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

Running TFLite on mobile devices

In this section, we will cover how TFLite can be run on the two major mobile OSes: Android and iOS.

TFLite on Android

Using TFLite on Android is as easy as adding TFLite to the dependencies field in the build.gradle file in Android Studio, and importing it into Android Studio:

dependencies {
implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly'
}

import org.tensorflow.lite.Interpreter;

Once this is done, the next step is to create an instance of the interpreter and load the model. This can be done using a helper function from the TFLite sample on GitHub called getModelPath, and by using loadModelFile to load the converted TFLite file. Now, to run the model, simply use the...