Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementation, including CycleGAN, SimGAN, DCGAN, and imitation learning with GANs. Each chapter builds on a common architecture in Python and Keras to explore increasingly difficult GAN architectures in an easy-to-read format.
The Generative Adversarial Networks Cookbook starts by covering the different types of GAN architecture to help you understand how the model works. You will learn how to perform key tasks and operations, such as creating false and high-resolution images, text-to-image synthesis, and generating videos with this recipe-based guide. You will also work with use cases such as DCGAN and deepGAN. To become well versed in the working of complex applications, you will take different real-world datasets and put them to use.
By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models thanks to easy-to-follow code solutions that you can implement right away.
This book is for data scientists, machine learning (ML) developers, and deep learning practitioners looking for a quick reference to tackle challenges and tasks in the GAN domain. Familiarity with machine learning concepts and a working knowledge of the Python programming language will help you get the most out of the book.
Chapter 1, What is a Generative Adversarial Network?, introduces you to GAN architectures and looks at the implementation of each of them.
Chapter 2, Data First – Easy Environment and Data Preparation, lays down the groundwork for manipulating data, augmenting your data, and balancing imbalanced datasets or data with massive outliers.
Chapter 3, My First GAN in Under 100 Lines, covers how to take the theory we'll have discussed and produce a simple GAN model using Keras, TensorFlow, and Docker.
Chapter 4, Dreaming of New Outdoor Structures Using DCGAN, covers the building blocks required to build your first deep convolutional generative adversarial network (DCGAN) implementation.
Chapter 5, Pix2Pix Image-to-Image Translation, covers Pix2Pix, how it works, and how it is implemented.
Chapter 6, Style Transfering Your Image Using CycleGAN, explains what CycleGAN is, and how to parse the CycleGAN datasets and implementations.
Chapter 7, Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN, demonstrates how SimGAN works, and how it is implemented.
Chapter 8, From Images to 3D Models Using GANs, talks about 3D models and techniques to implement these 3D models using images.
A basic knowledge of Python is a prerequisite, while a familiarity with machine learning concepts will be helpful.
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There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You can run the nvidia-smi command to know which version of driver is installed on your system."
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docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
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sudo ./build.shBold: Indicates a new term, an important word, or words that you see on screen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Now, click Save and let's check to make sure that we have the appropriate directory structure with our files."
In this book, you will find several headings that appear frequently (Getting ready, How to do it..., How it works..., There's more..., and See also).
To give clear instructions on how to complete a recipe, use these sections as follows:
This section tells you what to expect in the recipe and describes how to set up any software or any preliminary settings required for the recipe.
This section usually consists of a detailed explanation of what happened in the previous section.
This section consists of additional information about the recipe in order to increase your knowledge of it.
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