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SNIP

This repository contains code for the paper SNIP: Single-shot Network Pruning based on Connection Sensitivity (ICLR 2019).

Prerequisites

Dependencies

• tensorflow < 2
• python 2.7 or python 3.6
• packages in requirements.txt

Datasets

Put the following datasets in your preferred location (e.g., ./data).

• MNIST
• CIFAR-10

Usage

To run the code (LeNet on MNIST by default):

python main.py --path_data=./data

For example, in order to reproduce results for VGG-D:

python main.py --logdir ./reproduce-vgg --path_data ./data --datasource cifar-10 --aug_kinds fliplr translate_px --arch vgg-d --target_sparsity 0.95 --batch_size 128 --train_iterations 150000 --optimizer momentum --lr_decay_type piecewise --decay_boundaries 30000 60000 90000 120000 --decay_values 0.1 0.02 0.004 0.0008 0.00016

See main.py to run with other options.

Citation

If you use this code for your work, please cite the following:

@inproceedings{lee2018snip,
  title={SNIP: Single-shot network pruning based on connection sensitivity},
  author={Lee, Namhoon and Ajanthan, Thalaiyasingam and Torr, Philip HS},
  booktitle={ICLR},
  year={2019},
}

License

This project is licensed under the MIT License. See the LICENSE file for details.