GAN Playground

Welcome to the GAN Playground! This project allows users to explore and experiment with Generative Adversarial Networks (GANs) through an interactive web interface. Users can choose different GAN models, set training parameters, and visualize the results.

Website

Visit the GAN Playground at gan-playground.vercel.app

Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Frontend
  • Backend
• API Endpoints
  • DCGAN
  • WGAN-GP
  • Future Models
• Contributing
• License
• Acknowledgements

Features

• Choose from multiple GAN models (DCGAN, WGAN-GP, and more coming soon).
• Set training parameters such as number of epochs, learning rate, etc.
• Visualize training progress and results at each step of every epoch.
• API access to integrate GAN functionalities into other projects.

Getting Started

Prerequisites

• Python 3.8+
• Node.js 14+
• npm 6+

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/gan-playground.git
   cd gan-playground

2. Backend setup:

   cd backend
   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt

   To install the required packages with CUDA support (cu117), follow these steps:

   1. Make sure you have the correct CUDA version installed (CUDA 11.7).
   2. Run the following command to install the required packages:

      pip install -r requirements.txt --index-url https://download.pytorch.org/whl/cu117

3. Frontend setup:

   cd ../frontend
   npm install

Usage

Frontend

To start the frontend:

cd playground-frontend
npm start

This will start the React app on localhost:3000.

Backend

To start the backend on a new terminal:

cd backend
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
python main.py

This will start the React app on localhost:5000.

API Endpoints

DCGAN

• Endpoint: /api/dcgan/train
  • Method: POST
  • Description: Train the DCGAN model with the provided parameters.
  • Parameters:
    • epochs (int): Number of epochs to train.
    • batch_size (int): Batch size for training.
    • learning_rate (float): Learning rate for the optimizer.
  • Example Request:

    {
      "epochs": 100,
      "batch_size": 64,
      "learning_rate": 0.0002
    }

WGAN-GP

• Endpoint: /api/wgan_gp/train
  • Method: POST
  • Description: Train the WGAN-GP model with the provided parameters.
  • Parameters:
    • epochs (int): Number of epochs to train.
    • batch_size (int): Batch size for training.
    • learning_rate (float): Learning rate for the optimizer.
  • Example Request:

    {
      "epochs": 100,
      "batch_size": 64,
      "learning_rate": 0.0001
    }

Future Models

Additional models will be added soon. Stay tuned for updates!

Contributing

Contributions are welcome! Please fork the repository and create a pull request with your changes.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

• TensorFlow
• Keras
• React
• Flask