Chapter06/ch6_nb1_discover_autoencoders.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "<p style=\"border: 1px solid #e7692c; border-left: 15px solid #e7692c; padding: 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c\">Tip.</strong> <a style=\"color: #000000;\" href=\"https://nbviewer.jupyter.org/github/PacktPublishing/Hands-On-Computer-Vision-with-Tensorflow/blob/master/ch6/ch6_nb1_discover_autoencoders.ipynb\" title=\"View with Jupyter Online\">Click here to view this notebook on <code>nbviewer.jupyter.org</code></a>. \n",
    "    <br/>These notebooks are better read there, as Github default viewer ignores some of the formatting and interactive content.\n",
    "    </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"font-size: 1em; padding: 0; margin: 0;\">\n",
    "    <tr style=\"vertical-align: top; padding: 0; margin: 0;\">\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; padding-right: 15px;\">\n",
    "    <p style=\"background: #363636; color:#ffffff; text-align:justify; padding: 10px 25px;\">\n",
    "        <strong style=\"font-size: 1.0em;\"><span style=\"font-size: 1.2em;\"><span style=\"color: #e7692c;\">Hands-on</span> Computer Vision with TensorFlow 2</span><br/>by <em>Eliot Andres</em> & <em>Benjamin Planche</em> (Packt Pub.)</strong><br/><br/>\n",
    "        <strong>> Chapter 6: Enhancing and Segmenting Images </strong><br/>\n",
    "    </p>\n",
    "\n",
    "<h1 style=\"width: 100%; text-align: left; padding: 0px 25px;\"><small style=\"color: #e7692c;\">\n",
    "    Notebook 1:</small><br/>Discovering Auto-Encoders</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    In Chapter 6 of the book, we introduced <em><strong>encoders-decoders</em></strong> and their variant the <em><strong>auto-encoders</em></strong>. We detailed how auto-encoders' goal is to properly encode and then decode images without impacting their quality, despite their bottleneck (their latent space of lower dimensionality).\n",
    "<br/><br/>\n",
    "In this first notebook for Chapter 6, we will present how to build a simple fully-connected encoder-decoder, and how to apply it to the auto-encoding of <em>MNIST</em> images. We will finally demonstrate how this network can be used after training to embed the dataset and highlight its structure.\n",
    "</p>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #e7692c; padding: 0 10px; text-align:justify;\">\n",
    "    <strong style=\"color: #e7692c;\">Tip.</strong> The notebooks shared on this git repository illustrate some of notions from the book \"<em><strong>Hands-on Computer Vision with TensorFlow 2</strong></em>\" written by Eliot Andres and Benjamin Planche and published by Packt. If you enjoyed the insights shared here, <strong>please consider acquiring the book!</strong>\n",
    "<br/><br/>\n",
    "The book provides further guidance for those eager to learn about computer vision and to harness the power of TensorFlow 2 and Keras to build performant recognition systems for object detection, segmentation, video processing, smartphone applications, and more.</p>\n",
    "        </td>\n",
    "        <td style=\"vertical-align: top; padding: 0; margin: 0; width: 250px;\">\n",
    "    <a href=\"https://www.packtpub.com\" title=\"Buy on Packt!\">\n",
    "        <img src=\"../banner_images/book_cover.png\" width=250>\n",
    "    </a>\n",
    "    <p style=\"background: #e7692c; color:#ffffff; padding: 10px; text-align:justify;\"><strong>Leverage deep learning to create powerful image processing apps with TensorFlow 2 and Keras. <br/></strong>Get the book for more insights!</p>\n",
    "    <ul style=\"height: 32px; white-space: nowrap; text-align: center; margin: 0px; padding: 0px; padding-top: 10px;\">\n",
    "    <li style=\"display: inline-block; height: 100%; vertical-align: middle; float: left; margin: 5px; padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com\" title=\"Get the book on Amazon!\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_amazon.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    <li style=\"display: inline-block; height: 100%; vertical-align: middle; float: left; margin: 5px; padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com\" title=\"Get your Packt book!\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_packt.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    <li style=\"display: inline-block; height: 100%; vertical-align: middle; float: left; margin: 5px; padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com\" title=\"Get the book on O'Reilly Safari!\">\n",
    "        <img style=\"vertical-align: middle; max-width: 72px; max-height: 32px;\" src=\"../banner_images/logo_oreilly.png\" width=\"75px\">\n",
    "        </a>\n",
    "    </li>\n",
    "    </ul>\n",
    "        </td>\n",
    "        </tr>\n",
    "        </table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "# Some hyper-parameters:\n",
    "batch_size  = 128           # Images per batch (reduce/increase according to the machine's capability)\n",
    "num_epochs  = 60            # Max number of training epochs\n",
    "random_seed = 42            # Seed for some random operations, for reproducibility"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, we should be quite familiar with the [MNIST](http://yann.lecun.com/exdb/mnist) dataset[$^1$](#ref) (c.f. Chapters 1, 2, 3, and their respective notebooks). We prepare the dataset as done previously:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install tensorflow-datasets # Uncomment to install the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfds.core.DatasetInfo(\n",
      "    name='mnist',\n",
      "    version=1.0.0,\n",
      "    description='The MNIST database of handwritten digits.',\n",
      "    urls=['http://yann.lecun.com/exdb/mnist/'],\n",
      "    features=FeaturesDict({\n",
      "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
