43 lines
1.3 KiB
Lua
43 lines
1.3 KiB
Lua
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local ShakeShakeTable, parent = torch.class('w2nn.ShakeShakeTable','nn.Module')
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function ShakeShakeTable:__init()
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parent.__init(self)
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self.alpha = torch.Tensor()
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self.beta = torch.Tensor()
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self.first = torch.Tensor()
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self.second = torch.Tensor()
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self.train = true
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end
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function ShakeShakeTable:updateOutput(input)
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local batch_size = input[1]:size(1)
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if self.train then
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self.alpha:resize(batch_size):uniform()
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self.beta:resize(batch_size):uniform()
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self.second:resizeAs(input[1]):copy(input[2])
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for i = 1, batch_size do
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self.second[i]:mul(self.alpha[i])
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end
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self.output:resizeAs(input[1]):copy(input[1])
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for i = 1, batch_size do
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self.output[i]:mul(1.0 - self.alpha[i])
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end
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self.output:add(self.second):mul(2)
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else
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self.output:resizeAs(input[1]):copy(input[1]):add(input[2])
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end
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return self.output
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end
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function ShakeShakeTable:updateGradInput(input, gradOutput)
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local batch_size = input[1]:size(1)
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self.first:resizeAs(gradOutput):copy(gradOutput)
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for i = 1, batch_size do
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self.first[i]:mul(self.beta[i])
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end
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self.second:resizeAs(gradOutput):copy(gradOutput)
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for i = 1, batch_size do
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self.second[i]:mul(1.0 - self.beta[i])
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end
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self.gradOutput = {self.first, self.second}
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return self.gradOutput
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end
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