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https://github.com/team-approx-bayes/dl-with-bayes
Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"
https://github.com/team-approx-bayes/dl-with-bayes
Last synced: 4 months ago
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Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"
- Host: GitHub
- URL: https://github.com/team-approx-bayes/dl-with-bayes
- Owner: team-approx-bayes
- Created: 2019-10-27T04:06:14.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2019-12-03T22:34:36.000Z (over 4 years ago)
- Last Synced: 2023-11-07T16:24:59.987Z (8 months ago)
- Language: Python
- Homepage:
- Size: 7.53 MB
- Stars: 238
- Watchers: 14
- Forks: 22
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
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- awesome-stars - team-approx-bayes/dl-with-bayes - Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles" (Python)
README
# Practical Deep Learning with Bayesian Principles
This repository contains code that demonstrate
practical applications of Bayesian principles to Deep Learning.
Our implementation contains an Adam-like optimizer, called
[VOGN](http://proceedings.mlr.press/v80/khan18a.html),
to obtain uncertainty in Deep Learning.
- 2D-binary classification (see [toy example](./toy_example))
- Image classification ([MNIST](./classification),
[CIFAR-10/100](./classification),
and [ImageNet](./distributed/classification))
- Continual learning for image classification (permuted MNIST)
- Per-pixel semantic labeling & segmentation (Cityscapes)
## Setup
This repository uses [PyTorch-SSO](https://github.com/cybertronai/pytorch-sso), a PyTorch extension for second-order optimization, variational inference, and distributed training.
```bash
$ git clone [email protected]:cybertronai/pytorch-sso.git
$ cd pytorch-sso
$ python setup.py install
```
Please follow the
[Installation](https://github.com/cybertronai/pytorch-sso#installation)
of PyTorch-SSO for CUDA/MPI support.
## Bayesian Uncertainty Estimation
Decision boundary and entropy plots on 2D-binary classification by MLPs trained
with Adam and VOGN.
VOGN optimizes the posterior distribution of each weight (i.e., mean and variance of the Gaussian).
A model with the mean weights draws the red boundary, and models with the MC samples from the posterior distribution draw light red boundaries.
VOGN converges to a similar solution as Adam while keeping uncertainty in its predictions.
With PyTorch-SSO (`torchsso`), you can run VOGN training by changing a line in your train script:
```diff
import torch
+import torchsso
train_loader = torch.utils.data.DataLoader(train_dataset)
model = MLP()
-optimizer = torch.optim.Adam(model.parameters())
+optimizer = torchsso.optim.VOGN(model, dataset_size=len(train_loader.dataset))
for data, target in train_loader:
def closure():
optimizer.zero_grad()
output = model(data)
loss = F.binary_cross_entropy_with_logits(output, target)
loss.backward()
return loss, output
loss, output = optimizer.step(closure)
```
To train MLPs by VOGN and Adam and create GIF
```bash
$ cd toy_example
$ python main.py
```
For detail, please see [VOGN implementation in PyTorch-SSO](https://github.com/cybertronai/pytorch-sso/blob/master/torchsso/optim/vi.py).
## Bayes for Image Classification
This repository contains code for the NeurIPS 2019 paper "[Practical Deep Learning with Bayesian Principles](https://arxiv.org/abs/1906.02506),"
[[poster](./neurips2019_poster.pdf)]
which includes the results of **Large-scale Variational Inference on ImageNet classification**.
VOGN achieves similar performance in about the same number of epochs as Adam and SGD.
Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated (rightmost figure),
uncertainties on out-of-distribution data are improved (please refer the paper),
and continual-learning performance is boosted (please refer the paper, an example is to be prepared).
See [classification](./classification) (single CPU/GPU) or [distributed/classification](./distributed/classification) (multiple GPUs) for example scripts.
## Citation
NeurIPS 2019 paper
```
@article{osawa2019practical,
title = {Practical Deep Learning with Bayesian Principles},
author = {Osawa, Kazuki and Swaroop, Siddharth and Jain, Anirudh and Eschenhagen, Runa and Turner, Richard E. and Yokota, Rio and Khan, Mohammad Emtiyaz},
journal = {arXiv preprint arXiv:1906.02506},
year = {2019}
}
```