      "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10)\n",
      "    },\n",
      "    total_num_examples=70000,\n",
      "    splits={\n",
      "        'test': <tfds.core.SplitInfo num_examples=10000>,\n",
      "        'train': <tfds.core.SplitInfo num_examples=60000>\n",
      "    },\n",
      "    supervised_keys=('image', 'label'),\n",
      "    citation='\"\"\"\n",
      "        @article{lecun2010mnist,\n",
      "          title={MNIST handwritten digit database},\n",
      "          author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n",
      "          journal={ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist},\n",
      "          volume={2},\n",
      "          year={2010}\n",
      "        }\n",
      "        \n",
      "    \"\"\"',\n",
      ")\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "mnist_builder = tfds.builder(\"mnist\")\n",
    "mnist_builder.download_and_prepare()\n",
    "\n",
    "mnist_info = mnist_builder.info\n",
    "print(mnist_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "def _prepare_data_fn(features, target='label', flatten=True,\n",
    "                     return_batch_as_tuple=True, seed=None):\n",
    "    \"\"\"\n",
    "    Resize image to expected dimensions, and opt. apply some random transformations.\n",
    "    :param features:              Data\n",
    "    :param target                 Target/ground-truth data to be returned along the images\n",
    "                                  ('label' for categorical labels, 'image' for images, or None)\n",
    "    :param flatten:               Flag to flatten the images, from (28, 28, 1) to (784,)\n",
    "    :param return_batch_as_tuple: Flag to return the batch data as tuple instead of dict\n",
    "    :param seed:                  Seed for random operations\n",
    "    :return:                      Processed data\n",
    "    \"\"\"\n",
    "    \n",
    "    # Tensorflow-Dataset returns batches as feature dictionaries, expected by Estimators.\n",
    "    # To train Keras models, it is more straightforward to return the batch content as tuples.\n",
    "    \n",
    "    image = features['image']\n",
    "    # Convert the images to float type, also scaling their values from [0, 255] to [0., 1.]:\n",
    "    image = tf.image.convert_image_dtype(image, tf.float32)\n",
    "    \n",
    "    if flatten:\n",
    "        is_batched = len(image.shape) > 3\n",
    "        flattened_shape = (-1, 784) if is_batched else (784,)\n",
    "        image = tf.reshape(image, flattened_shape)\n",
    "        \n",
    "    if target is None:\n",
    "        return image if return_batch_as_tuple else {'image': image}\n",
    "    else:\n",
    "        features['image'] = image\n",
    "        return (image, features[target]) if return_batch_as_tuple else features\n",
    "\n",
    "def get_mnist_dataset(phase='train', target='label', batch_size=32, num_epochs=None, \n",
    "                      shuffle=True, flatten=True, return_batch_as_tuple=True, seed=None):\n",
    "    \"\"\"\n",
    "    Instantiate a CIFAR-100 dataset.\n",
    "    :param phase:                 Phase ('train' or 'val')\n",
    "    :param target                 Target/ground-truth data to be returned along the images\n",
    "                                  ('label' for categorical labels, 'image' for images, or None)\n",
    "    :param batch_size:            Batch size\n",
    "    :param num_epochs:            Number of epochs (to repeat the iteration - infinite if None)\n",
    "    :param shuffle:               Flag to shuffle the dataset (if True)\n",
    "    :param flatten:               Flag to flatten the images, from (28, 28, 1) to (784,)\n",
    "    :param return_batch_as_tuple: Flag to return the batch data as tuple instead of dict\n",
    "    :param seed:                  Seed for random operations\n",
    "    :return:                      Iterable Dataset\n",
    "    \"\"\"\n",
    "\n",
    "    assert(phase == 'train' or phase == 'test')\n",
    "    \n",
    "    prepare_data_fn = functools.partial(_prepare_data_fn, return_batch_as_tuple=return_batch_as_tuple,\n",
    "                                        target=target, flatten=flatten, seed=seed)\n",
    "\n",
    "    mnist_dataset = mnist_builder.as_dataset(split=tfds.Split.TRAIN if phase =='train' else tfds.Split.TEST)\n",
    "    mnist_dataset = mnist_dataset.repeat(num_epochs)\n",
    "    if shuffle:\n",
    "        mnist_dataset = mnist_dataset.shuffle(10000, seed=seed)\n",
    "    mnist_dataset = mnist_dataset.batch(batch_size)\n",
    "    mnist_dataset = mnist_dataset.map(prepare_data_fn, num_parallel_calls=4)\n",
    "    mnist_dataset = mnist_dataset.prefetch(1)\n",
    "    \n",
    "    return mnist_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of classes:\n",
    "num_classes = mnist_info.features['label'].num_classes\n",
    "\n",
    "# Number of images:\n",
    "num_train_imgs = mnist_info.splits['train'].num_examples\n",
    "num_val_imgs = mnist_info.splits['test'].num_examples\n",
    "\n",
    "train_steps_per_epoch = math.ceil(num_train_imgs / batch_size)\n",
    "val_steps_per_epoch   = math.ceil(num_val_imgs / batch_size)\n",
    "\n",
    "train_mnist_dataset = get_mnist_dataset(\n",
    "    phase='train', target='image', batch_size=batch_size, num_epochs=num_epochs, \n",
    "    shuffle=True, flatten=True, seed=random_seed)\n",
    "\n",
    "val_mnist_dataset = get_mnist_dataset(\n",
    "    phase='test', target='image', batch_size=batch_size, num_epochs=1, \n",
    "    shuffle=False, flatten=True, seed=random_seed)\n",
    "\n",
    "input_shape = mnist_info.features['image'].shape\n",
    "flattened_input_shape = [np.prod(input_shape)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Simple Auto-Encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the chapter, an auto-encoder is composed of two modules:\n",
