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Generative Adversarial Networks Cookbook

By : Kalin

3 (4)

Generative Adversarial Networks Cookbook

3 (4)

By: Kalin

Overview of this book

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 easy-to-read 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 real-world 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 easy-to-follow code solutions that you can implement right away.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Sections

Get in touch

Free Chapter

What Is a Generative Adversarial Network?

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

Data First, Easy Environment, and Data Prep

Introduction

Is data that important?

But first, set up your development environment

Data types

Data preprocessing

Anomalous data

Balancing data

Data augmentation

Exercise

My First GAN in Under 100 Lines

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

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 Image-to-Image Translation

Pix2Pix Image-to-Image Translation

Introduction

Introducing Pix2Pix with pseudocode

Parsing our dataset

Code implementation – generator

Code – the GAN network

Code implementation – discriminator

Training

Exercise

Style Transfering Your Image Using CycleGAN

Style Transfering Your Image Using CycleGAN

Introduction

Pseudocode – how does it work?

Parsing the CycleGAN dataset

Code implementation – generator

Code implementation – discriminator

Code implementation – GAN

On to training

Exercise

Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN

Using Simulated Images To Create Photo-Realistic 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

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

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Understanding the benefits of a GAN structure

So, what kinds of cool tidbits did you learn in this particular chapter? I'll try to use this final chapter as a recap of why the GAN structure is cool and what kinds of things make this a powerful tool for your future research.

How to do it...

As a recap, we start with three key questions:

Are GANs all the same architecture?
Are there any new concepts within the GAN architecture?
How do we practically construct the GAN Architecture?

We'll also review the key takeaways from this chapter.

How it works...

Let's address these three key questions:

Are GANs all the same architecture?
- GANs come in all shapes and sizes. There are simple implementations and complex ones. It just depends what domain you are approaching and what kind of accuracy you need in the generated input.
Are there any new concepts within the GAN architecture?
- GANs rely heavily on advances in the deep learning world around Deep Neural Networks. The novel part of a GAN lies in the architecture and the adversarial nature of training two (or more) neural networks against each other.
How do we practically construct the GAN architecture architecture?:
- The generator, discriminator, and associated loss functions are fundamental building blocks that we'll pull on for each of the chapters in order to build these models.

What are the key things to remember from this chapter?

The initial GAN paper was only the beginning of a movement within the machine learning space
The generator and discriminator are neural networks in a unique training configuration
The loss functions are critical to ensuring that the architecture can converge during training

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