update 2.0 api.

doc/paddle/api/alias_api_mapping

@@ -203,7 +203,7 @@ paddle.nn.layer.loss.L1Loss	paddle.nn.L1Loss,paddle.nn.layer.L1Loss
 paddle.fluid.dygraph.io.TranslatedLayer	paddle.jit.TranslatedLayer
 paddle.nn.functional.conv.conv_transpose2d	paddle.nn.functional.conv_transpose2d
 paddle.tensor.manipulation.split	paddle.split,paddle.tensor.split
-paddle.fluid.layers.tensor.create_parameter	paddle.create_parameter,paddle.framework.create_parameter
+paddle.fluid.layers.tensor.create_parameter	paddle.static.create_parameter,paddle.framework.create_parameter
 paddle.nn.layer.activation.Softsign	paddle.nn.Softsign
 paddle.nn.layer.loss.CrossEntropyLoss	paddle.nn.CrossEntropyLoss,paddle.nn.layer.CrossEntropyLoss
 paddle.nn.layer.norm.BatchNorm3d	paddle.nn.BatchNorm3d
@@ -419,7 +419,7 @@ paddle.fluid.layers.iou_similarity	paddle.nn.functional.iou_similarity,paddle.nn
 paddle.fluid.layers.transpose	paddle.transpose,paddle.tensor.transpose,paddle.tensor.linalg.transpose,paddle.tensor.manipulation.transpose
 paddle.nn.functional.activation.elu	paddle.nn.functional.elu
 paddle.tensor.manipulation.broadcast_to	paddle.broadcast_to,paddle.tensor.broadcast_to
-paddle.fluid.layers.tensor.create_global_var	paddle.create_global_var,paddle.framework.create_global_var
+paddle.fluid.layers.tensor.create_global_var	paddle.static.create_global_var,paddle.framework.create_global_var
 paddle.fluid.layers.elementwise_add	paddle.elementwise_add,paddle.tensor.elementwise_add,paddle.tensor.math.elementwise_add
 paddle.fluid.layers.affine_channel	paddle.nn.functional.affine_channel,paddle.nn.functional.vision.affine_channel
 paddle.tensor.creation.full_like	paddle.full_like,paddle.tensor.full_like

doc/paddle/api/paddle/fluid/executor/scope_guard_cn.rst

@@ -25,11 +25,11 @@ scope_guard
 
 .. code-block:: python
 
-    import paddle.fluid as fluid
+    import paddle
     import numpy
 
-    new_scope = fluid.Scope()
-    with fluid.scope_guard(new_scope):
-         fluid.global_scope().var("data").get_tensor().set(numpy.ones((1, 2)), fluid.CPUPlace())
+    new_scope = paddle.static.Scope()
+    with paddle.static.scope_guard(new_scope):
+         paddle.static.global_scope().var("data").get_tensor().set(numpy.ones((1, 2)), paddle.CPUPlace())
     data = numpy.array(new_scope.find_var("data").get_tensor())
     print(data)  # [[1. 1.]]

doc/paddle/api/paddle/fluid/layers/nn/py_func_cn.rst

@@ -9,7 +9,7 @@ py_func
 
 
 
-PaddlePaddle Fluid通过py_func在Python端注册OP。py_func的设计原理在于Paddle中的LodTensor与numpy数组可以方便的互相转换，从而可使用Python中的numpy API来自定义一个Python OP。
+PaddlePaddle 通过py_func在Python端注册OP。py_func的设计原理在于Paddle中的LodTensor与numpy数组可以方便的互相转换，从而可使用Python中的numpy API来自定义一个Python OP。
 
 该自定义的Python OP的前向函数是 ``func``, 反向函数是 ``backward_func`` 。 Paddle将在前向部分调用 ``func`` ，并在反向部分调用 ``backward_func`` （如果 ``backward_func`` 不是None)。 ``x`` 为 ``func`` 的输入，必须为LoDTensor类型； ``out``  为 ``func`` 的输出， 既可以是LoDTensor类型, 也可以是numpy数组。
 
@@ -34,7 +34,7 @@ PaddlePaddle Fluid通过py_func在Python端注册OP。py_func的设计原理在
 
 ..  code-block:: python
 
-    import paddle.fluid as fluid
+    import paddle
     import six
 
     # 自定义的前向函数，可直接输入LoDTenosor
@@ -51,36 +51,38 @@ PaddlePaddle Fluid通过py_func在Python端注册OP。py_func的设计原理在
         print(x)
     
     def create_tmp_var(name, dtype, shape):
-        return fluid.default_main_program().current_block().create_var(
+        return paddle.static.default_main_program().current_block().create_var(
             name=name, dtype=dtype, shape=shape)
 
     def simple_net(img, label):
         hidden = img
         for idx in six.moves.range(4):
-            hidden = fluid.layers.fc(hidden, size=200)
+            hidden = paddle.static.nn.fc(hidden, size=200)
             new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
                 dtype=hidden.dtype, shape=hidden.shape)
 
