added a warning (spent 1 hour figuring out what was happening)
setting a learning rate (>=0) disables adagrad
created generic nxn.ExtractInterpolate class
Added Texture functions (nxn.TexFun*) : they perform 2D image transformations on RGB inputs.
All on GPU, based on nxn.ExtractInterpolate
Merge branch 'master' of https://github.com/leonbottou/torch7-custom
deleted some useless files
deleted more files
removed convproto
added a self.gpucompatible flag on modules
(becomes true at initialization, if the module is compatible)
added a function Module:isGPUCompatible() to check if a module and its submodules are GPU compatible
NeuralNet:GPUWrap() is called at the beginning of NeuralNet:train(), and wraps the network between nxn.Copy modules
NeuralNet:updateConfusion() method
small fix in confusion matrix usage
all the forward passes through the network go through NeuralNet:forward()
We plug in the GPUWrap function there to ensure the network is CUDA'ed every time it is used (except by a raw call)
Put the cost values in Tensors rather than in tables
fixed mistake of previous commit (costs : tables -> tensors)
added sensitivity parameter to adagrad (lr = masterLR / sqrt (1+sens*sum(grads)))
default values are unchanged yet
added comment
added debugmode flag to bypass protected calls in nxn.NeuralNet:train()
fix'd position of setBackProp() in nxn.NeuralNet:train()