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-- Copyright 2004-present Facebook. All Rights Reserved.
--[[
`TrueNLLCriterion` computes the negative log-loss criterion directly.
]]
local TrueNLLCriterion, parent = torch.class('nn.TrueNLLCriterion',
'nn.Criterion')
-- For numerical stability
local eps = 0.00000001
function TrueNLLCriterion:__init()
parent.__init(self)
self.sizeAverage = true
end
function TrueNLLCriterion:updateOutput(input, target)
if input:dim() == 1 then
self.output = -math.log(input[target] + eps)
elseif input:dim() == 2 then
local output = 0
for i=1,target:size(1) do
output = output - math.log(input[i][target[i]] + eps)
end
if self.sizeAverage then
output = output / target:size(1)
end
self.output = output
else
error('matrix or vector expected')
end
return self.output
end
function TrueNLLCriterion:updateGradInput(input, target)
self.gradInput:resizeAs(input)
self.gradInput:zero()
if input:dim() == 1 then
self.gradInput[target] = -1 / (input[target] + eps)
else
local z = -1
if self.sizeAverage then
z = z / target:size(1)
end
local gradInput = self.gradInput
for i=1,target:size(1) do
gradInput[i][target[i]] = z / (input[i][target[i]] + eps)
end
end
return self.gradInput
end