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

New abstractions in TF 2.0

Abstractions are a very popular tool used in the process of programming and software development. In a very high-level sense, an abstraction refers to the process of isolating and describing the central idea of a particular task or set of tasks without necessarily specifying the physical, spatial, or temporal details. When done right, an abstraction can significantly reduce the amount of code that needs to be written for a particular task. It also boosts the reusability of existing code and makes it compatible with TF 2.0.

While working with machine learning systems, there are some common high-level tasks, such as training data, modeling, model evaluation, prediction, model storing, and model loading, that are common across a wide variety of tasks. An end programmer might also want to just modify one small component of the application while leaving the...