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local Add, parent = torch.class('nn.Add', 'nn.Module')
function Add:__init(inputSize,scalar)
parent.__init(self)
local size = inputSize
if scalar then size=1 end
self.scalar = scalar
self.bias = torch.Tensor(size)
self.gradBias = torch.Tensor(size)
self._ones = torch.Tensor{1}
self:reset()
end
function Add:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.bias:size(1))
end
self.bias:uniform(-stdv, stdv)
end
function Add:updateOutput(input)
self.output:resizeAs(input):copy(input)
if self.scalar then
self.output:add(self.bias[1]);
else
if input:isSameSizeAs(self.bias) then
self.output:add(self.bias)
else
local batchSize = input:size(1)
if self._ones:size(1) ~= batchSize then
self._ones:resize(batchSize):fill(1)
end
local bias = self.bias:view(-1)
local output = self.output:view(batchSize, -1)
output:addr(1, self._ones, bias)
end
end
return self.output
end
function Add:updateGradInput(input, gradOutput)
if self.gradInput then
self.gradInput:resizeAs(gradOutput):copy(gradOutput)
return self.gradInput
end
end
function Add:accGradParameters(input, gradOutput, scale)
scale = scale or 1
if self.gradBias:size(1) == 1 then
self.gradBias[1] = self.gradBias[1] + scale*gradOutput:sum();
else
if input:isSameSizeAs(self.bias) then
self.gradBias:add(scale, gradOutput)
else
local gradOutput = gradOutput:view(input:size(1), -1)
self.gradBias:view(-1):addmv(scale, gradOutput:t(), self._ones)
end
end
end
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