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:
- Neural Networks with Few Multiplications
- BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
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!