Chapter06/ch6_nb2_denoise_with_autoencoders.ipynb

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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-2/blob/master/Chapter06/ch6_nb2_denoise_with_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;background-color: #ffffff\">\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 2:</small><br/>Denoising with Auto-Encoders</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    Reusing the simple fully-connected auto-encoder implemented in the previous notebook, we will now demonstrate how such network can be used to <strong>recover corrupted/noisy images</strong>.\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 notions from the book \"<em><strong>Hands-on Computer Vision with TensorFlow 2</strong></em>\" written by Eliot Andres and Benjamin Planche, published by Packt. If you enjoyed the insights shared here, <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\"><strong>please consider acquiring the book!</strong></a>\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 efficient 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: 280px;\">\n",
    "    <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Learn more about the book!\" target=\"_blank\">\n",
    "        <img src=\"../banner_images/book_cover.png\" width=280>\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: block;height: 100%;float: left;vertical-align: middle;margin: 0 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.amazon.com/Hands-Computer-Vision-TensorFlow-processing/dp/1788830644\" title=\"Get the book on Amazon (paperback or Kindle version)!\" target=\"_blank\">\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: right;margin: -5px 25px 10px;padding: 0px;\">\n",
    "        <a href=\"https://www.packtpub.com/application-development/hands-computer-vision-tensorflow-2\" title=\"Get your Packt book (paperback, PDF, ePUB, or MOBI version)!\" target=\"_blank\">\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",
    "    </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",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Choosing which GPU this notebook can access\n",
    "# (useful when running multiple experiments in parallel, on different GPUs):\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]= \"0\" \n",
    "\n",
    "# Some hyper-parameters:\n",
    "batch_size  = 32            # Images per batch (reduce/increase according to the machine's capability)\n",
    "num_epochs  = 50            # 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": [
    "Once again, we will first demonstrate on the [MNIST](http://yann.lecun.com/exdb/mnist) dataset[$^1$](#ref). Though we could use again the efficient `tensorflow-datasets` module to instantiate and prepare the data (c.f. previous notebook), we will stick this time to using the MNIST dataset as `numpy` array provided by Keras, simply to illustrate some of the Keras utilities to handle input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of training set: (60000, 28, 28, 1)\n",
      "Shape of testing set: (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "img_height, img_width = x_train.shape[1:]\n",
    "img_channels = 1\n",
    "input_shape  = (img_height, img_width, img_channels)\n",
    "\n",
    "# For the following experiments, we will however not use the class labels:\n",
    "del y_train\n",
    "del y_test\n",
    "\n",
    "# Normalizing the image data:\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# Even though we will use again a basic fully-coonected network, we need to preserve the image format\n",
    "# of the sample this time, to use the Keras image pre-processing tool to add noise. Therefore, \n",
    "# the augmented images will be flattened by the network itself via a initial Flatten() layer.\n",
    "x_train = x_train.reshape((-1, img_height, img_width, img_channels))\n",
    "x_test = x_test.reshape((-1, img_height, img_width, img_channels))\n",
    "\n",
    "print(\"Shape of training set: {}\".format(x_train.shape))\n",
    "print(\"Shape of testing set: {}\".format(x_test.shape))\n",
    "\n",
    "# Setting some variables:\n",
    "batch_size = 64\n",
    "train_steps_per_epoch = len(x_train) // batch_size\n",
    "val_steps_per_epoch = len(x_test) // batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building an Auto-Encoder for Image Denoising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned in the book, denoising auto-encoders are normal auto-encoders, but **fed with corrupted samples** while still trying to recover the original images. Therefore, we need to instantiate our network, and build a method to corrupt our images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple auto-encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this experiment, we will reuse the exact same architecture as in the previous notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "code_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input (InputLayer)           [(None, 28, 28, 1)]       0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (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",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \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, Flatten, Reshape\n",
