diff --git a/mindspore/nn/layer/activation.py b/mindspore/nn/layer/activation.py
index 8845247a65..8152a81e9b 100644
--- a/mindspore/nn/layer/activation.py
+++ b/mindspore/nn/layer/activation.py
@@ -48,6 +48,11 @@ class Softmax(Cell):
     Outputs:
         Tensor, which has the same type and shape as `x` with values in the range[0,1].
 
+    Examples:
+        >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
+        >>> softmax = nn.Softmax()
+        >>> softmax(input_x)
+        [0.03168  0.01166  0.0861  0.636  0.2341]
     """
     def __init__(self, axis=-1):
         super(Softmax, self).__init__()
@@ -78,6 +83,12 @@ class LogSoftmax(Cell):
     Outputs:
         Tensor, which has the same type and shape as the input as `x` with values in the range[-inf,0).
 
+    Examples:
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
+        >>> log_softmax = nn.LogSoftmax()
+        >>> log_softmax(input_x)
+        [[-5.00672150e+00 -6.72150636e-03 -1.20067215e+01]
+         [-7.00091219e+00 -1.40009127e+01 -9.12250078e-04]]
     """
 
     def __init__(self, axis=-1):
@@ -134,6 +145,11 @@ class ReLU(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_data`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([-1, 2, -3, 2, -1]), mindspore.float16)
+        >>> relu = nn.ReLU()
+        >>> relu(input_x)
+        [0.  2.  0.  2.  0.]
     """
     def __init__(self):
         super(ReLU, self).__init__()
@@ -157,6 +173,11 @@ class ReLU6(Cell):
     Outputs:
         Tensor, which has the same type with `input_data`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
+        >>> relu6 = nn.ReLU6()
+        >>> relu6(input_x)
+        [0.  0.  0.  2.  1.]
     """
     def __init__(self):
         super(ReLU6, self).__init__()
@@ -188,6 +209,12 @@ class LeakyReLU(Cell):
     Outputs:
         Tensor, has the same type and shape with the `input_x`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
+        >>> leaky_relu = nn.LeakyReLU()
+        >>> leaky_relu(input_x)
+        [[-0.2  4.  -1.6]
+         [ 2   -1.   9.]]
     """
     def __init__(self, alpha=0.2):
         super(LeakyReLU, self).__init__()
@@ -224,6 +251,11 @@ class Tanh(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_data`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([1, 2, 3, 2, 1]), mindspore.float16)
+        >>> tanh = nn.Tanh()
+        >>> tanh(input_x)
+        [0.7617  0.964  0.995  0.964 0.7617]
     """
     def __init__(self):
         super(Tanh, self).__init__()
@@ -249,6 +281,12 @@ class GELU(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_data`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
+        >>> gelu = nn.GELU()
+        >>> gelu(input_x)
+        [[-1.5880802e-01  3.9999299e+00 -3.1077917e-21]
+         [ 1.9545976e+00 -2.2918017e-07  9.0000000e+00]]
     """
     def __init__(self):
         super(GELU, self).__init__()
@@ -273,6 +311,11 @@ class Sigmoid(Cell):
     Outputs:
         Tensor, with the same type and shape as the `input_data`.
 
+    Examples:
+        >>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
+        >>> sigmoid = nn.Sigmoid()
+        >>> sigmoid(input_x)
+        [0.2688  0.11914  0.5  0.881  0.7305]
     """
     def __init__(self):
         super(Sigmoid, self).__init__()