BIG UPDATE.

1 files changed, 0 insertions, 65 deletions

diff --git a/SpatialLinear.lua b/SpatialLinear.lua
deleted file mode 100644
index 92767f4..0000000
--- a/SpatialLinear.lua
+++ /dev/null
@@ -1,65 +0,0 @@
-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 torch.uniform(-stdv, stdv)
-                                     end)
-      self.bias[i] = torch.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:updateOutput(input)
-   self.output:resize(self.fanout, input:size(2), input:size(3))
-   input.nn.SpatialLinear_updateOutput(self, input)
-   return self.output
-end
-
-function SpatialLinear:updateGradInput(input, gradOutput)
-   self.gradInput:resize(self.fanin, input:size(2), input:size(3))
-   input.nn.SpatialLinear_updateGradInput(self, input, gradOutput)
-   return self.gradInput
-end