public SimpleLayer[] InnerDecode(Sequence pSequence, out SimpleLayer[] outputHiddenLayer, out Matrix <double> rawOutputLayer)
{
int numStates = pSequence.States.Length;
SimpleLayer[] mForward = null;
SimpleLayer[] mBackward = null;
Parallel.Invoke(() =>
{
//Computing forward RNN
forwardRNN.netReset(false);
mForward = new SimpleLayer[numStates];
for (int curState = 0; curState < numStates; curState++)
{
State state = pSequence.States[curState];
forwardRNN.SetInputLayer(state, curState, numStates, null);
forwardRNN.computeHiddenLayer(state);
mForward[curState] = forwardRNN.GetHiddenLayer();
}
},
() =>
{
//Computing backward RNN
backwardRNN.netReset(false);
mBackward = new SimpleLayer[numStates];
for (int curState = numStates - 1; curState >= 0; curState--)
{
State state = pSequence.States[curState];
backwardRNN.SetInputLayer(state, curState, numStates, null, false);
backwardRNN.computeHiddenLayer(state); //compute probability distribution
mBackward[curState] = backwardRNN.GetHiddenLayer();
}
});
//Merge forward and backward
SimpleLayer[] mergedHiddenLayer = new SimpleLayer[numStates];
Parallel.For(0, numStates, parallelOption, curState =>
{
mergedHiddenLayer[curState] = new SimpleLayer(L1);
SimpleLayer cells = mergedHiddenLayer[curState];
SimpleLayer forwardCells = mForward[curState];
SimpleLayer backwardCells = mBackward[curState];
int i = 0;
while (i < forwardRNN.L1 - Vector <double> .Count)
{
Vector <double> v1 = new Vector <double>(forwardCells.cellOutput, i);
Vector <double> v2 = new Vector <double>(backwardCells.cellOutput, i);
Vector <double> v = (v1 + v2) / vecConst2;
v.CopyTo(cells.cellOutput, i);
i += Vector <float> .Count;
}
while (i < forwardRNN.L1)
{
cells.cellOutput[i] = (forwardCells.cellOutput[i] + backwardCells.cellOutput[i]) / 2.0;
i++;
}
});
//Calculate output layer
Matrix <double> tmp_rawOutputLayer = new Matrix <double>(numStates, L2);
SimpleLayer[] seqOutput = new SimpleLayer[numStates];
Parallel.For(0, numStates, parallelOption, curState =>
{
seqOutput[curState] = new SimpleLayer(L2);
SimpleLayer outputCells = seqOutput[curState];
matrixXvectorADD(outputCells, mergedHiddenLayer[curState], Hidden2OutputWeight, L2, L1, 0);
double[] tmp_vector = tmp_rawOutputLayer[curState];
outputCells.cellOutput.CopyTo(tmp_vector, 0);
//Activation on output layer
SoftmaxLayer(outputCells);
});
outputHiddenLayer = mergedHiddenLayer;
rawOutputLayer = tmp_rawOutputLayer;
return(seqOutput);
}
