Chapter04/ch4_nb1_implement_resnet_from_scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "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/ch4/ch4_nb1_implement_resnet_from_scratch.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 4: Influential Classification Tools</strong><br/>\n",
    "    </p>\n",
    "\n",
    "<h1 style=\"width: 100%; text-align: left; padding: 0px 25px;\"><small style=\"color: #e7692c;\">Notebook 1:</small><br/>Implementing ResNet from Scratch</h1>\n",
    "<br/>\n",
    "<p style=\"border-left: 15px solid #363636; text-align:justify; padding: 0 10px;\">\n",
    "    In this notebook, we will cover one of the state-of-the-art classifiers presented in Chapter 4: <strong><em>ResNet</em></strong>. Reimplementing this CNN is a great exercise to demonstrate how far we have gone so far, coding in a modular way a performant network from 18 to 152 layer deep!\n",
    "<br/><br/>\n",
    "    We will train and test our implementation on <em>CIFAR-100</em>, a well-known computer vision dataset.\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import os\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\"]= \"2\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _CIFAR-10_ and _CIFAR-100_ datasets are famous classification datasets, collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton[$^1$](#ref). The first version contains 60,000 $32 \\times 32$ images from 10 different classes, and the second contains 60,000 $32 \\times 32$ images from 100 different classes. In this notebook and the next ones for this chapter, we will build and train models to deal with _CIFAR-100_.\n",
    "\n",
    "\n",
    "***Note:*** Though this classification task is more complex than our previous experiments with _MNIST_, its complexity is not on par with the _ImageNet_ challenge for instance, mentioned in the chapter. We opted for _CIFAR-100_ as this dataset is easy to acquire and light enough for our readers to be able to reproduce our experiments. For the more ambitious ones, we detail in a supplementary notebook how to get started with *ImageNet* and its subset *Tiny-ImageNet*[$^2$](#ref)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TensorFlow-Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like other datasets, _CIFAR-100_ is commonly used by researchers to test their algorithms (though the more challenging *ImageNet*[$^2$](#ref) or *Open-Images*[$^3$](#ref) are nowadays preffered to *CIFAR-100*).\n",
    "\n",
    "To help researchers and amateurs, the TensorFlow team has come up with an optional package: **`tensorflow_datasets`** ([https://www.tensorflow.org/datasets](https://www.tensorflow.org/datasets)). This package provides helper functions to download and some common datasets.\n",
    "\n",
    "***Note:*** As mentioned in their [disclaimer](https://github.com/tensorflow/datasets#disclaimers):\n",
    "\n",
    "    \"This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.\"\n",
    "    \n",
    "Let us have a look, listing the datasets available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install tensorflow-datasets # Uncomment to install the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bair_robot_pushing_small',\n",
       " 'cats_vs_dogs',\n",
       " 'celeb_a',\n",
       " 'celeb_a_hq',\n",
       " 'cifar10',\n",
       " 'cifar100',\n",
       " 'coco2014',\n",
       " 'diabetic_retinopathy_detection',\n",
       " 'dummy_dataset_shared_generator',\n",
       " 'dummy_mnist',\n",
       " 'fashion_mnist',\n",
       " 'image_label_folder',\n",
       " 'imagenet2012',\n",
       " 'imdb_reviews',\n",
       " 'lm1b',\n",
       " 'lsun',\n",
       " 'mnist',\n",
       " 'moving_mnist',\n",
       " 'nsynth',\n",
       " 'omniglot',\n",
       " 'open_images_v4',\n",
       " 'quickdraw_bitmap',\n",
       " 'squad',\n",
       " 'starcraft_video',\n",
       " 'svhn_cropped',\n",
       " 'tf_flowers',\n",
       " 'wmt_translate_ende',\n",
       " 'wmt_translate_enfr']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "tfds.list_builders()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A detailed list can also be found on their website: [https://www.tensorflow.org/datasets/datasets](https://www.tensorflow.org/datasets/datasets).\n",
    "\n",
    "We will select `\"cifar100\"`, let the module download it, and display its meta-information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tfds.core.DatasetInfo(\n",
      "    name='cifar100',\n",
      "    version=1.3.1,\n",
      "    description='This dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a \"fine\" label (the class to which it belongs) and a \"coarse\" label (the superclass to which it belongs).',\n",
      "    urls=['https://www.cs.toronto.edu/~kriz/cifar.html'],\n",
      "    features=FeaturesDict({\n",
      "        'coarse_label': ClassLabel(shape=(), dtype=tf.int64, num_classes=20),\n",
      "        'image': Image(shape=(32, 32, 3), dtype=tf.uint8),\n",
      "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=100)\n",
      "    },\n",
      "    total_num_examples=60000,\n",
      "    splits={\n",
      "        'test': <tfds.core.SplitInfo num_examples=10000>,\n",
      "        'train': <tfds.core.SplitInfo num_examples=50000>\n",
      "    },\n",
      "    supervised_keys=('image', 'label'),\n",
      "    citation='\"\"\"\n",
      "        @TECHREPORT{Krizhevsky09learningmultiple,\n",
      "            author = {Alex Krizhevsky},\n",
      "            title = {Learning multiple layers of features from tiny images},\n",
      "            institution = {},\n",
      "            year = {2009}\n",
      "        }\n",
      "        \n",
      "    \"\"\"',\n",
      ")\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cifar_builder = tfds.builder(\"cifar100\")\n",
    "cifar_builder.download_and_prepare()\n",
    "\n",
    "print(cifar_builder.info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow-Datasets provides us with some useful information, like the sample numbers, types, and sizes, the dataset description, etc.\n",
    "\n",
    "We can also access the class names as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm']\n"
     ]
    }
   ],
   "source": [
    "print(cifar_builder.info.features[\"label\"].names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_(Yes, this dataset has... an interesting choice of classes...)_\n",
    "\n",
    "As we can see from the meta-information, _CIFAR-100_ images can also be classified among 20 coarser classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['aquatic_mammals', 'fish', 'flowers', 'food_containers', 'fruit_and_vegetables', 'household_electrical_devices', 'household_furniture', 'insects', 'large_carnivores', 'large_man-made_outdoor_things', 'large_natural_outdoor_scenes', 'large_omnivores_and_herbivores', 'medium_mammals', 'non-insect_invertebrates', 'people', 'reptiles', 'small_mammals', 'trees', 'vehicles_1', 'vehicles_2']\n"
     ]
    }
   ],
   "source": [
    "print(cifar_builder.info.features[\"coarse_label\"].names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our experiments, we will however stick to the more complex 100 classes.\n",
    "\n",
    "### Input Pipeline Preparation\n",
    "\n",
    "We have our training and testing data available. We can now explicitly define the input pipeline for the models we will train:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# Some hyper-parameters:\n",
    "input_shape = [224, 224, 3] # We will resize the input images to this shape\n",
    "batch_size  = 32            # Images per batch (reduce/increase according to the machine's capability)\n",
    "num_epochs  = 300           # Max number of training epochs\n",
    "\n",
    "# Train/val Datasets:\n",
    "train_cifar_dataset = cifar_builder.as_dataset(split=tfds.Split.TRAIN)\n",
    "val_cifar_dataset = cifar_builder.as_dataset(split=tfds.Split.TEST)\n",
    "\n",
    "# Number of classes:\n",
    "num_classes = cifar_builder.info.features['label'].num_classes\n",
    "\n",
    "# Number of images:\n",
    "num_train_imgs = cifar_builder.info.splits['train'].num_examples\n",
    "num_val_imgs = cifar_builder.info.splits['test'].num_examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset instance: <_OptionsDataset shapes: {image: (32, 32, 3), label: (), coarse_label: ()}, types: {image: tf.uint8, label: tf.int64, coarse_label: tf.int64}>\n"
     ]
    }
   ],
   "source": [
    "print('Training dataset instance: {}'.format(train_cifar_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is provided by the TensorFlow-Datasets module as a **`tf.data.Dataset`** instance.\n",
    "\n",
    "Chapter 7 is partially dedicated to the **`tf.data`** API and the creation of efficient input pipelines, so we won't detail too much here. We will only mention that `tf.data.Dataset` instances behave like optimized data iterators. They also possess multiple methods to pre-process the data.\n",
    "\n",
    "For example, we will tell TensorFlow to iterate over the samples `epochs` times and to shuffle them before returning them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cifar_dataset = train_cifar_dataset.repeat(num_epochs).shuffle(10000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to pre-process the data itself, resizing the images, ignoring the coarser labels, and optionally applying some random _augmentations_ to the images (random operations to artificially increase the number of different images our model can train on - more details in Chapter 7).\n",
    "\n",
    "We thus implement a function pre-processing the samples accordingly, and passed it to the `tf.data.Dataset` instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _prepare_data_fn(features, input_shape, augment=False):\n",
    "    \"\"\"\n",
    "    Resize image to expected dimensions, and opt. apply some random transformations.\n",
    "    :param features:    Data\n",
    "    :param input_shape: Shape expected by the models (images will be resized accordingly)\n",
    "    :param augment:     Flag to apply some random augmentations to the images\n",
    "    :return:            Augmented Images, Labels\n",
    "    \"\"\"\n",
    "    input_shape = tf.convert_to_tensor(input_shape)\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",
    "    image = features['image']\n",
    "    label = features['label']\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 augment:\n",
    "        # Randomly applied horizontal flip:\n",
    "        image = tf.image.random_flip_left_right(image)\n",
    "\n",
    "        # Random B/S changes:\n",
    "        image = tf.image.random_brightness(image, max_delta=0.1)\n",
    "        image = tf.image.random_saturation(image, lower=0.5, upper=1.5)\n",
    "        image = tf.clip_by_value(image, 0.0, 1.0) # keeping pixel values in check\n",
    "\n",
    "        # Random resize and random crop back to expected size:\n",
    "        \n",
    "        random_scale_factor = tf.random.uniform([1], minval=1., maxval=1.4, dtype=tf.float32)\n",
    "        scaled_height = tf.cast(tf.cast(input_shape[0], tf.float32) * random_scale_factor, \n",
    "                                tf.int32)\n",
    "        scaled_width = tf.cast(tf.cast(input_shape[1], tf.float32) * random_scale_factor, \n",
    "                               tf.int32)\n",
    "        scaled_shape = tf.squeeze(tf.stack([scaled_height, scaled_width]))\n",
    "        image = tf.image.resize(image, scaled_shape)\n",
