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

Interacting with the graph using Python

Python is the language of choice to train a TensorFlow model; however, after defining a computational graph in Python, there are no constraints regarding using it with another language to execute the learning operations defined.

Always keep in mind that we use Python to define a graph and this definition can be exported in a portable and language-agnostic representation (Protobuf)—this representation can then be used in any other language to create a concrete graph and using it within a session.

The TensorFlow Python API is complete and easy to use. Therefore, we can extend the previous example to measure the accuracy (defining the accuracy measurement operation in the graph) and use this metric to perform model selection.

Selecting the best model means storing the model parameters at the end of each epoch and moving the parameters...