Conditional Generative Adversarial Network (cGAN) for MNIST

This repository contains an implementation of a Conditional Generative Adversarial Network (cGAN) for generating MNIST digits conditioned on the label.

Quick Start

1. Setup the environment:

You can create a conda environment with all the necessary dependencies using:

conda env create -f conda_env.yml

After the environment is set up, activate it:

conda activate cgan_env

2. Train the model:

Navigate to the main directory and start the training:

python train.py

Features

• Modular and well-structured code for easy understanding and customization.
• Uses PyTorch for model definition, training, and inference.
• Training script with command line arguments for hyperparameters.
• Loading/Saving checkpoints functionality.
• Option to visualize tensor sizes for debugging purposes.

Future work:

• Implement evaluation metrics for model assessment.
• Extend to other datasets.