    "        image = tf.image.random_crop(image, input_shape)\n",
    "    else:\n",
    "        image = tf.image.resize(image, input_shape[:2])\n",
    "        \n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "prepare_data_fn_for_train = functools.partial(_prepare_data_fn, \n",
    "                                              input_shape=input_shape,\n",
    "                                              augment=True)\n",
    "\n",
    "train_cifar_dataset = train_cifar_dataset.map(prepare_data_fn_for_train, num_parallel_calls=4)\n",
    "\n",
    "# We also ask the dataset to batch the samples:\n",
    "train_cifar_dataset = train_cifar_dataset.batch(batch_size)\n",
    "\n",
    "train_cifar_dataset = train_cifar_dataset.prefetch(1) # improve time performance c.f. Chapter 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we further prepare the validation dataset (though not shuffling or augmenting it):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepare_data_fn_for_val = functools.partial(_prepare_data_fn, \n",
    "                                              input_shape=input_shape,\n",
    "                                              augment=False)\n",
    "\n",
    "val_cifar_dataset = (val_cifar_dataset\n",
    "                     .repeat()\n",
    "                     .map(prepare_data_fn_for_val, num_parallel_calls=4)\n",
    "                     .batch(batch_size)\n",
    "                     .prefetch(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have our datasets ready to be iterated over for training.\n",
    "\n",
    "These objects are indeed compatible with Keras methods such as `model.fit(...)`. However, to work properly together, we need to specify to Keras how many batches per epoch such Dataset objects have (when calling `model.fit(...)`, with the parameter `steps_per_epoch` for the number of training batches, and `validation_steps` for the validation ones):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_steps_per_epoch = math.ceil(num_train_imgs / batch_size)\n",
    "val_steps_per_epoch   = math.ceil(num_val_imgs / batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** For the next notebooks, we will wrap this data preparation into functions and save them into [`cifar_utils.py`](./cifar_utils.py), for reuse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing ResNet with Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, we will opt for the Keras API to speed up the implementation. By the end of this section, we will have a _ResNet builder_ tool, able to instantiate any of the usual residual networks (ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-102, etc.)[$^4$](#ref)!\n",
    "\n",
    "***Note***: We will use Keras Functional API to implement the model. This API can also be used in a more *object-oriented* manner. In the [Appendix](#appendix) of this notebook, we propose a second implementation of ResNet from scratch, following this paradigm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import (\n",
    "    Input, Activation, Dense, Flatten, Conv2D, MaxPooling2D, \n",
    "    GlobalAveragePooling2D, AveragePooling2D, BatchNormalization, add)\n",
    "import tensorflow.keras.regularizers as regulizers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the Residual Blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to implement such a model-builder function, able to generate networks of various sizes, we need to make the ResNet implementation modular. Therefore, we will implement the ResNet main components, the residual blocks, in a modular way, so that we can chain them indefinitely.\n",
    "\n",
    "In Chapter 4, we saw that the residual blocks are composed of:\n",
    " - one _residual_ branch, applying a 3x3 convolution to the input, followed by batch normalization and ReLu activation;\n",
    " - one _shortcut_ branch, directly forwarding the input without modification, or only applying a 1x1 convolution to adapt the input volume in case it changed in the other branch;\n",
    " - one _merge_ operation, performing an element-wise addition between the results of the two branches.\n",
    "\n",
    "We will now implement each of this sub-modules. First, let us wrap the `conv-batnchnorm-relu` stack of layers into a single function we could reuse at will:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def _res_conv(filters, kernel_size=3, padding='same', strides=1, use_relu=True, use_bias=False, name='cbr',\n",
    "              kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "    \"\"\"\n",
    "    Return a layer block chaining conv, batchnrom and reLU activation.\n",
    "    :param filters:                 Number of filters.\n",
    "    :param kernel_size:             Kernel size.\n",
    "    :param padding:                 Convolution padding.\n",
    "    :param strides:                 Convolution strides.\n",
    "    :param use_relu:                Flag to apply ReLu activation at the end.\n",
    "    :param use_bias:                Flag to use bias or not in Conv layer.\n",
    "    :param name:                    Name suffix for the layers.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :return:                        Callable layer block\n",
    "    \"\"\"\n",
    "\n",
    "    def layer_fn(x):\n",
    "        conv = Conv2D(\n",
    "            filters=filters, kernel_size=kernel_size, padding=padding, strides=strides, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer, \n",
    "            name=name + '_c')(x)\n",
    "        res = BatchNormalization(axis=-1, name=name + '_bn')(conv)\n",
    "        if use_relu:\n",
    "            res = Activation(\"relu\", name=name + '_r')(res)\n",
    "        return res\n",
    "\n",
    "    return layer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Thanks to this function, we can add this stack of 3 layers to our network simply with `x = _conv_batchnorm_relu(**params)(x)`. We are therefore almost done covering the implementation of the 1st branch in the residual blocks.\n",
    "\n",
    "Now we will deal with the _shortcut_ branch. For simplicity, we will wrap its operation along with the following _merge_ operation since. Indeed, thisfunction already needs to receive as parameters both the input tensor and the result of the _residual_ branch to the _shortcut_ one, so that it can compare the tensors' shapes to check if the dimensions were changed. If so, a 1x1  convolution is applied to resize the input tensor accordingly before the _merge_. Otherwise, the input tensor is left untouched. And since the function has both thew _residual_ and the _shortcut_ results, it is a matter of a single line to do the _merge_ right away:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def _merge_with_shortcut(kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4), \n",
    "                         name='block'):\n",
    "    \"\"\"\n",
    "    Return a layer block which merge an input tensor and the corresponding \n",
    "    residual output tensor from another branch.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :param name:                    Name suffix for the layers.\n",
    "    :return:                        Callable layer block\n",
    "    \"\"\"\n",
    "\n",
    "    def layer_fn(x, x_residual):\n",
    "        # We check if `x_residual` was scaled down. If so, we scale `x` accordingly with a 1x1 conv:\n",
    "        x_shape = tf.keras.backend.int_shape(x)\n",
    "        x_residual_shape = tf.keras.backend.int_shape(x_residual)\n",
    "        if x_shape == x_residual_shape:\n",
    "            shortcut = x\n",
    "        else:\n",
    "            strides = (\n",
    "                int(round(x_shape[1] / x_residual_shape[1])), # vertical stride\n",
    "                int(round(x_shape[2] / x_residual_shape[2]))  # horizontal stride\n",
    "            )\n",
    "            x_residual_channels = x_residual_shape[3]\n",
    "            shortcut = Conv2D(\n",
    "                filters=x_residual_channels, kernel_size=(1, 1), padding=\"valid\", strides=strides,\n",
    "                kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "                name=name + '_shortcut_c')(x)\n",
    "\n",
    "        merge = add([shortcut, x_residual])\n",
    "        return merge\n",
    "\n",
    "    return layer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "We now have everything to create complete residual blocks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _residual_block_basic(filters, kernel_size=3, strides=1, use_bias=False, name='res_basic',\n",
    "                          kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "    \"\"\"\n",
    "    Return a basic residual layer block.\n",
    "    :param filters:                 Number of filters.\n",
    "    :param kernel_size:             Kernel size.\n",
    "    :param strides:                 Convolution strides\n",
    "    :param use_bias:                Flag to use bias or not in Conv layer.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :return:                        Callable layer block\n",
    "    \"\"\"\n",
    "\n",
    "    def layer_fn(x):\n",
    "        x_conv1 = _res_conv(\n",
    "            filters=filters, kernel_size=kernel_size, padding='same', strides=strides, \n",
    "            use_relu=True, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            name=name + '_cbr_1')(x)\n",
    "        x_residual = _res_conv(\n",
    "            filters=filters, kernel_size=kernel_size, padding='same', strides=1, \n",
    "            use_relu=False, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            name=name + '_cbr_2')(x_conv1)\n",
    "        merge = _merge_with_shortcut(kernel_initializer, kernel_regularizer,name=name)(x, x_residual)\n",
    "        merge = Activation('relu')(merge)\n",
    "        return merge\n",
    "\n",
    "    return layer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "With this implementation, we can easily chain residual blocks, e.g.:\n",
    "    # x = previous layer\n",
    "    x = _residual_block_basic(filters=64, strides=2)(x)\n",
    "    x = _residual_block_basic(filters=64, strides=1)(x)\n",
    "    # ...\n",
    "    \n",
    "Before building our complete model, we will however implement a different type of residual blocks. Indeed, this basic block becomes quite costly in terms of computations the deeper the networks goes. In their paper introducing ResNets, He et al. proposed a second type of blocks to replace the basic ones for deeper architectures. To keep in check the number of computations, those blocks make use of the _bottleneck_ technique we presented with the Inception models. The two $3 \\times 3$ convolutions are replaced by a $3 \\times 3$ convolution surrounded by $1 \\times 1$ ones as follow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def _residual_block_bottleneck(filters, kernel_size=3, strides=1, use_bias=False, name='res_bottleneck',\n",
    "                               kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "    \"\"\"\n",
    "    Return a residual layer block with bottleneck, recommended for deep ResNets (depth > 34).\n",
    "    :param filters:                 Number of filters.\n",
    "    :param kernel_size:             Kernel size.\n",
    "    :param strides:                 Convolution strides\n",
    "    :param use_bias:                Flag to use bias or not in Conv layer.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :return:                        Callable layer block\n",
    "    \"\"\"\n",
    "\n",
    "    def layer_fn(x):\n",
    "        x_bottleneck = _res_conv(\n",
    "            filters=filters, kernel_size=1, padding='valid', strides=strides, \n",
    "            use_relu=True, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            name=name + '_cbr1')(x)\n",
    "        x_conv = _res_conv(\n",
    "            filters=filters, kernel_size=kernel_size, padding='same', strides=1, \n",
    "            use_relu=True, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            name=name + '_cbr2')(x_bottleneck)\n",
    "        x_residual = _res_conv(\n",
    "            filters=filters * 4, kernel_size=1, padding='valid', strides=1, \n",
    "            use_relu=False, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            name=name + '_cbr3')(x_conv)\n",
    "        merge = _merge_with_shortcut(kernel_initializer, kernel_regularizer, name=name)(x, x_residual)\n",
    "        merge = Activation('relu')(merge)\n",
    "        return merge\n",
    "\n",
    "    return layer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chaining Blocks into Modular Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Chapter 4, we saw that residual blocks in usual ResNet models are chained according to a specific pattern (c.f. figure presenting the ResNet architecture). Every `b` blocks, we find a residual block with `strides = 2` reducing the spatial dimensionality (followed by `b - 1` blocks with `strides = 1`). It will make the rest of the implementation much easier if we wrap the instantiation of these `b` blocks together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _residual_macroblock(block_fn, filters, repetitions=3, kernel_size=3, strides_1st_block=1, use_bias=False,\n",
    "                         kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4),\n",
    "                         name='res_macroblock'):\n",
    "    \"\"\"\n",
    "    Return a layer block, composed of a repetition of `N` residual blocks.\n",
    "    :param block_fn:                Block layer method to be used.\n",
    "    :param repetitions:             Number of times the block should be repeated inside.\n",
    "    :param filters:                 Number of filters.\n",
    "    :param kernel_size:             Kernel size.\n",
    "    :param strides_1st_block:       Convolution strides for the 1st block.\n",
    "    :param use_bias:                Flag to use bias or not in Conv layer.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :return:                        Callable layer block\n",
    "    \"\"\"\n",
    "\n",
    "    def layer_fn(x):\n",
    "        for i in range(repetitions):\n",
    "            block_name = \"{}_{}\".format(name, i) \n",
    "            strides = strides_1st_block if i == 0 else 1\n",
    "            x = block_fn(filters=filters, kernel_size=kernel_size, \n",
    "                         strides=strides, use_bias=use_bias,\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "                         name=block_name)(x)\n",
    "        return x\n",
    "\n",
    "    return layer_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, the function we just wrote makes it possible to easily chain these macro-blocks together into a network as deep as we wish! It is now just a matter of adding the 3 layers preceeding the residual blocks (a 7x7 convolution and a max-pooling) as well as the final layers leading to the predictions (avg-pooling and FC):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResNet(input_shape, num_classes=1000, block_fn=_residual_block_basic, repetitions=(2, 2, 2, 2),\n",
    "           use_bias=False, kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "    \"\"\"\n",
    "    Build a ResNet model for classification.\n",
    "    :param input_shape:             Input shape (e.g. (224, 224, 3))\n",
    "    :param num_classes:             Number of classes to predict\n",
    "    :param block_fn:                Block layer method to be used.\n",
    "    :param repetitions:             List of repetitions for each macro-blocks the network should contain.\n",
    "    :param use_bias:                Flag to use bias or not in Conv layer.\n",
    "    :param kernel_initializer:      Kernel initialisation method name.\n",
    "    :param kernel_regularizer:      Kernel regularizer.\n",
    "    :return:                        ResNet model.\n",
    "    \"\"\"\n",
    "\n",
    "    # Input and 1st layers:\n",
    "    inputs = Input(shape=input_shape)\n",
    "    conv = _res_conv(\n",
    "        filters=64, kernel_size=7, strides=2, use_relu=True, use_bias=use_bias,\n",
    "        kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)(inputs)\n",
    "    maxpool = MaxPooling2D(pool_size=3, strides=2, padding='same')(conv)\n",
    "\n",
    "    # Chain of residual blocks:\n",
    "    filters = 64\n",
    "    strides = 2\n",
    "    res_block = maxpool\n",
    "    for i, repet in enumerate(repetitions):\n",
    "        # We do not further reduce the input size for the 1st block (max-pool applied just before):\n",
    "        block_strides = strides if i != 0 else 1\n",
    "        macroblock_name = \"block_{}\".format(i) \n",
    "        res_block = _residual_macroblock(\n",
    "            block_fn=block_fn, repetitions=repet, name=macroblock_name,\n",
    "            filters=filters, strides_1st_block=block_strides, use_bias=use_bias,\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)(res_block)\n",
    "        filters = min(filters * 2, 1024) # we limit to 1024 filters max\n",
    "\n",
    "    # Final layers for prediction:\n",
    "    res_spatial_dim = tf.keras.backend.int_shape(res_block)[1:3]\n",
    "    avg_pool = AveragePooling2D(pool_size=res_spatial_dim, strides=1)(res_block)\n",
    "    flatten = Flatten()(avg_pool)\n",
    "    predictions = Dense(units=num_classes, kernel_initializer=kernel_initializer, \n",
    "                        activation='softmax')(flatten)\n",
    "\n",
    "    # Model:\n",
    "    model = Model(inputs=inputs, outputs=predictions)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have our ResNet-builder function! Playing with the `repetitions` parameter, we can now spawn networks as deep as we want.\n",
    "\n",
    "With this, we can easily define the classic ResNet models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResNet18(input_shape, num_classes=1000, use_bias=True,\n",
    "             kernel_initializer='he_normal', kernel_regularizer=None):\n",
    "    return ResNet(input_shape, num_classes, block_fn=_residual_block_basic, repetitions=(2, 2, 2, 2),\n",
    "                  use_bias=use_bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "\n",
    "def ResNet34(input_shape, num_classes=1000, use_bias=True,\n",
    "             kernel_initializer='he_normal', kernel_regularizer=None):\n",
    "    return ResNet(input_shape, num_classes, block_fn=_residual_block_basic, repetitions=(3, 4, 6, 3),\n",
    "                  use_bias=use_bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "\n",
    "def ResNet50(input_shape, num_classes=1000, use_bias=True,\n",
    "             kernel_initializer='he_normal', kernel_regularizer=None):\n",
    "    # Note: ResNet50 is similar to ResNet34,\n",
    "    # with the basic blocks replaced by bottleneck ones (3 conv layers each instead of 2)\n",
    "    return ResNet(input_shape, num_classes, block_fn=_residual_block_bottleneck, repetitions=(3, 4, 6, 3),\n",
    "                  use_bias=use_bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "\n",
    "def ResNet101(input_shape, num_classes=1000, use_bias=True,\n",
    "             kernel_initializer='he_normal', kernel_regularizer=None):\n",
    "    return ResNet(input_shape, num_classes, block_fn=_residual_block_bottleneck, repetitions=(3, 4, 23, 3),\n",
    "                  use_bias=use_bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "\n",
    "def ResNet152(input_shape, num_classes=1000, use_bias=True,\n",
    "             kernel_initializer='he_normal', kernel_regularizer=None):\n",
    "    return ResNet(input_shape, num_classes, block_fn=_residual_block_bottleneck, repetitions=(3, 8, 36, 3),\n",
    "                  use_bias=use_bias, kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demonstrating with ResNet-50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the rest of this notebook, we will opt for ResNet-50. \n",
    "\n",
    "Note that for their ImageNet experiments, the ResNet authors used 224x224 images as input. In order to have a similar network, we will consider the same input dimensions (adding a proper scaling operation to our input pipeline, to resize the Tiny-ImageNet samples from 64x64 to 224x224):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "cbr_c (Conv2D)                  (None, 112, 112, 64) 9472        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "cbr_bn (BatchNormalizationV2)   (None, 112, 112, 64) 256         cbr_c[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "cbr_r (Activation)              (None, 112, 112, 64) 0           cbr_bn[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 56, 56, 64)   0           cbr_r[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr1_c (Conv2D)       (None, 56, 56, 64)   4160        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr1_bn (BatchNormali (None, 56, 56, 64)   256         block_0_0_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr1_r (Activation)   (None, 56, 56, 64)   0           block_0_0_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr2_c (Conv2D)       (None, 56, 56, 64)   36928       block_0_0_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr2_bn (BatchNormali (None, 56, 56, 64)   256         block_0_0_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr2_r (Activation)   (None, 56, 56, 64)   0           block_0_0_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr3_c (Conv2D)       (None, 56, 56, 256)  16640       block_0_0_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_shortcut_c (Conv2D)   (None, 56, 56, 256)  16640       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_0_0_cbr3_bn (BatchNormali (None, 56, 56, 256)  1024        block_0_0_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 56, 56, 256)  0           block_0_0_shortcut_c[0][0]       \n",
      "                                                                 block_0_0_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 56, 56, 256)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr1_c (Conv2D)       (None, 56, 56, 64)   16448       activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr1_bn (BatchNormali (None, 56, 56, 64)   256         block_0_1_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr1_r (Activation)   (None, 56, 56, 64)   0           block_0_1_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr2_c (Conv2D)       (None, 56, 56, 64)   36928       block_0_1_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr2_bn (BatchNormali (None, 56, 56, 64)   256         block_0_1_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr2_r (Activation)   (None, 56, 56, 64)   0           block_0_1_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr3_c (Conv2D)       (None, 56, 56, 256)  16640       block_0_1_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_1_cbr3_bn (BatchNormali (None, 56, 56, 256)  1024        block_0_1_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 56, 56, 256)  0           activation[0][0]                 \n",
