Update L1Decay and L2Decay (#1367)

* modify L1DecayRegularizer, L1Decay, L2DecayRegularizer and L2Decay, test=develop

doc/fluid/api_cn/regularizer_cn/L1DecayRegularizer_cn.rst

@@ -5,33 +5,7 @@ L1DecayRegularizer
 
 .. py:class:: paddle.fluid.regularizer.L1DecayRegularizer(regularization_coeff=0.0)
 
-L1DecayRegularizer实现L1权重衰减正则化，用于模型训练，使得权重矩阵稀疏。
+**注意：paddle.fluid.regularizer.L1DecayRegularizer是paddle.fluid.regularizer.L1Decay的别名，推荐使用paddle.fluid.regularizer.L1Decay。**
 
-具体实现中，L1权重衰减正则化的计算公式如下：
+详见 :ref:`cn_api_fluid_regularizer_L1Decay` 接口的使用文档。
 
-.. math::
-            \\L1WeightDecay=reg\_coeff∗sign(parameter)\\
-
-参数：
-  - **regularization_coeff** (float) – L1正则化系数，默认值为0.0。
-
-**代码示例**
-
-.. code-block:: python
-
-    import paddle.fluid as fluid
-
-    main_prog = fluid.Program()
-    startup_prog = fluid.Program()
-    with fluid.program_guard(main_prog, startup_prog):
-        data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32')
-        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-        hidden = fluid.layers.fc(input=data, size=128, act='relu')
-        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
-        loss = fluid.layers.cross_entropy(input=prediction, label=label)
-        avg_loss = fluid.layers.mean(loss)
-    optimizer = fluid.optimizer.Adagrad(
-        learning_rate=1e-4,
-        regularization=fluid.regularizer.L1DecayRegularizer(
-            regularization_coeff=0.1))
-    optimizer.minimize(avg_loss)

doc/fluid/api_cn/regularizer_cn/L1Decay_cn.rst

@@ -6,8 +6,6 @@ L1Decay
 
 .. py:attribute::   paddle.fluid.regularizer.L1Decay(regularization_coeff=0.0)
 
-``L1Decay`` 是 ``L1DecayRegularizer`` 的别名。
-
 L1Decay实现L1权重衰减正则化，用于模型训练，使得权重矩阵稀疏。
 
 具体实现中，L1权重衰减正则化的计算公式如下：

doc/fluid/api_cn/regularizer_cn/L2DecayRegularizer_cn.rst

@@ -5,33 +5,8 @@ L2DecayRegularizer
 
 .. py:class:: paddle.fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
 
-L2DecayRegularizer实现L2权重衰减正则化，用于模型训练，有助于防止模型对训练数据过拟合。
+**注意：paddle.fluid.regularizer.L2DecayRegularizer是paddle.fluid.regularizer.L2Decay的别名，推荐使用paddle.fluid.regularizer.L2Decay。**
 
-具体实现中，L2权重衰减正则化的计算公式如下：
+详见 :ref:`cn_api_fluid_regularizer_L2Decay` 接口的使用文档。
 
-.. math::
-            \\L2WeightDecay=reg\_coeff*parameter\\
 
-参数:
-  - **regularization_coeff** (float) – 正则化系数，默认值为0.0。
-  
-**代码示例**
-
-.. code-block:: python
-
-    import paddle.fluid as fluid
-
-    main_prog = fluid.Program()
-    startup_prog = fluid.Program()
-    with fluid.program_guard(main_prog, startup_prog):
-        data = fluid.layers.data(name='image', shape=[3, 28, 28], dtype='float32')
-        label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-        hidden = fluid.layers.fc(input=data, size=128, act='relu')
-        prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
-        loss = fluid.layers.cross_entropy(input=prediction, label=label)
-        avg_loss = fluid.layers.mean(loss)
-    optimizer = fluid.optimizer.Adagrad(
-        learning_rate=1e-4,
-        regularization=fluid.regularizer.L2DecayRegularizer(
-            regularization_coeff=0.1))
-    optimizer.minimize(avg_loss)

doc/fluid/api_cn/regularizer_cn/L2Decay_cn.rst

@@ -5,8 +5,6 @@ L2Decay
 
 .. py:attribute::   paddle.fluid.regularizer.L2Decay
 
-``L2Decay`` 是 ``L2DecayRegularizer`` 的别名。
-
 L2Decay实现L2权重衰减正则化，用于模型训练，有助于防止模型对训练数据过拟合。
 
 具体实现中，L2权重衰减正则化的计算公式如下：