LR parameter, train accuracy.

training.py

@@ -23,6 +23,8 @@ if __name__ == '__main__':
                         help='num threads (default: 4)')
     parser.add_argument('--dropout', type=float, default=0.4, metavar='D',
                         help='dropout rate (default: 0.4)')
+    parser.add_argument('--lr', type=float, default=3e-3, metavar='D',
+                        help='learning rate (default: 3e-3)')
     parser.add_argument('--bert_model_name', type=str, default='bert-base-multilingual-cased',
                         help='Bert model name (ex. "bert-base-multilingual-cased")')
     parser.add_argument('--tensorboard', type=str, default='default_tb', metavar='TB',
@@ -67,16 +69,18 @@ if __name__ == '__main__':
     model = add_adapters(model, config)
 
     model = ClassificationModel(model, n_labels=len(train_dataset.y_labels),
-                                dropout_prob=0.0)
+                                dropout_prob=args.dropout)
 
     model.eval()
     model.to(device)
 
-    optimizer = Adam(model.parameters(), lr=0.001, amsgrad=True)
+    optimizer = Adam(model.parameters(), lr=args.lr, amsgrad=True)
 
     print('Model have initialized')
     for i in range(args.num_epochs):
         model.train()
+        labels = []
+        predictions = []
         for batch in tqdm(train_loader):
             optimizer.zero_grad()
 
@@ -88,10 +92,15 @@ if __name__ == '__main__':
             y = batch['y']
             y = y.to(device)
 
-            loss, _ = model.forward(input_ids, input_mask, segment_ids, labels=y)
+            loss, logits = model.forward(input_ids, input_mask, segment_ids, labels=y)
             loss.backward()
 
             optimizer.step()
+            labels.append(torch.argmax(y, dim=1))
+            predictions.append(torch.argmax(logits, dim=1))
+        labels = torch.cat(labels).long()
+        predictions = torch.cat(predictions).long()
+        train_accuracy = (labels == predictions).float().mean()
 
         model.eval()
         labels = []
@@ -112,7 +121,9 @@ if __name__ == '__main__':
             predictions.append(torch.argmax(logits, dim=1))
         labels = torch.cat(labels).long()
         predictions = torch.cat(predictions).long()
-        print(f'Epoch: {i}\tVal Accuracy: {(labels == predictions).float().mean()}')
+
+        valid_accuracy = (labels == predictions).float().mean()
+        print(f'Epoch: {i}\tTrain Accuracy: {train_accuracy}\tVal Accuracy: {valid_accuracy}')
 
     model.eval()
     labels = []