giannifranchi / LP_BNN

This repository contains an official implementation of LPBNN.

LP-BNN CIFAR-10, CIFAR-100 official implementation using PyTorch BatchEnsemble CIFAR-10, CIFAR-100 unofficial implementation using PyTorch

Please if you use this code please cite the following papers:

• LP-BNN
• BatchEnsemble
• Wide Residual Networks

Requirements

see the requirement of CIFAR code In addition to this requirement our code needs a big GPU to have a big batch. Our code where tested and implemented on a V100 tesla GPU thanks to Jeanzay cluster. Please proceed to the installation of Cuda, and Pytorch to as explained on PyTorch web page to be able to use our code.

How to TRAIN the Deep Neural Network with LP-BNN

After you have cloned the repository, you can train each dataset of either cifar10, cifar100 by running the script below. To have better results we advise you to perform several trainings(minimum 3).

 python main_LPBNN.py --dataset [cifar10/cifar100] --dirsave_out LPBNN_C10_T0
 python main_LPBNN.py --dataset [cifar10/cifar100] --dirsave_out LPBNN_C10_T1
 python main_LPBNN.py --dataset [cifar10/cifar100] --dirsave_out LPBNN_C10_T2

How to train the Deep Neural Network with LP-BNN BatchEnsemble

After you have cloned the repository, you can train each dataset of either cifar10, cifar100 by running the script below. To have better results we advise you to perform several trainings(minimum 3).

 python main_BatchEnsemble.py --dataset [cifar10/cifar100] --dirsave_out BE_C10_T0
 python main_BatchEnsemble.py --dataset [cifar10/cifar100] --dirsave_out BE_C10_T1
 python main_BatchEnsemble.py --dataset [cifar10/cifar100] --dirsave_out BE_C10_T2

How to evaluate the code

here are the comand line to test for CIFAR10. For CIFAR100 please adapt the code

 python  evaluate_uncertainty.py --algo 'BE' --dataset cifar10 --dirsave_out './checkpoint/cifar10/BE_C10_T'
 python  evaluate_uncertainty.py --algo 'LPBNN' --dataset cifar10 --dirsave_out './checkpoint/cifar10/LPBNN_C10_T'

Implementation Details

Hyper-parameter	CIFAR-10	CIFAR-100
Ensemble size J	4	4
initial learning rate	0.1	0.1
batch size	128	128
lr decay ratio	0.1	0.1
lr decay epochs	80, 160, 200	80, 160, 200
cutout	True	True
SyncEnsemble BN	False	False
Size of the latent space $	32	32

CIFAR-10 Results

Below is the result of the test set accuracy for CIFAR-10 dataset training.

Accuracy is the average of 3 runs

network	Accuracy (%)	AUC	AUPR	FPR-95-TPR	ECE (%)	cA(%)	cE (%)
BatchEnsemble	96.48	0.9540	0.9731	0.132	0.0167	47.44	0.2909
LP-BNN	94.76	0.9670	0.9812	0.104	0.0148	69.92	0.2421

If you are interrested about the corrupted accuraccy and corrupted expected calibration error please download the dataset from CIFAR-10-C