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

Image Classification Using TensorFlow Hub

We have discussed the image classification task in all of the previous chapters of this book. We have seen how it is possible to define a convolutional neural network by stacking several convolutional layers and how to train it using Keras. We also looked at eager execution and saw that using AutoGraph is straightforward.

So far, the convolutional architecture used has been a LeNet-like architecture, with an expected input size of 28 x 28, trained end to end every time to make the network learn how to extract the correct features to solve the fashion-MNIST classification task.

Building a classifier from scratch, defining the architecture layer by layer, is an excellent didactical exercise that allows you to experiment with how different layer configurations can change the network performance. However, in real-life scenarios, the amount...