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

Examples of complete end-to-end data pipelines

So far, we have covered the creation of dataset objects and how to create batches of data to feed into a model. In this section, we will look at an example of an end-to-end input data pipeline and model training. We will build an image classifier using the CIFAR10 data.

In order to run the CIFAR10-based end-to-end example, you need to download the necessary data from The dataset has been taken from a paper called Learning Multiple Layers of Features from Tiny Images, by Alex Krizhevsky, 2009 ( This dataset contains the following information:

  • 50,000 images with labels for training 
  • 10,000 images with labels for testing
  • 10 class labels

After downloading and untarring the dataset, you will see a folder...