fix some mistakes

02.recognize_digits/README.cn.md

@@ -524,7 +524,7 @@ Test with Epoch 0, avg_cost: 0.053097883707459624, acc: 0.9822850318471338
 
 ### 生成预测输入数据
 
-`infer_3.png` 是数字 3 的一个示例图像。把它变成一个 numpy 数组以匹配数据feed格式。
+[infer_3.png](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/infer_3.png) 是数字 3 的一个示例图像。把它变成一个 numpy 数组以匹配数据feed格式。
 
 ```python
 def load_image(file):
@@ -558,7 +558,7 @@ with fluid.scope_guard(inference_scope):
     lab = numpy.argsort(results)
 
     # 打印 infer_3.png 这张图片的预测结果
-    img=Image.open('image/infer_3.png')
+    img=Image.open(cur_dir + 'image/infer_3.png')
     plt.imshow(img)
     print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
 ```

02.recognize_digits/README.md

@@ -508,7 +508,7 @@ You can use trained model to classify handwriting pictures of digits. The progra
 
 ### Generate input data to be inferred
 
-`infer_3.png` is an example picture of number 3. Transform it into a numpy to match feed data format
+[infer_3.png](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/infer_3.png) is an example picture of number 3. Transform it into a numpy to match feed data format
 
 
 ```python
@@ -547,7 +547,7 @@ with fluid.scope_guard(inference_scope):
     lab = numpy.argsort(results)
 
     # Print prediction result of  infer_3.png
-    img=Image.open('image/infer_3.png')
+    img=Image.open(cur_dir + 'image/infer_3.png')
     plt.imshow(img)
     print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
 ```

02.recognize_digits/index.cn.html

@@ -566,7 +566,7 @@
 
 ### 生成预测输入数据
 
-`infer_3.png` 是数字 3 的一个示例图像。把它变成一个 numpy 数组以匹配数据feed格式。
+[infer_3.png](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/infer_3.png) 是数字 3 的一个示例图像。把它变成一个 numpy 数组以匹配数据feed格式。
 
 ```python
 def load_image(file):
@@ -600,7 +600,7 @@
     lab = numpy.argsort(results)
 
     # 打印 infer_3.png 这张图片的预测结果
-    img=Image.open('image/infer_3.png')
+    img=Image.open(cur_dir + 'image/infer_3.png')
     plt.imshow(img)
     print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
 ```

02.recognize_digits/index.html

@@ -550,7 +550,7 @@
 
 ### Generate input data to be inferred
 
-`infer_3.png` is an example picture of number 3. Transform it into a numpy to match feed data format
+[infer_3.png](https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/infer_3.png) is an example picture of number 3. Transform it into a numpy to match feed data format
 
 
 ```python
@@ -589,7 +589,7 @@
     lab = numpy.argsort(results)
 
     # Print prediction result of  infer_3.png
-    img=Image.open('image/infer_3.png')
+    img=Image.open(cur_dir + 'image/infer_3.png')
     plt.imshow(img)
     print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1])
 ```

03.image_classification/README.cn.md

@@ -146,7 +146,7 @@ ResNet(Residual Network) \[[15](#参考文献)\] 是2015年ImageNet图像分类
 图9. 残差模块
 </p>
 
-图10展示了50、101、152层网络连接示意图，使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快，成功的训练了上百乃至近千层的卷积神经网络。
+图10展示了50、116、152层网络连接示意图，使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快，成功的训练了上百乃至近千层的卷积神经网络。
 
 <p align="center">
 <img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/resnet.png?raw=true"><br/>

03.image_classification/README.md

@@ -140,7 +140,7 @@ Figure 9 illustrates the ResNet architecture. To the left is the basic building
 Figure 9. Residual block
 </p>
 
-Figure 10 illustrates ResNets with 50, 101, 152 layers, respectively. All three networks use bottleneck blocks and their difference lies in the repetition time of residual blocks. ResNet converges very fast and can be trained with hundreds or thousands of layers.
+Figure 10 illustrates ResNets with 50, 116, 152 layers, respectively. All three networks use bottleneck blocks and their difference lies in the repetition time of residual blocks. ResNet converges very fast and can be trained with hundreds or thousands of layers.
 
 <p align="center">
 <img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/resnet.png?raw=true"><br/>

03.image_classification/index.cn.html

@@ -188,7 +188,7 @@
 图9. 残差模块
 </p>
 
-图10展示了50、101、152层网络连接示意图，使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快，成功的训练了上百乃至近千层的卷积神经网络。
+图10展示了50、116、152层网络连接示意图，使用的是瓶颈模块。这三个模型的区别在于每组中残差模块的重复次数不同(见图右上角)。ResNet训练收敛较快，成功的训练了上百乃至近千层的卷积神经网络。
 
 <p align="center">
 <img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/resnet.png?raw=true"><br/>

03.image_classification/index.html

@@ -182,7 +182,7 @@
 Figure 9. Residual block
 </p>
 
-Figure 10 illustrates ResNets with 50, 101, 152 layers, respectively. All three networks use bottleneck blocks and their difference lies in the repetition time of residual blocks. ResNet converges very fast and can be trained with hundreds or thousands of layers.
+Figure 10 illustrates ResNets with 50, 116, 152 layers, respectively. All three networks use bottleneck blocks and their difference lies in the repetition time of residual blocks. ResNet converges very fast and can be trained with hundreds or thousands of layers.
 
 <p align="center">
 <img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/resnet.png?raw=true"><br/>

05.recommender_system/README.cn.md

@@ -136,7 +136,7 @@ Paddle在API中提供了自动加载数据的模块。数据模块为 `paddle.da
 ```python
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -152,7 +152,7 @@ print movie_info.values()[0]
 
 ```python
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
     <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
@@ -163,7 +163,7 @@ print movie_info.values()[0]
 
 ```python
 user_info = paddle.dataset.movielens.user_info()
-print user_info.values()[0]
+print(list(user_info.values())[0])
 ```
 
     <UserInfo id(1), gender(F), age(1), job(10)>
@@ -216,7 +216,7 @@ train_set_creator = paddle.dataset.movielens.train()
 train_sample = next(train_set_creator())
 uid = train_sample[0]
 mov_id = train_sample[len(user_info[uid].value())]
-print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
+print("User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1]))
 ```
 
     User <UserInfo id(1), gender(F), age(1), job(10)> rates Movie <MovieInfo id(1193), title(One Flew Over the Cuckoo's Nest ), categories(['Drama'])> with Score [5.0]
@@ -533,13 +533,13 @@ train_loop()
 ```python
 infer_movie_id = 783
 infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
-user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
-gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
-age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
-job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
-movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place) # Hunchback of Notre Dame
-category_id = fluid.create_lod_tensor([np.array([10, 8, 9], dtype='int64')], [[3]], place) # Animation, Children's, Musical
-movie_title = fluid.create_lod_tensor([np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
+user_id = fluid.create_lod_tensor([[1]], [[1]], place)
+gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
+age_id = fluid.create_lod_tensor([[0]], [[1]], place)
+job_id = fluid.create_lod_tensor([[10]], [[1]], place)
+movie_id = fluid.create_lod_tensor([[783]], [[1]], place) # Hunchback of Notre Dame
+category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place) # Animation, Children's, Musical
+movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
                                       place) # 'hunchback','of','notre','dame','the'
 ```
 

05.recommender_system/README.md

@@ -122,7 +122,7 @@ Paddle provides modules for automatically loading data in the API. The data modu
 ```python
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -138,7 +138,7 @@ For example, one of the movie features is:
 
 ```python
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
     <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
@@ -149,7 +149,7 @@ This means that the movie id is 1, and the title is 《Toy Story》, which is di
 
 ```python
 user_info = paddle.dataset.movielens.user_info()
-print user_info.values()[0]
+print(list(user_info.values())[0])
 ```
 
     <UserInfo id(1), gender(F), age(1), job(10)>
@@ -202,7 +202,7 @@ train_set_creator = paddle.dataset.movielens.train()
 train_sample = next(train_set_creator())
 uid = train_sample[0]
 mov_id = train_sample[len(user_info[uid].value())]
-print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
+print("User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1]))
 ```
 
 ```python

05.recommender_system/index.cn.html

@@ -178,7 +178,7 @@
 ```python
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -194,7 +194,7 @@
 
 ```python
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
     <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
@@ -205,7 +205,7 @@
 
 ```python
 user_info = paddle.dataset.movielens.user_info()
-print user_info.values()[0]
+print(list(user_info.values())[0])
 ```
 
     <UserInfo id(1), gender(F), age(1), job(10)>
@@ -258,7 +258,7 @@
 train_sample = next(train_set_creator())
 uid = train_sample[0]
 mov_id = train_sample[len(user_info[uid].value())]
-print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
+print("User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1]))
 ```
 
     User <UserInfo id(1), gender(F), age(1), job(10)> rates Movie <MovieInfo id(1193), title(One Flew Over the Cuckoo's Nest ), categories(['Drama'])> with Score [5.0]
@@ -575,13 +575,13 @@
 ```python
 infer_movie_id = 783
 infer_movie_name = paddle.dataset.movielens.movie_info()[infer_movie_id].title
-user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
-gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
-age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
-job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
-movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place) # Hunchback of Notre Dame
-category_id = fluid.create_lod_tensor([np.array([10, 8, 9], dtype='int64')], [[3]], place) # Animation, Children's, Musical
-movie_title = fluid.create_lod_tensor([np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
+user_id = fluid.create_lod_tensor([[1]], [[1]], place)
+gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
+age_id = fluid.create_lod_tensor([[0]], [[1]], place)
+job_id = fluid.create_lod_tensor([[10]], [[1]], place)
+movie_id = fluid.create_lod_tensor([[783]], [[1]], place) # Hunchback of Notre Dame
+category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place) # Animation, Children's, Musical
+movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]],
                                       place) # 'hunchback','of','notre','dame','the'
 ```
 

05.recommender_system/index.html

@@ -164,7 +164,7 @@
 ```python
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -180,7 +180,7 @@
 
 ```python
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
     <MovieInfo id(1), title(Toy Story ), categories(['Animation', "Children's", 'Comedy'])>
@@ -191,7 +191,7 @@
 
 ```python
 user_info = paddle.dataset.movielens.user_info()
-print user_info.values()[0]
+print(list(user_info.values())[0])
 ```
 
     <UserInfo id(1), gender(F), age(1), job(10)>
@@ -244,7 +244,7 @@
 train_sample = next(train_set_creator())
 uid = train_sample[0]
 mov_id = train_sample[len(user_info[uid].value())]
-print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1])
+print("User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id], train_sample[-1]))
 ```
 
 ```python

09.gan/README.cn.md

@@ -1,6 +1,6 @@
 # 生成对抗网络
 
-本教程源代码目录在book/09.gan，初次使用请您参考Book文档使用说明。
+本教程源代码目录在[book/09.gan](https://github.com/PaddlePaddle/book/tree/develop/09.gan)，初次使用请您参考Book文档使用说明。
 
 ### 说明: ###
 1. 硬件环境要求：

09.gan/index.cn.html

@@ -42,7 +42,7 @@
 <div id="markdown" style='display:none'>
 # 生成对抗网络
 
-本教程源代码目录在book/09.gan，初次使用请您参考Book文档使用说明。
+本教程源代码目录在[book/09.gan](https://github.com/PaddlePaddle/book/tree/develop/09.gan)，初次使用请您参考Book文档使用说明。
 
 ### 说明: ###
 1. 硬件环境要求：