bayesianmnist's Introduction

This is a fork of the tutorial to fiddle around and understand core principles

Bayesian MNIST is a companion toy example for our tutorial "Hands-on Bayesian Neural Networks - A Tutorial for Deep Learning Users". It is just a hello world project showing how a BNN can be implemented to perform classification on MNIST.

Dependancies

The code depends on:

numpy (tested with version 1.19.2),
pytorch (tested with version 1.8.1),
torchvision (tested with version 0.9.1),
matplotlib (tested with version 3.1.1),

and two libraries from the base python distribution: argparse and os.

It has been tested with python 3.6.9.

Usage

The project is split into multiple files:

dataset.py implement a few routines to filter out the mnist dataset, allowing us to train the model without one digit, as it will be presented later to the model to see how it reacts.
viModel.py implement the variational inference layers and model we are using.
viExperiment.py is the script running the actual experiment. It can be called with the -h option to get a contextual help message:

python viExperiment.py -h

Citation

If you use our code in your project please cite our tutorial:

@ARTICLE{9756596,
author={Jospin, Laurent Valentin and Laga, Hamid and Boussaid, Farid and Buntine, Wray and Bennamoun, Mohammed},
journal={IEEE Computational Intelligence Magazine}, 
title={Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users}, 
year={2022},
volume={17},
number={2},
pages={29-48},
doi={10.1109/MCI.2022.3155327}
}

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