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

Bayesian CNN with Variational Inference

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.

Filter weight distributions in a Bayesian Vs Frequentist approach

Fully Bayesian perspective of an entire CNN

Make your custom Bayesian Network?

To make a custom Bayesian Network, inherit layers.misc.ModuleWrapper instead of torch.nn.Module and use layers.BBBLinear.BBBLinear and layers.BBBConv.BBBConv2d instead of torch.nn.Conv2d and torch.nn.Linear. Moreover, no need to define forward method. It’ll automatically be taken care of.

For example:

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:

class Net(ModuleWrapper):

  def __init__(self):
    super().__init__()
    self.conv = BBBConv2d(3, 16, 5, strides=2, alpha_shape=(1,1), name='conv')
    self.bn = nn.BatchNorm2d(16)
    self.relu = nn.ReLU()
    self.flatten = FlattenLayer(800)
    self.fc = BBBLinear(800, 10, alpha_shape=(1,1), name='fc')

Notes:

1. Add FlattenLayer before first BBBLinear block.
2. forward method of the model will return a tuple as (logits, kl).
3. Keyword argument name is optional and is required to use only when recording mean and variances in turned ON.

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

Recording Mean and Variance:

If record_mean_var is True, then mean and variances for layers in record_layers list will be logged in checkpoints directory. Recording frequency per epoch can be defined. All these mentioned parameters can be modified in the config_bayesian.py file.

DistPlots

LinePlots

Notes:

1. The recording will only take place during the training phase of the model.
2. Choose recording_freq_per_epoch as a multiple of number of training iterations. It’s not necessary but this will record exactly that many times.
   Example: for num_iteration = 96, recording_freq_per_epoch = 48. Therefore, step_size will be 2 and will record exactly 48 times.
   But for num_iteration = 96, recording_freq_per_epoch = 49. Therefore, step_size will be 1 and will record 96 times.
3. Choosing recording_freq_per_epoch higher than number of training iterations will raise ZeroDivisionError.

In order to visualize the recorded values, visualize_mean_var.py contains draw_distributions and draw_lineplot methods. Following are the arguments which needs to be passed to visualize_mean_var.py:

• --filename: Path to log file.
• --data_type: Draw plots for what? mean, std or both? Default is 'mean'.
• --node_no: Index of the node for which to draw plots. Index is after flattening of the layer. Default is 0 i.e, first node.
• --plot_type: Which plot to draw? Currently we support lineplot and distplot. Default is 'lineplot'.
• --plot_time: Pauses the plot for this much amount of time before updating it. Default is 1 second.
• --save_plots: Whether to save plots or not. Default is 0 for No. 1 is for Yes.
• --save_dir: Directory for saving plots. Must end with '/'. If not provided, default directory will be filename_directory/plots/.

Directory Structure:

layers/: Contains ModuleWrapper, FlattenLayer, Bayesian layers (BBBConv2d and BBBLinear).
models/BayesianModels/: Contains standard Bayesian models (BBBLeNet, BBBAlexNet, BBB3Conv3FC).
models/NonBayesianModels/: Contains standard Non-Bayesian models (LeNet, AlexNet).
checkpoints/: Checkpoint directory for the best model 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.
visualize_mean_var.py: Plotting Distributions and Line graphs of mean and variances.

If you are using this work, please cite the authors:

@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}
}