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InfoGAN

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.

Dependencies

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:

  • prettytensor
  • progressbar2
  • python-dateutil

Modifying PrettyTensor

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.

Running Experiment

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