This repository contains the Pytorch implementation of the PMCnet and PMCnet-light applied to the problem of approximating the complete posterior distribution of the unknown weight and bias parameters of neural network in Bayesian neural networks (BNNs). It provides uncertainty quantification when predicting new data. Adaptive importance sampling (AIS) is one of the most prominent Monte Carlo methodologies benefiting from sounded convergence guarantees and ease for adaptation. This work aims to show that AIS constitutes a successful approach for designing BNNs. More precisely, we propose a novel algorithm PMCnet that includes an efficient adaptation mechanism, exploiting geometric information on the complex (often multimodal) posterior distribution.
Python version 3.6.10
Pytorch 1.7.0
CUDA 11.0
scikit-learn 0.24.2
numpy 1.19.5
To initialize the parameters of BNN, we make use of the learnt parameters derived by MLE, which is put in the folder params. To start the training, run
CUDA_VISIBLE_DEVICE=0 python3 train_PMCnet_binray_classification.py
Here we give three examples of applying PMCnet under binay classification, multi-class classification and regression task respectively.
train_PMCnet_binray_classification.py: shows how to train PMCnet under binay classification task for dataset Ionosphere
train_PMCnet_multiclass_classification.py: shows how to train PMCnet under multi-class classification task for dataset Glass
train_PMCnet_regression.py: shows how to train PMCnet under regression task for dataset autoMPG
PMCnet_algo.py: the PMCnet algorithm under classification task and regression task
PMCnet_light_algo.py: the PMCnet-light algorithm under classification task for large-scale problem
PMCnet_light_algo_regression.py: the PMCnet-light algorithm under regression task for large-scale problem
Yunshi Huang - e-mail: yunshi.huang@centralesupelec.fr - PhD Student
Víctor Elvira -website
Emilie Chouzenoux -website
Jean-Christophe Pesquet -website