public void PrintTrainingProgress(Trainer trainer, int minibatchIdx)
{
if (trainer.PreviousMinibatchSampleCount() != 0)
{
float trainLossValue = (float)trainer.PreviousMinibatchLossAverage();
float evaluationValue = (float)trainer.PreviousMinibatchEvaluationAverage();
Debug.WriteLine($"Minibatch: {minibatchIdx} CrossEntropyLoss = {trainLossValue}, EvaluationCriterion = {evaluationValue}");
}
}
public void PrintTrainingProgress(Trainer trainer, int minibatchIdx)
{
if (trainer.PreviousMinibatchSampleCount() != 0)
{
float trainLossValue = (float)trainer.PreviousMinibatchLossAverage();
float evaluationValue = (float)trainer.PreviousMinibatchEvaluationAverage();
Debug.WriteLine($"{minibatchIdx};{trainLossValue};{evaluationValue};");
}
}
public static void PrintTrainingProgress(Trainer trainer, int minibatchIdx, int outputFrequencyInMinibatches)
{
if ((minibatchIdx % outputFrequencyInMinibatches) == 0 && trainer.PreviousMinibatchSampleCount() != 0)
{
float trainLossValue = (float)trainer.PreviousMinibatchLossAverage();
float evaluationValue = (float)trainer.PreviousMinibatchEvaluationAverage();
Console.WriteLine($"Minibatch: {minibatchIdx} CrossEntropyLoss = {trainLossValue}, EvaluationCriterion = {evaluationValue}");
}
}
private void RunTraining(Trainer trainer, GenericMinibatchSequenceSource minibatchSource, int numMinibatchesToTrain, DeviceDescriptor device)
{
double aggregate_metric = 0;
for (int minibatchCount = 0; minibatchCount < numMinibatchesToTrain; minibatchCount++)
{
IDictionary <Variable, MinibatchData> data = minibatchSource.GetNextRandomMinibatch();
trainer.TrainMinibatch(data, device);
double samples = trainer.PreviousMinibatchSampleCount();
double avg = trainer.PreviousMinibatchEvaluationAverage();
aggregate_metric += avg * samples;
double nbSampleSeen = trainer.TotalNumberOfSamplesSeen();
double train_error = aggregate_metric / nbSampleSeen;
Debug.WriteLine($"{minibatchCount} Average training error: {train_error:p2}");
}
}