Gaussian-Bernoulli Restricted Boltzmann Machines (GRBMs)

This is the official PyTorch implementation of Gaussian-Bernoulli RBMs Without Tears as described in the following paper:

@article{liao2022grbm,
  title={Gaussian-Bernoulli RBMs Without Tears}, 
  author={Liao, Renjie and Kornblith, Simon and Ren, Mengye and Fleet, David J and Hinton, Geoffrey}, 
  journal={arXiv preprint arXiv:2210.10318},
  year={2022}
}

Sampling processes of learned GRBMs on MNIST and CelebA(32 X 32):

Dependencies

Python 3, PyTorch(1.12.0). Other dependencies can be installed via pip install -r requirements.txt

Run Demos

Train

• To run the training of experiment X where X is one of {gmm_iso, gmm_aniso, mnist, fashionmnist, celeba, celeba2K}:

  python main.py -d X

Note:

• Please check the folder config for the configuration jason files where most hyperparameters are self-explanatory.
• Important hyperparameters include:
  • CD_step: #CD steps to generate negative samples
  • inference_method: must be one of Gibbs, Langevin, Gibbs-Langevin
  • Langevin_step: # inner loop Langevin steps for Gibbs-Langevin sampling method
  • Langevin_eta: step size of both Langevin and Gibbs-Langevin sampling methods
  • Langevin_adjust_step: when set to X, it enables Metropolis adjustment from X-th to #CD-th steps
  • is_vis_verbose: when set to True, it saves learned filters and hidden activations (conisder turning it off for better efficiency if you have too many filters and images are large)
• For CelebA experiments, you need to download the dataset and set the relative path as data/celeba

Cite

Please consider citing our paper if you use this code in your research work.

Questions/Bugs

Please submit a Github issue or contact rjliao@ece.ubc.ca if you have any questions or find any bugs.