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.
The network responsible for generating new data from training a data.
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.
- Importing the necessary modules
- Building a simple Generator network
- Building a simple Discriminator network
- Building a GAN by stacking the generator and discriminator
- Plotting the generated images
- A training method for GAN
- Loading and processing MNIST data
- Training The GAN
References & Articles