fix bug in api document

mindspore/nn/wrap/cell_wrapper.py

@@ -659,7 +659,7 @@ class ParameterUpdate(Cell):
         KeyError: If parameter with the specified name does not exist.
 
     Supported Platforms:
-        ``Ascend`` ``CPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> network = nn.Dense(3, 4)

mindspore/train/dataset_helper.py

@@ -196,7 +196,8 @@ class DatasetHelper:
         The iteration of DatasetHelper will provide one epoch data.
 
     Args:
-        dataset (Dataset): The training dataset iterator.
+        dataset (Dataset): The training dataset iterator. The dataset can be generated by dataset generator API in
+                           :class:`mindspore.dataset`, such as :class:`mindspore.dataset.ImageFolderDataset`.
         dataset_sink_mode (bool): If true use GetNext to fetch the data, or else feed the data
                                   from host. Default: True.
         sink_size (int): Control the amount of data in each sink.
@@ -210,8 +211,9 @@ class DatasetHelper:
         >>>
         >>> train_dataset = create_custom_dataset()
         >>> set_helper = DatasetHelper(train_dataset, dataset_sink_mode=False)
+        >>> # Object of DatasetHelper is iterable
         >>> for next_element in set_helper:
-        ...     print(next_element)
+        ...     next_element
     """
 
     def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1):

mindspore/train/loss_scale_manager.py

@@ -54,7 +54,7 @@ class FixedLossScaleManager(LossScaleManager):
         >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
         >>>
         >>> #2) Execute parameter update even if overflow occurs
-        >>> loss_scale = 1024
+        >>> loss_scale = 1024.0
         >>> loss_scale_manager = FixedLossScaleManager(loss_scale, False)
         >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=loss_scale)
         >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)