From db3cdc054049e49779d89563aec079cac7da8e34 Mon Sep 17 00:00:00 2001 From: lvmengsi Date: Sat, 12 Oct 2019 19:05:38 +0800 Subject: [PATCH] upgrade fluid.data api (#814) --- 01.fit_a_line/README.cn.md | 4 ++-- 01.fit_a_line/README.md | 4 ++-- 01.fit_a_line/index.cn.html | 4 ++-- 01.fit_a_line/index.html | 4 ++-- 01.fit_a_line/train.py | 4 ++-- 5 files changed, 10 insertions(+), 10 deletions(-) diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md index 412bc3c..715bc98 100644 --- a/01.fit_a_line/README.cn.md +++ b/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 diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md index 6f5ed25..29fdc1a 100644 --- a/01.fit_a_line/README.md +++ b/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 diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html index 10447f8..40d9128 100644 --- a/01.fit_a_line/index.cn.html +++ b/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 diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html index 2c3823e..184e825 100644 --- a/01.fit_a_line/index.html +++ b/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 diff --git a/01.fit_a_line/train.py b/01.fit_a_line/train.py index b2c2157..2ebe21e 100644 --- a/01.fit_a_line/train.py +++ b/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() -- GitLab