func (l *lstmGate) Batch(in autofunc.Result, n int) autofunc.Result {
if l.Peephole == nil {
return l.Activation.Apply(l.Dense.Batch(in, n))
}
return autofunc.Pool(in, func(in autofunc.Result) autofunc.Result {
vecSize := len(in.Output()) / n
var weightedInputs []autofunc.Result
var peepholed []autofunc.Result
for i := 0; i < n; i++ {
start := vecSize * i
weightedEnd := start + vecSize - len(l.Peephole.Vector)
weightedInputs = append(weightedInputs, autofunc.Slice(in, start, weightedEnd))
peepholeMe := autofunc.Slice(in, weightedEnd, (i+1)*vecSize)
peepholed = append(peepholed, autofunc.Mul(l.Peephole, peepholeMe))
}
weighted := l.Dense.Batch(autofunc.Concat(weightedInputs...), n)
return l.Activation.Apply(autofunc.Add(autofunc.Concat(peepholed...), weighted))
})
}
func (s *LogSoftmaxLayer) Apply(in autofunc.Result) autofunc.Result {
return autofunc.Pool(in, func(in autofunc.Result) autofunc.Result {
// Compute the log of the sum of the exponents by
// factoring out the largest exponent so that all
// the exponentials fit nicely inside floats.
maxIdx := maxVecIdx(in.Output())
maxValue := autofunc.Slice(in, maxIdx, maxIdx+1)
exponents := autofunc.AddFirst(in, autofunc.Scale(maxValue, -1))
expSum := autofunc.SumAll(autofunc.Exp{}.Apply(exponents))
expLog := autofunc.Log{}.Apply(expSum)
denomLog := autofunc.Add(expLog, maxValue)
return autofunc.AddFirst(in, autofunc.Scale(denomLog, -1))
})
}