From 47a52a4d02844dcbbd01230110f627a7754f286b Mon Sep 17 00:00:00 2001
From: liuyang_655 <liuyang655@huawei.com>
Date: Sun, 28 Nov 2021 22:18:51 -0500
Subject: [PATCH] Modify error info

---
 mindspore/nn/cell.py            | 22 +++++++++++-----------
 mindspore/nn/optim/optimizer.py |  2 +-
 2 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/mindspore/nn/cell.py b/mindspore/nn/cell.py
index f8f272444d4..2dd10bf469e 100755
--- a/mindspore/nn/cell.py
+++ b/mindspore/nn/cell.py
@@ -244,8 +244,8 @@ class Cell(Cell_):
     @parameter_layout_dict.setter
     def parameter_layout_dict(self, value):
         if not isinstance(value, dict):
-            raise TypeError("The type of parameter 'value' must be a dict type, "
-                            "but got the type : {}.".format(type(value)))
+            raise TypeError(f"For 'Cell', the property 'parameter_layout_dict' must be dict type, "
+                            f"but got type {type(value)}.")
         self._parameter_layout_dict = value
 
     @property
@@ -255,8 +255,8 @@ class Cell(Cell_):
     @parallel_parameter_name_list.setter
     def parallel_parameter_name_list(self, value):
         if not isinstance(value, list):
-            raise TypeError("The type of parameter 'parallel_parameter_name_list' must be a list type, "
-                            "but got the type : {}.".format(type(value)))
+            raise TypeError(f"For 'Cell', the property 'parallel_parameter_name_list' must be list type, "
+                            f"but got type {type(value)}.")
         self._parallel_parameter_name_list = value
 
     @property
@@ -265,13 +265,13 @@ class Cell(Cell_):
 
     @pipeline_stage.setter
     def pipeline_stage(self, value):
-        if not isinstance(value, int) or isinstance(value, bool):
-            raise TypeError("The parameter 'pipeline_stage' must be an int type, "
-                            "but got the type : {}.".format(type(value)))
+        if not isinstance(value, int):
+            raise TypeError(f"For 'Cell', the property 'pipeline_stage' must be int type, "
+                            f"but got type {type(value)}.")
 
         if value < 0:
-            raise TypeError("The parameter 'pipeline_stage' can not be less than 0,  "
-                            "but got the value : {}".format(value))
+            raise TypeError("For 'Cell', the property 'pipeline_stage' can not be less than 0, "
+                            "but got {}".format(value))
         self._pipeline_stage = value
         for item in self.trainable_params():
             item.add_pipeline_stage(value)
@@ -283,8 +283,8 @@ class Cell(Cell_):
     @parallel_parameter_merge_net_dict.setter
     def parallel_parameter_merge_net_dict(self, value):
         if not isinstance(value, dict):
-            raise TypeError("The parameter 'parallel_parameter_merge_net_dict' must be a dict type, "
-                            "but got the type : {}".format(type(value)))
+            raise TypeError(f"For 'Cell', the property 'parallel_parameter_merge_net_dict' must be dict type, "
+                            f"but got type {type(value)}.")
         self._parallel_parameter_merge_net_dict = value
 
     def get_func_graph_proto(self):
diff --git a/mindspore/nn/optim/optimizer.py b/mindspore/nn/optim/optimizer.py
index f3b369eea85..fa9cc724582 100644
--- a/mindspore/nn/optim/optimizer.py
+++ b/mindspore/nn/optim/optimizer.py
@@ -398,7 +398,7 @@ class Optimizer(Cell):
                 raise ValueError(f"For 'Optimizer', the dim of Tensor type 'learning_rate' should be a 0 or 1, "
                                  f"but got {learning_rate.ndim}.")
             if learning_rate.ndim == 1 and learning_rate.size < 2:
-                logger.warning("If use `Tensor` type dynamic learning rate, please make sure that the number"
+                logger.warning("If use `Tensor` type dynamic learning rate, please make sure that the number "
                                "of elements in the tensor is greater than 1.")
             return learning_rate
         if isinstance(learning_rate, LearningRateSchedule):