Initial commit

LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2015 Alexander Jung
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+

LeakyReLU.lua

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+--[[ from https://github.com/nagadomi/waifu2x/blob/master/lib/LeakyReLU.lua ]]--
+if nn.LeakyReLU then
+   return
+end
+local LeakyReLU, parent = torch.class('nn.LeakyReLU','nn.Module')
+
+function LeakyReLU:__init(negative_scale)
+   parent.__init(self)
+   self.negative_scale = negative_scale or 0.333
+   self.negative = torch.Tensor()
+end
+
+function LeakyReLU:updateOutput(input)
+   self.output:resizeAs(input):copy(input):abs():add(input):div(2)
+   self.negative:resizeAs(input):copy(input):abs():add(-1.0, input):mul(-0.5*self.negative_scale)
+   self.output:add(self.negative)
+
+   return self.output
+end
+
+function LeakyReLU:updateGradInput(input, gradOutput)
+   self.gradInput:resizeAs(gradOutput)
+   -- filter positive
+   self.negative:sign():add(1)
+   torch.cmul(self.gradInput, gradOutput, self.negative)
+   -- filter negative
+   self.negative:add(-1):mul(-1 * self.negative_scale):cmul(gradOutput)
+   self.gradInput:add(self.negative)
+
+   return self.gradInput
+end