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 set up learning and optimization functions, compile the model, fit it to training and testing data, and then actually run the model and see an animation indicating the effects on loss and accuracy.

In the following screenshot, we are compiling our model with loss, optimizer, and metrics:

Compiling model

The loss function is a mathematical function that tells optimizer how well it's doing. An optimizer function is a mathematical program that searches the available parameters in order to minimize the loss function. The metrics parameter are outputs from your machine learning model that should be human readable so that you can understand how well your model is running. Now, these loss and optimizer parameters are laden with math. By and large, you can approach this as a cookbook. When you are running a machine learning model with Keras, you...