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local THNN = require 'nn.THNN'
local BCECriterion, parent = torch.class('nn.BCECriterion', 'nn.Criterion')
function BCECriterion:__init(weights, sizeAverage)
parent.__init(self)
if sizeAverage ~= nil then
self.sizeAverage = sizeAverage
else
self.sizeAverage = true
end
if weights ~= nil then
assert(weights:dim() == 1, "weights input should be 1-D Tensor")
self.weights = weights
end
end
function BCECriterion:__len()
return self.weights and #self.weights or 0
end
function BCECriterion:updateOutput(input, target)
-- - log(input) * target - log(1 - input) * (1 - target)
assert( input:nElement() == target:nElement(),
"input and target size mismatch")
self.output_tensor = self.output_tensor or input.new(1)
local weights = self.weights
if weights ~= nil and target:dim() ~= 1 then
weights = self.weights:view(1, target:size(2)):expandAs(target)
end
input.THNN.BCECriterion_updateOutput(
input:cdata(),
target:cdata(),
self.output_tensor:cdata(),
self.sizeAverage,
THNN.optionalTensor(weights)
)
self.output = self.output_tensor[1]
return self.output
end
function BCECriterion:updateGradInput(input, target)
-- - (target - input) / ( input (1 - input) )
assert( input:nElement() == target:nElement(),
"input and target size mismatch")
local weights = self.weights
if weights ~= nil and target:dim() ~= 1 then
weights = self.weights:view(1, target:size(2)):expandAs(target)
end
input.THNN.BCECriterion_updateGradInput(
input:cdata(),
target:cdata(),
self.gradInput:cdata(),
self.sizeAverage,
THNN.optionalTensor(weights)
)
return self.gradInput
end
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