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

Built-in datasets in TF 2.0

TF 2.0 also provides a collection of datasets that are ready to be used with TensorFlow. It handles downloading, preparing the data, and even building on its own, which can then be directly fed into the model.

Follow these steps to use these built-in datasets:

  1. Install the TensorFlow datasets:
pip3 install tensorflow-datasets
Please note that tensorflow-datasets expects you to have a correct and complete installation of TF 2.0.
  1. After tensorflow-datasets has been installed, you can view a list of available datasets by using the following code:
import tensorflow_datasets as tfds

This will give the following output:

['abstract_reasoning', 'bair_robot_pushing_small', 'caltech101', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'chexpert', &apos...