updata fluid.layers.data to fluid.data (#1816)

doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_cn.rst

@@ -33,16 +33,16 @@
 数据层
 ------
 
-PaddlePaddle提供了 :code:`fluid.layers.data()` 算子来描述输入数据的格式。
+PaddlePaddle提供了 :code:`fluid.data()` 算子来描述输入数据的格式。
 
-:code:`fluid.layers.data()` 算子的输出是一个Variable。这个Variable的实际类型是Tensor。Tensor具有强大的表征能力，可以表示多维数据。为了精确描述数据结构，通常需要指定数据shape以及数值类型type。其中shape为一个整数向量，type可以是一个字符串类型。目前支持的数据类型参考    :ref:`user_guide_paddle_support_data_types` 。 模型训练一般会使用batch的方式读取数据，而batch的size在训练过程中可能不固定。data算子会依据实际数据来推断batch size，所以这里提供shape时不用关心batch size，只需关心一条样本的shape即可，更高级用法请参考 :ref:`user_guide_customize_batch_size_rank`。从上知，:math:`x` 为 :math:`13` 维的实数向量，:math:`y` 为实数，可使用下面代码定义数据层：
+:code:`fluid.data()` 算子的输出是一个Variable。这个Variable的实际类型是Tensor。Tensor具有强大的表征能力，可以表示多维数据。为了精确描述数据结构，通常需要指定数据shape以及数值类型type。其中shape为一个整数向量，type可以是一个字符串类型。目前支持的数据类型参考    :ref:`user_guide_paddle_support_data_types` 。 模型训练一般会使用batch的方式读取数据，而batch的size在训练过程中可能不固定。data算子会依据实际数据来推断batch size，所以这里提供shape时不用关心batch size，只需关心一条样本的shape即可，更高级用法请参考 :ref:`user_guide_customize_batch_size_rank`。从上知，:math:`x` 为 :math:`13` 维的实数向量，:math:`y` 为实数，可使用下面代码定义数据层：
 
 .. code-block:: python
 
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    x = fluid.data(name='x', shape=[13], dtype='float32')
+    y = fluid.data(name='y', shape=[1], dtype='float32')
 
-该模型使用的数据比较简单，事实上data算子还可以描述变长的、嵌套的序列数据。也可以使用 :code:`open_files` 打开文件进行训练。更详细的文档可参照 :ref:`user_guide_prepare_data`。
+该模型使用的数据比较简单，事实上data算子还可以描述变长的、嵌套的序列数据。更详细的文档可参照 :ref:`user_guide_prepare_data`。
 
 前向计算逻辑
 ------------

doc/fluid/beginners_guide/coding_practice/configure_simple_model/index_en.rst

@@ -35,16 +35,16 @@ After getting clear of the of input data format, model structure, loss function
 Data Layer
 -----------
 
-PaddlePaddle provides :code:`fluid.layers.data()` to describe format of input data.
+PaddlePaddle provides :code:`fluid.data()` to describe format of input data.
 
-The output of :code:`fluid.layers.data()` is a Variable which is in fact a Tensor. Tensor can represent multi-demensional data with its great expressive feature.In order to accurately describe data structure, it is usually necessary to indicate the shape and type of data. The shape is int vector and type can be a string. About current supported data type, please refer to    :ref:`user_guide_paddle_support_data_types_en` . Data is often read in form of batch to train model. Since batch size may vary and data operator infers batch size according to actual data, here the batch size is ignored when shape is provided. It's enough to care for the shape of single sample. For more advanced usage, please refer to :ref:`user_guide_customize_batch_size_rank_en` .  :math:`x` is real number vector of :math:`13` dimenstions while :math:`y` is a real number. Data layer can be defined as follows:
+The output of :code:`fluid.data()` is a Variable which is in fact a Tensor. Tensor can represent multi-demensional data with its great expressive feature.In order to accurately describe data structure, it is usually necessary to indicate the shape and type of data. The shape is int vector and type can be a string. About current supported data type, please refer to    :ref:`user_guide_paddle_support_data_types_en` . Data is often read in form of batch to train model. Since batch size may vary and data operator infers batch size according to actual data, here the batch size is ignored when shape is provided. It's enough to care for the shape of single sample. For more advanced usage, please refer to :ref:`user_guide_customize_batch_size_rank_en` .  :math:`x` is real number vector of :math:`13` dimenstions while :math:`y` is a real number. Data layer can be defined as follows:
 
 .. code-block:: python
 
-    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+    x = fluid.data(name='x', shape=[13], dtype='float32')
+    y = fluid.data(name='y', shape=[1], dtype='float32')
 
-Data in this example model are relatively simple. In fact, data operator can describe variable-length and nested sequence data. You can also use :code:`open_files` to open file to train. For more detailed documentation, please refer to :ref:`user_guide_prepare_data_en` .
+Data in this example model are relatively simple. In fact, data operator can describe variable-length and nested sequence data. For more detailed documentation, please refer to :ref:`user_guide_prepare_data_en` .
 
 Logic of Forward Computing
 ---------------------------