chapter08_intro-to-dl-for-computer-vision.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n\n**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n\nThis notebook was generated for TensorFlow 2.6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "# Introduction to deep learning for computer vision"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Introduction to convnets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Instantiating a small convnet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "inputs = keras.Input(shape=(28, 28, 1))\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(inputs)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying the model's summary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training the convnet on MNIST images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype(\"float32\") / 255\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype(\"float32\") / 255\n",
    "model.compile(optimizer=\"rmsprop\",\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    metrics=[\"accuracy\"])\n",
    "model.fit(train_images, train_labels, epochs=5, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Evaluating the convnet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(f\"Test accuracy: {test_acc:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The convolution operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Understanding border effects and padding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Understanding convolution strides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The max-pooling operation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**An incorrectly structured convnet missing its max-pooling layers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(28, 28, 1))\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(inputs)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
    "model_no_max_pool = keras.Model(inputs=inputs, outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model_no_max_pool.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Training a convnet from scratch on a small dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### The relevance of deep learning for small-data problems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Downloading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from google.colab import files\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!mkdir ~/.kaggle\n",
    "!cp kaggle.json ~/.kaggle/\n",
    "!chmod 600 ~/.kaggle/kaggle.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!kaggle competitions download -c dogs-vs-cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!unzip -qq dogs-vs-cats.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "!unzip -qq train.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Copying images to training, validation, and test directories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import os, shutil, pathlib\n",
    "\n",
    "original_dir = pathlib.Path(\"train\")\n",
    "new_base_dir = pathlib.Path(\"cats_vs_dogs_small\")\n",
    "\n",
    "def make_subset(subset_name, start_index, end_index):\n",
    "    for category in (\"cat\", \"dog\"):\n",
    "        dir = new_base_dir / subset_name / category\n",
    "        os.makedirs(dir)\n",
    "        fnames = [f\"{category}.{i}.jpg\" for i in range(start_index, end_index)]\n",
    "        for fname in fnames:\n",
    "            shutil.copyfile(src=original_dir / fname,\n",
    "                            dst=dir / fname)\n",
    "\n",
    "make_subset(\"train\", start_index=0, end_index=1000)\n",
    "make_subset(\"validation\", start_index=1000, end_index=1500)\n",
    "make_subset(\"test\", start_index=1500, end_index=2500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Building the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Instantiating a small convnet for dogs vs. cats classification**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "inputs = keras.Input(shape=(180, 180, 3))\n",
    "x = layers.Rescaling(1./255)(inputs)\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Configuring the model for training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"binary_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Using `image_dataset_from_directory` to read images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.utils import image_dataset_from_directory\n",
    "\n",
    "train_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"train\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)\n",
    "validation_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"validation\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)\n",
    "test_dataset = image_dataset_from_directory(\n",
    "    new_base_dir / \"test\",\n",
    "    image_size=(180, 180),\n",
    "    batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "random_numbers = np.random.normal(size=(1000, 16))\n",
    "dataset = tf.data.Dataset.from_tensor_slices(random_numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "for i, element in enumerate(dataset):\n",
    "    print(element.shape)\n",
    "    if i >= 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "batched_dataset = dataset.batch(32)\n",
    "for i, element in enumerate(batched_dataset):\n",
    "    print(element.shape)\n",
    "    if i >= 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "reshaped_dataset = dataset.map(lambda x: tf.reshape(x, (4, 4)))\n",
    "for i, element in enumerate(reshaped_dataset):\n",
    "    print(element.shape)\n",
    "    if i >= 2:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying the shapes of the data and labels yielded by the `Dataset`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "for data_batch, labels_batch in train_dataset:\n",
    "    print(\"data batch shape:\", data_batch.shape)\n",
    "    print(\"labels batch shape:\", labels_batch.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Fitting the model using a `Dataset`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath=\"convnet_from_scratch.keras\",\n",
    "        save_best_only=True,\n",
    "        monitor=\"val_loss\")\n",
    "]\n",
    "history = model.fit(\n",
    "    train_dataset,\n",
    "    epochs=30,\n",
    "    validation_data=validation_dataset,\n",
    "    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying curves of loss and accuracy during training**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "accuracy = history.history[\"accuracy\"]\n",
    "val_accuracy = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(accuracy) + 1)\n",
    "plt.plot(epochs, accuracy, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_accuracy, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Evaluating the model on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_model = keras.models.load_model(\"convnet_from_scratch.keras\")\n",
    "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
    "print(f\"Test accuracy: {test_acc:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Using data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Define a data augmentation stage to add to an image model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.RandomFlip(\"horizontal\"),\n",
    "        layers.RandomRotation(0.1),\n",
    "        layers.RandomZoom(0.2),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Displaying some randomly augmented training images**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "for images, _ in train_dataset.take(1):\n",
