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

Summary

In this chapter, we have taken a detailed look at the inference stage. Starting off by obtaining a basic understanding of what the end-to-end machine learning workflow looks like, we learned about the main steps involved in each stage. We also learned about the different abstractions that come into play while transferring models from the training phase to the inference phase. Taking a detailed look at the SavedModel format and the underlying dataflow model, we learned about the different options available to build and export models. We also learned about cool features such as tf.function and tf.autograph, which enable us to build TensorFlow graphs using native Python code. In the latter half of this chapter, we learned how to build inference pipelines for running TensorFlow models in different environments such as backend servers, web browsers, and even edge devices...