private void DeepLearningNet(Sequence pSequence, SimpleLayer[] seqOutput, List <double[][]> fErrLayers,
List <double[][]> bErrLayers, List <SimpleLayer[]> layerList)
{
int numStates = pSequence.States.Length;
int numLayers = forwardHiddenLayers.Count;
//Learning output layer
Parallel.Invoke(() =>
{
for (int curState = 0; curState < numStates; curState++)
{
seqOutput[curState].LearnFeatureWeights(numStates, curState);
}
},
() =>
{
Parallel.For(0, numLayers, parallelOption, i =>
{
Parallel.Invoke(() =>
{
SimpleLayer forwardLayer = forwardHiddenLayers[i];
forwardLayer.netReset(true);
for (int curState = 0; curState < numStates; curState++)
{
forwardLayer.computeLayer(layerList[i][curState].SparseFeature, layerList[i][curState].DenseFeature, true);
forwardLayer.er = fErrLayers[i][curState];
forwardLayer.LearnFeatureWeights(numStates, curState);
}
},
() =>
{
SimpleLayer backwardLayer = backwardHiddenLayers[i];
backwardLayer.netReset(true);
for (int curState = 0; curState < numStates; curState++)
{
int curState2 = numStates - curState - 1;
backwardLayer.computeLayer(layerList[i][curState2].SparseFeature, layerList[i][curState2].DenseFeature, true);
backwardLayer.er = bErrLayers[i][curState2];
backwardLayer.LearnFeatureWeights(numStates, curState);
}
});
});
});
}