    "\n",
    "- An **_encoder_**, which converts the input images into **_codes_**, tensors of lower dimensionality;\n",
    "- A **_decoder_**, which receives the encoder's codes and try to recover the original images from them.\n",
    "\n",
    "To demonstrate how auto-encoders can compress then recover data, we will define the _codes_ as 32-dimensional vectors (while the MNIST images are 28 * 28 = 784-dimensional):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us know create a simple fully-connected auto-encoder. We will use, e.g., 3 dense layers for the encoder, slowly decreasing the dimensionality of the input to `code_size`. We will then add some layers for the decoder to upsample back to the original dimensions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "enc_dense1 (Dense)           (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "enc_dense2 (Dense)           (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "enc_dense3 (Dense)           (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dec_dense1 (Dense)           (None, 64)                2112      \n",
      "_________________________________________________________________\n",
      "dec_dense2 (Dense)           (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dec_dense3 (Dense)           (None, 784)               101136    \n",
      "=================================================================\n",
      "Total params: 222,384\n",
      "Trainable params: 222,384\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "\n",
    "inputs = Input(shape=flattened_input_shape, name='input')\n",
    "\n",
    "# Encoding layers:\n",
    "enc_1 = Dense(128, activation='relu', name='enc_dense1')(inputs)\n",
    "enc_2 = Dense(64, activation='relu', name='enc_dense2')(enc_1)\n",
    "code_layer_name = 'enc_dense3'\n",
    "code = Dense(code_size, activation='relu', name=code_layer_name)(enc_2)\n",
    "\n",
    "# Decoding layers:\n",
    "dec_1 = Dense(64, activation='relu', name='dec_dense1')(code)\n",
    "dec_2 = Dense(128, activation='relu', name='dec_dense2')(dec_1)\n",
    "decoded = Dense(flattened_input_shape[0], activation='sigmoid', name='dec_dense3')(dec_2)\n",
    "# note: we use a sigmoid for the last activation, as we want the output values\n",
    "# to be between 0 and 1, like the input ones.\n",
    "\n",
    "# Auto-encoder model:\n",
    "autoencoder = Model(inputs, decoded)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the chapter, we mentioned that while trained together as an auto-encoder, the encoder and the decoder are usually used separately afterwards. Therefore, it is often convenient to define a separate model for each, wrapping up their respective layers.\n",
    "\n",
    "For the encoder, it is simply a matter of wrapping into a model the layers from input to code:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 784)]             0         \n",
      "_________________________________________________________________\n",
      "enc_dense1 (Dense)           (None, 128)               100480    \n",
      "_________________________________________________________________\n",
      "enc_dense2 (Dense)           (None, 64)                8256      \n",
      "_________________________________________________________________\n",
      "enc_dense3 (Dense)           (None, 32)                2080      \n",
      "=================================================================\n",
      "Total params: 110,816\n",
      "Trainable params: 110,816\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder = Model(inputs, code)\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `encoder` model takes images as inputs (sharing the same `Input` placeholder as the complete `autoencoder` model) and returns their codes, e.g., for storing.\n",
    "\n",
    "For the decoder, things are a bit more complex. We want to define a model composed of the auto-encoder's decoding layers, which would take for inputs codes provided by us and return the corresponding images.\n",
    "The problem is that in our `autoencoder` model, the decoding layers are linked to the encoding ones. We want them instead to be connected to a new `Input`, representing our user-provided codes. For that, we need to fetch the decoding layers and build a new graph with them, starting from this new input:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_code (InputLayer)      [(None, 32)]              0         \n",
      "_________________________________________________________________\n",
      "dec_dense1 (Dense)           (None, 64)                2112      \n",
      "_________________________________________________________________\n",
      "dec_dense2 (Dense)           (None, 128)               8320      \n",
      "_________________________________________________________________\n",
      "dec_dense3 (Dense)           (None, 784)               101136    \n",
      "=================================================================\n",
      "Total params: 111,568\n",
      "Trainable params: 111,568\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_code = Input(shape=(code_size,), name='input_code')\n",
    "\n",
    "num_decoder_layers = 3 # c.f. code above\n",
    "dec_i = input_code\n",
    "for i in range(num_decoder_layers, 0, -1): # Iterate from 2 to 0 (excluded):\n",
    "    # We get the decoder layers from the auto-encoder models, one by one:\n",
    "    dec_layer = autoencoder.layers[-i]\n",
    "    # Then we apply each layer to the new data, to construct a new graph \n",
    "    # with the same parameters:\n",
    "    dec_i = dec_layer(dec_i)\n",
    "\n",
