CEL

Code/train.py

@@ -152,7 +152,6 @@ def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer,
 
 	return evaluation_test, evaluation_train
 
-#BINARY
 def cross_entropy(logits, target, l):
 	results = torch.tensor([], device='cuda')
 	for i in range (logits.shape[0]):
@@ -165,9 +164,7 @@ def cross_entropy(logits, target, l):
 			one_hot = torch.tensor([1-value,value], device='cuda:0') #creating one-hot vector e.g. [0. ,1.]
 			#class 1 and 2 mixed
 			loss_clear_labels = -((one_hot[0] * logprobs[0][0]) + (one_hot[1] * logprobs[0][1]))
-			#print("Clear Labels: ", loss_clear_labels)
 			results = torch.cat((loss_clear_labels.view(1), results), dim=0)
-			#print("Results Clear Label: ", results)
 		else:
 			value_r = round(value, 1) #to make it equal to lambda_value e.g. 0.4
 			#Wert mit Flag
@@ -185,13 +182,6 @@ def cross_entropy(logits, target, l):
 	#print("Batch Loss: ", batch_loss)
 	return batch_loss
 
-#noch in eine if schleife (beide mixed labels)
-#evt. nicht immer torch.cat aufrufen
-
-#Matrix -> 2 dim
-#pro Zeile Label wenn gleich -> Standard instanz
-# wenn ungleich mixed
-
 
 
 def mixup_function(batch_of_matrices, batch_of_labels, l):