Chapter07/ch7_nb8_generate_images_with_gan_models.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-2/blob/master/Chapter07/ch7_nb8_generate_images_with_gan_models.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 7: Training on Complex and Scarce Datasets</strong><br/>\n",
    "    </p>\n",
    "\n",
    "<h1 style=\"width: 100%; text-align: left; padding: 0px 25px;\"><small style=\"color: #e7692c;\">\n",
    "    Notebook 8:</small><br/>Generating Images with GANs</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    We just saw how neural networks can be built and trained to understand the distribution of a dataset, in order to generate new samples. <br/><br/>\n",
    "    In the previous Notebook <a href=\"./ch7_nb7_generate_images_with_vae_models.ipynb\">7-7</a>, we did so with <em>VAEs</em>, teaching one simple model to generate digit image by learning from the MNIST dataset. We will now tackle the same task bit with a different type of generative models: the <strong>Generative Adversarial Networks (<em>GANs</em>)</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 os\n",
    "import math\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "random_seed = 42\n",
    "np.random.seed(random_seed)\n",
    "tf.random.set_seed(random_seed)\n",
    "\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]= \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Dataset\n",
    "\n",
    "The following code is copy-pasted from the previous notebook. The [MNIST](http://yann.lecun.com/exdb/mnist) dataset[$^1$](#ref) should not have any secret to our readers anymore..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# num_classes = 10\n",
    "img_rows, img_cols, img_ch = 28, 28, 1\n",
    "input_shape = (img_rows, img_cols, img_ch)\n",
    "batch_size = 200\n",
    "num_epochs = 31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_train, x_test = x_train / 127.5 - 1., x_test / 127.5 - 1.\n",
    "\n",
    "x_train = x_train.reshape(x_train.shape[0], *input_shape).astype(np.float32)\n",
    "x_test = x_test.reshape(x_test.shape[0], *input_shape).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices(x_train)\n",
    "train_dataset = train_dataset.apply(\n",
    "    tf.data.experimental.shuffle_and_repeat(len(x_train), num_epochs, random_seed))\n",
    "train_dataset = train_dataset.batch(batch_size)\n",
    "\n",
    "val_dataset = tf.data.Dataset.from_tensor_slices(x_test)\n",
    "val_dataset = val_dataset.batch(batch_size)\n",
    "\n",
    "\n",
    "train_steps_per_epoch = math.ceil(x_train.shape[0] / batch_size)\n",
    "val_steps_per_epoch   = math.ceil(x_test.shape[0] / batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plot_utils import plot_image_grid\n",
    "\n",
    "num_show = 9\n",
    "val_dataset_samples = val_dataset.take(1)\n",
    "val_samples = next(val_dataset_samples.__iter__())[:num_show]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 3240x360 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "val_samples_imgs = (val_samples.numpy() + 1.) / 2\n",
    "val_samples_imgs = np.tile(val_samples_imgs, (1, 1, 1, 3))\n",
    "\n",
    "figure = plot_image_grid([val_samples_imgs], grayscale=True, transpose=True)\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a Generative Adversarial Network\n",
    "\n",
    "In this section, we will demonstrate how to build a GAN[$^2$](#ref).\n",
    "\n",
    "### Generator and Discriminator Networks\n",
    "\n",
    "As presented in Chapter 7, the idea of GANs is to pit a generative network against a discriminative one. The former is taked to generate realistic images, and the latter is trained to distinguish between fake and real images.\n",
    "\n",
    "For these two networks, we reuse the simple architectures defined in previous notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Network hyper-parameters:\n",
    "kernel_size       = 4\n",
    "noise_dim         = 2\n",
    "filters_orig_gen  = 64\n",
    "layer_depth_gen   = 3\n",
    "filters_orig_disc = 64\n",
    "layer_depth_disc  = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import Model, Sequential\n",
    "from tensorflow.keras.layers import (Input, InputLayer, Lambda, Conv2D, \n",
    "                                     BatchNormalization, Flatten, Dense, Reshape, \n",
    "                                     Conv2DTranspose, Activation, LeakyReLU, Dropout)\n",
    "\n",
    "# c.f. DAE architecture in Chapter 6, Notebook 3\n",
