Deep Networks on classification tasks using Torch

This is a complete training example for {Cifar10/100, STL10, SVHN, MNIST} tasks

##Data You can get the needed data using @soumith's repo: https://github.com/soumith/cifar.torch.git

##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.

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

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

##Training You can start training using:

th Main.lua -dataset Cifar10 -network Cifar10_Model -LR 0.1

or,

th Main.lua -dataset Cifar100 -network Cifar100_Model -LR 0.1

##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
optimization	sgd	optimization method
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
whiten	false	whiten data
augment	false	Augment training data
preProcDir	./PreProcData/	Data for pre-processing (means,Pinv,P)