TensorFlow 2.0 Quick Start Guide by Holdroyd, Tony -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

TensorFlow 2.0 Quick Start Guide

Dedication About Packt

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Contributors

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Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Conventions used

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Section 1: Introduction to TensorFlow 2.00 Alpha Introducing TensorFlow 2

Looking at the modern TensorFlow ecosystem Installing TensorFlow Housekeeping and eager operations

Importing TensorFlow Coding style convention for TensorFlow Using eager execution Declaring eager variables Declaring TensorFlow constants Shaping a tensor Ranking (dimensions) of a tensor Specifying an element of a tensor Casting a tensor to a NumPy/Python variable Finding the size (number of elements) of a tensor Finding the datatype of a tensor Specifying element-wise primitive tensor operations Broadcasting Transposing TensorFlow and matrix multiplication Casting a tensor to another (tensor) datatype Declaring ragged tensors

Providing useful TensorFlow operations

Finding the squared difference between two tensors Finding a mean

Finding the mean across all axes Finding the mean across columns Finding the mean across rows

Generating tensors filled with random values

Using tf.random.normal() Using tf.random.uniform() Using a practical example of random values

Finding the indices of the largest and smallest element Saving and restoring tensor values using a checkpoint Using tf.function

Summary

Keras, a High-Level API for TensorFlow 2

The adoption and advantages of Keras The features of Keras The default Keras configuration file The Keras backend Keras data types Keras models

The Keras Sequential model

The first way to create a Sequential model The second way to create a Sequential model

The Keras functional API Subclassing the Keras Model class Using data pipelines Saving and loading Keras models Keras datasets

Summary

ANN Technologies Using TensorFlow 2

Presenting data to an ANN

Using NumPy arrays with datasets Using comma-separated value (CSV) files with datasets

CSV example 1 CSV example 2 CSV example 3

TFRecords

TFRecord example 1 TFRecord example 2

One-hot encoding

OHE example 1 OHE example 2

Layers

Dense (fully connected) layer Convolutional layer Max pooling layer Batch normalization layer and dropout layer Softmax layer

Activation functions Creating the model Gradient calculations for gradient descent algorithms Loss functions Summary

Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha Supervised Machine Learning Using TensorFlow 2

Supervised learning Linear regression Our first linear regression example The Boston housing dataset Logistic regression (classification) k-Nearest Neighbors (KNN) Summary

Unsupervised Learning Using TensorFlow 2

Autoencoders

A simple autoencoder

Preprocessing the data Training Displaying the results

An autoencoder application – denoising

Setup Preprocessing the data The noisy images Creating the encoding layers Creating the decoding layers Model summary Model instantiation, compiling, and training Denoised images TensorBoard output

Summary

Section 3: Neural Network Applications of TensorFlow 2.00 Alpha Recognizing Images with TensorFlow 2

Quick Draw – image classification using TensorFlow

Acquiring the data Setting up our environment Preprocessing the data Creating the model Training and testing the model TensorBoard callback Saving, loading, and retesting the model Saving and loading NumPy image data using the .h5 format Loading and inference with a pre-trained model

CIFAR 10 image classification using TensorFlow

Introduction The application

Summary

Neural Style Transfer Using TensorFlow 2

Setting up the imports Preprocessing the images Viewing the original images Using the VGG19 architecture Creating the model Calculating the losses Performing the style transfer Final displays Summary

Recurrent Neural Networks Using TensorFlow 2

Neural network processing modes Recurrent architectures An application of RNNs The code for our RNN example Building and instantiating our model Using our model to get predictions Summary

TensorFlow Estimators and TensorFlow Hub

TensorFlow Estimators

The code

TensorFlow Hub

IMDb (database of movie reviews) The dataset The code

Summary

Converting from tf1.12 to tf2 Other Books You May Enjoy

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