KC Tung - TensorFlow 2 Pocket Reference

by KC Tung

Copyright 2021 Favola Vera, LLC. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

• Acquisitions Editor: Rebecca Novack
• Development Editor: Sarah Grey
• Production Editor: Beth Kelly
• Copyeditor: Penelope Perkins
• Proofreader: Audrey Doyle
• Indexer: Potomac Indexing, LLC
• Interior Designer: David Futato
• Cover Designer: Karen Montgomery
• Illustrator: Kate Dullea

• August 2021: First Edition

See https://oreil.ly/tf2pr for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. TensorFlow 2 Pocket Reference, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the author, and do not represent the publishers views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-492-08918-6

[LSI]

The TensorFlow ecosystem has evolved into many different frameworks to serve a variety of roles and functions. That flexibility is part of the reason for its widespread adoption, but it also complicates the learning curve for data scientists, machine learning (ML) engineers, and other technical stakeholders. There are so many ways to manage TensorFlow models for common taskssuch as data and feature engineering, data ingestions, model selection, training patterns, cross validation against overfitting, and deployment strategiesthat the choices can be overwhelming.

This pocket reference will help you make choices about how to do your work with TensorFlow, including how to set up common data science and ML workflows using TensorFlow 2.0 design patterns in Python. Examples describe and demonstrate TensorFlow coding patterns and other tasks you are likely to encounter frequently in the course of your ML project work. You can use it as both a how-to book and a reference.

This book is intended for current and potential ML engineers, data scientists, and enterprise ML solution architects who want to advance their knowledge and experience in reusable patterns and best practices in TensorFlow modeling. Perhaps youve already read an introductory TensorFlow book, and you stay up to date with the field of data science generally. This book assumes that you have hands-on experience using Python (and possibly NumPy, pandas, and JSON libraries) for data engineering, feature engineering routines, and building TensorFlow models. Experience with common data structures such as lists, dictionaries, and NumPy arrays will also be very helpful.

Unlike many other TensorFlow books, this book is structured around the tasks youll likely need to do, such as:

• When and why should you feed training data as a NumPy array or streaming dataset? (Chapters )

• How can you leverage a pretrained model using transfer learning? (Chapters )

• Should you use a generic fit function to do your training or write a custom training loop? ()

• How should you manage and make use of model checkpoints? ()

• How can you review the training process using TensorBoard? ()

• If you cant fit all of your data into your runtimes memory, how can you perform distributed training using multiple accelerators, such as GPUs? ()

• How do you pass data to your model during inferencing and how do you handle output? ()

• Is your model fair? ()

If you are wrestling with questions like these, this book will be helpful to you.