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Hands-On Neural Networks with TensorFlow 2.0

By : Galeone

3.7 (7)

Buy this Book

Hands-On Neural Networks with TensorFlow 2.0

3.7 (7)

By: Galeone

Buy this Book

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

Who this book is for

What this book covers

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Section 1: Neural Network Fundamentals

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

Optimization

Convolutional neural networks

Regularization

Summary

Exercises

Section 2: TensorFlow Fundamentals

TensorFlow Graph Architecture

Environment setup

Dataflow graphs

Model definition and training

Interacting with the graph using Python

Summary

Exercises

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

Estimator API

Summary

Exercises

Section 3: The Application of Neural Networks

Image Classification Using TensorFlow Hub

Getting the data

Transfer learning

Fine-tuning

Summary

Exercises

Introduction to Object Detection

Getting the data

Object localization

Classification and localization

Summary

Exercises

Semantic Segmentation and Custom Dataset Builder

Semantic segmentation

Create a TensorFlow DatasetBuilder

Model training and evaluation

Summary

Exercises

Generative Adversarial Networks

Understanding GANs and their applications

Unconditional GANs

Conditional GANs

Summary

Exercises

Bringing a Model to Production

The SavedModel serialization format

Python deployment

Supported deployment platforms

Summary

Exercises

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Codebase migration

As we have already seen, TensorFlow 2.0 brings a lot of breaking changes, which means that we have to relearn how to use the framework. TensorFlow 1.x is the most widely used machine learning framework and so there is a lot of existing code that needs to be upgraded.

The TensorFlow engineers developed a conversion tool that can help in the conversion process: unfortunately, it relies on the tf.compat.v1 module, and it does not remove the graph nor the session execution. Instead, it just rewrites the code, prefixing it using tf.compat.v1, and applies some source code transformations to fix some easy API changes.

However, it is a good starting point to migrate a whole codebase. In fact, the suggested migration process is as follows: