textSimilarityConvNet/models.lua at master · hohoCode/textSimilarityConvNet · GitHub

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function createModel(mdl, vocsize, Dsize, nout, KKw)

-- define model to train

local network = nn.Sequential()

local featext = nn.Sequential()

local classifier = nn.Sequential()

local conCon1 = nn.Sequential()

local conCon2 = nn.Sequential()

local conCon3 = nn.Sequential()

local conCon4 = nn.Sequential()

local parallelConcat1 = nn.Concat(1)

local parallelConcat2 = nn.Concat(1)

local parallelConcat3 = nn.Concat(1)

local parallelConcat4 = nn.Concat(1)

local parallelConcat5 = nn.Concat(1)

local D = Dsize

local kW = KKw

local dW = 1

local noExtra = false

local nhid1 = 250

local nhid2 = 250

local NumFilter = D

local pR = 2

local layers=1

if mdl == 'deepQueryRankingNgramSimilarityOnevsGroupMaxMinMeanLinearExDGpPoinPercpt' then

dofile "PaddingReshape.lua"

deepQuery=nn.Sequential()

D = Dsize

local incep1max = nn.Sequential()

incep1max:add(nn.TemporalConvolution(D,NumFilter,1,dw))

if pR == 1 then

incep1max:add(nn.PReLU())

else

incep1max:add(nn.Tanh())

end

incep1max:add(nn.Max(1))

incep1max:add(nn.Reshape(NumFilter,1))

local incep2max = nn.Sequential()

incep2max:add(nn.Max(1))

incep2max:add(nn.Reshape(NumFilter,1))

local combineDepth = nn.Concat(2)

combineDepth:add(incep1max)

combineDepth:add(incep2max)

local ngram = kW

for cc = 2, ngram do

local incepMax = nn.Sequential()

if not noExtra then

incepMax:add(nn.TemporalConvolution(D,D,1,dw)) --set

if pR == 1 then

incepMax:add(nn.PReLU())

else

incepMax:add(nn.Tanh())

end

incepMax:add(nn.TemporalConvolution(D,NumFilter,cc,dw))

if pR == 1 then

incepMax:add(nn.PReLU())

else

incepMax:add(nn.Tanh())

end

incepMax:add(nn.Max(1))

incepMax:add(nn.Reshape(NumFilter,1))

combineDepth:add(incepMax)

end

local incep1min = nn.Sequential()

incep1min:add(nn.TemporalConvolution(D,NumFilter,1,dw))

if pR == 1 then

incep1min:add(nn.PReLU())

else

incep1min:add(nn.Tanh())

end

incep1min:add(nn.Min(1))

incep1min:add(nn.Reshape(NumFilter,1))

local incep2min = nn.Sequential()

incep2min:add(nn.Min(1))

incep2min:add(nn.Reshape(NumFilter,1))

combineDepth:add(incep1min)

combineDepth:add(incep2min)

for cc = 2, ngram do

local incepMin = nn.Sequential()

if not noExtra then

incepMin:add(nn.TemporalConvolution(D,D,1,dw)) --set

if pR == 1 then

incepMin:add(nn.PReLU())

else

incepMin:add(nn.Tanh())

end

incepMin:add(nn.TemporalConvolution(D,NumFilter,cc,dw))

if pR == 1 then

incepMin:add(nn.PReLU())

else

incepMin:add(nn.Tanh())

end

incepMin:add(nn.Min(1))

incepMin:add(nn.Reshape(NumFilter,1))

combineDepth:add(incepMin)

end

local incep1mean = nn.Sequential()

incep1mean:add(nn.TemporalConvolution(D,NumFilter,1,dw))

if pR == 1 then

incep1mean:add(nn.PReLU())

else

incep1mean:add(nn.Tanh())

end

incep1mean:add(nn.Mean(1))

incep1mean:add(nn.Reshape(NumFilter,1))

local incep2mean = nn.Sequential()

incep2mean:add(nn.Mean(1))

incep2mean:add(nn.Reshape(NumFilter,1))

combineDepth:add(incep1mean)

combineDepth:add(incep2mean)

for cc = 2, ngram do

local incepMean = nn.Sequential()

if not noExtra then

incepMean:add(nn.TemporalConvolution(D,D,1,dw)) --set

if pR == 1 then

incepMean:add(nn.PReLU())

else

incepMean:add(nn.Tanh())

end

incepMean:add(nn.TemporalConvolution(D,NumFilter,cc,dw))

if pR == 1 then

incepMean:add(nn.PReLU())

