def labeled_loss_calculation(self, labeled_examples, labels):
"""Calculates the labeled loss."""
bin_index_labels = real_numbers_to_bin_indexes(labels, self.bins)
predicted_logits = self.D(labeled_examples)
labeled_loss = self.labeled_criterion(predicted_logits, bin_index_labels)
labeled_loss *= self.settings.labeled_loss_multiplier
return labeled_loss
def labeled_loss_calculation(self, labeled_examples, labels):
"""Calculates the labeled loss."""
predictions = self.D(labeled_examples)
density_labels = labels
predicted_density_labels, predicted_count_logits = predictions
density_loss = torch.abs(predicted_density_labels - density_labels).pow(2).sum(1).sum(1).mean()
count_labels = density_labels.sum(1).sum(1)
bin_index_count_labels = real_numbers_to_bin_indexes(count_labels, self.bins)
count_loss = self.labeled_criterion(predicted_count_logits, bin_index_count_labels)
labeled_loss = count_loss + (density_loss * 10)
labeled_loss *= self.settings.labeled_loss_multiplier
return labeled_loss