A generative adversarial network, or GAN, is a deep neural network framework which is able to learn from a set of training data and generate new data with the same characteristics as the training data.

This repository is to demonstrate how we can create new images of a distribution of images with a Generative Adversarial Network (GAN).

GAN is a architecture which makes use of multiple neural networks that compete against each other to make the predictions.

Generator:

The network responsible for generating new data from training a data.

Discriminator:

Identifies and distinguishes a generated image/fake image from an original image of the training combined together form a GAN, both these networks learn based on their previous predictions, competing with each other for the better outcome.

How To Build A GAN In 8 Simple Steps We will follow the steps given below to build a simple Generative Adversarial Network.

1. Importing the necessary modules
2. Building a simple Generator network
3. Building a simple Discriminator network
4. Building a GAN by stacking the generator and discriminator
5. Plotting the generated images
6. A training method for GAN
7. Loading and processing MNIST data
8. Training The GAN

References & Articles

1. Intro to GAN
2. How To Build A Generative Adversarial Network In 8 Simple Steps