diff --git a/doc/fluid/dev/api_doc_std_cn.md b/doc/fluid/dev/api_doc_std_cn.md
index 9e9e77177f268154e1b717cf020643e2f30948ec..5596b2653ae6ed9917f77dad08f926bcb1fb3419 100644
--- a/doc/fluid/dev/api_doc_std_cn.md
+++ b/doc/fluid/dev/api_doc_std_cn.md
@@ -40,7 +40,7 @@ API文档须包含以下几个模块（排列顺序为文档撰写顺序）：
 
 ## 格式及示例
 
-API文档须使用rst格式撰写，该格式详情请参考[链接](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html)。API文档各模块的内容格式及示例如下（以下以fc为例进行说明）：
+API文档须使用reStructuredText格式撰写，该格式详情请参考[链接](http://sphinx-doc-zh.readthedocs.io/en/latest/rest.html)。API文档各模块的内容格式及示例如下（以下以fc为例进行说明）：
 
 - Python API Definition
 
@@ -137,7 +137,8 @@ API文档须使用rst格式撰写，该格式详情请参考[链接](http://sphi
 
       ```
       Args:
-          input (Variable|list of Variable): This layer's input tensor(s) which is at least 2-dimensional.
+          input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+              the input tensor(s) is at least 2.
           param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
               parameters/weights of this layer.
           name (str, default None): The name of this layer.
diff --git a/doc/fluid/dev/src/fc.py b/doc/fluid/dev/src/fc.py
index 14a3c4cd01e2b0dc85b1156db9fe903f5d04c581..3b074821cc2276a29b2a8639e82199fcf4d72020 100644
--- a/doc/fluid/dev/src/fc.py
+++ b/doc/fluid/dev/src/fc.py
@@ -48,7 +48,8 @@ def fc(input,
     * :math:`Out`: The output tensor.
 
     Args:
-        input (Variable|list of Variable): This layer's input tensor(s) which is at least 2-dimensional.
+        input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+            the input tensor(s) is at least 2.
         size(int): The number of output units in this layer.
         num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
             two dimensions. If this happens, the multidimensional tensor will first be flattened
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index ffa477ba9b88126d8ff0ed404e64830b087314e9..63e110251a8d5d4e4c81a15fa6effb78ca639f73 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -85,13 +85,12 @@ def fc(input,
     **Fully Connected Layer**
 
     The fully connected layer can take multiple tensors as its inputs. It
-    creates a variable (one for each input tensor) called weights for each
-    input tensor, which represents a fully connected weight matrix from
-    each input unit to each output unit. The fully connected layer
-    multiplies each input tensor with its coresponding weight to produce
-    an output Tensor. If multiple input tensors are given, the results of
-    multiple multiplications will be sumed up. If bias_attr is not None,
-    a biases variable will be created and added to the output. Finally,
+    creates a variable called weights for each input tensor, which represents
+    a fully connected weight matrix from each input unit to each output unit.
+    The fully connected layer multiplies each input tensor with its coresponding
+    weight to produce an output Tensor. If multiple input tensors are given,
+    the results of multiple multiplications will be sumed up. If bias_attr is
+    not None, a bias variable will be created and added to the output. Finally,
     if activation is not None, it will be applied to the output as well.
 
     This process can be formulated as follows:
@@ -110,44 +109,27 @@ def fc(input,
     * :math:`Out`: The output tensor.
 
     Args:
-       input(Variable|list): The input tensor(s) to the fully connected layer.
-       size(int): The number of output units in the fully connected layer.
-       num_flatten_dims(int): The fc layer can accept an input tensor with more
-                              than two dimensions. If this happens, the
-                              multidimensional tensor will first be flattened
-                              into a 2-dimensional matrix. The parameter
-                              `num_flatten_dims` determines how the input tensor
-                              is flattened: the first `num_flatten_dims`
-                              (inclusive, index starts from 1) dimensions will
-                              be flatten to form the first dimension of the
-                              final matrix (height of the matrix), and the rest
-                              `rank(X) - num_flatten_dims` dimensions are
-                              flattened to form the second dimension of the
-                              final matrix (width of the matrix). For example,
-                              suppose `X` is a 6-dimensional tensor with a shape
-                              [2, 3, 4, 5, 6], and `num_flatten_dims` = 3. Then,
-                              the flattened matrix will have a shape
-                              [2 x 3 x 4, 5 x 6] = [24, 30]. By default,
-                              `num_flatten_dims` is set to 1.
-       param_attr(ParamAttr|list): The parameter attribute for learnable
-                                   parameters/weights of the fully connected
-                                   layer.
-       param_initializer(ParamAttr|list): The initializer used for the
-                                          weight/parameter. If set None,
-                                          XavierInitializer() will be used.
-       bias_attr(ParamAttr|list): The parameter attribute for the bias parameter
-                                  for this layer. If set None, no bias will be
-                                  added to the output units.
-       bias_initializer(ParamAttr|list): The initializer used for the bias.
-                                        If set None, then ConstantInitializer()
-                                        will be used.
-       act(str): Activation to be applied to the output of the fully connected
-                 layer.
-       name(str): Name/alias of the fully connected layer.
-
+        input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
+            the input tensor(s) is at least 2.
+        size(int): The number of output units in this layer.
+        num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
+            two dimensions. If this happens, the multidimensional tensor will first be flattened
+            into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
+            tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
+            dimensions will be flatten to form the first dimension of the final matrix (height of
+            the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
+            form the second dimension of the final matrix (width of the matrix). For example, suppose
+            `X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
+            Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
+        param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
+            parameters/weights of this layer.
+        bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
+            of this layer. If it is set to None, no bias will be added to the output units.
+        act (str, default None): Activation to be applied to the output of this layer.
+        name (str, default None): The name of this layer.
 
     Returns:
-        Variable: The output tensor variable.
+        A tensor variable storing the transformation result.
 
     Raises:
         ValueError: If rank of the input tensor is less than 2.