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A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).
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Code release for "Bayesian Compression for Deep Learning"

In "Bayesian Compression for Deep Learning" we adopt a Bayesian view for the compression of neural networks. By revisiting the connection between the minimum description length principle and variational inference we are able to achieve up to 700x compression and up to 50x speed up (CPU to sparse GPU) for neural networks.

We visualize the learning process in the following figures for a dense network with 300 and 100 connections. White color represents redundancy whereas red and blue represent positive and negative weights respectively.

First layer weights Second Layer weights
alt text alt text

For dense networks it is also simple to reconstruct input feature importance. We show this for a mask and 5 randomly chosen digits. alt text


Model Method Error [%] Compression
after pruning
Compression after
precision reduction
LeNet-5-Caffe DC 0.7 6* -
DNS 0.9 55* -
SWS 1.0 100* -
Sparse VD 1.0 63* 228
BC-GNJ 1.0 108* 361
BC-GHS 1.0 156* 419
VGG BC-GNJ 8.6 14* 56
BC-GHS 9.0 18* 59


We provide an implementation in PyTorch for fully connected and convolutional layers for the group normal-Jeffreys prior (aka Group Variational Dropout) via:

import BayesianLayers

The layers can be then straightforwardly included eas follows:

    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            # activation
            self.relu = nn.ReLU()
            # layers
            self.fc1 = BayesianLayers.LinearGroupNJ(28 * 28, 300, clip_var=0.04)
            self.fc2 = BayesianLayers.LinearGroupNJ(300, 100)
            self.fc3 = BayesianLayers.LinearGroupNJ(100, 10)
            # layers including kl_divergence
            self.kl_list = [self.fc1, self.fc2, self.fc3]

        def forward(self, x):
            x = x.view(-1, 28 * 28)
            x = self.relu(self.fc1(x))
            x = self.relu(self.fc2(x))
            return self.fc3(x)

        def kl_divergence(self):
            KLD = 0
            for layer in self.kl_list:
                KLD += layer.kl_divergence()
            return KLD

The only additional effort is to include the KL-divergence in the objective. This is necessary if we want to the optimize the variational lower bound that leads to sparse solutions:

N = 60000.
discrimination_loss = nn.functional.cross_entropy

def objective(output, target, kl_divergence):
    discrimination_error = discrimination_loss(output, target)
    return discrimination_error + kl_divergence / N

Run an example

We provide a simple example, the LeNet-300-100 trained with the group normal-Jeffreys prior:


Retraining a regular neural network

Instead of training a network from scratch we often need to compress an already existing network. In this case we can simply initialize the weights with those of the pretrained network:

    BayesianLayers.LinearGroupNJ(28*28, 300, init_weight=pretrained_weight, init_bias=pretrained_bias)


The paper "Bayesian Compression for Deep Learning" has been accepted to NIPS 2017. Please cite us:

  title={Bayesian Compression for Deep Learning},
  author={Louizos, Christos and Ullrich, Karen and Welling, Max},
  journal={Conference on Neural Information Processing Systems (NIPS)},
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