1 files changed, 57 insertions, 0 deletions
diff --git a/contrib/lua-torch/nn/CReLU.lua b/contrib/lua-torch/nn/CReLU.lua
new file mode 100644
index 000000000..8da6e7974
--- /dev/null
+++ b/contrib/lua-torch/nn/CReLU.lua
@@ -0,0 +1,57 @@
+local CReLU, parent = torch.class('nn.CReLU', 'nn.Sequential')
+
+-- Implements the CReLU activation function as described by
+-- W. Shang et al. in "Understanding and Improving Convolutional Neural Networks
+-- via Concatenated Rectified Linear Units"
+function CReLU:__init(nInputDims, inplace)
+   parent.__init(self)
+   self.nInputDims = nInputDims
+   self.inplace = inplace or false
+
+   local concatTable = nn.ConcatTable()
+   concatTable:add(nn.Identity())
+   concatTable:add(nn.MulConstant(-1))
+   self:add(concatTable)
+   self:add(nn.JoinTable(2))
+   self:add(nn.ReLU(self.inplace))
+end
+
+function CReLU:updateOutput(input)
+   local input_
+   local batched = input:dim() == (self.nInputDims + 1)
+   if not batched then
+      input_ = input:view(1, -1)
+  else
+      input_ = input:view(input:size(1), -1)
+  end
+   parent.updateOutput(self, input_)
+   local osize = input:size()
+   if not batched then
+      osize[1] = osize[1] * 2
+   else
+      osize[2] = osize[2] * 2
+   end
+   self.output:resize(osize)
+   return self.output
+end
+
+function CReLU:backward(input, gradOutput)
+   return self:updateGradInput(input, gradOutput)
+end
+
+function CReLU:updateGradInput(input, gradOutput)
+   local batched = input:dim() == (self.nInputDims + 1)
+   if not batched then
+      parent.updateGradInput(self, input:view(1, -1), gradOutput:view(1, -1))
+   else
+      parent.updateGradInput(self, input:view(input:size(1), -1),
+                                   gradOutput:view(input:size(1), -1))
+   end
+
+   self.gradInput:resizeAs(input)
+   return self.gradInput
+end
+
+function CReLU:__tostring__()
+   return "CReLU()"
+end