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Restricted Boltzmann Machine

This module describes how to employ a restricted Boltzmann machine (rbm) to learn different physical distributions from raw data, obtained by performing measurements on the systems. Contents of the folder:

  • rbm.py: rbm class
  • main.py: main script
  • data/: folder containing the data of the 2d classical Ising model and the 1d transverse field quantum Ising model
  • tutorial/: folder containing the scripts for the tutorial

Usage:

$ python main.py [COMMAND] [ARGUMENTS]

Command:

  • train: train the rbm
  • sample: sample the rbm given a set of trained parameters

Arguments:

  • -nV: number of visible units
  • -nH: number of hidden units
  • -lr: learning rate
  • -CD: number of Gibbs updates in the contrastive divergence algorithm
  • -bs: batch size
  • -step: number of training steps
  • -nC: number of chains sampled in contrastive divergence

Useful references: Training of RBMs: https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf