diff --git a/doc/getstarted/concepts/src/infer.py b/doc/getstarted/concepts/src/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cc58dfee0bd6dade0340b4fd0ee1adb49ffebf6
--- /dev/null
+++ b/doc/getstarted/concepts/src/infer.py
@@ -0,0 +1,18 @@
+import paddle.v2 as paddle
+import numpy as np
+
+paddle.init(use_gpu=False)
+x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(2))
+y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+# loading the model which generated by training
+with open('params_pass_90.tar', 'r') as f:
+    parameters = paddle.parameters.Parameters.from_tar(f)
+
+# Input multiple sets of data，Output the infer result in a array.
+i = [[[1, 2]], [[3, 4]], [[5, 6]]]
+print paddle.infer(output_layer=y_predict, parameters=parameters, input=i)
+# Will print:
+# [[ -3.24491572]
+#  [ -6.94668722]
+#  [-10.64845848]]
diff --git a/doc/getstarted/concepts/src/train.py b/doc/getstarted/concepts/src/train.py
index 8aceb23406a476f08639cc6223cdf730b728a705..4bccbfca3c70c12aec564e2cae3b8ca174b68777 100644
--- a/doc/getstarted/concepts/src/train.py
+++ b/doc/getstarted/concepts/src/train.py
@@ -26,6 +26,11 @@ def event_handler(event):
         if event.batch_id % 1 == 0:
             print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
                                                   event.cost)
+    # product model every 10 pass
+    if isinstance(event, paddle.event.EndPass):
+        if event.pass_id % 10 == 0:
+            with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
+                trainer.save_parameter_to_tar(f)
 
 
 # define training dataset reader
diff --git a/doc/getstarted/concepts/use_concepts_cn.rst b/doc/getstarted/concepts/use_concepts_cn.rst
index c243083794bb3c4659242de99b3b2715af9d7c24..e695ff283e2e806377a51c559b37e8068360a4ff 100644
--- a/doc/getstarted/concepts/use_concepts_cn.rst
+++ b/doc/getstarted/concepts/use_concepts_cn.rst
@@ -147,4 +147,9 @@ PaddlePaddle支持不同类型的输入数据，主要包括四种类型，和
 ..  literalinclude:: src/train.py
     :linenos:
 
+使用以上训练好的模型进行预测，取其中一个模型params_pass_90.tar，输入需要预测的向量组，然后打印输出：
+
+..  literalinclude:: src/infer.py
+    :linenos:
+
 有关线性回归的实际应用，可以参考PaddlePaddle book的 `第一章节 <http://book.paddlepaddle.org/index.html>`_。