This project is a VS Code-ready implementation of GANs across multiple domains, including synthetic data, medical imaging, cybersecurity, and creative AI.
gan_vscode_project/ ├── .vscode/ ├── data/ │ ├── categories.txt │ ├── pizza.npy │ └── Wednesday-workingHours.pcap_ISCX.csv ├── notebooks/ ├── outputs/ │ ├── bloodmnist/ │ ├── cicids/ │ ├── part1/ │ └── quickdraw/ ├── src/ │ ├── init.py │ ├── common.py │ ├── part1_synthetic_gan.py │ ├── part2_bloodmnist_dcgan.py │ ├── part2_cicids_tabular_gan.py │ └── part2_quickdraw_dcgan.py ├── requirements.txt ├── run_all.py ├── setup.ps1 ├── setup.sh └── README.md
python -m venv .venv ..venv\Scripts\Activate.ps1 pip install -r requirements.txt
python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt
Before Running the project add the datasets Wednesday-workingHours.pcap_ISCX.csv ====https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset pizza.npy===https://storage.googleapis.com/quickdraw_dataset/full/numpy_bitmap/pizza.npy (https://github.com/googlecreativelab/quickdraw-dataset)
python src/part1_synthetic_gan.py
python src/part2_bloodmnist_dcgan.py
python src/part2_cicids_tabular_gan.py --input_csv data/Wednesday-workingHours.pcap_ISCX.csv
python src/part2_quickdraw_dcgan.py --input_npy data/pizza.npy
python run_all.py
All outputs are saved in: outputs/
Includes:
- Generated images
- Loss graphs
- PCA plots
- Metrics
- Use fewer epochs for testing
- Increase epochs for final results
- Use generated outputs in report
If torch not found: pip install torch torchvision torchaudio
If file path error: Check: data/pizza.npy data/Wednesday-workingHours.pcap_ISCX.csv
This project demonstrates how GANs can learn patterns and generate synthetic data across multiple domains.