      "                                                                 block_0_1_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 56, 56, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr1_c (Conv2D)       (None, 56, 56, 64)   16448       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr1_bn (BatchNormali (None, 56, 56, 64)   256         block_0_2_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr1_r (Activation)   (None, 56, 56, 64)   0           block_0_2_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr2_c (Conv2D)       (None, 56, 56, 64)   36928       block_0_2_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr2_bn (BatchNormali (None, 56, 56, 64)   256         block_0_2_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr2_r (Activation)   (None, 56, 56, 64)   0           block_0_2_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr3_c (Conv2D)       (None, 56, 56, 256)  16640       block_0_2_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_0_2_cbr3_bn (BatchNormali (None, 56, 56, 256)  1024        block_0_2_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 56, 56, 256)  0           activation_1[0][0]               \n",
      "                                                                 block_0_2_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 256)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr1_c (Conv2D)       (None, 28, 28, 128)  32896       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr1_bn (BatchNormali (None, 28, 28, 128)  512         block_1_0_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr1_r (Activation)   (None, 28, 28, 128)  0           block_1_0_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr2_c (Conv2D)       (None, 28, 28, 128)  147584      block_1_0_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr2_bn (BatchNormali (None, 28, 28, 128)  512         block_1_0_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr2_r (Activation)   (None, 28, 28, 128)  0           block_1_0_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr3_c (Conv2D)       (None, 28, 28, 512)  66048       block_1_0_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_shortcut_c (Conv2D)   (None, 28, 28, 512)  131584      activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_1_0_cbr3_bn (BatchNormali (None, 28, 28, 512)  2048        block_1_0_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 28, 28, 512)  0           block_1_0_shortcut_c[0][0]       \n",
      "                                                                 block_1_0_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 28, 28, 512)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr1_c (Conv2D)       (None, 28, 28, 128)  65664       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr1_bn (BatchNormali (None, 28, 28, 128)  512         block_1_1_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr1_r (Activation)   (None, 28, 28, 128)  0           block_1_1_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr2_c (Conv2D)       (None, 28, 28, 128)  147584      block_1_1_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr2_bn (BatchNormali (None, 28, 28, 128)  512         block_1_1_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr2_r (Activation)   (None, 28, 28, 128)  0           block_1_1_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr3_c (Conv2D)       (None, 28, 28, 512)  66048       block_1_1_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_1_cbr3_bn (BatchNormali (None, 28, 28, 512)  2048        block_1_1_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 28, 28, 512)  0           activation_3[0][0]               \n",
      "                                                                 block_1_1_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 28, 28, 512)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr1_c (Conv2D)       (None, 28, 28, 128)  65664       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr1_bn (BatchNormali (None, 28, 28, 128)  512         block_1_2_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr1_r (Activation)   (None, 28, 28, 128)  0           block_1_2_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr2_c (Conv2D)       (None, 28, 28, 128)  147584      block_1_2_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr2_bn (BatchNormali (None, 28, 28, 128)  512         block_1_2_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr2_r (Activation)   (None, 28, 28, 128)  0           block_1_2_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr3_c (Conv2D)       (None, 28, 28, 512)  66048       block_1_2_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_2_cbr3_bn (BatchNormali (None, 28, 28, 512)  2048        block_1_2_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 28, 28, 512)  0           activation_4[0][0]               \n",
      "                                                                 block_1_2_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 28, 28, 512)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr1_c (Conv2D)       (None, 28, 28, 128)  65664       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr1_bn (BatchNormali (None, 28, 28, 128)  512         block_1_3_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr1_r (Activation)   (None, 28, 28, 128)  0           block_1_3_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr2_c (Conv2D)       (None, 28, 28, 128)  147584      block_1_3_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr2_bn (BatchNormali (None, 28, 28, 128)  512         block_1_3_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr2_r (Activation)   (None, 28, 28, 128)  0           block_1_3_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr3_c (Conv2D)       (None, 28, 28, 512)  66048       block_1_3_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_1_3_cbr3_bn (BatchNormali (None, 28, 28, 512)  2048        block_1_3_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 28, 28, 512)  0           activation_5[0][0]               \n",
      "                                                                 block_1_3_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 28, 28, 512)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr1_c (Conv2D)       (None, 14, 14, 256)  131328      activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_0_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_0_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_0_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_0_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_0_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_0_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_shortcut_c (Conv2D)   (None, 14, 14, 1024) 525312      activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_2_0_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_0_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 14, 14, 1024) 0           block_2_0_shortcut_c[0][0]       \n",
      "                                                                 block_2_0_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 14, 14, 1024) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr1_c (Conv2D)       (None, 14, 14, 256)  262400      activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_1_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_1_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_1_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_1_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_1_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_1_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_1_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_1_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 14, 14, 1024) 0           activation_7[0][0]               \n",
      "                                                                 block_2_1_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 14, 14, 1024) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr1_c (Conv2D)       (None, 14, 14, 256)  262400      activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_2_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_2_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_2_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_2_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_2_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_2_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_2_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_2_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 14, 14, 1024) 0           activation_8[0][0]               \n",
      "                                                                 block_2_2_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 14, 14, 1024) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr1_c (Conv2D)       (None, 14, 14, 256)  262400      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_3_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_3_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_3_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_3_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_3_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_3_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_3_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_3_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 14, 14, 1024) 0           activation_9[0][0]               \n",
      "                                                                 block_2_3_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 14, 14, 1024) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr1_c (Conv2D)       (None, 14, 14, 256)  262400      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_4_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_4_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_4_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_4_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_4_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_4_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_4_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_4_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 14, 14, 1024) 0           activation_10[0][0]              \n",
      "                                                                 block_2_4_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 14, 14, 1024) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr1_c (Conv2D)       (None, 14, 14, 256)  262400      activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr1_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_5_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr1_r (Activation)   (None, 14, 14, 256)  0           block_2_5_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr2_c (Conv2D)       (None, 14, 14, 256)  590080      block_2_5_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr2_bn (BatchNormali (None, 14, 14, 256)  1024        block_2_5_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr2_r (Activation)   (None, 14, 14, 256)  0           block_2_5_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr3_c (Conv2D)       (None, 14, 14, 1024) 263168      block_2_5_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_2_5_cbr3_bn (BatchNormali (None, 14, 14, 1024) 4096        block_2_5_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 14, 14, 1024) 0           activation_11[0][0]              \n",
