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

Presenting data to an ANN

The canonical way to present data to a TensorFlow ANN, as recommended by Google, is via a data pipeline composed of a tf.data.Dataset object and a tf.data.Iterator method. A tf.data.Dataset object consists of a sequence of elements in which each element contains one or more tensor objects. The tf.data.Iterator is a method used to loop over a dataset so that successive individual elements in it may be accessed.

We will look at two important ways of constructing a data pipeline, firstly, from in-memory NumPy arrays, and, secondly, from Comma-Separated Value (CSV) files. We will also look at a binary TFRecord format.

Using NumPy arrays with datasets

Let's look at some straightforward examples first...