diff --git a/01.fit_a_line/README.en.md b/01.fit_a_line/README.en.md
index 7f42b7da50adec54a779d388cec6572177dd88f3..3ed49f0a0dad05488100abca064aa702664ab041 100644
--- a/01.fit_a_line/README.en.md
+++ b/01.fit_a_line/README.en.md
@@ -163,40 +163,31 @@ feeding={'x': 0, 'y': 1}
 Moreover, an event handler is provided to print the training progress:
 
 ```python
-import matplotlib.pyplot as plt
-from IPython import display
-import cPickle
+# event_handler to print training and testing info
+from paddle.v2.plot import Ploter
 
-step=0
+train_title = "Train cost"
+test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
 
-train_costs=[],[]
-test_costs=[],[]
+step = 0
 
 def event_handler(event):
     global step
-    global train_costs
-    global test_costs
     if isinstance(event, paddle.event.EndIteration):
-        need_plot = False
         if step % 10 == 0:  # every 10 batches, record a train cost
-            train_costs[0].append(step)
-            train_costs[1].append(event.cost)
+            plot_cost.append(train_title, step, event.cost)
 
-        if step % 1000 == 0: # every 1000 batches, record a test cost
+        if step % 100 == 0: # every 100 batches, record a test cost
             result = trainer.test(
                 reader=paddle.batch(
                     uci_housing.test(), batch_size=2),
                 feeding=feeding)
-            test_costs[0].append(step)
-            test_costs[1].append(result.cost)
+            plot_cost.append(test_title, step, result.cost)
 
         if step % 100 == 0: # every 100 batches, update cost plot
-            plt.plot(*train_costs)
-            plt.plot(*test_costs)
-            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
-            display.clear_output(wait=True)
-            display.display(plt.gcf())
-            plt.gcf().clear()
+            plot_cost.plot()
+
         step += 1
 ```
 
@@ -213,7 +204,7 @@ trainer.train(
     num_passes=30)
 ```
 
-![png](./image/train-and-test.png)
+![png](./image/train_and_test.png)
 
 ## 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 2c3f1fd4dd61aec8ad6e97881d6229f4e6ef91b1..0eee811ec19e8d3c7486b1abea773bc23cfe55bc 100644
--- a/01.fit_a_line/README.md
+++ b/01.fit_a_line/README.md
@@ -159,40 +159,30 @@ feeding={'x': 0, 'y': 1}
 
 ```python
 # event_handler to print training and testing info
-import matplotlib.pyplot as plt
-from IPython import display
-import cPickle
+from paddle.v2.plot import Ploter
 
-step=0
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
 
-train_costs=[],[]
-test_costs=[],[]
+step = 0
 
 def event_handler(event):
     global step
-    global train_costs
-    global test_costs
     if isinstance(event, paddle.event.EndIteration):
-        need_plot = False
         if step % 10 == 0:  # every 10 batches, record a train cost
-            train_costs[0].append(step)
-            train_costs[1].append(event.cost)
+            cost_ploter.append(train_title, step, event.cost)
 
-        if step % 1000 == 0: # every 1000 batches, record a test cost
+        if step % 100 == 0: # every 100 batches, record a test cost
             result = trainer.test(
                 reader=paddle.batch(
                     uci_housing.test(), batch_size=2),
                 feeding=feeding)
-            test_costs[0].append(step)
-            test_costs[1].append(result.cost)
+            cost_ploter.append(test_title, step, result.cost)
 
         if step % 100 == 0: # every 100 batches, update cost plot
-            plt.plot(*train_costs)
-            plt.plot(*test_costs)
-            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
-            display.clear_output(wait=True)
-            display.display(plt.gcf())
-            plt.gcf().clear()
+            cost_ploter.plot()
+
         step += 1
 ```
 
@@ -209,7 +199,7 @@ trainer.train(
     num_passes=30)
 ```
 
-![png](./image/train-and-test.png)
+![png](./image/train_and_test.png)
 
