vamsi3/simple-GAN

A simple Generative Adversarial Network (GAN) implemented on the MNIST Dataset

Simple GAN using PyTorch

This project is a basic Generative Adversarial Network (GAN) implemented in PyTorch on the MNIST Database

This is one of my initial steps towards GANs in general. This mostly follows from the idea of GAN first published in arXiv:1406.2661 [stat.ML] by GoodFellow et.al.

Getting the code to work

Follow the instructions below to get our project running on your local machine.

1. Clone the repository and make sure you have Python 3 to run the project.
2. Go to src/PyTorch/ and run python gan-mnist-pytorch.py
3. All the outputs and related plots can be found in src/PyTorch/output folder generated.
4. The various parameters that can be tweaked before run can be found at python gan-mnist-pytorch.py --help

Prerequisites

• PyTorch 0.4.0 or above
• CUDA 9.1 (or other version corresponding to PyTorch) to utilize any compatible GPU present for faster training

Results

Images generated by Generator at various epochs -

Generated on Epoch 0	Generated on Epoch 40	Generated on Epoch 80
Generated on Epoch 120	Generated on Epoch 160	Generated on Epoch 200

Author

• Vamsi Krishna Reddy Satti - vamsi3

License

This project is licensed under the MIT License - see the LICENSE file for details.