fix batch repeat usage order

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)
     ```
 
 

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
 ```
 

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)
     ```
 
 

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
 ```
 

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
 
 

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__':