    "def simple_encoder(input_shape, latent_dim=32, \n",
    "                   kernel_size=4, filters_orig=32, layer_depth=4,\n",
    "                   final_activation=None, name='encoder'):\n",
    "    \"\"\"\n",
    "    Build a simple encoder network.\n",
    "    :param input_shape:      Shape of the input image (H x W x C)\n",
    "    :param latent_dim:       Size of the latent space\n",
    "    :param kernel_size:      Kernel size for the convolutions\n",
    "    :param filters_orig:     Number of filters for the 1st CNN layer (then multiplied by 2 each layer)\n",
    "    :param layer_depth:      Number of layers composing the encoder/decoder\n",
    "    :param final_activation: Activation function for the last layer\n",
    "    :param name:             Model name\n",
    "    :return:                 Encoder model\n",
    "    \"\"\"\n",
    "    \n",
    "    layers = []\n",
    "    layers += [\n",
    "        # Taking images as inputs:\n",
    "        InputLayer(input_shape=input_shape)\n",
    "    ]\n",
    "    for i in range(layer_depth):\n",
    "        # Adding conv layers to extract features:\n",
    "        layers += [\n",
    "            Conv2D(filters=min(filters_orig * (2 ** i), 512),\n",
    "                   kernel_size=kernel_size, activation=None, strides=2, \n",
    "                   padding='same'),\n",
    "            BatchNormalization(),\n",
    "            LeakyReLU(),\n",
    "            Dropout(0.3)\n",
    "        ]\n",
    "    layers += [\n",
    "        # Flattening feature vectors and outputting result vector:\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(latent_dim, activation=final_activation)\n",
    "    ]\n",
    "    \n",
    "    return Sequential(layers, name=name)\n",
    "    \n",
    "def simple_decoder(output_shape, latent_dim=32, \n",
    "                   kernel_size=4, filters_orig=32, layer_depth=4,\n",
    "                   final_activation=None, name='decoder'):\n",
    "    \"\"\"\n",
    "    Build a simple decoder network.\n",
    "    :param output_shape:     Shape of the output image (H x W x C)\n",
    "    :param latent_dim:       Size of the latent space\n",
    "    :param kernel_size:      Kernel size for the convolutions\n",
    "    :param filters_orig:     Number of filters for the 1st CNN layer (then multiplied by 2 each layer)\n",
    "    :param layer_depth:      Number of layers composing the encoder/decoder\n",
    "    :param final_activation: Activation function for the last layer\n",
    "    :param name:             Model name\n",
    "    :return:                 Decoder model\n",
    "    \"\"\"\n",
    "    \n",
    "    # c.f. Chapter 6, Notebook 3\n",
    "    ResizeToOutputShapeLayer = lambda name: Lambda(\n",
    "            lambda images: tf.image.resize(images, output_shape[:2]), name=name)\n",
    "    \n",
    "    divided_size = np.ceil(np.asarray(output_shape[:2]) / (2 ** layer_depth)).astype(np.int32)\n",
    "    filters = filters_orig * (2 ** layer_depth)\n",
    "    \n",
    "    layers = []\n",
    "    layers += [\n",
    "        # Taking codes from the latent space as inputs:\n",
    "        InputLayer(input_shape=(latent_dim,)),\n",
    "        # Extending and reshaping into a 2D feature map:\n",
    "        Dense(units=divided_size[0] * divided_size[1] * filters, activation=None),\n",
    "        BatchNormalization(),\n",
    "        Activation('relu'), # LeakyReLU(),\n",
    "        Reshape(target_shape=(*divided_size, filters)),\n",
    "        Dropout(0.3)\n",
    "    ]\n",
    "    for i in range(layer_depth):\n",
    "        # Upsampling/convolving to decode into an image:\n",
    "        layers += [\n",
    "            Conv2DTranspose(filters=min(filters_orig * (2 ** (layer_depth - i - 1)), 512), \n",
    "                            kernel_size=kernel_size, activation=None, strides=2, \n",
    "                            padding='same'),\n",
    "            BatchNormalization(),\n",
    "            Activation('relu') # LeakyReLU()\n",
    "        ]\n",
    "    \n",
    "    layers += [\n",
    "        # Making sure to get back the original dimensions:\n",
    "        ResizeToOutputShapeLayer('resize_to_orig'),\n",
    "        # Decoding and outputting the image:\n",
    "        Conv2DTranspose(filters=input_shape[-1], kernel_size=kernel_size, \n",
    "                        activation=final_activation,\n",
    "                        strides=1, padding='same')\n",
    "    ]\n",
