modify example

mindspore/nn/optim/adam.py

@@ -337,7 +337,7 @@ class Adam(Optimizer):
         """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
            optimizer operation."""
         if not isinstance(value, str):
-            raise ValueError("The value must be str type, but got value type is {}".format(type(value)))
+            raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 
         if value not in ('CPU', 'Ascend'):
             raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(value))

mindspore/nn/optim/ftrl.py

@@ -190,7 +190,7 @@ class FTRL(Optimizer):
         """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
            optimizer operation."""
         if not isinstance(value, str):
-            raise ValueError("The value must be str type, but got value type is {}".format(type(value)))
+            raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 
         if value not in ('CPU', 'Ascend'):
             raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(value))

mindspore/nn/optim/lazyadam.py

@@ -255,7 +255,7 @@ class LazyAdam(Optimizer):
         """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
            optimizer operation."""
         if not isinstance(value, str):
-            raise ValueError("The value must be str type, but got value type is {}".format(type(value)))
+            raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 
         if value not in ('CPU', 'Ascend'):
             raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(value))

mindspore/nn/optim/proximal_ada_grad.py

@@ -159,7 +159,7 @@ class ProximalAdagrad(Optimizer):
         """If the input value is set to "CPU", the parameters will be updated on the host using the Fused
            optimizer operation."""
         if not isinstance(value, str):
-            raise ValueError("The value must be str type, but got value type is {}".format(type(value)))
+            raise TypeError("The value must be str type, but got value type is {}".format(type(value)))
 
         if value not in ('CPU', 'Ascend'):
             raise ValueError("The value must be 'CPU' or 'Ascend', but got value {}".format(value))

mindspore/nn/wrap/loss_scale.py

@@ -79,13 +79,13 @@ class DynamicLossScaleUpdateCell(Cell):
         >>> net_with_loss = Net()
         >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> manager = nn.DynamicLossScaleUpdateCell(loss_scale_value=2**12, scale_factor=2, scale_window=1000)
-        >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
+        >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
         >>> train_network.set_train()
         >>>
         >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
         >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
         >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
-        >>> output = train_network(inputs, label, scaling_sens)
+        >>> output = train_network(inputs, label, scale_sense=scaling_sens)
     """
 
     def __init__(self,
@@ -145,13 +145,13 @@ class FixedLossScaleUpdateCell(Cell):
         >>> net_with_loss = Net()
         >>> optimizer = nn.Momentum(net_with_loss.trainable_params(), learning_rate=0.1, momentum=0.9)
         >>> manager = nn.FixedLossScaleUpdateCell(loss_scale_value=2**12)
-        >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_update_cell=manager)
+        >>> train_network = nn.TrainOneStepWithLossScaleCell(net_with_loss, optimizer, scale_sense=manager)
         >>> train_network.set_train()
         >>>
         >>> inputs = Tensor(np.ones([16, 16]).astype(np.float32))
         >>> label = Tensor(np.zeros([16, 16]).astype(np.float32))
         >>> scaling_sens = Tensor(np.full((1), np.finfo(np.float32).max), dtype=mindspore.float32)
-        >>> output = train_network(inputs, label, scaling_sens)
+        >>> output = train_network(inputs, label, scale_sense=scaling_sens)
     """
 
     def __init__(self, loss_scale_value):