Book Image

Generative Adversarial Networks Cookbook

By : Josh Kalin
Book Image

Generative Adversarial Networks Cookbook

By: Josh 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.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
About Packt
Dedication
Contributors
Preface
Dedication2
Index

Code implementation – generator


In this case, the generator in also known as the refiner network. This generator, therefore, is the networkthat will take and refine the simulated data.

Getting ready

Check that you have the following files in the correct place:

├── data
├── docker
│   ├── build.sh
│   ├── clean.sh
│   ├── Dockerfile
│   └── kaggle.json
├── out
├── README.md
├── run.sh
└── src
    ├── generator.py
    loss.py

How to do it...

In this section, we'll look at build boilerplate items, model development, and helper functions in order to help us to build the full generator.

Boilerplate items

There are two key steps in the boilerplate, and they are as follows:

  1. Add all of the following import statements needed to create the generator (refiner) network:
#!/usr/bin/env python
import sys
import numpy as np
from keras.layers import Dense, Reshape, Input, BatchNormalization, Concatenate, Activation
from keras.layers.core import Activation
from keras.layers.convolutional import UpSampling2D, Convolution2D...