This repo holds the code associated with the ideas I discuss in my post Flexible posteriors for Bayesian deep learning. For the general framework, I extended @martinferianc's basic BNN architecture to accomodate the different types of networks I discuss in my post, using the same approaches outlined in the post. For the Kumaraswamy and NN-parameterized Bayesian networks, I use uniform priors.
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|-experiments # Where all the experiments are located
|---data # Where all the data is stored
|---scripts # Pre-configured scripts for running the experiments
|-src # Main source folder, also containing the model descriptions
|---models
|-----pointwise
|-----stochastic
|-------bbb # Implementation of Bayes-by-Backprop on MLP, Kumaraswamy nets, and Net-Nets.
There are in total three different experiments, regression, binary classification and MNIST digit classification. The default runners for the experiments are under the experiments folder, where the scripts for easy pre-configured runs can be found under experiments/scripts/. However, I only built the nets for regression, so your mileage may vary on the other types of experiments.
To run the experiments, simply prepare youself a a virtual environment and navigate to the experiments/scripts folder and pick one of the methods/tasks and run it as:
python3 bbb_binary_classification.py
No additional tuning should be necessary. However, the scripts assume that you have a GPU available for training and inference. In case you just wanto to use CPU, do:
python3 bbb_binary_classification.py --gpu -1
The main requirement is PyTorch>=1.5.0.
To be able to run this project and install the requirements simply execute (will work for GPUs or CPUs):
git clone https://github.com/martinferianc/BayesianNeuralNet-Tutorial
conda create --name venv --file requirements.txt --python=python3.6
conda activate venv