL1ifSF5teuXTuPd95556Rt/vz5Hm+99dZJW5yG/QUt\nXWdm9u9//9tjnQr40UcfJf10Sv8666yTtGlfne6flxpT77JK/ealcsU2PX46nTpO08yakhv7rbHG\nGh536dIladPPS14Z9byy26WmC2T9TUuz5pprJttDhgzx+KSTTvI4jjf9POn4NTN79NFHK7mLdS2m\nKOjnNKaSaqqgTsmO6WWaNvgZR2MWAAAgAElEQVT66697HFM786Zal5rKGPdf6bjS60hjUrDqQanX\n21LTF+IxX3vttT3eZZddkrZtttnG4zFjxnj86quvJv0WLFjgcaml3+tNXqphHIvrrruux3qM9Tia\npWm7Oi7LTVXRcx/3Sb9b11tvvaRNx5zuo94rNWY/iqbavxOyUt3ie1UifUVfP9776PX3448/9jhe\nU1WtntNSZV1fS/13x2Pcu3dvj6+99tqkTcd6/Du1xRZbeNynT5+k7fbbb/f4/vvv9zjeR9X7eatX\n5aaAZ2FGDQAAAAAAQEHwoAYAAAAAAKAgqpL6lDVlepVVVkn66bSxOFVIpw9qnDd1O2/KYWOq3GT9\n97x0i1LVUhqXTrHUKbZTp05N+mmlnpge8d5773mslWDuvvvupJ9OxdXPyW677Zb0O/HEEz3WFdfN\n0pXUn3nmGY+LfpwrqdRKD405JllpTOVWkdD0jTglVD8vr7zyiscxtUOn+eZNIc8bb3npCPVOPyda\nmc0sTXdaf/31Pc6r1NahQ4ekTa8dsboM8uWlGel06q222srjmL6mFfDK/d7K2g/9TJil6RZxnGpa\nRUx3bUni2Mm6LpWT/mKWVmT73//936RtrbXW8vipp57y+Jxzzkn6aQpzS6LnIo4j/awfdNBBSZum\ngGvKwrBhw5J+eamHWfuRR8+9VtI0M9txxx093n777ZO2cePGeTx69GiPP/zww6SffsdXegp/peVV\nRyqHHltNJzQz69+/v8d6bIcPH5700/TCmLqfJW/fY3pb1nIC8dzkpUK1FHnHVavhnXzyyUmbVnaK\nn4O8VF+l34s6Ls3S30L6e6fo463o4rnR6l16PszScaX3qPEeptT08LzPRTm/mxQzagAAAAAAAAqC\nBzUAAAAAAAAFwYMaAAAAAACAgqjIGjUxN0tz7jbddFOPW7dunfTT9UximTPN5dVcy8WLFyf9dG0T\nzQOLeZy6jkps09w1zeeO+/uvf/3LY833NUtz8fPy0Wop51DPgebwvfXWW0k/PX66ToxZWgJ08uTJ\nHseS2Uo/P3HtCy1Jq/mHZmYDBw70+LrrrvM45hyWcw5qJXe03NKQlV6vRY9PvD507tzZ43h+Z86c\n6fHs2bM9jnn0pa7rUOp5qpXzuzz036hl0W+88cakn56TUsuQ6nXezGz//ff3+L777vM4loKtRCnT\nosr7TJW6Vln8Dtphhx08HjBggMfTpk1L+ul3la6TUO4aNZrPvdlmmyX9unXr5vEbb7yRtE2aNMnj\nWHq03lVzDay43t+xxx7rcceOHTP/Tu+zFi5cmLRVYg2+WqHnRteg0JLbZunaFYMHD07aVl99dY9f\nfvllj6dMmZL00/uPctd1y2qLnzFdC6NTp05Jm36f5t2j6jW/6NfnSqz5qGNC12u74YYbkn69evVq\n8L0GDRqU9DvhhBM8Hjt2bNKWd9+bJf6N3gvpb5m4/pH+bqq3+5t475+1Pkj87/qb87LLLvNY71fM\n0rEd1/rRbV1TMe6Tno/4OXjuuec81t+0tX5eKilv/Sm9H2nXrp3Hu+yyS9Lv6KOP9ljX9zNLz7He\nPw0dOjTp9/DDD3u8aNGipC1rHag4Fsu9B/sCM2oAAAAAAAAKggc1AAAAAAAABVF26pNOKdMpRGZm\nm2yyicea5qBlec3M1ltvPY9jWWdt0+mXH3/8cdJPpwFutNFGHsfShDq9NabC6GvoPsZ/l05pjaUt\nR44c2eD+5k1/zitJXgQ6fUunrcdyuzqFOqY2aGpaqdM+9bO16667Jm2aZhU/C1oWMU5RW161MiWx\n3CmuedOpl3c/4jT9bbfd1uOYhqipc3lTQmvlfBRJ1rTfrbfeOukX01C/kDdFPqbDXHrppR6ffvrp\nHt99991Jv7/85S8ez5o1K2nLK2VbVKVOxc9r09fQlF0zs5122slj/Z596KGHkn5z5szxOO+6mzfW\ns1Jh2rRpk/TTEsbxHOo1Wr/fivZd15zKuZZpiraZWdeuXT2OU/D1HDz++OMet6Q0xEiPkV5n2rdv\nn/TTNLJYJlnviW699VaPY2p41nGNaRnlfA5iOXH9d82dOzdpGzVqlMd6z5ZXOr5oKpHCE4+7pu1q\nafu+fftmvremPMR7GE3Bjynb+hsi3r8q/XfF67d+XvU1KpECXmR6XxJ/m2mKsJ6P+HnRFKfu3bt7\nHNNANQX/zDPPTNpeeOEFj/W3ZPwc6HvH30z6uWjJZdTj+dGxqfcZBx98cNJvn3328VjTVeP1W+9N\nsu5rzdLvzxNPPDFp69+/v8fx2n7PPfd4PHXqVI/jd+vyXlOZUQMAAAAAAFAQPKgBAAAAAAAoCB7U\nAAAAAAAAFERFynPH9WV0W/NzY45vXs7YkiVLPJ43b16Dr2dmttpqqzX4+nF9gxkzZnisJaPN0nxv\nXWshlmrUsrVaEswsuzxcnlrKCdfyYjGn8t133/U4Hvdy1pnQ0sE9evRI2jQvXMv+mpmdf/75HpdT\nEjNPrZQ3LHe/8krhZeVXlvpeut6UWVrmMo4bLSusudnVON76mvFaVIvro0QxZ/rss8/2eLfddvM4\nXpeVXqNirnze2ieaM67fB2eccUbST9t++ctfJm36HVDU8Zan1DUo4vjS87bffvslbT179vRYS4Pq\nGmlm6fU6b5/0cx8/87q/us6UrpNjlq6VM27cuKRN11CohzHVGKWuT5dXDjlr7TDNqTcza9u2beb7\n6v3TI4884nE5pYJrVd53mh5/XQPPLL0HjNdJXZPg6aef9jjvuOp7xdfLu9bq/ur96gEHHJD007U7\ntASwWbp+lK6ZUfS1ElW8hpaz9kP8Xtx555093mOPPTLfS4+ZluyN64PpZ0bXtzBLr6MTJ070OF6v\n9b3j+dB/c16Z9VoXz63eK+y+++5Jm675Mn78eI/jcX3iiSc81t8xeq9hlp5TvX6alf49jobpPUdc\na23ffff1+NRTT/W4U6dOST+9Pup6pHEtoAkTJng8ffr0pG327Nke6z1NfK+NN97YY/2ta2b2xhtv\neKzjOa4/tbxjkxk1AAAAAAAABcGDGgAAAAAAgIKoSOpTnF6m09B02liclqTpKbGMnU7p03SXOH1a\np5LqFKVYvk1Tn+bPn5+06d9169bN45iyoVPl4mtkTRfNK2lb9KmKWdMvS52K2Rg65fhXv/qVx3F6\nsJbGO+uss5I2PSellr8tp3x1PcibCh7bstIj8tLB9HO+1VZbJf222GILj6dNm5a0lXoOy5E3NbXI\n073LFctuH3PMMR7Hss9Kz7FOK9W0NLP03MWSzVo2Vs9/HM+77rqrx5qOZZaWEtZrb5HHYqnTn7Wf\npu+apdNw99prr6RNj+v111/vsU7BNUs/z/pesXRz3hR7HcOdO3f2+MADD8x8r2HDhiVt+p1f5PNW\nCXnnvjEpZ1n03MWUuHi/ozQdTdNf6v185NFjrteWmAKr24sXL07a9J5SXyPvu1XTbmIKjt4Dx3Oj\n5/eUU07xePDgwUk/TcnReyWzNFUy734uK92uof1qCnn3a1lpg3ljMd7XDxo0yGNNqYjXVE2t11S3\nmC73wx/+0GMtI2yWpkKddNJJHuu5MSu91Hbeuap18biec845HmuaiVma4jJnzhyP42dbyya//fbb\nHutvUbPSy56rxoyNejtXkf774jVV03Rjuvvhhx/usZ7/WO5aly+59tprPR4zZkzST/8uvobu12mn\nndZgbJamK8ZULf29kpeGSOoTAAAAAABAneBBDQAAAAAAQEFUJPUprnCclb4QqwXptOjYptOISp0a\nrKs4R/oacRqSvteCBQs8jivv6+vHlI1S97FWpxuXOhWzVHE63FFHHeVxv379PI6fixtvvNHjUldj\nj9PO81K1avX8VFIlqj7pNP2DDz44adtggw08fvnll5M2PaelnotKTCOtl/OuqUVxCqdWTdBjFq/f\nWhnhiiuu8FirnJilU0njONXqeeedd57HMb1JU2oOOeSQpE2nM2uaQa2cq7xxpNe/tddeO+m3zTbb\neBwrEOgxueGGGzyOacVZ7xuPXV5KmX6WfvKTn3isqYtm6XehTic3a3mVnkqVlb6QV9VG0940XcMs\n/TzF8fynP/0ps62lyJuKrt9VMQVi5syZHscqoPp3em2NafzaT18jL40npkUdd9xxHh9//PEea2qq\nWVr9LVYo0fvZUr8zi3CtrUQqll7LOnbsmLRtuummHo8dO9bjq6++Oun32GOPeaznKlZ2GjJkiMcx\nJVi/MytdsakI52p56fmNKfN6vGL6mp63rIqHZukYyEvLbcpjWc5SDEWUVUlPv7fMzL75zW96fNhh\nhyVteh3V76oHHngg6acpU3qNbswSBpr+r+nmMeVO6TIsZmZTpkzxWMd2XgXNcjCjBgAAAAAAoCB4\nUAMAAAAAAFAQPKgBAAAAAAAoiLLXqNFcsCVLliRtWfmvMXdX87gqsVZIXn5aXp6rltzacMMNPY75\nyg899JDHmhcXX7/S67nUC82j33PPPZO2s88+22PNaRw/fnzSb9KkSR7nnW89x7EkcF5pSvy3vHGq\n9JhrLv7//M//ZP7NI488kmzn5RdnvVcRSogWheb4DhgwIGnT46TnNJ6Db3/72x7rtb0xedyay6tr\n3uy8885JP12LYffdd0/adP0aXW+hyGNW9y2ujaVrVeg1qV27dkk/XfMgltO+7bbbPNZSy6V+98V1\n11QcR1tuuaXHuiZKXF9M87R1LaGv2q+WJI6Vcq6pej422WSTzL+J5+DZZ5/N3I9S3rcxf1eL9PMc\n13rSNWDi/WuXLl081vW1Xn/99aRf69atPdbrc7Rw4UKP43fmAQcc4LGW6n7llVeSfi+99JLHeesk\naByvU7V0rrP2NX5+9Xtmxx13TNr0HGvZbY3N0uO5/fbbe3zRRRcl/fQ3RNw/LcMdf18g/b7r06dP\n0jZw4ECP//a3vyVtpd43lnP9q8Z4qJd1aVTWb2xdC8bMbJdddvE4rs+n9Hp47733Jm16f6nXr7y1\nt7TMtlm6Lqrec8Xroa6Vo2XBzdI1+Sq95pRiRg0AAAAAAEBB8KAGAAAAAACgICpSnjuKU0S/EKdi\n5pXMVpUuJRink2u5MC0dPH369KTf8OHDPY7TFpni3TA91j179vT4yiuvTPqtv/76Hmu6xbhx45J+\nmnKWV/5Wp7nFfjr9v9wpajo9rkjnvhJT1vPK15c6TrW0YiylqGksTz31VNJWajnfvNQnVS/TSrPE\nf7tOyY6lQfVYzJkzx+OTTz456bd48eIG/6YxtOzzm2++6XGcoqylEHVKv5nZDjvs4LGmb9RKWkZe\nSoGmW2y00UZJP02F0um/ZmYvvviix6WOFR2/eWMlpjT9+Mc/9linKOvnwywt4R73t6jnpqnlpQ3m\nHSM9J3vssYfH8R5G77luueWWpC2mpmcp9Zqq6uH86jUpfrb1HrBbt25Jm46JwYMHexyvY6uttprH\nei7mz5+f9NP0nN69e2e+l75GTAnQ+9K8Uu+qls5h3r9J4zg+8kruvvXWWx6/9tprme+laWunnnqq\nx+3bt8/cp5hqqudcU/zzloUoVfy+KdJ9aan0vj2W59bjpb8XzNLrZF56r8pbDqMp7ynzfkvU0tjM\nossgmJl16NAhs6+eOx0Ten01M+vXr1+DfxPTfrVk9j777JO0aTq3XnvjOZg3b57Hzz//fOb+VhMz\nagAAAAAAAAqCBzUAAAAAAAAFUZHUpzhVKCtVIm8V+ihrSmOcklbqtG6dXhan1B1zzDEe6xS6MWPG\nJP2yVnjGl+I50OoUl19+ucdxuqiurK1pDldddVXST1foj58fPcd5lU7KOXd56RZFSsUo973zpuKX\nU1lBKw7FKbnPPPOMx7F6WpbGTE0tVT1MK43pKlotJG/1+t///vcex+mipZ7vUukUVp2KapZO6Y/v\nq2O9FsXrTEyJ+EI8T3pM4vHKuu6UmzqsbbGS0P7779/g34waNSrZfuGFFzwuJ3UxqodxaVZ6epOK\nx0U/C1opMX5m5s6d67FWpzQr/fuu1M9JPd/7xGuOVlWKU/i32247j/X4aNqnWZrqqxWhYr8tttjC\n4/h50XsY3ae///3vSb9yxl8tj7dSv6v0MxvTjDT1TStCxZRU/d2g1QvjMdfzquMy9tVURr2Gxr+r\ndEWjotFzpfeQugyFWZoS3Ldv36RNx87kyZM9LrWiXqwIlFcZNqtiXyWWUWhMZc0i0/3WCk1mZhMn\nTvR44403TtqyzteBBx6YbGsqo44Pvcc1S++PtbqzWXrO9ZwuWLAg6Tds2DCPR44cmbTptaSa54oZ\nNQAAAAAAAAXBgxoAAAAAAICC4EENAAAAAABAQVSlPHdW2e1yc7jKWRsh/o2uD/Ctb30radPcNc0n\n1tw0s7S0Xj3naS+PlVdeOdnW0r9du3b1OB6/l19+2eNf/OIXHr/xxhtJv7wcbP18aa5iYz53pa6N\npLmPMS+ypYjrJGgZ7t12283jmBM+YsQIj2OZe5V3/PPWCCrl9eJr1Kq4Rs3WW2+d2Vdzb2+++WaP\n88aUHrO8ctOR5pr36dPH49atW2f+zdKlS5Pt0aNHe1yJXPCmllcuVc9bvBZqiWAtSWpm1r9/f4/f\ne+89j2Netb6m7oeWCjYza9Wqlcc/+9nPkjYt767XOP3smJVe/jlPPayZUWo55DyxrHD37t09juWh\n1bRp0xqM85S6Jo1ZeWsjFUnev0fXf4nluV966SWP41peeq50/MXvO72u6biP10Id23EdRR3PF154\nocezZs1K+um1vBLruNWSvLLYKq4N1KVLF487d+7scVyTSM+BvsbUqVOTfrrunq5raZauOfXzn/88\n8zXOP/98j3XNlbgfKn7fZP0OK5qs60lcU0T/3T179kza/vznP3usa6DEMaCvqWWX43pEWrI9nkM9\nH7reYlzfqpxjXi9jVv8d8d7kd7/7ncc33HBD0qZjTs/J17/+9aTflltu6bGuNRPXAWzbtq3H8bep\njmH93a/rN5qZPfjggx7HfwvluQEAAAAAAFoYHtQAAAAAAAAURFVSnyqd7lROacg4zUnTbmJpN50+\nev3113v8/PPPJ/1aaorLV9Hzo2XyzMy+8Y1veKzpEDpt38zsjDPO8HjSpEkex7SMUsu6llP63Syd\nmpyXnlBqGcwiKXVaZaljNk617dChQ4PxnDlzkn5aXrTU8ol5aSR5r1Hq56DIU4PzxNQnnU4fp3jr\nVN84PV/pcYmpGErHQNwPnbaqqaZxOrnuxzvvvJO06Xatnh+VdX2aPn160k9L+Pbo0SNp23vvvT3W\nVJg4PVun3+t04Hjd0mn/sRy3nnud1h3PUzkqkSZUBJUoM55XJnbffff1WMdOPI9PPfWUxzF9J+u9\nGrPvlbina055nzf9/shLxY1lZlVeaqZu6/dYPP6bbrqpx/H6fN1113n87LPPepw39T7v31yP5dbz\n/h2anqn3l2ZmgwYN8nj99df3OI5FTYe56aabPI6ltfW9tKS0WZpeuvnmm3us597M7Mgjj/T4//7v\n/5K2999/3+O8cVkr9ze6b1qSW1OYzMx22WUXj+N9yTbbbOOxpovGVJisa148Pnp9zUtl1DTgCy64\nIOmn1+G841/qvWwt0X+Hfl7NzF577bWSXkOvlTrezNL73O23397jE044Iem3xx57eByvlZrK+sMf\n/tDj5557LumXt4xGU6XkM6MGAAAAAACgIHhQAwAAAAAAUBA8qAEAAAAAACiIqq9RU+nXy8v/1XUS\nNtxww6Tfaaed5rGWETZLy3BrzmEsO1rkPM9KKLd8sZZ81dLaZmabbbZZg3+jOfVmZv/85z89zlv/\nJW/dmKz8zvjv0tzjWK5W19HROJYOXrhwYeY+thRx3Zh+/fp5vM4663j8j3/8I+k3e/Zsj0tdoybm\nJGu+aanlpePnQLdrcc0hs7TcZ9yO63RpvrbGcU2LrNz2eD3Qsajn28zslltu8VhLKcZzoPnfug5D\n3K9avPbGfc5aT0LHg5nZrbfe6nEsB6lle7WUrB5jM7OPPvrIY71WTZgwIXMf47nRMaFx/MzpdaDc\ncVSrufl5x68ccV2MgQMHeqzjOY7ZBx54wGM991HeNVXPQa1eD0uVdV0r9zpTzrpuuuaGmdnRRx/t\ncVwPZ8SIER7nrS+Wpx7XpVF5a7LomNB7TTOzv/71rx7repVastfM7M477/RYr9nx3lDF8ukjR470\neLvttvM4jkX9Pl1jjTWStrx1lFStfGfqedP1DMeMGZP0e/rppz2Ov+H0XqRjx44ex/v7eM/6hbz1\nfdZcc82krVWrVh4fe+yxHse1V2677TaP42ek1tf8+iqVuL7oa8R7J70G6rVS74/M0u/TWD79j3/8\no8daZj2uRVuEc8WMGgAAAAAAgILgQQ0AAAAAAEBBVCX1qRJKnTqlU9R0mtuZZ56Z9NMyXePGjUva\nhg4d6rGmO9XjlLQ85ZQTNUunm+2+++5Jm07p1Olrcfqpnm99/Zi+odNAY9m3rGnoceritttu2+C+\nm6VTKnXaatxf7afldIssb5p+Oed+rbXWStr22Wcfj3V66Ny5c5N+Og251BKijVFqOkI9jO84ZTpO\n9VU6DjRN7aGHHkr66TjVKcVxCnavXr08vuyyy5K2rbfe2mM9B3EK69///nePH3zwwaQtryxiLYjf\nYXostXR6LKM+atQoj6dMmZK0abqTXsdi6plOIR89erTHWmLWLL2+6pg1MzvqqKM81lS5HXbYIek3\nfPhwQ2U+o1rWPm7rOIrpcpMnT/Y4fu6yroeN2d9aHH+lqsS/Le97RtMttPzzueeem/TTNPG77ror\naYspNPhvpZZAjmlljz/+uMd67Y3pf5puod9jeSn4MUXxscce83jffff1OKby6D7GdJ2sf2etjlHd\nb/399fDDDyf9XnzxRY/jfYTei+61114eH3HEEUk/XRJD02Lyxm+bNm2Sbf0e1+9Mvf81y09XrLdz\nWG3x/Oh19IorrvBY097M0jEcy25rye+83yRFwIwaAAAAAACAguBBDQAAAAAAQEEUNvWpVDotcNdd\nd/X4sMMOS/rptH2dfmiWTist4rSnoonT0HbccUeP4xTOrCmFmhphZrbJJpt4rNMa+/btm/Tr2rWr\nxzEdaeLEiR7rFLj99tsv6adT97Wyk1k69VKnH+v+maVTzWtRuZ9zHW9atcDMrEePHh5rSkVcDT+m\nemTtl8Z50/mjrM9cPY7tuJL9q6++6nH79u2TNp2m+6Mf/cjjmDajU3u1Mkn37t2TfptvvrnHMb0w\nK8UiTuHXKnH1XmVPP8N5/zZN6YwVCLQKlI6rOD1ep9zr6+VV7orjVPvqdbJ169ZJv6xKGiiNHr9u\n3bolbTqu9HsxpgWUWiGt3GtqPSs35btUen71PkKn75ulY10rkZplV4zLk1flsKVVfcqr3KIVeTRF\nRa+NZmmasabN5KW1xPSpN954w2NNjenSpUvS78knn/Q4Vv7L+rdUoupcc9B/g94bxqUNNB0sL2X+\n7bff9lirbJmZ9e7d22NNHdbYLD0fpR7XWA1R04Xz0tda6nW3MeI9qqZb631pPJb6mzAuh6LVMIt+\nDrjDAgAAAAAAKAge1AAAAAAAABQED2oAAAAAAAAKoubWqIn5glo67aKLLvI45v9qieBY+rCc/N+W\nLJ4DPbYxJ1dpzm8sm9e/f3+PNbczloDW14hrWrz33nser7vuupmvoeunxPxiLXu6/fbbe6x5r2Zp\nmb9p06ZZPdPzrWs9xXOox1xzjV944YWkX6n58Xm53vWYY1+ODz74INm+8847Pe7Tp0/SpqV+dT2h\nuPaMrkeiY6Uxa5HoudPrw/e///2kn5aLLnqecCWV+m+N6znp5z6ee6XfaaWuWRLzwLPWtIjvW+ny\nxi3hc6D/Xv2+GzhwYNJPv+90LY2bb7456VfO9TDvb1rCOWgqeiz1eqqxmdm8efM8fvfdd5O2UseH\nXqNLLetcL/LWaym1dLces/gacT3DrNfOey8tA6zrp7z55ptJv6lTp3qc9/uk3tYdylqvpjF0LZvX\nX389aWvbtq3Huv5eXM9P72Xz7nv0vjSuv1fOdzC+tNpqq3l8zTXXJG16/6pjIK5rdPzxx3s8ZcqU\npK2WxgszagAAAAAAAAqCBzUAAAAAAAAFUROpTzq1KZaBPe644zzWcsFxWtP999/vsU63R+PFYztq\n1CiPn3766aRNU5p0KpvGZmadOnXyOK8cnk4TjFMSdWqqllKMr5dXAvCOO+7w+Iknnsjc3wkTJmTu\nY1OrdtqAvr6WX9cy59H06dM91mm8jVHtEob1kG4Rx+IzzzzjsZb/NDM7/PDDPdYSy7EMaanlKPWY\nxXH06KOPeqwluCdNmpT0q6Xpp41Vic9UfA2dal2Jae96XYslYvU6qaWD8659jUk5KKdfPdKUs65d\nuyZtel41/XbGjBlJv3KOc7nnqqUqt4y3Xl+32GILj+P9i6a2dejQIWnTMafjMqZP6XvHdGEdw/VI\n/+2l3kPmtcXjp8dd01ry0v3zaAppTJvRNH5SFBtHj0lc2kDPW7du3TzWtHCzNB01Hn+919Fxed11\n1yX9Yln1rH3El/Q33Mknn+zx/vvvn/TTa6qOvwsuuCDp99JLL3lcy8ecGTUAAAAAAAAFwYMaAAAA\nAACAguBBDQAAAAAAQEFUZY2aSq/9oPlo2267bdL23e9+t8F+77zzTtLv0ksv9bjcsm/4j3hONb/2\ne9/7XtJ2yCGHNBhvueWWST/N09e861iaUPNDZ86cmbRpXq+uZRT7vfbaax6PHTs2adOcUy3zF9fg\niNvNqSlzL/XcxJLlG2ywgce6JlS5xypvPYVK/F09rI8Sz72Wwr744ouTNh1LRx55pMdaitIs+/od\nr5vjxo3z+NRTT03aRo8e7bGWJK3lPOHGasxnttLvlXWc47oYmpuvpUvNzGbPnu2xXkPzSgfH188a\nYy3pc9AQPWbrr7++x3SrwiMAAAYdSURBVPG46Loier3NW5uEdYH+W1OuxxPfa7PNNvNYy8W2a9cu\n6afXyf322y9p09KyS5Ys8Xjx4sWZr6GxWX183+XR4x7XXdPzHY9D1v1CXHsm6z6m1HLpZmZrrLFG\ng6+v965mZqussorHut6iGb9fvkreWkW6Np+eG10fysxs4cKFHsf1gy666CKPX3nlFY/nzZuX9NM1\niBpTwr0lieND12g74YQTPI5rhOrx0xLsV1xxRWa/WsaMGgAAAAAAgILgQQ0AAAAAAEBBVD31qdw0\nKJ0StdZaa3m85557Jv20jJpOPRs2bFjST8sFU+q3svTfO2fOnKTt6quvbjDOSwvQcx+nfeqU0Fiq\nXdt0qpxOYzRLpwTH8n26X3HqsCrqOa7GFG99TZ3+e8899yT9tEz7yJEjPY7TSssR/x15n5+mTDkp\nGp3Wrdc8M7MzzzzT45tvvtljTUk0M+vUqZPHmt40dOjQpJ+mw8TSr0UdH02p3HK+pabu5b1+1t/F\nlBm9Tk6ePDlp06n5+lmKqQN63Y3T8rPuBcotaVsv9HutTZs2Hmtai1mafqZpjTG1oyXff5Qi7/uj\n0scrjrEddtjB4/bt2ze4D3E/dEyZpempet3V9Gyz9Docx1i9fy4qcU7178p9Db1/jZ+FVq1aeay/\na+I1Nd6zojzxuD7xxBMeazphTMHRVLR4LvR+Vl+/1LLvLZ2O03idGzRokMeaEpyXhvjTn/7U47zf\nbLWMGTUAAAAAAAAFwYMaAAAAAACAgqhI6lPe9Gyd+hf76bSxmOKiq3P37NnT45j6pKkr06ZN83j8\n+PGZ+1iN9BCmtjVO3vHSaW5xyptObYvTflVLnQpejX+rjlOdfn/fffdlvreep1i5q9LjrSkretSS\neBx0yu6LL77YYIymUWrqXiU+y3Fat9LPhFawMDN78803Pf7Xv/7lsabjmKXX6Pjv0u16rzrTGHrM\nJk2a5PFVV12V9NPjp6lpMcWYa17jZB2vSnyXxL/RqiTPPfecx9ttt13ST+9fH3nkkaTtH//4h8c6\nZuN3a2P2qx5kpVPG86i/L2K6haZrlpr6lFfpbtVVV818r7XXXrvB99WUqLi9YMGCzP3Af8urVDl/\n/nyP9bjW49goijgWdXxoKqiZ2e677+6xnpO4RIX+9tBrar1iRg0AAAAAAEBB8KAGAAAAAACgIHhQ\nAwAAAAAAUBAVWaMm5vdl5QjGfE3NG9Xyg2Zm3bt39/jYY4/1uEuXLkk/LdGs62dEWs6y2iWMyXds\nfpyD8pVaEviDDz7I/DvNF6/2ueBco15V4rtK14aJa1rMmDHD4+HDhydt+t2qr6F5/vE180oCM06/\nlLXu1+OPP57002OmpZcbszYJSleJz2hci0nXE7r44os93mijjZJ+uq7irFmzkra41gb+I+t8xf+e\nd43SNWby1rJU+tslbw2Odu3aJW3rrLOOx3r/FNf9Wrx4ceb+stZXZfB91DTicdZ1azfeeOOkTdei\nWbJkicdvv/120m/EiBEe6/divWJGDQAAAAAAQEHwoAYAAAAAAKAgKpL6lEen6eVNgYp0+qCWYY5T\noNZaay2Pp06d6rGWOjTLnt7Y0HaWvKlyTKNDS5BXApMpuUBpmuv7Ii8lYNGiRUnbvHnzPNbU4TgV\nP27nvR/+IyulKU7j5vjVnjgedBxpae0pU6Yk/d5///3q7lgLlpeCWc59i55jTYMyS89j/B2ycOFC\njzX1KZYf1n3Ku74CtUY/95MnT07aLrjgAo/79evn8auvvpr0e+mllzxuCWmhzKgBAAAAAAAoCB7U\nAAAAAAAAFAQPagAAAAAAAApihWU5SdClrt1SDVll8rRkqFm6Ro2WtIt5ozH/PktRcsIruR/NeR5b\nukqdx6Y8h3klgXWtp5ayJg1jsT7U4lishLz9zWsr4viux7Go+1GU+49qa0ljsdSSz0Ucb3nqcSy2\nRC1pLNYrxmJ9yDqPzKgBAAAAAAAoCB7UAAAAAAAAFERu6hMAAAAAAACaDjNqAAAAAAAACoIHNQAA\nAAAAAAXBgxoAAAAAAICC4EENAAAAAABAQfCgBgAAAAAAoCB4UAMAAAAAAFAQ/x8PjIlnM0qX3wAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0750af4be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5NgENol9ZUIV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Adding a sparsity constraint on the encoded representations"