    "decoder = Model(input_code, dec_i)\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before going through the training, we will set up some useful tools to monitor this process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSNR as custom metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will pick a metric to estimate how well our auto-encoder is recovering the original images. The _**Peak Signal-to-Noise Ration (PSNR)**_ is commonly used, as it measures the quality of a corrupted or recovered signal/image compared to its original version. The higher the value, the closer to the original image... (the value is in decibels, i.e. following a logarithmic scale). In TensorFlow, this metric can be implemented as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_n(x, n=10):\n",
    "    \"\"\"\n",
    "    Compute log_n(x), i.e. the log base `n` value of `x`.\n",
    "    :param x:   Input tensor\n",
    "    :param n:   Value of the log base\n",
    "    :return:    Log result\n",
    "    \"\"\"\n",
    "    log_e = tf.math.log(x)\n",
    "    div_log_n = tf.math.log(tf.constant(n, dtype=log_e.dtype))\n",
    "    return log_e / div_log_n\n",
    "\n",
    "\n",
    "def psnr(img_a, img_b, max_img_value=255):\n",
    "    \"\"\"\n",
    "    Compute the PSNR (Peak Signal-to-Noise Ratio) between two images.\n",
    "    :param img_a:           Image A\n",
    "    :param img_b:           Image B\n",
    "    :param max_img_value:   Maximum possible pixel value of the images\n",
    "    :return:                PSNR value\n",
    "    \"\"\"\n",
    "    mse = tf.reduce_mean((img_a - img_b) ** 2)\n",
    "    return 20 * log_n(max_img_value, 10) - 10 * log_n(mse, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code is mostly for teaching purpose, as TensorFlow actually provides an implementation of this metric: [`tf.image.psnr(a, b, max_val)`](https://www.tensorflow.org/api_docs/python/tf/image/psnr). \n",
    "\n",
    "Still, let us use our implementation for now. We wrap it to fit Keras interface for metrics (c.f. previous notebooks): \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "psnr_metrics = functools.partial(psnr, max_img_value=1.)\n",
    "psnr_metrics.__name__ = 'psnr'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom callback for monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be fancy and play a bit more with Keras API, we will also define a custom callback. We have been using various pre-defined callbacks in the previous chapters, and even implemented a simple logging callback in Chapter 4. This time, we will implement our own, inheriting from the abstract `Callback` class. This class defines an interface composed of several methods which will be called by Keras along the training (before each epoch, before each batch iteration, after each batch iteration, etc.).\n",
    "\n",
    "For demonstration, we will implement a callback which plots the training losses/metrics and updates the figure after each epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "class DynamicPlotCallback(tf.keras.callbacks.Callback):\n",
    "        \n",
    "    def on_train_begin(self, logs={}):\n",
    "        # This method will be called when the training start.\n",
    "        # Therefore, we use it to initialize some elements for our Callback:\n",
    "        self.logs = dict()\n",
    "        self.fig, self.ax = None, None\n",
    " \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        # This method will be called after each epoch.\n",
    "        # Keras will call this function, providing the current epoch number,\n",
    "        # and the values of the various losses/metrics for this epoch (`logs` dict).\n",
    "        \n",
    "        # We add the new log values to the list...\n",
    "        for key, val in logs.items():\n",
    "            if key not in self.logs:\n",
    "                self.logs[key] = []\n",
    "            self.logs[key].append(val)\n",
    "        # ... then we plot everything:\n",
    "        self._plot_logs()\n",
    " \n",
    "    def on_train_end(self, logs={}):\n",
    "        pass    # our callback does nothing special at the end of the training\n",
    " \n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        pass   # ... nor at the beginning of a new epoch\n",
    " \n",
    "    def on_batch_begin(self, batch, logs={}):\n",
    "        pass   # ... nor at the beginning of a new batch\n",
    " \n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        pass   # ... nor after.\n",
    "    \n",
    "    def _plot_logs(self):\n",
    "        # Method to clear the figures and draw them over with new values:\n",
    "        if self.fig is None: # First call - we initialize the figure:\n",
    "            num_metrics = len(self.logs) \n",
    "            self.fig, self.ax = plt.subplots(math.ceil(num_metrics / 2), 2, figsize=(10, 8))\n",
    "            self.fig.show()\n",
    "            self.fig.canvas.draw()\n",
    "        \n",
    "        # Plotting:\n",
    "        i = 0\n",
    "        for key, val in self.logs.items():\n",
    "            id_vert, id_hori = i // 2, i % 2\n",
    "            self.ax[id_vert, id_hori].clear()\n",
    "            self.ax[id_vert, id_hori].set_title(key)\n",
    "            self.ax[id_vert, id_hori].plot(val)\n",
    "            i += 1\n",
    "        \n",
    "        #self.fig.tight_layout()\n",
    "        self.fig.subplots_adjust(right=0.75, bottom=0.25)\n",
    "        self.fig.canvas.draw()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have everything to monitor the training. As explained in the chapter, our auto-encoder will receive the training **images both as inputs and targets** (`x_train`), and will try to minimize a **cross-entropy loss**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"999.4166358888987\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0328 11:54:30.521862 140301929522944 training_generator.py:228] Your dataset ran out of data; interrupting training. Make sure that your dataset can generate at least `steps_per_epoch * epochs` batches (in this case, 28140 batches). You may need to use the repeat() function when building your dataset.\n"
