Modify the code to support python3. (#793)

Modify the code to support python3: fix problem in book05 doc and code

05.recommender_system/README.cn.md

@@ -134,9 +134,10 @@ Paddle在API中提供了自动加载数据的模块。数据模块为 `paddle.da
 
 
 ```python
+from __future__ import print_function
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -152,7 +153,7 @@ 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 +164,7 @@ 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 +217,7 @@ 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]
@@ -232,7 +233,6 @@ print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id]
 
 
 ```python
-from __future__ import print_function
 import math
 import sys
 import numpy as np

05.recommender_system/README.md

@@ -120,9 +120,10 @@ Paddle provides modules for automatically loading data in the API. The data modu
 
 
 ```python
+from __future__ import print_function
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -138,7 +139,7 @@ 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 +150,7 @@ 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 +203,7 @@ 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
@@ -220,7 +221,6 @@ Below we begin to configure the model based on the form of the input data. First
 
 
 ```python
-from __future__ import print_function
 import math
 import sys
 import numpy as np

05.recommender_system/index.cn.html

@@ -176,9 +176,10 @@ Paddle在API中提供了自动加载数据的模块。数据模块为 `paddle.da
 
 
 ```python
+from __future__ import print_function
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -194,7 +195,7 @@ 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'])>
@@ -205,7 +206,7 @@ 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)>
@@ -258,7 +259,7 @@ 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]
@@ -274,7 +275,6 @@ print "User %s rates Movie %s with Score %s"%(user_info[uid], movie_info[mov_id]
 
 
 ```python
-from __future__ import print_function
 import math
 import sys
 import numpy as np

05.recommender_system/index.html

@@ -162,9 +162,10 @@ Paddle provides modules for automatically loading data in the API. The data modu
 
 
 ```python
+from __future__ import print_function
 import paddle
 movie_info = paddle.dataset.movielens.movie_info()
-print movie_info.values()[0]
+print(list(movie_info.values())[0])
 ```
 
 
@@ -180,7 +181,7 @@ 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'])>
@@ -191,7 +192,7 @@ 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)>
@@ -244,7 +245,7 @@ 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
@@ -262,7 +263,6 @@ Below we begin to configure the model based on the form of the input data. First
 
 
 ```python
-from __future__ import print_function
 import math
 import sys
 import numpy as np