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group-sparsity-sbp

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Structured Bayesian Pruning, NIPS 2017

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<h1>Structured Bayesian Pruning <br/> via Log-Normal Multiplicative Noise </h1>

This repo contains the code for our NIPS17 paper, [Structured Bayesian Pruning via Log-Normal Multiplicative Noise](https://arxiv.org/pdf/1705.07283.pdf) ([poster](https://bayesgroup.github.io/pdf/sbp-poster.pdf)). In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e.g. removes neurons and/or convolutional
channels in CNNs. To do this we inject noise to the neurons outputs while keeping the weights unregularized.

## Spotlight video

[![](http://i3.ytimg.com/vi/3zEYjw-cB4Y/maxresdefault.jpg)](https://youtu.be/3zEYjw-cB4Y)

# Launch experiments

Example for launching LeNet5 experiment.  
```
python ./scripts/lenet5-sbp.py
```

Example for launching VGG-like experiment. To obtain sparse VGG-like architecture we use pretrained network, so you can use your own weights, or train the network from scratch using the following command.
```
python ./scripts/vgglike.py --num_gpus <num GPUs>
```
Don't forget to adjust batch size to obtain the same number of iterations. For instance, for one GPU we use batch_size=100, for 2 GPUs we use batch_size=50.

Finally, use the following command to launch SBP model for VGG-like architecture.
```
python ./scripts/vgglike-sbp.py --num_gpus <num GPUs> --checkpoint <path to pretrained checkpoint>
```

# MNIST Experiments

Results for LeNet architectures on MNIST

| Network  | Method       | Error        | Neurons per Layer    | CPU        | GPU        | FLOPs       |
| -------- | ------------ | ------------ | -------------------- | ---------- | ---------- | ----------- |
| Lenet-fc | Original     | 1.54         | 784 - 500 - 300 - 10 | 1.00 X     | 1.00 X     | 1.00 X      |  
|          | SparseVD     | 1.57         | 537 - 217 - 130 - 10 | 1.19 X     | 1.03 X     | 3.73 X      |  
|          | SSL          | 1.49         | 434 - 174 -  78 - 10 | 2.21 X     | 1.04 X     | 6.06 X      |  
|          | StructuredBP | 1.55         | 245 - 160 -  55 - 10 | **2.33 X** | **1.08 X** | **11.23 X** |  
|||||||
| LeNet5   | Original     | 0.80         | 20 - 50 - 800 - 500  | 1.00 X     | 1.00 X     | 1.00 X      |
|          | SparseVD     | 0.75         | 17 - 32 - 329 - 75   | 1.48 X     | 1.41 X     | 2.19 X      |
|          | SSL          | 1.00         | 3 - 12 - 800 - 500   | 5.17 X     | 1.80 X     | 3.90 X      |
|          | StructuredBP | 0.86         | 3 - 18 - 284 - 283   | **5.41 X** | **1.91 X** | **10.49 X** |

# CIFAR-10 Experiments

Results for VGG-like architecture on CIFAR-10 dataset. Here speed-up is reported for CPU. More detailed results are provided in the paper.

# Citation

If you found this code useful please cite our paper

```
@incollection{
  neklyudov2018structured,
  title = {Structured Bayesian Pruning via Log-Normal Multiplicative Noise},
  author = {Neklyudov, Kirill and Molchanov, Dmitry and Ashukha, Arsenii and Vetrov, Dmitry P},
  booktitle = {Advances in Neural Information Processing Systems 30},
  editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
  pages = {6778--6787},
  year = {2017},
  publisher = {Curran Associates, Inc.},
  url = {http://papers.nips.cc/paper/7254-structured-bayesian-pruning-via-log-normal-multiplicative-noise.pdf}
}
```

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