{
"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"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "chapter08_intro-to-dl-for-computer-vision.i",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
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