    "\n",
    "inputs = Input(shape=input_shape, name='input')\n",
    "\n",
    "# As previously mentioned, the image flattening is done here:\n",
    "inputs_flat = Flatten()(inputs)\n",
    "\n",
    "# Encoding layers:\n",
    "enc_1 = Dense(128, activation='relu', name='enc_dense1')(inputs_flat)\n",
    "enc_2 = Dense(64, activation='relu', name='enc_dense2')(enc_1)\n",
    "code = Dense(code_size, activation='relu', name='enc_dense3')(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(np.prod(input_shape), 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",
    "# Finally, we reshape the decoded data so it has the same shape as the input samples:\n",
    "decoded_reshape = Reshape(input_shape)(decoded)\n",
    "\n",
    "# Auto-encoder model:\n",
    "autoencoder = Model(inputs, decoded_reshape)\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator of noisy images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now set up our input pipeline, adding a pre-processing step to corrupt our images. We will use this simple function for that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_noise(img, min_noise_factor=.3, max_noise_factor=.6):\n",
    "    \"\"\"\n",
    "    Add some random noise to an image, from a uniform distribution.\n",
    "    :param img:               Image to corrupt\n",
    "    :param min_noise_factor:  Min. value for the noise random average amplitude\n",
    "    :param max_noise_factor:  Max. value for the noise random average amplitude\n",
    "    :return:                  Corrupted image\n",
    "    \"\"\"\n",
    "    # Generating and applying noise to image:\n",
    "    noise_factor = np.random.uniform(min_noise_factor, max_noise_factor)\n",
    "    noise = np.random.normal(loc=0.0, scale=noise_factor, size=img.shape) \n",
    "    img_noisy = img + noise\n",
    "    \n",
    "    # Making sure the image value are still in the proper range:\n",
    "    img_noisy = np.clip(img_noisy, 0., 1.)\n",
    "    \n",
    "    return img_noisy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us have a look how this affects our images. To visualize some results, we first implement the following method to draw grids of results. This will come handy later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "\n",
    "def plot_image_grid(images, titles=None, figure=None,\n",
    "                    grayscale=False, transpose=False):\n",
    "    \"\"\"\n",
    "    Plot a grid of n x m images.\n",
    "    :param images:       Images in a n x m array\n",
    "    :param titles:       (opt.) List of m titles for each image column\n",
    "    :param figure:       (opt.) Pyplot figure (if None, will be created)\n",
    "    :param grayscale:    (opt.) Flag to draw the images in grayscale\n",
    "    :param transpose:    (opt.) Flag to transpose the grid\n",
    "    :return:             Pyplot figure filled with the images\n",
    "    \"\"\"\n",
    "    num_cols, num_rows = len(images), len(images[0])\n",
    "    img_ratio = images[0][0].shape[1] / images[0][0].shape[0]\n",
    "\n",
    "    if transpose:\n",
    "        vert_grid_shape, hori_grid_shape = (1, num_rows), (num_cols, 1)\n",
    "        figsize = (int(num_rows * 5 * img_ratio), num_cols * 5)\n",
    "        wspace, hspace = 0.2, 0.\n",
    "    else:\n",
    "        vert_grid_shape, hori_grid_shape = (num_rows, 1), (1, num_cols)\n",
    "        figsize = (int(num_cols * 5 * img_ratio), num_rows * 5)\n",
    "        hspace, wspace = 0.2, 0.\n",
    "\n",
    "    if figure is None:\n",
    "        figure = plt.figure(figsize=figsize)\n",
    "    imshow_params = {'cmap': plt.get_cmap('gray')} if grayscale else {}\n",
    "    grid_spec = gridspec.GridSpec(*hori_grid_shape, wspace=0, hspace=0)\n",
    "\n",
    "    for j in range(num_cols):\n",
    "        grid_spec_j = gridspec.GridSpecFromSubplotSpec(\n",
    "            *vert_grid_shape, subplot_spec=grid_spec[j], wspace=wspace, hspace=hspace)\n",
    "\n",
    "        for i in range(num_rows):\n",
    "            ax_img = figure.add_subplot(grid_spec_j[i])\n",
    "            # ax_img.axis('off')\n",
    "            ax_img.set_yticks([])\n",
    "            ax_img.set_xticks([])\n",
    "            if titles is not None:\n",
    "                if transpose:\n",
    "                    ax_img.set_ylabel(titles[j], fontsize=25)\n",
    "                else:\n",
    "                    ax_img.set_title(titles[j], fontsize=15)\n",
    "            ax_img.imshow(images[j][i], **imshow_params)\n",
    "\n",
    "    figure.tight_layout()\n",
    "    return figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** We will save this method in [plot_utils.py](./plot_utils.py) and use it in the next notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/benjamin/anaconda3/envs/tf2/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 4320x720 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_show = 12\n",
    "random_image_indices = np.random.choice(len(x_test), size=num_show)\n",
    "\n",