      ]
    },
    {
      "metadata": {
        "id": "mMeOJk_CZWNp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "cc0e0ece-cf36-4e46-e23e-c9bb6b92da62"
      },
      "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',\n",
        "                activity_regularizer=regularizers.l1(10e-5))(input_img)\n",
        "decoded = Dense(784, activation='sigmoid')(encoded)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_6 (InputLayer)         (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 32)                25120     \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 784)               25872     \n",
            "=================================================================\n",
            "Total params: 50,992\n",
            "Trainable params: 50,992\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NPm-9nMAb8oM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1771
        },
        "outputId": "c39bba9c-85e6-4c60-9395-d82ba4aca54d"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "60000/60000 [==============================] - 2s 29us/step - loss: 0.6737 - val_loss: 0.6485\n",
            "Epoch 2/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.6284 - val_loss: 0.6090\n",
            "Epoch 3/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5916 - val_loss: 0.5749\n",
            "Epoch 4/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5598 - val_loss: 0.5454\n",
            "Epoch 5/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5323 - val_loss: 0.5198\n",
            "Epoch 6/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.5084 - val_loss: 0.4975\n",
            "Epoch 7/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.4875 - val_loss: 0.4780\n",
            "Epoch 8/50\n",
            "15616/60000 [======>.......................] - ETA: 1s - loss: 0.4756"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.4692 - val_loss: 0.4609\n",
            "Epoch 9/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.4531 - val_loss: 0.4457\n",
            "Epoch 10/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.4388 - val_loss: 0.4324\n",
            "Epoch 11/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.4262 - val_loss: 0.4205\n",
            "Epoch 12/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.4150 - val_loss: 0.4098\n",
            "Epoch 13/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.4049 - val_loss: 0.4003\n",
            "Epoch 14/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3959 - val_loss: 0.3918\n",
            "Epoch 15/50\n",
            "52224/60000 [=========================>....] - ETA: 0s - loss: 0.3882"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3877 - val_loss: 0.3840\n",
            "Epoch 16/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3804 - val_loss: 0.3771\n",
            "Epoch 17/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3737 - val_loss: 0.3707\n",
            "Epoch 18/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3676 - val_loss: 0.3649\n",
            "Epoch 19/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3621 - val_loss: 0.3596\n",
            "Epoch 20/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3570 - val_loss: 0.3548\n",
            "Epoch 21/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3524 - val_loss: 0.3503\n",
            "Epoch 22/50\n",
            "56064/60000 [===========================>..] - ETA: 0s - loss: 0.3482"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3481 - val_loss: 0.3463\n",
            "Epoch 23/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3442 - val_loss: 0.3425\n",
            "Epoch 24/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3406 - val_loss: 0.3390\n",
            "Epoch 25/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3372 - val_loss: 0.3357\n",
            "Epoch 26/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3341 - val_loss: 0.3327\n",
            "Epoch 27/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3312 - val_loss: 0.3299\n",
            "Epoch 28/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3285 - val_loss: 0.3273\n",
            "Epoch 29/50\n",
            "57344/60000 [===========================>..] - ETA: 0s - loss: 0.3259"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3259 - val_loss: 0.3249\n",
            "Epoch 30/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3236 - val_loss: 0.3226\n",
            "Epoch 31/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3213 - val_loss: 0.3204\n",
            "Epoch 32/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3193 - val_loss: 0.3184\n",
            "Epoch 33/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3173 - val_loss: 0.3165\n",
            "Epoch 34/50\n",
            "60000/60000 [==============================] - 2s 26us/step - loss: 0.3155 - val_loss: 0.3147\n",
            "Epoch 35/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3138 - val_loss: 0.3131\n",
            "Epoch 36/50\n",
            "52480/60000 [=========================>....] - ETA: 0s - loss: 0.3123"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3121 - val_loss: 0.3115\n",
            "Epoch 37/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3106 - val_loss: 0.3100\n",
            "Epoch 38/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3091 - val_loss: 0.3085\n",
            "Epoch 39/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3077 - val_loss: 0.3072\n",
            "Epoch 40/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3064 - val_loss: 0.3059\n",
            "Epoch 41/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3052 - val_loss: 0.3047\n",
            "Epoch 42/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3040 - val_loss: 0.3035\n",
            "Epoch 43/50\n",
            "50688/60000 [========================>.....] - ETA: 0s - loss: 0.3029"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3029 - val_loss: 0.3024\n",
            "Epoch 44/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3018 - val_loss: 0.3014\n",
            "Epoch 45/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.3008 - val_loss: 0.3004\n",
            "Epoch 46/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.2998 - val_loss: 0.2994\n",
            "Epoch 47/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.2989 - val_loss: 0.2985\n",
            "Epoch 48/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.2980 - val_loss: 0.2976\n",
            "Epoch 49/50\n",
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.2971 - val_loss: 0.2968\n",
            "Epoch 50/50\n",
            "49152/60000 [=======================>......] - ETA: 0s - loss: 0.2964"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 27us/step - loss: 0.2963 - val_loss: 0.2960\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9hE8O2pzb9NP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "b9ecbc8b-9cd4-41f5-90cc-42fc3afc4c86"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzs3Xl0m+Wd9//3rc22LNmWbMlbvMWx\nszj7RhI3KYQECFCgUyhp+ZF22imlD3m6wczTyXOYMDO/pJwz7dPf0206pQxTKEPDUNNJoSWUsIMT\nh+xxNtuJl3iVvMj7Kv3+cHAJS+yAEnn5vM7ROcjSLX/1PQ6fc1/XdV+3EQqFQoiIiMi4YYp0ASIi\nInIhhbOIiMg4o3AWEREZZxTOIiIi44zCWUREZJxROIuIiIwzlkgX8C6fryOsn+dy2Wlt7Q7rZ05V\n6mX4qJfho16Gh/oYPpfaS4/H+ZGvTdozZ4vFHOkSJg31MnzUy/BRL8NDfQyfcPZy0oaziIjIRKVw\nFhERGWcUziIiIuOMwllERGScUTiLiIiMMwpnERGRcUbhLCIiMs4onEVERMYZhbOIiMg4o3AWEREZ\nZ8bN3trhdM7XSV1rL2mu6EiXIiIicskm5Znzs6+f4aFfFdPdOxjpUkRERC7ZpAzndI+DwaEQp8+1\nRboUERGRSzYpw3l2ZgIAJ6taI1yJiIjIpZuU4ZybHo/FbOJktcJZREQmnkkZzjarmVnZLmoaO+nq\nHYh0OSIiIpdkUoYzwPzcJELA6WrNO4uIyMQyacN53owkAE5oaFtERCaYSRvOM7NcWC0mTlbpzFlE\nRCaWSRvOVouZGenxnPN10tHdH+lyRERExmzShjPArPOXVJ3SvLOIiEwgkzucs1wAuqRKREQmlEkd\nzjmpcdisJk7qzFlERCaQSR3OFrOJvGkJ1Pm7CHRp3llERCaGSR3O8N55Zw1ti4jIxDCmW0Zu376d\nw4cPYxgGW7ZsYf78+SOv1dfX893vfpeBgQHmzJnDP/3TP7F3716+9a1vkZeXB0B+fj4PPvjg5fkG\noxiZd65qZfns5IjUICIicilGDeeSkhKqqqrYsWMHFRUVbNmyhR07doy8/vDDD/OVr3yF9evX84//\n+I/U1dUBsHz5cn784x9fvsrHKDvFSbTNzAnNO4uIyAQx6rB2cXEx69atAyA3N5dAIEBnZycAwWCQ\n/fv3s3btWgC2bt1KWlraZSz30plNJvIzEmhs6aa1oy/S5YiIiIxq1DNnv99PQUHByHO3243P58Ph\ncNDS0kJsbCzf//73KS0tZenSpdx///0AlJeXc++99xIIBNi8eTOFhYUX/T0ulx2LxfwJv86wjr5O\nmjr9eD3DW3gumZ3CkYpm6lp7yJ+eFJbfMdV4PM5IlzBpqJfho16Gh/oYPuHq5ZjmnN8rFApd8N+N\njY1s2rSJ9PR07rnnHl599VVmz57N5s2b2bBhAzU1NWzatIkXX3wRm832kZ/b2tr98b7Bh/jV0Sc4\n1VbOtlX/G5vZRkZSDAAlx+opOL9ATMbO43Hi83VEuoxJQb0MH/UyPNTH8LnUXl4syEcd1vZ6vfj9\n/pHnTU1NeDweAFwuF2lpaWRmZmI2m1m5ciVlZWUkJydz4403YhgGmZmZJCUl0djYOOaCP6mkmES6\nB3o43VoBQKbXiT3Kos1IRERkQhg1nAsLC9m1axcApaWleL1eHA4HABaLhYyMDCorK0dez8nJYefO\nnTz66KMA+Hw+mpubSU6+ciul5ybNBuBY80kATCaD/IwEfG29NAd6r1gdIiIiH8eow9qLFy+moKCA\njRs3YhgGW7dupaioCKfTyfr169myZQvf+973CIVC5Ofns3btWrq7u3nggQfYvXs3AwMDPPTQQxcd\n0g63nLhMYq0xHPOfIJR/G4ZhMCvLxaFyPyerWymcl3rFahEREblUY5pzfuCBBy54PmvWrJH/zsrK\n4qmnnrrgdYfDwS9+8YswlPfxmE1mFqTM4e2a/dR3NZLmSBnZjEThLCIi492k3SFscdo8AErPD21P\n8zqIjbbo/s4iIjLuTdpwXpgyBwODY80nADAZBjMzXTS39+Jr64lwdSIiIh9t0oZzXLSTrLgMzgSq\n6B4YDuORoe0qrdoWEZHxa9KGM8DcxFkEQ0FOtJwGdH9nERGZGCZ1OBckDi9ce3feOT0pFqfdysnq\ntgs2UxERERlPJnU4T3OmEWdzUtp8kmAoiHF+3rm1o4+mVs07i4jI+DSpw9lkmChInEXnQBfVHecA\nmH1+3vmEhrZFRGScmtThDH8Z2j7mHx7afu/9nUVERMajSR/Os9x5mAzTyLxzittOfKxN884iIjJu\nTfpwjrFEMyM+h+qOcwT6Oka28mzv6qe+OXx3whIREQmXSR/OAAVJw0Pbx1tOATAne3ho+0hFc8Rq\nEhER+ShTIpznJg7fparUP7xb2IIZSRgGHCjzRbIsERGRDzUlwjnZ7iEx2s2JljKGgkPE2W3kTUug\n4lyAQFd/pMsTERG5wJQIZ8MwmJs0i96hXioClQAszksiBBwu90e0NhERkfebEuEMUHB+aPvdG2Es\nzPcAcOC0hrZFRGR8mTLhnJcwHavJSun56529CTFM8zg4XtlCT99ghKsTERH5iykTzjazlZmuGTR0\nN+HvaQFgcX4Sg0Mhjp1tiXB1IiIifzFlwhk+eCOMxeeHtg9qaFtERMaRKRXOc89f7/zuvHOG10Fi\nXDSHK5oZHApGsjQREZERUyqc3dEu0mJTKGutoH+oH8MwWJSfRE/fIKeq2yJdnoiICDDFwhmGh7YH\ngoOcbq0AYHHe+VXb2pBERETGiSkZzgDHzs8752XE44ixcvC0j6BuhCEiIuPAlAvn6fFZxFiiKW0+\nSSgUwmwysWBGIm2d/VTWd0S6PBERkakXzmaTmdnufFp6W6nvagT+MrR9UEPbIiIyDky5cAaYlzQH\ngIO+owDMyXFjs5i0W5iIiIwLUzacLSYLB5qOABBlNVOQ46a+uZv65q4IVyciIlPdlAznGEs0BYmz\naOhqpK6zAfjLhiSHynQjDBERiawxhfP27du588472bhxI0eOHLngtfr6er7whS9w++238w//8A9j\nOmY8WOydD8CBpsPA8D2eTYahoW0REYm4UcO5pKSEqqoqduzYwbZt29i2bdsFrz/88MN85Stf4Zln\nnsFsNlNXVzfqMePB3MTZWE1WDjQdIRQK4Yixkp8RT0VdO22dfZEuT0REprBRw7m4uJh169YBkJub\nSyAQoLOzE4BgMMj+/ftZu3YtAFu3biUtLe2ix4wX0ZYo5ibOorHbR21nPQCLNLQtIiLjgGW0N/j9\nfgoKCkaeu91ufD4fDoeDlpYWYmNj+f73v09paSlLly7l/vvvv+gxH8XlsmOxmD/h17mQx+O86OtX\n513FQd9RTnaeZNH0may7KpunXirjWFUrd1w3K6y1THSj9VLGTr0MH/UyPNTH8AlXL0cN5/cLvWcX\nrVAoRGNjI5s2bSI9PZ177rmHV1999aLHfJTW1u5LLeWiPB4nPt/FNxXJtGZjM1l5o3If16Zcg2EY\nZCY7OHzaR/W5VmKiLrk9k9JYeiljo16Gj3oZHupj+FxqLy8W5KMOa3u9Xvz+vwzzNjU14fEMD/+6\nXC7S0tLIzMzEbDazcuVKysrKLnrMeGIz25iXNAd/TzM1nbXA8IYkQ8EQR880R7g6ERGZqkYN58LC\nQnbt2gVAaWkpXq93ZHjaYrGQkZFBZWXlyOs5OTkXPWa8WZy8AIADjcMryt+9pEqrtkVEJFJGHbdd\nvHgxBQUFbNy4EcMw2Lp1K0VFRTidTtavX8+WLVv43ve+RygUIj8/n7Vr12IymT5wzHg1xz2TKLON\nA02HuTV3A+meWDwJ0RypaGZgMIjVMiUvBRcRkQga06TqAw88cMHzWbP+slgqKyuLp556atRjxiub\n2cq8pDm803iI6o5zZMVlsCjPw4v7ajhZ3cq86YmRLlFERKYYnRYCS7zDQ9v7G4c3JHl3aHvfyaaI\n1SQiIlOXwhmYnTiTaHP0yIYkM6bF446LYv+pJvoHhiJdnoiITDEKZ8BqsrDAU0BrXxtn26sxGQYr\n5qTQ0zfEoXJtSCIiIleWwvm89++1vXJuCgB7ShsjVpOIiExNCufzZrnziLHEcLDpKMFQkPSkWDKT\nHRw900x7d3+kyxMRkSlE4Xye5fzQdltfgDOBKgBWFaQwFAyx74QWhomIyJWjcH6PxedXbR9oGt6Q\n5Ko5yRgGvH2sIZJliYjIFKNwfo9ZrhnEWuwcbDpCMBQk3hFFQY6bs/XtNLSEd+9vERGRj6Jwfg+z\nycwCz1za+zuoaDsLDA9tAxTr7FlERK4QhfP7LE4eXrW9//zQ9qI8D1FWM8WlDWO6u5aIiMgnpXB+\nn/yEXBzWWA41HWUoOESUzcySmR78gV7KzgUiXZ6IiEwBCuf3MZvMLPTMpWOgk7K2M8BfrnkuLtXQ\ntoiIXH4K5w+x5PxtJPc1HgRgdqaLBIeNfSeaGBgMRrI0ERGZAhTOH2JGwnTc0S4ONB2hd7AXk2l4\nO8/uvkGOVGg7TxERubwUzh/CZJhYmbqU/qF+9r9vO09d8ywiIpebwvkjrExdhoHB23X7AMjwOpjm\ncXCkopnOnoEIVyciIpOZwvkjuKITmJ2YT2V7NbWd9QCsmvvudp66GYaIiFw+CueLKExdDkDx+bPn\nq+YkYwDFulOViIhcRgrni5ibNBun1UFJwwEGhgZwOaOYne2ivDZAU6u28xQRkctD4XwRFpOFq1KX\n0DXYzWF/KQAr393OU2fPIiJymSicR7EqdRkAb9eVALBkpgeb1UTxMW3nKSIil4fCeRTJsV5y43M4\n1VqOv6eZaJuFxfkemtp6OFPXHunyRERkElI4j0Fh2oULw94d2n5b23mKiMhloHAeg0XeeUSbo9nT\nsJ+h4BBzsl3Ex9rYW9pI38BQpMsTEZFJRuE8BjazjWUpi2jrC3Ci5TRmk4k1C9Lo7htk73EtDBMR\nkfBSOI/RqrQLF4ZdvSgdk2Hw8v5zWhgmIiJhpXAeo0znNDIcaRxtPkGgrwOXM4pF+UlUN3VSUauF\nYSIiEj6Wsbxp+/btHD58GMMw2LJlC/Pnzx95be3ataSkpGA2mwH4wQ9+QGVlJd/61rfIy8sDID8/\nnwcffPAylH9lrUpbzo7Tv2dvwztcl3UNaxdPY/8pHy8fOMeMafGRLk9ERCaJUcO5pKSEqqoqduzY\nQUVFBVu2bGHHjh0XvOeRRx4hNjZ25HllZSXLly/nxz/+cfgrjqClyYsoKn+Ot+tKWJ95NbMyE0hL\nimXfySbuvDaP+FhbpEsUEZFJYNRh7eLiYtatWwdAbm4ugUCAzs7Oy17YeGS3xrDIOx9fTzPlbWcw\nDINrFqUzFAzx+uG6SJcnIiKTxKhnzn6/n4KCgpHnbrcbn8+Hw+EY+dnWrVupra1lyZIl3H///QCU\nl5dz7733EggE2Lx5M4WFhRf9PS6XHYvF/HG/x4fyeJxh/TyAG2d/mpKGA+xvOciq/IXccvUMil6v\n4I3DdXzp5gLM5sk5jX85ejlVqZfho16Gh/oYPuHq5ZjmnN/r/SuTv/nNb7J69Wri4+O577772LVr\nF4sWLWLz5s1s2LCBmpoaNm3axIsvvojN9tHDvq1hvpGEx+PE5+sI62cCJJGCJyaRPTUHuCXzJuzW\nGFYUpPDKgVr+XFzJkpmesP/OSLtcvZyK1MvwUS/DQ30Mn0vt5cWCfNTTPK/Xi9/vH3ne1NSEx/OX\nALrttttITEzEYrGwZs0aTp8+TXJyMjfeeCOGYZCZmUlSUhKNjZPjemDDMFiVtpyB4CDvNB4EYO2i\ndABePnAukqWJiMgkMWo4FxYWsmvXLgBKS0vxer0jQ9odHR189atfpb+/H4B9+/aRl5fHzp07efTR\nRwHw+Xw0NzeTnJx8ub7DFXdVylJMhok36/YSCoVI9ziYlZnAiapW6vxdkS5PREQmuFGHtRcvXkxB\nQQEbN27EMAy2bt1KUVERTqeT9evXs2bNGu68806ioqKYM2cON9xwA11dXTzwwAPs3r2bgYEBHnro\noYsOaU808VFOFnrmcqDpCKday5nlzmPt4mmcrG7jlQO13HVdfqRLFBGRCcwIjZPtrcI953G551Eq\n26v5l3d+ypzEmdy34KsMDgX5u399m97+IX54XyExUZc8nT9uaU4qfNTL8FEvw0N9DJ8rOucsHy47\nLpPc+GyON5+irrMBi9nE1QvT6e0fYo/uViUiIp+AwvkTuDZzDQAv17wBwJqFaZhNBi8fqNV+2yIi\n8rEpnD+BeUlz8MYksa/hAIG+DhIcUSyZ6aHW38XpmrZIlyciIhOUwvkTMBkmrslYzWBoiNdr3wZg\n7eJpAOw+UBvJ0kREZAJTOH9CK1KXEGu180ZtMf1D/eRNi2eax8HB0z5aO/oiXZ6IiExACudPyGa2\nsTp9JV0D3eyp349hGKxdMrzf9muHdPYsIiKXTuEcBmvSV2ExzLxS8wbBUJCVc1KIibLw2qE6BoeC\nkS5PREQmGIVzGMRHOVmWspimHj9H/SeIsplZPT+VQFc/xcd0WZWIiFwahXOYrM1YDcDu6tcBuG5Z\nBmaTwR/3VBEM6rIqEREZO4VzmKQ5UpjjnklF4CxV7TW446IpnJdKY2sP+042Rbo8ERGZQBTOYfTu\npiTvnj3fuCITw4DniysJalMSEREZI4VzGM10zSDdkcpB31Gae1rxuuxcNSeZc74uDpf7R/8AERER\nFM5hZRgG12asIRgK8uq5NwG4aUUWAM+9XaUtPUVEZEwUzmG2JHkB8bY43qrbS/dAD+keB4vzPZyt\nb+d4VWukyxMRkQlA4RxmFpOFqzMK6Rvq5626vQDcvGr47Pn5tysjWJmIiEwUCufL4FNpV2Ez23j1\n3FsMBgfJToljbo6bk9VtlJ3TDTFEROTiFM6Xgd1qpzBtOW19AYrr3wHg5lXZwPDcs4iIyMUonC+T\n9ZnXYDVZeaFyNwNDA+RnJJCfkcDRM81UNXREujwRERnHFM6XSXyUk6unFdLWF+DN9809P1dcGbnC\nRERk3FM4X0brsj5NtDmKXZUv0zfUT0G2m+wUJwdO+ajzd0W6PBERGacUzpeRwxrLNRmr6Rjo5LVz\nb2EYBjevyiYEPF+suWcREflwCufLbG3GauyWGP5c9So9gz0szEsiPSmWvccbaWrriXR5IiIyDimc\nLzO7NYZ1mZ+me7CHl2vexGQY3LQyi2AoxJ/26OxZREQ+SOF8BXx6WiEOaywvV79B50AXy2Z78SbE\n8NbRelo7+iJdnoiIjDMK5ysg2hLF9VnX0DvUy0tVr2E2mbhxZRaDQyH+8NbZSJcnIiLjjML5CvlU\n+kribXG8eu4tAn0drJqbQmqindcP11OrldsiIvIeCucrxGa2ckP2tQwEB/hz1StYzCZuvzqXYCjE\nM6+UR7o8EREZRyxjedP27ds5fPgwhmGwZcsW5s+fP/La2rVrSUlJwWw2A/CDH/yA5OTkix4zVa1K\nW8ZL1a/yRm0x12auYeGMJPIzEjhc0czJqlZmZbkiXaKIiIwDo545l5SUUFVVxY4dO9i2bRvbtm37\nwHseeeQRnnjiCZ544gmSk5PHdMxUZDFZ2JCznsHQEC9U7sYwDO5cOwOAHa+UE9T9nkVEhDGEc3Fx\nMevWrQMgNzeXQCBAZ2dn2I+ZKpYnL8JrT+Lt+n34e5rJSY1j+WwvVQ0dlBxvjHR5IiIyDow6rO33\n+ykoKBh57na78fl8OByOkZ9t3bqV2tpalixZwv333z+mY97P5bJjsZg/7vf4UB6PM6yfFy5fWHAL\n/7f433m5/jXuu+pLfO2z8zlw+mV+/+ZZri+cjs0a3j6Ew3jt5USkXoaPehke6mP4hKuXY5pzfq/Q\n+4Zev/nNb7J69Wri4+O577772LVr16jHfJjW1u5LLeWiPB4nPt/4vPvTjOh80mJTeL1yL2uSC0mJ\nTWbt4nRe3FfDjl0nueGqzEiXeIHx3MuJRr0MH/UyPNTH8LnUXl4syEcd1vZ6vfj9/pHnTU1NeDye\nkee33XYbiYmJWCwW1qxZw+nTp0c9ZqozGSZunn49IUL8rvw5QqEQN6/Kxh5l4bm3K+nsGYh0iSIi\nEkGjhnNhYeHI2XBpaSler3dkeLqjo4OvfvWr9Pf3A7Bv3z7y8vIueowMm580h5muGRxvPsWx5hM4\nYqzcvCqb7r5Bnnu7MtLliYhIBI06rL148WIKCgrYuHEjhmGwdetWioqKcDqdrF+/njVr1nDnnXcS\nFRXFnDlzuOGGGzAM4wPHyIUMw+CO/FvZXvIjnjm9k1muPK5dMo2XD5xj9/5zrF0yDW9CTKTLFBGR\nCDBCY5kQvgLCPecxUeZRflf2B16ueYPPTL+eG7KvZe/xRv5tZynLZ3u599a5kS4PmDi9nAjUy/BR\nL8NDfQyfKzrnLJfXjTnrcNoc7Kp8mdbeNpbP9pKT6qTkRBNn6tojXZ6IiESAwjnCYiwx3Jp7I/3B\nAYrKn8MwDD5/zfDGJE+/XDamle4iIjK5KJzHgatSFpMTl8mBpiOcbi1nZqaLhTOSOH0uwMEy/+gf\nICIik4rCeRwwGSbuyL8VA4P/Or2ToeAQd1yTi8kw+O3uMvoGhiJdooiIXEEK53EiKy6DlanLqOtq\n4PXaYlITY7lueQb+QC87dc9nEZEpReE8jtySewMxlhieP/siHf2d3FqYQ1J8NLv21lDTpL3JRUSm\nCoXzOOK0Obg55zp6BnvZWfEnomxm7r5+JsFQiF+/cJJgUIvDRESmAoXzOLM6fQVpsSkU179DZXs1\n86Ynsny2lzN17bxysDbS5YmIyBWgcB5nzCYzn8+/lRAhnj713wRDQb6wLh97lIXfvVZBa0dfpEsU\nEZHLTOE8DuW5clniXUBVRw176vcTH2vjjmty6e0f4j//fDrS5YmIyGWmcB6nPjvjJmxmG8+WP0db\nX4DVC9KYMS2e/ad9HCzzRbo8ERG5jBTO45QrOoHP5t5E92APT50swgC+dMMszCaD37x4mp6+wUiX\nKCIil4nCeRz7VPpVzHTN4FjzCfY27Cc9KZYNK7Jo7ejj92/o2mcRkclK4TyOmQwTd826gyizjWfK\ndtLa28ZnVmWR7Irhpf01VDboxhgiIpORwnmcS4xx8bkZn6FnsJf/PPk7LGYTm66fSSgE//GnkwwF\ng5EuUUREwkzhPAGsSlvObHc+x1tOUVy/j9nZbgrnplDd2MlL75yLdHkiIhJmCucJwDAM7pp1O9Hm\naH5X9gdaelv5/NoZOGKsPPv6GeqbuyJdooiIhJHCeYJwRSdwe95n6B3q48kTz+CIsXL39TPpHwzy\ny53HGRzS8LaIyGShcJ5AVqQupSBxFidby3izbi/LZnn51LxUqho7KHr9TKTLExGRMFE4TyCGYfDF\nWZ8jxhJDUflz+Hta+OL6PLyuGF7YW83xypZIlygiImGgcJ5gEqLiuSPvFvqH+nnyxH9hs5r4+i0F\nmE0Gv3ruOJ09A5EuUUREPiGF8wS0PGUx85LmcLqtgjdr95CTGsdtq3No6+znsT+eIBTSrSVFRCYy\nhfMEZBgGX5j5OWItdorKn6eus4ENV2UxMyOBg2V+XjtcF+kSRUTkE1A4T1DxUU7umn07A8EBfnXs\nCfqDfXztM3OwR1n47UtlurxKRGQCUzhPYAs8c1mbsZrGbh//efJ3uJxRfGnDLPoHg/zbzlJdXiUi\nMkEpnCe423JvZHp8FvubDvNGbfHI5VXVjZ26vEpEZIJSOE9wZpOZrxTchcMay+/K/kBVe40urxIR\nmeDGFM7bt2/nzjvvZOPGjRw5cuRD3/PDH/6Qu+++G4C9e/eyYsUK7r77bu6++27++Z//OXwVywe4\nohP48pwvMBQK8uix3xA0+kcur3rkueO0d/dHukQREbkEo4ZzSUkJVVVV7Nixg23btrFt27YPvKe8\nvJx9+/Zd8LPly5fzxBNP8MQTT/Dggw+Gr2L5ULMT87kh+1qae1t5/MQOslIc3LY6h0BnP//67DHN\nP4uITCCjhnNxcTHr1q0DIDc3l0AgQGdn5wXvefjhh/nOd75zeSqUMbsxZx2zXHkc9Z9gd/XrbFiR\nxeJ8D6dq2tjxcnmkyxMRkTGyjPYGv99PQUHByHO3243P58PhcABQVFTE8uXLSU9Pv+C48vJy7r33\nXgKBAJs3b6awsPCiv8flsmOxmD/Od/hIHo8zrJ83Edy/5m/4uxe3s/PMCyzMnMX/+tIy/vYnb7B7\n/znmzkhi3fKsj/W5U7GXl4t6GT7qZXioj+ETrl6OGs7v997dp9ra2igqKuKxxx6jsbFx5OfZ2dls\n3ryZDRs2UFNTw6ZNm3jxxRex2Wwf+bmtrd2XWspFeTxOfL6OsH7mxGDw5dlf5P8e/Dd+9NYjfG/5\nt/kftxbwz79+h589cxiHzUxuevwlfeLU7WX4qZfho16Gh/oYPpfay4sF+ajD2l6vF7/fP/K8qakJ\nj8cDwJ49e2hpaeGuu+5i8+bNlJaWsn37dpKTk7nxxhsxDIPMzEySkpIuCG+5vGYk5HDL9BsI9Hfw\nWOlTJCVEc++tcxkKhvjps0dp7eiLdIkiInIRo4ZzYWEhu3btAqC0tBSv1zsypH3DDTfwxz/+kaef\nfpqf/vSnFBQUsGXLFnbu3Mmjjz4KgM/no7m5meTk5Mv4NeT91mV+enj/7dZyisqeoyDHzR1XzyDQ\n2c/Pnz3KwKAWiImIjFejDmsvXryYgoICNm7ciGEYbN26laKiIpxOJ+vXr//QY9auXcsDDzzA7t27\nGRgY4KGHHrrokLaEn2EYfGnOnfxw/8955dybJMUkcv3yVVQ3dbCntJEnXjzFX2+YhWEYkS5VRETe\nxwiNk1sYhXvOQ/Mow5p7WvmX/T+hs7+Lr8//EjPjZ/L93xygqrGDu9bnc+2SaaN+hnoZPupl+KiX\n4aE+hs8VnXOWiS0xxsU35v81FpOFfy/9Txp669n8V/Nw2q38dncZp6pbI12iiIi8j8J5CsiKy+DL\nBV9gYGiAXxx+DFNUL/d9dh4AP3v2GP5AT4QrFBGR91I4TxELPXP57IybCPR38K9HHiMzNZovrsuj\ns2eAHz19mM6egUiXKCIi5ylynOH5AAAgAElEQVScp5C1GatZnb6S2s56Hi19ktULUrhuWQb1zd38\nf/91mL7+oUiXKCIiKJynFMMwuCPvFua4Z3K8+RTPlP+BO67JZWVBCmfq2vnZ749qD24RkXFA4TzF\nmE1mvjL3LtIdqbxRW8xr597kr2+cxfzcRI6daeHf/3iC4PhYwC8iMmUpnKegGEs035j/18Tb4igq\nf54jzcf4xq1zyU2LY09pI0+/XM44ucJORGRKUjhPUa7oBL6x4K+xma38R+lTnG4/xbfuWEBqop0X\n99Xwp73VkS5RRGTKUjhPYRnOdL4x/yuYDBO/OvoENT1nuf/Ohbjjonjm1QreOFwX6RJFRKYkhfMU\nl+eazr3zvwyGwb8d+TXNQ7V89/MLiY228B8vnORgmS/SJYqITDkKZ2GWO4+vzb2bYCjIz488Rq/V\nz7c/vwCrxcQv/ruU0jPNkS5RRGRKUTgLAHOTZvOVuXcxGBzkZ4cexeJo577PziMYDPGPv9rD6Zq2\nSJcoIjJlKJxlxELPXL48ZyN9Q3389NCvcHn6+PotBfQPDPGjpw9rH24RkStE4SwXWJK8kLtnf56e\nwV5+cugR0jNCfO9LyxgcCvKjpw9zvLIl0iWKiEx6Cmf5gKtSl7Bx5mfpHOjiJwd/SVaWmc1/NY9g\nKMT/feYIRzUHLSJyWSmc5UN9Kn0Ft+fdQqC/g4de+T94Ugb55ufmA/CT3x3hULk/whWKiExeCmf5\nSNdkfIrb826htSfAjw78K/bETr59+3xMJoOfFR1l/yldZiUicjkonOWirsn4FPct/xK9Q3385OAv\nCTl9fOeOBVjMJn7x38fYd7Ip0iWKiEw6CmcZ1adzVnDPvE2ECPGLI/9BZ1QV373z3eugj7GntCHS\nJYqITCoKZxmTeUlzuG/B32A1WXms9CnqOcH9GxcSbbPwyB+O8+d3aiJdoojIpKFwljHLc03n24u/\njsMay47Tz3K67x3+duNC4mJtPPVSGb/dXabbTYqIhIHCWS5JhjOd7y75Bu5oF8+d3cU7Ha/y93cv\nIi0plhf31fCvzx6jf2Ao0mWKiExoCme5ZF67h/uX/A9SY5N55dyb/LH2D/ztF+czKzOB/ad9/Mtv\nD9Le3R/pMkVEJiyFs3wsCVHxfGfxN8iJy2Rf4wEePfEYf3PbDFbMSaaitp3tT+ynsbU70mWKiExI\nCmf52GKtdr656B4We+dTEajkR4d+xo3XuLh5VRZNrT1se3w/5bWBSJcpIjLhKJzlE7GZbXyl4C5u\nzrmelt5Wfnjg5+TO7mXTDTPp7h3kX546yP5TuhZaRORSKJzlEzMMgw051/K1uXdDKMQvj/6a3oRT\n/M/PzcNkGPz82WM893alVnKLiIzRmMJ5+/bt3HnnnWzcuJEjR4586Ht++MMfcvfdd1/SMTK5LPTO\n47tL7sMVlcAfzrzAgb4Xuf8L80hwRlH0+hl+VnSU7t7BSJcpIjLujRrOJSUlVFVVsWPHDrZt28a2\nbds+8J7y8nL27dt3ScfI5JThTOPvlv1Ppsdn8U7jIYpqn+Q7d81kdpaLg2V+/vnxd6j1dUa6TBGR\ncW3UcC4uLmbdunUA5ObmEggE6Oy88H+uDz/8MN/5zncu6RiZvOJsTr656OusSFlKVUcNPz/2C267\nIYENV2XS2NLN//v4fkpONEa6TBGRccsy2hv8fj8FBQUjz91uNz6fD4fDAUBRURHLly8nPT19zMd8\nGJfLjsVi/lhf4qN4PM6wft5U9nF6+R3vV3juVBa/OVLEjw/9G1+Ydyv/K38pP376IL/471Ia2nr5\n8k1zMJun1tIH/V2Gj3oZHupj+ISrl6OG8/uF3rOop62tjaKiIh577DEaGz/6TCg0hoVArWG+Jtbj\nceLzdYT1M6eqT9LLFYlXkbgwicdKn+LJI88yJ/E43/7CZ3hs5xl+/1oFJ840c+9tc4mPtYW56vFJ\nf5fho16Gh/oYPpfay4sF+ajh7PV68fv9I8+bmprweDwA7Nmzh5aWFu666y76+/uprq5m+/btFz1G\npp48Vy5/v/zbPH58B8ebT1HbUc8Xb7uTV9+M5cBpH//4WAnfuG0uedMSIl2qiMi4MOp4YmFhIbt2\n7QKgtLQUr9c7Mjx9ww038Mc//pGnn36an/70pxQUFLBly5aLHiNTk9Pm4BsL/prbcm+kY6CTXxz7\nFbmLGvncp3MIdPXz8JMH+P0bZxgKBiNdqohIxI165rx48WIKCgrYuHEjhmGwdetWioqKcDqdrF+/\nfszHiJgME+uzriY3IZt/P/afPHf2RWa6ZnDfHRt46oVqdr5VybGzLXztM3NIdtkjXa6ISMQYobFM\nCF8B4Z7z0DxK+FyOXnYNdPPEiac56j+O0+rgzhm3s29fiD3HG4mymvniujw+NT8VwzDC+nsjTX+X\n4aNehof6GD7hnHOeWstkZdyItdr5+rwv8bm8z9A92MOvTvwHjvwTfPmmXEwmeOxPJ/n5s8fo7BmI\ndKkiIlfcJa/WFgkXwzBYm7GavIRcnjixg7fqSnBFnWbT7bfwymt97D/to6IuwFdvnkNBtjvS5YqI\nXDE6c5aIy3Cm8XdL/ycbsq8l0N/O42WPk7W0ilvXTKOje4Af/vYQ//nSafr6hyJdqojIFaFwlnHB\nYrJw8/Tr+dslm0mLTeHNuj3s53f8P3+VSLLbzkvvnOPBR/dy7GxzpEsVEbnsFM4yrmTGTePvln2T\n67PW0tLbxtPVv2H+6jquuyqNlvY+/s+Owzzyh+N0dPdHulQRkctG4SzjjtVk4ZbcG/jbpZtJiU3m\nzfo9HI96li9+Lo6sFCfFpQ3870f2UlzaMKbd50REJhqFs4xbWXEZfG/pN7ku6xpa+wL8rnoH3kXH\n+MzVXvoHh3jkD8f50X8dxh/oiXSpIiJhpXCWcc1qtnJr7gb+ftm3yUuYzrHmE7zW+xTrb+pjTk48\nx8608OCvSnhxX412FxORSUPhLBNCmiOFby36Ol+as5FocxQv1+2mO+tlbr7OgdVi4re7y3jo3/dx\nvLIl0qWKiHxiCmeZMAzDYHnKYv5hxd/y6WmraOr2s7vtGQqurmTFggTq/F384LeH+GnRUZraNNQt\nIhOXNiGRCcdujeHz+bexInUpvz31LEeajxJlP8X6mwqpOOLmwGkfRyr8XL88k5tWZhFt05+5iEws\nOnOWCSvTOY0HltzHF2b+FVaTlTd8r9Cd/WeuXRfCYbfyfHEVf//LPbx9rJ6gVnWLyASicJYJzWSY\n+FT6Ch5a+Xdcl3UNXQPdvN2+i8Sl+yhcaaa7d5BfPXeC7U/sp+xcW6TLFREZE433yaQQY4nh1twN\nrElfyR/O7KKk4QB1PM+sq2cQqpvNsePtfP83B5ifm8hfrZlOZvJH3w1GRCTSFM4yqbiiE9g0506u\nyVjN78uf52RrGYajgqXr5tNSlsmRimaOVDRz1Zxkbludo/tGi8i4pHCWSSnDmcbmhX/D8ZbT/L78\neUrbD2NJOcbSGfOpP5HK3uONvHOyidXzU/lMYQ4uZ1SkSxYRGaFwlknLMAwKEmcy251HScMB/lS5\nm9KOg1gyjrAwfx61pam8eqiOt441cO2Sady4IgtHjDXSZYuIKJxl8jMZJlakLmVZ8iJKGg/ywtmX\nONV9CMv0Y8yfPZfqo15e2FvNKwdquWZROtcvzyDeoTNpEYkchbNMGWaTmZWpS1mevIi9DQd4oXI3\nZb2HsORbKLAUcK40mRdKqnlp/znWLEjlhqsySYqPiXTZIjIFKZxlyjGbzKxKW8ZVKYvZ27CfFyp3\nc6b3MOaZZmZbZ9J4KpWXD9Ty2qE6VhakcOPKLFLcWjgmIleOwlmmrOGQXs7ylMWUNBzgz9WvUtl9\nHHKOM2PmdAJnp/Hm0TreOlbPslleblyRpUuwROSKUDjLlGcxWViVtpwVqUs56j/OS9WvcSZwBtLP\nkJmdSl9tNiUnQpScaGJ2lovrlmUwLzcRk2FEunQRmaQUziLnmQwTCzxzWeCZS0VbJS9Vv8YRfyl4\n60lJc2FuzuVE2SAnqlpJdttZv3QahXNTibKZI126iEwyCmeRD5GbkE1uQjYNXU3srn6dkob9DMa/\nQ8LyKOL7c6k9mcRvXuym6LUzfHphGtcumYY7LjrSZYvIJKFwFrmIlFgvd82+nZunX8ebtXt4s24v\nTcHjWOdCipFBa2Uqf9o7wK6SGpbO8nDNonTyMxIwNOQtIp+AwllkDOKj4rhp+nVcn72WQ75jvH7u\nbSoClZBVQ1JOHCFfFiWn+yg50URqop2rF6azal4KsdHa1ERELp3CWeQSWEwWliYvZGnyQmo66nj9\n3NvsazzIQOJRHEkWnP2Z+M4k8dTuLp55rYJls7xcvSid3LQ4nU2LyJgZodDoN7rdvn07hw8fxjAM\ntmzZwvz580dee/rpp3nmmWcwmUzMmjWLrVu3UlJSwre+9S3y8vIAyM/P58EHH7zo7/D5Oj7hV7mQ\nx+MM+2dOVerlxXUPdFNc/w5v1u6hqccPgN2IY9A3jUCNFwaimeaJ5dML07n50zPo6eyNcMWTg/4u\nw0N9DJ9L7aXH89GXZo565lxSUkJVVRU7duygoqKCLVu2sGPHDgB6enp4/vnnefLJJ7FarWzatImD\nBw8CsHz5cn784x+PuUiRicputXNt5hrWZqymvO0sxfX7ONB0hIGk49iTThA7kEZDlZcnX+rg6VfK\nWZSXxKq5qRTkuDCbdEt1EfmgUcO5uLiYdevWAZCbm0sgEKCzsxOHw0FMTAy//vWvgeGg7uzsxOPx\nUFdXd3mrFhmHDMMgzzWdPNd07si/lf2Nh3i7fh9V7TVYZ9QSSwxGIJ191a2UnGgkPjaKlQUprJqb\nwjSvI9Lli8g4Mmo4+/1+CgoKRp673W58Ph8Ox1/+Z/LLX/6Sxx9/nE2bNpGRkUFdXR3l5eXce++9\nBAIBNm/eTGFh4UV/j8tlx2IJ7/WiFxsykEujXl4qJ5mp6/nswvVUt9Xy8tm3eaNyLx3x5UTHlxND\nPL2NKew63MoLJdVMT4/n2qUZrFk0jQTdvnLM9HcZHupj+ISrl5e8IOzDpqjvueceNm3axNe+9jWW\nLFlCdnY2mzdvZsOGDdTU1LBp0yZefPFFbDbbR35ua2v3pZZyUZpHCR/18pOJIY6bpt3A9WnrqBs6\nx0un3uKI/zih5FNEJ58iejCJ6loPjzzfxKM7S5md7WL5LC+LZ3q02vsi9HcZHupj+FzROWev14vf\n7x953tTUhMfjAaCtrY2ysjKWLVtGdHQ0a9as4cCBAyxZsoQbb7wRgMzMTJKSkmhsbCQjI2PMRYtM\nNhaThSXJ88i0ZtM72MthXyn7Gg9ysqUMa5YfW9ZJrD1eTjYkUfrnZB7fFcXcHDfL5ySzcEYSMVG6\nuEJkqhj1X3thYSE/+clP2LhxI6WlpXi93pEh7cHBQb73ve+xc+dOYmNjOXr0KLfccgs7d+7E5/Px\n1a9+FZ/PR3NzM8nJyZf9y4hMFNGWaK5KXcJVqUsI9HVwoOkw+xoOUkUNtpxGjJzjWHo8HGtI4vCf\nkrESw/zcRJbPTmbedDfRNgW1yGQ26r/wxYsXU1BQwMaNGzEMg61bt1JUVITT6WT9+vXcd999bNq0\nCYvFwsyZM7n22mvp6urigQceYPfu3QwMDPDQQw9ddEhbZCqLj3JyTcanuCbjU/h7WjjkO8rBpqNU\nUo0tpwlyTmDuSeRQo4f9zydjCdopyHaxON/DwrwknHb92xKZbMZ0nfOVoOucxy/1MnwupZctva0c\n8h3jYNNRzgQqR35u7nXR60tiqM0LvQ5mZrhYlO9hcZ6HxPips7+3/i7DQ30Mnys65ywikeGOdrE2\nYzVrM1bT1hfgkO8Yh5uOUR44izWjFWtGGeZBOxX+JE6XeHlqt5ssbzwLZiSyYEYSWSlO3dZSZIJS\nOItMAAlR8Vw9rZCrpxXSPdDN8eZTHPEf53jLKYZSqrGkVGMKWqlvS+TcKQ87S5KIszmZN93Ngtwk\n5mS7sUfrn7vIRKF/rSITjN1qZ2nKIpamLGIoOER521mO+o9zxF9Ks6kBs7sBgIGeePa2JlK8Owmj\n20Veuov5uUnMy00kLdGuvb5FxjGFs8gEZjaZmemewUz3DD6X9xnquxo53nKKE82nKTfOQEwA0s5g\nBC2caUuk7FgS//VWIvG2BApy3BRku5mT7SYuVovKRMYThbPIJGEYBmmOFNIcKazL/DR9Q/2UtVZw\nvOUUx5tP4TM1YnY3AtDXZ6ck4GbPW4kM/SmRTLebOTluCnLc5E+Lxxrm3fpE5NIonEUmqSizjblJ\ns5mbNBuApm4/x1tOcaqlnLK2CnqizoH33PnXnNTXu3nxZCKm7iRmpLqZleliVpaLnNQ4rBbdoEPk\nSlI4i0wRXnsSXnsSV08rZCg4RE1nLadayjnVWk6FqRLDXoUlpQpCBme74iivcrHzmBtLTyK5KUnM\nynIxO9NFdqoTi1lhLXI5KZxFpiCzyUx2XCbZcZlcn72WgaEBzgSqONU6fFZdZTqHyRGA1EoIwZlu\nJ+WVLv77qBtLbyK5Xi950+LJy0ggNy1OO5aJhJn+RYkIVrN1ZGEZQP9QP2cD1ZS3naGs7QxnTdUM\nxnZgSakG4EyvnfK6BJ4/7YIuF+lxKeRPSyB/WgJ50+KJd+jOWiKfhMJZRD7AZrZdENYDwUGq2mso\nbztLedsZzgaq6Y2uA8/wvdubBi00tCfwakkCwZdduC1eZqQkMT0tjtz0eDK8Dg2Fi1wChbOIjMpq\nsjAjIYcZCTnAWoKhIA1dTZwJVHImUMWZQCU+ix9zwvAd7LpCcLDHwf6KeIKH4zF6XGTFp5GblkBu\nejzTU+Nwx0XpWmuRj6BwFpFLZjJMI5dtfSp9BQAd/Z2cDVRxJlBFZXs1VeZz9NtrwVMLQG3QRE1n\nHK8ciCfYFY99KJHsxFRyUuLISY0jOzWOeF1vLQIonEUkTJw2B/M9Bcz3FACMnF1XttdQ1V7N2UA1\ndaYGQs42AAaBsiEzp5rjCNXEEeyKx2kkMd2dSk5qPJnJTjKTnQpsmZIUziJyWbz37HpV2jJgeKFZ\ndUct1R3nqG6vpTJQg8/sg7hWAPqA40NmjjXFEap0Eux2EhtKJDM+lexkF1nJDjKTnSRNobtvydSk\ncBaRK8Zmtr1n7npY72AftZ31w4HdcY7KtnP4zD5CzuHAHgDKQwZl7bEEG5yEuuOwDiSQ5UonLd5F\nptfJNI+DdE8sUVbtbCaTg8JZRCIq2hJFbkI2uQnZIz/rH+qnvquRcx11nOusp6r9HHWmBgbs9UA9\nAFVA5YCVN886CZU6CPU4ibd4yIxLIcvrGg7spFg8CTGYTFp4JhOLwllExh2b2UZWXAZZcRkjPwuG\ngjT3tHKus47azjqa+nxU+Gtos7ZAXAsA3cBJ4HgghlCDg2CPA1O/k8SoJDLiUsj0uEhLiiU9KZbE\n+Gjd71rGLYWziEwIJsOEx56Ix57IIu88PB4nPl8HfUP91Hc1UNfZMDw8HqijztRIb7QPs8sHQNv5\nx+HWaEJ1ToI9sZjPh3aa00tGYiKpbjupSbEku2J0TbZEnMJZRCa0KLNtZCvS9+ro76Shq4mG7kbq\nOxupDtTTaGqiO8qHOWE4tFvPP451WAn6HIQOxUJfLHFmNymxHqYleElzO0hJtJPstuOMserabLki\nFM4iMik5bQ6cNgd5rukX/Lx7oJuG7ibquxpp7PJRE2igsdtHwNoG5xehdQNngIohg1BtDKGKWEK9\ndqxDTlw2N6kOL9MSPKQmxuJNiMHriiEmSv87lfDRX5OITCl2q53p8dlMj8++4OcDwUH8Pc00djXR\n0O3jXHsDDZ0+Wswt9EUPn2mHgJbzj2M9JkIVMYR67YT67NiCTlxRbpLtSUyLTyLF7cTrisGTEIMj\nxnqlv6ZMcApnERGGtyhNjU0mNTb5A691D3TT1OOnqdtPY5ePc+2NNHT5aDO1MhgzHNxBoPn8o7TP\nIFQZTeiUnVBfDJYhB3GWBJJiXKQ4PaQlJOB12fEkxOB2RmmOWz5A4SwiMgq71U629YPz2qFQiK7B\nbvw9zfi7m2ns9lMbaKKxu5lWUwt90c0j720//zgTgpDfTKh2OLhDfTHEGE7ircPhnRqXREp8PJ6E\nGJLiY0hw2jCbFN5TjcJZRORjMgwDhzUWhzX2A8ENw9drN/e2Dod3Twt17T4aOptp6W2hwxxgyN4B\nDG+04j//ODkAoUYzoeoYQv0x0BdDtOHAaY3DHe0i2eEmJc6NN95OYnw0bmc0UTZtvjLZKJxFRC4T\nm9n2kUPloVCIroFuWnpbhwO8u4W6Dh9NXS209rXRaW5nyN4JDIf3u3Pd5UMQajEINUQNn3kPRGMZ\nshNrdhJvjcNtTxgOcKeLxPhoXHHRuBxRWC06+55IFM4iIhFgGAYOWywOWyyZcdM+9D3dA9209gVo\n6W3F391KfUczvq4WWnvb6DAC9NrawAgB0HX+UQcc64VQt0HoXDSh/mhC/VFYQ3bsJgdxVieumASS\n7AmkON3kZiRhBIO4HDZioiy6VGycUDiLiIxTdqsdu9VOuiP1Q18fCg4R6G+nrS9AS08bTV2tNHa2\n0NzdSqA/QKepg35bKxjDK83fDfB6gH6gGUIN1uEAH4jCGIommljsFgfxtjhcMXF47Al4nQm4HXbi\nHTYSHFFE28wK8ctsTOG8fft2Dh8+jGEYbNmyhfnz54+89vTTT/PMM89gMpmYNWsWW7duxTCMix4j\nIiKfnNlkxh3twh3tYnr8h79nKDhEe38HbX3DIe4bCfAAgb4A3aZOui1dBI3h+e/+8482hvcvp3v4\nETpnJTQQ9Z4QtxNjjsVpdZIQ5cRtjyMxNgGPI44ERxTxDhtOu1WL2T6mUcO5pKSEqqoqduzYQUVF\nBVu2bGHHjh0A9PT08Pzzz/Pkk09itVrZtGkTBw8eZHBw8COPERGRK8dsMuOKTsAVnfChr7+7DWrv\nYC+B/g7a+9pp6Q3Q1NGKr6uNlt4AHf0ddNNFn6V7ZB783RAPAOdg+H6ffRDyG4QGomDQRmggCkso\nmmhjOMgd1ljiopy4Y+JItMeT5HCS4Igmzm7DabdpXvw9Rg3n4uJi1q1bB0Bubi6BQIDOzk4cDgcx\nMTH8+te/BoaDurOzE4/HQ1FR0UceIyIi40+0JZpoSzTJds/wDz58JJ3+oQHa+zuGz8Z7AzR2tNHc\nHaC1t52O/g66BrroMbrot3URMtoJAT3nHy3vfkjv8CPUzPkQtxEatGEeisJqxBBtsmM323HYHMRH\nx+KKisMdG0dirJP42CgcMdZJH+ajhrPf76egoGDkudvtxufzXRC0v/zlL3n88cfZtGkTGRkZYzrm\n/VwuOxZLeC8H8HicYf28qUy9DB/1MnzUy/C41D6m4x71PaFQiJ7BXgK9wyHe0tVOQ3srvo42WroD\nBHo76OjvpNt0/ozc+MvK9AGgA2gEGOIvQ+tNwKCV0OB7wtwUTbQpBrtleHFdfLSDhBgnibFxJDnj\n8DjjiHdE47SfH2a/zBu+hOtv8pIXhIVCoQ/87J577mHTpk187WtfY8mSJWM65v1aW7svtZSLeneo\nRj459TJ81MvwUS/D43L30UIMScSQFJtCfiwfeUY+GBykc6CLzv4u2vs7aelup7mrndaedgJ9nXQN\ndNE92E2vpYd+cy9D0cMr1T8Q5u8dbwdCIeN8oA8/zMEoLERhM6KJNsdgt8QQa4nFGWUnLtpBQkws\n7hgn8XY7jhgrsTFWYqIsY7q96KX28mJBPmo4e71e/H7/yPOmpiY8nuFhj7a2NsrKyli2bBnR0dGs\nWbOGAwcOXPQYERGR97OYLCRExZMQdX5lW+LF3x8MBeke7KGrv4uO/i5aujto6WmnraeT9r4uOvs7\n6Rropmeohz5LDwPmXoaiu8EIMQgMMnxCPjLUPgh0nn8AoeD/3979hTS9/3Ecf07nfh7N/mhuVPSP\nqJQsIigwKworSKKLIFgR0kVRrEE3VsMKL0JT88LaRUXmjREtVoR3RZAQYYZdhEpQxiEkVqk/jrXc\nd8ctz0WcUb9+51dt67fvxutx9/2ML7z3wvFin49+tUD0c6ETySFr0saKwpUc3LD5F7z7b323nCsq\nKvB6vTidTgYGBrDb7bHt6UgkgsfjobOzk/z8fPr6+tixYweFhYX/eI+IiEiisixZsaezOfKBGd+/\n59PkJ4xImI8T43yc+MgfRpB/f/zAmBFkzAgSnBj/XOiREAYGf2IQyQ7HSn340++//H397bvlvGrV\nKpYtW4bT6cRisVBXV8etW7coKChgy5YtHD58mOrqaqxWK0uXLqWyshKLxfLNPSIiIqmUZckiL+c3\n8nJ+o5gi+Ic/P/tPk5OTGNEwudn/+rUDfsEy+SMHwv8HyT7z0HlU8ijL5FGWyaMsk0M5Jk8yz5wz\n9/fQRURE0pTKWURExGRUziIiIiajchYRETEZlbOIiIjJqJxFRERMRuUsIiJiMipnERERk1E5i4iI\nmIzKWURExGRUziIiIiZjmmdri4iIyGf65iwiImIyKmcRERGTUTmLiIiYjMpZRETEZFTOIiIiJqNy\nFhERMRlrqgf4FRoaGnj69CkWi4Xa2lpWrFiR6pHSyvPnz3G5XOzbt4+9e/cSCAQ4duwY0WiU4uJi\nzp49i81mS/WYaaG5uZknT54QiUQ4ePAgy5cvV5Y/KRQK4fF4GB0dJRwO43K5KCkpUY4JMAyD7du3\n43K5KC8vV5Zx6Onp4ciRIyxevBiAJUuWsH///qRlmXHfnB8/fsyrV6/w+XzU19dTX1+f6pHSyvj4\nOKdPn6a8vDy2dv78efbs2cO1a9eYP38+fr8/hROmj0ePHvHixQt8Ph9tbW00NDQoyzjcv3+fsrIy\nrl69SmtrK42NjcoxQRcuXGDatGmAPt+JWLNmDR0dHXR0dHDq1KmkZplx5dzd3c3mzZsBWLRoEWNj\nYwSDwRRPlT5sNhuXL3jLxtgAAALASURBVF/GbrfH1np6eqisrARg06ZNdHd3p2q8tLJ69WrOnTsH\nwNSpUwmFQsoyDlVVVRw4cACAQCCAw+FQjgl4+fIlg4ODbNy4EdDnO5mSmWXGlfPIyAgzZsyIXRcW\nFjI8PJzCidKL1WolNzf3q7VQKBTbmikqKlKePyg7O5u8vDwA/H4/GzZsUJYJcDqd1NTUUFtbqxwT\n0NTUhMfjiV0ry/gNDg5y6NAhdu/ezcOHD5OaZUaeOX9JTydNLuX58+7du4ff76e9vZ2tW7fG1pXl\nz7l+/TrPnj3j6NGjX2WnHH/c7du3WblyJXPnzv2vryvLH7dgwQLcbjfbtm1jaGiI6upqotFo7PVE\ns8y4crbb7YyMjMSu3717R3FxcQonSn95eXkYhkFubi5v3779astb/rcHDx5w8eJF2traKCgoUJZx\n6O/vp6ioiFmzZlFaWko0GiU/P185xqGrq4uhoSG6urp48+YNNptNP5NxcjgcVFVVATBv3jxmzpxJ\nX19f0rLMuG3tiooK7ty5A8DAwAB2u50pU6akeKr0tnbt2limd+/eZf369SmeKD18+PCB5uZmLl26\nxPTp0wFlGY/e3l7a29uBz8dW4+PjyjFOra2t3Lx5kxs3brBr1y5cLpeyjFNnZydXrlwBYHh4mNHR\nUXbu3Jm0LDPyv1K1tLTQ29uLxWKhrq6OkpKSVI+UNvr7+2lqauL169dYrVYcDgctLS14PB7C4TCz\nZ8/mzJkz5OTkpHpU0/P5fHi9XhYuXBhba2xs5OTJk8ryJxiGwYkTJwgEAhiGgdvtpqysjOPHjyvH\nBHi9XubMmcO6deuUZRyCwSA1NTW8f/+eiYkJ3G43paWlScsyI8tZREQknWXctraIiEi6UzmLiIiY\njMpZRETEZFTOIiIiJqNyFhERMRmVs4iIiMmonEVERExG5SwiImIyfwHaqjRJNzRTUgAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0746f4ac50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0uRa7D3lb_b2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "ee78c7d7-77a5-4964-fbf2-41c528807405"
      },
      "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)\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": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAADjCAYAAADdR/IFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f075236f828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "MqC-grhpbzpa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Deep autoencoder"
      ]
    },
    {
      "metadata": {
        "id": "25iopg9dZeZq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "f336f86a-0604-4a5e-e00d-e967a5098154"
      },
      "cell_type": "code",
      "source": [
        "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)\n",
        "\n",
        "autoencoder = Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 32)                2080      \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 784)               101136    \n",
            "=================================================================\n",
            "Total params: 222,384\n",
            "Trainable params: 222,384\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "m1Pr88jKZjMk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3508
        },
        "outputId": "50cf2945-cd8d-483a-d8ca-6fa7c5baa54a"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit(x_train, x_train, epochs=100, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/100\n",
            "60000/60000 [==============================] - 3s 45us/step - loss: 0.3413 - val_loss: 0.2630\n",
            "Epoch 2/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2555 - val_loss: 0.2458\n",
            "Epoch 3/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2400 - val_loss: 0.2340\n",
            "Epoch 4/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.2266 - val_loss: 0.2172\n",
            "Epoch 5/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.2111 - val_loss: 0.2051\n",
            "Epoch 6/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1995 - val_loss: 0.1928\n",
            "Epoch 7/100\n",
            "  256/60000 [..............................] - ETA: 3s - loss: 0.1883"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1895 - val_loss: 0.1856\n",
            "Epoch 8/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1832 - val_loss: 0.1785\n",
            "Epoch 9/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1768 - val_loss: 0.1701\n",
            "Epoch 10/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1696 - val_loss: 0.1629\n",
            "Epoch 11/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1636 - val_loss: 0.1595\n",
            "Epoch 12/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1593 - val_loss: 0.1549\n",
            "Epoch 13/100\n",
            "33024/60000 [===============>..............] - ETA: 1s - loss: 0.1560"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1556 - val_loss: 0.1559\n",
            "Epoch 14/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1528 - val_loss: 0.1505\n",
            "Epoch 15/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1501 - val_loss: 0.1490\n",
            "Epoch 16/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1474 - val_loss: 0.1449\n",
            "Epoch 17/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1447 - val_loss: 0.1416\n",
            "Epoch 18/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1424 - val_loss: 0.1406\n",
            "Epoch 19/100\n",
            "35072/60000 [================>.............] - ETA: 0s - loss: 0.1405"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1405 - val_loss: 0.1403\n",
            "Epoch 20/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1386 - val_loss: 0.1355\n",
            "Epoch 21/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1367 - val_loss: 0.1346\n",
            "Epoch 22/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1349 - val_loss: 0.1325\n",
            "Epoch 23/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1332 - val_loss: 0.1330\n",
            "Epoch 24/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1317 - val_loss: 0.1294\n",
            "Epoch 25/100\n",
            "33280/60000 [===============>..............] - ETA: 0s - loss: 0.1308"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1306 - val_loss: 0.1290\n",
            "Epoch 26/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1294 - val_loss: 0.1299\n",
            "Epoch 27/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1284 - val_loss: 0.1271\n",
            "Epoch 28/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1276 - val_loss: 0.1251\n",
            "Epoch 29/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1266 - val_loss: 0.1260\n",
            "Epoch 30/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1257 - val_loss: 0.1257\n",
            "Epoch 31/100\n",
            "36608/60000 [=================>............] - ETA: 0s - loss: 0.1249"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1250 - val_loss: 0.1238\n",
            "Epoch 32/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1241 - val_loss: 0.1221\n",
            "Epoch 33/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1232 - val_loss: 0.1219\n",
            "Epoch 34/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1225 - val_loss: 0.1224\n",
            "Epoch 35/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1216 - val_loss: 0.1201\n",
            "Epoch 36/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1210 - val_loss: 0.1185\n",
            "Epoch 37/100\n",
            "35328/60000 [================>.............] - ETA: 0s - loss: 0.1203"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1202 - val_loss: 0.1187\n",
            "Epoch 38/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1196 - val_loss: 0.1197\n",