    "    \n",
    "    return Sequential(layers, name=name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then instantiate our two networks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"generator\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 8192)              24576     \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2 (Batc (None, 8192)              32768     \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose (Conv2DTran (None, 8, 8, 256)         2097408   \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_1 (Ba (None, 8, 8, 256)         1024      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 16, 16, 128)       524416    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_2 (Ba (None, 16, 16, 128)       512       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 32, 32, 64)        131136    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_3 (Ba (None, 32, 32, 64)        256       \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "resize_to_orig (Lambda)      (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         1025      \n",
      "=================================================================\n",
      "Total params: 2,813,121\n",
      "Trainable params: 2,795,841\n",
      "Non-trainable params: 17,280\n",
      "_________________________________________________________________\n",
      "Model: \"discriminator\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 14, 14, 64)        1088      \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_4 (Ba (None, 14, 14, 64)        256       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 7, 7, 128)         131200    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_5 (Ba (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 4, 4, 256)         524544    \n",
      "_________________________________________________________________\n",
      "batch_normalization_v2_6 (Ba (None, 4, 4, 256)         1024      \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2)                 8194      \n",
      "=================================================================\n",
      "Total params: 666,818\n",
      "Trainable params: 665,922\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Generator (transforming a noise vector of shape `(laten_dim,)`\n",
    "# into an image of shape `(img_row, img_col, img_ch)`:\n",
    "generator = simple_decoder(input_shape, noise_dim, kernel_size,\n",
    "                           filters_orig_gen, layer_depth_gen, 'tanh', 'generator')\n",
    "generator.summary()\n",
    "\n",
    "# Discriminator performing a binary classification, judging if\n",
    "# images are real or fake (i.e., 2 classes):\n",
    "discriminator = simple_encoder(input_shape, 2, kernel_size, \n",
    "                               filters_orig_disc, layer_depth_disc, None, 'discriminator')\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and Testing a GAN\n",
    "\n",
    "Because the two networks should be trained one against the other, we cannot use a classic training loop.\n",
    "We have to define our own training step, to update the weight of both networks following the operations described in Chapter 7.\n",
    "\n",
    "***Note:*** While we could use the method `.train_on_batch(...)` of Keras models to train each network over its batch inside our custom training step, we will instead manually define the feed-forwarding and back-propagating, as an excuse to remind ourselves how `tf.GradientTape` works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
    "\n",
    "generator_optimizer = tf.keras.optimizers.Adam()\n",
    "discriminator_optimizer = tf.keras.optimizers.SGD()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def gan_train_step(image_batch):\n",
    "    \"\"\"\n",
    "    Perform one training step for the GAN model.\n",
    "    :param image_batch:     Batch of real training images\n",
    "    :return:                Loss of the generator, Loss of the discriminator\n",
    "    \"\"\"\n",
    "\n",
    "    batch_size  = tf.shape(image_batch)[0]\n",
    "    \n",
    "    real_images = image_batch\n",
    "    real_labels = tf.ones((batch_size,))\n",
    "    fake_labels = tf.zeros((batch_size,))\n",
    "    all_labels  = tf.concat((real_labels, fake_labels), axis=0)\n",
    "    \n",
    "    z_noise = tf.random.normal((batch_size, noise_dim))\n",
    "    with tf.GradientTape() as discriminator_tape, tf.GradientTape() as generator_tape:\n",
