Generative Adversarial Network designed to produce realistic human faces after being trained on a sample of 200,000+ images of human faces
What is this repository for?
- This repository is for the Jupyter notebook and supporting python files
- The data files used for this project can be found either in my Bitbucket (as it was too much for my Github) or at the websites for the MNIST and CelebA datasets.
- Version: stable
How do I get set up?
- To set up, clone the repository or download a .zip file with the repository to your desired directory. Make sure that the directory contains a folder titled "data" to include the MNIST and CelebA folders in.
- The helper.py and unittests.py files are dependencies that should also be included in the directory
- Unit tests are set up to automatically be run by the python notebook before the running of the Genrative Adversarial Network.
- To start, run the Jupyter Notebook application in an environment (such as that created by Anaconda) for
- This repository can easily be forked and contributed to on Github. Feel free to send a pull request.
- The current GAN makes realistic enough faces, but further hyperparameter optimization is needed.
- DRAW: A Recurrent Neural Network For Image Generation has demonstrated promising results on the MNIST generation task. Next step will involve applying this model to the CelebA dataset.
- Refactoring the code to make use of Google Brain's TFGAN library will make future modifications easier, as well as provide more useful metrics of performance.