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fchollet--deep-learning-wit…/chapter08_image-classification.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"This is a companion notebook for the book [Deep Learning with Python, Third Edition](https://www.manning.com/books/deep-learning-with-python-third-edition). 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\nThe book's contents are available online at [deeplearningwithpython.io](https://deeplearningwithpython.io)."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"!pip install keras keras-hub --upgrade -q"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"KERAS_BACKEND\"] = \"jax\""
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab_type": "code"
},
"outputs": [],
"source": [
"# @title\n",
"import os\n",
"from IPython.core.magic import register_cell_magic\n",
"\n",
"@register_cell_magic\n",
"def backend(line, cell):\n",
" current, required = os.environ.get(\"KERAS_BACKEND\", \"\"), line.split()[-1]\n",
" if current == required:\n",
" get_ipython().run_cell(cell)\n",
" else:\n",
" print(\n",
" f\"This cell requires the {required} backend. To run it, change KERAS_BACKEND to \"\n",
" f\"\\\"{required}\\\" at the top of the notebook, restart the runtime, and rerun the notebook.\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Image classification"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"### Introduction to ConvNets"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import keras\n",
"from keras import layers\n",
"\n",
"inputs = keras.Input(shape=(28, 28, 1))\n",
"x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(inputs)\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.GlobalAveragePooling2D()(x)\n",
"outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
"model = keras.Model(inputs=inputs, outputs=outputs)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"model.summary(line_length=80)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"from 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(\n",
" optimizer=\"adam\",\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" metrics=[\"accuracy\"],\n",
")\n",
"model.fit(train_images, train_labels, epochs=5, batch_size=64)"
]
},
{
"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": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"inputs = keras.Input(shape=(28, 28, 1))\n",
"x = layers.Conv2D(filters=64, kernel_size=3, activation=\"relu\")(inputs)\n",
"x = layers.Conv2D(filters=128, kernel_size=3, activation=\"relu\")(x)\n",
"x = layers.Conv2D(filters=256, kernel_size=3, activation=\"relu\")(x)\n",
"x = layers.GlobalAveragePooling2D()(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(line_length=80)"
]
},
{
"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": [
"import kagglehub\n",
"\n",
"kagglehub.login()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"download_path = kagglehub.competition_download(\"dogs-vs-cats\")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import zipfile\n",
"\n",
"with zipfile.ZipFile(download_path + \"/train.zip\", \"r\") as zip_ref:\n",
" zip_ref.extractall(\".\")"
]
},
{
"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(\"dogs_vs_cats_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, 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 your model"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import keras\n",
"from keras import layers\n",
"\n",
"inputs = keras.Input(shape=(180, 180, 3))\n",
"x = layers.Rescaling(1.0 / 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=512, kernel_size=3, activation=\"relu\")(x)\n",
"x = layers.GlobalAveragePooling2D()(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(line_length=80)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"accuracy\"],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"#### Data preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"from keras.utils import image_dataset_from_directory\n",
"\n",
"batch_size = 64\n",
"image_size = (180, 180)\n",
"train_dataset = image_dataset_from_directory(\n",
" new_base_dir / \"train\", image_size=image_size, batch_size=batch_size\n",
")\n",
"validation_dataset = image_dataset_from_directory(\n",
" new_base_dir / \"validation\", image_size=image_size, batch_size=batch_size\n",
")\n",
"test_dataset = image_dataset_from_directory(\n",
" new_base_dir / \"test\", image_size=image_size, batch_size=batch_size\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"##### Understanding TensorFlow Dataset objects"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"\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(\n",
" lambda x: tf.reshape(x, (4, 4)),\n",
" num_parallel_calls=8)\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": [
"##### Fitting the model"
]
},
{
"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": "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",
"]\n",
"history = model.fit(\n",
" train_dataset,\n",
" epochs=50,\n",
" validation_data=validation_dataset,\n",
" callbacks=callbacks,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\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",
"\n",
"plt.plot(epochs, accuracy, \"r--\", 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",
"\n",
"plt.plot(epochs, loss, \"r--\", 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": "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": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"data_augmentation_layers = [\n",
" layers.RandomFlip(\"horizontal\"),\n",
" layers.RandomRotation(0.1),\n",
" layers.RandomZoom(0.2),\n",
"]\n",
"\n",
"def data_augmentation(images, targets):\n",
" for layer in data_augmentation_layers:\n",
