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Hi!
I want to collaborate in your development of MSR weight filler.
I added support for inner product layer. Current code doesn't work for ip because fan_in and fan_out are computed differently for ip and conv weight blobs.
Because ip and conv layers can have same blob dimension(100->100 ip, kernelsize = 100, in and out channels = 1, so we can't detect blob type by looking ant first 2 dimensions of blob) with different meaning, added optional FillerLayerType layer_type to FillerParameter message.
Added support for non-zero negative slope ReLU as described in original paper.
Also i added scale field to FillerParameter message.
Scale is needed in case scale of network inputs and outputs are not same ( in original paper they use scale<1 for top ip layers), or we want to manualy change amplitude of network's signal in any place.
Maybe Xavier filler also need support for this field.
Tests now expects all random variable's deviations not to exceed 5*std.