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

Keras models

Keras is based on the concept of a neural network model. The predominant model is called a Sequence, being a linear stack of layers. There is also a system using the Keras functional API.

The Keras Sequential model

To build a Keras Sequential model, you add layers to it in the same order that you want the computations to be undertaken by the network.

After you have built your model, you compile it; this optimizes the computations that are to be undertaken, and is where you allocate the optimizer and the loss function you want your model to use.

The next stage is to fit the model to the data. This is commonly known as training the model, and is where all the computations take place. It is possible to present the...