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local CReLU, parent = torch.class('nn.CReLU', 'nn.Sequential')
-- Implements the CReLU activation function as described by
-- W. Shang et al. in "Understanding and Improving Convolutional Neural Networks
-- via Concatenated Rectified Linear Units"
function CReLU:__init(nInputDims, inplace)
parent.__init(self)
self.nInputDims = nInputDims
self.inplace = inplace or false
local concatTable = nn.ConcatTable()
concatTable:add(nn.Identity())
concatTable:add(nn.MulConstant(-1))
self:add(concatTable)
self:add(nn.JoinTable(2))
self:add(nn.ReLU(self.inplace))
end
function CReLU:updateOutput(input)
local input_
local batched = input:dim() == (self.nInputDims + 1)
if not batched then
input_ = input:view(1, -1)
else
input_ = input:view(input:size(1), -1)
end
parent.updateOutput(self, input_)
local osize = input:size()
if not batched then
osize[1] = osize[1] * 2
else
osize[2] = osize[2] * 2
end
self.output:resize(osize)
return self.output
end
function CReLU:backward(input, gradOutput)
return self:updateGradInput(input, gradOutput)
end
function CReLU:updateGradInput(input, gradOutput)
local batched = input:dim() == (self.nInputDims + 1)
if not batched then
parent.updateGradInput(self, input:view(1, -1), gradOutput:view(1, -1))
else
parent.updateGradInput(self, input:view(input:size(1), -1),
gradOutput:view(input:size(1), -1))
end
self.gradInput:resizeAs(input)
return self.gradInput
end
function CReLU:__tostring__()
return "CReLU()"
end
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