/csgmcmc

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

Primary LanguageJupyter Notebook

Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

This repository contains code for the paper Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning, accepted in International Conference on Learning Representations (ICLR), 2020 as Oral Presentation (acceptance rate = 1.85%).

@article{zhang2020csgmcmc,
  title={Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning},
  author={Zhang, Ruqi and Li, Chunyuan and Zhang, Jianyi and Chen, Changyou and Wilson, Andrew Gordon},
  journal={International Conference on Learning Representations},
  year={2020}
}

Introduction

Cyclical Stochastic Gradient MCMC (cSG-MCMC) is proposed to efficiently explore complex multimodal distributions, such as those encountered for modern deep neural networks.

The key idea is to adapt a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We prove that our proposed learning rate schedule provides faster convergence to samples from a stationary distribution than SG-MCMC with standard decaying schedules. The figure below is an illustration of the proposed cyclical stepsize schedule (red) and the traditional decreasing stepsize schedule (blue) for SG-MCMC algorithms. The cSG-MCMC consists of two stages: Exploration and Sampling.

Dependencies

Experimental Results

Gaussian Mixture Density (25 Gaussians)

To generate samples from mixture of Gaussians (single chain results), please run experiments/mog25.m

The two different stepsize schedules are called in lines:

    dsgld = sgld( gradUNoise, etaSGLD, L, x0, V );
    dcsgld = csgld( gradUNoise, etacSGLD, L, M, x0, V );

To visualize the results, please use ipython notebook to open the file experiments/plot_density.ipynb. Cached results from our runs are included.

Sampling from a mixture of 25 Gaussians in the non-parallel setting (one single chain).

SGLD cSGLD

Sampling from a mixture of 25 Gaussians in the parallel setting (4 chains).

SGLD cSGLD

CIFAR-10

To train models with cSGLD on CIFAR-10, run:

cd experiments
python cifar_csgmcmc.py --dir=<DIR> \
                        --data_path=<PATH> \
                        --temperature=<TEMPERATURE>

To train models with cSGHMC on CIFAR-10, run:

cd experiments
python cifar_csghmc.py --dir=<DIR> \
                       --data_path=<PATH> \
                       --temperature=<TEMPERATURE> \
                       --alpha=<ALPHA>

Parameters:

  • DIR — path to training directory where samples will be stored
  • PATH — path to the data directory
  • ALPHA — One minus the momentum term. One is corresponding to SGLD and a number which is less than one is corresponding to SGHMC
  • TEMPERATURE — temperature in the posterior

To test the ensemble of the collected samples on CIFAR-10, run experiments/cifar_ensemble.py

CIFAR-100

Similarly, for CIFAR-100, run

cd experiments
python cifar100_csgmcmc.py --dir=<DIR> \
                           --data_path=<PATH> \
                           --temperature=<TEMPERATURE>

cd experiments
python cifar100_csghmc.py --dir=<DIR> \
                          --data_path=<PATH> \
                          --temperature=<TEMPERATURE> \
                          --alpha=<ALPHA>

To test the ensemble of the collected samples on CIFAR-100, run experiments/cifar100_ensemble.py

Test Error (%) on CIFAR-10 and CIFAR-100.

Dataset SGLD cSGLD SGHMC cSGHMC
CIFAR-10 5.20 ± 0.06 4.29 ± 0.06 4.93 ± 0.1 4.27 ± 0.03
CIFAR-100 23.23 ± 0.01 20.55 ± 0.06 22.60 ± 0.17 20.50 ± 0.11

References