update 01 and 02 markdown

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.

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。

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.

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。

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)
 ```
 

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)
 ```
 

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)
 ```
 

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)
 ```