From 78c34be34b00f18a6bbbacf25ea56e20fd45e4cc Mon Sep 17 00:00:00 2001 From: xiaoting <31891223+tink2123@users.noreply.github.com> Date: Mon, 14 Oct 2019 11:05:38 +0800 Subject: [PATCH] Revert "update 02 book for 1.6 (#817)" (#821) This reverts commit 104c4078709d1f4aef4bee69c9035ab4683cc353. --- 02.recognize_digits/README.cn.md | 10 +++++----- 02.recognize_digits/README.md | 10 +++++----- 02.recognize_digits/index.cn.html | 10 +++++----- 02.recognize_digits/index.html | 10 +++++----- 02.recognize_digits/train.py | 4 ++-- 5 files changed, 22 insertions(+), 22 deletions(-) diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md index 31c2adb..33b528f 100644 --- a/02.recognize_digits/README.cn.md +++ b/02.recognize_digits/README.cn.md @@ -195,7 +195,7 @@ import paddle.fluid as fluid ### Program Functions 配置 我们需要设置 `inference_program` 函数。我们想用这个程序来演示三个不同的分类器，每个分类器都定义为 Python 函数。 -我们需要将图像数据输入到分类器中。Paddle 为读取数据提供了一个特殊的层 `fluid.data` 层。 +我们需要将图像数据输入到分类器中。Paddle 为读取数据提供了一个特殊的层 `layer.data` 层。 让我们创建一个数据层来读取图像并将其连接到分类网络。 - Softmax回归：只通过一层简单的以softmax为激活函数的全连接层，就可以得到分类的结果。 @@ -209,7 +209,7 @@ def softmax_regression(): predict_image -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 以softmax为激活函数的全连接层，输出层的大小必须为数字的个数10 predict = fluid.layers.fc( input=img, size=10, act='softmax') @@ -229,7 +229,7 @@ def multilayer_perceptron(): predict_image -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 第一个全连接层，激活函数为ReLU hidden = fluid.layers.fc(input=img, size=200, act='relu') # 第二个全连接层，激活函数为ReLU @@ -251,7 +251,7 @@ def convolutional_neural_network(): predict -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 第一个卷积-池化层 # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu conv_pool_1 = fluid.nets.simple_img_conv_pool( @@ -296,7 +296,7 @@ def train_program(): """ # 标签层，名称为label,对应输入图片的类别标签 - label = fluid.data(name='label', shape=[-1, 1], dtype='int64') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') # predict = softmax_regression() # 取消注释将使用 Softmax回归 # predict = multilayer_perceptron() # 取消注释将使用 多层感知器 diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md index b3ca3c8..b0832f5 100644 --- a/02.recognize_digits/README.md +++ b/02.recognize_digits/README.md @@ -174,7 +174,7 @@ import paddle.fluid as fluid ### Program Functions Configuration We need to configure `inference_program` function. We want to use this program to show three different classifiers, each of which is defined as a Python function. -We need to input the image data into the classifier. Paddle provides a special layer `fluid.data` for reading data. +We need to input the image data into the classifier. Paddle provides a special layer `layer.data` for reading data. Let's create a data layer to read the image and connect it to the network of classification. -Softmax regression: The results of the classification can be obtained only through a simple layer of simple fully connected layer with softmax as the activation function. @@ -188,7 +188,7 @@ def softmax_regression(): predict_image -- result of classification """ # input original image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # With softmax as the fully connected layer of the activation function, the size of the output layer must be 10 predict = fluid.layers.fc( input=img, size=10, act='softmax') @@ -208,7 +208,7 @@ def multilayer_perceptron(): predict_image -- result of classification """ # input raw image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # the first fully connected layer, whose activation function is ReLU hidden = fluid.layers.fc(input=img, size=200, act='relu') # the second fully connected layer, whose activation function is ReLU @@ -230,7 +230,7 @@ def convolutional_neural_network(): predict -- result of classification """ # input raw image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # the first convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. conv_pool_1 = fluid.nets.simple_img_conv_pool( @@ -275,7 +275,7 @@ def train_program(): """ # label layer, called label, correspondent with label category of input picture - label = fluid.data(name='label', shape=[-1, 1], dtype='int64') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') # predict = softmax_regression() # cancel note and run Softmax regression # predict = multilayer_perceptron() # cancel note and run multiple perceptron diff --git a/02.recognize_digits/index.cn.html b/02.recognize_digits/index.cn.html index 3288152..5733f2d 100644 --- a/02.recognize_digits/index.cn.html +++ b/02.recognize_digits/index.cn.html @@ -237,7 +237,7 @@ import paddle.fluid as fluid ### Program Functions 配置 我们需要设置 `inference_program` 函数。