fix Initializer docs, test=develop (#1949)

doc/fluid/api_cn/initializer_cn/BilinearInitializer_cn.rst

@@ -19,12 +19,13 @@ BilinearInitializer
     import math
     factor = 2
     C = 2
+    B = 8
     H = W = 32
-    w_attr = fluid.ParamAttr(
+    w_attr = fluid.param_attr.ParamAttr(
         learning_rate=0.,
         regularizer=fluid.regularizer.L2Decay(0.),
-        initializer=fluid.initializer.BilinearInitializer())
-    x = fluid.layers.data(name="data", shape=[4, H, W],
+        initializer=fluid.initializer.Bilinear())
+    x = fluid.data(name="data", shape=[B, 3, H, W],
                           dtype="float32")
     conv_up = fluid.layers.conv2d_transpose(
         input=x,

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

@@ -19,7 +19,7 @@ Random Truncated Normal(高斯)分布初始化函数
 .. code-block:: python
 
         import paddle.fluid as fluid
-        x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+        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))