fix Initializer docs, test=develop (#1949)

doc/fluid/api_cn/initializer_cn/BilinearInitializer_cn.rst

-.. _cn_api_fluid_initializer_BilinearInitializer:
-
-BilinearInitializer
--------------------------------
-
-.. py:class:: paddle.fluid.initializer.BilinearInitializer())
-
+.. _cn_api_fluid_initializer_BilinearInitializer:
+
+BilinearInitializer
+-------------------------------
+
+.. py:class:: paddle.fluid.initializer.BilinearInitializer())
+
 该接口为参数初始化函数，用于转置卷积函数中，对输入进行上采样。用户通过任意整型因子放大shape为(B，C，H，W)的特征图。
-
+
 返回：对象
 
 用法如下：
-
-**代码示例**:
-
-.. code-block:: python
-
-    import paddle.fluid as fluid
+
+**代码示例**:
+
+.. code-block:: python
+
+    import paddle.fluid as fluid
     import math
-    factor = 2
-    C = 2
-    H = W = 32
-    w_attr = fluid.ParamAttr(
-        learning_rate=0.,
-        regularizer=fluid.regularizer.L2Decay(0.),
-        initializer=fluid.initializer.BilinearInitializer())
-    x = fluid.layers.data(name="data", shape=[4, H, W],
-                          dtype="float32")
-    conv_up = fluid.layers.conv2d_transpose(
-        input=x,
-        num_filters=C,
-        output_size=None,
-        filter_size=2 * factor - factor % 2,
-        padding=int(math.ceil((factor - 1) / 2.)),
-        stride=factor,
-        groups=C,
-        param_attr=w_attr,
-        bias_attr=False)
-
-上述代码实现的是将输入x（shape=[-1, 4, H, W]）经过转置卷积得到shape=[-1, C, H*factor, W*factor]的输出，num_filters = C和groups = C 表示这是按通道转置的卷积函数，输出通道为C，转置卷积的groups为C。滤波器shape为(C,1,K,K)，K为filter_size。该初始化函数为滤波器的每个通道设置(K,K)插值核。输出特征图的最终输出shape为(B,C,factor*H,factor*W)。注意学习率和权重衰减设为0，以便在训练过程中双线性插值的系数值保持不变
-
-
-
-
+    factor = 2
+    C = 2
+    B = 8
+    H = W = 32
+    w_attr = fluid.param_attr.ParamAttr(
+        learning_rate=0.,
+        regularizer=fluid.regularizer.L2Decay(0.),
+        initializer=fluid.initializer.Bilinear())
+    x = fluid.data(name="data", shape=[B, 3, H, W],
+                          dtype="float32")
+    conv_up = fluid.layers.conv2d_transpose(
+        input=x,
+        num_filters=C,
+        output_size=None,
+        filter_size=2 * factor - factor % 2,
+        padding=int(math.ceil((factor - 1) / 2.)),
+        stride=factor,
+        groups=C,
+        param_attr=w_attr,
+        bias_attr=False)
+
+上述代码实现的是将输入x（shape=[-1, 4, H, W]）经过转置卷积得到shape=[-1, C, H*factor, W*factor]的输出，num_filters = C和groups = C 表示这是按通道转置的卷积函数，输出通道为C，转置卷积的groups为C。滤波器shape为(C,1,K,K)，K为filter_size。该初始化函数为滤波器的每个通道设置(K,K)插值核。输出特征图的最终输出shape为(B,C,factor*H,factor*W)。注意学习率和权重衰减设为0，以便在训练过程中双线性插值的系数值保持不变
+
+
+
+

doc/fluid/api_cn/initializer_cn/ConstantInitializer_cn.rst

@@ -18,9 +18,9 @@ ConstantInitializer
 .. code-block:: python
 
         import paddle.fluid as fluid
-        x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+        x = fluid.data(name="data", shape=[32, 32], dtype="float32")
         fc = fluid.layers.fc(input=x, size=10,
-            param_attr=fluid.initializer.ConstantInitializer(value=2.0))
+            param_attr=fluid.initializer.Constant(value=2.0))
 
 
 

doc/fluid/api_cn/initializer_cn/TruncatedNormalInitializer_cn.rst

-.. _cn_api_fluid_initializer_TruncatedNormalInitializer:
-
-TruncatedNormalInitializer
--------------------------------
-
-.. py:class:: paddle.fluid.initializer.TruncatedNormalInitializer(loc=0.0, scale=1.0, seed=0)
-
-Random Truncated Normal(高斯)分布初始化函数
-
-参数：
-    - **loc** (float16|float32) - 正态分布的平均值
-    - **scale** (float16|float32) - 正态分布的标准差
-    - **seed** (int32) - 随机种子
-
-返回：对象
-
-**代码示例**
-
-.. code-block:: python
-
-        import paddle.fluid as fluid
-        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
-        fc = fluid.layers.fc(input=x, size=10,
-            param_attr=fluid.initializer.TruncatedNormal(loc=0.0, scale=2.0))
-
-
-
-
-
-
-
-
+.. _cn_api_fluid_initializer_TruncatedNormalInitializer:
+
+TruncatedNormalInitializer
+-------------------------------
+
+.. py:class:: paddle.fluid.initializer.TruncatedNormalInitializer(loc=0.0, scale=1.0, seed=0)
+
+Random Truncated Normal(高斯)分布初始化函数
+
+参数：
+    - **loc** (float16|float32) - 正态分布的平均值
+    - **scale** (float16|float32) - 正态分布的标准差
+    - **seed** (int32) - 随机种子
+
+返回：对象
+
+**代码示例**
+
+.. code-block:: python
+
+        import paddle.fluid as fluid
+        x = fluid.data(name='x', shape=[None, 1], dtype='float32')
+        fc = fluid.layers.fc(input=x, size=10,
+            param_attr=fluid.initializer.TruncatedNormal(loc=0.0, scale=2.0))
+
+
+
+
+
+
+
+