wehub-resource-sync
786 lines
18 KiB
Plaintext
786 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"# Fundamentals of machine learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Generalization: The goal of machine learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Underfitting and overfitting"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Noisy training data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Ambiguous features"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Rare features and spurious correlations"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Adding white-noise channels or all-zeros channels to MNIST**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow.keras.datasets import mnist\n",
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"import numpy as np\n",
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"\n",
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"(train_images, train_labels), _ = mnist.load_data()\n",
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"train_images = train_images.reshape((60000, 28 * 28))\n",
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"train_images = train_images.astype(\"float32\") / 255\n",
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"\n",
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"train_images_with_noise_channels = np.concatenate(\n",
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" [train_images, np.random.random((len(train_images), 784))], axis=1)\n",
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"\n",
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"train_images_with_zeros_channels = np.concatenate(\n",
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" [train_images, np.zeros((len(train_images), 784))], axis=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Training the same model on MNIST data with noise channels or all-zero channels**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"from tensorflow import keras\n",
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"from tensorflow.keras import layers\n",
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"\n",
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"def get_model():\n",
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" model = keras.Sequential([\n",
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" layers.Dense(512, activation=\"relu\"),\n",
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" layers.Dense(10, activation=\"softmax\")\n",
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" ])\n",
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" model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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" return model\n",
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"\n",
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"model = get_model()\n",
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"history_noise = model.fit(\n",
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" train_images_with_noise_channels, train_labels,\n",
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" epochs=10,\n",
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" batch_size=128,\n",
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" validation_split=0.2)\n",
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"\n",
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"model = get_model()\n",
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"history_zeros = model.fit(\n",
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" train_images_with_zeros_channels, train_labels,\n",
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" epochs=10,\n",
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Plotting a validation accuracy comparison**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"val_acc_noise = history_noise.history[\"val_accuracy\"]\n",
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"val_acc_zeros = history_zeros.history[\"val_accuracy\"]\n",
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"epochs = range(1, 11)\n",
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"plt.plot(epochs, val_acc_noise, \"b-\",\n",
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" label=\"Validation accuracy with noise channels\")\n",
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"plt.plot(epochs, val_acc_zeros, \"b--\",\n",
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" label=\"Validation accuracy with zeros channels\")\n",
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"plt.title(\"Effect of noise channels on validation accuracy\")\n",
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"plt.xlabel(\"Epochs\")\n",
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"plt.ylabel(\"Accuracy\")\n",
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"plt.legend()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### The nature of generalization in deep learning"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Fitting a MNIST model with randomly shuffled labels**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"(train_images, train_labels), _ = mnist.load_data()\n",
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"train_images = train_images.reshape((60000, 28 * 28))\n",
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"train_images = train_images.astype(\"float32\") / 255\n",
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"\n",
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"random_train_labels = train_labels[:]\n",
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"np.random.shuffle(random_train_labels)\n",
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"\n",
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"model = keras.Sequential([\n",
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" layers.Dense(512, activation=\"relu\"),\n",
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" layers.Dense(10, activation=\"softmax\")\n",
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"])\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(train_images, random_train_labels,\n",
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" epochs=100,\n",
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### The manifold hypothesis"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Interpolation as a source of generalization"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Why deep learning works"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Training data is paramount"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Evaluating machine-learning models"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Training, validation, and test sets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Simple hold-out validation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### K-fold validation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"#### Iterated K-fold validation with shuffling"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Beating a common-sense baseline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Things to keep in mind about model evaluation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"## Improving model fit"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Tuning key gradient descent parameters"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**Training a MNIST model with an incorrectly high learning rate**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"(train_images, train_labels), _ = mnist.load_data()\n",
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"train_images = train_images.reshape((60000, 28 * 28))\n",
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"train_images = train_images.astype(\"float32\") / 255\n",
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"\n",
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"model = keras.Sequential([\n",
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" layers.Dense(512, activation=\"relu\"),\n",
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" layers.Dense(10, activation=\"softmax\")\n",
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"])\n",
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"model.compile(optimizer=keras.optimizers.RMSprop(1.),\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(train_images, train_labels,\n",
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" epochs=10,\n",
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**The same model with a more appropriate learning rate**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"model = keras.Sequential([\n",
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" layers.Dense(512, activation=\"relu\"),\n",
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" layers.Dense(10, activation=\"softmax\")\n",
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"])\n",
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"model.compile(optimizer=keras.optimizers.RMSprop(1e-2),\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"model.fit(train_images, train_labels,\n",
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" epochs=10,\n",
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Leveraging better architecture priors"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"### Increasing model capacity"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text"
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},
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"source": [