    "        # Generating fake images:\n",
    "        fake_images = generator(z_noise, training=True)\n",
    "    \n",
    "        # Classifying real/fake images:\n",
    "        pred_labels_on_real = discriminator(real_images, training=True)\n",
    "        pred_labels_on_fake = discriminator(fake_images, training=True)\n",
    "\n",
    "        pred_labels_on_all = tf.concat((pred_labels_on_real, pred_labels_on_fake), axis=0)\n",
    "    \n",
    "        # Computing the losses:\n",
    "        discriminator_loss = loss(all_labels, pred_labels_on_all)\n",
    "        generator_loss = loss(real_labels, pred_labels_on_fake)\n",
    "        \n",
    "    \n",
    "    # Finally, back-propagating the gradients into both networks:\n",
    "    discriminator_grad = discriminator_tape.gradient(discriminator_loss, \n",
    "                                                     discriminator.trainable_variables)\n",
    "    generator_grad = generator_tape.gradient(generator_loss, \n",
    "                                             generator.trainable_variables)\n",
    "    discriminator_optimizer.apply_gradients(\n",
    "        zip(discriminator_grad, discriminator.trainable_variables))\n",
    "    generator_optimizer.apply_gradients(\n",
    "        zip(generator_grad, generator.trainable_variables))\n",
    "    \n",
    "    return generator_loss, discriminator_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Note: In the above function, data is first feed-forwarded into the 2 networks, \n",
    "then back-propagation is performed. However, it is common to have instead the discriminator\n",
    "trained first over the batch (feed-forwarding then back-propagating), and only after to\n",
    "train the generator. Find below an implementation of such a variant of the GAN training step.\n",
    "\"\"\"\n",
    "\n",
    "# @tf.function\n",
    "# def gan_train_step(image_batch, num_iter_for_discriminator=1):\n",
    "# \n",
    "#     batch_size  = tf.shape(image_batch)[0]\n",
    "#     \n",
    "#     real_images = image_batch\n",
    "#     real_labels = tf.ones((batch_size,))\n",
    "#     fake_labels = tf.zeros((batch_size,))\n",
    "#     \n",
    "#     soft_real_labels = real_labels + tf.random.normal((batch_size,), mean=0., stddev=.1)\n",
    "#     soft_fake_labels = tf.math.abs(\n",
    "#         fake_labels + tf.random.normal((batch_size,), mean=0., stddev=.1))\n",
    "#     soft_all_labels  = tf.concat((soft_real_labels, soft_fake_labels), axis=0)\n",
    "#     \n",
    "#     # First, we train the discriminator:\n",
    "#     discriminator_loss_avg = []\n",
    "#     for i in range(num_iter_for_discriminator):\n",
    "#         z_noise = tf.random.normal((batch_size, noise_dim))\n",
    "#         with tf.GradientTape() as discriminator_tape:\n",
    "#             # Generating fake images:\n",
    "#             fake_images = generator(z_noise, training=False)\n",
    "#     \n",
    "#             # Classifying real/fake images:\n",
    "#             pred_labels_on_real = discriminator(real_images, training=True)\n",
    "#             pred_labels_on_fake = discriminator(fake_images, training=True)\n",
    "# \n",
    "#             pred_labels_on_all = tf.concat((pred_labels_on_real, pred_labels_on_fake), axis=0)\n",
    "# \n",
    "#             # Computing the loss:\n",
    "#             discriminator_loss = loss(soft_all_labels, pred_labels_on_all)\n",
    "#         \n",
    "#         # Back-propagating:\n",
    "#         discriminator_grad = discriminator_tape.gradient(\n",
    "#             discriminator_loss, discriminator.trainable_variables)\n",
    "#         discriminator_optimizer.apply_gradients(\n",
    "#             zip(discriminator_grad, discriminator.trainable_variables))\n",
    "#         discriminator_loss_avg.append(discriminator_loss)\n",
    "#         \n",
    "#     # Keeping average loss value for logs:    \n",
    "#     discriminator_loss_avg = tf.reduce_mean(tf.stack(discriminator_loss_avg, axis=0))\n",
    "#         \n",
    "#     # Then we train the generator:\n",
    "#     with tf.GradientTape() as generator_tape:\n",
    "#         z_noise = tf.random.normal((batch_size, noise_dim))\n",
    "#         \n",
    "#         # Generating fake images:\n",
    "#         fake_images = generator(z_noise, training=True)\n",
    "#         \n",
    "#         # Classifying if they look real-ish:\n",
    "#         pred_labels_on_fake = discriminator(fake_images, training=False)\n",
