diff --git a/mindspore/dataset/engine/datasets.py b/mindspore/dataset/engine/datasets.py
index 73bd025e19..784f10f3e8 100644
--- a/mindspore/dataset/engine/datasets.py
+++ b/mindspore/dataset/engine/datasets.py
@@ -2294,11 +2294,11 @@ def _iter_fn(dataset, num_samples):
             except StopIteration:
                 return
             # convert output tensors to ndarrays
-            yield tuple([np.array(x) for x in val])
+            yield tuple([np.array(x, copy=False) for x in val])
     else:
         for val in dataset:
             # convert output tensors to ndarrays
-            yield tuple([np.array(x) for x in val])
+            yield tuple([np.array(x, copy=False) for x in val])
 
 
 def _generator_fn(generator, num_samples):
@@ -2332,12 +2332,12 @@ def _py_sampler_fn(sampler, num_samples, dataset):
                 return
             val = dataset[idx]
             # convert output tensors to ndarrays
-            yield tuple([np.array(x) for x in val])
+            yield tuple([np.array(x, copy=False) for x in val])
     else:
         for i in sampler:
             val = dataset[i]
             # convert output tensors to ndarrays
-            yield tuple([np.array(x) for x in val])
+            yield tuple([np.array(x, copy=False) for x in val])
 
 
 def _cpp_sampler_fn(sampler, dataset):
@@ -2348,7 +2348,7 @@ def _cpp_sampler_fn(sampler, dataset):
     for i in indices:
         val = dataset[i]
         # convert output tensors to ndarrays
-        yield tuple([np.array(x) for x in val])
+        yield tuple([np.array(x, copy=False) for x in val])
 
 
 def _cpp_sampler_fn_mp(sampler, dataset, num_worker):
@@ -2437,7 +2437,7 @@ def _sampler_fn_mp(indices, dataset, num_worker):
         # Set eoe event once all indices are sent
         if idx_cursor == len(indices) and not eoe.is_set():
             eoe.set()
-        yield tuple([np.array(x) for x in result])
+        yield tuple([np.array(x, copy=False) for x in result])
 
 
 def _generator_worker_loop(dataset, idx_queue, result_queue, eoe):
@@ -2549,35 +2549,35 @@ class GeneratorDataset(SourceDataset):
             when num_shards is also specified. Random accessible input is required.
 
     Examples:
-        >>> import mindspore.dataengine as de
+        >>> import mindspore.dataset as ds
         >>> # 1) Multidimensional generator function as callable input
         >>> def generator_md():
         >>>     for i in range(64):
         >>>         yield (np.array([[i, i + 1], [i + 2, i + 3]]),)
         >>> # create multi_dimension_generator_dataset with GeneratorMD and column name "multi_dimensional_data"
-        >>> multi_dimension_generator_dataset = de.GeneratorDataset(generator_md, ["multi_dimensional_data"])
+        >>> multi_dimension_generator_dataset = ds.GeneratorDataset(generator_md, ["multi_dimensional_data"])
         >>> # 2) Multi-column generator function as callable input
         >>> def generator_mc(maxid = 64):
         >>>     for i in range(maxid):
         >>>         yield (np.array([i]), np.array([[i, i + 1], [i + 2, i + 3]]))
         >>> # create multi_column_generator_dataset with GeneratorMC and column names "col1" and "col2"
-        >>> multi_column_generator_dataset = de.GeneratorDataset(generator_mc, ["col1", "col2"])
+        >>> multi_column_generator_dataset = ds.GeneratorDataset(generator_mc, ["col1", "col2"])
         >>> # 3) Iterable dataset as iterable input
         >>> class MyIterable():
         >>>     def __iter__(self):
         >>>         return # User implementation
         >>> # create iterable_generator_dataset with MyIterable object
-        >>> iterable_generator_dataset = de.GeneratorDataset(MyIterable(), ["col1"])
+        >>> iterable_generator_dataset = ds.GeneratorDataset(MyIterable(), ["col1"])
         >>> # 4) Random accessible dataset as Random accessible input
         >>> class MyRA():
         >>>     def __getitem__(self, index):
         >>>         return # User implementation
         >>> # create ra_generator_dataset with MyRA object
-        >>> ra_generator_dataset = de.GeneratorDataset(MyRA(), ["col1"])
+        >>> ra_generator_dataset = ds.GeneratorDataset(MyRA(), ["col1"])
         >>> # List/Dict/Tuple is also random accessible
-        >>> list_generator = de.GeneratorDataset([(np.array(0),), (np.array(1)), (np.array(2))], ["col1"])
+        >>> list_generator = ds.GeneratorDataset([(np.array(0),), (np.array(1)), (np.array(2))], ["col1"])
         >>> # 5) Built-in Sampler
-        >>> my_generator = de.GeneratorDataset(my_ds, ["img", "label"], sampler=samplers.RandomSampler())
+        >>> my_generator = ds.GeneratorDataset(my_ds, ["img", "label"], sampler=samplers.RandomSampler())
         >>>
     """