Layer-wise Optimal Brain Surgeon

This repo is for Layer-wise Optimal Brain Surgeon (L-OBS), which will appear in NIPS 2017. Codes are based on Tensorflow r1.0+

Paper link: Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon

This repo contains:

1. Experiment codes for MNIST using lenet300-100: (folder: lenet300-100)

   This is a toy code for L-OBS, you can know how L-OBS is implemented using lenet300-100 as an examples.

   How to use it:

   Run lenet300-100/LOBS.py to prune lenet300-100

2. Experiment codes for Imagenet using ResNet-50: (folder: ResNet-50)

   Explaination:

   This folder is for conducting L-OBS on ResNet-50

   To facilitate the process of getting Imagenet data, we use this to generate image batches and build network models. But we also make some modifications.

   To run this code, you need to first download the whole ILSVRC2012 dataset. Then specify the dataset root in the codes.

   How to use it:

   run the following .py file:

   • calculate_hessian_inverse.py:

     This code calculates hessian inverse for every layer in ResNet-50. It will generate 54 hessian_inverse.npy files in hessian_inverse/ folder

   • prune_weights.py:

     This code prunes weights and biases for ResNet-50.

   • validate.py:

     This code test the pruned weights efficiency.

   Notice:

   • It may takes some time to calculate the hessian inverse. In our experiment server: 64-CPUs Intel(R) Xeon(R) CPU E5-2697A v4 @ 2.60GHz, 512Gb memory (without GPUs, haha, maybe you can speed up with GPUs), it takes about 33 hours to calculate all the hessian inverse.

   • This folder has already contains all the layer that current popular models use: fully-connected, convolution, res. And it implements APIs for calculating hessian inverse and performing pruning. The utility folder is still under maintain, we are making our best to provide you user-friendly interface for conducting experiments on L-OBS. But currently, if you want to conduct experiments on other models, please first get familiar with the APIs provided in this folder. Then you can easily deploy L-OBS on other models.

3. A utility code folder, which provides APIs for any network model and dataset. It is under maintain.

4. To deploy L-OBS on large-scale dataset efficiently, multiprocessor-version codes will be released later.

Support

Please use github issues for any problem related to the code. Send email to the authors for general questions related to the paper.