fixed range

mindspore/nn/layer/math.py

@@ -15,6 +15,7 @@
 """math"""
 import math
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _inner_ops as inner
 from mindspore.common.tensor import Tensor
 from ..cell import Cell
 from ...common import dtype as mstype
@@ -114,7 +115,7 @@ class Range(Cell):
             limit = float(limit)
         if isinstance(delta, int):
             delta = float(delta)
-        self.range_x = P.Range(start, limit, delta)
+        self.range_x = inner.Range(start, limit, delta)
         if limit is None:
             length_input = math.ceil(start / delta)
         else:

mindspore/nn/layer/normalization.py

@@ -375,7 +375,7 @@ class GlobalBatchNorm(_BatchNorm):
 
     Args:
         num_features (int): `C` from an expected input of size (N, C, H, W).
-        device_num_each_group (int): The number of devices in each group.
+        device_num_each_group (int): The number of devices in each group. Default: 1.
         eps (float): A value added to the denominator for numerical stability. Default: 1e-5.
         momentum (float): A floating hyperparameter of the momentum for the
             running_mean and running_var computation. Default: 0.9.

mindspore/ops/operations/array_ops.py

@@ -122,8 +122,8 @@ class SameTypeShape(PrimitiveWithInfer):
     Checks whether data type and shape of two tensors are the same.
 
     Raises:
-        TypeError - If data type not the same.
-        ValueError - If shape of two tensors not the same.
+        TypeError: If data type not the same.
+        ValueError: If shape of two tensors not the same.
 
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.

mindspore/ops/operations/nn_ops.py

@@ -673,7 +673,7 @@ class BatchNorm(PrimitiveWithInfer):
         >>> mean = Tensor(np.ones([64]), mindspore.float32)
         >>> variance = Tensor(np.ones([64]), mindspore.float32)
         >>> batch_norm = P.BatchNorm()
-        >>> output = batch_norm(input_x, scale, bias, mean, variance
+        >>> output = batch_norm(input_x, scale, bias, mean, variance)
     """
 
     @prim_attr_register