    "#         \n",
    "#         # Computing the loss:\n",
    "#         generator_loss = loss(real_labels, pred_labels_on_fake)\n",
    "#     \n",
    "#     # Back-propagating:\n",
    "#     generator_grad = generator_tape.gradient(\n",
    "#         generator_loss, generator.trainable_variables)\n",
    "#     generator_optimizer.apply_gradients(\n",
    "#         zip(generator_grad, generator.trainable_variables))\n",
    "#     \n",
    "#     return generator_loss, discriminator_loss_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a validation step, to collect validation metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def gan_val_step(image_batch):\n",
    "\n",
    "    batch_size  = tf.shape(image_batch)[0]\n",
    "    \n",
    "    real_images = image_batch\n",
    "    real_labels = tf.ones((batch_size,))\n",
    "    fake_labels = tf.zeros((batch_size,))\n",
    "    all_labels  = tf.concat((real_labels, fake_labels), axis=0)\n",
    "    \n",
    "    z_noise = tf.random.normal((batch_size, noise_dim))\n",
    "    \n",
    "    # Generating fake images:\n",
    "    fake_images = generator(z_noise, training=False)\n",
    "    \n",
    "    # Classifying real/fake images:\n",
    "    pred_labels_on_real = discriminator(real_images, training=False)\n",
    "    pred_labels_on_fake = discriminator(fake_images, training=False)\n",
    "    \n",
    "    pred_labels_on_all = tf.concat((pred_labels_on_real, pred_labels_on_fake), axis=0)\n",
    "    \n",
    "    # Computing the losses:\n",
    "    discriminator_loss = loss(all_labels, pred_labels_on_all)\n",
    "    generator_loss = loss(real_labels, pred_labels_on_fake)\n",
    "    \n",
    "    return generator_loss, discriminator_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also set up some of the usual metrics and callbacks. Since we will use a custom training loop, we will have to call the callbacks manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 4500x4500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import collections\n",
    "from keras_custom_callbacks import SimpleLogCallback, TensorBoardImageGridCallback\n",
    "\n",
    "model_name = 'mnist_gan'\n",
    "model_dir = os.path.join('models', model_name)\n",
    "\n",
    "# Callback to postprocess some validation results and display them in Tensorboard:\n",
    "# Fixed noise vectors to generate images from:\n",
    "z_noise_samples = tf.random.normal((num_show, noise_dim))\n",
    "\n",
    "# Function to post-process the results, i.e. to convert noise vectors in images before plotting,\n",
    "# as down in Notebook 6-1:\n",
    "def process_images_before_plot(input_z, pred_imgs, target_imgs=None):\n",
    "    # Transforming noise vectors into plottable images:\n",
    "    noise_min, noise_max = tf.reduce_min(input_z), tf.reduce_max(input_z)\n",
    "    input_z = (input_z - noise_min) / (noise_max - noise_min)\n",
    "    input_z_imgs = np.tile(np.expand_dims(np.expand_dims(input_z, 1), -1), \n",
    "                           (1, 2, 1, 3))\n",
    "    # Normalizing generated images from [-1, 1] to [0, 1] and tiling channels:\n",
    "    pred_imgs = (pred_imgs + 1.) / 2\n",
    "    pred_imgs = np.tile(pred_imgs, (1, 1, 1, 3))\n",
    "\n",
    "    return input_z_imgs, pred_imgs, target_imgs\n",
    "\n",
    "callback_tb_grid = TensorBoardImageGridCallback(\n",
    "    log_dir=model_dir, preprocess_fn=process_images_before_plot,\n",
    "    input_images=z_noise_samples, target_images=None, \n",
    "    grayscale=True, transpose=True,\n",
    "    tag=model_name + '_results', figsize=(15, 15))\n",
    "\n",
    "# Callback to simply log metrics at the end of each epoch (saving space compared to verbose=1):\n",
    "metrics_to_print = collections.OrderedDict([(\"g_loss\", \"g_loss\"), \n",
    "                                            (\"d_loss\", \"d_loss\"),\n",
    "                                            (\"val_g_loss\", \"val_g_loss\"), \n",
    "                                            (\"val_d_loss\", \"val_d_loss\")])\n",
    "callback_simple_log = SimpleLogCallback(metrics_to_print, num_epochs=num_epochs, log_frequency=2)\n",
    "\n",
    "\n",
    "callback_tb_grid.set_model(generator)\n",
