Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo Galeone

Overview of this book

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Summary

In this chapter, we looked at the SavedModel serialization format. This standardized serialization format was designed with the goal of simplifying the deployment of machine learning models on many different platforms.

SavedModel is a language-agnostic, self-contained representation of the computation, and the whole TensorFlow ecosystem supports it. Deploying a trained machine learning model on embedded devices, smartphones, browsers, or using many different languages is possible thanks to the conversion tools based on the SavedModel format or the native support offered by the TensorFlow bindings for other languages.

The easiest way to deploy a model is by using Python since the TensorFlow 2.0 API has complete support for the creation, restoration, and manipulation of SavedModel objects. Moreover, the Python API offers additional features and integrations between the Keras...