else

incepMean:add(nn.Tanh())

end

incepMean:add(nn.Mean(1))

incepMean:add(nn.Reshape(NumFilter,1))

combineDepth:add(incepMean)

end

local conceptFNum = 20

for cc = 1, ngram do

local perConcept = nn.Sequential()

perConcept:add(nn.PaddingReshape(2,2)) --set

perConcept:add(nn.SpatialConvolutionMM(1,conceptFNum,1,cc)) --set

perConcept:add(nn.Max(2)) --set

if pR == 1 then

perConcept:add(nn.PReLU())

else

perConcept:add(nn.Tanh())

end

perConcept:add(nn.Transpose({1,2}))

combineDepth:add(perConcept)

end

for cc = 1, ngram do

local perConcept = nn.Sequential()

perConcept:add(nn.PaddingReshape(2,2)) --set

perConcept:add(nn.SpatialConvolutionMM(1,conceptFNum,1,cc)) --set

perConcept:add(nn.Min(2)) --set

if pR == 1 then

perConcept:add(nn.PReLU())

else

perConcept:add(nn.Tanh())

end

perConcept:add(nn.Transpose({1,2}))

combineDepth:add(perConcept)

end

featext:add(combineDepth)

local items = (ngram+1)*3

local separator = items+2*conceptFNum*ngram

local sepModel = 0

if sepModel == 1 then

modelQ= featext:clone()

else

modelQ= featext:clone('weight','bias','gradWeight','gradBias')

end

paraQuery=nn.ParallelTable()

paraQuery:add(modelQ)

paraQuery:add(featext)

deepQuery:add(paraQuery)

deepQuery:add(nn.JoinTable(2))

d=nn.Concat(1)

for i=1,items do

if i <= items/3 then

for j=1,items/3 do

local connection = nn.Sequential()

local minus=nn.Concat(2)

local c1=nn.Sequential()

local c2=nn.Sequential()

c1:add(nn.Select(2,i)) -- == D, not D*1

c1:add(nn.Reshape(NumFilter,1)) --D*1 here

c2:add(nn.Select(2,separator+j))

c2:add(nn.Reshape(NumFilter,1))

minus:add(c1)

minus:add(c2)

connection:add(minus) -- D*2

local similarityC=nn.Concat(1) -- multi similarity criteria

local s1=nn.Sequential()

s1:add(nn.SplitTable(2))

s1:add(nn.PairwiseDistance(2)) -- scalar

local s2=nn.Sequential()

if 1 < 3 then

s2:add(nn.SplitTable(2))

else

s2:add(nn.Transpose({1,2}))

s2:add(nn.SoftMax())

s2:add(nn.SplitTable(1))

end

s2:add(nn.CsDis()) -- scalar

local s3=nn.Sequential()

s3:add(nn.SplitTable(2))

s3:add(nn.CSubTable()) -- linear

s3:add(nn.Abs()) -- linear

similarityC:add(s1)

similarityC:add(s2)

similarityC:add(s3)

connection:add(similarityC) -- scalar

d:add(connection)

end

elseif i <= 2*items/3 then

for j=1+items/3, 2*items/3 do

local connection = nn.Sequential()

local minus=nn.Concat(2)

local c1=nn.Sequential()

local c2=nn.Sequential()

c1:add(nn.Select(2,i)) -- == NumFilter, not NumFilter*1

c1:add(nn.Reshape(NumFilter,1)) --NumFilter*1 here

c2:add(nn.Select(2,separator+j))

c2:add(nn.Reshape(NumFilter,1))

minus:add(c1)

minus:add(c2)

connection:add(minus) -- D*2

local similarityC=nn.Concat(1) -- multi similarity criteria

local s1=nn.Sequential()

s1:add(nn.SplitTable(2))

s1:add(nn.PairwiseDistance(2)) -- scalar

local s2=nn.Sequential()

if 1 < 3 then

s2:add(nn.SplitTable(2))

else

s2:add(nn.Transpose({1,2})) -- D*2 -> 2*D

s2:add(nn.SoftMax())

s2:add(nn.SplitTable(1))

end

s2:add(nn.CsDis()) -- scalar

local s3=nn.Sequential()

s3:add(nn.SplitTable(2))

s3:add(nn.CSubTable()) -- linear

s3:add(nn.Abs()) -- linear

similarityC:add(s1)

similarityC:add(s2)

similarityC:add(s3)

connection:add(similarityC) -- scalar

d:add(connection)

end

else

for j=1+2*items/3, items do

local connection = nn.Sequential()

