diff --git a/01.fit_a_line/README.cn.md b/01.fit_a_line/README.cn.md
index 936142bfbc427fa085797f2e62b136709e085405..a969c752767a575d78e86bd51d763702b91eb551 100644
--- a/01.fit_a_line/README.cn.md
+++ b/01.fit_a_line/README.cn.md
@@ -100,6 +100,7 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 import paddle
 import paddle.fluid as fluid
 import numpy
+from __future__ import print_function
 ```
 
 我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
@@ -179,7 +180,7 @@ feed_order=['x', 'y']
 除此之外，可以定义一个事件相应器来处理类似`打印训练进程`的事件：
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -190,11 +191,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: # record the test cost every 10 seconds
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -251,10 +252,20 @@ inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10
-tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
+test_data = test_reader().next()
+test_feat = numpy.array([data[0] for data in test_data]).astype("float32")
+test_label = numpy.array([data[1] for data in test_data]).astype("float32")
 
-results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+results = inferencer.infer({'x': test_feat})
+
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
+
+print("\nground truth:")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, test_label[k]))
 ```
 
 ## 总结
diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md
index e672caf1206b13b50abbd57eb2fc465fb656cce1..407d0411eb7a2f3cd990618a22b34eaca0abfd28 100644
--- a/01.fit_a_line/README.md
+++ b/01.fit_a_line/README.md
@@ -108,6 +108,7 @@ Our program starts with importing necessary packages:
 import paddle
 import paddle.fluid as fluid
 import numpy
+from __future__ import print_function
 ```
 
 We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
@@ -189,7 +190,7 @@ feed_order=['x', 'y']
 Moreover, an event handler is provided to print the training progress:
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -200,11 +201,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: #record a test cost every 10 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -263,10 +264,20 @@ inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10
-tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
+test_data = test_reader().next()
+test_feat = numpy.array([data[0] for data in test_data]).astype("float32")
+test_label = numpy.array([data[1] for data in test_data]).astype("float32")
 
-results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+results = inferencer.infer({'x': test_feat})
+
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
+
+print("\nground truth:")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, test_label[k]))
 ```
 
 ## Summary
diff --git a/01.fit_a_line/image/ranges.png b/01.fit_a_line/image/ranges.png
index 5d86b12715f46afbafb7d50e2938e184219b5b95..5325df4800985983e17476f007658d1cdb170b1c 100644
Binary files a/01.fit_a_line/image/ranges.png and b/01.fit_a_line/image/ranges.png differ
diff --git a/01.fit_a_line/index.cn.html b/01.fit_a_line/index.cn.html
index 32760a22092f136746a72328b48502548caf7692..9a0b9918dcbbb31c4615908b3cc323eb59b36835 100644
--- a/01.fit_a_line/index.cn.html
+++ b/01.fit_a_line/index.cn.html
@@ -142,6 +142,7 @@ $$MSE=\frac{1}{n}\sum_{i=1}^{n}{(\hat{Y_i}-Y_i)}^2$$
 import paddle
 import paddle.fluid as fluid
 import numpy
+from __future__ import print_function
 ```
 
 我们通过uci_housing模块引入了数据集合[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)
@@ -221,7 +222,7 @@ feed_order=['x', 'y']
 除此之外，可以定义一个事件相应器来处理类似`打印训练进程`的事件：
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -232,11 +233,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: # record the test cost every 10 seconds
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -293,10 +294,20 @@ inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10
-tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
+test_data = test_reader().next()
+test_feat = numpy.array([data[0] for data in test_data]).astype("float32")
+test_label = numpy.array([data[1] for data in test_data]).astype("float32")
 
-results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+results = inferencer.infer({'x': test_feat})
+
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
+
+print("\nground truth:")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, test_label[k]))
 ```
 
 ## 总结
diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html
index cab234ff49188aa0be1941c5c81c4c1fc3a9d2a3..4c6cf7f431c599abdae0f6e4109ca571ba613f4d 100644
--- a/01.fit_a_line/index.html
+++ b/01.fit_a_line/index.html
@@ -150,6 +150,7 @@ Our program starts with importing necessary packages:
 import paddle
 import paddle.fluid as fluid
 import numpy
+from __future__ import print_function
 ```
 
 We encapsulated the [UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing) in our Python module `uci_housing`.  This module can
@@ -231,7 +232,7 @@ feed_order=['x', 'y']
 Moreover, an event handler is provided to print the training progress:
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -242,11 +243,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: #record a test cost every 10 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -305,10 +306,20 @@ inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
 batch_size = 10
-tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
+test_reader = paddle.batch(paddle.dataset.uci_housing.test(),batch_size=batch_size)
+test_data = test_reader().next()
+test_feat = numpy.array([data[0] for data in test_data]).astype("float32")
+test_label = numpy.array([data[1] for data in test_data]).astype("float32")
 
-results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+results = inferencer.infer({'x': test_feat})
+
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
+
+print("\nground truth:")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, test_label[k]))
 ```
 
