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

Neural network processing modes

The following diagram illustrates the variety of neural network processing modes:

Rectangles represent tensors, arrows represent functions, red is input, blue is output, and green is the tensor state.

From left to right, we have the following:

  • Plain feed-forward network, fixed-size input, and fixed-size output, for example, image classification
  • Sequence output, for example, image captioning that takes one image and outputs a set of words identifying items in the image
  • Sequence input, for example, sentiment identification (like our IMDb application) where a sentence is classed as being of positive or negative sentiment
  • Both sequence input and output, for example, machine translation where an RNN takes an English sentence and translates it into a French output
  • Synced sequence both input and output, for example, video classification that is like...