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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

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Contains code for the NeurIPS 2019 paper "Practical Deep Learning with Bayesian Principles"

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# 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.
![](./docs/boundary.gif)
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**.

![](./docs/curves.png)
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}
}
```