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

Code implementation – generator

The generator uses the U-NET architecture in the pseudocode we introduced previously. This recipe is going to cover the practical side of implementing that network. 

Getting ready

Spot check! Make sure you have the following files in your working directory:

├── docker
│   ├──
│   ├──
│   └── Dockerfile
└── src
|   ├──

Don't pass this step until you've completed the previous recipes and added the file to your working directory for the Pix2Pix implementation.



How to do it...

In the file, input the following steps into the file to create the network architecture:

  1. With all of these networks, we'll need to import the necessary libraries to implement the class:
#!/usr/bin/env python3
import sys
import numpy as np
from keras.layers import Dense, Reshape, Input, BatchNormalization, Concatenate
from keras.layers.core import Activation
from keras.layers.convolutional import UpSampling2D, Convolution2D...