upgrade fluid.data api (#814)

01.fit_a_line/README.cn.md

@@ -194,8 +194,8 @@ test_reader = paddle.batch(
 训练程序的目的是定义一个训练模型的网络结构。对于线性回归来讲，它就是一个从输入到输出的简单的全连接层。更加复杂的结果，比如卷积神经网络，递归神经网络等会在随后的章节中介绍。训练程序必须返回`平均损失`作为第一个返回值，因为它会被后面反向传播算法所用到。
 
 ```python
-x = fluid.layers.data(name='x', shape=[13], dtype='float32') # 定义输入的形状和数据类型
-y = fluid.layers.data(name='y', shape=[1], dtype='float32') # 定义输出的形状和数据类型
+x = fluid.data(name='x', shape=[-1, 13], dtype='float32') # 定义输入的形状和数据类型
+y = fluid.data(name='y', shape=[-1, 1], dtype='float32') # 定义输出的形状和数据类型
 y_predict = fluid.layers.fc(input=x, size=1, act=None) # 连接输入和输出的全连接层
 
 main_program = fluid.default_main_program() # 获取默认/全局主函数

01.fit_a_line/README.md

@@ -196,8 +196,8 @@ test_reader = paddle.batch(
 The aim of the program for training is to define a network structure of a training model. For linear regression, it is a simple fully connected layer from input to output. More complex result, such as Convolutional Neural Network and Recurrent Neural Network, will be introduced in later chapters. It must return `mean error` as the first return value in program for training, for that `mean error` will be used for BackPropagation.
 
 ```python
-x = fluid.layers.data(name='x', shape=[13], dtype='float32') # define shape and data type of input
-y = fluid.layers.data(name='y', shape=[1], dtype='float32') # define shape and data type of output
+x = fluid.data(name='x', shape=[-1, 13], dtype='float32') # define shape and data type of input
+y = fluid.data(name='y', shape=[-1, 1], dtype='float32') # define shape and data type of output
 y_predict = fluid.layers.fc(input=x, size=1, act=None) # fully connected layer connecting input and output
 
 main_program = fluid.default_main_program() # get default/global main function

01.fit_a_line/index.cn.html

@@ -236,8 +236,8 @@ test_reader = paddle.batch(
 训练程序的目的是定义一个训练模型的网络结构。对于线性回归来讲，它就是一个从输入到输出的简单的全连接层。更加复杂的结果，比如卷积神经网络，递归神经网络等会在随后的章节中介绍。训练程序必须返回`平均损失`作为第一个返回值，因为它会被后面反向传播算法所用到。
 
 ```python
-x = fluid.layers.data(name='x', shape=[13], dtype='float32') # 定义输入的形状和数据类型
-y = fluid.layers.data(name='y', shape=[1], dtype='float32') # 定义输出的形状和数据类型
+x = fluid.data(name='x', shape=[-1, 13], dtype='float32') # 定义输入的形状和数据类型
+y = fluid.data(name='y', shape=[-1, 1], dtype='float32') # 定义输出的形状和数据类型
 y_predict = fluid.layers.fc(input=x, size=1, act=None) # 连接输入和输出的全连接层
 
 main_program = fluid.default_main_program() # 获取默认/全局主函数

01.fit_a_line/index.html

@@ -238,8 +238,8 @@ test_reader = paddle.batch(
 The aim of the program for training is to define a network structure of a training model. For linear regression, it is a simple fully connected layer from input to output. More complex result, such as Convolutional Neural Network and Recurrent Neural Network, will be introduced in later chapters. It must return `mean error` as the first return value in program for training, for that `mean error` will be used for BackPropagation.
 
 ```python
-x = fluid.layers.data(name='x', shape=[13], dtype='float32') # define shape and data type of input
-y = fluid.layers.data(name='y', shape=[1], dtype='float32') # define shape and data type of output
+x = fluid.data(name='x', shape=[-1, 13], dtype='float32') # define shape and data type of input
+y = fluid.data(name='y', shape=[-1, 1], dtype='float32') # define shape and data type of output
 y_predict = fluid.layers.fc(input=x, size=1, act=None) # fully connected layer connecting input and output
 
 main_program = fluid.default_main_program() # get default/global main function

01.fit_a_line/train.py

@@ -87,8 +87,8 @@ def main():
             batch_size=batch_size)
 
     # feature vector of length 13
-    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=[-1, 13], dtype='float32')
+    y = fluid.data(name='y', shape=[-1, 1], dtype='float32')
 
     main_program = fluid.default_main_program()
     startup_program = fluid.default_startup_program()