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local SparseLinear, parent = torch.class('nn.SparseLinear', 'nn.Module')
function SparseLinear:__init(inputSize, outputSize)
parent.__init(self)
self.weightDecay = 0
self.weight = torch.Tensor(outputSize, inputSize)
self.bias = torch.Tensor(outputSize)
self.gradWeight = torch.Tensor(outputSize, inputSize)
self.gradBias = torch.Tensor(outputSize)
self.lastInput = torch.Tensor()
-- state
self.gradInput:resize(inputSize)
self.output:resize(outputSize)
self:reset()
end
function SparseLinear:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.weight:size(1))
end
-- we do this so the initialization is exactly
-- the same than in previous torch versions
for i=1,self.weight:size(1) do
self.weight:select(1, i):apply(function()
return torch.uniform(-stdv, stdv)
end)
self.bias[i] = torch.uniform(-stdv, stdv) * 0.000001
end
end
function SparseLinear:updateOutput(input)
return input.nn.SparseLinear_updateOutput(self, input)
end
function SparseLinear:accGradParameters(input, gradOutput, scale)
return input.nn.SparseLinear_accGradParameters(self, input, gradOutput, scale)
end
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