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Rethinking Binarized Neural Network Optimization

Description

This repository aims to reproduce the results in "Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization" as part of the NeurIPS 2019 reproducibility challenge. We have implemented the Binary optimization algorithm in PyTorch and with it are able to train a binary neural network on CIFAR-10. See the reproducibility report for details.

How to run

First, install dependencies

# clone project   
git clone https://github.com/nikvaessen/Rethinking-Binarized-Neural-Network-Optimization

# install project   
cd https://github.com/nikvaessen/Rethinking-Binarized-Neural-Network-Optimization
pip install -e .   
pip install requirements.txt

If you are interested in training a BNN on cifar-10, you can navigate to research_seed/cifar and run cifar_trainer.py.

# module folder
cd research_seed/cifar/   

# run module 
python cifar_trainer.py    

Main Contribution

In order to reproduce the original paper we have implemented the following:

• Bytorch implements binary optimisation and binary layers in PyTorch
• cifar implement BinaryNet (from this paper) for CIFAR-10
• theoretical implements experiments to disprove the approximation viewpoint as well as behaviour of learning rates under latent-weight optimisation
• experiments contains convenience scripts to reproduce the experiments of section 5.1 and 5.2 of the original paper