            "Epoch 39/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1188 - val_loss: 0.1175\n",
            "Epoch 40/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1182 - val_loss: 0.1182\n",
            "Epoch 41/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1177 - val_loss: 0.1153\n",
            "Epoch 42/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1170 - val_loss: 0.1155\n",
            "Epoch 43/100\n",
            "35072/60000 [================>.............] - ETA: 0s - loss: 0.1165"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1164 - val_loss: 0.1162\n",
            "Epoch 44/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1159 - val_loss: 0.1159\n",
            "Epoch 45/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1153 - val_loss: 0.1148\n",
            "Epoch 46/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1149 - val_loss: 0.1134\n",
            "Epoch 47/100\n",
            "60000/60000 [==============================] - 2s 41us/step - loss: 0.1143 - val_loss: 0.1128\n",
            "Epoch 48/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1140 - val_loss: 0.1130\n",
            "Epoch 49/100\n",
            "32768/60000 [===============>..............] - ETA: 1s - loss: 0.1136"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1134 - val_loss: 0.1125\n",
            "Epoch 50/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1129 - val_loss: 0.1106\n",
            "Epoch 51/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1124 - val_loss: 0.1133\n",
            "Epoch 52/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1120 - val_loss: 0.1122\n",
            "Epoch 53/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1115 - val_loss: 0.1100\n",
            "Epoch 54/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1111 - val_loss: 0.1095\n",
            "Epoch 55/100\n",
            "36608/60000 [=================>............] - ETA: 0s - loss: 0.1107"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1107 - val_loss: 0.1090\n",
            "Epoch 56/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1103 - val_loss: 0.1094\n",
            "Epoch 57/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1100 - val_loss: 0.1078\n",
            "Epoch 58/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1095 - val_loss: 0.1072\n",
            "Epoch 59/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1091 - val_loss: 0.1108\n",
            "Epoch 60/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1087 - val_loss: 0.1078\n",
            "Epoch 61/100\n",
            "34816/60000 [================>.............] - ETA: 0s - loss: 0.1084"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1084 - val_loss: 0.1069\n",
            "Epoch 62/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1080 - val_loss: 0.1063\n",
            "Epoch 63/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1077 - val_loss: 0.1071\n",
            "Epoch 64/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1073 - val_loss: 0.1078\n",
            "Epoch 65/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1071 - val_loss: 0.1060\n",
            "Epoch 66/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1067 - val_loss: 0.1074\n",
            "Epoch 67/100\n",
            "36096/60000 [=================>............] - ETA: 0s - loss: 0.1065"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1064 - val_loss: 0.1051\n",
            "Epoch 68/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1062 - val_loss: 0.1055\n",
            "Epoch 69/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1060 - val_loss: 0.1031\n",
            "Epoch 70/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1056 - val_loss: 0.1041\n",
            "Epoch 71/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1051 - val_loss: 0.1034\n",
            "Epoch 72/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1050 - val_loss: 0.1044\n",
            "Epoch 73/100\n",
            "36864/60000 [=================>............] - ETA: 0s - loss: 0.1048"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1047 - val_loss: 0.1043\n",
            "Epoch 74/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1045 - val_loss: 0.1023\n",
            "Epoch 75/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1043 - val_loss: 0.1025\n",
            "Epoch 76/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1039 - val_loss: 0.1018\n",
            "Epoch 77/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1037 - val_loss: 0.1059\n",
            "Epoch 78/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1035 - val_loss: 0.1024\n",
            "Epoch 79/100\n",
            "38144/60000 [==================>...........] - ETA: 0s - loss: 0.1034"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1033 - val_loss: 0.1024\n",
            "Epoch 80/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1029 - val_loss: 0.1025\n",
            "Epoch 81/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1028 - val_loss: 0.1034\n",
            "Epoch 82/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1027 - val_loss: 0.1016\n",
            "Epoch 83/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1024 - val_loss: 0.1030\n",
            "Epoch 84/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1021 - val_loss: 0.1000\n",
            "Epoch 85/100\n",
            "37120/60000 [=================>............] - ETA: 0s - loss: 0.1021"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1020 - val_loss: 0.1022\n",
            "Epoch 86/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1018 - val_loss: 0.1015\n",
            "Epoch 87/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1017 - val_loss: 0.1015\n",
            "Epoch 88/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1014 - val_loss: 0.1004\n",
            "Epoch 89/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1012 - val_loss: 0.1000\n",
            "Epoch 90/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1011 - val_loss: 0.1015\n",
            "Epoch 91/100\n",
            "36352/60000 [=================>............] - ETA: 0s - loss: 0.1011"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1010 - val_loss: 0.1012\n",
            "Epoch 92/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1008 - val_loss: 0.0994\n",
            "Epoch 93/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1006 - val_loss: 0.0986\n",
            "Epoch 94/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1004 - val_loss: 0.1012\n",
            "Epoch 95/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1004 - val_loss: 0.0990\n",
            "Epoch 96/100\n",
            "60000/60000 [==============================] - 2s 38us/step - loss: 0.1002 - val_loss: 0.0994\n",
            "Epoch 97/100\n",
            "36352/60000 [=================>............] - ETA: 0s - loss: 0.1001"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.1000 - val_loss: 0.1004\n",
            "Epoch 98/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.0999 - val_loss: 0.0981\n",
            "Epoch 99/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.0998 - val_loss: 0.0983\n",
            "Epoch 100/100\n",
            "60000/60000 [==============================] - 2s 39us/step - loss: 0.0997 - val_loss: 0.0984\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "K0vW89E7Zrzi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "6078c928-a2e9-4651-e06c-439478b88ef2"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0746c4bf28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kqkYp-3DZtxU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "c1787225-885f-4cd5-d257-b63d25cc7388"
      },
      "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)\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": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAADjCAYAAADdR/IFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f0749649320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "5NoK4RzRcRcR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Convolutional autoencoder"
      ]
    },
    {
      "metadata": {
        "id": "fdufEx-vcUSg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "outputId": "763f266c-2ad0-4b81-9358-77d5b68bfbfd"
      },
      "cell_type": "code",
      "source": [
        "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\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",
        "\n",
        "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_9 (InputLayer)         (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 28, 28, 16)        160       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 14, 14, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 14, 14, 8)         1160      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 7, 7, 8)           0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 7, 7, 8)           584       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 8)           0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 4, 4, 8)           584       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_6 (UpSampling2 (None, 8, 8, 8)           0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 8, 8, 8)           584       \n",
            "_________________________________________________________________\n",
            "up_sampling2d_7 (UpSampling2 (None, 16, 16, 8)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 14, 14, 16)        1168      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_8 (UpSampling2 (None, 28, 28, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 28, 28, 1)         145       \n",
            "=================================================================\n",
            "Total params: 4,385\n",
            "Trainable params: 4,385\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EATjxxBpcXJC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(x_train, _), (x_test, _) = 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": "FDQdNCfLcgvL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1771
        },
        "outputId": "8670c40b-bcec-4c07-a198-abd9d1cfa2d3"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit(x_train, x_train, epochs=50, batch_size=256, shuffle=True, validation_data=(x_test, x_test))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "60000/60000 [==============================] - 6s 95us/step - loss: 0.2345 - val_loss: 0.1835\n",
            "Epoch 2/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1716 - val_loss: 0.1577\n",
            "Epoch 3/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1547 - val_loss: 0.1461\n",
            "Epoch 4/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1448 - val_loss: 0.1417\n",
            "Epoch 5/50\n",
            "11776/60000 [====>.........................] - ETA: 3s - loss: 0.1404"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1383 - val_loss: 0.1348\n",
            "Epoch 6/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1333 - val_loss: 0.1306\n",
            "Epoch 7/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1299 - val_loss: 0.1272\n",
            "Epoch 8/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1266 - val_loss: 0.1256\n",
            "Epoch 9/50\n",
            "45568/60000 [=====================>........] - ETA: 1s - loss: 0.1246"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1247 - val_loss: 0.1221\n",
            "Epoch 10/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1229 - val_loss: 0.1212\n",
            "Epoch 11/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1214 - val_loss: 0.1185\n",
            "Epoch 12/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1201 - val_loss: 0.1205\n",
            "Epoch 13/50\n",
            "48640/60000 [=======================>......] - ETA: 0s - loss: 0.1192"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1190 - val_loss: 0.1165\n",
            "Epoch 14/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1175 - val_loss: 0.1162\n",
            "Epoch 15/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1166 - val_loss: 0.1154\n",
            "Epoch 16/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1158 - val_loss: 0.1135\n",
            "Epoch 17/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1149"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 75us/step - loss: 0.1150 - val_loss: 0.1107\n",
            "Epoch 18/50\n",
