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--[[
-- A MultiLabel multiclass criterion based on sigmoid:
--
-- the loss is:
-- l(x,y) = - sum_i y[i] * log(p[i]) + (1 - y[i]) * log (1 - p[i])
-- where p[i] = exp(x[i]) / (1 + exp(x[i]))
--
-- and with weights:
-- l(x,y) = - sum_i weights[i] (y[i] * log(p[i]) + (1 - y[i]) * log (1 - p[i]))
--
-- This uses the stable form of the loss and gradients.
--]]
local MultiLabelSoftMarginCriterion, parent = torch.class('nn.MultiLabelSoftMarginCriterion', 'nn.Criterion')
function MultiLabelSoftMarginCriterion:__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
self.sigmoid = nn.Sigmoid()
end
function MultiLabelSoftMarginCriterion:updateOutput(input, target)
local weights = self.weights
if weights ~= nil and target:dim() ~= 1 then
weights = self.weights:view(1, target:size(2)):expandAs(target)
end
local x = input:view(input:nElement())
local t = target:view(target:nElement())
self.sigmoid:updateOutput(x)
self._buffer1 = self._buffer1 or input.new()
self._buffer2 = self._buffer2 or input.new()
self._buffer1:ge(x, 0) -- indicator
-- log(1 + exp(x - cmul(x, indicator):mul(2)))
self._buffer2:cmul(x, self._buffer1):mul(-2):add(x):exp():add(1):log()
-- cmul(x, t - indicator)
self._buffer1:mul(-1):add(t):cmul(x)
-- log(1 + exp(x - cmul(x, indicator):mul(2))) - cmul(x, t - indicator)
self._buffer2:add(-1, self._buffer1)
if weights ~= nil then
self._buffer2:cmul(weights)
end
self.output = self._buffer2:sum()
if self.sizeAverage then
self.output = self.output / input:nElement()
end
return self.output
end
function MultiLabelSoftMarginCriterion:updateGradInput(input, target)
local weights = self.weights
if weights ~= nil and target:dim() ~= 1 then
weights = self.weights:view(1, target:size(2)):expandAs(target)
end
self.gradInput:resizeAs(input):copy(self.sigmoid.output)
self.gradInput:add(-1, target)
if weights ~= nil then
self.gradInput:cmul(weights)
end
if self.sizeAverage then
self.gradInput:div(target:nElement())
end
return self.gradInput
end
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