Book Image

Hands-On Deep Learning for Images with TensorFlow

By : Will Ballard
Book Image

Hands-On Deep Learning for Images with TensorFlow

By: Will Ballard

Overview of this book

TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
Table of Contents (7 chapters)


In this section, we'll learn about the output activation function known as softmax. We'll be taking a look at how it relates to output classes, as well as learning about how softmax generates probability.

Let's take a look! When we're building a classifier, the neural network is going to output a stack of numbers, usually an array with one slot corresponding to each of our classes. In the case of the model we're looking at here, it's going to be digits from zero to nine. What softmax does is smooth out a big stack of numbers into a set of probability scores that all sum up to one:

Stack of numbers

This is important so that you can know which answer is the most probable. So, as an example that we can use to understand softmax, let's look at our array of values. We can see that there are three values. Let's assume that the neural...