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local L1Penalty, parent = torch.class('nn.L1Penalty','nn.Module')
--This module acts as an L1 latent state regularizer, adding the
--[gradOutput] to the gradient of the L1 loss. The [input] is copied to
--the [output].
function L1Penalty:__init(l1weight, sizeAverage, provideOutput)
parent.__init(self)
self.l1weight = l1weight
self.sizeAverage = sizeAverage or false
if provideOutput == nil then
self.provideOutput = true
else
self.provideOutput = provideOutput
end
end
function L1Penalty:updateOutput(input)
local m = self.l1weight
if self.sizeAverage == true then
m = m/input:nElement()
end
local loss = m*input:norm(1)
self.loss = loss
self.output = input
return self.output
end
function L1Penalty:updateGradInput(input, gradOutput)
local m = self.l1weight
if self.sizeAverage == true then
m = m/input:nElement()
end
self.gradInput:resizeAs(input):copy(input):sign():mul(m)
if self.provideOutput == true then
self.gradInput:add(gradOutput)
end
return self.gradInput
end
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