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What's New in TensorFlow 2.0
What's New in TensorFlow 2.0

What's New in TensorFlow 2.0: Use the new and improved features of TensorFlow to enhance machine learning and deep learning

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Profile Icon Baranwal Profile Icon Alizishaan Khatri
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€10.99 €16.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
eBook Aug 2019 202 pages 1st Edition
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€10.99 €16.99
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Arrow left icon
Profile Icon Baranwal Profile Icon Alizishaan Khatri
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€10.99 €16.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (2 Ratings)
eBook Aug 2019 202 pages 1st Edition
eBook
€10.99 €16.99
Paperback
€20.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€10.99 €16.99
Paperback
€20.99
Subscription
Free Trial
Renews at €18.99p/m

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What's New in TensorFlow 2.0

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:

  • What's new?
  • TF 2.0 installation and setup
  • Using TF 2.0
  • Rich extensions
...

Technical requirements

You will need the following before you can start executing the steps described in the sections ahead:

  • Python 3.4 or higher
  • A computer with Ubuntu 16.04 or later (The instructions remain similar for most *NIX-based systems such as macOS or other Linux variants)

What's new?

The philosophy of TF 2.0 is based on simplicity and ease of use. The major updates include easy model building with tf.keras and eager execution, robust model deployment for production and commercial use for any platform, powerful experimentation techniques and tools for research, and API simplification for a more intuitive organization of APIs.

The new organization of TF 2.0 is simplified by the following diagram:

The preceding diagram is focused on using the Python API for training and deploying; however, the same process is followed with the other supported languages including Julia, JavaScript, and R. The flow of TF 2.0 is separated into two sections—model training and model deployment, where model training includes the data pipelines, model creation, training, and distribution strategies; and model deployment includes the variety of means of deployment...

TF 2.0 installation and setup

This section describes the steps required to install TF 2.0 on your system using different methods and on different system configurations. Entry-level users are recommended to start with the pip- and virtualenv-based methods. For users of the GPU version, docker is the recommended method.

Installing and using pip

For the uninitiated, pip is a popular package management system in the Python community. If this is not installed on your system, please install it before proceeding further. On many Linux installations, Python and pip are installed by default. You can check whether pip is installed by typing the following command:

python3 -m pip --help

If you see a blurb describing the different commands...

Using TF 2.0

TF 2.0 can be used in two main ways—using low-level APIs and using high-level APIs. To use the low-level APIs in TF 2.0, APIs such as tf.GradientTape and tf.function are implemented.

The code flow for writing low-level code is to define a forward pass inside of a function that takes the input data as an argument. This function is then annotated with the tf.function decorator in order to run it in graph mode along with all of its benefits. To record and get the gradients of the forward pass, both the decorator function and the loss function are run inside the tf.GradientTape context manager, from which gradients can be calculated and applied on the model variables.

Training code can also be written using the low-level APIs for tf.keras models by using tf.GradientTape. This is for when more control and customizability is needed over the default tf.keras.Model...

Technical requirements

In order to run the code excerpts given in this chapter, you will need the following hardware and software: 

  • TF 2.0 or higher (either of the CPU or GPU versions will suffice)
  • Python 3.4+ (currently, the highest Python version supported by TensorFlow is 3.6)
  • NumPy (if not automatically installed by TensorFlow)

The code files for this chapter are available at https://github.com/PacktPublishing/What-s-New-in-TensorFlow-2.0/tree/master/Chapter02.

New abstractions in TF 2.0

Abstractions are a very popular tool used in the process of programming and software development. In a very high-level sense, an abstraction refers to the process of isolating and describing the central idea of a particular task or set of tasks without necessarily specifying the physical, spatial, or temporal details. When done right, an abstraction can significantly reduce the amount of code that needs to be written for a particular task. It also boosts the reusability of existing code and makes it compatible with TF 2.0.

While working with machine learning systems, there are some common high-level tasks, such as training data, modeling, model evaluation, prediction, model storing, and model loading, that are common across a wide variety of tasks. An end programmer might also want to just modify one small component of the application while leaving the...

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Key benefits

  • Explore TF Keras APIs and strategies to run GPUs, TPUs, and compatible APIs across the TensorFlow ecosystem
  • Learn and implement best practices for building data ingestion pipelines using TF 2.0 APIs
  • Migrate your existing code from TensorFlow 1.x to TensorFlow 2.0 seamlessly

Description

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.

Who is this book for?

If you’re a data scientist, machine learning practitioner, deep learning researcher, or AI enthusiast who wants to migrate code to TensorFlow 2.0 and explore the latest features of TensorFlow 2.0, this book is for you. Prior experience with TensorFlow and Python programming is necessary to understand the concepts covered in the book.

What you will learn

  • Implement tf.keras APIs in TF 2.0 to build, train, and deploy production-grade models
  • Build models with Keras integration and eager execution
  • Explore distribution strategies to run models on GPUs and TPUs
  • Perform what-if analysis with TensorBoard across a variety of models
  • Discover Vision Kit, Voice Kit, and the Edge TPU for model deployments
  • Build complex input data pipelines for ingesting large training datasets

Product Details

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Publication date : Aug 12, 2019
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781838828837
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Product Details

Publication date : Aug 12, 2019
Length: 202 pages
Edition : 1st
Language : English
ISBN-13 : 9781838828837
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

12 Chapters
Section 1: TensorFlow 2.0 - Architecture and API Changes Chevron down icon Chevron up icon
Getting Started with TensorFlow 2.0 Chevron down icon Chevron up icon
Keras Default Integration and Eager Execution Chevron down icon Chevron up icon
Section 2: TensorFlow 2.0 - Data and Model Training Pipelines Chevron down icon Chevron up icon
Designing and Constructing Input Data Pipelines Chevron down icon Chevron up icon
Model Training and Use of TensorBoard Chevron down icon Chevron up icon
Section 3: TensorFlow 2.0 - Model Inference and Deployment and AIY Chevron down icon Chevron up icon
Model Inference Pipelines - Multi-platform Deployments Chevron down icon Chevron up icon
AIY Projects and TensorFlow Lite Chevron down icon Chevron up icon
Section 4: TensorFlow 2.0 - Migration, Summary Chevron down icon Chevron up icon
Migrating From TensorFlow 1.x to 2.0 Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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Sanjay Kumar Gupta Oct 12, 2019
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Very eloquent and clear book that explains core concepts of Machine Learning in relation to TensorFlow quite well. Some parts read almost like you’re having a friend teach you directly, without getting lost in the jargon.
Amazon Verified review Amazon
Sudarshan Apr 07, 2020
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Very good book for understanding TensorFlow 2.0 APIs in comparison to 1.x version. Organized really well to maintain the continuity of the content.
Amazon Verified review Amazon
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