Book description
Simplify nextgeneration deep learning by implementing powerful generative models using Python, TensorFlow and Keras
Key Features
 Understand the common architecture of different types of GANs
 Train, optimize, and deploy GAN applications using TensorFlow and Keras
 Build generative models with realworld data sets, including 2D and 3D data
Book Description
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 implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easytoread format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different realworld data sets 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 easytofollow code solutions that you can implement right away.
What you will learn
 Structure a GAN architecture in pseudocode
 Understand the common architecture for each of the GAN models you will build
 Implement different GAN architectures in TensorFlow and Keras
 Use different datasets to enable neural network functionality in GAN models
 Combine different GAN models and learn how to finetune them
 Produce a model that can take 2D images and produce 3D models
 Develop a GAN to do style transfer with Pix2Pix
Who this book is for
This book is for data scientists, machine learning 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 working knowledge of Python programming language will help you get the most out of the book.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Dedication
 Contributors
 Preface
 Dedication2

What Is a Generative Adversarial Network?
 Introduction
 Generative and discriminative models
 A neural network love story
 Deep neural networks
 Architecture structure basics
 Basic building block – generator
 Basic building block – discriminator
 Basic building block – loss functions
 Training
 GAN pieces come together in different ways
 What does a GAN output?
 Understanding the benefits of a GAN structure
 Exercise
 Data First, Easy Environment, and Data Prep

My First GAN in Under 100 Lines
 Introduction
 From theory to code – a simple example
 Building a neural network in Keras and TensorFlow
 Explaining your first GAN component – discriminator
 Explaining your second GAN component – generator
 Putting all the GAN pieces together
 Training your first GAN
 Training the model and understanding the GAN output
 Exercise

Dreaming of New Outdoor Structures Using DCGAN
 Introduction
 What is DCGAN? A simple pseudocode example
 Tools – do I need any unique tools?
 Parsing the data – is our data unique?
 Code implementation – generator
 Code implementation – discriminator
 Training
 Evaluation – how do we know it worked?
 Adjusting parameters for better performance
 Exercise
 Pix2Pix ImagetoImage Translation
 Style Transfering Your Image Using CycleGAN

Using Simulated Images To Create PhotoRealistic Eyeballs with SimGAN
 Introduction
 How SimGAN architecture works
 Pseudocode – how does it work?
 How to work with training data
 Code implementation – loss functions
 Code implementation – generator
 Code implementation – discriminator
 Code implementation – GAN
 Training the simGAN network
 Exercise

From Image to 3D Models Using GANs
 Introduction
 Introduction to using GANs in order to produce 3D models
 Environment preparation
 Encoding 2D data and matching to 3D objects
 Code implementation – generator
 Code implementation – discriminator
 Code implementation – GAN
 Training this model
 Exercise
 Other Books You May Enjoy
Product information
 Title: Generative Adversarial Networks Cookbook
 Author(s):
 Release date: December 2018
 Publisher(s): Packt Publishing
 ISBN: 9781789139907
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