The np_bnn library is a Python implementation of Bayesian neural networks for classification, using the Numpy and Scipy libraries. The program is used in our arXiv paper.
To install the np_bnn library you can use:
python -m pip install https://github.com/dsilvestro/npBNN/archive/refs/tags/v.0.1.16.tar.gz
Note that you may have to use python3 depending on which version of Python is set as default in your operating system.
The example file bnn_classify.py shows how to set up a BNN classification model, train it, and use it to make predictions.
The example file bnn_regress.py shows how to set up and run a BNN regression model.
The file block_bnns.py shows how to setup sparse networks with block layers (e.g. for interpretable NNs).
The npBNN package implements Markov Chain Monte Carlo (MCMC) to estimate the model parameters. A parallelized version using Metropolis Coupled MCMC (or MC3) is also available: bnn_runner_MC3.py.