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

Designing and Constructing Input Data Pipelines

This chapter will give an overview of how to build complex input data pipelines for ingesting large training/inference datasets in the most common formats, such as CSV files, images, text, and so on using tf.data APIs consisting of the TFRecords and tf.data.Dataset methods. You will also get a general idea about protocol buffers, protocol messages, and how they are implemented using the TFRecords and tf.Example methods in TensorFlow 2.0 (TF 2.0). This chapter also explains the best practices for using the tf.data.Dataset method with respect to the shuffling, batching, and prefetching of data, and provides recommendations in terms of TF 2.0. Finally, we will talk about the built-in TensorFlow datasets, which have been newly added and are extremely useful for building a prototype model training...