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local Euclidean, parent = torch.class('nn.Euclidean', 'nn.Module')
function Euclidean:__init(inputSize,outputSize)
parent.__init(self)
self.weight = torch.Tensor(inputSize,outputSize)
self.gradWeight = torch.Tensor(inputSize,outputSize)
-- state
self.gradInput:resize(inputSize)
self.output:resize(outputSize)
self.temp = torch.Tensor(inputSize)
self:reset()
end
function Euclidean:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.weight:size(1))
end
for i=1,self.weight:size(2) do
self.weight:select(2, i):apply(function()
return torch.uniform(-stdv, stdv)
end)
end
end
function Euclidean:updateOutput(input)
self.output:zero()
for o = 1,self.weight:size(2) do
self.output[o] = input:dist(self.weight:select(2,o))
end
return self.output
end
function Euclidean:updateGradInput(input, gradOutput)
self:updateOutput(input)
if self.gradInput then
self.gradInput:zero()
for o = 1,self.weight:size(2) do
if self.output[o] ~= 0 then
self.temp:copy(input):add(-1,self.weight:select(2,o))
self.temp:mul(gradOutput[o]/self.output[o])
self.gradInput:add(self.temp)
end
end
return self.gradInput
end
end
function Euclidean:accGradParameters(input, gradOutput, scale)
self:updateOutput(input)
scale = scale or 1
for o = 1,self.weight:size(2) do
if self.output[o] ~= 0 then
self.temp:copy(self.weight:select(2,o)):add(-1,input)
self.temp:mul(gradOutput[o]/self.output[o])
self.gradWeight:select(2,o):add(self.temp)
end
end
end
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