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($"Minibatch: {minibatchIdx} CrossEntropyLoss = {trainLossValue}, EvaluationCriterion = {evaluationValue}");
}
}
public void PrintTrainingProgress(Trainer trainer, int minibatchIdx)
{
if (trainer.PreviousMinibatchSampleCount() != 0)
{
float trainLossValue = (float)trainer.PreviousMinibatchLossAverage();
float evaluationValue = (float)trainer.PreviousMinibatchEvaluationAverage();
Debug.WriteLine($"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};");
}
}