Restricted Boltzmann Machines (RBMs) in PyTorch

Author: Gabriel Bianconi

Overview

This project implements Restricted Boltzmann Machines (RBMs) using PyTorch (see rbm.py). Our implementation includes momentum, weight decay, L2 regularization, and CD-k contrastive divergence. We also provide support for CPU and GPU (CUDA) calculations.

In addition, we provide an example file applying our model to the MNIST dataset (see mnist_dataset.py). The example trains an RBM, uses the trained model to extract features from the images, and finally uses a SciPy-based logistic regression for classification. It achieves 92.8% classification accuracy (this is obviously not a cutting-edge model).