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

Bringing a Model to Production

In this chapter, the ultimate goal of any real-life machine learning application will be presented—the deployment and inference of a trained model. As we saw in the previous chapters, TensorFlow allows us to train models and save their parameters in checkpoint files, making it possible to restore the model's status and continue with the training process, while also running the inference from Python.

The checkpoint files, however, are not in the right file format when the goal is to use a trained machine learning model with low latency and a low memory footprint. In fact, the checkpoint files only contain the models' parameters value, without any description of the computation; this forces the program to define the model structure first and then restore the model parameters. Moreover, the checkpoint files contain variable values that...