diff --git a/train.py b/train.py
index 7f3a66bf00..9d0471bd3c 100644
--- a/train.py
+++ b/train.py
@@ -80,8 +80,14 @@ def train(opt):
     model.train()
     if opt.saved_model != '':
         print(f'loading pretrained model from {opt.saved_model}')
+        # Fine tunning 목적
         if opt.FT:
-            model.load_state_dict(torch.load(opt.saved_model), strict=False)
+            checkpoint = torch.load(opt.saved_model)
+            checkpoint = {k: v for k, v in checkpoint.items() if (k in model.state_dict().keys()) and (model.state_dict()[k].shape == checkpoint[k].shape)}
+            for name in model.state_dict().keys() :
+                if name in checkpoint.keys() :
+                    model.state_dict()[name].copy_(checkpoint[name])
+            #model.load_state_dict(torch.load(opt.saved_model), strict=False)
         else:
             model.load_state_dict(torch.load(opt.saved_model))
     print("Model:")
diff --git a/utils.py b/utils.py
index e576358418..f6cad8cbc6 100644
--- a/utils.py
+++ b/utils.py
@@ -32,8 +32,20 @@ def encode(self, text, batch_max_length=25):
         batch_text = torch.LongTensor(len(text), batch_max_length).fill_(0)
         for i, t in enumerate(text):
             text = list(t)
-            text = [self.dict[char] for char in text]
-            batch_text[i][:len(text)] = torch.LongTensor(text)
+            # Could occur Dict Key Error. So, should check 'char' in self.dict.
+            # If there isn't char in self.dict, it will be ignored.
+            # Should drop all data including that char. because it could make train worse.
+            text_index = []
+            for char in text:
+                if char not in self.dict:
+                    text_index = []
+                    break
+                text_index.append(self.dict[char])
+
+            batch_text[i][:len(text_index)] = torch.LongTensor(text_index)
+
+            #text = [self.dict[char] for char in text if char in self.dict]
+            #batch_text[i][:len(text)] = torch.LongTensor(text)
         return (batch_text.to(device), torch.IntTensor(length).to(device))
 
     def decode(self, text_index, length):