Restricted Boltzmann Machines (RBMs) in PyTorch
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Restricted Boltzmann Machines (RBMs) in PyTorch

Author: Gabriel Bianconi


This project implements Restricted Boltzmann Machines (RBMs) using PyTorch (see 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 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).