            "60000/60000 [==============================] - 4s 75us/step - loss: 0.1142 - val_loss: 0.1105\n",
            "Epoch 19/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1135 - val_loss: 0.1132\n",
            "Epoch 20/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1129 - val_loss: 0.1130\n",
            "Epoch 21/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1122"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1119 - val_loss: 0.1104\n",
            "Epoch 22/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1116 - val_loss: 0.1154\n",
            "Epoch 23/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1108 - val_loss: 0.1092\n",
            "Epoch 24/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1104 - val_loss: 0.1081\n",
            "Epoch 25/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1101"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1100 - val_loss: 0.1086\n",
            "Epoch 26/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1096 - val_loss: 0.1070\n",
            "Epoch 27/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1093 - val_loss: 0.1105\n",
            "Epoch 28/50\n",
            "60000/60000 [==============================] - 4s 75us/step - loss: 0.1085 - val_loss: 0.1057\n",
            "Epoch 29/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1081"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 75us/step - loss: 0.1081 - val_loss: 0.1051\n",
            "Epoch 30/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1080 - val_loss: 0.1058\n",
            "Epoch 31/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1077 - val_loss: 0.1047\n",
            "Epoch 32/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1072 - val_loss: 0.1070\n",
            "Epoch 33/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1074"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1072 - val_loss: 0.1049\n",
            "Epoch 34/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1069 - val_loss: 0.1050\n",
            "Epoch 35/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1063 - val_loss: 0.1060\n",
            "Epoch 36/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1061 - val_loss: 0.1045\n",
            "Epoch 37/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1059"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1057 - val_loss: 0.1044\n",
            "Epoch 38/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1054 - val_loss: 0.1053\n",
            "Epoch 39/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1051 - val_loss: 0.1091\n",
            "Epoch 40/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1048 - val_loss: 0.1032\n",
            "Epoch 41/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1047"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1046 - val_loss: 0.1012\n",
            "Epoch 42/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1044 - val_loss: 0.1040\n",
            "Epoch 43/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1044 - val_loss: 0.1003\n",
            "Epoch 44/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1041 - val_loss: 0.1026\n",
            "Epoch 45/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1040"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1039 - val_loss: 0.1026\n",
            "Epoch 46/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1036 - val_loss: 0.1026\n",
            "Epoch 47/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1036 - val_loss: 0.1021\n",
            "Epoch 48/50\n",
            "60000/60000 [==============================] - 5s 76us/step - loss: 0.1032 - val_loss: 0.1037\n",
            "Epoch 49/50\n",
            "49408/60000 [=======================>......] - ETA: 0s - loss: 0.1032"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1032 - val_loss: 0.1030\n",
            "Epoch 50/50\n",
            "60000/60000 [==============================] - 5s 75us/step - loss: 0.1030 - val_loss: 0.1007\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ypAzkIuMcsfR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "8befe4d4-9ad2-430c-da39-befafd64f515"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFLCAYAAADs00TBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41dbe123c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wY0ZaSREcshy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "296ac387-e385-46e2-c640-81aec4580202"
      },
      "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+1):\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": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAADjCAYAAADdR/IFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAACKCBzUAAAAAAAAR8f8ASbDgjbYuHmoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f41d12f3518>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zu1QjtKwcslB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# n = 10\n",
        "# plt.figure(figsize=(20, 8))\n",
        "# for i in range(1, n+1):\n",
        "#     ax = plt.subplot(1, n, i)\n",
        "#     plt.imshow(encoded_imgs[i].reshape(4, 4 * 8).T)  # encoded_imgs was not defined in the code!\n",
        "#     plt.gray()\n",
        "#     ax.get_xaxis().set_visible(False)\n",
        "#     ax.get_yaxis().set_visible(False)\n",
        "# plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PaD2nz55dAgQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Application to image denoising"
      ]
    },
    {
      "metadata": {
        "id": "i3AM_KM5cscX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(x_train, _), (x_test, _) = 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": "UwjQSaGHdDoS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "dc9e8548-9688-4f92-dfb1-c6b139138630"
      },
      "cell_type": "code",
      "source": [
        "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": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAABsCAYAAAAyoVQIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVNxjLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4196bc23c8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6gegeSCJdFgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "13a6bdb3-e7be-4a8b-bbcf-8fa4e13d898a"
      },
      "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')\n",
        "\n",
        "autoencoder.summary()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_10 (InputLayer)        (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 28, 28, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 14, 14, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 7, 7, 32)          9248      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_9 (UpSampling2 (None, 14, 14, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 14, 14, 32)        9248      \n",
            "_________________________________________________________________\n",
            "up_sampling2d_10 (UpSampling (None, 28, 28, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 28, 28, 1)         289       \n",
            "=================================================================\n",
            "Total params: 28,353\n",
            "Trainable params: 28,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "l9eQjra3dHvx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3508
        },
        "outputId": "3433914d-f95a-4571-aa3c-c88ec51c6e61"
      },
      "cell_type": "code",
      "source": [
        "history = autoencoder.fit(x_train_noisy, x_train, epochs=100, batch_size=128, shuffle=True, validation_data=(x_test_noisy, x_test))\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/100\n",
            "60000/60000 [==============================] - 9s 146us/step - loss: 0.1742 - val_loss: 0.1211\n",
            "Epoch 2/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.1181 - val_loss: 0.1139\n",
            "Epoch 3/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1111 - val_loss: 0.1103\n",
            "Epoch 4/100\n",
            "13056/60000 [=====>........................] - ETA: 5s - loss: 0.1086"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.1073 - val_loss: 0.1052\n",
            "Epoch 5/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1052 - val_loss: 0.1028\n",
            "Epoch 6/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1037 - val_loss: 0.1025\n",
            "Epoch 7/100\n",
            "45952/60000 [=====================>........] - ETA: 1s - loss: 0.1030"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.1029 - val_loss: 0.1013\n",
            "Epoch 8/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1020 - val_loss: 0.1032\n",
            "Epoch 9/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.1014 - val_loss: 0.1013\n",
            "Epoch 10/100\n",
            "51968/60000 [========================>.....] - ETA: 1s - loss: 0.1006"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1006 - val_loss: 0.0987\n",
            "Epoch 11/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.1000 - val_loss: 0.0996\n",
            "Epoch 12/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0998 - val_loss: 0.1000\n",
            "Epoch 13/100\n",
            "52608/60000 [=========================>....] - ETA: 0s - loss: 0.0995"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0994 - val_loss: 0.0997\n",
            "Epoch 14/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0991 - val_loss: 0.0978\n",
            "Epoch 15/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0989 - val_loss: 0.0983\n",
            "Epoch 16/100\n",
            "51968/60000 [========================>.....] - ETA: 1s - loss: 0.0985"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0986 - val_loss: 0.0976\n",
            "Epoch 17/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0984 - val_loss: 0.0993\n",
            "Epoch 18/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0980 - val_loss: 0.0971\n",
            "Epoch 19/100\n",
            "51840/60000 [========================>.....] - ETA: 1s - loss: 0.0980"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0981 - val_loss: 0.0985\n",
            "Epoch 20/100\n",
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.0979 - val_loss: 0.0969\n",
            "Epoch 21/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0977 - val_loss: 0.0968\n",
            "Epoch 22/100\n",
            "51840/60000 [========================>.....] - ETA: 1s - loss: 0.0976"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0975 - val_loss: 0.0973\n",
            "Epoch 23/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0974 - val_loss: 0.0972\n",
            "Epoch 24/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0972 - val_loss: 0.0964\n",
            "Epoch 25/100\n",
            "51840/60000 [========================>.....] - ETA: 1s - loss: 0.0970"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0970 - val_loss: 0.0978\n",
            "Epoch 26/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0970 - val_loss: 0.0963\n",
            "Epoch 27/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0969 - val_loss: 0.0973\n",
            "Epoch 28/100\n",
            "52096/60000 [=========================>....] - ETA: 0s - loss: 0.0967"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0967 - val_loss: 0.0964\n",
            "Epoch 29/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0966 - val_loss: 0.0957\n",
            "Epoch 30/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0965 - val_loss: 0.0957\n",
            "Epoch 31/100\n",
            "53248/60000 [=========================>....] - ETA: 0s - loss: 0.0965"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0965 - val_loss: 0.0958\n",
            "Epoch 32/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0964 - val_loss: 0.0961\n",
            "Epoch 33/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0962 - val_loss: 0.0954\n",
            "Epoch 34/100\n",
            "51712/60000 [========================>.....] - ETA: 1s - loss: 0.0961"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0962 - val_loss: 0.0954\n",
            "Epoch 35/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0961 - val_loss: 0.0955\n",
            "Epoch 36/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0961 - val_loss: 0.0961\n",
            "Epoch 37/100\n",
            "52096/60000 [=========================>....] - ETA: 0s - loss: 0.0960"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0960 - val_loss: 0.0952\n",
