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

Major changes in TF 2.0

The major changes that you will experience while migrating from TF 1.x to TF 2.0 concern API cleanup.

Many of the APIs in TF 2.0 have either been removed or moved. Major changes include the removal of, tf.flags, and tf.logging in favor of other Python modules, such as absl-py and the built-in logging system.

One of the largest changes that has been made in TF 2.0 code-wise is eager execution. TF 1.x requires users to manually stitch an abstract syntax tree using tf.* calls to build a computational graph, which it will run with This means that TF 2.0 code runs line by line, and so tf.control_dependancies() is no longer needed.

The call in TF 1.x is very similar to a simple function. The user specifies the inputs and the function to be called, and it returns a set of outputs. This code flow is completely...