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https://github.com/kumar-shridhar/PyTorch-BayesianCNN

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
https://github.com/kumar-shridhar/PyTorch-BayesianCNN

aleatoric-uncertainties bayes bayes-by-backprop bayesian-convnets bayesian-deep-learning bayesian-inference bayesian-network bayesian-networks bayesian-neural-networks bayesian-statistics convolutional-neural-networks image-recognition python pytorch pytorch-cnn variational-bayes variational-inference

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Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

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README 

        

[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-376/)

[![Pytorch 1.3](https://img.shields.io/badge/pytorch-1.3.1-blue.svg)](https://pytorch.org/)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/kumar-shridhar/PyTorch-BayesianCNN/blob/master/LICENSE)

[![arxiv](https://img.shields.io/badge/stat.ML-arXiv%3A2002.02797-B31B1B.svg)](https://arxiv.org/abs/1901.02731)



We introduce **Bayesian convolutional neural networks with variational inference**, a variant of convolutional neural networks (CNNs), in which the intractable posterior probability distributions over weights are inferred by **Bayes by Backprop**. We demonstrate how our proposed variational inference method achieves performances equivalent to frequentist inference in identical architectures on several datasets (MNIST, CIFAR10, CIFAR100) as described in the [paper](https://arxiv.org/abs/1901.02731).



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### Filter weight distributions in a Bayesian Vs Frequentist approach



![Distribution over weights in a CNN's filter.](experiments/figures/BayesCNNwithdist.png)



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### Fully Bayesian perspective of an entire CNN



![Distributions must be over weights in convolutional layers and weights in fully-connected layers.](experiments/figures/CNNwithdist_git.png)



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### Layer types



This repository contains two types of bayesian lauer implementation:  

* **BBB (Bayes by Backprop):**  

  Based on [this paper](https://arxiv.org/abs/1505.05424). This layer samples all the weights individually and then combines them with the inputs to compute a sample from the activations.



* **BBB_LRT (Bayes by Backprop w/ Local Reparametrization Trick):**  

  This layer combines Bayes by Backprop with local reparametrization trick from [this paper](https://arxiv.org/abs/1506.02557). This trick makes it possible to directly sample from the distribution over activations.

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### Make your custom Bayesian Network?

To make a custom Bayesian Network, inherit `layers.misc.ModuleWrapper` instead of `torch.nn.Module` and use `BBBLinear` and `BBBConv2d` from any of the given layers (`BBB` or `BBB_LRT`) instead of `torch.nn.Linear` and `torch.nn.Conv2d`. Moreover, no need to define `forward` method. It'll automatically be taken care of by `ModuleWrapper`. 



For example:  

```python

class Net(nn.Module):



  def __init__(self):

    super().__init__()

    self.conv = nn.Conv2d(3, 16, 5, strides=2)

    self.bn = nn.BatchNorm2d(16)

    self.relu = nn.ReLU()

    self.fc = nn.Linear(800, 10)



  def forward(self, x):

    x = self.conv(x)

    x = self.bn(x)

    x = self.relu(x)

    x = x.view(-1, 800)

    x = self.fc(x)

    return x

```

Above Network can be converted to Bayesian as follows:

```python

class Net(ModuleWrapper):



  def __init__(self):

    super().__init__()

    self.conv = BBBConv2d(3, 16, 5, strides=2)

    self.bn = nn.BatchNorm2d(16)

    self.relu = nn.ReLU()

    self.flatten = FlattenLayer(800)

    self.fc = BBBLinear(800, 10)

```



#### Notes:

1. Add `FlattenLayer` before first `BBBLinear` block.  

2. `forward` method of the model will return a tuple as `(logits, kl)`.

3. `priors` can be passed as an argument to the layers. Default value is:  

```python

priors={

    'prior_mu': 0,

    'prior_sigma': 0.1,

    'posterior_mu_initial': (0, 0.1),  # (mean, std) normal_

    'posterior_rho_initial': (-3, 0.1),  # (mean, std) normal_

}

```



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### How to perform standard experiments?

Currently, following datasets and models are supported.  

* Datasets: MNIST, CIFAR10, CIFAR100  

* Models: AlexNet, LeNet, 3Conv3FC  



#### Bayesian



`python main_bayesian.py`

* set hyperparameters in `config_bayesian.py`



#### Frequentist



`python main_frequentist.py`

* set hyperparameters in `config_frequentist.py`



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### Directory Structure:

`layers/`:  Contains `ModuleWrapper`, `FlattenLayer`, `BBBLinear` and `BBBConv2d`.  

`models/BayesianModels/`: Contains standard Bayesian models (BBBLeNet, BBBAlexNet, BBB3Conv3FC).  

`models/NonBayesianModels/`: Contains standard Non-Bayesian models (LeNet, AlexNet).  

`checkpoints/`: Checkpoint directory: Models will be saved here.  

`tests/`: Basic unittest cases for layers and models.  

`main_bayesian.py`: Train and Evaluate Bayesian models.  

`config_bayesian.py`: Hyperparameters for `main_bayesian` file.  

`main_frequentist.py`: Train and Evaluate non-Bayesian (Frequentist) models.  

`config_frequentist.py`: Hyperparameters for `main_frequentist` file.  



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### Uncertainty Estimation:  

There are two types of uncertainties: **Aleatoric** and **Epistemic**.  

Aleatoric uncertainty is a measure for the variation of data and Epistemic uncertainty is caused by the model.  

Here, two methods are provided in `uncertainty_estimation.py`, those are `'softmax'` & `'normalized'` and are respectively based on equation 4 from [this paper](https://openreview.net/pdf?id=Sk_P2Q9sG) and equation 15 from [this paper](https://arxiv.org/pdf/1806.05978.pdf).  

Also, `uncertainty_estimation.py` can be used to compare uncertainties by a Bayesian Neural Network on `MNIST` and `notMNIST` dataset. You can provide arguments like:     

1. `net_type`: `lenet`, `alexnet` or `3conv3fc`. Default is `lenet`.   

2. `weights_path`: Weights for the given `net_type`. Default is `'checkpoints/MNIST/bayesian/model_lenet.pt'`.  

3. `not_mnist_dir`: Directory of `notMNIST` dataset. Default is `'data\'`. 

4. `num_batches`: Number of batches for which uncertainties need to be calculated.  



**Notes**:  

1. You need to download the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset from [here](http://yaroslavvb.com/upload/notMNIST/notMNIST_small.tar.gz).  

2. Parameters `layer_type` and `activation_type` used in `uncertainty_etimation.py` needs to be set from `config_bayesian.py` in order to match with provided weights. 



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If you are using this work, please cite:



```

@article{shridhar2019comprehensive,

  title={A comprehensive guide to bayesian convolutional neural network with variational inference},

  author={Shridhar, Kumar and Laumann, Felix and Liwicki, Marcus},

  journal={arXiv preprint arXiv:1901.02731},

  year={2019}

}

```



```

@article{shridhar2018uncertainty,

  title={Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference},

  author={Shridhar, Kumar and Laumann, Felix and Liwicki, Marcus},

  journal={arXiv preprint arXiv:1806.05978},

  year={2018}

}

}

```



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