diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index 2be6e2fa..2a6458c6 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -237,14 +237,10 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No
 
         # loss check
         if batch_count == 0:
-           _dict = _check_arg_dict_list(model.loss, [output, batch_y])
-            if len(_dict) != 0:
-                pass
-        loss_input = _build_args(model.loss, **output, **batch_y)
-        loss = model.loss(**loss_input)
-        if batch_count == 0:
-            if isinstance(loss, torch.Tensor):
-                pass
+           _check_loss(model=model, model_func=model.get_loss, check_level=check_level,
+                                 output=output, batch_y=batch_y)
+        loss_input = _build_args(model.get_loss, **output, **batch_y)
+        loss = model.get_loss(**loss_input)
 
         # check loss output
         if batch_count == 0:
@@ -281,7 +277,7 @@ def _check_code(dataset, model, batch_size=DEFAULT_CHECK_BATCH_SIZE, dev_data=No
                     truths[k].append(v)
                 if batch_count+1>DEFAULT_CHECK_NUM_BATCH:
                     break
-            _check_loss_evaluate(model=model, model_func=model.evaluate, check_level=check_level,
+            _check_loss(model=model, model_func=model.evaluate, check_level=check_level,
                                  output=outputs, batch_y=truths)
             refined_input = _build_args(model.evaluate, **outputs, **truths)
             metrics = model.evaluate(**refined_input)
@@ -323,16 +319,17 @@ def _check_forward_error(model, model_func, check_level, batch_x):
         elif check_level == WARNING_CHECK_LEVEL:
             warnings.warn(message=_unused)
 
-def _check_loss_evaluate(model, model_func, check_level, output, batch_y):
+def _check_loss(model, model_func, check_level, output, batch_y):
     check_res = _check_arg_dict_list(model_func, [output, batch_y])
     _missing = ''
     _unused = ''
     _duplicated = ''
     signature_str = get_func_signature(model_func)
-    func_signature = "{}.{}(self, {})".format(model.__class__.__name__, model_func.__name__, signature_str[1:-1])
-    forward_signature_str = get_func_signature(model.forward)
-    forward_func_signature = "{}.forward(self, {})".format(model.__class__.__name__, forward_signature_str[1:-1])
     model_name = model.__class__.__name__
+    model_func_name = model_func.__name__
+    func_signature = "{}.{}(self, {})".format(model_name, model_func_name, signature_str[1:-1])
+    forward_signature_str = get_func_signature(model.forward)
+    forward_func_signature = "{}.forward(self, {})".format(model_name, forward_signature_str[1:-1])
     if len(check_res.missing)>0:
         _missing = "Function {} misses argument {}, only provided with {}(from {}) and " \
                    "{}." \
@@ -384,6 +381,77 @@ def _check_loss_evaluate(model, model_func, check_level, output, batch_y):
         if _error_str:
             raise ValueError(_error_str)
 
+def _check_evaluate(model, model_func, check_level, output, batch_y):
+
+    check_res = _check_arg_dict_list(model_func, [output, batch_y])
+    _missing = ''
+    _unused = ''
+    _duplicated = ''
+    signature_str = get_func_signature(model_func)
+    model_name = model.__class__.__name__
+    model_func_name = model_func.__name__
+    func_signature = "{}.{}(self, {})".format(model_name, model_func_name, signature_str[1:-1])
+    if hasattr(model, 'predict'):
+        previous_func = model.predict
+        previous_func_name = 'predict'
+    else:
+        previous_func = model.forward
+        previous_func_name = 'forward'
+    previous_signature_str = get_func_signature(previous_func)
+    previous_func_signature = "{}.{}(self, {})".format(model_name, previous_func_name, previous_signature_str[1:-1])
+    if len(check_res.missing)>0:
+        _missing = "Function {} misses argument {}, only provided with {}(from {}) and " \
+                   "{}." \
+                   .format(func_signature, check_res.missing,
+                            list(output.keys()), previous_func_signature,
+                           list(batch_y.keys()))
+    if len(check_res.unused)>0:
+        if len(check_res.unused) > 1:
+            _unused = "{} are not used ".format(check_res.unused)
+        else:
+            _unused = "{} is not used ".format(check_res.unused)
+        _unused += "in function {}.\n".format(func_signature)
+    if len(check_res.duplicated)>0:
+        if len(check_res.duplicated) > 1:
+            _duplicated = "Duplicated keys {} are detected when calling function {}. \nDon't set {} as target and output " \
+                          "them in {} at the same time.\n".format(check_res.duplicated,
+                                                                            func_signature,
+                                                                            check_res.duplicated,
+                                                                  previous_func_signature)
+        else:
+            _duplicated = "Duplicated key {} is detected when calling function {}. \nDon't set {} as target and output " \
+                          "it in {} at the same time.\n".format(check_res.duplicated,
+                                                                func_signature,
+                                                                check_res.duplicated,
+                                                                previous_func_signature)
+    _number_errs = int(len(_missing)!=0) + int(len(_duplicated)!=0) + int(len(_unused)!=0)
+    if _number_errs > 0:
+        _error_str = ''
+        if _number_errs > 1:
+            count = 1
+            if _missing:
+                _error_str += '({}).{}'.format(count, _missing)
+                count += 1
+            if _duplicated:
+                _error_str += '({}).{}'.format(count, _duplicated)
+                count += 1
+            if _unused and check_level == STRICT_CHECK_LEVEL:
+                _error_str += '({}).{}'.format(count, _unused)
+        else:
+            if _unused:
+                if check_level == STRICT_CHECK_LEVEL:
+                    # TODO 这里可能需要自定义一些Error类型
+                    _error_str = _unused
+                elif check_level == WARNING_CHECK_LEVEL:
+                    _unused = _unused.strip()
+                    warnings.warn(_unused)
+            else:
+                _error_str = _missing + _duplicated
+        if _error_str:
+            raise ValueError(_error_str)
+
+
+
 
 if __name__ == '__main__':
     import torch
@@ -430,5 +498,13 @@ if __name__ == '__main__':
     # import inspect
     # print(inspect.getfullargspec(model.forward))
 
+    import numpy as np
+
+    a = [1, 3]
+    np.asarray(a)
+
+    import pandas
+    df = pandas.DataFrame(fake_data_dict)
+    df.infer_objects()