" images = layer(images)\n",
" return images, targets\n",
"\n",
"augmented_train_dataset = train_dataset.map(\n",
" data_augmentation, num_parallel_calls=8\n",
")\n",
"augmented_train_dataset = augmented_train_dataset.prefetch(tf.data.AUTOTUNE)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"plt.figure(figsize=(10, 10))\n",
"for image_batch, _ in train_dataset.take(1):\n",
" image = image_batch[0]\n",
" for i in range(9):\n",
" ax = plt.subplot(3, 3, i + 1)\n",
" augmented_image, _ = data_augmentation(image, None)\n",
" augmented_image = keras.ops.convert_to_numpy(augmented_image)\n",
" plt.imshow(augmented_image.astype(\"uint8\"))\n",
" plt.axis(\"off\")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"inputs = keras.Input(shape=(180, 180, 3))\n",
"x = layers.Rescaling(1.0 / 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=512, kernel_size=3, activation=\"relu\")(x)\n",
"x = layers.GlobalAveragePooling2D()(x)\n",
"x = layers.Dropout(0.25)(x)\n",
"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
"model = keras.Model(inputs=inputs, outputs=outputs)\n",
"\n",
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"accuracy\"],\n",
")"
]
},
{
"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",
"]\n",
"history = model.fit(\n",
" augmented_train_dataset,\n",
" epochs=100,\n",
" validation_data=validation_dataset,\n",
" callbacks=callbacks,\n",
")"
]
},
{
"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",
")\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 a pretrained model"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"#### Feature extraction with a pretrained model"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import keras_hub\n",
"\n",
"conv_base = keras_hub.models.Backbone.from_preset(\"xception_41_imagenet\")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"preprocessor = keras_hub.layers.ImageConverter.from_preset(\n",
" \"xception_41_imagenet\",\n",
" image_size=(180, 180),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"##### Fast feature extraction without data augmentation"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"def get_features_and_labels(dataset):\n",
" all_features = []\n",
" all_labels = []\n",
" for images, labels in dataset:\n",
" preprocessed_images = preprocessor(images)\n",
" features = conv_base.predict(preprocessed_images, verbose=0)\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": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"inputs = keras.Input(shape=(6, 6, 2048))\n",
"x = layers.GlobalAveragePooling2D()(inputs)\n",
"x = layers.Dense(256, activation=\"relu\")(x)\n",
"x = layers.Dropout(0.25)(x)\n",
"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
"model = keras.Model(inputs, outputs)\n",
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"accuracy\"],\n",
")\n",
"\n",
"callbacks = [\n",
" keras.callbacks.ModelCheckpoint(\n",
" filepath=\"feature_extraction.keras\",\n",
" save_best_only=True,\n",
" monitor=\"val_loss\",\n",
" )\n",
"]\n",
"history = model.fit(\n",
" train_features,\n",
" train_labels,\n",
" epochs=10,\n",
" validation_data=(val_features, val_labels),\n",
" callbacks=callbacks,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\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, \"r--\", 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, \"r--\", 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": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"test_model = keras.models.load_model(\"feature_extraction.keras\")\n",
"test_loss, test_acc = test_model.evaluate(test_features, test_labels)\n",
"print(f\"Test accuracy: {test_acc:.3f}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"##### Feature extraction together with data augmentation"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import keras_hub\n",
"\n",
"conv_base = keras_hub.models.Backbone.from_preset(\n",
" \"xception_41_imagenet\",\n",
" trainable=False,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"conv_base.trainable = True\n",
"len(conv_base.trainable_weights)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"conv_base.trainable = False\n",
"len(conv_base.trainable_weights)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"inputs = keras.Input(shape=(180, 180, 3))\n",
"x = preprocessor(inputs)\n",
"x = conv_base(x)\n",
"x = layers.GlobalAveragePooling2D()(x)\n",
"x = layers.Dense(256)(x)\n",
"x = layers.Dropout(0.25)(x)\n",
"outputs = layers.Dense(1, activation=\"sigmoid\")(x)\n",
"model = keras.Model(inputs, outputs)\n",
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" optimizer=\"adam\",\n",
" metrics=[\"accuracy\"],\n",
")"
]
},
{
"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",
"]\n",
"history = model.fit(\n",
" augmented_train_dataset,\n",
" epochs=30,\n",
" validation_data=validation_dataset,\n",
" callbacks=callbacks,\n",
")"
]
},
{
"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",
")\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": [
"model.compile(\n",
" loss=\"binary_crossentropy\",\n",
" optimizer=keras.optimizers.Adam(learning_rate=1e-5),\n",
" metrics=[\"accuracy\"],\n",
")\n",
"\n",
"callbacks = [\n",
" keras.callbacks.ModelCheckpoint(\n",
" filepath=\"fine_tuning.keras\",\n",
" save_best_only=True,\n",
" monitor=\"val_loss\",\n",
" )\n",
"]\n",
"history = model.fit(\n",
" augmented_train_dataset,\n",
" epochs=30,\n",
" validation_data=validation_dataset,\n",
" callbacks=callbacks,\n",
")"
]
},
{
"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}\")"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "chapter08_image-classification",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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