diff --git a/tutorials/source_en/advanced_use/computer_vision_application.md b/tutorials/source_en/advanced_use/computer_vision_application.md
index 0f2359f314616a4d6630f2e0c8943ba1688aa0f6..1e1c6e45288023585905f0d5b2a31fd77fd641f6 100644
--- a/tutorials/source_en/advanced_use/computer_vision_application.md
+++ b/tutorials/source_en/advanced_use/computer_vision_application.md
@@ -129,14 +129,14 @@ tar -zvxf cifar-10-binary.tar.gz
     Shuffle data randomly to disorder the data sequence and read data in batches for model training:
 
     ```python
-    # apply repeat operations
-    cifar_ds = cifar_ds.repeat(repeat_num)
-
     # apply shuffle operations
     cifar_ds = cifar_ds.shuffle(buffer_size=10)
 
     # apply batch operations
     cifar_ds = cifar_ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
+
+    # apply repeat operations
+    cifar_ds = cifar_ds.repeat(repeat_num)
     ```
 
 
diff --git a/tutorials/source_en/advanced_use/distributed_training.md b/tutorials/source_en/advanced_use/distributed_training.md
index 7d859196e5dd8319faee3469a886ecfcbc0ab0ea..c34146b9b3d5c92cdf16898a25b78876d22d4175 100644
--- a/tutorials/source_en/advanced_use/distributed_training.md
+++ b/tutorials/source_en/advanced_use/distributed_training.md
@@ -145,15 +145,15 @@ def create_dataset(repeat_num=1, batch_size=32, rank_id=0, rank_size=1):
     data_set = data_set.map(input_columns="label", operations=type_cast_op)
     data_set = data_set.map(input_columns="image", operations=c_trans)
 
-    # apply repeat operations
-    data_set = data_set.repeat(repeat_num)
-
     # apply shuffle operations
     data_set = data_set.shuffle(buffer_size=10)
 
     # apply batch operations
     data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
 
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
     return data_set
 ```
 
diff --git a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
index e6ad3d3cc803360f6093efadc6eb94a4f5dd6921..ca7c15ce61272d4d6e3cb566db73c22b8f69131e 100644
--- a/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
+++ b/tutorials/source_zh_cn/advanced_use/computer_vision_application.md
@@ -131,14 +131,14 @@ tar -zvxf cifar-10-binary.tar.gz
     最后通过数据混洗（shuffle）随机打乱数据的顺序，并按batch读取数据，进行模型训练：
 
     ```python
-    # apply repeat operations
-    cifar_ds = cifar_ds.repeat(repeat_num)
-
     # apply shuffle operations
     cifar_ds = cifar_ds.shuffle(buffer_size=10)
 
     # apply batch operations
     cifar_ds = cifar_ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
+
+    # apply repeat operations
+    cifar_ds = cifar_ds.repeat(repeat_num)
     ```
 
 
diff --git a/tutorials/source_zh_cn/advanced_use/distributed_training.md b/tutorials/source_zh_cn/advanced_use/distributed_training.md
index b5b097b71dec1a43c102a27a36e9e9123369751c..8071832fe081cdc1c39fee8314c802df402faf69 100644
--- a/tutorials/source_zh_cn/advanced_use/distributed_training.md
+++ b/tutorials/source_zh_cn/advanced_use/distributed_training.md
@@ -144,15 +144,15 @@ def create_dataset(repeat_num=1, batch_size=32, rank_id=0, rank_size=1):
     data_set = data_set.map(input_columns="label", operations=type_cast_op)
     data_set = data_set.map(input_columns="image", operations=c_trans)
 
-    # apply repeat operations
-    data_set = data_set.repeat(repeat_num)
-
     # apply shuffle operations
     data_set = data_set.shuffle(buffer_size=10)
 
     # apply batch operations
     data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
 
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
     return data_set
 ```
 
diff --git a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py
index 2db511100e77530273771361ffebeb36b34e1512..e363047ca876dd86c5f34ba3699c40bde59ad13c 100644
--- a/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py
+++ b/tutorials/tutorial_code/distributed_training/resnet50_distributed_training.py
@@ -69,15 +69,15 @@ def create_dataset(repeat_num=1, batch_size=32, rank_id=0, rank_size=1):
     data_set = data_set.map(input_columns="label", operations=type_cast_op)
     data_set = data_set.map(input_columns="image", operations=c_trans)
 
-    # apply repeat operations
-    data_set = data_set.repeat(repeat_num)
-
     # apply shuffle operations
     data_set = data_set.shuffle(buffer_size=10)
 
     # apply batch operations
     data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
 
+    # apply repeat operations
+    data_set = data_set.repeat(repeat_num)
+
     return data_set
 
 
diff --git a/tutorials/tutorial_code/resnet/cifar_resnet50.py b/tutorials/tutorial_code/resnet/cifar_resnet50.py
index da0620b7a469d75d03cb34957b5089000bc929c5..eedf7e0c4d172a42ba568649e32b60ea90c99904 100644
--- a/tutorials/tutorial_code/resnet/cifar_resnet50.py
+++ b/tutorials/tutorial_code/resnet/cifar_resnet50.py
@@ -91,15 +91,15 @@ def create_dataset(repeat_num=1, training=True):
     cifar_ds = cifar_ds.map(input_columns="label", operations=type_cast_op)
     cifar_ds = cifar_ds.map(input_columns="image", operations=c_trans)
 
-    # apply repeat operations
-    cifar_ds = cifar_ds.repeat(repeat_num)
-
     # apply shuffle operations
     cifar_ds = cifar_ds.shuffle(buffer_size=10)
 
     # apply batch operations
     cifar_ds = cifar_ds.batch(batch_size=args_opt.batch_size, drop_remainder=True)
 
+    # apply repeat operations
+    cifar_ds = cifar_ds.repeat(repeat_num)
+
     return cifar_ds
 
 if __name__ == '__main__':