 ## Summary
diff --git a/03.image_classification/README.cn.md b/03.image_classification/README.cn.md
index 59180d1db79c88e1986606cf06873559a2b7b961..3f21327ba257806aef5f7464d0708a95421b0d4a 100644
--- a/03.image_classification/README.cn.md
+++ b/03.image_classification/README.cn.md
@@ -160,6 +160,7 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 import sys
+from __future__ import print_function
 ```
 
 本教程中我们提供了VGG和ResNet两个模型的配置。
@@ -426,7 +427,7 @@ def event_handler(event):
 
 通过`trainer.train`函数训练:
 
-**注意:** CPU，每个 Epoch 将花费大约15～20分钟。这部分可能需要一段时间。请随意修改代码，在GPU上运行测试，以提高培训速度。
+**注意:** CPU，每个 Epoch 将花费大约15～20分钟。这部分可能需要一段时间。请随意修改代码，在GPU上运行测试，以提高训练速度。
 
 ```python
 trainer.train(
@@ -499,10 +500,10 @@ img = load_image(cur_dir + '/image/dog.png')
 ```python
 inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
-
+label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference
 results = inferencer.infer({'pixel': img})
-print("infer results: ", results)
+print("infer results: %s" % label_list[np.argmax(results[0])])
 ```
 
 ## 总结
diff --git a/03.image_classification/README.md b/03.image_classification/README.md
index f24fbdeb9458f1730fe64bd6d54b2c71400d4a4e..c3632e9e9a85e66ea8b7ab4ed14ed635c72c9258 100644
--- a/03.image_classification/README.md
+++ b/03.image_classification/README.md
@@ -171,6 +171,7 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 import sys
+from __future__ import print_function
 ```
 
 Now we are going to walk you through the implementations of the VGG and ResNet.
@@ -514,9 +515,10 @@ Now we are ready to do inference.
 inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
+label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference
 results = inferencer.infer({'pixel': img})
-print("infer results: ", results)
+print("infer results: %s" % label_list[np.argmax(results[0])])
 ```
 
 
diff --git a/03.image_classification/index.cn.html b/03.image_classification/index.cn.html
index dbd1f1c5978e91126104309e67527b9243a0fc75..0aa3cf2554cbeb07b57388e405fc0da0bba50665 100644
--- a/03.image_classification/index.cn.html
+++ b/03.image_classification/index.cn.html
@@ -202,6 +202,7 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 import sys
+from __future__ import print_function
 ```
 
 本教程中我们提供了VGG和ResNet两个模型的配置。
@@ -468,7 +469,7 @@ def event_handler(event):
 
 通过`trainer.train`函数训练:
 
-**注意:** CPU，每个 Epoch 将花费大约15～20分钟。这部分可能需要一段时间。请随意修改代码，在GPU上运行测试，以提高培训速度。
+**注意:** CPU，每个 Epoch 将花费大约15～20分钟。这部分可能需要一段时间。请随意修改代码，在GPU上运行测试，以提高训练速度。
 
 ```python
 trainer.train(
@@ -541,10 +542,10 @@ img = load_image(cur_dir + '/image/dog.png')
 ```python
 inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
-
+label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference
 results = inferencer.infer({'pixel': img})
-print("infer results: ", results)
+print("infer results: %s" % label_list[np.argmax(results[0])])
 ```
 
 ## 总结
diff --git a/03.image_classification/index.html b/03.image_classification/index.html
index 0a37e7016c2849bfe9b1c6bf4c1acda864a0b1d0..6c2f1f6643caaab13831f824fbbfdb85644c76ec 100644
--- a/03.image_classification/index.html
+++ b/03.image_classification/index.html
@@ -213,6 +213,7 @@ import paddle
 import paddle.fluid as fluid
 import numpy
 import sys
+from __future__ import print_function
 ```
 
 Now we are going to walk you through the implementations of the VGG and ResNet.
@@ -556,9 +557,10 @@ Now we are ready to do inference.
 inferencer = fluid.Inferencer(
     infer_func=inference_program, param_path=params_dirname, place=place)
 
+label_list = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
 # inference
 results = inferencer.infer({'pixel': img})
-print("infer results: ", results)
+print("infer results: %s" % label_list[np.argmax(results[0])])
 ```