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local SoftMaxForest, parent = torch.class("nn.SoftMaxForest", "nn.Container")
function SoftMaxForest:__init(inputSize, trees, rootIds, gaterSize, gaterAct, accUpdate)
local gaterAct = gaterAct or nn.Tanh()
local gaterSize = gaterSize or {}
-- experts
self.experts = nn.ConcatTable()
self.smts = {}
for i,tree in ipairs(trees) do
local smt = nn.SoftMaxTree(inputSize, tree, rootIds[i], accUpdate)
table.insert(self._smts, smt)
self.experts:add(smt)
end
-- gater
self.gater = nn.Sequential()
self.gater:add(nn.SelectTable(1)) -- ignore targets
for i,hiddenSize in ipairs(gaterSize) do
self.gater:add(nn.Linear(inputSize, hiddenSize))
self.gater:add(gaterAct:clone())
inputSize = hiddenSize
end
self.gater:add(nn.Linear(inputSize, self.experts:size()))
self.gater:add(nn.SoftMax())
-- mixture
self.trunk = nn.ConcatTable()
self.trunk:add(self._gater)
self.trunk:add(self._experts)
self.mixture = nn.MixtureTable()
self.module = nn.Sequential()
self.module:add(self.trunk)
self.module:add(self.mixture)
parent.__init(self)
self.modules[1] = self.module
end
function SoftMaxForest:updateOutput(input)
self.output = self.module:updateOutput(input)
return self.output
end
function SoftMaxForest:updateGradInput(input, gradOutput)
self.gradInput = self.module:updateGradInput(input, gradOutput)
return self.gradInput
end
function SoftMaxForest:accGradParameters(input, gradOutput, scale)
self.module:accGradParameters(input, gradOutput, scale)
end
function SoftMaxForest:accUpdateGradParameters(input, gradOutput, lr)
self.module:accUpdateGradParameters(input, gradOutput, lr)
end
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