      "                                                                 block_2_5_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 14, 14, 1024) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr1_c (Conv2D)       (None, 7, 7, 512)    524800      activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr1_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_0_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr1_r (Activation)   (None, 7, 7, 512)    0           block_3_0_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr2_c (Conv2D)       (None, 7, 7, 512)    2359808     block_3_0_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr2_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_0_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr2_r (Activation)   (None, 7, 7, 512)    0           block_3_0_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr3_c (Conv2D)       (None, 7, 7, 2048)   1050624     block_3_0_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_shortcut_c (Conv2D)   (None, 7, 7, 2048)   2099200     activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_3_0_cbr3_bn (BatchNormali (None, 7, 7, 2048)   8192        block_3_0_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 7, 7, 2048)   0           block_3_0_shortcut_c[0][0]       \n",
      "                                                                 block_3_0_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 7, 7, 2048)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr1_c (Conv2D)       (None, 7, 7, 512)    1049088     activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr1_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_1_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr1_r (Activation)   (None, 7, 7, 512)    0           block_3_1_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr2_c (Conv2D)       (None, 7, 7, 512)    2359808     block_3_1_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr2_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_1_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr2_r (Activation)   (None, 7, 7, 512)    0           block_3_1_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr3_c (Conv2D)       (None, 7, 7, 2048)   1050624     block_3_1_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_1_cbr3_bn (BatchNormali (None, 7, 7, 2048)   8192        block_3_1_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 7, 7, 2048)   0           activation_13[0][0]              \n",
      "                                                                 block_3_1_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 7, 7, 2048)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr1_c (Conv2D)       (None, 7, 7, 512)    1049088     activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr1_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_2_cbr1_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr1_r (Activation)   (None, 7, 7, 512)    0           block_3_2_cbr1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr2_c (Conv2D)       (None, 7, 7, 512)    2359808     block_3_2_cbr1_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr2_bn (BatchNormali (None, 7, 7, 512)    2048        block_3_2_cbr2_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr2_r (Activation)   (None, 7, 7, 512)    0           block_3_2_cbr2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr3_c (Conv2D)       (None, 7, 7, 2048)   1050624     block_3_2_cbr2_r[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_3_2_cbr3_bn (BatchNormali (None, 7, 7, 2048)   8192        block_3_2_cbr3_c[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 7, 7, 2048)   0           activation_14[0][0]              \n",
      "                                                                 block_3_2_cbr3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 7, 7, 2048)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 1, 1, 2048)   0           activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 2048)         0           average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 100)          204900      flatten[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 23,777,252\n",
      "Trainable params: 23,731,812\n",
      "Non-trainable params: 45,440\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "resnet50 = ResNet50(input_shape=input_shape, num_classes=num_classes)\n",
    "resnet50.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying Images with ResNet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now apply our ResNet-50 model to the classification of the _CIFAR-100_ pictures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Network\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw in previous chapters and notebooks, training a model with Keras is straightforward. We first instantiate the optimizer, as well as some callbacks to visualize results or regularly save the trained model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To imitate the ImageNet experiments, we will add the top-5 accuracy metrics we presented in the chapter.\n",
    "Conveniently, TensorFlow and Keras already provides multiple predefined losses and metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.Adam() #tf.keras.optimizers.SGD(momentum=0.9, nesterov=True)\n",
    "\n",
    "accuracy_metric = tf.metrics.SparseCategoricalAccuracy(name='acc')\n",
    "top5_accuracy_metric = tf.metrics.SparseTopKCategoricalAccuracy(k=5, name='top5_acc')\n",
    "resnet50.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', \n",
    "                 metrics=[accuracy_metric, top5_accuracy_metric])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have been using various pre-defined callbacks in the previous chapters. 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 and to have more concise logs (since this notebook is quite long already), we will implement a callback printing some results only every `log_frequency` epochs, with some fancier styling:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting some variables to format the logs:\n",
    "log_begin_red, log_begin_blue, log_begin_green = '\\033[91m', '\\033[94m', '\\033[92m'\n",
    "log_begin_bold, log_begin_underline = '\\033[1m', '\\033[4m'\n",
    "log_end_format = '\\033[0m'\n",
    "\n",
    "class SimpleLogCallback(tf.keras.callbacks.Callback):\n",
    "    \"\"\" Keras callback for simple, denser console logs.\"\"\"\n",
    "\n",
    "    def __init__(self, metrics_dict, num_epochs='?', log_frequency=1,\n",
    "                 metric_string_template='\\033[1m[[name]]\\033[0m = \\033[94m{[[value]]:5.3f}\\033[0m'):\n",
    "        \"\"\"\n",
    "        Initialize the Callback.\n",
    "        :param metrics_dict:            Dictionary containing mappings for metrics names/keys\n",
    "                                        e.g. {\"accuracy\": \"acc\", \"val. accuracy\": \"val_acc\"}\n",
    "        :param num_epochs:              Number of training epochs\n",
    "        :param log_frequency:           Log frequency (in epochs)\n",
    "        :param metric_string_template:  (opt.) String template to print each metric\n",
    "        \"\"\"\n",
    "        super().__init__()\n",
    "\n",
    "        self.metrics_dict = collections.OrderedDict(metrics_dict)\n",
    "        self.num_epochs = num_epochs\n",
    "        self.log_frequency = log_frequency\n",
    "\n",
    "        # We build a format string to later print the metrics, (e.g. \"Epoch 0/9: loss = 1.00; val-loss = 2.00\")\n",
    "        log_string_template = 'Epoch {0:2}/{1}: '\n",
    "        separator = '; '\n",
    "\n",
    "        i = 2\n",
    "        for metric_name in self.metrics_dict:\n",
    "            templ = metric_string_template.replace('[[name]]', metric_name).replace('[[value]]', str(i))\n",
    "            log_string_template += templ + separator\n",
    "            i += 1\n",
    "\n",
    "        # We remove the \"; \" after the last element:\n",
    "        log_string_template = log_string_template[:-len(separator)]\n",
    "        self.log_string_template = log_string_template\n",
    "\n",
    "    def on_train_begin(self, logs=None):\n",
    "        print(\"Training: {}start{}\".format(log_begin_red, log_end_format))\n",
    "\n",
    "    def on_train_end(self, logs=None):\n",
    "        print(\"Training: {}end{}\".format(log_begin_green, log_end_format))\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        if (epoch - 1) % self.log_frequency == 0 or epoch == self.num_epochs:\n",
    "            values = [logs[self.metrics_dict[metric_name]] for metric_name in self.metrics_dict]\n",
    "            print(self.log_string_template.format(epoch, self.num_epochs, *values))\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** This callback function is added to [`keras_custom_callbacks.py`](./keras_custom_callbacks.py), so we can reuse it in the next notebooks.\n",
    "\n",
    "We now instantiate and prepare all the callbacks we want to apply to our training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\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([(\"loss\", \"loss\"), \n",
    "                                            (\"v-loss\", \"val_loss\"),\n",
    "                                            (\"acc\", \"acc\"), \n",
    "                                            (\"v-acc\", \"val_acc\"),\n",
    "                                            (\"top5-acc\", \"top5_acc\"), \n",
    "                                            (\"v-top5-acc\", \"val_top5_acc\")])\n",
    "\n",
    "callback_simple_log = SimpleLogCallback(metrics_to_print, \n",
    "                                        num_epochs=num_epochs, log_frequency=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "model_dir = './models/resnet_from_scratch'\n",
    "callbacks = [\n",
    "    # Callback to interrupt the training if the validation loss/metrics stops improving for some epochs:\n",
    "    tf.keras.callbacks.EarlyStopping(patience=8, monitor='val_acc',\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 save the model (e.g., every 5 epochs), specifying the epoch and val-loss in the filename:\n",
    "    tf.keras.callbacks.ModelCheckpoint(\n",
    "        os.path.join(model_dir, 'weights-epoch{epoch:02d}-loss{val_loss:.2f}.h5'), period=5),\n",
    "    # Log callback:\n",
    "    callback_simple_log \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now compile our model with a classification loss and the desired metrics, before launching the training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training: \u001b[91mstart\u001b[0m\n",
      "Epoch  1/300: \u001b[1mloss\u001b[0m = \u001b[94m3.746\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m12.998\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.126\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.048\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.354\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.154\u001b[0m\n",
      "Epoch  3/300: \u001b[1mloss\u001b[0m = \u001b[94m3.119\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m3.005\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.235\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.271\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.526\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.562\u001b[0m\n",