            "Epoch 38/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0959 - val_loss: 0.0955\n",
            "Epoch 39/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0958 - val_loss: 0.0961\n",
            "Epoch 40/100\n",
            "52608/60000 [=========================>....] - ETA: 0s - loss: 0.0959"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0959 - val_loss: 0.0956\n",
            "Epoch 41/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0958 - val_loss: 0.0953\n",
            "Epoch 42/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0956 - val_loss: 0.0953\n",
            "Epoch 43/100\n",
            "52096/60000 [=========================>....] - ETA: 0s - loss: 0.0955"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0955 - val_loss: 0.0948\n",
            "Epoch 44/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0956 - val_loss: 0.0947\n",
            "Epoch 45/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0955 - val_loss: 0.0948\n",
            "Epoch 46/100\n",
            "51584/60000 [========================>.....] - ETA: 1s - loss: 0.0955"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0955 - val_loss: 0.0947\n",
            "Epoch 47/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0953 - val_loss: 0.0951\n",
            "Epoch 48/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0954 - val_loss: 0.0951\n",
            "Epoch 49/100\n",
            "51968/60000 [========================>.....] - ETA: 1s - loss: 0.0953"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0953 - val_loss: 0.0953\n",
            "Epoch 50/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0953 - val_loss: 0.0956\n",
            "Epoch 51/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0953 - val_loss: 0.0963\n",
            "Epoch 52/100\n",
            "52992/60000 [=========================>....] - ETA: 0s - loss: 0.0952"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0953 - val_loss: 0.0970\n",
            "Epoch 53/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0951 - val_loss: 0.0949\n",
            "Epoch 54/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0951 - val_loss: 0.0955\n",
            "Epoch 55/100\n",
            "53632/60000 [=========================>....] - ETA: 0s - loss: 0.0950"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0950 - val_loss: 0.0946\n",
            "Epoch 56/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0951 - val_loss: 0.0947\n",
            "Epoch 57/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0951 - val_loss: 0.0947\n",
            "Epoch 58/100\n",
            "51072/60000 [========================>.....] - ETA: 1s - loss: 0.0949"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.0949 - val_loss: 0.0944\n",
            "Epoch 59/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0949 - val_loss: 0.0943\n",
            "Epoch 60/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0949 - val_loss: 0.0943\n",
            "Epoch 61/100\n",
            "50816/60000 [========================>.....] - ETA: 1s - loss: 0.0949"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.0948 - val_loss: 0.0945\n",
            "Epoch 62/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0948 - val_loss: 0.0948\n",
            "Epoch 63/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0948 - val_loss: 0.0950\n",
            "Epoch 64/100\n",
            "52992/60000 [=========================>....] - ETA: 0s - loss: 0.0947"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0947 - val_loss: 0.0943\n",
            "Epoch 65/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0946 - val_loss: 0.0941\n",
            "Epoch 66/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0947 - val_loss: 0.0945\n",
            "Epoch 67/100\n",
            "52608/60000 [=========================>....] - ETA: 0s - loss: 0.0947"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0946 - val_loss: 0.0942\n",
            "Epoch 68/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0947 - val_loss: 0.0944\n",
            "Epoch 69/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0946 - val_loss: 0.0949\n",
            "Epoch 70/100\n",
            "52864/60000 [=========================>....] - ETA: 0s - loss: 0.0945"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0946 - val_loss: 0.0944\n",
            "Epoch 71/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0946 - val_loss: 0.0941\n",
            "Epoch 72/100\n",
            "60000/60000 [==============================] - 8s 134us/step - loss: 0.0945 - val_loss: 0.0941\n",
            "Epoch 73/100\n",
            "51712/60000 [========================>.....] - ETA: 1s - loss: 0.0946"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0945 - val_loss: 0.0940\n",
            "Epoch 74/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0945 - val_loss: 0.0940\n",
            "Epoch 75/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0945 - val_loss: 0.0941\n",
            "Epoch 76/100\n",
            "51712/60000 [========================>.....] - ETA: 1s - loss: 0.0944"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0944 - val_loss: 0.0950\n",
            "Epoch 77/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0944 - val_loss: 0.0944\n",
            "Epoch 78/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0944 - val_loss: 0.0943\n",
            "Epoch 79/100\n",
            "51456/60000 [========================>.....] - ETA: 1s - loss: 0.0944"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0944 - val_loss: 0.0941\n",
            "Epoch 80/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0944 - val_loss: 0.0938\n",
            "Epoch 81/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0943 - val_loss: 0.0939\n",
            "Epoch 82/100\n",
            "53248/60000 [=========================>....] - ETA: 0s - loss: 0.0943"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0943 - val_loss: 0.0944\n",
            "Epoch 83/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0943 - val_loss: 0.0942\n",
            "Epoch 84/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0943 - val_loss: 0.0940\n",
            "Epoch 85/100\n",
            "52608/60000 [=========================>....] - ETA: 0s - loss: 0.0942"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0943 - val_loss: 0.0941\n",
            "Epoch 86/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0943 - val_loss: 0.0940\n",
            "Epoch 87/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0942 - val_loss: 0.0940\n",
            "Epoch 88/100\n",
            "51840/60000 [========================>.....] - ETA: 1s - loss: 0.0942"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0942 - val_loss: 0.0938\n",
            "Epoch 89/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0942 - val_loss: 0.0939\n",
            "Epoch 90/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0942 - val_loss: 0.0937\n",
            "Epoch 91/100\n",
            "53376/60000 [=========================>....] - ETA: 0s - loss: 0.0942"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0942 - val_loss: 0.0943\n",
            "Epoch 92/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0941 - val_loss: 0.0939\n",
            "Epoch 93/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0942 - val_loss: 0.0938\n",
            "Epoch 94/100\n",
            "53376/60000 [=========================>....] - ETA: 0s - loss: 0.0941"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0941 - val_loss: 0.0941\n",
            "Epoch 95/100\n",
            "60000/60000 [==============================] - 8s 133us/step - loss: 0.0941 - val_loss: 0.0941\n",
            "Epoch 96/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0941 - val_loss: 0.0937\n",
            "Epoch 97/100\n",
            "53120/60000 [=========================>....] - ETA: 0s - loss: 0.0941"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0940 - val_loss: 0.0937\n",
            "Epoch 98/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0941 - val_loss: 0.0938\n",
            "Epoch 99/100\n",
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0940 - val_loss: 0.0939\n",
            "Epoch 100/100\n",
            "53760/60000 [=========================>....] - ETA: 0s - loss: 0.0941"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 8s 132us/step - loss: 0.0940 - val_loss: 0.0938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AjGlstesdVH7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "188839be-e182-47c1-c890-b4e4b41552a9"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFKCAYAAAAnj5dkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns4iIyCijcBYRERll/j9LJXMKy8kdugAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f417fa56438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "yIoyk0RTdVN7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "outputId": "102fb3b9-155d-41c6-effc-06c1998b0b95"
      },
      "cell_type": "code",
      "source": [
        "decoded_imgs = autoencoder.predict(x_test)\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": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAADjCAYAAADdR/IFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f417f5ef5c0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "vjLFLLWfgWQa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sequence-to-sequence autoencoder"
      ]
    },
    {
      "metadata": {
        "id": "UPjusRZkgVOf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# from keras.layers import LSTM, RepeatVector\n",
        "\n",
        "# timesteps = None\n",
        "# input_dim = None\n",
        "\n",
        "# inputs = Input(shape=(timesteps, input_dim))\n",
        "# encoded = LSTM(latent_dim)(inputs)\n",
        "\n",
        "# decoded = RepeatVector(timesteps)(encoded)\n",
        "# decoded = LSTM(input_dim, return_sequences=True)(decoded)\n",
        "\n",
        "# sequence_autoencoder = Model(inputs, decoded)\n",
        "# encoder = Model(inputs, encoded)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lJWJ7ZvYdVLc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PH3hIAnpgbLs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Variational autoencoder (VAE)\n",
        "\n",
        "The code below comes from the gist linked in the post: https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py"
      ]
    },
    {
      "metadata": {
        "id": "boG_7x8rgdhn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "image_size = x_train.shape[1]\n",
        "original_dim = image_size * image_size\n",
        "x_train = np.reshape(x_train, [-1, original_dim])\n",
        "x_test = np.reshape(x_test, [-1, original_dim])\n",
        "x_train = x_train.astype('float32') / 255\n",
        "x_test = x_test.astype('float32') / 255"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PyQYc2oqkVwF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# reparameterization trick\n",
        "# instead of sampling from Q(z|X), sample eps = N(0,I)\n",
        "# z = z_mean + sqrt(var)*eps\n",
        "def sampling(args):\n",
        "    z_mean, z_log_var = args\n",
        "    batch = K.shape(z_mean)[0]\n",
        "    dim = K.int_shape(z_mean)[1]\n",
        "    # by default, random_normal has mean=0 and std=1.0\n",
        "    epsilon = K.random_normal(shape=(batch, dim))\n",
        "    return z_mean + K.exp(0.5 * z_log_var) * epsilon\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QvDntOgmmITf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "input_shape = (original_dim, )\n",
        "intermediate_dim = 512\n",
        "batch_size = 128\n",
        "latent_dim = 2\n",
        "epochs = 50"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5xUrzut3gdM3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "outputId": "de7c918a-115f-41e1-b2bd-ff14325065c5"
      },
      "cell_type": "code",
      "source": [
        "inputs = Input(shape=input_shape, name='encoder_input')\n",
        "x = Dense(intermediate_dim, activation='relu')(inputs)\n",
        "z_mean = Dense(latent_dim, name='z_mean')(x)\n",
        "z_log_var = Dense(latent_dim, name='z_log_var')(x)\n",
        "\n",
        "# use reparameterization trick to push the sampling out as input\n",
        "# note that \"output_shape\" isn't necessary with the TensorFlow backend\n",
        "z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])\n",
        "\n",
        "# instantiate encoder model\n",
