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local ffi = require 'ffi'
local RReLU, parent = torch.class('nn.RReLU', 'nn.Module')
function RReLU:__init(l, u, ip)
parent.__init(self)
self.lower = l or 1/8
self.upper = u or 1/3
assert(self.lower <= self.upper and self.lower >= 0 and self.upper >= 0)
self.noise = torch.Tensor()
self.train = true
self.inplace = ip or false
end
function RReLU:updateOutput(input)
local gen = ffi.typeof('THGenerator**')(torch._gen)[0]
input.THNN.RReLU_updateOutput(
input:cdata(),
self.output:cdata(),
self.noise:cdata(),
self.lower,
self.upper,
self.train,
self.inplace,
gen
)
return self.output
end
function RReLU:updateGradInput(input, gradOutput)
input.THNN.RReLU_updateGradInput(
input:cdata(),
gradOutput:cdata(),
self.gradInput:cdata(),
self.noise:cdata(),
self.lower,
self.upper,
self.train,
self.inplace
)
return self.gradInput
end
function RReLU:__tostring__()
return string.format('%s (l:%f, u:%f)', torch.type(self), self.lower, self.upper)
end
function RReLU:clearState()
if self.noise then self.noise:set() end
return parent.clearState(self)
end
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