      "Epoch  5/300: \u001b[1mloss\u001b[0m = \u001b[94m2.653\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m2.460\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.328\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.372\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.639\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.671\u001b[0m\n",
      "Epoch  7/300: \u001b[1mloss\u001b[0m = \u001b[94m2.262\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m2.165\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.409\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.431\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.721\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.739\u001b[0m\n",
      "Epoch  9/300: \u001b[1mloss\u001b[0m = \u001b[94m1.951\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m2.124\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.478\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.451\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.780\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.750\u001b[0m\n",
      "Epoch 11/300: \u001b[1mloss\u001b[0m = \u001b[94m1.736\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.926\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.530\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.496\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.819\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.780\u001b[0m\n",
      "Epoch 13/300: \u001b[1mloss\u001b[0m = \u001b[94m1.536\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.746\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.574\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.536\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.851\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.819\u001b[0m\n",
      "Epoch 15/300: \u001b[1mloss\u001b[0m = \u001b[94m1.391\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.731\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.611\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.549\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.874\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.821\u001b[0m\n",
      "Epoch 17/300: \u001b[1mloss\u001b[0m = \u001b[94m1.234\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.776\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.650\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.540\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.897\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.821\u001b[0m\n",
      "Epoch 19/300: \u001b[1mloss\u001b[0m = \u001b[94m1.107\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.577\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.680\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.589\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.916\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.853\u001b[0m\n",
      "Epoch 21/300: \u001b[1mloss\u001b[0m = \u001b[94m0.989\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.639\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.709\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.582\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.931\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.850\u001b[0m\n",
      "Epoch 23/300: \u001b[1mloss\u001b[0m = \u001b[94m0.875\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.656\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.740\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.589\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.945\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.848\u001b[0m\n",
      "Epoch 25/300: \u001b[1mloss\u001b[0m = \u001b[94m0.778\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.652\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.766\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.592\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.955\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.849\u001b[0m\n",
      "Epoch 27/300: \u001b[1mloss\u001b[0m = \u001b[94m0.681\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.568\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.791\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.616\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.966\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.869\u001b[0m\n",
      "Epoch 29/300: \u001b[1mloss\u001b[0m = \u001b[94m0.606\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.619\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.814\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.613\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.973\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.868\u001b[0m\n",
      "Epoch 31/300: \u001b[1mloss\u001b[0m = \u001b[94m0.532\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.656\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.836\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.618\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.978\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.864\u001b[0m\n",
      "Epoch 33/300: \u001b[1mloss\u001b[0m = \u001b[94m0.468\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.693\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.854\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.618\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.984\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.864\u001b[0m\n",
      "Epoch 35/300: \u001b[1mloss\u001b[0m = \u001b[94m0.419\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.744\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.868\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.622\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.987\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.865\u001b[0m\n",
      "Epoch 37/300: \u001b[1mloss\u001b[0m = \u001b[94m0.371\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.810\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.885\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.616\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.990\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.859\u001b[0m\n",
      "Epoch 39/300: \u001b[1mloss\u001b[0m = \u001b[94m0.343\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.803\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.892\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.620\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.990\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.860\u001b[0m\n",
      "Epoch 41/300: \u001b[1mloss\u001b[0m = \u001b[94m0.309\u001b[0m; \u001b[1mv-loss\u001b[0m = \u001b[94m1.783\u001b[0m; \u001b[1macc\u001b[0m = \u001b[94m0.902\u001b[0m; \u001b[1mv-acc\u001b[0m = \u001b[94m0.624\u001b[0m; \u001b[1mtop5-acc\u001b[0m = \u001b[94m0.993\u001b[0m; \u001b[1mv-top5-acc\u001b[0m = \u001b[94m0.866\u001b[0m\n",
      "Training: \u001b[92mend\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "history = resnet50.fit(train_cifar_dataset,  \n",
    "                       epochs=num_epochs, steps_per_epoch=train_steps_per_epoch,\n",
    "                       validation_data=(val_cifar_dataset), validation_steps=val_steps_per_epoch,\n",
    "                       verbose=0, callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can either follow the training thanks to Keras' console logs, or in Tensorboard thanks to the callback we set up (Tensoboard can be launched with `tensorboard --logdir ./models`, and then accessed at [localhost:6006](localhost:6006)). \n",
    "\n",
    "Training a CNN from scratch is a long, expensive operation, as we can observe here (which is one of the reasons to apply transfer learning whenever it is possible, as we will demonstrate in the next notebooks). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `history` object returned by `model.fit()` can also be used to plot some of the training metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1a2cc0d9e8>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(3, 2, figsize=(15, 10), sharex='col') # add parameter `sharey='row'` for a more direct comparison\n",
    "ax[0, 0].set_title(\"loss\")\n",
    "ax[0, 1].set_title(\"val-loss\")\n",
    "ax[1, 0].set_title(\"acc\")\n",
    "ax[1, 1].set_title(\"val-acc\")\n",
    "ax[2, 0].set_title(\"top5-acc\")\n",
    "ax[2, 1].set_title(\"val-top5-acc\")\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['acc'])\n",
    "ax[1, 1].plot(history.history['val_acc'])\n",
    "ax[2, 0].plot(history.history['top5_acc'])\n",
    "ax[2, 1].plot(history.history['val_top5_acc'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best val acc:  62.79%\n",
      "Best val top5: 87.06%\n"
     ]
    }
   ],
   "source": [
    "best_val_acc = max(history.history['val_acc']) * 100\n",
    "best_val_top5 = max(history.history['val_top5_acc']) * 100\n",
    "\n",
    "print('Best val acc:  {:2.2f}%'.format(best_val_acc))\n",
    "print('Best val top5: {:2.2f}%'.format(best_val_top5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Guessing the exact correct class out of 200 almost 40% of the time is not a bad start. We should keep in mind that Tiny-ImageNet is a difficult dataset, with not that many training samples per class. Inspired by the notions covered in the book, we will demonstrate in the next notebooks how to increase the accuracy nonetheless."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting with the Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we know have a complex model, fully implemented and trained by ourselves, we cannot conclude this notebook without at least testing it once on some unseen images.\n",
    "\n",
    "For that purpose, we first implement some utility functions to load test images (simply as `numpy` arrays), to get the top-5 predictions and corresponing labels from the model's outputs, and finally to plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
    "import os\n",
    "import numpy as np\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "def load_image(image_path, size):\n",
    "    \"\"\"\n",
    "    Load an image as a Numpy array.\n",
    "    :param image_path:  Path of the image\n",
    "    :param size:        Target size\n",
    "    :return             Image array, normalized between 0 and 1\n",
    "    \"\"\"\n",
    "    image = img_to_array(load_img(image_path, target_size=size)) / 255.\n",
    "    return image\n",
    "\n",
    "\n",
    "def process_predictions(class_probabilities, class_readable_labels, k=5):\n",
    "    \"\"\"\n",
    "    Process a batch of predictions from our estimator.\n",
    "    :param class_probabilities:     Prediction results returned by the Keras classifier for a batch of data\n",
    "    :param class_readable_labels:   List of readable-class labels, for display\n",
    "    :param k:                       Number of top predictions to consider\n",
    "    :return                         Readable labels and probabilities for the predicted classes\n",
    "    \"\"\"\n",
    "    topk_labels, topk_probabilities = [], []\n",
    "    for i in range(len(class_probabilities)):\n",
    "        # Getting the top-k predictions:\n",
    "        topk_classes = sorted(np.argpartition(class_probabilities[i], -k)[-k:])\n",
    "    \n",
    "        # Getting the corresponding labels and probabilities:\n",
    "        topk_labels.append([class_readable_labels[predicted] for predicted in topk_classes])\n",
    "        topk_probabilities.append(class_probabilities[i][topk_classes])\n",
