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local SpatialLinear, parent = torch.class('nn.SpatialLinear', 'nn.Module')
function SpatialLinear:__init(fanin, fanout)
parent.__init(self)
self.fanin = fanin or 1
self.fanout = fanout or 1
self.weightDecay = 0
self.weight = torch.Tensor(self.fanout, self.fanin)
self.bias = torch.Tensor(self.fanout)
self.gradWeight = torch.Tensor(self.fanout, self.fanin)
self.gradBias = torch.Tensor(self.fanout)
self.output = torch.Tensor(fanout,1,1)
self.gradInput = torch.Tensor(fanin,1,1)
self:reset()
end
function SpatialLinear: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(1) do
self.weight:select(1, i):apply(function()
return random.uniform(-stdv, stdv)
end)
self.bias[i] = random.uniform(-stdv, stdv)
end
end
function SpatialLinear:zeroGradParameters(momentum)
if momentum then
self.gradWeight:mul(momentum)
self.gradBias:mul(momentum)
else
self.gradWeight:zero()
self.gradBias:zero()
end
end
function SpatialLinear:updateParameters(learningRate)
self.weight:add(-learningRate, self.gradWeight)
self.bias:add(-learningRate, self.gradBias)
end
function SpatialLinear:decayParameters(decay)
self.weight:add(-decay, self.weight)
self.bias:add(-decay, self.bias)
end
function SpatialLinear:forward(input)
self.output:resize(self.fanout, input:size(2), input:size(3))
input.nn.SpatialLinear_forward(self, input)
return self.output
end
function SpatialLinear:backward(input, gradOutput)
self.gradInput:resize(self.fanin, input:size(2), input:size(3))
input.nn.SpatialLinear_backward(self, input, gradOutput)
return self.gradInput
end
function SpatialLinear:write(file)
parent.write(self, file)
file:writeInt(self.fanin)
file:writeInt(self.fanout)
file:writeDouble(self.weightDecay)
file:writeObject(self.weight)
file:writeObject(self.bias)
file:writeObject(self.gradWeight)
file:writeObject(self.gradBias)
end
function SpatialLinear:read(file)
parent.read(self, file)
self.fanin = file:readInt()
self.fanout = file:readInt()
self.weightDecay = file:readDouble()
self.weight = file:readObject()
self.bias = file:readObject()
self.gradWeight = file:readObject()
self.gradBias = file:readObject()
end
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