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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):zero()
self.bias = torch.Tensor(outputSize):zero()
self.gradWeight = torch.Tensor(outputSize, inputSize):zero()
self.gradBias = torch.Tensor(outputSize):zero()
self.lastInput = nil
if torch.getnumthreads() > 1 and outputSize >= 128 then
self.shardBuffer = torch.Tensor(outputSize, torch.getnumthreads())
end
-- 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(2))
end
if nn.oldSeed then
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
else
self.weight:uniform(-stdv, stdv)
self.bias:uniform(-stdv, stdv):mul(0.000001)
end
end
function SparseLinear:updateOutput(input)
return input.nn.SparseLinear_updateOutput(self, input)
end
function SparseLinear:accGradParameters(input, gradOutput, scale)
if not self.lastInput then
self.lastInput = input:clone()
else
self.lastInput:resizeAs(input):copy(input)
end
return input.nn.SparseLinear_accGradParameters(self, input, gradOutput, scale)
end
function SparseLinear:updateGradInput(input, gradOutput)
if self.gradInput then
input.nn.SparseLinear_updateGradInput(self, input, gradOutput)
return self.gradInput
end
end
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