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"**A simple logistic regression on MNIST**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"model = keras.Sequential([layers.Dense(10, activation=\"softmax\")])\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
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"history_small_model = model.fit(\n",
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" train_images, train_labels,\n",
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" epochs=20,\n",
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"val_loss = history_small_model.history[\"val_loss\"]\n",
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"epochs = range(1, 21)\n",
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"plt.plot(epochs, val_loss, \"b--\",\n",
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" label=\"Validation loss\")\n",
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"plt.title(\"Effect of insufficient model capacity on validation loss\")\n",
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"plt.xlabel(\"Epochs\")\n",
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"plt.ylabel(\"Loss\")\n",
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"plt.legend()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 0,
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"metadata": {
|
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"colab_type": "code"
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},
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"outputs": [],
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"source": [
|
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"model = keras.Sequential([\n",
|
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" layers.Dense(96, activation=\"relu\"),\n",
|
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" layers.Dense(96, activation=\"relu\"),\n",
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" layers.Dense(10, activation=\"softmax\"),\n",
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"])\n",
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"model.compile(optimizer=\"rmsprop\",\n",
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" loss=\"sparse_categorical_crossentropy\",\n",
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" metrics=[\"accuracy\"])\n",
|
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"history_large_model = model.fit(\n",
|
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" train_images, train_labels,\n",
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" epochs=20,\n",
|
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" batch_size=128,\n",
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" validation_split=0.2)"
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]
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},
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|
{
|
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"cell_type": "markdown",
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|
"metadata": {
|
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"colab_type": "text"
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|
},
|
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"source": [
|
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"## Improving generalization"
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|
]
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},
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{
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|
"cell_type": "markdown",
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|
"metadata": {
|
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"colab_type": "text"
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|
},
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"source": [
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"### Dataset curation"
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|
]
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},
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{
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|
"cell_type": "markdown",
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|
"metadata": {
|
|
"colab_type": "text"
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|
},
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"source": [
|
|
"### Feature engineering"
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|
]
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|
},
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|
{
|
|
"cell_type": "markdown",
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|
"metadata": {
|
|
"colab_type": "text"
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|
},
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"source": [
|
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"### Using early stopping"
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|
]
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|
},
|
|
{
|
|
"cell_type": "markdown",
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|
"metadata": {
|
|
"colab_type": "text"
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|
},
|
|
"source": [
|
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"### Regularizing your model"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
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"#### Reducing the network's size"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
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|
},
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"source": [
|
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"**Original model**"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras.datasets import imdb\n",
|
|
"(train_data, train_labels), _ = imdb.load_data(num_words=10000)\n",
|
|
"\n",
|
|
"def vectorize_sequences(sequences, dimension=10000):\n",
|
|
" results = np.zeros((len(sequences), dimension))\n",
|
|
" for i, sequence in enumerate(sequences):\n",
|
|
" results[i, sequence] = 1.\n",
|
|
" return results\n",
|
|
"train_data = vectorize_sequences(train_data)\n",
|
|
"\n",
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(16, activation=\"relu\"),\n",
|
|
" layers.Dense(16, activation=\"relu\"),\n",
|
|
" layers.Dense(1, activation=\"sigmoid\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"binary_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"history_original = model.fit(train_data, train_labels,\n",
|
|
" epochs=20, batch_size=512, validation_split=0.4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Version of the model with lower capacity**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(4, activation=\"relu\"),\n",
|
|
" layers.Dense(4, activation=\"relu\"),\n",
|
|
" layers.Dense(1, activation=\"sigmoid\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"binary_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"history_smaller_model = model.fit(\n",
|
|
" train_data, train_labels,\n",
|
|
" epochs=20, batch_size=512, validation_split=0.4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Version of the model with higher capacity**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(512, activation=\"relu\"),\n",
|
|
" layers.Dense(512, activation=\"relu\"),\n",
|
|
" layers.Dense(1, activation=\"sigmoid\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"binary_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"history_larger_model = model.fit(\n",
|
|
" train_data, train_labels,\n",
|
|
" epochs=20, batch_size=512, validation_split=0.4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"#### Adding weight regularization"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Adding L2 weight regularization to the model**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras import regularizers\n",
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(16,\n",
|
|
" kernel_regularizer=regularizers.l2(0.002),\n",
|
|
" activation=\"relu\"),\n",
|
|
" layers.Dense(16,\n",
|
|
" kernel_regularizer=regularizers.l2(0.002),\n",
|
|
" activation=\"relu\"),\n",
|
|
" layers.Dense(1, activation=\"sigmoid\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"binary_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"history_l2_reg = model.fit(\n",
|
|
" train_data, train_labels,\n",
|
|
" epochs=20, batch_size=512, validation_split=0.4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Different weight regularizers available in Keras**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras import regularizers\n",
|
|
"regularizers.l1(0.001)\n",
|
|
"regularizers.l1_l2(l1=0.001, l2=0.001)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"#### Adding dropout"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Adding dropout to the IMDB model**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" layers.Dense(16, activation=\"relu\"),\n",
|
|
" layers.Dropout(0.5),\n",
|
|
" layers.Dense(16, activation=\"relu\"),\n",
|
|
" layers.Dropout(0.5),\n",
|
|
" layers.Dense(1, activation=\"sigmoid\")\n",
|
|
"])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"binary_crossentropy\",\n",
|
|
" metrics=[\"accuracy\"])\n",
|
|
"history_dropout = model.fit(\n",
|
|
" train_data, train_labels,\n",
|
|
" epochs=20, batch_size=512, validation_split=0.4)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Summary"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "chapter05_fundamentals-of-ml.i",
|
|
"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": "3.7.0"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |