diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md
index e95337d76003269caa27786500e32b91e3a53a21..ae935f2f67e1f1e4a88efe7539575255975d969b 100644
--- a/01.fit_a_line/README.cn.md
+++ b/01.fit_a_line/README.cn.md
@@ -154,30 +154,23 @@ test_reader = paddle.batch(
         batch_size=BATCH_SIZE)
 ```
 
-如果想直接从txt文件中读取数据的话，可以参考以下方式。
-
+如果想直接从txt文件中读取数据的话，可以参考以下方式(需要自行准备txt文件)。
+```text
 feature_names = [
     'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
     'PTRATIO', 'B', 'LSTAT', 'convert'
 ]
-
 feature_num = len(feature_names)
-
 data = numpy.fromfile(filename, sep=' ') # 从文件中读取原始数据
-
 data = data.reshape(data.shape[0] // feature_num, feature_num)
-
 maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0]
 
 for i in six.moves.range(feature_num-1):
- data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves可以兼容python2和python3
+   data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves可以兼容python2和python3
 
 ratio = 0.8 # 训练集和验证集的划分比例
-
 offset = int(data.shape[0]*ratio)
-
 train_data = data[:offset]
-
 test_data = data[offset:]
 
 def reader(data):
@@ -193,6 +186,7 @@ test_reader = paddle.batch(
     paddle.reader.shuffle(
         reader(test_data), buf_size=500),
         batch_size=BATCH_SIZE)
+```
 
 ### 配置训练程序
 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。
diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md
index f8a6e8ee27dbff873f907776d824db26e5512167..7bdb903cabadfcfbb81609a2669eafb1c112102f 100644
--- a/01.fit_a_line/README.md
+++ b/01.fit_a_line/README.md
@@ -156,30 +156,23 @@ test_reader = paddle.batch(
         batch_size=BATCH_SIZE)
 ```
 
-If you want to read data directly from \*.txt file, you can refer to the method as follows.
-
+If you want to read data directly from \*.txt file, you can refer to the method as follows(need to prepare txt file by yourself).
+```text
 feature_names = [
     'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
     'PTRATIO', 'B', 'LSTAT', 'convert'
 ]
-
 feature_num = len(feature_names)
-
 data = numpy.fromfile(filename, sep=' ') # Read primary data from file
-
 data = data.reshape(data.shape[0] // feature_num, feature_num)
-
 maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0]
 
 for i in six.moves.range(feature_num-1):
- data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3
+   data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3
 
 ratio = 0.8 # distribution ratio of train dataset and verification dataset
-
 offset = int(data.shape[0]\*ratio)
-
 train_data = data[:offset]
-
 test_data = data[offset:]
 
 train_reader = paddle.batch(
@@ -191,6 +184,7 @@ test_reader = paddle.batch(
     paddle.reader.shuffle(
         test_data, buf_size=500),
         batch_size=BATCH_SIZE)
+```
 
 ### Configure Program for Training
 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.
diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html
index 4c99b3e552f2bbaf0751f6a1f2034232dbfd26fa..d4df07293ba5bca2dbd54694b81a6b0117b755db 100644
--- a/01.fit_a_line/index.cn.html
+++ b/01.fit_a_line/index.cn.html
@@ -196,30 +196,23 @@ test_reader = paddle.batch(
         batch_size=BATCH_SIZE)
 ```
 
-如果想直接从txt文件中读取数据的话，可以参考以下方式。
-
+如果想直接从txt文件中读取数据的话，可以参考以下方式(需要自行准备txt文件)。
+```text
 feature_names = [
     'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
     'PTRATIO', 'B', 'LSTAT', 'convert'
 ]
-
 feature_num = len(feature_names)
-
 data = numpy.fromfile(filename, sep=' ') # 从文件中读取原始数据
-
 data = data.reshape(data.shape[0] // feature_num, feature_num)
-
 maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0]
 
 for i in six.moves.range(feature_num-1):
- data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves可以兼容python2和python3
+   data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves可以兼容python2和python3
 
 ratio = 0.8 # 训练集和验证集的划分比例
-
 offset = int(data.shape[0]*ratio)
-
 train_data = data[:offset]
-
 test_data = data[offset:]
 
 def reader(data):
@@ -235,6 +228,7 @@ test_reader = paddle.batch(
     paddle.reader.shuffle(
         reader(test_data), buf_size=500),
         batch_size=BATCH_SIZE)
+```
 
 ### 配置训练程序
 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。
diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html
index 5c29f8ed37f0159ea935da3684715ed8975e812d..0c046f0e276419d5fb51a58f42634cd02ea8464f 100644
--- a/01.fit_a_line/index.html
+++ b/01.fit_a_line/index.html
@@ -198,30 +198,23 @@ test_reader = paddle.batch(
         batch_size=BATCH_SIZE)
 ```
 
-If you want to read data directly from \*.txt file, you can refer to the method as follows.
-
+If you want to read data directly from \*.txt file, you can refer to the method as follows(need to prepare txt file by yourself).
+```text
 feature_names = [
     'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',
     'PTRATIO', 'B', 'LSTAT', 'convert'
 ]
-
 feature_num = len(feature_names)
-
 data = numpy.fromfile(filename, sep=' ') # Read primary data from file
-
 data = data.reshape(data.shape[0] // feature_num, feature_num)
-
 maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0]
 
 for i in six.moves.range(feature_num-1):
- data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3
+   data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i]) # six.moves is compatible to python2 and python3
 
 ratio = 0.8 # distribution ratio of train dataset and verification dataset
-
 offset = int(data.shape[0]\*ratio)
-
 train_data = data[:offset]
-
 test_data = data[offset:]
 
 train_reader = paddle.batch(
@@ -233,6 +226,7 @@ test_reader = paddle.batch(
     paddle.reader.shuffle(
         test_data, buf_size=500),
         batch_size=BATCH_SIZE)
+```
 
 ### Configure Program for Training
 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.
diff --git a/09.gan/README.cn.md b/09.gan/README.cn.md
index 10266d523f8fb96ed539f69a6a703a8320d7a97d..99cf01ed570e122248a38982eeaf5b284034226a 100644
--- a/09.gan/README.cn.md
+++ b/09.gan/README.cn.md
@@ -162,7 +162,7 @@ def bn(x, name=None, act='relu'):
 
 - 卷积层
 
-调用 `fluid.nets.simple_img_conv_pool` 实现卷积池化组，卷积核大小为3x3，池化窗口大小为2x2，窗口滑动步长为2，激活函数类型由具体网络结构指定。
+调用 `fluid.nets.simple_img_conv_pool` 实现卷积池化组，卷积核大小为5x5，池化窗口大小为2x2，窗口滑动步长为2，激活函数类型由具体网络结构指定。
 
 ```python
 def conv(x, num_filters, name=None, act=None):
diff --git a/09.gan/index.cn.html b/09.gan/index.cn.html
index ca7ee664d486e33ba84798f138df953cc2043400..8947f5a1725538fea260a17b89f57e6f028a2336 100644
--- a/09.gan/index.cn.html
+++ b/09.gan/index.cn.html
@@ -204,7 +204,7 @@ def bn(x, name=None, act='relu'):
 
 - 卷积层
 
-调用 `fluid.nets.simple_img_conv_pool` 实现卷积池化组，卷积核大小为3x3，池化窗口大小为2x2，窗口滑动步长为2，激活函数类型由具体网络结构指定。
+调用 `fluid.nets.simple_img_conv_pool` 实现卷积池化组，卷积核大小为5x5，池化窗口大小为2x2，窗口滑动步长为2，激活函数类型由具体网络结构指定。
 
 ```python
 def conv(x, num_filters, name=None, act=None):