我们想用这个程序来演示三个不同的分类器，每个分类器都定义为 Python 函数。 -我们需要将图像数据输入到分类器中。Paddle 为读取数据提供了一个特殊的层 `fluid.data` 层。 +我们需要将图像数据输入到分类器中。Paddle 为读取数据提供了一个特殊的层 `layer.data` 层。 让我们创建一个数据层来读取图像并将其连接到分类网络。 - Softmax回归：只通过一层简单的以softmax为激活函数的全连接层，就可以得到分类的结果。 @@ -251,7 +251,7 @@ def softmax_regression(): predict_image -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 以softmax为激活函数的全连接层，输出层的大小必须为数字的个数10 predict = fluid.layers.fc( input=img, size=10, act='softmax') @@ -271,7 +271,7 @@ def multilayer_perceptron(): predict_image -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 第一个全连接层，激活函数为ReLU hidden = fluid.layers.fc(input=img, size=200, act='relu') # 第二个全连接层，激活函数为ReLU @@ -293,7 +293,7 @@ def convolutional_neural_network(): predict -- 分类的结果 """ # 输入的原始图像数据，大小为28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # 第一个卷积-池化层 # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu conv_pool_1 = fluid.nets.simple_img_conv_pool( @@ -338,7 +338,7 @@ def train_program(): """ # 标签层，名称为label,对应输入图片的类别标签 - label = fluid.data(name='label', shape=[-1, 1], dtype='int64') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') # predict = softmax_regression() # 取消注释将使用 Softmax回归 # predict = multilayer_perceptron() # 取消注释将使用 多层感知器 diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html index 9ab5b1e..44c80a1 100644 --- a/02.recognize_digits/index.html +++ b/02.recognize_digits/index.html @@ -216,7 +216,7 @@ import paddle.fluid as fluid ### Program Functions Configuration We need to configure `inference_program` function. We want to use this program to show three different classifiers, each of which is defined as a Python function. -We need to input the image data into the classifier. Paddle provides a special layer `fluid.data` for reading data. +We need to input the image data into the classifier. Paddle provides a special layer `layer.data` for reading data. Let's create a data layer to read the image and connect it to the network of classification. -Softmax regression: The results of the classification can be obtained only through a simple layer of simple fully connected layer with softmax as the activation function. @@ -230,7 +230,7 @@ def softmax_regression(): predict_image -- result of classification """ # input original image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # With softmax as the fully connected layer of the activation function, the size of the output layer must be 10 predict = fluid.layers.fc( input=img, size=10, act='softmax') @@ -250,7 +250,7 @@ def multilayer_perceptron(): predict_image -- result of classification """ # input raw image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # the first fully connected layer, whose activation function is ReLU hidden = fluid.layers.fc(input=img, size=200, act='relu') # the second fully connected layer, whose activation function is ReLU @@ -272,7 +272,7 @@ def convolutional_neural_network(): predict -- result of classification """ # input raw image data in size of 28*28*1 - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') # the first convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. conv_pool_1 = fluid.nets.simple_img_conv_pool( @@ -317,7 +317,7 @@ def train_program(): """ # label layer, called label, correspondent with label category of input picture - label = fluid.data(name='label', shape=[-1, 1], dtype='int64') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') # predict = softmax_regression() # cancel note and run Softmax regression # predict = multilayer_perceptron() # cancel note and run multiple perceptron diff --git a/02.recognize_digits/train.py b/02.recognize_digits/train.py index 27cb3c7..04fbc29 100644 --- a/02.recognize_digits/train.py +++ b/02.recognize_digits/train.py @@ -101,8 +101,8 @@ def train(nn_type, test_reader = paddle.batch( paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) - img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32') - label = fluid.data(name='label', shape=[-1, 1], dtype='int64') + img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') if nn_type == 'softmax_regression': net_conf = softmax_regression -- GitLab