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

Efficient Data Input Pipelines and Estimator API

In this chapter, we will look at two of the most common modules of the TensorFlow API: and tf.estimator.

The TensorFlow 1.x design was so good that almost nothing changed in TensorFlow 2.0; in fact, and tf.estimator were the first two high-level modules introduced during the life cycle of TensorFlow 1.x.

The module is a high-level API that allows you to define high-efficiency input pipelines without worrying about threads, queues, synchronization, and distributed filesystems. The API was designed with simplicity in mind to overcome the usability issues of the previous low-level API.

The tf.estimator API was designed to simplify and standardize machine learning programming, allowing to train, evaluate, run inference, and export for serving a parametric model, letting the user focus on the model and input definition...