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

The future of TF 2.0

TF 2.0 is currently in beta and hence is still under development. Some key features that are coming up include modifications to packages such as TensorBoard, TensorFlow Lite, TensorFlow.js, Swift for TensorFlow, and TensorFlow Extended, and small changes being made to the base API. TensorBoard will see enhancements such as improved hyperparameter-tuning capabilities, the introduction of hosting capabilities to make sharing dashboards easy, and enabling plugins to use different frontend technologies, such as ReactJS. TensorFlow Lite will see increased coverage of supported operations, an easier conversion of TF 2.0 models to TFLite, and extended support for Edge TPUs and AIY boards. Both TensorFlow.js and Swift for TensorFlow will see improvements in speed and performance, and will soon include a rich set of examples and getting-started guides with end-to-end...