wehub-resource-sync
990 lines
20 KiB
Plaintext
990 lines
20 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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"# Introduction to Keras and TensorFlow"
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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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"## What's TensorFlow?"
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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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"## What's Keras?"
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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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"## Keras and TensorFlow: A brief history"
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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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"## Setting up a deep-learning workspace"
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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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"### Jupyter notebooks: The preferred way to run deep-learning experiments"
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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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"### Using Colaboratory"
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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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"#### First steps with Colaboratory"
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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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"#### Installing packages with pip"
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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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"#### Using the GPU runtime"
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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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"## First steps with TensorFlow"
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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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"#### Constant tensors and variables"
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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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"**All-ones or all-zeros tensors**"
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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 tensorflow as tf\n",
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"x = tf.ones(shape=(2, 1))\n",
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"print(x)"
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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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"x = tf.zeros(shape=(2, 1))\n",
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"print(x)"
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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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"**Random tensors**"
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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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"x = tf.random.normal(shape=(3, 1), mean=0., stddev=1.)\n",
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"print(x)"
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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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"x = tf.random.uniform(shape=(3, 1), minval=0., maxval=1.)\n",
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"print(x)"
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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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"**NumPy arrays are assignable**"
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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 numpy as np\n",
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"x = np.ones(shape=(2, 2))\n",
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"x[0, 0] = 0."
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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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"**Creating a TensorFlow variable**"
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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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"v = tf.Variable(initial_value=tf.random.normal(shape=(3, 1)))\n",
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"print(v)"
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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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"**Assigning a value to a TensorFlow variable**"
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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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"v.assign(tf.ones((3, 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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"**Assigning a value to a subset of a TensorFlow variable**"
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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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"v[0, 0].assign(3.)"
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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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"**Using `assign_add`**"
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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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"v.assign_add(tf.ones((3, 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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"#### Tensor operations: Doing math in TensorFlow"
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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 few basic math operations**"
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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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"a = tf.ones((2, 2))\n",
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"b = tf.square(a)\n",
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"c = tf.sqrt(a)\n",
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"d = b + c\n",
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"e = tf.matmul(a, b)\n",
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"e *= d"
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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 second look at the GradientTape API"
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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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"**Using the `GradientTape`**"
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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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"input_var = tf.Variable(initial_value=3.)\n",
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"with tf.GradientTape() as tape:\n",
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" result = tf.square(input_var)\n",
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"gradient = tape.gradient(result, input_var)"
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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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"**Using `GradientTape` with constant tensor inputs**"
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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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"input_const = tf.constant(3.)\n",
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"with tf.GradientTape() as tape:\n",
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" tape.watch(input_const)\n",
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" result = tf.square(input_const)\n",
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"gradient = tape.gradient(result, input_const)"
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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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"**Using nested gradient tapes to compute second-order gradients**"
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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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"time = tf.Variable(0.)\n",
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"with tf.GradientTape() as outer_tape:\n",
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" with tf.GradientTape() as inner_tape:\n",
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" position = 4.9 * time ** 2\n",
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" speed = inner_tape.gradient(position, time)\n",
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"acceleration = outer_tape.gradient(speed, time)"
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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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"#### An end-to-end example: A linear classifier in pure TensorFlow"
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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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"**Generating two classes of random points in a 2D plane**"
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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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"num_samples_per_class = 1000\n",
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"negative_samples = np.random.multivariate_normal(\n",
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" mean=[0, 3],\n",
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" cov=[[1, 0.5],[0.5, 1]],\n",
