modify code doc

mindspore/_checkparam.py

@@ -430,7 +430,7 @@ class Validator:
     @staticmethod
     def check_file_name_by_regular(target, reg=None, flag=re.ASCII, prim_name=None):
         if reg is None:
-            reg = r"^[0-9a-zA-Z\_\.\/\\]*$"
+            reg = r"^[0-9a-zA-Z\_\-\.\/\\]*$"
         if re.match(reg, target, flag) is None:
             prim_name = f'in `{prim_name}`' if prim_name else ""
             raise ValueError("'{}' {} is illegal, it should be match regular'{}' by flags'{}'".format(

mindspore/nn/layer/container.py

@@ -84,17 +84,16 @@ class SequentialCell(Cell):
         ``Ascend`` ``GPU``
 
     Examples:
-        >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid')
-        >>> bn = nn.BatchNorm2d(2)
+        >>> conv = nn.Conv2d(3, 2, 3, pad_mode='valid', weight_init="ones")
         >>> relu = nn.ReLU()
-        >>> seq = nn.SequentialCell([conv, bn, relu])
-        >>> x = Tensor(np.random.random((1, 3, 4, 4)), dtype=mindspore.float32)
+        >>> seq = nn.SequentialCell([conv, relu])
+        >>> x = Tensor(np.ones([1, 3, 4, 4]), dtype=mindspore.float32)
         >>> output = seq(x)
         >>> print(output)
-        [[[[0.02531557 0.        ]
-           [0.04933941 0.04880078]]
-          [[0.         0.        ]
-           [0.         0.        ]]]]
+        [[[[27. 27.]
+           [27. 27.]]
+          [[27. 27.]
+           [27. 27.]]]]
     """
     def __init__(self, *args):
         super(SequentialCell, self).__init__()

mindspore/train/loss_scale_manager.py

@@ -42,8 +42,10 @@ class FixedLossScaleManager(LossScaleManager):
         drop_overflow_update (bool): whether to execute optimizer if there is an overflow. Default: True.
 
     Examples:
+        >>> net = Net()
         >>> loss_scale_manager = FixedLossScaleManager()
-        >>> model = Model(net, loss_scale_manager=loss_scale_manager)
+        >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
     """
     def __init__(self, loss_scale=128.0, drop_overflow_update=True):
         if loss_scale < 1:
@@ -85,8 +87,10 @@ class DynamicLossScaleManager(LossScaleManager):
         scale_window (int): Maximum continuous normal steps when there is no overflow. Default: 2000.
 
     Examples:
+        >>> net = Net()
         >>> loss_scale_manager = DynamicLossScaleManager()
-        >>> model = Model(net, loss_scale_manager=loss_scale_manager)
+        >>> optim = Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9)
+        >>> model = Model(net, loss_scale_manager=loss_scale_manager, optimizer=optim)
     """
     def __init__(self,
                  init_loss_scale=2 ** 24,