BayTorch - Bayesian layers for PyTorch
Project description
BayTorch - Bayesian layers in PyTorch
Bayesian layers are probabilistic layers where weights are stochastic and sampled from the posterior distribution or an approximation.
So far, this module includes diagonal Gaussian linear and convolutional layers with mean and standard deviation of every weight as learnable parameters, as well as an implementation of the variational free energy loss for stochastic gradient VI according to the Bayes-by-Backprop algorithm [1].
[1] Blundell et al., Weight Uncertainty in Neural Networks, 2015.
Installation
Install from PyPI
pip install baytorch
or clone the repository, build and install from source
git clone https://jugit.fz-juelich.de/ias-8/baytorch.git && cd baytorch
python -m build
pip install dist/baytorch*whl
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