This folder contains examples related to Bayesian Methods.
We currently have Stochastic Gradient Langevin Dynamics (SGLD) (Welling and Teh, 2011) and Bayesian Dark Knowledge (BDK) (Balan, Rathod, Murphy and Welling, 2015).
sgld.ipynb shows how to use MXNet to repeat the toy experiment in the original SGLD paper.
bdk.ipynb shows how to use MXNet to implement the DistilledSGLD algorithm in Bayesian Dark Knowledge.
bdk_demo.py contains scripts (more than the notebook) related to Bayesian Dark Knowledge. Use python bdk_demo.py -d 1 -l 2 -t 50000 to run classification on MNIST.
View parameters we can use with the following command.
python bdk_demo.py -h
usage: bdk_demo.py [-h] [-d DATASET] [-l ALGORITHM] [-t TRAINING] [--gpu GPU]
Examples in the paper [NIPS2015]Bayesian Dark Knowledge and [ICML2011]Bayesian
Learning via Stochastic Gradient Langevin Dynamics
optional arguments:
-h, --help show this help message and exit
-d DATASET, --dataset DATASET
Dataset to use. 0 --> TOY, 1 --> MNIST, 2 -->
Synthetic Data in the SGLD paper
-l ALGORITHM, --algorithm ALGORITHM
Type of algorithm to use. 0 --> SGD, 1 --> SGLD,
other-->DistilledSGLD
-t TRAINING, --training TRAINING
Number of training samples
--gpu GPU 0 to use GPU, not set to use CPU