CEL results

Code/test_loss.py

@@ -40,9 +40,9 @@ for i in range(logits.shape[0]):
     if value == 1 or value == 0:
         one_hot = torch.tensor([1-value,value]) #creating one-hot vector e.g. [0. ,1.]
         loss_clear_labels = -((one_hot[0] * logprobs[0][0]) + (one_hot[1] * logprobs[0][1]))
-        print("Clear Labels: ", loss_clear_labels)
+        print("Clear Labels Loss: ", loss_clear_labels)
         results = torch.cat((loss_mixed_labels.view(1), results), dim=0)
-        print("Results: ", results)
+        print("Results Clear Labels: ", results)
     else:
         if value_r == l:
             mixed_vec = torch.tensor([l, 1-l])
@@ -53,7 +53,7 @@ for i in range(logits.shape[0]):
             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: ", results)
+            print("Results Mixed 1: ", results)
         else:
             mixed_vec = torch.tensor([1-l, l])
             logprobs = torch.nn.functional.log_softmax(lg, dim=1)
@@ -61,7 +61,7 @@ for i in range(logits.shape[0]):
             loss_mixed_labels = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))        
             print("Mixed Labels 1-l: ", loss_mixed_labels)
             results = torch.cat((loss_mixed_labels.view(1), results), dim=0)
-            print("Results: ", results)
+            print("Results Mixed 2: ", results)
 print("Results all", results)
 average_loss = results.mean() #compute the average
 print("Avg", average_loss)

Code/train.py

@@ -160,16 +160,17 @@ def cross_entropy(logits, target, l):
 			if value_r == l:
 				#create vector: e.g. [l, 1-l]
 				mixed_vec = torch.tensor([l, 1-l], device='cuda')
-				loss_mixed_labels = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
-				print("Mixed Labels: ", loss_mixed_labels)
-				results = torch.cat((loss_mixed_labels.view(1), results), dim=0)
-				print("Results Mixed Label: ", results)
+				loss_mixed_labels_1 = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
+				print("Mixed Labels1: ", loss_mixed_labels_1)
+				results = torch.cat((loss_mixed_labels_1.view(1), results), dim=0)
+				print("Results Mixed Label1: ", results)
 			else:
 				mixed_vec = torch.tensor([1-l, l], device='cuda')
-				loss_mixed_labels = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
-				print("Mixed Labels: ", loss_mixed_labels)
-				results = torch.cat((loss_mixed_labels.view(1), results), dim=0)
-				print("Results Mixed Label: ", results)
+				loss_mixed_labels_2 = -((mixed_vec[0] * logprobs[0][0]) + (mixed_vec[1] * logprobs[0][1]))
+				print("Mixed Labels2: ", loss_mixed_labels_2)
+				results = torch.cat((loss_mixed_labels_2.view(1), results), dim=0)
+				print("Results Mixed Label2: ", results)
+	print("ALL BATCH Results: ", results)
 	batch_loss = results.mean() #compute average
 	print("Batch Loss: ", batch_loss)
 	return batch_loss