     ]
    }
   ],
   "source": [
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy',\n",
    "                    metrics=[psnr_metrics])\n",
    "\n",
    "history = autoencoder.fit(\n",
    "    train_mnist_dataset, epochs=num_epochs, steps_per_epoch=train_steps_per_epoch,\n",
    "    validation_data=val_mnist_dataset, validation_steps=val_steps_per_epoch,\n",
    "    verbose=0, callbacks=[DynamicPlotCallback()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems our network converged. To have a better look, we build a method to show the input/target images and the auto-encoder's results:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "\n",
    "def show_pairs(samples_a, samples_b, \n",
    "               plot_fn_a=\"imshow\", plot_fn_b=\"imshow\"):\n",
    "    \"\"\"\n",
    "    Plot pairs of data\n",
    "    :param samples_a:   List of samples A\n",
    "    :param samples_b:   List of samples B\n",
    "    :param plot_fn_a:   Name of Matplotlib function to plot A (default: \"imshow\")\n",
    "    :param plot_fn_b:   Name of Matplotlib function to plot B (default: \"imshow\")\n",
    "    :return:            /\n",
    "    \"\"\"\n",
    "    assert(len(samples_a) == len(samples_b))\n",
    "    num_images = len(samples_a)\n",
    "    \n",
    "    figure = plt.figure(figsize=(num_images, 2))\n",
    "    grid_spec = gridspec.GridSpec(1, num_images)\n",
    "    for i in range(num_images):\n",
    "        \n",
    "        grid_spec_i = gridspec.GridSpecFromSubplotSpec(\n",
    "            2, 1, subplot_spec=grid_spec[i], hspace=0)\n",
    "        \n",
    "        # Drawing image A:\n",
    "        ax_img = figure.add_subplot(grid_spec_i[0])\n",
    "        getattr(ax_img, plot_fn_a)(samples_a[i])\n",
    "        plt.gray()\n",
    "        ax_img.get_xaxis().set_visible(False)\n",
    "        ax_img.get_yaxis().set_visible(False)\n",
    "        \n",
    "        # Drawing image B:\n",
    "        ax_img = figure.add_subplot(grid_spec_i[1])\n",
    "        getattr(ax_img, plot_fn_b)(samples_b[i])\n",
    "        plt.gray()\n",
    "        ax_img.get_xaxis().set_visible(False)\n",
    "        ax_img.get_yaxis().set_visible(False)\n",
    "        \n",
    "    # plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mRXPq1KnO+hU0lLqqcKBUKBRyz10prK666ioGDhwI4Ir2BDGwVMyYMYPx48cDXr/0jPzKs9wHli1b5gK+/O8BcMEFF8S8vabMGoZhGIZhGElL3MvZiuLKuQ0ePBiAO++8031PtavlKD5jxoxAJcTeG9nZ2Zx00kkA7us555zjyi5efPHFgKfQKiVNEJk7d26x32/atKn7d7du3YBI2WIVTVC6DtV1vuyyy/Zaaz1oKLWcghL8Ne+l1paUrgQiquBdd90VwxZGH9XQDjJSYWXR+POf/1wkRZpKu/pRCdAmTZq4Z9uoUaMCr1mzZo2bp0Fk27ZtgBdo6S8DqzRXt9xyC7fccgsQsXgBrnx4EKwjRx55ZLFlLvv16wdAhw4dgIIKlp6JVNuPP/44sKVcC6v9laVwIDV4vqlBo3Bwt9RYffWzbt06N4eV3lEqZpD3Cq0/hc8kS5cudUHdKqoDOKVz9OjRQLCV2V9//dXNTQXd+9F5QAr09OnTncVZfZ46dWo8mgrE6TCbk5PjOqeNX5N8/Pjx7gO7++67gYgZWxGNmqh+87sCbZSrLwjoIeoAIFm9S5cuHHrooXv9fW0+WVlZBQZ/svHqq6+6rzrM3nTTTUDBfIkyy8hhXjkwE8kRRxwBeOYtHcI7duzoDjzFXaB0CPr222/d/y9evLjAawr/f1BQZbMqVaq4aFtdSPyHc7mYKLtIEEhPT3cBCSVl2iiJsuQ/rFGjBkcffTRQ/AacaJS/Un3v2LGjcylQblN/lpEHH3wQwEXSd+3a1blelJRVJtYsX77ciRhy/fjnP//pzOlyUVJVvXA47HLPKvC3QYMGJT7DIGUYqQy6qGi99PPKK6/EuzllomXLlgX+v6yXp7Lmww4CMrsXDnhavXp1sft44ddpj1GO66ChdpUm1Eho7N69u5uHEiuLEy1jhbkZGIZhGIZhGElLXJTZJUuWOCfhE044AfBk606dOjlzgj+w6OOPPwa82tz+FCdlrRoWT2TKVHUdP1LsRPPmzZ1iogow6lPNmjW58MILY9jS+KEAgM8//xzwUqz07NnT1Y5/9tlngYhalOjb6datWwFcnl99nTJlCqtXrwa8yjsyE4E3Rv1pS4LOAQccAHiK10EHHcQTTzwBeGlj/OY91VUPUvW9q6++uogi+/vvv5eYjqpdu3YuH2RZaNGihQseUxoa5W8NEgoomjhxolNQ5NLSq1cvGjZsWOD1Mo2uWLHCWchKciGKJ1L9165d6wLA5NbjH4uy7ulraem3gtCvaCC3i/3337/A9zdu3Bgoa4kfrTHlRap80KlXrx777bcfUNTNoKTUW4Vfp3NQacpnUFFlN1nNQ6EQvXr1AuDf//533NtjyqxhGIZhGIaRtMTNs1rK46JFiyJ/+P9u2m+99Vaxr7/++uuL/f7kyZMDVSELIkEMhVNqSY3t06eP8xV64YUXgIgy+/e//x3w/E3kV6qqPkGjVq1aTgkp7+cv3yH5802aNMl9PioWcfzxxyc8xYyU4dICtXbt2lXke/L/TSYUJKXUMuApzMWh6m3PP/884Cm6eyOWVV9mzZpV5HubNm3i6aefLvb1O3fuLDaQQciCIP/tM844w1mS3nvvPQDGjh3rfPuDjNajxx9/3M07FVLwo+ph8k0NAjNnzmTmzJkAvP7664CnSMrf109aWpqLOUgWVa+8KBajsArdr1+/QPpzA061FHsrYNGiRQsABgwYAMC8efOA2FeOqigNGzZ08TCF+6a2FybRRTyiidZZqcy//vorH374YcLaY8qsYRiGYRiGkbTETZn9/vvvAS/t1vDhwwGvhGFJKP3M7bffDsC4ceNi1cQK88QTTzil69133wW8qNP09HQXRXzKKacAkawNUsGUSLo0VSyRKBXMdddd556FPytBRSiuhHGyoHKSyY7UdSUoHzJkiCsrWhxKfVS4pvreiGXRgR9//NGVvVaBhMMOO6zMqrHQnO3Zsyfgqe9t27Z1KfNUJOPGG290teJHjBhRyR7Enj59+pSaYq1u3brxa0wFkDIrSir3LYuBnqHQvpPMNGvWzKWrkgqmbD9SsIOIYiZEaWtBeno61113HeD1cdq0abFrXBTwFw7wf68kmjVrllRpRYtDMQdDhgxxmRjUl/bt27vYkkQQt8Os0qkoSEYHmqFDh7o8pTJj+9FmFcRDrKhatapLayQTiQ7p06ZNc+nFlN8xGTZBoUCtdu3aOXOPnLyfeeaZcr2XDviHHnqo+3x+/PFHAH766aeotDfWFM5hCpGqU8mGUjLdd999QCQQT5uN8iMWZ5YWWVlZLvWONla/C8Zjjz0W/UYXIj8/nzfffBOg3HW/5TYxe/Zs595SOJXOnDlzXCCqXn/FFVe4wIfCF9J4o8uHP/eozPCat/7UXIVJS0sLZDBtNAnyYa+sXHPNNUUCvxYsWADgLlZBRFUtVSFRKf8GDBjg8q1Wr14diMwvpbNUqjG9JqisXr2aL7/8EvAC24ubb3IhHDhwoHMzUFq9ZAv80p5/7rnnOrc8rb2JFqnMzcAwDMMwDMNIWhJWWuPFF190X88++2zAC0Zo3Lgx999/PxBsFVO3yjp16jB79mzAq5k9duxYIBJEokIC1157bQJaWTn8ZpGjjjoK8FLoyNT84YcfMn36dMBL4D5jxgxXk1rvIQUzKyvL3WAVCJboW11lUHq1ZMZf6UqpnhSwWBx16tThoosuArz0akptFk8UBCblVCmMwLPqSCECr066VIW9mfukNis5+rfffuvGfZUqVYD4KLMKDL3mmmucu1JmZibgVRb0o/nlV4pUGGHDhg1AJChXAbnJjgJPClfIuuuuu4oE5yYLKsbitzrIUjBo0KCEtKkiyPVDFpBHHnnEWWhlDcrOzuazzz4DoH///gDk5eXFu6nlYuPGjS6dodzP/Cm3VL1Urkr+tebee++NZ1MrjdZSBdCGw2FXkCSRrgV+TJk1DMMwDMMwkpZQeRyRQ6FQcnot/x/hcLjUvBgV7d/8+fOdn6GQL9OMGTNcapxY32D21j8ofx+VYP3yyy93CoEUKTmAl1QSVP5BhX+2ceNG5z8slVd+l3sjVs+wrChgavjw4U7patWqFRA1dfmLcDh8QmkvSPV5CKnfx/L2T8Uq/P7me/OHhUiqMlmJpLiX19e9OIL2DNu0aQMUTYm0YMEC97Pykui1RvEXsiaAF6zYsWPHaPyJuK41KtAxaNAgVx5cKTqnTJnC5MmTAcjNzY3Wn4zbOJUFWaXQ/Xuifx+UJUl7qXxnK0Msx6msUFKXFdcUDoddYGlJaciiRVmeISTQzSCVePvttznkkEOASK1x8B7+p59+mrB2RQMd0IYNG+ac1WvWrAkUzFGqQ55yC7744oslDvK5c+eyePHimLU5liigJBQKubrTyewiYSQvukzNnz+/yM9UBW3ixImsXbs2ru1KBHJX0tfmzZsDXpaDZKS4YFNVpEtG5syZU+BrKvHkk08C3v539dVXFxFxRo0aVWoO86AxbNgw5w6ii7MuVjNmzIj5Iba8mJuBYRiGYRiGkbSYm4GPVO8fpH4fk71/mJsBkPp9LG//FESTnZ3NySefDHhuA0qvFk+C+gz79OkD4AJzBg0a5KotlpdErzWqROfP/6xKbUrNVUlsrSH1+1jR/t1+++1OmZ0xYwaAS58WT8rqZmDKrGEYhmEYhpG0mDLrI9X7B6nfx2TvH6aWAKnfx1TvH6R+H2PVP1VYVPrKjIwMFyDbpUsXIDqBQ9haA6R+H1O9f8KUWcMwDMMwDCNpsWwGhmEYhhEQlOpPaavAKw8dJUXWMFIOO8wahmEYRkBQkJdYuHChy8ttGEbxmJuBYRiGYRiGkbSUV5n9FfgpFg2JAw3L8JpU7x+kfh+TuX+Q+n20cRoh1fsHqd/HmPSvc+fO0X7LkrBxGiHV+5jq/QPKmc3AMAzDMAzDMIKEuRkYhmEYhmEYSYsdZg3DMAzDMIykxQ6zhmEYhmEYRtJih1nDMAzDMAwjabHDrGEYhmEYhpG0lCs1V6rX+E31/kHq9zHZ+wf8Gg6H65X2gmTvo43T1O8fpH4fk71/2FoDpH4fU71/IpDKbNWqVRPdBMNIFMmaD9AwjOTC1hojZQhUOduzzjrLfX3//fcBmDVrFgA7duxIVLMMw0hCQqHIhf6AAw4AoHnz5rRt2xaAv/zlLwDcddddLFq0CIA//vgDgD179sS7qYZhFCItzdPalA/f8uIbJRFIZdYwDMMwDMMwykIglNlq1aoB0KNHDwDOOOMMtm7dCsAHH3wAmDKbaKRyFYfdlo1EIxUnHA4XGY9SZq+//npOPfVUAKpXrw7Au+++y3fffQfAFVdcAcDixYvdexmGER/23XdfAEaMGAF454GMjAzuueceAP79738DsHPnzgS00AgypswahmEYhmEYSUvCldm0tDQGDRoEwLnnngvA8uXLeeeddwDYtm1bwtpWGaRk7rfffvTu3RuAdu3auZ+rX6NHjwY8NSg3NzeOrSyI30cpIyMyNE488UQALr74Ylq0aAHAEUccAcA+++wDRG7JUtI3bdoEwHvvvef8nufPnw94PommeBnRJj8/v8Sf1axZE4gotH4FV7930EEHAXD66acDOKV29+7dMWtvedFcq169ulOw6tatC3hWq82bN7t1RcrV/9pc07qr9WvPnj3/c59BMpKRkcGQIUMAuPDCCwv8bNmyZaxatSoBrYodmruXXHIJAL1793b744YNGwC46aabWLt2LVD6+hZENA+rVKkCRIL6tTbFqi+mzBqGYRiGYRhJS8KV2TPOOIOBAwcCkJ6eDsC8efP44osvgMQqlZWhdu3aADz99NOcdtppgKd85uTkOLXg6KOPBuDRRx8FYNKkSeTl5cW1rfrcpf5kZGS4aO/hw4cDkZukFCG9Tr9XvXp1DjzwwALv2bJlS6677joAfvzxRwDn9/Tmm29axLgRczTHvvnmGwAmTJjg1FeN5S1bttCmTRsArr32WsBTNSdMmBAYVU9rR61atejatSvgWbKaNGkCRFQQKZKbN28GYNy4cUyfPh2AX375BQh+toa0tDTXj8zMTMDbB3Jzc4s8k1Ao5Nbb2267DYhYkiBi+Ro3bhzwv6dSJwPaS4YMGUK/fv0Az4L3wAMPADBlyhRnfQiCQukfk+UZU5mZmfz1r38FIllUIGK5hcheqvfS/p+RkcENN9wABHvuSoWtVasWnTp1AuDss88GoHHjxgBkZ2cXOQcsXLgwqnMyVJ43i2byXQV9rVy50i1Eq1evBiKO3zK7R5N4JBfWgz3zzDMBePLJJ11f9TA//fRTDj74YMAz42uS9ujRgwULFlTob1c0QbTy+v7pT39y/3/ppZcCONeCmjVr8sMPPwCwbt06IGL+gchAbdCgAeC5IDRo0IA6deoUeP9du3YBMGbMGMaPHw+UP7CvMs9Qzybgm9oX4XD4hNJeUJlxqkNRVlYW4G0m++yzD9u3bwc8F5hYXarinchcm8/+++/v/q2xWLt2bbfB9OrVC/AuaT169GDOnDkV+pvRXmuys7OByKZ/3nnnAXDIIYcAXnurVKni/q2xnpeXx8aNGwFYsWIFAK+//joAjz32GL///nt5muGI5TOsXr06zZs3B7x9Yvny5UDkkF7YXalatWrOheuxxx4DoH79+gCsWbOGZs2aAd4zLyupnoyeGK81pSHz85gxY4CIuf2///0vAHfccQcAM2fOBConaFV2nOpSFQ6H3R5d3v1Dc/LSSy/lH//4BwA1atQo8F75+fluvdXczcnJ4fbbbwfgrbfeAjwXBH8bEjVOtXf0798fgOuuu865dEkQ0LMLhULufCeB7IMPPijT5SSpiyYYhmEYhmEYRllImJuBbmS1a9d20vngwYMBzyyYjOgmV69epErgO++84wKhdLv6/fffnRp99dVXA5G0QRAxi3Xp0gWIXzoyff5S7fLz83n11VcBmDZtmvvZt99+C+CCvYpTOnTjPuCAAzjuuOMAnFm0c+fOANxyyy0cdthhQMTJ3d+GWKCbcceOHQH46KOPYu6MnmhCoZDrtxTyoUOHumchtxC/kqdnILVu9uzZjBw5EvCUsWREz/i3335zSoGUjf/+97/ODK+iLTJoHWBmAAAazElEQVSNjR49uojlJFHoOTVo0MC1RfNP/5+RkeHcJzQP8/PznSlTKcpUOOLWW2/llFNOAWDp0qXx6EapqI/9+/d3bk4KwpPJ+bfffiuikG3fvp2vv/4a8KwKUrdq1qxZalrBeKJ2hEKhShcB0HulpaUVUPeSgVAoxMSJEwHPVWbp0qVuL1yyZAmQ2P5o/oi8vLwKPytZBkaNGuXmp1RonQ1WrVrlrBCyHjVp0sRZNbVuaY4k2t2gVatW3HvvvYBnvd2xYwfPPvssAFOnTgW8+duyZUtnjf7pp9gUnjNl1jAMwzAMw0ha4u4zK18RpZzIyspi3rx5AHTo0AGI3a0jHr4lujkp3U84HHY+a7qlhMNhd7PWbeWjjz4CIk7Uf/7znwHvhlpWKusfJB+YtLS0Amm61Hb59JTlxhwKhZxK3apVKwBeeOEFIKIQbdmyBcD1Vf64e6Miz7Bp06YAPPLIIwB88sknzi9ZquUvv/ziFKyjjjoK8Hycv//+e/e85He4ZcuWIspYlKiUH5va2aBBA5fyTqnhsrOzizzX4lB/8vPznSIwZcoUAK655pq9/v7eiLfPrL/PxT0rKSGzZ88GcIUVdu/e7RTsnJyccv3NaK818nE++eSTXRCbFHT5sP/xxx+0b98ewPmc1qlTxwWIaX772sioUaMAuPvuu4Gyj+VYPEO1eebMmS510ZdffgngfPh/+eWXIm0MhUJuX9G8lrq+c+dOatWqBSTGZzY9Pd2NIQUYNmrUyLVJY1Nq1a+//ur6J7X822+/dfP6nHPOASLjACI+lP/85z8B+Oyzz4ByrUcJ8Znt1q2bK36gwKbTTz/d/TuaMQ0VHaeF1fyKtEmFWebOnQtE9qH169cDEeskRFJYQmQcyC/eb0nR+FdAp84Xubm5bj+Ox7lGSrX20PPPP999JoorGDZsmEuhpjGo8X3YYYcVsIxCxAIfTZ/ZuLsZKMhCC+vvv//uNshES+fRQANMh/VCjtpF/i2HbpkdqlWr5iZBvNFi75/I/jaX11yn3z300EMB3ALuN7NVNAClLOgAcOyxxwKeU/p5553ngt0+/fRTIGIqkflV7VRA1KpVq9wioue1aNEiZxZatGgR4C1MiTSPqZ033HCDC2zyBzHocKogGl0qNP7A6//BBx/szGJ9+/YFvAPfiy++GNN+RJO9PQ99Jv/5z38AzwwfCoVcQOPChQtj2MK9I5ejuXPnug1O81WX5IyMDDcWFVXcp0+fIiZTsWvXLhcwlcgxq3XlqquuAiIXMa2jL730EuDNu+LaGQ6H3RgvvHYm2sWgadOmPP/88wA0bNgQiKxLalfhTd/fXv1sz5497uea3/r/3NxcZ8bWATdI+ZH9aC0ZNWqUW4uHDRsGRA61QQrMrWxbqlSpwp133gl4bgY7duxwWQwU3OZ38dOarLV4yZIl7ucaC+URlKKBLvqTJ08GPFfBnJwc5xb68ssvA8WPO32O9evXdy5NOsxG+3mbm4FhGIZhGIaRtMRNmdWN8/LLLwe8m8W8efPKbGJOJsp6c9JNS4pKzZo1nVtCoijuxuTP/Sj8SroUA33NyMhwLgS33nor4Jkqtm/fzuOPPw5EzGqxQrd/qWpS4I444giXnkjpQkKhkHO2V5oxmXbAU+uk8vbv39+puxrbMqNUNJ1TNJBafOyxx7pqbDLfzZkzhyeeeALwzOZSn/Pz810/FJx4//33c9lllwHec1VaqOnTpwdKSakM6oeCHqWoZGVlOXehRCuzYteuXU4B8QcUQcSNRCqQ6tofeeSRRVRAPfszzzzTuSgkElkCNLbS0tLcvFRw3t7SMxVWLMWOHTsSMk7VjiuvvNK5eUjlgqIKm1+hLRwclpaWViTlmkhPT3fvVRYXokQit6fDDjuMr776CoBZs2YlsklRR3tc3759XUCb9s333nvPqZjaO/3Be5rXpZ0d4m1BUY58uV7JTeKKK67g888/B0o+LwAcc8wxAIwcOdKl7Src92gR7NFvGIZhGIZhGKUQN2VWfpNSs3TTnjRpUrG+srrhyLdWt5Y9e/YkTQqSsqDbifxlNm3aFAi1REgJqFu3Lt27dwfg8MMPB7xn1Lhx4yIVWvbZZx/nIybFUze5SZMmudQdsXyWem8p/wroguILAkil/O2334r8TD57Sht31llnFfFFTJSvsx/16/nnn3fPRP5Z27ZtK9NtWLfvG2+80anrjRo1Ajwf27S0tLhXqos1sogo8KZFixZOWZCSHwQ1Wm2Q4qMCARMnTqRly5aA55+4Z88eVq5cCcB9990H4IJvZLlIJKFQyFVg0/wJh8OuzZp3e0Pj8ueffwY868L69esTMk6lpDZu3LhIsZb8/Hw3x+Q/qLUxFAo5n0nN31NPPdWNQ6U0VOBwXl6eW6+CqsxKkVPQ07777uuek/b3zMxMF+Og/S+Zqn/qGSuIcdSoUW486zlOnTq1iF9pcenagkK1atVc0KJQ0Ycvvvii2PZq3VEFMAWM1alTxwVnxipOJpij3zAMwzAMwzDKQFyU2bS0NBf9rPQTUsFKSj8lPypF/ykx74YNG5wP5po1a4BgqCUVReqK0rd88803CVNMiiv3qtv++eefz9ChQwFc2hzdqouLwM3Ly3OJ9uUfK5/E9evXJyTitjIKjSLHNV4nT57sErkLFZVIJFIznnvuuQL17CvC1q1bnZotv7+jjz4aKOirlypoHPv9vqV6aqwHQc0U8t+Warzffvu5uatsKk899RQPPvggQJEysEEgFAq5YhVSdUKhkLOAlDXDjV4nK5A/E0AiMhqo1LBfLdV8yc3N5dFHHwUi5c7BK0RTXHL+6dOnu8/mkksuAeC2224DIiWatQ9q3AYN9dFfwlW+6PIr7d69u3t2sqQpu0Us4yqihax0Kj9bo0YNN+501vFbXLXv+32mgzQvAVq3bu2Kl2js+sufa1/Rma5169aukIIs8P51UxYhf+acaBKXw2xWVpYzf+kBa2HVouunZs2aboHWgu1/+FootAjKxJSMqEqWDrPTpk1L2KAu7u/6N0eZYXXREP7Nwh+IoUOvzGAKqErmFGxafObPn+/MR5roQTjc6bAVDbNVlSpVXI5gPWOlqsrIyAhsCqCKooOr341GC3WQNho9iwEDBgBetcFQKOTGp/IhT5w4MZCHWJGRkeHyOvsrEGqj9H+vNLR+am/wV0rS3hGPdUfPRsGXH330kQueUR8+++wzt7Fr7yqtf+Fw2M3rZcuWue9B5POTy0GQLlrgPTsFEYldu3a5vLpyn2