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local SoftMin, parent = torch.class('nn.SoftMin', 'nn.Module')
function SoftMin:updateOutput(input)
self.mininput = self.mininput or input.new()
self.mininput:resizeAs(input):copy(input):mul(-1)
input.THNN.SoftMax_updateOutput(
self.mininput:cdata(),
self.output:cdata()
)
return self.output
end
function SoftMin:updateGradInput(input, gradOutput)
self.mininput = self.mininput or input.new()
self.mininput:resizeAs(input):copy(input):mul(-1)
input.THNN.SoftMax_updateGradInput(
self.mininput:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.output:cdata()
)
self.gradInput:mul(-1)
return self.gradInput
end
function SoftMin:clearState()
if self.mininput then self.mininput:set() end
return parent.clearState(self)
end
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