    "orig_samples = x_test[random_image_indices]\n",
    "noisy_samples = add_noise(orig_samples)\n",
    "\n",
    "fig = plot_image_grid([np.squeeze(orig_samples), np.squeeze(noisy_samples)],\n",
    "                      grayscale=True, transpose=True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our corrupting function is doing its job, adding some _salt-and-pepper_ noise. Some digits are even hard to recognize... Let us see how our denoising model will manage...\n",
    "\n",
    "We now have a choice. We could simply apply our noise function to the whole training set (`x_noisy = add_noise(x_train)`) and pass it to our model for training (`autoencoder.fit(x_noisy, x_train)`). While straightforward, this solution has one inconvenient: each original image have only one noisy version. Epoch after epoch, the network will receive the same noisy images. There is thus a risk that it may overfit some of the data.\n",
    "\n",
    "Another solution would be to corrupt each batch of images at each iteration, thus creating different corrupted versions each time. While a bit heavier, this solution provides our network with new samples each time, making it more robust. We will therefore opt for this solution.\n",
    "\n",
    "To implement it, we will use a _**generator**_, a Python object yielding new elements each time it is called. Keras models can be trained directly on datasets (`model.fit(...)`), or on generators (`model.fit_generator(...)`). Though less advanced that TensorFlow `tf.data.Dataset`, generators share some common advantages (e.g., for datasets too big to be loaded at once, a generator can be used to load only the images for the next batches).\n",
    "\n",
    "Keras offers several pre-implemented generators, to iterate over image folders, image arrays, etc. Here, we will use `ImageDataGenerator`, which can iterate over numpy arrays to generate batches. This generator can also be configured to pre-process each batch before yielding it. This is exactly what we need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<keras_preprocessing.image.NumpyArrayIterator at 0x7effe4328e80>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# We define our Keras generator, passing our noisy function as pre-processing step:\n",
    "train_datagen = ImageDataGenerator(preprocessing_function=add_noise)\n",
    "\n",
    "# Then we pass our dataset to the generator and specify how the yielded batch should be\n",
    "# (batch size, shuffling or not, etc.).\n",
    "train_generator = train_datagen.flow(x_train, x_train, \n",
    "                                     batch_size=batch_size, shuffle=True)\n",
    "train_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our input pipeline is ready, though we need to prepare the validation data too.\n",
    "\n",
    "To be able to consistently compare metrics from one epoch to another, we augment the validation images with noise only once, and saved the resulting images so they can be reused for each epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_noisy = add_noise(x_test)\n",
    "\n",
    "val_datagen = tf.keras.preprocessing.image.ImageDataGenerator()\n",
    "val_generator = train_datagen.flow(x_test_noisy, x_test,\n",
    "                                   batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As in previous experiments, we will set up some monitoring tools, then train our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PSNR as custom metric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We reuse PSNR, to evaluate how well our model recovered the corrupted sampled, compared to the original ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "psnr_metric = functools.partial(tf.image.psnr, max_val=1.)\n",
    "psnr_metric.__name__ = 'psnr'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom callback to plot result grids"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To keep experimenting with Keras and TensorFlow, we will implement another **custom callback**. Instead of waiting the end of the training to visualize some predicted images, would it be not better to **plot some result grids after each epoch** for instance? Even better: we could reuse the same input/target images each time, so we can compare the predicted versions from one epoch to another!\n",
    "\n",
    "The following custom callback does exactly that. At the end of each epoch, it feeds the model with some pre-selected images, then use `matplotlib` to draw a result grid. It then converts the `pyplot.Figure` into a `tensorflow.Summary` which can be saved by TensorFlow an read by Tensorboard for display (image summaries can be seen in [http://localhost:6006/#images](http://localhost:6006/#images) once Tensorboard started.\n",
    "\n",
    "This is what will appear in Tensorboard (with the slider to visualize other steps/epochs):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Tensorboard screenshot](./notebook_images/tensorboard_result_grid.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "\n",
    "def figure_to_rgb_array(fig):\n",
    "    \"\"\"\n",
    "    Convert figure into a RGB array\n",
