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LinearNB.lua
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LinearNB.lua
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-- Copyright (c) 2015-present, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the BSD-style license found in the
-- LICENSE file in the root directory of this source tree. An additional grant
-- of patent rights can be found in the PATENTS file in the same directory.
local LinearNB, parent = torch.class('nn.LinearNB', 'nn.Module')
function LinearNB:__init(inputSize, outputSize)
parent.__init(self)
self.outputSize = outputSize
self.inputSize = inputSize
self.weight = torch.Tensor(outputSize, inputSize)
self.gradWeight = torch.Tensor(outputSize, inputSize)
self:reset()
end
function LinearNB:reset(stdv)
if stdv then
stdv = stdv * math.sqrt(3)
else
stdv = 1./math.sqrt(self.weight:size(2))
end
if nn.oldSeed then
for i=1,self.weight:size(1) do
self.weight:select(1, i):apply(function()
return torch.uniform(-stdv, stdv)
end)
end
else
self.weight:uniform(-stdv, stdv)
end
end
function LinearNB:updateOutput(input)
if input:dim() == 1 then
self.output:resize(self.outputSize)
self.output:zero()
self.output:addmv(1, self.weight, input)
elseif input:dim() == 2 then
local nframe = input:size(1)
local nunit = self.outputSize
self.output:resize(nframe, nunit):zero()
if not self.addBuffer or self.addBuffer:size(1) ~= nframe then
self.addBuffer = input.new(nframe):fill(1)
end
if nunit == 1 then
-- Special case to fix output size of 1 bug:
self.output:select(2,1):addmv(1, input, self.weight:select(1,1))
else
self.output:addmm(1, input, self.weight:t())
end
else
error('input must be vector or matrix')
end
return self.output
end
function LinearNB:updateGradInput(input, gradOutput)
if self.gradInput then
local nElement = self.gradInput:nElement()
self.gradInput:resizeAs(input)
if self.gradInput:nElement() ~= nElement then
self.gradInput:zero()
end
if input:dim() == 1 then
self.gradInput:addmv(0, 1, self.weight:t(), gradOutput)
elseif input:dim() == 2 then
self.gradInput:addmm(0, 1, gradOutput, self.weight)
end
return self.gradInput
end
end
function LinearNB:accGradParameters(input, gradOutput, scale)
scale = scale or 1
if input:dim() == 1 then
self.gradWeight:addr(scale, gradOutput, input)
elseif input:dim() == 2 then
local nunit = self.outputSize
if nunit == 1 then
-- Special case to fix output size of 1 bug:
self.gradWeight:select(1,1):addmv(scale,
input:t(), gradOutput:select(2,1))
else
self.gradWeight:addmm(scale, gradOutput:t(), input)
end
end
end
-- we do not need to accumulate parameters when sharing
LinearNB.sharedAccUpdateGradParameters = LinearNB.accUpdateGradParameters