        "encoder = Model(inputs, [z_mean, z_log_var, z], name='encoder')\n",
        "encoder.summary()\n",
        "\n",
        "# build decoder model\n",
        "latent_inputs = Input(shape=(latent_dim,), name='z_sampling')\n",
        "x = Dense(intermediate_dim, activation='relu')(latent_inputs)\n",
        "outputs = Dense(original_dim, activation='sigmoid')(x)\n",
        "\n",
        "# instantiate decoder model\n",
        "decoder = Model(latent_inputs, outputs, name='decoder')\n",
        "decoder.summary()\n",
        "\n",
        "# instantiate VAE model\n",
        "outputs = decoder(encoder(inputs)[2])\n",
        "vae = Model(inputs, outputs, name='vae_mlp')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "encoder_input (InputLayer)      (None, 784)          0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 512)          401920      encoder_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "z_mean (Dense)                  (None, 2)            1026        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "z_log_var (Dense)               (None, 2)            1026        dense_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "z (Lambda)                      (None, 2)            0           z_mean[0][0]                     \n",
            "                                                                 z_log_var[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 403,972\n",
            "Trainable params: 403,972\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "z_sampling (InputLayer)      (None, 2)                 0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 512)               1536      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 784)               402192    \n",
            "=================================================================\n",
            "Total params: 403,728\n",
            "Trainable params: 403,728\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uFlFzhOkgdVL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "models = (encoder, decoder)\n",
        "data = (x_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uRH1OG9imebr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "e2bf68a6-ab23-4818-f292-18e8d6585557"
      },
      "cell_type": "code",
      "source": [
        "from keras.losses import mse, binary_crossentropy\n",
        "\n",
        "reconstruction_loss = binary_crossentropy(inputs, outputs)\n",
        "reconstruction_loss *= original_dim\n",
        "kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)\n",
        "kl_loss = K.sum(kl_loss, axis=-1)\n",
        "kl_loss *= -0.5\n",
        "vae_loss = K.mean(reconstruction_loss + kl_loss)\n",
        "vae.add_loss(vae_loss)\n",
        "\n",
        "vae.compile(optimizer='rmsprop')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: UserWarning: Output \"decoder\" missing from loss dictionary. We assume this was done on purpose, and we will not be expecting any data to be passed to \"decoder\" during training.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "27pVeQS6nAwp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1771
        },
        "outputId": "930aec00-2601-4fff-a5a4-c8142b19f2cb"
      },
      "cell_type": "code",
      "source": [
        "history = vae.fit(x_train, epochs=epochs, batch_size=batch_size, validation_data=(x_test, None))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/50\n",
            "60000/60000 [==============================] - 4s 69us/step - loss: 187.8497 - val_loss: 170.9464\n",
            "Epoch 2/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 168.4644 - val_loss: 166.3602\n",
            "Epoch 3/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 164.8357 - val_loss: 164.1692\n",
            "Epoch 4/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 162.3976 - val_loss: 161.7899\n",
            "Epoch 5/50\n",
            "27264/60000 [============>.................] - ETA: 1s - loss: 160.9295"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 63us/step - loss: 160.4477 - val_loss: 160.1159\n",
            "Epoch 6/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 159.0365 - val_loss: 158.9030\n",
            "Epoch 7/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 157.9886 - val_loss: 157.9983\n",
            "Epoch 8/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 157.1356 - val_loss: 157.3934\n",
            "Epoch 9/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 156.4746 - val_loss: 156.6287\n",
            "Epoch 10/50\n",
            " 1920/60000 [..............................] - ETA: 3s - loss: 155.4853"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 63us/step - loss: 155.8801 - val_loss: 156.0617\n",
            "Epoch 11/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 155.3866 - val_loss: 155.7948\n",
            "Epoch 12/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 154.9615 - val_loss: 155.5228\n",
            "Epoch 13/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 154.6028 - val_loss: 155.7931\n",
            "Epoch 14/50\n",
            "58368/60000 [============================>.] - ETA: 0s - loss: 154.2234"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 154.2496 - val_loss: 154.9207\n",
            "Epoch 15/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 153.9161 - val_loss: 154.6858\n",
            "Epoch 16/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 153.6263 - val_loss: 154.8412\n",
            "Epoch 17/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 153.3878 - val_loss: 154.8123\n",
            "Epoch 18/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 153.1122 - val_loss: 154.5481\n",
            "Epoch 19/50\n",
            " 3712/60000 [>.............................] - ETA: 3s - loss: 152.8213"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 152.9062 - val_loss: 154.2370\n",
            "Epoch 20/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 152.6820 - val_loss: 153.6737\n",
            "Epoch 21/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 152.4765 - val_loss: 153.4333\n",
            "Epoch 22/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 152.2654 - val_loss: 153.5419\n",
            "Epoch 23/50\n",
            "59520/60000 [============================>.] - ETA: 0s - loss: 152.0652"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 152.0735 - val_loss: 153.6573\n",
            "Epoch 24/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 151.8932 - val_loss: 153.3757\n",
            "Epoch 25/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 151.7147 - val_loss: 153.0858\n",
            "Epoch 26/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 151.5438 - val_loss: 153.0902\n",
            "Epoch 27/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 151.3995 - val_loss: 153.3639\n",
            "Epoch 28/50\n",
            " 2816/60000 [>.............................] - ETA: 3s - loss: 150.3032"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 63us/step - loss: 151.2215 - val_loss: 153.1643\n",
            "Epoch 29/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 151.0692 - val_loss: 152.9782\n",
            "Epoch 30/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 150.9147 - val_loss: 152.8225\n",
            "Epoch 31/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.7653 - val_loss: 152.7728\n",
            "Epoch 32/50\n",
            "59392/60000 [============================>.] - ETA: 0s - loss: 150.6037"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.5858 - val_loss: 152.9356\n",
            "Epoch 33/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.4819 - val_loss: 152.7417\n",
            "Epoch 34/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.3202 - val_loss: 152.7474\n",
            "Epoch 35/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.2194 - val_loss: 152.6655\n",
            "Epoch 36/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 150.0672 - val_loss: 153.2423\n",
            "Epoch 37/50\n",
            " 4480/60000 [=>............................] - ETA: 3s - loss: 148.6243"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.8979 - val_loss: 152.3737\n",
            "Epoch 38/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.8169 - val_loss: 153.3604\n",
            "Epoch 39/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 149.7056 - val_loss: 151.8972\n",
            "Epoch 40/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.5619 - val_loss: 151.9712\n",
            "Epoch 41/50\n",
            "58752/60000 [============================>.] - ETA: 0s - loss: 149.4464"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.4385 - val_loss: 151.9070\n",
            "Epoch 42/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.3384 - val_loss: 151.7646\n",
            "Epoch 43/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.2212 - val_loss: 151.9907\n",
            "Epoch 44/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 149.0637 - val_loss: 151.7400\n",
            "Epoch 45/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 148.9959 - val_loss: 151.4312\n",
            "Epoch 46/50\n",
            " 4608/60000 [=>............................] - ETA: 3s - loss: 148.6496"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 63us/step - loss: 148.8776 - val_loss: 151.6564\n",
            "Epoch 47/50\n",
            "60000/60000 [==============================] - 4s 64us/step - loss: 148.7657 - val_loss: 151.3038\n",
            "Epoch 48/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 148.6621 - val_loss: 151.4221\n",
            "Epoch 49/50\n",
            "60000/60000 [==============================] - 4s 63us/step - loss: 148.5723 - val_loss: 151.2866\n",
            "Epoch 50/50\n",
            "57472/60000 [===========================>..] - ETA: 0s - loss: 148.3961"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "60000/60000 [==============================] - 4s 63us/step - loss: 148.4573 - val_loss: 151.6321\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PlTxJjCagdSh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "c4c97add-c251-4740-c061-d5a37b6aaf08"
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFKCAYAAADMuCxnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\natF04gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f3945b73eb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "LJ2piDz7gdQi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "outputId": "254ae23e-2dbf-4c4b-bbdc-1dd8a1d15af4"
      },
      "cell_type": "code",
      "source": [
        "# display a 2D plot of the digit classes in the latent space\n",
        "z_mean, _, _ = encoder.predict(x_test, batch_size=batch_size)\n",
        "\n",
        "plt.figure(figsize=(12, 10))\n",
        "plt.scatter(z_mean[:, 0], z_mean[:, 1], c=y_test)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAJDCAYAAAB0c84HAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f393a5028d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "A6aRwBKZsLG2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "outputId": "ea85dde0-54b4-4fe2-858c-b48565ce39a8"
      },
      "cell_type": "code",
      "source": [
        "# display a 30x30 2D manifold of digits\n",
        "n = 30\n",
        "digit_size = 28\n",
        "figure = np.zeros((digit_size * n, digit_size * n))\n",
        "# linearly spaced coordinates corresponding to the 2D plot\n",
        "# of digit classes in the latent space\n",
        "grid_x = np.linspace(-4, 4, n)\n",
        "grid_y = np.linspace(-4, 4, n)[::-1]\n",
        "\n",
        "for i, yi in enumerate(grid_y):\n",
        "    for j, xi in enumerate(grid_x):\n",
        "        z_sample = np.array([[xi, yi]])\n",
        "        x_decoded = decoder.predict(z_sample)\n",
        "        digit = x_decoded[0].reshape(digit_size, digit_size)\n",
        "        figure[i * digit_size: (i + 1) * digit_size,\n",
        "               j * digit_size: (j + 1) * digit_size] = digit\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "start_range = digit_size // 2\n",
        "end_range = n * digit_size + start_range + 1\n",
        "pixel_range = np.arange(start_range, end_range, digit_size)\n",
        "sample_range_x = np.round(grid_x, 1)\n",
        "sample_range_y = np.round(grid_y, 1)\n",
        "\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.imshow(figure)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAIuCAYAAACYSoPfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\