update 02 book for 1.6 (#817)

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 `layer.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 `fluid.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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='label', shape=[1], dtype='int64')
+    label = fluid.data(name='label', shape=[-1, 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

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 `layer.data` for reading data.
+We need to input the image data into the classifier. Paddle provides a special layer `fluid.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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='label', shape=[1], dtype='int64')
+    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
 
     # predict = softmax_regression() # cancel note and run Softmax regression
     # predict = multilayer_perceptron() # cancel note and run multiple perceptron

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 `layer.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 `fluid.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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='label', shape=[1], dtype='int64')
+    label = fluid.data(name='label', shape=[-1, 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

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 `layer.data` for reading data.
+We need to input the image data into the classifier. Paddle provides a special layer `fluid.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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
+    img = fluid.data(name='img', shape=[-1, 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.layers.data(name='label', shape=[1], dtype='int64')
+    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
 
     # predict = softmax_regression() # cancel note and run Softmax regression
     # predict = multilayer_perceptron() # cancel note and run multiple perceptron

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.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
-    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    img = fluid.data(name='img', shape=[-1, 1, 28, 28], dtype='float32')
+    label = fluid.data(name='label', shape=[-1, 1], dtype='int64')
 
     if nn_type == 'softmax_regression':
         net_conf = softmax_regression