GAN_Demo

This is a demonstratory exercise in using the GAN framework, as proposed by Goodfellow in 2014 (https://arxiv.org/abs/1406.2661).

Purpose

To create a pseudo random number generator using the GAN framework with least squares loss criterion, aimed to mimic a chosen probability distribution.

Use

To run the demo excecute

python main.py

A generator (G) and discriminator (D) will be saved to /TrainedModels. These models can be tested by calling

python test.py "MODEL_NAME_HERE"

For example, to test the example network provided, call

python test.py "26_01_2023-19_14"

This will create a pyplot figure to compare the generated distribution (orange line) to the target distribution (indicated by blue bars).

To train your own model, parameters can be set within Hyperparameters.py. It is often commented that a successful GAN requires the correct balancing of power between G and D. In this repo, power can be measured by the number of layers and channels per layer for both G and D. As such, it is easy for the user to run experiments to investigate the training process for poorly balanced models using this repo.

About

A probability distribution can be reasonably well approximated by simply approximating a function, e.g. $e^{-x^2}$ for a Gaussian or normal distribution, assuming we already have access to a uniform random variable, e.g. torch.rand. Using uniformly sampled numbers as seeds for our generative model, the generator G has a fully connected architecture, consisting of a small number of linear (or fully connected) + ReLU layers.

The discriminator D is similary simple, differing only in that the output dimension of each layer decreases by half.

To avoid the common vanishing gradient problem, we train using the least squares criterion proposed by Mao et al in 2016 (https://arxiv.org/abs/1611.04076). This indeed results in a better outcome.

To do

• Add GPU functionality
• Adam optimiser proved very poor for this model - Investigate!
• Improve graphical outputs
• Find successful parameters to train with classical GAN loss