torchbnn 0.3

Bayesian Neural Network for PyTorch

Bayesian-Neural-Network-Pytorch

This is a lightweight repository of bayesian neural network for Pytorch. There are bayesian versions of pytorch layers and some utils. To help construct bayesian neural network intuitively, all codes are modified based on the original pytorch codes.

Usage

Dependencies

• torch 1.2.0
• python 3.6

Installation

• pip install torchbnn or
• git clone https://github.com/Harry24k/bayesian-neural-network-pytorch

import torchbnn

Thanks to

• @kumar-shridhar github:PyTorch-BayesianCNN
• @xuanqing94 github:BayesianDefense

Update Records

Version 0.1

• modules : BayesLinear, BayesConv2d, BayesBatchNorm2d
• utils : convert_model(nonbayes_to_bayes, bayes_to_nonbayes)
• functional : bayesian_kl_loss

Version 0.2

• prior_sigma is used when initialize modules and functions instead of prior_log_sigma
  • Modules(BayesLinear, BayesConv2d, BayesBatchNorm2d) are re-defined with prior_sigma instead of prior_log_sigma.
  • convert_model(nonbayes_to_bayes, bayes_to_nonbayes) is also changed with prior_sigma instead of prior_log_sigma.
• Modules(BayesLinear, BayesConv2d, BayesBatchNorm2d) : Base initialization method is changed to the method of Adv-BNN from the original torch method.
• functional : bayesian_kl_loss is changed similar to ones in torch.functional
• loss : BKLLoss is added based on bayesian_kl_loss similar to ones in torch.loss

Version 0.3

• bayesian_kl_loss/BKLLoss returns tensor.Tensor([0]) as default
  • In the previous version, bayesian_kl_loss/BKLLoss returns 0 of int type if there is no Bayesian layers. However, considering all torch loss returns tensor and .item() is used to make them to int type, they are changed to return tensor.Tensor([0]) if there is no Bayesian layers.