diff --git a/mindspore/nn/layer/pooling.py b/mindspore/nn/layer/pooling.py
index 2c13957636..21f49101a5 100644
--- a/mindspore/nn/layer/pooling.py
+++ b/mindspore/nn/layer/pooling.py
@@ -334,7 +334,7 @@ class AvgPool1d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, L_{out})`.
 
     Examples:
-        >>> pool = nn.AvgPool1d(kernel_size=6, strides=1)
+        >>> pool = nn.AvgPool1d(kernel_size=6, stride=1)
         >>> x = Tensor(np.random.randint(0, 10, [1, 3, 6]), mindspore.float32)
         >>> output = pool(x)
         >>> output.shape
diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py
index cb3f0580f0..dbc8f9b8a9 100644
--- a/mindspore/ops/operations/array_ops.py
+++ b/mindspore/ops/operations/array_ops.py
@@ -1376,8 +1376,9 @@ class ArgMinWithValue(PrimitiveWithInfer):
         - output_x (Tensor) - The minimum value of input tensor, with the same shape as index.
 
     Examples:
-        >>> input_x = Tensor(np.random.rand(5))
+        >>> input_x = Tensor(np.random.rand(5), mindspore.float32)
         >>> index, output = P.ArgMinWithValue()(input_x)
+        0 0.0496291
     """
 
     @prim_attr_register
diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py
index 2a011f9d31..399e5ce73f 100644
--- a/mindspore/ops/operations/nn_ops.py
+++ b/mindspore/ops/operations/nn_ops.py
@@ -5740,9 +5740,13 @@ class LRN(PrimitiveWithInfer):
         Tensor, with the same shape and data type as the input tensor.
 
     Examples:
-        >>> x = Tensor(np.random.rand(1, 10, 4, 4)), mindspore.float32)
+        >>> x = Tensor(np.random.rand(1, 2, 2, 2), mindspore.float32)
         >>> lrn = P.LRN()
         >>> lrn(x)
+        [[[[0.18990143 0.59475636]
+           [0.6291904  0.1371534 ]]
+          [[0.6258911  0.4964315 ]
+           [0.3141494  0.43636137]]]]
     """
     @prim_attr_register
     def __init__(self, depth_radius=5, bias=1.0, alpha=1.0, beta=0.5, norm_region="ACROSS_CHANNELS"):