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Hands-On Neural Networks with TensorFlow 2.0

By : Galeone

3.7 (7)

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)

By: Galeone

Overview of this book

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Section 1: Neural Network Fundamentals

Section 1: Neural Network Fundamentals

What is Machine Learning?

What is Machine Learning?

The importance of the dataset

Supervised learning

Unsupervised learning

Semi-supervised learning

Summary

Exercises

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Neural networks

Optimization

Convolutional neural networks

Regularization

Summary

Exercises

Section 2: TensorFlow Fundamentals

Section 2: TensorFlow Fundamentals

TensorFlow Graph Architecture

TensorFlow Graph Architecture

Environment setup

Dataflow graphs

Model definition and training

Interacting with the graph using Python

Summary

Exercises

TensorFlow 2.0 Architecture

TensorFlow 2.0 Architecture

Relearning the framework

The Keras framework and its models

Eager execution and new features

Codebase migration

Summary

Exercises

Efficient Data Input Pipelines and Estimator API

Efficient Data Input Pipelines and Estimator API

Efficient data input pipelines

Estimator API

Summary

Exercises

Section 3: The Application of Neural Networks

Section 3: The Application of Neural Networks

Image Classification Using TensorFlow Hub

Image Classification Using TensorFlow Hub

Getting the data

Transfer learning

Fine-tuning

Summary

Exercises

Introduction to Object Detection

Introduction to Object Detection

Getting the data

Object localization

Classification and localization

Summary

Exercises

Semantic Segmentation and Custom Dataset Builder

Semantic Segmentation and Custom Dataset Builder

Semantic segmentation

Create a TensorFlow DatasetBuilder

Model training and evaluation

Summary

Exercises

Generative Adversarial Networks

Generative Adversarial Networks

Understanding GANs and their applications

Unconditional GANs

Conditional GANs

Summary

Exercises

Bringing a Model to Production

Bringing a Model to Production

The SavedModel serialization format

Python deployment

Supported deployment platforms

Summary

Exercises

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Relearning the framework

As we introduced in Chapter 3, TensorFlow Graph Architecture, TensorFlow works by building a computational graph first and then executing it. In TensorFlow 2.0, this graph definition is hidden and simplified; the execution and the definition can be mixed, and the flow of execution is always the one that's found in the source code—there's no need to worry about the order of execution in 2.0.

Prior to the 2.0 release, developers had to design the graph and the source by following this pattern:

How can I define the graph? Is my graph composed of multiple layers that are logically separated? If so, I have to define every logical block inside a different tf.variable_scope.
During the training or inference phase, do I have to use a part of the graph more than once in the same execution step? If so, I have to define this part by wrapping it...

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