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

Model definition and training

Disclaimer: the layer module has been completely removed in TensorFlow 2.0, and the layer definition using tf.keras.layers is the new standard; however, an overview of tf.layers is still worth reading because it shows how reasoning layer by layer to define deep models is the natural way to proceed and it also gives us an idea of the reasons behind the migration from tf.layers to tf.keras.layers.

Defining models with tf.layers

As shown in the previous section, TensorFlow provides all the primitive features to define a neural network layer: the user should take care when defining the variables, the operation nodes, the activation functions, and the logging, and define a proper interface to handle...