update api list (#506) (#508)

doc/fluid/api/index_cn.rst

-=============
-API Reference
-=============
-
-..  toctree::
-    :maxdepth: 1
-
-    fluid.rst
-    average.rst
-    backward.rst
-    clip.rst
-    data.rst
-    data_feeder.rst
-    executor.rst
-    initializer.rst
-    io.rst
-    layers.rst
-    metrics.rst
-    nets.rst
-    optimizer.rst
-    param_attr.rst
-    profiler.rst
-    recordio_writer.rst
-    regularizer.rst
-    transpiler.rst
-

doc/fluid/api/index_en.rst

@@ -18,7 +18,6 @@ API Reference
     metrics.rst
     nets.rst
     optimizer.rst
-    param_attr.rst
     profiler.rst
     recordio_writer.rst
     regularizer.rst

doc/fluid/api/param_attr.rst

-..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
-    !DO NOT EDIT THIS FILE MANUALLY!
-
-================
-fluid.param_attr
-================
-
-.. _api_fluid_param_attr_ParamAttr:
-
-ParamAttr
----------
-
-..  autoclass:: paddle.fluid.param_attr.ParamAttr
-    :members:
-    :noindex:
-
-.. _api_fluid_param_attr_WeightNormParamAttr:
-
-WeightNormParamAttr
--------------------
-
-..  autoclass:: paddle.fluid.param_attr.WeightNormParamAttr
-    :members:
-    :noindex:
-

doc/fluid/api_cn/index_cn.rst

@@ -18,7 +18,6 @@ API
     metrics_cn.rst
     nets_cn.rst
     optimizer_cn.rst
-    param_attr_cn.rst
     profiler_cn.rst
     regularizer_cn.rst
     transpiler_cn.rst

doc/fluid/api_cn/param_attr_cn.rst

-#################
- fluid.param_attr
-#################
-
-
-
-
-.. _cn_api_fluid_param_attr_ParamAttr:
-
- 
-ParamAttr
--------------------------------
-
-
-.. py:class:: paddle.fluid.param_attr.ParamAttr(name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None, do_model_average=False)
-
-该类代表了参数的各种属性。 为了使神经网络训练过程更加流畅，用户可以根据需要调整参数属性。比如learning rate（学习率）, regularization（正则化）, trainable（可训练性）, do_model_average(平均化模型)和参数初始化方法.
-
-参数:	
-    - **name** (str) – 参数名。默认为None。
-    - **initializer** (Initializer) – 初始化该参数的方法。 默认为None
-    - **learning_rate** (float) – 参数的学习率。计算方法为 global_lr*parameter_lr∗scheduler_factor。 默认为1.0
-    - **regularizer** (WeightDecayRegularizer) – 正则因子. 默认为None
-    - **trainable** (bool) – 该参数是否可训练。默认为True
-    - **gradient_clip** (BaseGradientClipAttr) – 减少参数梯度的方法。默认为None
-    - **do_model_average** (bool) – 该参数是否服从模型平均值。默认为False
-    
-**代码示例**
-
-..  code-block:: python
-
-   w_param_attrs = fluid.ParamAttr(name="fc_weight",
-                                   learning_rate=0.5,
-                                   regularizer=fluid.L2Decay(1.0),
-                                   trainable=True)
-   y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
-
-
-
-
-
-
-
-
-
-
-.. _cn_api_fluid_param_attr_WeightNormParamAttr:
-
-WeightNormParamAttr
--------------------------------
-
-.. py:class:: paddle.fluid.param_attr.WeightNormParamAttr(dim=None, name=None, initializer=None, learning_rate=1.0, regularizer=None, trainable=True, gradient_clip=None, do_model_average=False)
-  
-权重归一化。权范数是神经网络中权向量的再参数化，它将权向量的长度与其方向解耦。该paper对权值归一化的实现进行了讨论： `Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks <https://arxiv.org/pdf/1602.07868.pdf>`_ 
-
-参数:
-  - **dim**  (list) – 参数维度. Default None.
-  - **name** (str) – 参数名称. Default None.
-  - **initializer**  (Initializer) – 初始化参数的方法. Default None.
-  - **learning_rate**  (float) – 参数的学习率. 优化的参数学习率为 :math:`global\_lr*parameter\_lr*scheduler\_factor` . Default 1.0
-  - **regularizer**  (WeightDecayRegularizer) – 正则化因子. Default None.
-  - **trainable**  (bool) – 参数是否可训练. Default True.
-  - **gradient_clip**  (BaseGradientClipAttr) – 修剪这个参数的梯度的方法. Default None.
-  - **do_model_average**  (bool) – 这个参数是否应该做模型平均. Default False.
-
-
-**代码示例**
-
-
-..  code-block:: python
-  
-    data = fluid.layers.data(name="data", shape=[3, 32, 32], dtype="float32")
-    fc = fluid.layers.fc(input=data,
-                          size=1000,
-                          param_attr=WeightNormParamAttr(
-                          dim=None,
-                          name='weight_norm_param'))
-                          
-             
-
-
-
-
-
-
-
-
-
-
-