    "custom_callbacks = [callback_simple_log, callback_tb_grid]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define our custom training loop. We define a first function for an epoch loop, and a second to wrap the whole training together, with validation and calls to the callback functions after each epoch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "def loop_for_epoch(step_fn, dataset_iter, train_steps_per_epoch,\n",
    "                   generator_loss_accumulator, discriminator_loss_accumulator):\n",
    "    for i in range(train_steps_per_epoch):\n",
    "        batch = next(dataset_iter)\n",
    "        generator_loss, discriminator_loss = step_fn(batch)\n",
    "        generator_loss_accumulator.update_state(generator_loss)\n",
    "        discriminator_loss_accumulator.update_state(discriminator_loss)\n",
    "    \n",
    "    # End of training epoch:\n",
    "    accumulated_generator_loss = generator_loss_accumulator.result()\n",
    "    accumulated_discriminator_loss = discriminator_loss_accumulator.result()\n",
    "    tf.summary.scalar('gen_loss', accumulated_generator_loss, \n",
    "                      step=generator_optimizer.iterations)\n",
    "    tf.summary.scalar('disc_loss', accumulated_discriminator_loss, \n",
    "                      step=discriminator_optimizer.iterations)\n",
    "    generator_loss_accumulator.reset_states()\n",
    "    discriminator_loss_accumulator.reset_states()\n",
    "    return accumulated_generator_loss.numpy(), accumulated_discriminator_loss.numpy()\n",
    "\n",
    "\n",
    "def train(train_dataset, epochs, train_steps_per_epoch,\n",
    "          val_dataset=None, val_steps_per_epoch=None,\n",
    "          summary_dir='./summaries', callbacks=[]):  \n",
    "    \n",
    "    train_summary_writer = tf.summary.create_file_writer(\n",
    "        os.path.join(summary_dir, 'train'))\n",
    "    if val_dataset is not None:\n",
    "        val_summary_writer = tf.summary.create_file_writer(\n",
    "            os.path.join(summary_dir, 'val'))\n",
    "    avg_generator_loss = tf.keras.metrics.Mean(name='avg_gen_loss', dtype=tf.float32)\n",
    "    avg_discriminator_loss = tf.keras.metrics.Mean(name='avg_disc_loss', dtype=tf.float32)\n",
    "    \n",
    "    for cb in custom_callbacks:\n",
    "        cb.on_train_begin()\n",
    "        \n",
    "    train_dataset_iter = train_dataset.__iter__()\n",
    "    for epoch in range(epochs):\n",
    "        with train_summary_writer.as_default():\n",
    "            g_train_loss, d_train_loss = loop_for_epoch(\n",
    "                gan_train_step, train_dataset_iter, train_steps_per_epoch,\n",
    "                avg_generator_loss, avg_discriminator_loss)\n",
    "\n",
    "        if val_dataset is not None:\n",
    "            val_dataset_iter = val_dataset.__iter__()\n",
    "            with val_summary_writer.as_default():\n",
    "                g_val_loss, d_val_loss = loop_for_epoch(\n",
    "                    gan_val_step, val_dataset_iter, val_steps_per_epoch,\n",
    "                    avg_generator_loss, avg_discriminator_loss)\n",
    "        else: \n",
    "            g_val_loss, d_val_loss = np.nan, np.nan\n",
    "        \n",
    "        logs = {'g_loss': g_train_loss, 'd_loss': d_train_loss,\n",
    "                'val_g_loss': g_val_loss, 'val_d_loss': d_val_loss}\n",
    "        for cb in custom_callbacks:\n",
    "            cb.on_epoch_end(epoch, logs)\n",
    "                    \n",
    "    for cb in custom_callbacks:\n",
    "        cb.on_train_end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: \u001b[92mstart\u001b[0m.\n",
      "Epoch  1/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.851\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.690\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.455\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.734\u001b[0m\n",
      "Epoch  3/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.723\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.769\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.403\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.771\u001b[0m\n",
      "Epoch  5/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.801\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.689\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.491\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.723\u001b[0m\n",
      "Epoch  7/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.819\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.675\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.538\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.687\u001b[0m\n",
