Status: Archive (code is provided as-is, no updates expected)
Code for reproducing key results in the paper InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets by Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel.
This project currently requires the dev version of TensorFlow available on Github: https://github.com/tensorflow/tensorflow. As of the release, the latest commit is 79174a.
In addition, please pip install the following packages:
prettytensorprogressbarpython-dateutil
$ git clone [email protected]:openai/InfoGAN.git
$ docker run -v $(pwd)/InfoGAN:/InfoGAN -w /InfoGAN -it -p 8888:8888 gcr.io/tensorflow/tensorflow:r0.9rc0-devel
root@X:/InfoGAN# pip install -r requirements.txt
root@X:/InfoGAN# python launchers/run_mnist_exp.pyWe provide the source code to run the MNIST example:
PYTHONPATH='.' python launchers/run_mnist_exp.pyYou can launch TensorBoard to view the generated images:
tensorboard --logdir logs/mnist