diff --git a/fastNLP/core/__init__.py b/fastNLP/core/__init__.py
index 03f284d5..1003c824 100644
--- a/fastNLP/core/__init__.py
+++ b/fastNLP/core/__init__.py
@@ -7,4 +7,5 @@ from .sampler import SequentialSampler, BucketSampler, RandomSampler, BaseSample
 from .tester import Tester
 from .trainer import Trainer
 from .vocabulary import Vocabulary
-
+from .optimizer import Optimizer
+from .loss import Loss
diff --git a/fastNLP/core/tester.py b/fastNLP/core/tester.py
index 2a0d33e0..d6ef9c1e 100644
--- a/fastNLP/core/tester.py
+++ b/fastNLP/core/tester.py
@@ -39,7 +39,6 @@ class Tester(object):
 
         for req_key in required_args:
             if req_key not in kwargs:
-                logger.error("Tester lacks argument {}".format(req_key))
                 raise ValueError("Tester lacks argument {}".format(req_key))
 
         for key in default_args:
@@ -49,7 +48,6 @@ class Tester(object):
                 else:
                     msg = "Argument %s type mismatch: expected %s while get %s" % (
                         key, type(default_args[key]), type(kwargs[key]))
-                    logger.error(msg)
                     raise ValueError(msg)
             else:
                 # Tester doesn't care about extra arguments
@@ -85,8 +83,7 @@ class Tester(object):
                 for k, v in batch_y.items():
                     truths[k].append(v)
             eval_results = self.evaluate(**output, **truths)
-        # print("[tester] {}".format(self.print_eval_results(eval_results)))
-        # logger.info("[tester] {}".format(self.print_eval_results(eval_results)))
+        print("[tester] {}".format(self.print_eval_results(eval_results)))
         self.mode(network, is_test=False)
         self.metrics = eval_results
         return eval_results
diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index b879ad11..b4f11090 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -100,9 +100,9 @@ class Trainer(object):
             for name, param in self.model.named_parameters():
                 if param.requires_grad:
                     self._summary_writer.add_scalar(name + "_mean", param.mean(), global_step=self.step)
-                    self._summary_writer.add_scalar(name + "_std", param.std(), global_step=self.step)
-                    self._summary_writer.add_scalar(name + "_grad_sum", param.sum(), global_step=self.step)
-            if n_print > 0 and self.step % n_print == 0:
+                    # self._summary_writer.add_scalar(name + "_std", param.std(), global_step=self.step)
+                    # self._summary_writer.add_scalar(name + "_grad_sum", param.sum(), global_step=self.step)
+            if kwargs["n_print"] > 0 and self.step % kwargs["n_print"] == 0:
                 end = time.time()
                 diff = timedelta(seconds=round(end - kwargs["start"]))
                 print_output = "[epoch: {:>3} step: {:>4}] train loss: {:>4.6} time: {}".format(