diff --git a/python/paddle/nn/functional/activation.py b/python/paddle/nn/functional/activation.py
index 92d6c25ea3ac62e11d37deb42b0aaec1712fb556..53871b26b0100119e5de4001ed872f3e66ed50f7 100644
--- a/python/paddle/nn/functional/activation.py
+++ b/python/paddle/nn/functional/activation.py
@@ -813,9 +813,9 @@ def maxout(x, groups, axis=1, name=None):
     Parameters:
         x (Tensor): The input is 4-D Tensor with shape [N, C, H, W] or [N, H, W, C], the data type
             of input is float32 or float64.
-        groups (int, optional): The groups number of maxout. `groups` specifies the
+        groups (int): The groups number of maxout. `groups` specifies the
             index of channel dimension where maxout will be performed. This must be
-            a factor of number of features. Default is 1.
+            a factor of number of features.
         axis (int, optional): The axis along which to perform maxout calculations.
             It should be 1 when data format is NCHW, be -1 or 3 when data format
             is NHWC. If ``axis`` < 0, it works the same way as :math:`axis + D` ,
diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py
index 4fbb792ce85a41a5f173b87d483b4e1cb813fe05..83c216483e1bd8e3abc43d253da3d0f53d54fbca 100644
--- a/python/paddle/nn/functional/common.py
+++ b/python/paddle/nn/functional/common.py
@@ -70,17 +70,17 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
                                   data type can be float32 or float64
         kernel_sizes(int|list):   The size of convolution kernel, should be [k_h, k_w]
                                   or an integer k treated as [k, k].
-        strides(int|list):        The strides, should be [stride_h, stride_w]
+        strides(int|list, optional):        The strides, should be [stride_h, stride_w]
                                   or an integer stride treated as [sride, stride].
                                   For default, strides will be [1, 1].
-        paddings(int|list):       The paddings of each dimension, should be
+        paddings(int|list, optional):       The paddings of each dimension, should be
                                   [padding_top, padding_left, padding_bottom, padding_right]
                                   or [padding_h, padding_w] or an integer padding.
                                   If [padding_h, padding_w] was given, it will expanded to
                                   [padding_h, padding_w, padding_h, padding_w]. If an integer
                                   padding was given, [padding, padding, padding, padding] will
                                   be used. For default, paddings will be [0, 0, 0, 0]
-        dilations(int|list):      the dilations of convolution kernel, should be
+        dilations(int|list, optional):      the dilations of convolution kernel, should be
                                   [dilation_h, dilation_w], or an integer dilation treated as
                                   [dilation, dilation]. For default, it will be [1, 1].
         name(str, optional): The default value is None.
diff --git a/python/paddle/nn/layer/activation.py b/python/paddle/nn/layer/activation.py
index 1433b5870010924c0e4b3a043136acfec985101e..99df2ffe43227a06d3b933e1868a31b131613d09 100644
--- a/python/paddle/nn/layer/activation.py
+++ b/python/paddle/nn/layer/activation.py
@@ -1401,7 +1401,7 @@ class Maxout(Layer):
         \end{array}
 
     Parameters:
-        groups (int, optional): The groups number of maxout. `groups` specifies the
+        groups (int): The groups number of maxout. `groups` specifies the
             index of channel dimension where maxout will be performed. This must be
             a factor of number of features. Default is 1.
         axis (int, optional): The axis along which to perform maxout calculations.
diff --git a/python/paddle/nn/layer/common.py b/python/paddle/nn/layer/common.py
index 921643ef9d85536b78aa374ca7ed2cfa4d207e35..3dd0eb3ccce290c550e94f4acd9642b1db7c3146 100644
--- a/python/paddle/nn/layer/common.py
+++ b/python/paddle/nn/layer/common.py
@@ -1533,7 +1533,7 @@ class Embedding(Layer):
 
 class Unfold(Layer):
     """
-    This op returns a col buffer of sliding local blocks of input x, also known
+    Returns a col buffer of sliding local blocks of input x, also known
     as im2col for batched 2D image tensors. For each block under the convolution filter,
     all element will be rearranged as a column. While the convolution filter sliding over
     the input feature map, a series of such columns will be formed.
diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py
index 2d5f57f2c585eddc266c0461f626001ddfa5f99b..51bad327b93fb4232db30af60e7c6751471f7f14 100644
--- a/python/paddle/nn/layer/loss.py
+++ b/python/paddle/nn/layer/loss.py
@@ -539,7 +539,7 @@ class MSELoss(Layer):
         Out = \operatorname{sum}((input - label)^2)
     where `input` and `label` are `float32` tensors of same shape.
     Parameters:
-        reduction (string, optional): The reduction method for the output,
+        reduction (str, optional): The reduction method for the output,
             could be 'none' | 'mean' | 'sum'.
             If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned.
             If :attr:`size_average` is ``'sum'``, the reduced sum loss is returned.