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local Linear, parent = torch.class('nn.Linear', 'nn.Module')
function Linear:__init(inputSize, outputSize, bias)
parent.__init(self)
local bias = ((bias == nil) and true) or bias
self.weight = torch.Tensor(outputSize, inputSize)
self.gradWeight = torch.Tensor(outputSize, inputSize)
if bias then
self.bias = torch.Tensor(outputSize)
self.gradBias = torch.Tensor(outputSize)
end
self:reset()
end
function Linear:noBias()
self.bias = nil
self.gradBias = nil
return self
end
function Linear: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)
end
if self.bias then
for i=1,self.bias:nElement() do
self.bias[i] = torch.uniform(-stdv, stdv)
end
end
else
self.weight:uniform(-stdv, stdv)
if self.bias then self.bias:uniform(-stdv, stdv) end
end
return self
end
function Linear:updateAddBuffer(input)
local nframe = input:size(1)
self.addBuffer = self.addBuffer or input.new()
if self.addBuffer:nElement() ~= nframe then
self.addBuffer:resize(nframe):fill(1)
end
end
function Linear:updateOutput(input)
if input:dim() == 1 then
self.output:resize(self.weight:size(1))
if self.bias then self.output:copy(self.bias) else self.output:zero() end
self.output:addmv(1, self.weight, input)
elseif input:dim() == 2 then
local nframe = input:size(1)
local nElement = self.output:nElement()
self.output:resize(nframe, self.weight:size(1))
if self.output:nElement() ~= nElement then
self.output:zero()
end
self:updateAddBuffer(input)
self.output:addmm(0, self.output, 1, input, self.weight:t())
if self.bias then self.output:addr(1, self.addBuffer, self.bias) end
else
error('input must be vector or matrix')
end
return self.output
end
function Linear:updateGradInput(input, gradOutput)
if self.gradInput then
local nElement = self.gradInput:nElement()
self.gradInput:resizeAs(input)
if self.gradInput:nElement() ~= nElement then
self.gradInput:zero()
end
if input:dim() == 1 then
self.gradInput:addmv(0, 1, self.weight:t(), gradOutput)
elseif input:dim() == 2 then
self.gradInput:addmm(0, 1, gradOutput, self.weight)
end
return self.gradInput
end
end
function Linear:accGradParameters(input, gradOutput, scale)
scale = scale or 1
if input:dim() == 1 then
self.gradWeight:addr(scale, gradOutput, input)
if self.bias then self.gradBias:add(scale, gradOutput) end
elseif input:dim() == 2 then
self.gradWeight:addmm(scale, gradOutput:t(), input)
if self.bias then
-- update the size of addBuffer if the input is not the same size as the one we had in last updateGradInput
self:updateAddBuffer(input)
self.gradBias:addmv(scale, gradOutput:t(), self.addBuffer)
end
end
end
function Linear:sharedAccUpdateGradParameters(input, gradOutput, lr)
-- we do not need to accumulate parameters when sharing:
self:defaultAccUpdateGradParameters(input, gradOutput, lr)
end
function Linear:clearState()
if self.addBuffer then self.addBuffer:set() end
return parent.clearState(self)
end
function Linear:__tostring__()
return torch.type(self) ..
string.format('(%d -> %d)', self.weight:size(2), self.weight:size(1)) ..
(self.bias == nil and ' without bias' or '')
end
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