Generative Adversarial Network (GAN) in JAX

Introduction to GANs

Generative Adversarial Networks (GANs) are a revolutionary idea in the field of machine learning, particularly in unsupervised learning. They were introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two models: a generator and a discriminator. The generator creates data (like images), while the discriminator evaluates them. Together, they improve each other's performance.

For a detailed understanding, refer to the original paper: Generative Adversarial Nets by Ian Goodfellow et al.

JAX and FLAX Setup

This guide will help you set up JAX, Flax, and Optax in your Jupyter Notebook environment for high-performance machine learning research.

Steps to Install

1. Open a New Jupyter Notebook:

   • Start by opening your Jupyter Notebook environment.

2. Install JAX:

   • JAX can be installed directly in your notebook. Run the following command in a cell to install JAX for a CPU-only version:

     pip install --upgrade jax jaxlib

   • For a GPU-enabled version, you'll need to install specific versions based on your CUDA setup. Refer to the JAX GitHub repository for detailed instructions.

3. Install Flax and Optax:

   • Flax and Optax can also be installed via pip directly in your notebook:

     pip install flax optax

Post-Installation

After installing the packages, you can import and use them in your notebook:

import jax
import flax
import optax