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