func (s *LogSoftmaxLayer) ApplyR(v autofunc.RVector, in autofunc.RResult) autofunc.RResult {
return autofunc.PoolR(in, func(in autofunc.RResult) autofunc.RResult {
// See comment in Apply() for details on how this works.
maxIdx := maxVecIdx(in.Output())
maxValue := autofunc.SliceR(in, maxIdx, maxIdx+1)
exponents := autofunc.AddFirstR(in, autofunc.ScaleR(maxValue, -1))
expSum := autofunc.SumAllR(autofunc.Exp{}.ApplyR(v, exponents))
expLog := autofunc.Log{}.ApplyR(v, expSum)
denomLog := autofunc.AddR(expLog, maxValue)
return autofunc.AddFirstR(in, autofunc.ScaleR(denomLog, -1))
})
}
func (l *lstmGate) BatchR(rv autofunc.RVector, in autofunc.RResult, n int) autofunc.RResult {
if l.Peephole == nil {
return l.Activation.ApplyR(rv, l.Dense.BatchR(rv, in, n))
}
return autofunc.PoolR(in, func(in autofunc.RResult) autofunc.RResult {
vecSize := len(in.Output()) / n
var weightedInputs []autofunc.RResult
var peepholed []autofunc.RResult
peephole := autofunc.NewRVariable(l.Peephole, rv)
for i := 0; i < n; i++ {
start := vecSize * i
weightedEnd := start + vecSize - len(l.Peephole.Vector)
weightedInputs = append(weightedInputs, autofunc.SliceR(in, start, weightedEnd))
peepholeMe := autofunc.SliceR(in, weightedEnd, (i+1)*vecSize)
peepholed = append(peepholed, autofunc.MulR(peephole, peepholeMe))
}
weighted := l.Dense.BatchR(rv, autofunc.ConcatR(weightedInputs...), n)
joinedPeep := autofunc.ConcatR(peepholed...)
return l.Activation.ApplyR(rv, autofunc.AddR(joinedPeep, weighted))
})
}
