Deep Networks on classification tasks using Torch

This is a complete training example for BinaryNets using Binary-Backpropagation algorithm as explained in "Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio' on following datasets: Cifar10/100, SVHN, MNIST

Data

We use dp library to extract all the data please view installation section

Dependencies

• Torch (http://torch.ch)
• "DataProvider.torch" (https://github.com/eladhoffer/DataProvider.torch) for DataProvider class.
• "cudnn.torch" (https://github.com/soumith/cudnn.torch) for faster training. Can be avoided by changing "cudnn" to "nn" in models.
• "dp" (https://github.com/nicholas-leonard/dp.git) for data extraction
• "unsup" (https://github.com/koraykv/unsup.git) for data pre-processing

To install all dependencies (assuming torch is installed) use:

luarocks install https://raw.githubusercontent.com/eladhoffer/DataProvider.torch/master/dataprovider-scm-1.rockspec
luarocks install cudnn
luarocks install dp
luarocks install unsup

Training

Create pre-processing folder:

cd BinaryNet
mkdir PreProcData

Start training using:

th Main_BinaryNet_Cifar10.lua -network BinaryNet_Cifar10_Model

or,

th Main_BinaryNet_MNIST.lua -network BinaryNet_MNIST_Model

Run with Docker

The Docker is built from nvidia/cuda:8.0-cudnn5-devel with Torch commit 0219027e6c4644a0ba5c5bf137c989a0a8c9e01b

• To build image, run: docker build -t binarynet:torch-gpu-cuda-8.0 -f Dockerfile/binarynet-torch-gpu-cuda-8.0 . or to pull docker image: docker pull hychiang/binarynet:torch-gpu-cuda-8.0

• To launch image with gpu, run: docker run -it --gpus all binarynet:torch-gpu-cuda-8.0

• To train BNN with Cifar10: th Main_BinaryNet_Cifar10.lua -network BinaryNet_Cifar10_Model

Additional flags

Flag	Default Value	Description
modelsFolder	./Models/	Models Folder
network	Model.lua	Model file - must return valid network.
LR	0.1	learning rate
LRDecay	0	learning rate decay (in # samples
weightDecay	1e-4	L2 penalty on the weights
momentum	0.9	momentum
batchSize	128	batch size
stcNeurons	true	using stochastic binarization for the neurons or not
stcWeights	false	using stochastic binarization for the weights or not
optimization	adam	optimization method
SBN	true	use shift based batch-normalization or not
runningVal	true	use running mean and std or not
epoch	-1	number of epochs to train (-1 for unbounded)
threads	8	number of threads
type	cuda	float or cuda
devid	1	device ID (if using CUDA)
load	none	load existing net weights
save	time-identifier	save directory
dataset	Cifar10	Dataset - Cifar10, Cifar100, STL10, SVHN, MNIST
dp_prepro	false	preprocessing using dp lib
whiten	false	whiten data
augment	false	Augment training data
preProcDir	./PreProcData/	Data for pre-processing (means,Pinv,P)