initial revamp of torch7 tree

1 files changed, 42 insertions, 0 deletions

diff --git a/SparseLinear.lua b/SparseLinear.lua
new file mode 100644
index 0000000..ec8845e
--- /dev/null
+++ b/SparseLinear.lua
@@ -0,0 +1,42 @@
+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