             # 用户自定义的前向反向计算
-            hidden = fluid.layers.py_func(func=tanh, x=hidden,
+            hidden = paddle.static.nn.py_func(func=tanh, x=hidden,
                 out=new_hidden, backward_func=tanh_grad,
                 skip_vars_in_backward_input=hidden)
 
             # 用户自定义的调试函数，打印出输入的LodTensor
-            fluid.layers.py_func(func=debug_func, x=hidden, out=None)
+            paddle.static.nn.py_func(func=debug_func, x=hidden, out=None)
 
-        prediction = fluid.layers.fc(hidden, size=10, act='softmax')
-        loss = fluid.layers.cross_entropy(input=prediction, label=label)
-        return fluid.layers.mean(loss)
+        prediction = paddle.static.nn.fc(hidden, size=10, act='softmax')
+        loss = paddle.static.nn.cross_entropy(input=prediction, label=label)
+        return paddle.mean(loss)
 
 **示例代码2**:
 
 ..  code-block:: python
     
     # 该示例展示了如何将LoDTensor转化为numpy数组，并利用numpy API来自定义一个OP
-    import paddle.fluid as fluid
+    import paddle
     import numpy as np
 
+    paddle.enable_static()
+
     def element_wise_add(x, y): 
         # 必须先手动将LodTensor转换为numpy数组，否则无法支持numpy的shape操作
         x = np.array(x)    
@@ -97,24 +99,24 @@ PaddlePaddle Fluid通过py_func在Python端注册OP。py_func的设计原理在
         return result
 
     def create_tmp_var(name, dtype, shape):
-        return fluid.default_main_program().current_block().create_var(
+        return paddle.static.default_main_program().current_block().create_var(
                     name=name, dtype=dtype, shape=shape)
 
     def py_func_demo():
-        start_program = fluid.default_startup_program()
-        main_program = fluid.default_main_program()
+        start_program = paddle.static.default_startup_program()
+        main_program = paddle.static.default_main_program()
 
         # 创建前向函数的输入变量
-        x = fluid.data(name='x', shape=[2,3], dtype='int32')
-        y = fluid.data(name='y', shape=[2,3], dtype='int32')
+        x = paddle.data(name='x', shape=[2,3], dtype='int32')
+        y = paddle.data(name='y', shape=[2,3], dtype='int32')
         
         # 创建前向函数的输出变量，必须指明变量名称name/数据类型dtype/维度shape
         output = create_tmp_var('output','int32', [3,1])
 
         # 输入多个LodTensor以list[Variable]或tuple(Variable)形式
-        fluid.layers.py_func(func=element_wise_add, x=[x,y], out=output)
+        paddle.static.nn.py_func(func=element_wise_add, x=[x,y], out=output)
 
-        exe=fluid.Executor(fluid.CPUPlace())
+        exe=paddle.static.Executor(fluid.CPUPlace())
         exe.run(start_program)
 
         # 给program喂入numpy数组

doc/paddle/api/paddle/fluid/layers/tensor/create_global_var_cn.rst

@@ -26,9 +26,8 @@ create_global_var
 
 .. code-block:: python
 
-    import paddle.fluid as fluid
-    import paddle.fluid.layers as layers
-    var = layers.create_global_var(shape=[2,3], value=1.0, dtype='float32',
+    import paddle
+    var = paddle.static.create_global_var(shape=[2,3], value=1.0, dtype='float32',
                                    persistable=True, force_cpu=True, name='new_var')
 
 

doc/paddle/api/paddle/fluid/layers/tensor/create_parameter_cn.rst

@@ -29,9 +29,8 @@ create_parameter
 
 .. code-block:: python
 
-    import paddle.fluid as fluid
-    import paddle.fluid.layers as layers
-    W = layers.create_parameter(shape=[784, 200], dtype='float32')
+    import paddle
+    W = paddle.static.create_parameter(shape=[784, 200], dtype='float32')
 
 
 

doc/paddle/api/paddle/fluid/param_attr/ParamAttr_cn.rst

@@ -34,14 +34,15 @@ ParamAttr
 
 .. code-block:: python
 
-   import paddle.fluid as fluid
+   import paddle
    
-   w_param_attrs = fluid.ParamAttr(name="fc_weight",
-                                   learning_rate=0.5,
-                                   regularizer=fluid.regularizer.L2Decay(1.0),
-                                   trainable=True)
+   w_param_attrs = paddle.ParamAttr(name="fc_weight",
+                                    learning_rate=0.5,
+                                    regularizer=fluid.regularizer.L2Decay(1.0),
+                                    trainable=True)
    print(w_param_attrs.name) # "fc_weight"
-   x = fluid.layers.data(name='X', shape=[1], dtype='float32')
-   y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs)
+   paddle.enable_static()
+   x = paddle.data(name='X', shape=[1], dtype='float32')
+   y_predict = paddle.static.nn.fc(input=x, size=10, param_attr=w_param_attrs)