jUqJGba8oRPGnSJAB69uwZeJcDrYvKAR0Khdx+oPl33HHHOfcSHdCDPDdPPfVU5/oh5CrStWtX50ahyoIHHHCAu5AUDjpdsGCBqw4Wq33D3AwMwzAMwzCMpCUuyuzRRx9dxAQix3epW+CZmebNm+dkat1Y/CZA3eQVlBKUtDllJSMjg9atWwOR+u/gme4XL16csHYVhz7/N9980xUFUOoqVaPp1auXU7N0uwavTwMHDgS8W9rcuXOdqSEIamZZ0PhTep3+/fu7W6vUFVkRNm/e7FQSv+JSOPF1LInGTb9Zs2bOTCRl5OuvvwbKX00pXmRmZrqAE1V+Ux9+/PFHF1Ck/uTl5bl0Zn/7298AT2nYs2ePW1uCZE3Qs1V6o8Lp08BL1N6tWzcXbClTdpBUoFAo5Myvmhf+NV6qc2lFEzIzM13BC81JqYJ16tRxJuB4KJf6bLUmzJw50z0L9e/BBx90Cl1ZA2ALp82TGpuVleWCq4MWGC1Lh5BlbsyYMTz11FOAt54+//zzrsqb9kaptoceeqizNAQNjbN77rkH8IIYc3Nz3TOWCn3eeec51VkVFeVOE0RWrFjh5oyek6qT1a5du4AVECKWrMIuPdrnu3btyrZt22LaXlNmDcMwDMMwjKQlLsqsP9m+bo9KFwSecqKSaU2aNHF+Fbq164a+7777ultC0BKa+5FKcuCBBzp/4S5dugCRm6dUZSmZUh7kaxUUpDTk5uY6NUupi/R1zJgxTq0dPnw4ECmnKcVLwUMPPfQQEEnarnri8osKkk+Unp3an5WV5dSC66+/HogUUZDio69XXnklEPGfknKi9/jyyy958803geAGLupWLXXrvPPOcwqTgvnuuOMOIDiKutYWBYvcfffdzqdbz1Frjr+cr1SCn3/+2T0jpT2S2rJ161bn7x20ZwUwYcIEwAsKatmypfs86tSpA0RSBN10000AvPXWWwAuSKOsaa9iSW5urlOpVIK3Tp06zs/y7bffBnDFPubMmeP8DKVIDhw40M3PwumptmzZkhDFUuNl6dKlzrdZe1pF1jrNyd69ewPevhEKhdyzDpoye+SRRwKe9VVxMI888kiRVI6rVq1yZwKNV5W5b9SoUSCV2VAoxEUXXQTAueeeC3hrztq1a7n77rsBr/+XXnqpU59ledDcDWLcz7Rp05wSqwD8VatWARG/b41j+YTfdNNNbi1V+q14xjXF5TC7dOnSIlVr5EZw+OGHu42oa9euQGQh0EFp7ty5gGfCPemkk9wBN4iHWOVWfffddwv8P3gTNxQKFclRqv+Xq0U8KS6LQXkIh8Mub2C/fv2AyCFOzvCKWD3++OOBSASuslQoAPCNN95I+GKsz14bhr5u27bNmYdkOqtSpYr7vHQomD17NhA5IClyV3kHt23bxhtvvAEE42CkTV8XyVq1arlLl3ILHnPMMc6UpEpMJWUfSQSZmZnuYKaNb9999y0ynv3VkzTPdDjYf//9i1TS0lq1YcMGd4jXe4TD4UA8P/DM5t26dQMiploF7CkQo0aNGs7tQuus5uUll1zinmei5l5+fj7vv/8+4FUKHD16tDOna9NXfkvwDoN+t53Cbmz62aJFi9zhNxHk5eW5aPaKEgqF3OW4Xbt27ntC605QxqXYf//9AU+gkdkdih9veq7KCiNXpsIBx0EhLS2Nv//974Bnhpc70htvvOHWTD2rVq1aucNd+/btAc9lL4iH2V27drk8scI/xjTn5A6SkZHh5lrfvn0Br7JpPDA3A8MwDMMwDCNpiYsyu3nzZidP64apChEnnnhikdrV27Ztc6qDXAkUYJSTk8PIkSMBAlMpKz093eVje/jhhwEvRUwoFHI3c39e1sK3aPW9fv36ri55vIjGjV7vIVVr06ZNvPTSS4AX7Cc19sILL3TKmBznFy5cyC+//FLpdlQG3aq//PJLwFMPNmzYwIknnghAnz59gIgpVH2WMjJt2jQgYmKRReFf//qXe+9Y5dcrD1JkpXaccsopQOQm3bZtW8ALMsnLyyMnJwfwAhv89bULuxqEw+G4Kny1a9d2Zjspkbm5uU7tV8UZmWSbNm3qghLVD7+iVziVTlpaGh07dizwNzdt2uTcRPzuC4lEKfN69erlFJHLL78ciFhI1H+5IEjt/OSTT7jwwgsBnAtMItC8U6Da4sWLGTVqFBCxxIE3Jv1uBPr3H3/84dZPv0sbRMyiibb4RANV4iusUv72228ut2nQ0HzS/JAKqWdVGD3PHj16AF66tXjvh2UlOzvbrSNCa8fjjz/u9n2tMVu2bCnimiZLShBylBdHaXNHfdcenpmZyTvvvAPg9v54YsqsYRiGYRiGkbTERZnNy8tzgTNy9pdyWVyi3aysLOcjK8dj3XgefPBBnnnmGSDxiojae+qppzonf/m+qOrVxo0bXWoWqQz16tVztzWly1F1rKD4JPrTbUBE+ZIPk5SUvQUB6efylVLqo4MPPtgpDUo23a5dO5deJ1HIZ2v+/PmAp+yFw2GXlkzO/EOHDnVKuz4PqZi7d+92loWgBfQJzTkpzs2aNXOqjxSSUCjkfH+lgKoC2CeffOL67S+koEp/+gz1mcSCo446ygUcqC1r1qxh4sSJgKewy7rTu3dvl3ZLfQyHw86aIMuAFPSqVavSvXt3AC6++GIgkppMfmTfffcdQKV9IiuL1sF169a5oDAFe3Xs2NGl7pIyJvWyWrVqjBs3DvBSeSUyEFMq0OLFi4uoc1Kw/ONUFr2DDz7YjUv1TZ9Joq090aBKlSoMGjQIKFgMAuDRRx8NhMWnOBQ8pDiDwsUxCiPfS/l0a159//33MW1nRalWrVoRS4ACRleuXFnk9VpzwRvrQQp8Lg/77LOPSw2oNTgnJ8dZhBIRIGzKrGEYhmEYhpG0xEWZBc+vUFHHSsmUkZHhbjdSAzMyMpwiqJuL/C4ffvjhwKQFUvR769at3S1SKad0m6xatSo33HAD4KWrWLlypbvVqKyhoqaDUpJQn78Ukj59+jiVQ7XelS5lz549park+pnU9U2bNhUoewsRn+iylq6MNWqvP1G+UjlJSa9ataprr559UMZlaeizVTlFRZueddZZRT733bt3uz7JZ1FpWJo3b+5u5PKBy8zM5OSTTwbg/vvvB3AlDGPxTKtVq+bmi/xCa9So4dSBCy64APDU5MzMzAJZCSCSTUN+Xi+88EKB9zrxxBNdYRBFZh900EEu5ZAi7GVRiIelKC0trUipSM3V9PR0pzLrZ3Xr1nVKpl7nj4QP6pjV3FOWFH199dVX3TPUM+nVq5fLjqO+qV/yb05mDj/8cGf90BiTn/SYMWMSbqEsCVm2ZM3yx4wUVmdr167N008/DeCsQbJWBtW6tWPHDjfO1B8VPdqxY0eRgk/+IhKap8k2PhU7MXXqVGfx0hntwgsvdOMyEcTtMCs0QGVKGDt2rDN1+lPoaDGbN28e4KWfCVIlHuX3a9q0qdtUNai10Xfo0MFt8D/88AMAgwcPdmaIRB/cSkIHFOXPUx/Aq52tlFurV692k1pf/f3SJtqmTRsAzjzzTDcp9LkpFVtQ0cKkPqSlpbnvKUVcUJ9lcWgxfe2114BIdSyl5JI7yapVq1wVLbkF+Q+NuoTqML99+3bnVqCAhlhutAsXLnRpnZSXNDs726VDK5yuCbxLiYKN7rzzTtdmrS0arytXrnQHQT138BZ0fYaa87E8GCpg0u+ipPGmQIzatWu7w7fW10aNGrm1qHDKsp07d7pa68li7gyHw+45KQD45Zdf5oorrgC8qpAak7qEJTMDBgxwY1JjbMSIEUDiXVxKQ9WtJIJo7TjttNOKuAD169fP5ZLXmiThK6iH9a1btzpBR+NOa0lWVpZbR+U+cc4557i+yA3r008/jWuby4vWDF0wFODevn17Nxafe+45wBMsE4W5GRiGYRiGYRhJS6g8t55QKBT1K9KBBx7I4MGDAejcuTMQURyUmkXpjqKhHITD4VBpPy9v/xSUMHr0aFccQWmBVIf6qKOOcjdTVYiKVTWTvfUPyt5HqT9SX++55x6n8EiRUgDM999/78yAUuvy8/OdeUmO/1KLatWq5W6oUvC6devmPru9uCxE9RmWFfVLKYw6dOjgbuEK7IuS2v5FOBw+obQXxKKPfvO1X2GU6iilXiqgP/hK6le1atWc4vL5558XeS8RrXEaCoVcqi25FLRq1cqlyVPwkIIyx4wZw9ixYwFvDJdGenq6Mw1K8WzZsqVTdx944AGAYk1r0RqnUoHlqtSkSRM3N1WxTOrxIYcc4tQvv5VL41Hz8euvvwbg5ptvdgpRedWvaK41lSUzM9MFSCnQWM935MiRrnpYtPsY6/5JjV+xYoVzqdBYU/Cb3J4qSFzWGqX/Uwqn+vXrF7Fg7tmzx7ke3nfffUDZ5ujeiPU4lUvT6NGjAa/Ny5cvd+uEgp3r1q3rzgX6TKJhkYzVOA2FQs4id+eddwJeRa86deqwbNkywOuL1pdoU5ZnCKbMGoZhGIZhGElM3H1mC7N+/XpXi1nlDOOdfL2iSGHdvHmzU2mlBvkdvKVuBrG+dElIUVPS/5UrV/LQQw8BXh8VdNGyZUv3vPyKkJAiotesX7+ezz77DIA77rgDiBQmCKpvFHj+lF988QUAxx13nPMRUjnbZBizJVFS2/V9+Tbrq/oM3rP2FwOJx7MMh8MukG38+PFF2uN/XUXIy8tzFocnn3wSiKQSlDqtNDyxIi0trYC/HUQC75Tip/BcK9xviPg/S32VuiuLSjSUryCwe/dul4pMCr3WqOzs7CLprJIFxZL4/X4/+OADINi+soVR3IvKKT/33HMuRZ7SNg4cOJCPP/4YSK51VKkIFRh70UUXATjrkJ/c3Fy3TknVDDJZWVnO+tSlSxfAs9Dl5OS4GItYKbLlJeGHWT9BjawtCWUnuPfee92mp/y4OvSMGjUq5pteLNGGN2vWLFfVSgE2Z5xxBhA5zCpPpUyfoVDIZS9QxOaHH34IRA4GMsnLST7IB1k/MpWtWLHCuRxU0tSX9MTzAFsWYtEOHYTWrl3r3GHigS4P+vv5+flunSx8iPXnzFUQzaBBg5ybU7Ktr+VBfdN64g+Ok4tQsh1mVekyMzPT9ef1118HkuvAJyTmtG3b1h2K9LySdWxqj7vuuusAz9XsxhtvdONO+8Pbb7/t9o+grJWl0aRJExf4qsxN6u+4ceNctoqgYG4GhmEYhmEYRtKS8ACweJJoh/5YE6SgjFAoVCQVUDRuo0F5hunp6bFSExISABZPgjROY0W0xqnUHQUAnXTSSS5Nk1wsZPlYu3ZtEWUyVgTtGSqQSOZepXKcNWuWs6AUzsG7NxK11kgFU2Wv7Oxs91wV+CVLYCWxtYbYB9QWdrOLNtEep2r3sccey8033wxEquxBxAoN8P7778dNTbcAMMMwDMMwDCPlMWXWR6r3D1K/j8neP0wtAVK/j6neP0hMH5XyyZ8isKK+sol6hi1atAC8GIPq1as7FV4FaxSvUUlsrSH1+1iZ1FwKNlVq1ET4nZsyaxiGYRiGYaQ8gcpmYBjJjFShZIw0NoxUQHMvWdOOZWRkuNLh/jLEUmaV3cIwYk04HHbZC5IBO8waRpSwQ6xhGJUhLy/P5WAVmzdvZsSIEUDyHtINI9aYm4FhGIZhGIaRtJQ3AGwj8FPsmhNTGobD4XqlvSDV+wep38ck7x+kfh9tnJL6/YPU72OS9w9Sv482Tkn9/olyHWYNwzAMwzAMI0iYm4FhGIZhGIaRtNhh1jAMwzAMw0ha7DBrGIZhGIZhJC12mDUMwzAMwzCSFjvMGoZhGIZhGEmLHWYNwzAMwzCMpMUOs4ZhGIZhGEbSYodZwzAMwzAMI2mxw6xhGIZhGIaRtPx/bAzORmWexzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x144 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_show = 12\n",
