Remove utils plot until paddle support (#639)

* Remove Utils until paddle support

* delete figures until paddle support

01.fit_a_line/README.cn.md

@@ -196,28 +196,24 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
-# Plot data
-from paddle.utils import Ploter
 
 train_title = "Train cost"
 test_title = "Test cost"
-plot_cost = Ploter(train_title, test_title)
 
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -242,7 +238,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 ```
 

01.fit_a_line/README.md

@@ -212,28 +212,24 @@ Moreover, an event handler is provided to print the training progress:
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
-# Plot data
-from paddle.utils import Ploter
 
 train_title = "Train cost"
 test_title = "Test cost"
-plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
+
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
-            if test_metrics[0] < 10.0:
             print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
+            if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
                 print('loss is less than 10.0, stop')
                 trainer.stop()
@@ -258,7 +254,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 ```

01.fit_a_line/index.cn.html

@@ -238,28 +238,24 @@ feed_order=['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
-# Plot data
-from paddle.utils import Ploter
 
 train_title = "Train cost"
 test_title = "Test cost"
-plot_cost = Ploter(train_title, test_title)
 
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
 
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
 
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
@@ -284,7 +280,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 ```
 

01.fit_a_line/index.html

@@ -254,28 +254,24 @@ Moreover, an event handler is provided to print the training progress:
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
-# Plot data
-from paddle.utils import Ploter
 
 train_title = "Train cost"
 test_title = "Test cost"
-plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:   # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" % (train_title, step, event.metrics[0]))
+
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
-            if test_metrics[0] < 10.0:
             print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
+            if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
                 print('loss is less than 10.0, stop')
                 trainer.stop()
@@ -300,7 +296,7 @@ We now can start training by calling `trainer.train()`.
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 ```

01.fit_a_line/train.py

@@ -69,26 +69,23 @@ feed_order = ['x', 'y']
 # Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
-from paddle.utils import Ploter
-
 train_title = "Train cost"
 test_title = "Test cost"
-plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
 
 # event_handler prints training and testing info
-def event_handler_plot(event):
+def event_handler(event):
     global step
     if isinstance(event, EndStepEvent):
         if step % 10 == 0:  # record a train cost every 10 batches
-            plot_cost.append(train_title, step, event.metrics[0])
+            print("%s, Step %d, Cost %f" %
+                  (train_title, step, event.metrics[0]))
         if step % 100 == 0:  # record a test cost every 100 batches
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
-            plot_cost.append(test_title, step, test_metrics[0])
-            plot_cost.plot()
+            print("%s, Step %d, Cost %f" % (test_title, step, test_metrics[0]))
             if test_metrics[0] < 10.0:
                 # If the accuracy is good enough, we can stop the training.
                 print('loss is less than 10.0, stop')
@@ -106,7 +103,7 @@ def event_handler_plot(event):
 trainer.train(
     reader=train_reader,
     num_epochs=100,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     feed_order=feed_order)
 
 

02.recognize_digits/README.cn.md

@@ -321,42 +321,6 @@ def event_handler(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
-`event_handler_plot` 可以用来在训练过程中画图如下：
-
-<div align="center">
-<img src="https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/train_and_test.png?raw=true" width="400"><br/>
-图7 训练结果
-</div>
-
-
-```python
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-step = 0
-lists = []
-
-# event_handler to plot a figure
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 100 == 0:
-            # event.metrics maps with train program return arguments.
-            # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        # save parameters
-        trainer.save_params(params_dirname)
-
-        avg_cost, acc = trainer.test(
-            reader=test_reader, feed_order=['img', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-        lists.append((event.epoch, avg_cost, acc))
-```
 
 #### 开始训练
 

02.recognize_digits/README.md

@@ -324,38 +324,6 @@ def event_handler(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
-`event_handler_plot` is used to plot a figure like below：
-
-![png](./image/train_and_test.png)
-
-```python
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-step = 0
-lists = []
-
-# event_handler to plot a figure
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 100 == 0:
-            # event.metrics maps with train program return arguments.
-            # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        # save parameters
-        trainer.save_params(params_dirname)
-
-        avg_cost, acc = trainer.test(
-            reader=test_reader, feed_order=['img', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-        lists.append((event.epoch, avg_cost, acc))
-```
 
 #### Start training
 
@@ -365,7 +333,7 @@ Now that we setup the event_handler and the reader, we can start training the mo
 # Train the model now
 trainer.train(
     num_epochs=5,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     reader=train_reader,
     feed_order=['img', 'label'])
 ```

02.recognize_digits/index.cn.html

@@ -363,42 +363,6 @@ def event_handler(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
-`event_handler_plot` 可以用来在训练过程中画图如下：
-
-<div align="center">
-<img src="https://github.com/PaddlePaddle/book/blob/develop/02.recognize_digits/image/train_and_test.png?raw=true" width="400"><br/>
-图7 训练结果
-</div>
-
-
-```python
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-step = 0
-lists = []
-
-# event_handler to plot a figure
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 100 == 0:
-            # event.metrics maps with train program return arguments.
-            # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        # save parameters
-        trainer.save_params(params_dirname)
-
-        avg_cost, acc = trainer.test(
-            reader=test_reader, feed_order=['img', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-        lists.append((event.epoch, avg_cost, acc))
-```
 
 #### 开始训练
 

02.recognize_digits/index.html

@@ -366,38 +366,6 @@ def event_handler(event):
         lists.append((event.epoch, avg_cost, acc))
 ```
 
