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What's New in TensorFlow 2.0

By : Baranwal, Alizishaan Khatri

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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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Getting Started with TensorFlow 2.0

This book aims to familiarize readers with the new features introduced in TensorFlow 2.0 (TF 2.0) and to empower you to unlock its potential while building machine learning applications. This chapter provides a bird's-eye view of new architectural and API-level changes in TF 2.0. We will cover TF 2.0 installation and setup, and will compare the changes with respect to TensorFlow 1.x (TF 1.x), such as Keras APIs and layer APIs. We will also cover the addition of rich extensions, such as TensorFlow Probability, Tensor2Tensor, Ragged Tensors, and the newly available custom training logic for loss functions. This chapter also summarizes the changes to the layers API and other APIs.

The following topics will be covered in this chapter: