diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md
index 33b528fefd05ab3d8f58be909378e719596b3ca8..42c7bf78af4941518f90e829bffe45dc4a9f7e20 100644
--- a/02.recognize_digits/README.cn.md
+++ b/02.recognize_digits/README.cn.md
@@ -239,6 +239,37 @@ def multilayer_perceptron():
     return prediction
 ```
 
+- 卷积池化层：在LeNet-5中会出现多个卷积-池化的操作，为避免代码重复书写，将串联的卷积-池化写成conv_pool函数。
+
+```python
+def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"):
+    """
+    定义卷积池化层：
+        含有一个卷积层和一个池化层
+    Args：
+        input —— 网络输入
+        num_filters —— 卷积核的个数
+        filter_size —— 卷积核的大小
+        pool_size —— 池化核的大小
+        pool_stride —— 池化的步长
+        act —— 卷积层的激活函数
+
+    Return:
+        out -- 经过卷积池化后的特征图
+    """
+    conv_out = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act)
+    out = fluid.layers.pool2d(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_stride=pool_stride)
+    return out
+```
+
+
 - 卷积神经网络LeNet-5: 输入的二维图像，首先经过两次卷积层到池化层，再经过全连接层，最后使用以softmax为激活函数的全连接层作为输出层。
 
 ```python
@@ -254,7 +285,7 @@ def convolutional_neural_network():
     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(
+    conv_pool_1 = conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
@@ -264,7 +295,7 @@ def convolutional_neural_network():
     conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     # 第二个卷积-池化层
     # 使用50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_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,
diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md
index b0832f537c0dc1da49a66dc023f5fc7aa17f0196..a03b6d4da9addb4caddbe4ed2310ffe8e03f93f2 100644
--- a/02.recognize_digits/README.md
+++ b/02.recognize_digits/README.md
@@ -218,6 +218,36 @@ def multilayer_perceptron():
     return prediction
 ```
 
+-Conv_pool layer: LeNet-5 has multiple convolution-pooling operations. In order to avoid repeated code writing, the convolution-pooling in series is written as conv_pool function.
+
+```python
+def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"):
+    """
+    Define convolution-pooling layer：
+        Conv_pool layer has a convolutional layer and a pooling layer
+    Args：
+        input —— Input
+        num_filters —— The number of filter
+        filter_size —— The filter size
+        pool_size —— The pool kernel size
+        pool_stride —— The pool stride size
+        act —— Activation type
+
+    Return:
+        out -- output
+    """
+    conv_out = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act)
+    out = fluid.layers.pool2d(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_stride=pool_stride)
+    return out
+
+```
 -Convolutional neural network LeNet-5: The input two-dimensional image first passes through two convolutional layers to the pooling layer, then passes through the fully connected layer, and finally fully connection layer with softmax as activation function is used as output layer.
 
 ```python
@@ -233,7 +263,7 @@ def convolutional_neural_network():
     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(
+    conv_pool_1 = conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
@@ -243,7 +273,7 @@ def convolutional_neural_network():
     conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     # the second 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_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,
diff --git a/02.recognize_digits/index.cn.html b/02.recognize_digits/index.cn.html
index 5733f2db12e1f20d629becfcd95ac8c4ef3cafb4..655d7ce205f433986a420ff26c19835036cff26c 100644
--- a/02.recognize_digits/index.cn.html
+++ b/02.recognize_digits/index.cn.html
@@ -281,6 +281,37 @@ def multilayer_perceptron():
     return prediction
 ```
 
+- 卷积池化层：在LeNet-5中会出现多个卷积-池化的操作，为避免代码重复书写，将串联的卷积-池化写成conv_pool函数。
+
+```python
+def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"):
+    """
+    定义卷积池化层：
+        含有一个卷积层和一个池化层
+    Args：
+        input —— 网络输入
+        num_filters —— 卷积核的个数
+        filter_size —— 卷积核的大小
+        pool_size —— 池化核的大小
+        pool_stride —— 池化的步长
+        act —— 卷积层的激活函数
+
+    Return:
+        out -- 经过卷积池化后的特征图
+    """
+    conv_out = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act)
+    out = fluid.layers.pool2d(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_stride=pool_stride)
+    return out
+```
+
+
 - 卷积神经网络LeNet-5: 输入的二维图像，首先经过两次卷积层到池化层，再经过全连接层，最后使用以softmax为激活函数的全连接层作为输出层。
 
 ```python
@@ -296,7 +327,7 @@ def convolutional_neural_network():
     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(
+    conv_pool_1 = conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
@@ -306,7 +337,7 @@ def convolutional_neural_network():
     conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     # 第二个卷积-池化层
     # 使用50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_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,
diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html
index 44c80a1644a2bc3f1f06c1eaab95221354d45003..72f9382937a337bf718ed19f3e69598947331054 100644
--- a/02.recognize_digits/index.html
+++ b/02.recognize_digits/index.html
@@ -260,6 +260,36 @@ def multilayer_perceptron():
     return prediction
 ```
 
+-Conv_pool layer: LeNet-5 has multiple convolution-pooling operations. In order to avoid repeated code writing, the convolution-pooling in series is written as conv_pool function.
+
+```python
+def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"):
+    """
+    Define convolution-pooling layer：
+        Conv_pool layer has a convolutional layer and a pooling layer
+    Args：
+        input —— Input
+        num_filters —— The number of filter
+        filter_size —— The filter size
+        pool_size —— The pool kernel size
+        pool_stride —— The pool stride size
+        act —— Activation type
+
+    Return:
+        out -- output
+    """
+    conv_out = fluid.layers.conv2d(
+        input=input,
+        num_filters=num_filters,
+        filter_size=filter_size,
+        act=act)
+    out = fluid.layers.pool2d(
+        input=conv_out,
+        pool_size=pool_size,
+        pool_stride=pool_stride)
+    return out
+
+```
 -Convolutional neural network LeNet-5: The input two-dimensional image first passes through two convolutional layers to the pooling layer, then passes through the fully connected layer, and finally fully connection layer with softmax as activation function is used as output layer.
 
 ```python
@@ -275,7 +305,7 @@ def convolutional_neural_network():
     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(
+    conv_pool_1 = conv_pool(
         input=img,
         filter_size=5,
         num_filters=20,
@@ -285,7 +315,7 @@ def convolutional_neural_network():
     conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
     # the second 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_2 = fluid.nets.simple_img_conv_pool(
+    conv_pool_2 = conv_pool(
         input=conv_pool_1,
         filter_size=5,
         num_filters=50,