Book Image

What's New in TensorFlow 2.0

By : Ajay Baranwal, Alizishaan Khatri, Tanish Baranwal
Book Image

What's New in TensorFlow 2.0

By: Ajay Baranwal, Alizishaan Khatri, Tanish Baranwal

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.
Table of Contents (13 chapters)
Title Page

Frequently asked questions

In this section, some frequently asked questions about the migration from TF 1.x to TF 2.0 will be addressed.

Does code written in TF 2.0 have the same speed as graph-based TF 1.x code?

Yes, code written in TF 2.0 using tf.function or tf.keras will have the same speed and optimality as it does in TF 1.x. As we mentioned earlier in this chapter, using tf.function to annotate major functions allows the model to be run in graph mode, and all the computations and logic in the function will be compiled into a computational graph. The same goes for using tf.keras to define and train TensorFlow models. Using the method will also train the model in graph mode and has all of the benefits and optimizations that come with this. When writing eager executed code, there is a slight performance drop, something which is more than compensated for by the...