Project containing different GAN implementations:
- WacGAN (GAN model combining ACGAN & Wasserstein GAN)
- WacGAN-info (GAN model combining ACGAN, info-GAN and Wasserstein GAN)
Run the code with the following command:
python main.py [--make_dataset] [--process_dataset] [--train_model]
For more information about run options use:
python main.py -h
├── LICENSE
├── main.py <- Makefile with commands like `make_data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download, generate and/or process the dataset
│ │ ├── process_dataset.py
│ │ └── make_dataset.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ └── BasicModel.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience