!10034 modify Note

From: @Somnus2020
Reviewed-by: @zhunaipan,@kingxian,@zhoufeng54
Signed-off-by: @kingxian

mindspore/nn/wrap/cell_wrapper.py

@@ -412,7 +412,7 @@ class ParameterUpdate(Cell):
         >>> update = nn.ParameterUpdate(param)
         >>> update.phase = "update_param"
         >>> weight = Tensor(np.arange(12).reshape((4, 3)), mindspore.float32)
-        >>> network_updata = update(weight)
+        >>> output = update(weight)
     """
 
     def __init__(self, param):

mindspore/nn/wrap/loss_scale.py

@@ -102,7 +102,9 @@ class DynamicLossScaleUpdateCell(Cell):
         >>> net_with_loss = WithLossCell(net, loss)
         >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
-        >>> train_network.set_train()
+        >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32)
+        >>> labels = Tensor(np.ones([out_features,]), mindspore.float32)
+        >>> output = train_network(input, labels)
     """
 
     def __init__(self,
@@ -185,7 +187,9 @@ class FixedLossScaleUpdateCell(Cell):
         >>> net_with_loss = WithLossCell(net, loss)
         >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
-        >>> train_network.set_train()
+        >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32)
+        >>> labels = Tensor(np.ones([out_features,]), mindspore.float32)
+        >>> output = train_network(input, labels)
     """
 
     def __init__(self, loss_scale_value):
@@ -255,7 +259,9 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         >>> net_with_loss = WithLossCell(net, loss)
         >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
-        >>> train_network.set_train()
+        >>> input = Tensor(np.ones([out_features, in_features]), mindspore.float32)
+        >>> labels = Tensor(np.ones([out_features,]), mindspore.float32)
+        >>> output = train_network(input, labels)
         >>>
         >>> #2) when the type of scale_sense is Tensor:
         >>> net = Net(in_features, out_features)
@@ -267,8 +273,6 @@ class TrainOneStepWithLossScaleCell(TrainOneStepCell):
         >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mstype.float32)
         >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=scaling_sens)
         >>> output = train_network(inputs, label)
-        >>> print(output[0])
-        256.0
     """
     def __init__(self, network, optimizer, scale_sense):
         super(TrainOneStepWithLossScaleCell, self).__init__(network, optimizer, sens=None)