    "    for i in range(9):\n",
    "        augmented_images = data_augmentation(images)\n",
    "        ax = plt.subplot(3, 3, i + 1)\n",
    "        plt.imshow(augmented_images[0].numpy().astype(\"uint8\"))\n",
    "        plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Defining a new convnet that includes image augmentation and dropout**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(180, 180, 3))\n",
    "x = data_augmentation(inputs)\n",
    "x = layers.Rescaling(1./255)(x)\n",
    "x = layers.Conv2D(filters=32, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.MaxPooling2D(pool_size=2)(x)\n",
    "x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Training the regularized convnet**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath=\"convnet_from_scratch_with_augmentation.keras\",\n",
    "        save_best_only=True,\n",
    "        monitor=\"val_loss\")\n",
    "]\n",
    "history = model.fit(\n",
    "    train_dataset,\n",
    "    epochs=100,\n",
    "    validation_data=validation_dataset,\n",
    "    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Evaluating the model on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_model = keras.models.load_model(\n",
    "    \"convnet_from_scratch_with_augmentation.keras\")\n",
    "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
    "print(f\"Test accuracy: {test_acc:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Leveraging a pretrained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Feature extraction with a pretrained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Instantiating the VGG16 convolutional base**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base = keras.applications.vgg16.VGG16(\n",
    "    weights=\"imagenet\",\n",
    "    include_top=False,\n",
    "    input_shape=(180, 180, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Fast feature extraction without data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Extracting the VGG16 features and corresponding labels**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def get_features_and_labels(dataset):\n",
    "    all_features = []\n",
    "    all_labels = []\n",
    "    for images, labels in dataset:\n",
    "        preprocessed_images = keras.applications.vgg16.preprocess_input(images)\n",
    "        features = conv_base.predict(preprocessed_images)\n",
    "        all_features.append(features)\n",
    "        all_labels.append(labels)\n",
    "    return np.concatenate(all_features), np.concatenate(all_labels)\n",
    "\n",
    "train_features, train_labels =  get_features_and_labels(train_dataset)\n",
    "val_features, val_labels =  get_features_and_labels(validation_dataset)\n",
    "test_features, test_labels =  get_features_and_labels(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "train_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Defining and training the densely connected classifier**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "inputs = keras.Input(shape=(5, 5, 512))\n",
    "x = layers.Flatten()(inputs)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "      filepath=\"feature_extraction.keras\",\n",
    "      save_best_only=True,\n",
    "      monitor=\"val_loss\")\n",
    "]\n",
    "history = model.fit(\n",
    "    train_features, train_labels,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels),\n",
    "    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Plotting the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "acc = history.history[\"accuracy\"]\n",
    "val_acc = history.history[\"val_accuracy\"]\n",
    "loss = history.history[\"loss\"]\n",
    "val_loss = history.history[\"val_loss\"]\n",
    "epochs = range(1, len(acc) + 1)\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
    "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "#### Feature extraction together with data augmentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Instantiating and freezing the VGG16 convolutional base**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base  = keras.applications.vgg16.VGG16(\n",
    "    weights=\"imagenet\",\n",
    "    include_top=False)\n",
    "conv_base.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Printing the list of trainable weights before and after freezing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base.trainable = True\n",
    "print(\"This is the number of trainable weights \"\n",
    "      \"before freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base.trainable = False\n",
    "print(\"This is the number of trainable weights \"\n",
    "      \"after freezing the conv base:\", len(conv_base.trainable_weights))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Adding a data augmentation stage and a classifier to the convolutional base**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "data_augmentation = keras.Sequential(\n",
    "    [\n",
    "        layers.RandomFlip(\"horizontal\"),\n",
    "        layers.RandomRotation(0.1),\n",
    "        layers.RandomZoom(0.2),\n",
    "    ]\n",
    ")\n",
    "\n",
    "inputs = keras.Input(shape=(180, 180, 3))\n",
    "x = data_augmentation(inputs)\n",
    "x = keras.applications.vgg16.preprocess_input(x)\n",
    "x = conv_base(x)\n",
    "x = layers.Flatten()(x)\n",
    "x = layers.Dense(256)(x)\n",
    "x = layers.Dropout(0.5)(x)\n",
    "outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
    "model = keras.Model(inputs, outputs)\n",
    "model.compile(loss=\"binary_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath=\"feature_extraction_with_data_augmentation.keras\",\n",
    "        save_best_only=True,\n",
    "        monitor=\"val_loss\")\n",
    "]\n",
    "history = model.fit(\n",
    "    train_dataset,\n",
    "    epochs=50,\n",
    "    validation_data=validation_dataset,\n",
    "    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Evaluating the model on the test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "test_model = keras.models.load_model(\n",
    "    \"feature_extraction_with_data_augmentation.keras\")\n",
    "test_loss, test_acc = test_model.evaluate(test_dataset)\n",
    "print(f\"Test accuracy: {test_acc:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "### Fine-tuning a pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Freezing all layers until the fourth from the last**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "conv_base.trainable = True\n",
    "for layer in conv_base.layers[:-4]:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "**Fine-tuning the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model.compile(loss=\"binary_crossentropy\",\n",
    "              optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "callbacks = [\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath=\"fine_tuning.keras\",\n",
    "        save_best_only=True,\n",
    "        monitor=\"val_loss\")\n",
    "]\n",
    "history = model.fit(\n",
    "    train_dataset,\n",
    "    epochs=30,\n",
    "    validation_data=validation_dataset,\n",
    "    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab_type": "code"
   },
   "outputs": [],
   "source": [
    "model = keras.models.load_model(\"fine_tuning.keras\")\n",
    "test_loss, test_acc = model.evaluate(test_dataset)\n",
    "print(f\"Test accuracy: {test_acc:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text"
   },
   "source": [
    "## Summary"
   ]
  }
 ],
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