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local BCECriterion, parent = torch.class('nn.BCECriterion', 'nn.Criterion')
function BCECriterion:__init()
parent.__init(self)
self.sizeAverage = true
end
function BCECriterion:updateOutput(input, target)
-- log(input) * target + log(1 - input) * (1 - target)
self.term1 = self.term1 or input.new()
self.term2 = self.term2 or input.new()
self.term3 = self.term3 or input.new()
self.term1:resizeAs(input)
self.term2:resizeAs(input)
self.term3:resizeAs(input)
self.term1:fill(1):add(-1,target)
self.term2:fill(1):add(-1,input):log():cmul(self.term1)
self.term3:copy(input):log():cmul(target)
self.term3:add(self.term2)
if self.sizeAverage then
self.term3:div(target:size(1))
end
return self.term3:sum()
end
function BCECriterion:updateGradInput(input, target)
-- target / input - (1 - target) / (1 - input)
self.term1 = self.term1 or input.new()
self.term2 = self.term2 or input.new()
self.term1:resizeAs(input)
self.term2:resizeAs(input)
self.term1:fill(1):add(-1,target)
self.term2:fill(1):add(-1,input)
self.term1:cdiv(self.term2)
self.gradInput:resizeAs(input)
self.gradInput:copy(target):cdiv(input)
self.gradInput:add(-1,self.term1)
if self.sizeAverage then
self.gradInput:div(target:size(1))
end
return self.gradInput
end
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