    "    :param fig:         PyPlot Figure\n",
    "    :return:            RGB array\n",
    "    \"\"\"\n",
    "    figure_buffer = io.BytesIO()\n",
    "    fig.savefig(figure_buffer, format='png')\n",
    "    figure_buffer.seek(0)\n",
    "    figure_string = figure_buffer.getvalue()\n",
    "    return figure_string\n",
    "\n",
    "\n",
    "def figure_to_summary(fig, name, step):\n",
    "    \"\"\"\n",
    "    Convert figure into TF summary\n",
    "    :param fig:             Figure\n",
    "    :param tag:             Summary name\n",
    "    :return:                Summary step\n",
    "    \"\"\"\n",
    "    # Transform figure into PNG buffer:\n",
    "    figure_string = figure_to_rgb_array(fig)\n",
    "\n",
    "    # Transform PNG buffer into image tensor:\n",
    "    figure_tensor = tf.image.decode_png(figure_string, channels=4)\n",
    "    figure_tensor = tf.expand_dims(figure_tensor, 0) # adding batch dimension\n",
    "\n",
    "    # Using Proto to convert the image string into a summary:\n",
    "    figure_summary = tf.summary.image(name, figure_tensor, step)\n",
    "\n",
    "    return figure_summary\n",
    "\n",
    "\n",
    "\n",
    "class TensorBoardImageGridCallback(tf.keras.callbacks.Callback):\n",
    "    \"\"\" Keras callback for generative models, to draw grids of\n",
    "        input/predicted/target images into Tensorboard every epoch.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, log_dir, input_images, target_images=None, tag='images',\n",
    "                 figsize=(10, 10), dpi=300, grayscale=False, transpose=False,\n",
    "                 preprocess_fn=None):\n",
    "        \"\"\"\n",
    "        Initialize the Callback.\n",
    "        :param log_dir:         Folder to write the image summaries into\n",
    "        :param input_images:    List of input images to use for the grid\n",
    "        :param target_images:   (opt.) List of target images for the grid\n",
    "        :param tag:             Tag to name the Tensorboard summary\n",
    "        :param figsize:         Pyplot figure size for the grid\n",
    "        :param dpi:             Pyplot figure DPI\n",
    "        :param grayscale:       Flag to plot the images as grayscale\n",
    "        :param transpose:       Flag to transpose the image grid\n",
    "        :param preprocess_fn:   (opt.) Function to pre-process the\n",
    "                                input/predicted/target image lists before plotting\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.summary_writer = tf.summary.create_file_writer(log_dir)\n",
    "\n",
    "        self.input_images, self.target_images = input_images, target_images\n",
    "        self.tag = tag\n",
    "        self.postprocess_fn = preprocess_fn\n",
    "\n",
    "        self.image_titles = ['images', 'predicted']\n",
    "        if self.target_images is not None:\n",
    "            self.image_titles.append('ground-truth')\n",
    "\n",
    "        # Initializing the figure:\n",
    "        self.fig = plt.figure(num=0, figsize=figsize, dpi=dpi)\n",
    "        self.grayscale = grayscale\n",
    "        self.transpose = transpose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \"\"\"\n",
    "        Plot into Tensorboard a grid of image results.\n",
    "        :param epoch:   Epoch num\n",
    "        :param logs:    (unused) Dictionary of loss/metrics value for the epoch\n",
    "        \"\"\"\n",
    "\n",
    "        # Get predictions with current model:\n",
    "        predicted_images = self.model.predict_on_batch(self.input_images)\n",
    "        if self.postprocess_fn is not None:\n",
    "            input_images, predicted_images, target_images = self.postprocess_fn(\n",
    "                self.input_images, predicted_images, self.target_images)\n",
    "        else:\n",
    "            input_images, target_images = self.input_images, self.target_images\n",
    "\n",
    "        # Fill figure with images:\n",
    "        grid_imgs = [input_images, predicted_images]\n",
    "        if target_images is not None:\n",
    "            grid_imgs.append(target_images)\n",
    "        self.fig.clf()\n",
    "        self.fig = plot_image_grid(grid_imgs, titles=self.image_titles, figure=self.fig,\n",
    "                                   grayscale=self.grayscale, transpose=self.transpose)\n",
    "\n",
    "        with self.summary_writer.as_default():\n",
    "            # Transform into summary:\n",
    "            figure_summary = figure_to_summary(self.fig, self.tag, epoch)\n",
    "\n",
    "            # # Finally, log it:\n",
    "            # self.summary_writer.add_summary(figure_summary, global_step=epoch)\n",
    "        self.summary_writer.flush()\n",
    "\n",
    "    def on_train_end(self, logs={}):\n",
    "        \"\"\"\n",
    "        Close the resources used to plot the grids.\n",
    "        :param logs:    (unused) Dictionary of loss/metrics value for the epoch\n",
    "        \"\"\"\n",
    "        self.summary_writer.close()\n",
    "        plt.close(self.fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is now just a matter of launching the training, and monitoring it here or in Tensorboard:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: \u001b[92mstart\u001b[0m.\n",