      "Epoch  9/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.774\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.701\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.980\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.484\u001b[0m\n",
      "Epoch 11/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.734\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.722\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.469\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.747\u001b[0m\n",
      "Epoch 13/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.757\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.696\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.537\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.696\u001b[0m\n",
      "Epoch 15/17: \u001b[1mg_loss\u001b[0m = \u001b[94m0.732\u001b[0m; \u001b[1md_loss\u001b[0m = \u001b[94m0.712\u001b[0m; \u001b[1mval_g_loss\u001b[0m = \u001b[94m0.546\u001b[0m; \u001b[1mval_d_loss\u001b[0m = \u001b[94m0.718\u001b[0m\n",
      "Training: \u001b[91mend\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "train(train_dataset=train_dataset, epochs=num_epochs, \n",
    "      train_steps_per_epoch=train_steps_per_epoch,\n",
    "      val_dataset=val_dataset, val_steps_per_epoch=val_steps_per_epoch,\n",
    "      summary_dir=os.path.join(model_dir, 'summaries'), callbacks=custom_callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like the encode in VAEs, the discriminator is usually discarded once the model trained, to keep only the generative network.\n",
    "\n",
    "As done in Notebook [7-7](ch7_nb7_generate_images_with_vae_models.ipynb) for our VAE, we now display some images generated from random noise vectors, as well as images generated from a grid of vectors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 5760x360 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_examples_to_generate = 16\n",
    "\n",
    "# keeping the random vector constant for generation (prediction) so\n",
    "# it will be easier to see the improvement.\n",
    "random_vector_for_generation = tf.random.normal(\n",
    "    shape=[num_examples_to_generate, noise_dim])\n",
    "images = generator(random_vector_for_generation)\n",
    "\n",
    "images = (images + 1.) / 2\n",
    "images = np.tile(images, (1, 1, 1, 3))\n",
    "\n",
    "figure = plot_image_grid([images], grayscale=True, transpose=True)\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 400 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We built a 2D latent space, so let us explore it.\n",
    "\n",
    "dim1_num, dim2_num = 20, 20\n",
    "dim1_grid = np.linspace(-1.5, 1.5, dim1_num)\n",
    "dim2_grid = np.linspace(-1.5, 1.5, dim2_num)\n",
    "\n",
    "z_samples = []\n",
    "for dim1_val in dim1_grid:\n",
    "    for dim2_val in dim2_grid:\n",
    "        z_sample = np.array([dim1_val, dim2_val])\n",
    "        z_samples.append(z_sample)\n",
    "\n",
    "z_samples = np.stack(z_samples, axis=0)\n",
    "images = generator.predict(z_samples) \n",
    "images = (images + 1.) / 2\n",
    "images = np.tile(images, (1, 1, 1, 3))\n",
    "images = np.reshape(images, (dim1_num, dim2_num, img_rows, img_cols, 3))\n",
    "   \n",
    "figure = plot_image_grid(images, \n",
    "                         figure=plt.figure(figsize=(10, 10)),\n",
    "                         grayscale=True)\n",
    "figure.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "As we can observe in the results above, even though the generator itself was never fed with actual images, it learnt how to generate _digit-like_ images (our architectures and losses were too simple here to expect high-quality results). Note that GANs are infamously hard to train (we recommend having a look at the training hack listed by Soumith Chintala et al.: https://github.com/soumith/ganhacks).\n",
    "\n",
    "Even though, GANs are popular and powerful tools, and many advanced models have been proposed to generate detailed images or to transform existing images (as GANs can not only be conditioned on noise vector, but also any kind of data, images included).\n",
    "\n",
    "<a id=\"ref\"></a>\n",
    "#### References\n",
    "\n",
    "1. 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.\n",
    "2. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. and Bengio, Y., 2014. Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680)."
   ]
  }
 ],
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