Merge branch 'master' of...

Merge branch 'master' of https://gitlab.cl.uni-heidelberg.de/friebolin/swp-data-augmentation-for-metonymy-resolution

Code/train.py

@@ -139,34 +139,40 @@ def train(model, name, imdb, seed,mixup,lambda_value, mixepoch, tmix, mixlayer,
 
 	return evaluation_test, evaluation_train
 
-def cross_entropy(logits, target, l):
+def cross_entropy(logits, target):
+	"""
+    Computes the cross-entropy loss between the predicted logits and the target labels.
+    
+    Args:
+    - logits (torch.Tensor): A tensor of shape (batch_size, num_classes) 
+	  representing the predicted logits for each input example.
+    - target (torch.Tensor): A tensor of shape (batch_size,) 
+	  representing the target labels for each input example.
+    
+    Returns:
+    - batch_loss (torch.Tensor): A scalar tensor representing 
+	  the average cross-entropy loss across the batch.
+    """
 	results = torch.tensor([], device='cuda')
 	for i in range (logits.shape[0]):
-		lg = logits[i:i+1,:] #comment to explain the process in this Code Line
+		lg = logits[i:i+1,:] # Extract a row from the logits tensor for the i-th input example in the batch
 		t = target[i]
-		#makes the logits in log (base e) probabilities
-		logprobs = torch.nn.functional.log_softmax(lg, dim=1)
-		value = t.item() #gets Item (0. or 1.)
-		if value == 1 or value == 0:
+		logprobs = torch.nn.functional.log_softmax(lg, dim=1) #logits in log (base e) probabilities
+		value = t.item() #get scalar value
+		if value == 1 or value == 0: #check if non-mixed label
 			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]))
+			#calculation with indexing (- 1-label * )
 			results = torch.cat((loss_clear_labels.view(1), results), dim=0)
 		else:
 			value_r = round(value, 1) #to make it equal to lambda_value e.g. 0.4
-			#Wert mit Flag
-			mixed_vec = torch.tensor([value_r, 1-value_r])
-			print("Mixed Vec: ", mixed_vec)
-			logprobs = torch.nn.functional.log_softmax(lg, dim=1)
-			print("Log:", logprobs)
-        	#loss_mixed_labels = -torch.mul(mixed_vec, logprobs).sum()
+			mixed_vec = torch.tensor([value_r, 1-value_r]) #creating on-hot mixed vec.
+			logprobs = torch.nn.functional.log_softmax(lg, dim=1)#logits in log probabilities
 			loss_mixed_labels = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
-			print("Loss Mixed Lables l: ", loss_mixed_labels)
-			results = torch.cat((loss_mixed_labels.view(1), results), dim=0)
-			print("Results Mixed 1: ", results)
-	print("ALL BATCH Results: ", results)
-	batch_loss = results.mean() #compute average
-	#print("Batch Loss: ", batch_loss)
+			#calculation for mixed with indexing
+			results = torch.cat((loss_mixed_labels.view(1), results), dim=0)#append resultts to result tensor
+	print("LOSS BATCH (Results): ", results)
+	batch_loss = results.mean() #compute average of all results
 	return batch_loss