What's New in TensorFlow 2.0

By : Baranwal, Alizishaan Khatri

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Buy this Book

What's New in TensorFlow 2.0

5 (2)

By: Baranwal, Alizishaan Khatri

Buy this Book

Overview of this book

TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This book will help you understand and utilize the latest TensorFlow features. What's New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The book then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You'll explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This book delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis. By the end of the book, you'll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.

Preface

Who this book is for

What this book covers

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Section 1: TensorFlow 2.0 - Architecture and API Changes

Getting Started with TensorFlow 2.0

Technical requirements

What's new?

TF 2.0 installation and setup

Using TF 2.0

Rich extensions

Summary

Keras Default Integration and Eager Execution

Technical requirements

New abstractions in TF 2.0

Diving deep into the Keras API

Estimators

Evaluating TensorFlow graphs

Summary

Section 2: TensorFlow 2.0 - Data and Model Training Pipelines

Designing and Constructing Input Data Pipelines

Technical requirements

Designing and constructing the data pipeline

Transforming datasets

Feeding the created dataset to the model

Examples of complete end-to-end data pipelines

Best practices and the performance optimization of a data pipeline in TF 2.0

Built-in datasets in TF 2.0

Summary

Further reading

Section 3: TensorFlow 2.0 - Model Inference and Deployment and AIY

Model Inference Pipelines - Multi-platform Deployments

Technical requirements

Machine learning workflow – the inference phase

Model artifact – the SavedModel format

Inference on backend servers

Inference in the browser

Inference on mobile and IoT devices

Summary

AIY Projects and TensorFlow Lite

Introduction to TFLite

Getting started with TFLite

Running TFLite on mobile devices

Running TFLite on low-power machines

Comparing TFLite and TF

AIY

Summary

Section 4: TensorFlow 2.0 - Migration, Summary

Migrating From TensorFlow 1.x to 2.0

Major changes in TF 2.0

Recommended techniques to employ for idiomatic TF 2.0

Making code TF 2.0-native

Frequently asked questions

The future of TF 2.0

Summary

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Migrating From TensorFlow 1.x to 2.0

This chapter will cover how you can convert TensorFlow 1.x (TF 1.x) code into TensorFlow 2.0 (TF 2.0) code in two ways. The first method is to use the update script, which changes most of the TF 1.x code so that it can run in TF 2.0. This, however, simply converts all tf.x API calls into tf.compat.v1.x format. The other method is to convert TF 1.x code into idiomatic TF2.0 code by taking into account the core changes that have been made to the library. We will discuss the conceptual differences between TF 1.x and TF 2.0, the compatibility criteria between them, and the ways we can migrate syntactically and semantically. We will also show several examples of syntactic and semantic migration from TF 1.x to TF 2.0, with which we will provide references and future information.

The following topics will be covered in this chapter: