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 fork of the original changes part of the code to accomodate the new TensorFlow API. I am using version 1.3.0. Since this project also used
prettytensor, which has it's own problems, you'll need to make a fix to this library as well (instructions below).
In addition, please
pip install the following packages:
You'll likely get an error like
ValueError: too many values to unpack. You can fix this by either pulling the relevant commit from the pull request or merely making this trivial change.
We provide the source code to run the MNIST example:
PYTHONPATH='.' python launchers/run_mnist_exp.py
You can launch TensorBoard to view the generated images:
tensorboard --logdir logs/mnist