diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md
index fd8160a5396f0b8b66b2f73d1379b437baa0590e..edb4ea2d8d0fddef6dbfebf30d3a43d6bd5ab3d5 100644
--- a/02.recognize_digits/README.cn.md
+++ b/02.recognize_digits/README.cn.md
@@ -399,7 +399,7 @@ prediction, [avg_loss, acc] = train_program()
 # 输入的原始图像数据，名称为img，大小为28*28*1
 # 标签层，名称为label,对应输入图片的类别标签
 # 告知网络传入的数据分为两部分，第一部分是img值，第二部分是label值
-feeder = fluid.DataFeeder(feed_list=[‘img’, ‘label’], place=place)
+feeder = fluid.DataFeeder(feed_list=['img', 'label'], place=place)
 
 # 选择Adam优化器
 optimizer = optimizer_program()
diff --git a/02.recognize_digits/index.cn.html b/02.recognize_digits/index.cn.html
index e40718b27a2910b21813e6c28fa19becbe5ed96a..816f56a631b184aa0383bfaf23cc0ffe15bd5704 100644
--- a/02.recognize_digits/index.cn.html
+++ b/02.recognize_digits/index.cn.html
@@ -441,7 +441,7 @@ prediction, [avg_loss, acc] = train_program()
 # 输入的原始图像数据，名称为img，大小为28*28*1
 # 标签层，名称为label,对应输入图片的类别标签
 # 告知网络传入的数据分为两部分，第一部分是img值，第二部分是label值
-feeder = fluid.DataFeeder(feed_list=[‘img’, ‘label’], place=place)
+feeder = fluid.DataFeeder(feed_list=['img', 'label'], place=place)
 
 # 选择Adam优化器
 optimizer = optimizer_program()
diff --git a/09.gan/README.cn.md b/09.gan/README.cn.md
index 5934efa1907b7db4b3bd63367289056a3e99340f..10266d523f8fb96ed539f69a6a703a8320d7a97d 100644
--- a/09.gan/README.cn.md
+++ b/09.gan/README.cn.md
@@ -368,7 +368,7 @@ for pass_id in range(epoch):
         # 虚假图片
         generated_image = exe.run(g_program,
                                   feed={'noise': noise_data},
-                                  fetch_list={g_img})[0]
+                                  fetch_list=[g_img])[0]
 
         total_images = np.concatenate([real_image, generated_image])
 
@@ -378,7 +378,7 @@ for pass_id in range(epoch):
                                'img': generated_image,
                                'label': fake_labels,
                            },
-                           fetch_list={d_loss})[0][0]
+                           fetch_list=[d_loss])[0][0]
 
         # D 判断真实图片为真的loss
         d_loss_2 = exe.run(d_program,
@@ -386,7 +386,7 @@ for pass_id in range(epoch):
                                'img': real_image,
                                'label': real_labels,
                            },
-                           fetch_list={d_loss})[0][0]
+                           fetch_list=[d_loss])[0][0]
 
         d_loss_n = d_loss_1 + d_loss_2
         losses[0].append(d_loss_n)
@@ -398,7 +398,7 @@ for pass_id in range(epoch):
                 size=[batch_size, NOISE_SIZE]).astype('float32')
             dg_loss_n = exe.run(dg_program,
                                  feed={'noise': noise_data},
-                                 fetch_list={dg_loss})[0][0]
+                                 fetch_list=[dg_loss])[0][0]
             losses[1].append(dg_loss_n)
         t_time += (time.time() - s_time)
         if batch_id % 10 == 0 :
@@ -407,7 +407,7 @@ for pass_id in range(epoch):
             # 每轮的生成结果
             generated_images = exe.run(g_program_test,
                                        feed={'noise': const_n},
-                                       fetch_list={g_img})[0]
+                                       fetch_list=[g_img])[0]
             # 将真实图片和生成图片连接
             total_images = np.concatenate([real_image, generated_images])
             fig = plot(total_images)
diff --git a/09.gan/index.cn.html b/09.gan/index.cn.html
index a80ffc3912d7da340aa6c3cca739d232c001a8ce..ca7ee664d486e33ba84798f138df953cc2043400 100644
--- a/09.gan/index.cn.html
+++ b/09.gan/index.cn.html
@@ -410,7 +410,7 @@ for pass_id in range(epoch):
         # 虚假图片
         generated_image = exe.run(g_program,
                                   feed={'noise': noise_data},
-                                  fetch_list={g_img})[0]
+                                  fetch_list=[g_img])[0]
 
         total_images = np.concatenate([real_image, generated_image])
 
@@ -420,7 +420,7 @@ for pass_id in range(epoch):
                                'img': generated_image,
                                'label': fake_labels,
                            },
-                           fetch_list={d_loss})[0][0]
+                           fetch_list=[d_loss])[0][0]
 
         # D 判断真实图片为真的loss
         d_loss_2 = exe.run(d_program,
@@ -428,7 +428,7 @@ for pass_id in range(epoch):
                                'img': real_image,
                                'label': real_labels,
                            },
-                           fetch_list={d_loss})[0][0]
+                           fetch_list=[d_loss])[0][0]
 
         d_loss_n = d_loss_1 + d_loss_2
         losses[0].append(d_loss_n)
@@ -440,7 +440,7 @@ for pass_id in range(epoch):
                 size=[batch_size, NOISE_SIZE]).astype('float32')
             dg_loss_n = exe.run(dg_program,
                                  feed={'noise': noise_data},
-                                 fetch_list={dg_loss})[0][0]
+                                 fetch_list=[dg_loss])[0][0]
             losses[1].append(dg_loss_n)
         t_time += (time.time() - s_time)
         if batch_id % 10 == 0 :
@@ -449,7 +449,7 @@ for pass_id in range(epoch):
             # 每轮的生成结果
             generated_images = exe.run(g_program_test,
                                        feed={'noise': const_n},
-                                       fetch_list={g_img})[0]
+                                       fetch_list=[g_img])[0]
             # 将真实图片和生成图片连接
             total_images = np.concatenate([real_image, generated_images])
             fig = plot(total_images)