func splitVectors(in linalg.Vector, n int) []linalg.Vector {
res := autofunc.Split(n, &autofunc.Variable{Vector: in})
resList := make([]linalg.Vector, len(res))
for i, x := range res {
resList[i] = x.Output()
}
return resList
}
// ApplyBlock applies the LSTM to a batch of inputs.
func (l *LSTM) ApplyBlock(s []State, in []autofunc.Result) BlockResult {
var internalPool, lastOutPool []*autofunc.Variable
res := autofunc.PoolAll(in, func(in []autofunc.Result) autofunc.Result {
var weavedInputs []autofunc.Result
var internalResults []autofunc.Result
for i, sObj := range s {
state := sObj.(lstmState)
internalVar := &autofunc.Variable{Vector: state.Internal}
lastOutVar := &autofunc.Variable{Vector: state.Output}
internalPool = append(internalPool, internalVar)
lastOutPool = append(lastOutPool, lastOutVar)
weavedInputs = append(weavedInputs, in[i], lastOutVar, internalVar)
internalResults = append(internalResults, internalVar)
}
gateIn := autofunc.Concat(weavedInputs...)
inValue := l.inputValue.Batch(gateIn, len(in))
inGate := l.inputGate.Batch(gateIn, len(in))
rememberGate := l.rememberGate.Batch(gateIn, len(in))
lastState := autofunc.Concat(internalResults...)
newState := autofunc.Add(autofunc.Mul(rememberGate, lastState),
autofunc.Mul(inValue, inGate))
return autofunc.Pool(newState, func(newState autofunc.Result) autofunc.Result {
var newWeaved []autofunc.Result
for i, state := range autofunc.Split(len(in), newState) {
newWeaved = append(newWeaved, in[i], lastOutPool[i], state)
}
newGateIn := autofunc.Concat(newWeaved...)
outGate := l.outputGate.Batch(newGateIn, len(in))
outValues := neuralnet.HyperbolicTangent{}.Apply(newState)
return autofunc.Concat(newState, autofunc.Mul(outGate, outValues))
})
})
states, outs := splitLSTMOutput(len(in), res.Output())
return &lstmResult{
CellStates: states,
OutputVecs: outs,
InternalPool: internalPool,
LastOutPool: lastOutPool,
JoinedOut: res,
}
}