diff --git a/mindspore/nn/layer/normalization.py b/mindspore/nn/layer/normalization.py
index fdfa25e1838..d43c1c8ab40 100644
--- a/mindspore/nn/layer/normalization.py
+++ b/mindspore/nn/layer/normalization.py
@@ -249,7 +249,7 @@ class LayerNorm(Cell):
             'he_uniform', etc. Default: 'zeros'.
 
     Inputs:
-        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = `(x_1, x_2, ..., x_R)`,
+        - **input_x** (Tensor) - The shape of 'input_x' is input_shape = :math:`(x_1, x_2, ..., x_R)`,
           and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`.
 
     Outputs:
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index 8585f873e98..f6d563321c4 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1790,7 +1790,7 @@ class ScatterNd(PrimitiveWithInfer):
 
 
 class ResizeNearestNeighbor(PrimitiveWithInfer):
-    """
+    r"""
     Resize the input tensor by using nearest neighbor algorithm.
 
     Resize input tensor to given size by using nearest neighbor algorithm. The nearest
@@ -1806,7 +1806,7 @@ class ResizeNearestNeighbor(PrimitiveWithInfer):
          - **input_x** (Tensor) - The input tensor. The shape of the tensor is :math:`(N, C, H, W)`.
 
     Outputs:
-        Tensor, the shape of the output tensor is :math:`(N, NEW_C, NEW_H, W)`.
+        Tensor, the shape of the output tensor is :math:`(N, NEW\_C, NEW\_H, W)`.
 
     Examples:
         >>> input_tensor = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)