    "    \n",
    "    return topk_labels, topk_probabilities\n",
    "\n",
    "\n",
    "def display_predictions(images, topk_labels, topk_probabilities):\n",
    "    \"\"\"\n",
    "    Plot a batch of predictions.\n",
    "    :param images:                  Batch of input images\n",
    "    :param topk_labels:             String labels of predicted classes\n",
    "    :param topk_probabilities:      Probabilities for each class\n",
    "    \"\"\"\n",
    "    num_images = len(images)\n",
    "    num_images_sqrt = np.sqrt(num_images)\n",
    "    plot_cols = plot_rows = int(np.ceil(num_images_sqrt))\n",
    "    \n",
    "    figure = plt.figure(figsize=(13,10))\n",
    "    grid_spec = gridspec.GridSpec(plot_cols, plot_rows)\n",
    "    \n",
    "    for i in range(num_images):\n",
    "        img, pred_labels, pred_proba = images[i], topk_labels[i], topk_probabilities[i]\n",
    "        # Shortening the labels to better fit in the plot:\n",
    "        pred_labels = [label.split(',')[0][:20] for label in pred_labels]\n",
    "        \n",
    "        grid_spec_i = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=grid_spec[i], \n",
    "                                                       hspace=0.1)\n",
    "        \n",
    "        # Drawing the input image:\n",
    "        ax_img = figure.add_subplot(grid_spec_i[:2])\n",
    "        ax_img.axis('off')\n",
    "        ax_img.imshow(img)\n",
    "        ax_img.autoscale(tight=True)\n",
    "        \n",
    "        # Plotting a bar chart for the predictions:\n",
    "        ax_pred = figure.add_subplot(grid_spec_i[2])\n",
    "        ax_pred.spines['top'].set_visible(False)\n",
    "        ax_pred.spines['right'].set_visible(False)\n",
    "        ax_pred.spines['bottom'].set_visible(False)\n",
    "        ax_pred.spines['left'].set_visible(False)\n",
    "        y_pos = np.arange(len(pred_labels))\n",
    "        ax_pred.barh(y_pos, pred_proba, align='center')\n",
    "        ax_pred.set_yticks(y_pos)\n",
    "        ax_pred.set_yticklabels(pred_labels)\n",
    "        ax_pred.invert_yaxis()\n",
    "        \n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** This utility functions are added to [`classification_utils.py`](classification_utils.py) for later use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We made available several pictures, taken from our holiday photo albums. Let us see how our model fares on these real images!\n",
    "\n",
    "First we load them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Images: (16, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "test_filenames = glob.glob(os.path.join('res', '*'))\n",
    "test_images = np.asarray([load_image(file, size=input_shape[:2]) for file in test_filenames])\n",
    "print('Test Images: {}'.format(test_images.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use our model to predict image per image or batch per batch, and we can use the utility functions we just implemented to display the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted class probabilities: (16, 100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_batch = test_images[:16]\n",
    "\n",
    "# Our model was trained on CIFAR images, which originally are 32x32px. We scaled them up\n",
    "# to 224x224px to train our model on, but this means the resulting images had important\n",
    "# artifacts/low quality.\n",
    "# To test on images of the same quality, we first resize them to 32x32px, then to the \n",
    "#expected input size (i.e., 224x224px):\n",
    "cifar_original_image_size = cifar_builder.info.features['image'].shape[:2]\n",
    "image_batch_low_quality = tf.image.resize(image_batch, cifar_original_image_size)\n",
    "image_batch_low_quality = tf.image.resize(image_batch_low_quality, input_shape[:2])\n",
    "    \n",
    "\n",
    "predictions = resnet50.predict_on_batch(image_batch_low_quality)\n",
    "print('Predicted class probabilities: {}'.format(predictions.shape))\n",
    "\n",
    "class_readable_labels = cifar_builder.info.features[\"label\"].names\n",
    "top5_labels, top5_probabilities = process_predictions(predictions, class_readable_labels)\n",
    "    \n",
    "display_predictions(image_batch, top5_labels, top5_probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We covered quite some ground with this notebook! Applying the knowledge acquired from the book, we re-implemented a complete state-of-the-art CNN and applied it to a difficult dataset. We monitored its training and learned how to visualize classification predictions.\n",
    "These skills will be further honed in the remaining notebooks covering Chapter 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref\"></a>\n",
    "#### References\n",
    "\n",
    "1. Krizhevsky, Alex, Hinton, Geoffrey. \"*Learning multiple layers of features from tiny images*\" (2009), Citeseer.\n",
    "2. Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Duerig, T., others, 2018. The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale. arXiv preprint arXiv:1811.00982.\n",
    "3. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L., 2014. ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575 [cs].\n",
    "4. He, K., Zhang, X., Ren, S., Sun, J., 2015. Deep Residual Learning for Image Recognition. arXiv:1512.03385 [cs]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"appendix\"></a>\n",
    "## Appendix - Object-Oriented Model Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this appendix, we propose a different re-implementation of _ResNet_, following a more _object-oriented_ approach.\n",
    "\n",
    "We rewrite each of the functions to generate layers and blocks as classes, still using Keras Functional API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvWithBatchNorm(tf.keras.layers.Conv2D):\n",
    "    \"\"\" Convolutional layer with batch normalization\"\"\"\n",
    "\n",
    "    def __init__(self, activation='relu', name='convbn', **kwargs):\n",
    "        \"\"\"\n",
    "        Initialize the layer. \n",
    "        :param activation:   Activation function (name or callable)\n",
    "        :param name:         Name suffix for the sub-layers.\n",
    "        :param kwargs:       Mandatory and optional parameters of tf.keras.layers.Conv2D\n",
    "        \"\"\"\n",
    "        \n",
    "        self.activation = Activation(\n",
    "            activation, name=name + '_act') if activation is not None else None\n",
    "        \n",
    "        super().__init__(activation=None, name=name + '_c', **kwargs)\n",
    "        \n",
    "        self.batch_norm = BatchNormalization(axis=-1, name=name + '_bn')\n",
    "\n",
    "    def call(self, inputs, training=None):\n",
    "        \"\"\"\n",
    "        Call the layer. \n",
    "        :param inputs:         Input tensor to process\n",
    "        :param training:       Flag to let TF knows if it is a training iteration or not\n",
    "                               (this will affect the behavior of BatchNorm)\n",
    "        :return:               Convolved tensor\n",
    "        \"\"\"\n",
    "        x = super().call(inputs)\n",
    "        x = self.batch_norm(x, training=training)\n",
    "        if self.activation is not None:\n",
    "            x = self.activation(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import functools\n",
    "\n",
    "class ResidualMerge(tf.keras.layers.Layer):\n",
    "    \"\"\" Layer to merge the original tensor and the residual one in residual blocks\"\"\"\n",
    "\n",
    "    def __init__(self, name='block', **kwargs):\n",
    "        \"\"\"\n",
    "        Initialize the layer. \n",
    "        :param activation:   Activation function (name or callable)\n",
    "        :param name:         Name suffix for the sub-layers.\n",
    "        :param kwargs:       Optional parameters of tf.keras.layers.Conv2D\n",
    "        \"\"\"\n",
    "        \n",
    "        super().__init__(name=name)\n",
    "        self.shortcut = None\n",
    "        self.kwargs = kwargs\n",
    "        \n",
    "    def build(self, input_shape):\n",
    "        x_shape = input_shape[0]\n",
    "        x_residual_shape = input_shape[1]\n",
    "        if x_shape[1] == x_residual_shape[1] and \\\n",
    "           x_shape[2] == x_residual_shape[2] and \\\n",
    "           x_shape[3] == x_residual_shape[3]:\n",
    "            self.shortcut = functools.partial(tf.identity, name=self.name + '_shortcut')\n",
    "        else:\n",
    "            strides = (\n",
    "                int(round(x_shape[1] / x_residual_shape[1])), # vertical stride\n",
    "                int(round(x_shape[2] / x_residual_shape[2]))  # horizontal stride\n",
    "            )\n",
    "            x_residual_channels = x_residual_shape[3]\n",
    "            self.shortcut = ConvWithBatchNorm(\n",
    "                filters=x_residual_channels, kernel_size=(1, 1), strides=strides,\n",
    "                activation=None, name=self.name + '_shortcut_c', **self.kwargs)\n",
    "        \n",
    "\n",
    "    def call(self, inputs):\n",
    "        \"\"\"\n",
    "        Call the layer. \n",
    "        :param inputs:         Tuple of two input tensors to merge\n",
    "        :return:               Merged tensor\n",
    "        \"\"\"\n",
    "        x, x_residual = inputs\n",
    "        \n",
    "        x_shortcut = self.shortcut(x)\n",
    "        x_merge = add([x_shortcut, x_residual])\n",
    "        return x_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class BasicResidualBlock(tf.keras.Model):\n",
    "    \"\"\" Basic residual block\"\"\"\n",
    "\n",
    "    def __init__(self, filters=16, kernel_size=1, strides=1, activation='relu',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4),\n",
    "                 name='res_basic', **kwargs):\n",
    "        \"\"\"\n",
    "        Initialize the layer. \n",
    "        :param filters:                 Number of filters\n",
    "        :param kernel_size:             Kernel size\n",
    "        :param strides:                 Convolution strides\n",
    "        :param activation:              Activation function (name or callable)\n",
    "        :param kernel_initializer:      Kernel initialisation method name\n",
    "        :param kernel_regularizer:      Kernel regularizer\n",
    "        :param name:                    Name suffix for the sub-layers.\n",
    "        :param kwargs:                  Optional parameters of tf.keras.layers.Conv2D\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "        \n",
    "        self.conv_1 = ConvWithBatchNorm(\n",
    "            filters=filters, kernel_size=kernel_size, activation=activation, padding='same',\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            strides=strides, name=name + '_cb_1', **kwargs)\n",
    "        \n",
    "        self.conv_2 = ConvWithBatchNorm(\n",
    "            filters=filters, kernel_size=kernel_size, activation=None, padding='same',\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            strides=1, name=name + '_cb_2', **kwargs)\n",
    "        \n",
    "        self.merge = ResidualMerge(\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer, \n",
    "            name=name)\n",
    "        \n",
    "        self.activation = Activation(activation, name=name + '_act')\n",
    "\n",
    "    def call(self, inputs, training=None):\n",
    "        \"\"\"\n",
    "        Call the block. \n",
    "        :param inputs:         Input tensor to process\n",
    "        :param training:       Flag to let TF knows if it is a training iteration or not\n",
    "                               (this will affect the behavior of BatchNorm)\n",
    "        :return:               Block output tensor\n",
    "        \"\"\"\n",
    "        x = inputs\n",
    "        # Residual path:\n",
    "        x_residual = self.conv_1(x, training=training)\n",
    "        x_residual = self.conv_2(x_residual, training=training)\n",
    "        \n",
    "        # Merge residual result with original tensor:\n",
    "        x_merge = self.merge([x, x_residual])\n",
    "        x_merge = self.activation(x_merge)\n",
    "        return x_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualBlockWithBottleneck(tf.keras.Model):\n",
    "    \"\"\" Residual block with bottleneck, recommended for deep ResNets (depth > 34)\"\"\"\n",
    "    def __init__(self, filters=16, kernel_size=1, strides=1, activation='relu',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4),\n",
    "                 name='res_basic', **kwargs):\n",
    "        \"\"\"\n",
    "        Initialize the block. \n",
    "        :param filters:                 Number of filters\n",
    "        :param kernel_size:             Kernel size\n",
    "        :param strides:                 Convolution strides\n",
    "        :param activation:              Activation function (name or callable)\n",
    "        :param kernel_initializer:      Kernel initialisation method name\n",
    "        :param kernel_regularizer:      Kernel regularizer\n",
    "        :param name:                    Name suffix for the sub-layers.\n",
    "        :param kwargs:                  Optional parameters of tf.keras.layers.Conv2D\n",
    "        \"\"\"\n",
    "        super().__init__(name=name)\n",
    "        \n",
    "        self.conv_0 = ConvWithBatchNorm(\n",
    "            filters=filters, kernel_size=1, activation=activation, padding='valid',\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            strides=1, name=name + '_cb_0', **kwargs)\n",
    "        \n",
    "        self.conv_1 = ConvWithBatchNorm(\n",
    "            filters=filters, kernel_size=kernel_size, activation=activation, padding='same',\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            strides=strides, name=name + '_cb_1', **kwargs)\n",
    "        \n",
    "        self.conv_2 = ConvWithBatchNorm(\n",
    "            filters=4 * filters, kernel_size=1, activation=None, padding='valid',\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "            strides=1, name=name + '_cb_2', **kwargs)\n",
    "        \n",
    "        self.merge = ResidualMerge(\n",
    "            kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer, \n",
    "            name=name)\n",
    "        \n",
    "        self.activation = Activation(activation, name=name + '_act')\n",
    "\n",
    "    def call(self, inputs, training=None):\n",
    "        \"\"\"\n",
    "        Call the layer. \n",
    "        :param inputs:         Input tensor to process\n",
    "        :param training:       Flag to let TF knows if it is a training iteration or not\n",
    "                               (this will affect the behavior of BatchNorm)\n",
    "        :return:               Block output tensor\n",
    "        \"\"\"\n",
    "        x = inputs\n",
    "        # Residual path:\n",
    "        x_residual = self.conv_0(x, training=training)\n",
    "        x_residual = self.conv_1(x_residual, training=training)\n",
    "        x_residual = self.conv_2(x_residual, training=training)\n",
    "        \n",
    "        # Merge residual result with original tensor:\n",
    "        x_merge = self.merge([x, x_residual])\n",
    "        x_merge = self.activation(x_merge)\n",
    "        return x_merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResidualMacroBlock(tf.keras.models.Sequential):\n",
    "    \"\"\" Macro-block, chaining multiple residual blocks (as a Sequential model)\"\"\"\n",
    "\n",
    "    def __init__(self, block_class=ResidualBlockWithBottleneck, repetitions=3, \n",
    "                 filters=16, kernel_size=1, strides=1, activation='relu',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4),\n",
    "                 name='res_macroblock', **kwargs):\n",
    "        \"\"\"\n",
    "        Initialize the block. \n",
    "        :param block_class:             Block class to be used.\n",
    "        :param repetitions:             Number of times the block should be repeated inside.\n",
    "        :param filters:                 Number of filters\n",
    "        :param kernel_size:             Kernel size\n",
    "        :param strides:                 Convolution strides\n",
    "        :param activation:              Activation function (name or callable)\n",
    "        :param kernel_initializer:      Kernel initialisation method name\n",
    "        :param kernel_regularizer:      Kernel regularizer\n",
    "        :param name:                    Name suffix for the sub-layers.\n",
    "        :param kwargs:                  Optional parameters of tf.keras.layers.Conv2D\n",
    "        \"\"\"\n",
    "        super().__init__(\n",
    "            [block_class(\n",
    "                 filters=filters, kernel_size=kernel_size, activation=activation,\n",
    "                 strides=strides if i == 0 else 1, name=\"{}_{}\".format(name, i),\n",
    "                 kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "             for i in range(repetitions)], \n",
    "            name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ResNet(tf.keras.models.Sequential):\n",
    "    \"\"\" ResNet model for classification\"\"\"\n",
    "\n",
    "    def __init__(self, input_shape, num_classes=1000, \n",
    "                 block_class=ResidualBlockWithBottleneck, repetitions=(2, 2, 2, 2),\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4),\n",
    "                 name='resnet'):\n",
    "        \"\"\"\n",
    "        Initialize a ResNet model for classification.\n",
    "        :param input_shape:             Input shape (e.g. (224, 224, 3))\n",
    "        :param num_classes:             Number of classes to predict\n",
    "        :param block_class:             Block class to be used\n",
    "        :param repetitions:             List of repetitions for each macro-blocks the network should contain\n",
    "        :param kernel_initializer:      Kernel initialisation method name\n",
    "        :param kernel_regularizer:      Kernel regularizer\n",
    "        :param name:                    Model's name\n",
    "        :return:                        ResNet model.\n",
    "        \"\"\"\n",
    "    \n",
    "        filters = 64\n",
    "        strides = 2\n",
    "    \n",
    "        super().__init__(\n",
    "            # Initial conv + max-pool layers:\n",
    "            [Input(shape=input_shape, name='input'),\n",
    "             ConvWithBatchNorm(\n",
    "                 filters=filters, kernel_size=7, activation='relu', padding='same', strides=strides,\n",
    "                 kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer,\n",
    "                 name='conv'),\n",
    "             MaxPooling2D(pool_size=3, strides=strides, padding='same', name='max_pool')\n",
    "            ] + \\\n",
    "            # Residual blocks:\n",
    "            [ResidualMacroBlock(\n",
    "                 block_class=block_class, repetitions=repet, \n",
    "                 filters=min(filters * (2 ** i), 1024), kernel_size=3, activation='relu',\n",
    "                 strides=strides if i != 0 else 1, name='block_{}'.format(i),\n",
    "                 kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "             for i, repet in enumerate(repetitions)\n",
    "            ] + \\\n",
    "            # Final layers leading to classification output:\n",
    "            [GlobalAveragePooling2D(name='avg_pool'),\n",
    "             Dense(units=num_classes, kernel_initializer=kernel_initializer, activation='softmax')\n",
    "            ], name=name)\n",
    "\n",
    "\n",
    "# Standard ResNet versions:\n",
    "class ResNet18(ResNet):\n",
    "    def __init__(self, input_shape, num_classes=1000, name='resnet18',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "        super().__init__(input_shape, num_classes, \n",
    "                         block_class=BasicResidualBlock, repetitions=(2, 2, 2, 2),\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "        \n",
    "class ResNet34(ResNet):\n",
    "    def __init__(self, input_shape, num_classes=1000, name='resnet34',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "        super().__init__(input_shape, num_classes, \n",
    "                         block_class=BasicResidualBlock, repetitions=(3, 4, 6, 3),\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "        \n",
    "class ResNet50(ResNet):\n",
    "    def __init__(self, input_shape, num_classes=1000, name='resnet50',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "        super().__init__(input_shape, num_classes, \n",
    "                         block_class=ResidualBlockWithBottleneck, repetitions=(3, 4, 6, 3),\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "class ResNet101(ResNet):\n",
    "    def __init__(self, input_shape, num_classes=1000, name='resnet101',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "        super().__init__(input_shape, num_classes, \n",
    "                         block_class=ResidualBlockWithBottleneck, repetitions=(3, 4, 23, 3),\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)\n",
    "\n",
    "class ResNet152(ResNet):\n",
    "    def __init__(self, input_shape, num_classes=1000, name='resnet152',\n",
    "                 kernel_initializer='he_normal', kernel_regularizer=regulizers.l2(1e-4)):\n",
    "        super().__init__(input_shape, num_classes, \n",
    "                         block_class=ResidualBlockWithBottleneck, repetitions=(3, 8, 36, 3),\n",
    "                         kernel_initializer=kernel_initializer, kernel_regularizer=kernel_regularizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:*** This implementation slightly differs from the previous one. We leave it to our reader to adapt as they wish.\n",
    "\n",
    "Let us instantiate a network and have a look at its structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ResNet50(input_shape, num_classes)\n",
    "model.summary(line_length=80, positions=[.5, .85, 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is a much more concise summary as the one we printed earlier in the notebook. It may not be as informative though... But since our residual blocks are also `Model` instances, we can check their own summary to obtain as much detail as we wish:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary of the last macro-block:\n",
    "model.layers[-3].summary(line_length=80, positions=[.5, .85, 1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Summary of the last block of the last macro-block:\n",
    "model.layers[-3].layers[-1].summary(line_length=80, positions=[.5, .85, 1.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both approaches to the Keras Functional API have their own pros and cons. We leave it to our readers to decide which they prefer."
   ]
  }
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