      "Epoch  0/50: \u001b[1mloss\u001b[0m = \u001b[94m0.204\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.228\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m13.773\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m13.063\u001b[0m\n",
      "Epoch  1/50: \u001b[1mloss\u001b[0m = \u001b[94m0.152\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.186\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m15.760\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m14.165\u001b[0m\n",
      "Epoch  2/50: \u001b[1mloss\u001b[0m = \u001b[94m0.140\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.177\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m16.436\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m14.544\u001b[0m\n",
      "Epoch  3/50: \u001b[1mloss\u001b[0m = \u001b[94m0.134\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.175\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m16.818\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m14.674\u001b[0m\n",
      "Epoch  4/50: \u001b[1mloss\u001b[0m = \u001b[94m0.130\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.172\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.079\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m14.744\u001b[0m\n",
      "Epoch  5/50: \u001b[1mloss\u001b[0m = \u001b[94m0.127\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.168\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.265\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m14.928\u001b[0m\n",
      "Epoch  6/50: \u001b[1mloss\u001b[0m = \u001b[94m0.124\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.167\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.427\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.015\u001b[0m\n",
      "Epoch  7/50: \u001b[1mloss\u001b[0m = \u001b[94m0.123\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.166\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.549\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.022\u001b[0m\n",
      "Epoch  8/50: \u001b[1mloss\u001b[0m = \u001b[94m0.121\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.164\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.653\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.126\u001b[0m\n",
      "Epoch  9/50: \u001b[1mloss\u001b[0m = \u001b[94m0.120\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.165\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.750\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.112\u001b[0m\n",
      "Epoch 10/50: \u001b[1mloss\u001b[0m = \u001b[94m0.119\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.166\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.840\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.073\u001b[0m\n",
      "Epoch 11/50: \u001b[1mloss\u001b[0m = \u001b[94m0.118\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.164\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.921\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.135\u001b[0m\n",
      "Epoch 12/50: \u001b[1mloss\u001b[0m = \u001b[94m0.117\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.165\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m17.983\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.118\u001b[0m\n",
      "Epoch 13/50: \u001b[1mloss\u001b[0m = \u001b[94m0.116\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.163\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.034\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.199\u001b[0m\n",
      "Epoch 14/50: \u001b[1mloss\u001b[0m = \u001b[94m0.116\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.162\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.079\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.265\u001b[0m\n",
      "Epoch 15/50: \u001b[1mloss\u001b[0m = \u001b[94m0.115\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.165\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.122\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.121\u001b[0m\n",
      "Epoch 16/50: \u001b[1mloss\u001b[0m = \u001b[94m0.115\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.165\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.173\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.150\u001b[0m\n",
      "Epoch 17/50: \u001b[1mloss\u001b[0m = \u001b[94m0.114\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.162\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.212\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.297\u001b[0m\n",
      "Epoch 18/50: \u001b[1mloss\u001b[0m = \u001b[94m0.114\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.161\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.246\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.306\u001b[0m\n",
      "Epoch 19/50: \u001b[1mloss\u001b[0m = \u001b[94m0.114\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.162\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.275\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.275\u001b[0m\n",