    "\n",
    "x_test_sample = next(val_mnist_dataset.__iter__())[0][:num_show].numpy()\n",
    "x_decoded = autoencoder.predict_on_batch(x_test_sample)\n",
    "show_pairs(x_test_sample.reshape(num_show, *input_shape[:2]), \n",
    "           x_decoded.reshape(num_show, *input_shape[:2]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, results are indeed aright (though maybe a bit blurry). Despite its low-dimensional bottleneck and simplistic architecture, our network can nicely recover the digits.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embedding and Visualizing Datasets with Auto-Encoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Since we defined an `encoder` model, we can also plot the images and their corresponding 32-dimensional codes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_encoded = encoder.predict_on_batch(x_test_sample)\n",
    "\n",
    "# We scale up the code, to better visualize it:\n",
    "x_encoded_show = np.tile(x_encoded.reshape(num_show, 1, code_size), (1, 15, 1))\n",
    "\n",
    "show_pairs(x_test_sample.reshape(num_show, *input_shape[:2]), \n",
    "           x_encoded_show, plot_fn_b=\"matshow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While interesting to visualize, these codes don't tell us much about the data at first glance. With still 32 dimensions, it is **hard to interpret** these features extracted by the encoder, even though they seem to contain enough information for the decoder to recover the original images from them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset embedding with t-SNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make this 32-dimensional latent space more understandable, we will use a method to find a 2D projection which best preserves the data distribution. Applying such a method to the MNIST codes, we could then visualize the complete dataset as a 2D scatter plot. \n",
    "\n",
    "While multiple solutions exist to find interesting projections given a multi-dimensional dataset (PCA, LDA, etc.), we will use here **_t-SNE_**. The _t-Distributed Stochastic Neighbor Embedding_[$^2$](#ref) is an unsupervised technique developed by Laurens van der Maatens and Geoffrey Hinton in 2008. Given a dataset, this method returns a non-linear projection which tries to maximize the distance between all the elements after projection. This is a really useful tool, and multiple implementation already exist.\n",
    "\n",
    "We will use here a version provided by `scikit`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install scikit-learn  # uncomment to install the module if it isn't done yet.\n",
    "from sklearn.manifold import TSNE\n",
    "# Creating a t-SNE instance to project our codes into 2D elements:\n",
    "tsne = TSNE(n_components=2, verbose=1, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After converting the whole dataset into codes, we apply this method to them, and finally visualize them all together as a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 10000 samples in 0.013s...\n",
      "[t-SNE] Computed neighbors for 10000 samples in 4.971s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 3000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 4000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 5000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 6000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 7000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 8000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 9000 / 10000\n",
      "[t-SNE] Computed conditional probabilities for sample 10000 / 10000\n",
      "[t-SNE] Mean sigma: 4.024734\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 85.299362\n",
      "[t-SNE] KL divergence after 1000 iterations: 1.773399\n"
     ]
    }
   ],
   "source": [
    "_, (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_test = x_test / 255.0\n",
    "x_test = x_test.reshape(x_test.shape[0], *flattened_input_shape)\n",
    "\n",
    "x_encoded = encoder.predict_on_batch(x_test)\n",
    "x_2d = tsne.fit_transform(x_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/benjamin/anaconda3/envs/tf2/lib/python3.6/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family ['normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "matplotlib.rc('font', **font)\n",
    "figure = plt.figure(figsize=(15, 10))\n",
    "plt.scatter(x_2d[:, 0], x_2d[:, 1], # 2D image projections\n",
    "            c=y_test                # per-class colors\n",
    "            ,cmap=plt.cm.get_cmap(\"jet\", num_classes) \n",
    "            # replace \"jet\" with \"plasma\" for colors more consistent when printed grayscale\n",
    "           )\n",
    "plt.colorbar(ticks=range(num_classes))\n",
    "plt.clim(-0.5, num_classes - 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each point represents a digit image, as encoded by our model. As we can see, the codes contain some features which clearly could be used to distinguish between classes, **even though at no point the network was aware of the image classes**. The auto-encoder managed to uncover by itself some similarities and structures in the dataset, to better compress the images without losing information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through this notebook, we got acquainted with auto-encoders, and how networks can be applied to pixel-level predictions. In the process, we presented and implemented various tools (custom metrics, losses, and callbacks, modular models, etc.) which will come handy for the next notebooks and chapters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref\"></a>\n",
    "#### References"
   ]
  },
  {
   "cell_type": "markdown",
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   ]
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