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
Section 1: Neural Network Fundamentals
Section 2: TensorFlow Fundamentals
Section 3: The Application of Neural Networks

The SavedModel serialization format

As we explained in Chapter 3, TensorFlow Graph Architecture, representing computations using DataFlow graphs has several advantages in terms of model portability since a graph is a language-agnostic representation of the computation.

SavedModel is a universal serialization format for TensorFlow models that extends the TensorFlow standard graph representation by creating a language-agnostic representation for the computation that is recoverable and hermetic. This representation has been designed not only to carry the graph description and values (like the standard graph) but also to offer additional features that were designed to simplify the usage of the trained models in heterogeneous production environments.

TensorFlow 2.0 has been designed with simplicity in mind. This design choice is visible in the following diagram, where it is possible...