      "Epoch 20/50: \u001b[1mloss\u001b[0m = \u001b[94m0.113\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.161\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.315\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.296\u001b[0m\n",
      "Epoch 21/50: \u001b[1mloss\u001b[0m = \u001b[94m0.113\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.162\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.344\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.262\u001b[0m\n",
      "Epoch 22/50: \u001b[1mloss\u001b[0m = \u001b[94m0.112\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.162\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.369\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.296\u001b[0m\n",
      "Epoch 23/50: \u001b[1mloss\u001b[0m = \u001b[94m0.112\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m0.163\u001b[0m; \u001b[1mpsnr\u001b[0m = \u001b[94m18.392\u001b[0m; \u001b[1mv-psnr\u001b[0m = \u001b[94m15.210\u001b[0m\n",
      "Training: \u001b[91mend\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "from keras_custom_callbacks import SimpleLogCallback\n",
    "\n",
    "model_dir  = os.path.join('.', 'models', 'ae_denoising_mnist')\n",
    "\n",
    "metrics_to_print = collections.OrderedDict([(\"loss\", \"loss\"), \n",
    "                                            (\"v-loss\", \"val_loss\"),\n",
    "                                            (\"psnr\", \"psnr\"), \n",
    "                                            (\"v-psnr\", \"val_psnr\")])\n",
    "callbacks  = [\n",
    "    # Callback to interrupt the training if the validation loss/metrics stops improving for some epochs:\n",
    "    tf.keras.callbacks.EarlyStopping(patience=5, monitor='val_loss',\n",
    "                                     restore_best_weights=True),\n",
    "    # Callback to log the graph, losses and metrics into TensorBoard:\n",
    "    tf.keras.callbacks.TensorBoard(log_dir=model_dir, histogram_freq=0, write_graph=True),\n",
    "    # Callback to simply log metrics at the end of each epoch (saving space compared to verbose=1/2):\n",
    "    SimpleLogCallback(metrics_to_print, num_epochs=num_epochs, log_frequency=1),\n",
    "    # Callback to log some validation results as image grids into TensorBoard:\n",
    "    TensorBoardImageGridCallback(\n",
    "        log_dir=model_dir, input_images=noisy_samples, target_images=orig_samples, \n",
    "        tag='ae_results', figsize=(len(noisy_samples) * 3, 3 * 3), \n",
    "        grayscale=True, transpose=True,\n",
    "        preprocess_fn=lambda img, pred, gt: (\n",
    "            # Squeezing the images from H x W x 1 to H x W, otherwise Pyplot complains:\n",
    "            np.squeeze(img, -1), np.squeeze(pred, -1), np.squeeze(gt, -1)))\n",
    "]\n",
    "\n",
    "autoencoder.compile(optimizer='adam', loss='binary_crossentropy',\n",
    "                    metrics=[psnr_metric])\n",
    "\n",
    "# `model.fit_generator()` works quite similarly to `model.fit()` \n",
    "# used with tf.data inputs (c.f. notebooks for Chapter 4).\n",
    "# In other words, we need to specify the number of batches (\"steps\") per epoch, \n",
    "# for Keras to keep track.\n",
    "history = autoencoder.fit_generator(\n",
    "    train_generator, steps_per_epoch=train_steps_per_epoch, epochs=num_epochs,\n",
    "    validation_data=val_generator, validation_steps=val_steps_per_epoch,\n",
    "    verbose=0, callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7eff940c95f8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(10, 5), sharex='col')\n",
    "ax[0, 0].set_title(\"loss\")\n",
    "ax[0, 1].set_title(\"val-loss\")\n",
    "ax[1, 0].set_title(\"psnr\")\n",
    "ax[1, 1].set_title(\"val-psnr\")\n",
    "\n",
    "ax[0, 0].plot(history.history['loss'])\n",
    "ax[0, 1].plot(history.history['val_loss'])\n",
    "ax[1, 0].plot(history.history['psnr'])\n",
    "ax[1, 1].plot(history.history['val_psnr'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though we already have results drawn in Tensorboard, let us add one last image grid to this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4320x1080 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted_samples = autoencoder.predict_on_batch(noisy_samples)\n",
    "\n",
    "fig = plot_image_grid([np.squeeze(noisy_samples),\n",
    "                       np.squeeze(predicted_samples),\n",
    "                       np.squeeze(orig_samples)],\n",
    "                      titles=['image', 'predicted', 'ground-truth'],\n",
    "                      grayscale=True, transpose=True)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, though the denoising is not perfect and information is sometimes lost, our simplistic auto-encoder is doing rather well. In the next notebook, we will implement a more advanced, convolutional network for the more complex problem of image super-resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref\"></a>\n",
    "#### References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. LeCun, Y., Cortes, C., Burges, C., 2010. MNIST handwritten digit database. AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2, 18."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}