public virtual void handleLearningEvent(LearningEvent @event)
{
BackPropagation bp = (BackPropagation)@event.Source;
Console.WriteLine("Current iteration: " + bp.CurrentIteration);
Console.WriteLine("Error: " + bp.TotalNetworkError);
}
public async Task <long> AddLearningEventAsync(LearningEvent learningEvent)
{
await Context.AddAsync(learningEvent);
await Context.SaveChangesAsync();
return(learningEvent.Id);
}
public virtual void handleLearningEvent(LearningEvent @event)
{
BackPropagation bp = (BackPropagation)@event.Source;
if (@event.EventType != LearningEvent.Type.LEARNING_STOPPED)
{
Console.WriteLine(bp.CurrentIteration + ". iteration : " + bp.TotalNetworkError);
}
}
// This private class is used to fire LearningEvents
protected internal virtual void fireLearningEvent(LearningEvent evt)
{
lock (this)
{
foreach (LearningEventListener listener in listeners)
{
listener.handleLearningEvent(evt);
}
}
}
public virtual void handleLearningEvent(LearningEvent @event)
{
BackPropagation bp = (BackPropagation)@event.Source;
LOG.info("Epoch no#: [{}]. Error [{}]", bp.CurrentIteration, bp.TotalNetworkError);
LOG.info("Epoch execution time: {} sec", (DateTimeHelperClass.CurrentUnixTimeMillis() - start) / 1000.0);
// neuralNetwork.save(bp.getCurrentIteration() + "_MNIST_CNN-MIC.nnet");
start = DateTimeHelperClass.CurrentUnixTimeMillis();
// if (bp.getCurrentIteration() % 5 == 0)
// Evaluation.runFullEvaluation(neuralNetwork, testSet);
}
public async Task <bool> Update(long id, LearningEvent learningEvent)
{
var ev = await GetEvent(id);
ev.Workers = learningEvent.Workers;
ev.MaxScore = learningEvent.MaxScore;
ev.Name = learningEvent.Name;
ev.PlannedDate = learningEvent.PlannedDate;
ev.Description = learningEvent.Description;
ev.CompetencesId = learningEvent.CompetencesId;
await Context.SaveChangesAsync();
return(true);
}
public virtual void handleLearningEvent(LearningEvent @event)
{
BackPropagation bp = (BackPropagation)@event.Source;
if (@event.EventType.Equals(LearningEvent.Type.LEARNING_STOPPED))
{
double error = bp.TotalNetworkError;
Console.WriteLine("Training completed in " + bp.CurrentIteration + " iterations, ");
Console.WriteLine("With total error: " + formatDecimalNumber(error));
}
else
{
Console.WriteLine("Iteration: " + bp.CurrentIteration + " | Network error: " + bp.TotalNetworkError);
}
}
// @Override
// public void update(Observable arg0, Object arg1) {
// SupervisedLearning rule = (SupervisedLearning)arg0;
// System.out.println( "Training, Network Epoch " + rule.getCurrentIteration() + ", Error:" + rule.getTotalNetworkError());
// }
public virtual void handleLearningEvent(LearningEvent @event)
{
SupervisedLearning rule = (SupervisedLearning)@event.getSource();
System.Console.WriteLine("Training, Network Epoch " + rule.CurrentIteration + ", Error:" + rule.TotalNetworkError);
}
public virtual void handleLearningEvent(LearningEvent @event)
{
BackPropagation bp = (BackPropagation)@event.getSource();
foldErrors[bp.CurrentIteration - 1] += bp.TotalNetworkError / foldSize;
}
public virtual void handleLearningEvent(LearningEvent @event)
{
LMS lr = (LMS)@event.Source;
Console.WriteLine(lr.CurrentIteration + ". iteration | Total network error: " + lr.TotalNetworkError);
}
