back again

Code/main.py

@@ -225,7 +225,7 @@ if __name__ == "__main__":
 	parser.add_argument(
 		"-tb",
 		"--test_batch_size",
-		help="The batch size for the training process",
+		help="The batch size for the test process",
 		type=int,
 		default=16)
 

Code/train.py

@@ -103,16 +103,16 @@ def train(model, name, seed,gradient_accumulation_steps,mixup, threshold, lambda
 					#print("span output: ", span_output)
 					logits=model.classifier(span_output.detach()) #target_value?
 						
-					print("logits: ", logits)
-					print("logits shape: ", list(logits.shape))
-					print("Newlabels: ", new_labels_batch)
-					print("labels shape: ", list(new_labels_batch.shape))
+					# print("logits: ", logits)
+					# print("logits shape: ", list(logits.shape))
+					# print("Newlabels: ", new_labels_batch)
+					# print("labels shape: ", list(new_labels_batch.shape))
 
-					preds = logits.view(-1, 2).to("cuda")
+					logits = logits.view(-1, 2).to("cuda")
 					target = new_labels_batch.view(-1).to("cuda")
-					# loss_2 = cross_entropy(preds, target)
+					loss_2 = cross_entropy(logits, target)
 					#loss_2 = SoftCrossEntropyLoss(logits.view(-1, 2).to("cuda"), new_labels_batch.view(-1).to("cuda"))
-					loss_2 = torch.nn.functional.cross_entropy(preds, target.long())
+					#loss_2 = torch.nn.functional.cross_entropy(preds, target.long())
 					print("MixUp Loss: ", loss_2)
 					#update entire model
 					loss_2.backward()
@@ -137,6 +137,11 @@ def train(model, name, seed,gradient_accumulation_steps,mixup, threshold, lambda
 
 	return evaluation_test, evaluation_train
 
+def cross_entropy(logits, target):
+    log_softmax = torch.nn.functional.log_softmax(logits, dim=1)
+    loss = -torch.sum(target * log_softmax, dim=1).mean()
+    return loss
+
 # def SoftCrossEntropyLoss (input, target):
 #     logprobs = torch.nn.functional.log_softmax (input, dim = 1)
 #     return  -(target * logprobs).sum() / input.shape[0]