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README.md

Please checkout our latest work,
BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1,
and the associated github repository.

BinaryConnect

Motivations

The goal of this repository is to enable the reproduction of the experiments described in
BinaryConnect: Training Deep Neural Networks with binary weights during propagations.
You may want to checkout our subsequent work:

Requirements

  • Python, Numpy, Scipy
  • Theano (Bleeding edge version)
  • Pylearn2
  • Lasagne
  • PyTables (only for the SVHN dataset)
  • a fast Nvidia GPU or a large amount of patience

MNIST

python mnist.py

This python script trains an MLP on MNIST with the stochastic version of BinaryConnect. It should run for about 30 minutes on a GTX 680 GPU. The final test error should be around 1.15%. Please note that this is NOT the experiment reported in the article (which is in the "master" branch of the repository).

CIFAR-10

python cifar10.py

This python script trains a CNN on CIFAR-10 with the stochastic version of BinaryConnect. It should run for about 20 hours on a Titan Black GPU. The final test error should be around 8.27%.

SVHN

export SVHN_LOCAL_PATH=/Tmp/SVHN/
python svhn_preprocessing.py

This python script (taken from Pylearn2) computes a preprocessed (GCN and LCN) version of the SVHN dataset in a temporary folder (SVHN_LOCAL_PATH).

python svhn.py

This python script trains a CNN on SVHN with the stochastic version of BinaryConnect. It should run for about 2 days on a Titan Black GPU. The final test error should be around 2.15%.

How to play with it

The python scripts mnist.py, cifar10.py and svhn.py contain all the relevant hyperparameters. It is very straightforward to modify them. binary_connect.py contains the binarization function (called binarization).

Have fun!

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Training Deep Neural Networks with binary weights during propagations

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