Deep Convolutional Generative Adversarial Network

About

DCGAN(Deep Convolutional Generative Adversarial Network) is an architecture used to generate new content.

Architecture

DCGANs comprises of two neural networks, Generator and Discriminator. Generator generates a fake image and has no
prior knowledge of real image. It interacts with the Discriminator in order to learn what kind of image it needs to generate.

Discriminator is a classifier which determines whether an image is fake or real. Discriminator neural network is fed with
both real(given by user) and fake images(generated by Generator), and it is then trained to accept the real images and reject
the fake images.

Main purpose of Generator is to fool the Discriminator Neural Network by generating fake images and make the Discriminator classify them as real images.

Discriminator neural network in this repository take images from MNIST dataset as real image input and Generator tries to generate fake hand written digits.

Requirements

• Python 2.7
• Pytorch (works perfectly on pytorch version 0.4.0)

Usage

• Run the python main.py after cloning or downloading the repository .

Result

Generated Image MNIST dataset

Real Image	Fake Image at 20th epoch	Fake Images During Training