 ## 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/image/ranges.png b/01.fit_a_line/image/ranges.png
index 3661ace169ad2bbda6355cff615b771108b258de..2f588f15c8a57f7f86699a4bc1b9879591928270 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/image/train-and-test.png b/01.fit_a_line/image/train-and-test.png
deleted file mode 100644
index bcd304a6a0baf30ecfbc43e08fc0aca179d05958..0000000000000000000000000000000000000000
Binary files a/01.fit_a_line/image/train-and-test.png and /dev/null differ
diff --git a/01.fit_a_line/index.en.html b/01.fit_a_line/index.en.html
index 06f5559174c252eaf951211d86ce86c7897d9ed9..82bcd531c7db05bf95462653a42ddf8537813be5 100644
--- a/01.fit_a_line/index.en.html
+++ b/01.fit_a_line/index.en.html
@@ -205,40 +205,31 @@ feeding={'x': 0, 'y': 1}
 Moreover, an event handler is provided to print the training progress:
 
 ```python
-import matplotlib.pyplot as plt
-from IPython import display
-import cPickle
+# event_handler to print training and testing info
+from paddle.v2.plot import Ploter
 
-step=0
+train_title = "Train cost"
+test_title = "Test cost"
+plot_cost = Ploter(train_title, test_title)
 
-train_costs=[],[]
-test_costs=[],[]
+step = 0
 
 def event_handler(event):
     global step
-    global train_costs
-    global test_costs
     if isinstance(event, paddle.event.EndIteration):
-        need_plot = False
         if step % 10 == 0:  # every 10 batches, record a train cost
-            train_costs[0].append(step)
-            train_costs[1].append(event.cost)
+            plot_cost.append(train_title, step, event.cost)
 
-        if step % 1000 == 0: # every 1000 batches, record a test cost
+        if step % 100 == 0: # every 100 batches, record a test cost
             result = trainer.test(
                 reader=paddle.batch(
                     uci_housing.test(), batch_size=2),
                 feeding=feeding)
-            test_costs[0].append(step)
-            test_costs[1].append(result.cost)
+            plot_cost.append(test_title, step, result.cost)
 
         if step % 100 == 0: # every 100 batches, update cost plot
-            plt.plot(*train_costs)
-            plt.plot(*test_costs)
-            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
-            display.clear_output(wait=True)
-            display.display(plt.gcf())
-            plt.gcf().clear()
+            plot_cost.plot()
+
         step += 1
 ```
 
@@ -255,7 +246,7 @@ trainer.train(
     num_passes=30)
 ```
 
-![png](./image/train-and-test.png)
+![png](./image/train_and_test.png)
 
 ## 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 a866e13bf3321c22973198d8ae639327348c4587..b7534329942e3d21d9a1eb2d9b120446a806f6a0 100644
--- a/01.fit_a_line/index.html
+++ b/01.fit_a_line/index.html
@@ -201,40 +201,30 @@ feeding={'x': 0, 'y': 1}
 
 ```python
 # event_handler to print training and testing info
-import matplotlib.pyplot as plt
-from IPython import display
-import cPickle
+from paddle.v2.plot import Ploter
 
-step=0
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
 
-train_costs=[],[]
-test_costs=[],[]
+step = 0
 
 def event_handler(event):
     global step
-    global train_costs
-    global test_costs
     if isinstance(event, paddle.event.EndIteration):
-        need_plot = False
         if step % 10 == 0:  # every 10 batches, record a train cost
-            train_costs[0].append(step)
-            train_costs[1].append(event.cost)
+            cost_ploter.append(train_title, step, event.cost)
 
-        if step % 1000 == 0: # every 1000 batches, record a test cost
+        if step % 100 == 0: # every 100 batches, record a test cost
             result = trainer.test(
                 reader=paddle.batch(
                     uci_housing.test(), batch_size=2),
                 feeding=feeding)
-            test_costs[0].append(step)
-            test_costs[1].append(result.cost)
+            cost_ploter.append(test_title, step, result.cost)
 
         if step % 100 == 0: # every 100 batches, update cost plot
-            plt.plot(*train_costs)
-            plt.plot(*test_costs)
-            plt.legend(['Train Cost', 'Test Cost'], loc='upper left')
-            display.clear_output(wait=True)
-            display.display(plt.gcf())
-            plt.gcf().clear()
+            cost_ploter.plot()
+
         step += 1
 ```
 
@@ -251,7 +241,7 @@ trainer.train(
     num_passes=30)
 ```
 
-![png](./image/train-and-test.png)
+![png](./image/train_and_test.png)
 
 ## 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/02.recognize_digits/README.en.md b/02.recognize_digits/README.en.md
index 15a70e75af4caa39f32d7b4db4233fc080a17eed..c5efb71f10bcaede1335e3acd955a2ae806e75b2 100644
--- a/02.recognize_digits/README.en.md
+++ b/02.recognize_digits/README.en.md
@@ -231,6 +231,34 @@ Here `shuffle` is a reader decorator, which takes a reader A as its parameter, a
 
 `batch` is a special decorator, whose input is a reader and output is a *batch reader*, which doesn't yield an instance at a time, but a minibatch.
 
