Book Image

TensorFlow 2.0 Quick Start Guide

By : Tony Holdroyd

Book Image

TensorFlow 2.0 Quick Start Guide

By: Tony Holdroyd

Overview of this book

TensorFlow is one of the most popular machine learning frameworks in Python. With this book, you will improve your knowledge of some of the latest TensorFlow features and will be able to perform supervised and unsupervised machine learning and also train neural networks. After giving you an overview of what's new in TensorFlow 2.0 Alpha, the book moves on to setting up your machine learning environment using the TensorFlow library. You will perform popular supervised machine learning tasks using techniques such as linear regression, logistic regression, and clustering. You will get familiar with unsupervised learning for autoencoder applications. The book will also show you how to train effective neural networks using straightforward examples in a variety of different domains. By the end of the book, you will have been exposed to a large variety of machine learning and neural network TensorFlow techniques.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Introduction to TensorFlow 2.00 Alpha

Section 1: Introduction to TensorFlow 2.00 Alpha

Introducing TensorFlow 2

Introducing TensorFlow 2

Looking at the modern TensorFlow ecosystem

Installing TensorFlow

Housekeeping and eager operations

Providing useful TensorFlow operations

Keras, a High-Level API for TensorFlow 2

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

ANN Technologies Using TensorFlow 2

ANN Technologies Using TensorFlow 2

Presenting data to an ANN

One-hot encoding

Activation functions

Creating the model

Gradient calculations for gradient descent algorithms

Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha

Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha

Supervised Machine Learning Using TensorFlow 2

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)

Unsupervised Learning Using TensorFlow 2

Unsupervised Learning Using TensorFlow 2

Section 3: Neural Network Applications of TensorFlow 2.00 Alpha

Section 3: Neural Network Applications of TensorFlow 2.00 Alpha

Recognizing Images with TensorFlow 2

Recognizing Images with TensorFlow 2

Quick Draw – image classification using TensorFlow

CIFAR 10 image classification using TensorFlow

Neural Style Transfer Using TensorFlow 2

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

Recurrent Neural Networks Using TensorFlow 2

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

TensorFlow Estimators and TensorFlow Hub

TensorFlow Estimators and TensorFlow Hub

TensorFlow Estimators

Converting from tf1.12 to tf2

Converting from tf1.12 to tf2

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Keras, a High-Level API for TensorFlow 2

In this chapter, we will discuss Keras, which is a high-level API for TensorFlow 2. Keras was developed by François Chollet at Google. Keras has become extremely popular for fast prototyping, for building and training deep learning models, and for research and production. Keras is a very rich API; it supports eager execution and data pipelines, and other features, as we will see.

Keras has been available for TensorFlow since 2017, but its use has been extended and further integrated into TensorFlow with the release of TensorFlow 2.0. TensorFlow 2.0 has embraced Keras as the API of choice for the majority of deep learning development work.

It is possible to import Keras as a standalone module, but in this book, we will concentrate on using Keras from within TensorFlow 2. The module is, thus, tensorflow.keras.

In this chapter, we will...