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+# API注释撰写标准
+
+- [API注释模块](#API注释模块)
+- [格式及示例](#格式及示例)
+- [完整示例](#完整示例)
+
+
+## API注释模块
+
+API文档须包含以下几个模块（排列顺序为文档撰写顺序）：
+
+- Python API Definition
+
+  API的代码定义。
+
+- Function Description
+
+  API的功能描述。描述该API的含义、作用或对输入所做的操作，及参考文献和对应链接（如果有），必要时给出公式，并解释公式中关键变量的含义。
+
+- Args Description
+
+  API参数介绍。按代码定义中的参数顺序逐个介绍，介绍内容包含数据类型、默认值（如果有）、含义等。
+
+- Returns
+
+  API返回值介绍。介绍返回值含义，必要时给出对应的形状。若返回值为包含多个参数的tuple，则按顺序逐个介绍各参数。
+
+- Raises（如果有）
+
+  可能抛出的异常或错误及可能的产生原因，当可能抛出多种异常或错误时应分条列出。
+
+- Note（如果有）
+
+  注意事项。当有多条注意事项时，应分条列出。
+
+- Examples
+
+  API的使用示例。
+
+
+## 格式及示例
+
+API文档各模块格式及示例如下(以下以fc为例进行说明）：
+
+- Python API Definition
+
+  - 格式：
+    
+      [Python API Definition]
+    
+  - 示例
+  
+      ```
+      fc(input,
+         size,
+         num_flatten_dims=1,
+         param_attr=None,
+         bias_attr=None,
+         act=None,
+         name=None,
+         main_program=None,
+         startup_program=None)
+      ```
+
+- Function Description
+  
+  - 格式
+
+      本模块应包含以下内容（排列顺序为文档撰写顺序）：
+
+      [Function Description]
+  
+      [Formula]
+    
+      [Symbols' Descriptions if necessary]
+    
+      [References if necessary]
+ 
+  - 示例
+
+      [Function Description]
+
+       ```
+       **Fully Connected Layer**
+
+       The fully connected layer can take multiple tensors as its inputs. It
+       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.
+       ```
+
+      [Formula]
+
+      ```
+      This process can be formulated as follows:
+
+      .. math::
+
+           Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
+      ```
+
+      [Symbols' Descriptions if necessary]
+
+      ```
+      In the above equation:
+
+      * :math:`N`: Number of the input.
+      * :math:`X_i`: The input tensor.
+      * :math:`W`: The weights created by this layer.
+      * :math:`b`: The bias parameter created by this layer (if needed).
+      * :math:`Act`: The activation function.
+      * :math:`Out`: The output tensor.
+      ```
+
+      [References if necessary]
+
+      因fc没有必要列出的参考文献，故该内容省略。其他情况下需明确给出对应的参考文献和对应连接，以 layer_norm 为例：
+      
+      ```
+      Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_ for more details.
+      ```
+  
+
+- Args Description
+  
+  - 格式
+  
+      \[Arg's Name\][(Data Type, Default Value)][Description]
+  
+  - 示例
+
+      fc的部分参数注释如下：
+
+      ```
+      Args:
+          input (Tensor): The input tensor(s) of the layer.
+          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.
+      ```
+
+- Returns
+  
+  - 格式
+  
+      [Name][Shape]
+  
+  - 示例
+  
+      ```
+      Returns:
+          A tensor variable storing the transformation result.
+      ```
+  
+      当返回值为包含多个参数的tuple时，应按顺序逐个介绍各参数，以dynamic_lstm为例：
+  
+      ```
+      Returns:
+          A tuple containing:
+            The hidden state of LSTM whose shape is (T X D).
+            The cell state of LSTM whose shape is (T X D).
+      ```
+  
+- Raises
+
+  - 格式
+  
+      [Exception Type][Condition]
+
+  - 示例
+  
+      ```
+      Raises:
+          ValueError: If the rank of the input is less than 2.
+      ```
+
+- Note
+
+  - 格式
+  
+     [Note]
+
+  - 示例
+
+      fc没有注意事项，故该模块省略不写。其他情况应明确给出，若有多条注意事项，须分条列出，以scaled\_dot\_product\_attention为例：
+
+      ```
+      Note:
+          1. When num_heads > 1, three linear projections are learned respectively
+             to map input queries, keys and values into queries', keys' and values'.
+             queries', keys' and values' have the same shapes with queries, keys
+             and values.
+          2. When num_heads == 1, scaled_dot_product_attention has no learnable
+             parameters.
+      ```
+  
+- Examples
+
+  - 格式
+
+      \[Python Code Snipper]
+  
+  - 示例
+  
+      ```
+      Examples:
+          .. code-block:: python
+
+            data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+            fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+      ```
+
+## 完整示例
+
+fc 的完整注释见[示例](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/dev/src/fc.py)。
diff --git a/doc/fluid/dev/src/fc.py b/doc/fluid/dev/src/fc.py
new file mode 100644
index 0000000000000000000000000000000000000000..40f3c7fd3ebb561b07d5ce1b845ca3ca8beb814c
--- /dev/null
+++ b/doc/fluid/dev/src/fc.py
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+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+def fc(input,
+       size,
+       num_flatten_dims=1,
+       param_attr=None,
+       bias_attr=None,
+       act=None,
+       name=None):
+    """
+    **Fully Connected Layer**
+
+    The fully connected layer can take multiple tensors as its inputs. It
+    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:
+
+    .. math::
+
+        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
+
+    In the above equation:
+
+    * :math:`N`: Number of the input.
+    * :math:`X_i`: The input tensor.
+    * :math:`W`: The weights created by this layer.
+    * :math:`b`: The bias parameter created by this layer (if needed).
+    * :math:`Act`: The activation function.
+    * :math:`Out`: The output tensor.
+
+    Args:
+        input (Tensor|list of Tensor): The input tensor(s) to this layer.
+        size(int): The number of output units in the fully connected 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
+            parameter of this layer. If set 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:
+        A tensor variable storing the transformation result.
+
+    Raises:
+        ValueError: If rank of the input tensor is less than 2.
+
+    Examples:
+        .. code-block:: python
+
+          data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+          fc = fluid.layers.fc(input=data, size=1000, act="tanh")
+    """