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Copy pathDropout.lua
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70 lines (63 loc) · 1.72 KB
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local Dropout, Parent = torch.class('nn.Dropout', 'nn.Module')
function Dropout:__init(p,v1,inplace,stochasticInference)
Parent.__init(self)
self.p = p or 0.5
self.train = true
self.inplace = inplace
self.stochastic_inference = stochasticInference or false
-- version 2 scales output during training instead of evaluation
self.v2 = not v1
if self.p >= 1 or self.p < 0 then
error('<Dropout> illegal percentage, must be 0 <= p < 1')
end
self.noise = torch.Tensor()
end
function Dropout:updateOutput(input)
if self.inplace then
self.output:set(input)
else
self.output:resizeAs(input):copy(input)
end
if self.p > 0 then
if self.train or self.stochastic_inference then
self.noise:resizeAs(input)
self.noise:bernoulli(1-self.p)
if self.v2 then
self.noise:div(1-self.p)
end
self.output:cmul(self.noise)
elseif not self.v2 then
self.output:mul(1-self.p)
end
end
return self.output
end
function Dropout:updateGradInput(input, gradOutput)
if self.inplace then
self.gradInput:set(gradOutput)
else
self.gradInput:resizeAs(gradOutput):copy(gradOutput)
end
if self.train then
if self.p > 0 then
self.gradInput:cmul(self.noise) -- simply mask the gradients with the noise vector
end
else
if not self.v2 and self.p > 0 then
self.gradInput:mul(1-self.p)
end
end
return self.gradInput
end
function Dropout:setp(p)
self.p = p
end
function Dropout:__tostring__()
return string.format('%s(%f)', torch.type(self), self.p)
end
function Dropout:clearState()
if self.noise then
self.noise:set()
end
return Parent.clearState(self)
end