Book Image

Generative Adversarial Networks Projects

By : Kailash Ahirwar
Book Image

Generative Adversarial Networks Projects

By: Kailash Ahirwar

Overview of this book

Generative Adversarial Networks (GANs) have the potential to build next-generation models, as they can mimic any distribution of data. Major research and development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning. This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the GAN domain. Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the book. The level of complexity of the operations required increases with every chapter, helping you get to grips with using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an understanding of the architecture and functioning of generative models through their practical implementation. By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in your own projects.
Table of Contents (11 chapters)

Setting up the project

If you haven't already cloned the repository with the complete code for all chapters, clone the repository now. The downloaded code has a directory called Chapter07, which contains the entire code for this chapter. Execute the following commands to set up the project:

  1. Start by navigating to the parent directory as follows:
cd Generative-Adversarial-Networks-Projects
  1. Now, change the directory from the current directory to Chapter07, as shown in the following example:
cd Chapter07
  1. Next, create a Python virtual environment for this project, as shown in the following code:
virtualenv venv
virtualenv venv -p python3 # Create a virtual environment using
python3 interpreter
virtualenv venv -p python2 # Create a virtual environment using
python2 interpreter

We will be using this newly created virtual environment for this project....