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" size=num_samples_per_class)\n",
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"positive_samples = np.random.multivariate_normal(\n",
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" mean=[3, 0],\n",
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" cov=[[1, 0.5],[0.5, 1]],\n",
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" size=num_samples_per_class)"
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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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"**Stacking the two classes into an array with shape (2000, 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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"inputs = np.vstack((negative_samples, positive_samples)).astype(np.float32)"
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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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"**Generating the corresponding targets (0 and 1)**"
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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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"targets = np.vstack((np.zeros((num_samples_per_class, 1), dtype=\"float32\"),\n",
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" np.ones((num_samples_per_class, 1), dtype=\"float32\")))"
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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 the two point classes**"
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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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"plt.scatter(inputs[:, 0], inputs[:, 1], c=targets[:, 0])\n",
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"plt.show()"
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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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"**Creating the linear classifier variables**"
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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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"input_dim = 2\n",
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"output_dim = 1\n",
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"W = tf.Variable(initial_value=tf.random.uniform(shape=(input_dim, output_dim)))\n",
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"b = tf.Variable(initial_value=tf.zeros(shape=(output_dim,)))"
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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 forward pass function**"
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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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"def model(inputs):\n",
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" return tf.matmul(inputs, W) + b"
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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 mean squared error loss function**"
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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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"def square_loss(targets, predictions):\n",
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" per_sample_losses = tf.square(targets - predictions)\n",
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" return tf.reduce_mean(per_sample_losses)"
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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 training step function**"
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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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"learning_rate = 0.1\n",
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"\n",
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"def training_step(inputs, targets):\n",
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" with tf.GradientTape() as tape:\n",
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" predictions = model(inputs)\n",
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" loss = square_loss(targets, predictions)\n",
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" grad_loss_wrt_W, grad_loss_wrt_b = tape.gradient(loss, [W, b])\n",
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" W.assign_sub(grad_loss_wrt_W * learning_rate)\n",
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" b.assign_sub(grad_loss_wrt_b * learning_rate)\n",
|
|
" return loss"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**The batch training loop**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"for step in range(40):\n",
|
|
" loss = training_step(inputs, targets)\n",
|
|
" print(f\"Loss at step {step}: {loss:.4f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"predictions = model(inputs)\n",
|
|
"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"x = np.linspace(-1, 4, 100)\n",
|
|
"y = - W[0] / W[1] * x + (0.5 - b) / W[1]\n",
|
|
"plt.plot(x, y, \"-r\")\n",
|
|
"plt.scatter(inputs[:, 0], inputs[:, 1], c=predictions[:, 0] > 0.5)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Anatomy of a neural network: Understanding core Keras APIs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Layers: The building blocks of deep learning"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"#### The base Layer class in Keras"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**A `Dense` layer implemented as a `Layer` subclass**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow import keras\n",
|
|
"\n",
|
|
"class SimpleDense(keras.layers.Layer):\n",
|
|
"\n",
|
|
" def __init__(self, units, activation=None):\n",
|
|
" super().__init__()\n",
|
|
" self.units = units\n",
|
|
" self.activation = activation\n",
|
|
"\n",
|
|
" def build(self, input_shape):\n",
|
|
" input_dim = input_shape[-1]\n",
|
|
" self.W = self.add_weight(shape=(input_dim, self.units),\n",
|
|
" initializer=\"random_normal\")\n",
|
|
" self.b = self.add_weight(shape=(self.units,),\n",
|
|
" initializer=\"zeros\")\n",
|
|
"\n",
|
|
" def call(self, inputs):\n",
|
|
" y = tf.matmul(inputs, self.W) + self.b\n",
|
|
" if self.activation is not None:\n",
|
|
" y = self.activation(y)\n",
|
|
" return y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"my_dense = SimpleDense(units=32, activation=tf.nn.relu)\n",
|
|
"input_tensor = tf.ones(shape=(2, 784))\n",
|
|
"output_tensor = my_dense(input_tensor)\n",
|
|
"print(output_tensor.shape)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"#### Automatic shape inference: Building layers on the fly"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras import layers\n",
|
|
"layer = layers.Dense(32, activation=\"relu\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"from tensorflow.keras import models\n",
|
|
"from tensorflow.keras import layers\n",
|
|
"model = models.Sequential([\n",
|
|
" layers.Dense(32, activation=\"relu\"),\n",
|
|
" layers.Dense(32)\n",
|
|
"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([\n",
|
|
" SimpleDense(32, activation=\"relu\"),\n",
|
|
" SimpleDense(64, activation=\"relu\"),\n",
|
|
" SimpleDense(32, activation=\"relu\"),\n",
|
|
" SimpleDense(10, activation=\"softmax\")\n",
|
|
"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### From layers to models"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### The \"compile\" step: Configuring the learning process"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([keras.layers.Dense(1)])\n",
|
|
"model.compile(optimizer=\"rmsprop\",\n",
|
|
" loss=\"mean_squared_error\",\n",
|
|
" metrics=[\"accuracy\"])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=keras.optimizers.RMSprop(),\n",
|
|
" loss=keras.losses.MeanSquaredError(),\n",
|
|
" metrics=[keras.metrics.BinaryAccuracy()])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Picking a loss function"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Understanding the fit() method"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Calling `fit()` with NumPy data**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = model.fit(\n",
|
|
" inputs,\n",
|
|
" targets,\n",
|
|
" epochs=5,\n",
|
|
" batch_size=128\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history.history"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Monitoring loss and metrics on validation data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"**Using the `validation_data` argument**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = keras.Sequential([keras.layers.Dense(1)])\n",
|
|
"model.compile(optimizer=keras.optimizers.RMSprop(learning_rate=0.1),\n",
|
|
" loss=keras.losses.MeanSquaredError(),\n",
|
|
" metrics=[keras.metrics.BinaryAccuracy()])\n",
|
|
"\n",
|
|
"indices_permutation = np.random.permutation(len(inputs))\n",
|
|
"shuffled_inputs = inputs[indices_permutation]\n",
|
|
"shuffled_targets = targets[indices_permutation]\n",
|
|
"\n",
|
|
"num_validation_samples = int(0.3 * len(inputs))\n",
|
|
"val_inputs = shuffled_inputs[:num_validation_samples]\n",
|
|
"val_targets = shuffled_targets[:num_validation_samples]\n",
|
|
"training_inputs = shuffled_inputs[num_validation_samples:]\n",
|
|
"training_targets = shuffled_targets[num_validation_samples:]\n",
|
|
"model.fit(\n",
|
|
" training_inputs,\n",
|
|
" training_targets,\n",
|
|
" epochs=5,\n",
|
|
" batch_size=16,\n",
|
|
" validation_data=(val_inputs, val_targets)\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"### Inference: Using a model after training"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 0,
|
|
"metadata": {
|
|
"colab_type": "code"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"predictions = model.predict(val_inputs, batch_size=128)\n",
|
|
"print(predictions[:10])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"colab_type": "text"
|
|
},
|
|
"source": [
|
|
"## Summary"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"collapsed_sections": [],
|
|
"name": "chapter03_introduction-to-keras-and-tf.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
|
|
} |