39 lines
1.7 KiB
Lua
39 lines
1.7 KiB
Lua
-- ref: https://en.wikipedia.org/wiki/Huber_loss
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local ClippedWeightedHuberCriterion, parent = torch.class('w2nn.ClippedWeightedHuberCriterion','nn.Criterion')
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function ClippedWeightedHuberCriterion:__init(w, gamma, clip)
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parent.__init(self)
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self.clip = clip
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self.gamma = gamma or 1.0
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self.weight = w:clone()
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self.diff = torch.Tensor()
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self.diff_abs = torch.Tensor()
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--self.outlier_rate = 0.0
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self.square_loss_buff = torch.Tensor()
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self.linear_loss_buff = torch.Tensor()
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end
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function ClippedWeightedHuberCriterion:updateOutput(input, target)
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self.diff:resizeAs(input):copy(input)
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self.diff:clamp(self.clip[1], self.clip[2])
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for i = 1, input:size(1) do
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self.diff[i]:add(-1, target[i]):cmul(self.weight)
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end
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self.diff_abs:resizeAs(self.diff):copy(self.diff):abs()
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local square_targets = self.diff[torch.lt(self.diff_abs, self.gamma)]
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local linear_targets = self.diff[torch.ge(self.diff_abs, self.gamma)]
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local square_loss = self.square_loss_buff:resizeAs(square_targets):copy(square_targets):pow(2.0):mul(0.5):sum()
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local linear_loss = self.linear_loss_buff:resizeAs(linear_targets):copy(linear_targets):abs():add(-0.5 * self.gamma):mul(self.gamma):sum()
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--self.outlier_rate = linear_targets:nElement() / input:nElement()
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self.output = (square_loss + linear_loss) / input:nElement()
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return self.output
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end
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function ClippedWeightedHuberCriterion:updateGradInput(input, target)
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local norm = 1.0 / input:nElement()
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self.gradInput:resizeAs(self.diff):copy(self.diff):mul(norm)
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local outlier = torch.ge(self.diff_abs, self.gamma)
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self.gradInput[outlier] = torch.sign(self.diff[outlier]) * self.gamma * norm
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return self.gradInput
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end
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