diff --git a/01.fit_a_line/README.en.md b/01.fit_a_line/README.en.md
index 31341db5e1b725ba6c646597537ef54f7bb14f9d..934b28efc1bca4ec4bdb9564fbf20feba168ff8a 100644
--- a/01.fit_a_line/README.en.md
+++ b/01.fit_a_line/README.en.md
@@ -222,6 +222,33 @@ trainer.train(
 
 ![png](./image/train_and_test.png)
 
+### Apply model
+
+#### 1. generate testing data
+
+```python
+test_data_creator = paddle.dataset.uci_housing.test()
+test_data = []
+
+for item in test_data_creator():
+    test_data.append((item[0], ))
+    if len(test_data) == 5:
+        break
+
+for data in test_data:
+    print data
+```
+
+#### 2. inference
+
+```python
+probs = paddle.infer(
+    output_layer=y_predict, parameters=parameters, input=test_data)
+
+for data in probs:
+    print data
+```
+
 ## Summary
 This chapter introduces *Linear Regression* and how to train and test this model with PaddlePaddle, using the UCI Housing Data Set. Because a large number of more complex models and techniques are derived from linear regression, it is important to understand its underlying theory and limitation.
 
diff --git a/01.fit_a_line/README.md b/01.fit_a_line/README.md
index 45950078e1c09d77d9e52c9b9160e3c3458557fc..7fbd4f32ed2b7070c64827e7ff35228f6c71828c 100644
--- a/01.fit_a_line/README.md
+++ b/01.fit_a_line/README.md
@@ -217,6 +217,33 @@ trainer.train(
 
 ![png](./image/train_and_test.png)
 
+### 应用模型
+
+#### 1. 生成测试数据
+
+```python
+test_data_creator = paddle.dataset.uci_housing.test()
+test_data = []
+
+for item in test_data_creator():
+    test_data.append((item[0], ))
+    if len(test_data) == 5:
+        break
+
+for data in test_data:
+    print data
+```
+
+#### 2. 推测 inference
+
+```python
+probs = paddle.infer(
+    output_layer=y_predict, parameters=parameters, input=test_data)
+
+for data in probs:
+    print data
+```
+
 ## 总结
 在这章里，我们借助波士顿房价这一数据集，介绍了线性回归模型的基本概念，以及如何使用PaddlePaddle实现训练和测试的过程。很多的模型和技巧都是从简单的线性回归模型演化而来，因此弄清楚线性模型的原理和局限非常重要。
 
diff --git a/01.fit_a_line/index.en.html b/01.fit_a_line/index.en.html
index fdb8c89cfad99aefea1727c89f414fc2b3ee4beb..59590c500ae744c34f3f5e08ad2ab8771c7a504f 100644
--- a/01.fit_a_line/index.en.html
+++ b/01.fit_a_line/index.en.html
@@ -264,6 +264,33 @@ trainer.train(
 
 ![png](./image/train_and_test.png)
 
+### Apply model
+
+#### 1. generate testing data
+
+```python
+test_data_creator = paddle.dataset.uci_housing.test()
+test_data = []
+
+for item in test_data_creator():
+    test_data.append((item[0], ))
+    if len(test_data) == 5:
+        break
+
+for data in test_data:
+    print data
+```
+
+#### 2. inference
+
+```python
+probs = paddle.infer(
+    output_layer=y_predict, parameters=parameters, input=test_data)
+
+for data in probs:
+    print data
+```
+
 ## Summary
 This chapter introduces *Linear Regression* and how to train and test this model with PaddlePaddle, using the UCI Housing Data Set. Because a large number of more complex models and techniques are derived from linear regression, it is important to understand its underlying theory and limitation.
 
diff --git a/01.fit_a_line/index.html b/01.fit_a_line/index.html
index cac6ce753a502d47aec931845eb014df4876d521..1f6f23efc968e8d3e2472e0aea68f2be73510772 100644
--- a/01.fit_a_line/index.html
+++ b/01.fit_a_line/index.html
@@ -259,6 +259,33 @@ trainer.train(
 
 ![png](./image/train_and_test.png)
 
+### 应用模型
+
+#### 1. 生成测试数据
+
+```python
+test_data_creator = paddle.dataset.uci_housing.test()
+test_data = []
+
+for item in test_data_creator():
+    test_data.append((item[0], ))
+    if len(test_data) == 5:
+        break
+
+for data in test_data:
+    print data
+```
+
+#### 2. 推测 inference
+
+```python
+probs = paddle.infer(
+    output_layer=y_predict, parameters=parameters, input=test_data)
+
+for data in probs:
+    print data
+```
+
 ## 总结
 在这章里，我们借助波士顿房价这一数据集，介绍了线性回归模型的基本概念，以及如何使用PaddlePaddle实现训练和测试的过程。很多的模型和技巧都是从简单的线性回归模型演化而来，因此弄清楚线性模型的原理和局限非常重要。