local minus=nn.Concat(2)

local c1=nn.Sequential()

local c2=nn.Sequential()

c1:add(nn.Select(2,i)) -- == D, not D*1

c1:add(nn.Reshape(NumFilter,1)) --D*1 here

c2:add(nn.Select(2,separator+j))

c2:add(nn.Reshape(NumFilter,1))

minus:add(c1)

minus:add(c2)

connection:add(minus) -- D*2

local similarityC=nn.Concat(1) -- multi similarity criteria

local s1=nn.Sequential()

s1:add(nn.SplitTable(2))

s1:add(nn.PairwiseDistance(2)) -- scalar

local s2=nn.Sequential()

if 1 < 3 then

s2:add(nn.SplitTable(2))

else

s2:add(nn.Transpose({1,2})) -- D*2 -> 2*D

s2:add(nn.SoftMax())

s2:add(nn.SplitTable(1))

end

s2:add(nn.CsDis()) -- scalar

local s3=nn.Sequential()

s3:add(nn.SplitTable(2))

s3:add(nn.CSubTable()) -- linear

s3:add(nn.Abs()) -- linear

similarityC:add(s1)

similarityC:add(s2)

similarityC:add(s3)

connection:add(similarityC) -- scalar

d:add(connection)

end

for i=1,NumFilter do

for j=1,3 do

local connection = nn.Sequential()

connection:add(nn.Select(1,i)) -- == 2items

connection:add(nn.Reshape(2*separator,1)) --2items*1 here

local minus=nn.Concat(2)

local c1=nn.Sequential()

local c2=nn.Sequential()

if j == 1 then

c1:add(nn.Narrow(1,1,ngram+1)) -- first half (items/3)*1

c2:add(nn.Narrow(1,separator+1,ngram+1)) -- first half (items/3)*1

elseif j == 2 then

c1:add(nn.Narrow(1,ngram+2,ngram+1)) --

c2:add(nn.Narrow(1,separator+ngram+2,ngram+1))

else

c1:add(nn.Narrow(1,2*(ngram+1)+1,ngram+1))

c2:add(nn.Narrow(1,separator+2*(ngram+1)+1,ngram+1)) --each is ngram+1 portion (max or min or mean)

end

minus:add(c1)

minus:add(c2)

connection:add(minus) -- (items/3)*2

local similarityC=nn.Concat(1)

local s1=nn.Sequential()

s1:add(nn.SplitTable(2))

s1:add(nn.PairwiseDistance(2)) -- scalar

local s2=nn.Sequential()

if 1 >= 2 then

s2:add(nn.Transpose({1,2})) -- (items/3)*2 -> 2*(items/3)

s2:add(nn.SoftMax()) --for softmax have to do transpose from (item/3)*2 -> 2*(item/3)

s2:add(nn.SplitTable(1)) --softmax only works on row

else

s2:add(nn.SplitTable(2)) --(items/3)*2

end

s2:add(nn.CsDis()) -- scalar

similarityC:add(s1)

similarityC:add(s2)

connection:add(similarityC) -- scalar

d:add(connection)

end

for i=items+1,separator do

local connection = nn.Sequential()

local minus=nn.Concat(2)

local c1=nn.Sequential()

local c2=nn.Sequential()

c1:add(nn.Select(2,i)) -- == D, not D*1

c1:add(nn.Reshape(NumFilter,1)) --D*1 here

c2:add(nn.Select(2,separator+i))

c2:add(nn.Reshape(NumFilter,1))

minus:add(c1)

minus:add(c2)

connection:add(minus) -- D*2

local similarityC=nn.Concat(1)

local s1=nn.Sequential()

s1:add(nn.SplitTable(2))

s1:add(nn.PairwiseDistance(2)) -- scalar

local s2=nn.Sequential()

if 1 < 3 then

s2:add(nn.SplitTable(2))

else

s2:add(nn.Transpose({1,2}))

s2:add(nn.SoftMax())

s2:add(nn.SplitTable(1))

end

s2:add(nn.CsDis()) -- scalar

local s3=nn.Sequential()

s3:add(nn.SplitTable(2))

s3:add(nn.CSubTable()) -- linear

s3:add(nn.Abs()) -- linear

similarityC:add(s1)

similarityC:add(s2)

similarityC:add(s3)

connection:add(similarityC) -- scalar

d:add(connection)

end

deepQuery:add(d)

return deepQuery

end

textSimilarityConvNet/models.lua at master · hohoCode/textSimilarityConvNet · GitHub

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