diff --git a/mindspore/context.py b/mindspore/context.py
index ad601f8fab95f50ca75aac10fe62485ee1f16332..bf01032bf8d0381ece801468fc5a53000967128f 100644
--- a/mindspore/context.py
+++ b/mindspore/context.py
@@ -564,6 +564,8 @@ def set_context(**kwargs):
         check_bprop (bool): Whether to check bprop. Default: False.
         max_device_memory (str): Sets the maximum memory available for device, currently only supported on GPU.
             The format is "xxGB". Default: "1024GB".
+        print_file_path (str): The path of print data to save. If this parameter is set, print data is saved to
+        a file by default，and turn off printing to the screen.
 
     Raises:
         ValueError: If input key is not an attribute in context.
@@ -584,6 +586,7 @@ def set_context(**kwargs):
         >>>                     save_graphs_path="/mindspore")
         >>> context.set_context(enable_profiling=True, profiling_options="training_trace")
         >>> context.set_context(max_device_memory="3.5GB")
+        >>> context.set_context(print_file_path="print.pb")
     """
     for key, value in kwargs.items():
         if not hasattr(_context(), key):
diff --git a/mindspore/train/serialization.py b/mindspore/train/serialization.py
index ce776d6821226f881995ec4fac873367e7a2eb13..c4fba9ba8a5a0258f772cec14a9f8a9be64b85d5 100644
--- a/mindspore/train/serialization.py
+++ b/mindspore/train/serialization.py
@@ -29,8 +29,7 @@ from mindspore.common.api import _executor
 from mindspore.common import dtype as mstype
 from mindspore._checkparam import check_input_data
 
-
-__all__ = ["save_checkpoint", "load_checkpoint", "load_param_into_net", "export"]
+__all__ = ["save_checkpoint", "load_checkpoint", "load_param_into_net", "export", "parse_print"]
 
 tensor_to_ms_type = {"Int8": mstype.int8, "Uint8": mstype.uint8, "Int16": mstype.int16, "Uint16": mstype.uint16,
                      "Int32": mstype.int32, "Uint32": mstype.uint32, "Int64": mstype.int64, "Uint64": mstype.uint64,
@@ -513,6 +512,13 @@ def parse_print(print_file_name):
                     tensor_list.append(Tensor(param_value, ms_type))
                 # Scale type
                 else:
+                    data_type_ = data_type.lower()
+                    if 'float' in data_type_:
+                        param_data = float(param_data[0])
+                    elif 'int' in data_type_:
+                        param_data = int(param_data[0])
+                    elif 'bool' in data_type_:
+                        param_data = bool(param_data[0])
                     tensor_list.append(Tensor(param_data, ms_type))
 
     except BaseException as e: