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TensorFlow 2.0 Quick Start Guide

By : Holdroyd

2.3 (3)

Buy this Book

TensorFlow 2.0 Quick Start Guide

2.3 (3)

By: Holdroyd

Buy this Book

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

Get in touch

Free Chapter

Section 1: Introduction to TensorFlow 2.00 Alpha

Introducing TensorFlow 2

Looking at the modern TensorFlow ecosystem

Installing TensorFlow

Housekeeping and eager operations

Providing useful TensorFlow operations

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

Summary

ANN Technologies Using TensorFlow 2

Presenting data to an ANN

One-hot encoding

Layers

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

Summary

Section 3: Neural Network Applications of TensorFlow 2.00 Alpha

Recognizing Images with TensorFlow 2

Quick Draw – image classification using TensorFlow

CIFAR 10 image classification using TensorFlow

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

TensorFlow Hub

Summary

Converting from tf1.12 to tf2

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Installing TensorFlow

The best programming support for TensorFlow is provided for Python (although libraries do exist for Java, C, and Go, while those for other languages are under active development).

There is a wealth of information on the web for installing TensorFlow for Python.

It is standard practice, also recommended by Google, to install TensorFlow in a virtual environment, that is, an environment that isolates a set of APIs and code from other APIs and code and from the system-wide environment.

There are two distinct versions of TensorFlow—one for execution on a CPU and another for execution on a GPU. This last requires that the numerical libraries CUDA and CuDNN are installed. Tensorflow will default to GPU execution where possible. See https://www.tensorflow.org/alpha/guide/using_gpu.

Rather than attempt to reinvent the wheel here, there follow resources for...