+`event_handler_plot` is used to plot a figure like below：
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+
+# event_handler to plot a figure
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 100 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(reader=paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=128))
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` is used to plot some text data when training.
 ```python
 lists = []
 
@@ -246,13 +274,15 @@ def event_handler(event):
             event.pass_id, result.cost, result.metrics)
         lists.append((event.pass_id, result.cost,
                       result.metrics['classification_error_evaluator']))
+```
 
+```python
 trainer.train(
     reader=paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=8192),
         batch_size=128),
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     num_passes=100)
 ```
 
diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md
index 85f0defa1989b3832a50564e46274ae7270bed60..97ba662923ebac27a06621816f22d09bfc8bdbdd 100644
--- a/02.recognize_digits/README.md
+++ b/02.recognize_digits/README.md
@@ -236,6 +236,34 @@ trainer = paddle.trainer.SGD(cost=cost,
 
 `batch`是一个特殊的decorator，它的输入是一个reader，输出是一个batched reader —— 在PaddlePaddle里，一个reader每次yield一条训练数据，而一个batched reader每次yield一个minbatch。
 
+`event_handler_plot`可以用来在训练过程中画图如下：
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+
+# event_handler to plot a figure
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 100 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(reader=paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=128))
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` 用来在训练过程中输出训练结果
 ```python
 lists = []
 
@@ -251,13 +279,15 @@ def event_handler(event):
             event.pass_id, result.cost, result.metrics)
         lists.append((event.pass_id, result.cost,
                       result.metrics['classification_error_evaluator']))
+```
 
+```python
 trainer.train(
     reader=paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=8192),
         batch_size=128),
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     num_passes=100)
 ```
 
diff --git a/02.recognize_digits/image/train_and_test.png b/02.recognize_digits/image/train_and_test.png
new file mode 100644
index 0000000000000000000000000000000000000000..5cb87b450d0398bcfaec0e647c362052069797e7
Binary files /dev/null and b/02.recognize_digits/image/train_and_test.png differ
diff --git a/02.recognize_digits/index.en.html b/02.recognize_digits/index.en.html
index 03960fd4b38172a6b821c9b612a7d7c2034bd8a2..45c85c8290baebb379c4f87a594230ee07836c2f 100644
--- a/02.recognize_digits/index.en.html
+++ b/02.recognize_digits/index.en.html
@@ -273,6 +273,34 @@ Here `shuffle` is a reader decorator, which takes a reader A as its parameter, a
 
 `batch` is a special decorator, whose input is a reader and output is a *batch reader*, which doesn't yield an instance at a time, but a minibatch.
 
+`event_handler_plot` is used to plot a figure like below：
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+
+# event_handler to plot a figure
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 100 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(reader=paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=128))
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` is used to plot some text data when training.
 ```python
 lists = []
 
@@ -288,13 +316,15 @@ def event_handler(event):
             event.pass_id, result.cost, result.metrics)
         lists.append((event.pass_id, result.cost,
                       result.metrics['classification_error_evaluator']))
+```
 
+```python
 trainer.train(
     reader=paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=8192),
         batch_size=128),
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     num_passes=100)
 ```
 
diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html
index 3b45d841952289b2fcd11ae149538596a5dd6a1f..38500236c3d6a4e27f3fbc8d859cb780625f7bad 100644
--- a/02.recognize_digits/index.html
+++ b/02.recognize_digits/index.html
@@ -278,6 +278,34 @@ trainer = paddle.trainer.SGD(cost=cost,
 
 `batch`是一个特殊的decorator，它的输入是一个reader，输出是一个batched reader —— 在PaddlePaddle里，一个reader每次yield一条训练数据，而一个batched reader每次yield一个minbatch。
 
+`event_handler_plot`可以用来在训练过程中画图如下：
+
+![png](./image/train_and_test.png)
+
+```python
+from paddle.v2.plot import Ploter
+
+train_title = "Train cost"
+test_title = "Test cost"
+cost_ploter = Ploter(train_title, test_title)
+
+step = 0
+
+# event_handler to plot a figure
+def event_handler_plot(event):
+    global step
+    if isinstance(event, paddle.event.EndIteration):
+        if step % 100 == 0:
+            cost_ploter.append(train_title, step, event.cost)
+            cost_ploter.plot()
+        step += 1
+    if isinstance(event, paddle.event.EndPass):
+        result = trainer.test(reader=paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=128))
+        cost_ploter.append(test_title, step, result.cost)
+```
+
+`event_handler` 用来在训练过程中输出训练结果
 ```python
 lists = []
 
@@ -293,13 +321,15 @@ def event_handler(event):
             event.pass_id, result.cost, result.metrics)
         lists.append((event.pass_id, result.cost,
                       result.metrics['classification_error_evaluator']))
+```
 
+```python
 trainer.train(
     reader=paddle.batch(
         paddle.reader.shuffle(
             paddle.dataset.mnist.train(), buf_size=8192),
         batch_size=128),
-    event_handler=event_handler,
+    event_handler=event_handler_plot,
     num_passes=100)
 ```