public void Train(TrainingSample sample, NetworkScorer scorer)
{
List <NetworkState> states = FeedForward(sample, scorer);
trainingState.Prepare(momentum: Momentum);
// Backpropogate through time.
for (int i = states.Count - 1; i >= 1; i--)
{
// Get the corresponding frame.
TrainingFrame frame = sample.Frames[i - 1];
NetworkState current = states[i];
NetworkState last = states[i - 1];
//bool annotatedOnsetNearby = IsAnnotatedOnsetNearby(sample, states, i);
double onCorrect = frame.IsOnset ? DetectionValue : NoDetectionValue;
//double offCorrect = frame.IsOnset ? 0.0 : 1.0;
trainingState.Errors[0] = (current.Output[0] - onCorrect);
// trainingState.Errors[1] = (current.Output[1] - offCorrect);
trainingState.BackPropogate(last: last, now: current);
}
// Apply the required weight changes.
trainingState.ApplyWeightChanges(LearningCoefficient);
}
public List <NetworkState> FeedForward(TrainingSample sample, NetworkScorer scorer)
{
List <NetworkState> states = new List <NetworkState>();
// Initialise with a zero before-starting state.
NetworkState state = new NetworkState(Network);
states.Add(state);
// Forward propogate through time
foreach (TrainingFrame trainingFrame in sample.Frames)
{
state = new NetworkState(Network, state);
Array.Copy(trainingFrame.Frame.Values, state.Input, state.Input.Length);
state.FeedForward();
states.Add(state);
}
scorer.Score(states, sample);
return(states);
}
