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.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Introduction to TensorFlow 2.00 Alpha
5
Section 2: Supervised and Unsupervised Learning in TensorFlow 2.00 Alpha
7
Unsupervised Learning Using TensorFlow 2
8
Section 3: Neural Network Applications of TensorFlow 2.00 Alpha
13
Converting from tf1.12 to tf2

CIFAR 10 image classification using TensorFlow

In this second section, we will look at training a model to recognize images in the CIFAR10 image dataset. This will give us the chance to exemplify a slightly different style of sequential model creation.

Introduction

The CIFAR 10 image dataset, with 10 categories, is a labeled subset of the 80 million tiny images dataset. These images were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Full details on this dataset can be found at https://www.cs.toronto.edu/~kriz/cifar.html.

In total, there are 60,000 32 x 32 color images in the 10 classes consisting of 50,000 training images and 10,000 test images.

The categories are as follows:

labels = ['airplane&apos...