-`event_handler_plot` is used to plot a figure like below：
-
-![png](./image/train_and_test.png)
-
-```python
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-step = 0
-lists = []
-
-# event_handler to plot a figure
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 100 == 0:
-            # event.metrics maps with train program return arguments.
-            # event.metrics[0] will yeild avg_cost and event.metrics[1] will yeild acc in this example.
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        # save parameters
-        trainer.save_params(params_dirname)
-
-        avg_cost, acc = trainer.test(
-            reader=test_reader, feed_order=['img', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-        lists.append((event.epoch, avg_cost, acc))
-```
 
 #### Start training
 
@@ -407,7 +375,7 @@ Now that we setup the event_handler and the reader, we can start training the mo
 # Train the model now
 trainer.train(
     num_epochs=5,
-    event_handler=event_handler_plot,
+    event_handler=event_handler,
     reader=train_reader,
     feed_order=['img', 'label'])
 ```

03.image_classification/README.cn.md

@@ -383,42 +383,6 @@ test_reader = paddle.batch(
 
 可以使用`event_handler`回调函数来观察训练过程，或进行测试等, 该回调函数是`trainer.train`函数里设定。
 
-`event_handler_plot`可以用来利用回调数据来打点画图:
-
-<p align="center">
-<img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/train_and_test.png?raw=true" width="350"><br/>
-图12. 训练结果
-</p>
-
-
-```python
-params_dirname = "image_classification_resnet.inference.model"
-
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-
-step = 0
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 1 == 0:
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        avg_cost, accuracy = trainer.test(
-            reader=test_reader,
-            feed_order=['pixel', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-
-        # save parameters
-        if params_dirname is not None:
-            trainer.save_params(params_dirname)
-```
-
 `event_handler` 用来在训练过程中输出文本日志
 
 ```python

03.image_classification/README.md

@@ -385,38 +385,6 @@ test_reader = paddle.batch(
 
 Callback function `event_handler` will be called during training when a pre-defined event happens.
 
-`event_handler_plot`is used to plot a figure like below：
-
-![png](./image/train_and_test.png)
-
-```python
-params_dirname = "image_classification_resnet.inference.model"
-
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-
-step = 0
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 1 == 0:
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        avg_cost, accuracy = trainer.test(
-            reader=test_reader,
-            feed_order=['pixel', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-
-        # save parameters
-        if params_dirname is not None:
-            trainer.save_params(params_dirname)
-```
-
 `event_handler` is used to plot some text data when training.
 
 ```python

03.image_classification/index.cn.html

@@ -425,42 +425,6 @@ test_reader = paddle.batch(
 
 可以使用`event_handler`回调函数来观察训练过程，或进行测试等, 该回调函数是`trainer.train`函数里设定。
 
-`event_handler_plot`可以用来利用回调数据来打点画图:
-
-<p align="center">
-<img src="https://github.com/PaddlePaddle/book/blob/develop/03.image_classification/image/train_and_test.png?raw=true" width="350"><br/>
-图12. 训练结果
-</p>
-
-
-```python
-params_dirname = "image_classification_resnet.inference.model"
-
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-
-step = 0
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 1 == 0:
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        avg_cost, accuracy = trainer.test(
-            reader=test_reader,
-            feed_order=['pixel', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-
-        # save parameters
-        if params_dirname is not None:
-            trainer.save_params(params_dirname)
-```
-
 `event_handler` 用来在训练过程中输出文本日志
 
 ```python

03.image_classification/index.html

@@ -427,38 +427,6 @@ test_reader = paddle.batch(
 
 Callback function `event_handler` will be called during training when a pre-defined event happens.
 
-`event_handler_plot`is used to plot a figure like below：
-
-![png](./image/train_and_test.png)
-
-```python
-params_dirname = "image_classification_resnet.inference.model"
-
-from paddle.utils import Ploter
-
-train_title = "Train cost"
-test_title = "Test cost"
-cost_ploter = Ploter(train_title, test_title)
-
-step = 0
-def event_handler_plot(event):
-    global step
-    if isinstance(event, EndStepEvent):
-        if step % 1 == 0:
-            cost_ploter.append(train_title, step, event.metrics[0])
-            cost_ploter.plot()
-        step += 1
-    if isinstance(event, EndEpochEvent):
-        avg_cost, accuracy = trainer.test(
-            reader=test_reader,
-            feed_order=['pixel', 'label'])
-        cost_ploter.append(test_title, step, avg_cost)
-
-        # save parameters
-        if params_dirname is not None:
-            trainer.save_params(params_dirname)
-```
-
 `event_handler` is used to plot some text data when training.
 
 ```python

05.recommender_system/README.cn.md

@@ -439,30 +439,26 @@ feed_order = [
 ```python
 # Specify the directory path to save the parameters
 params_dirname = "recommender_system.inference.model"
-
-from paddle.utils import Ploter
-test_title = "Test cost"
-plot_cost = Ploter(test_title)
-
-
 def event_handler(event):
     if isinstance(event, EndStepEvent):
+        test_reader = paddle.batch(
+            paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
         # get avg cost
         avg_cost = np.array(avg_cost_set).mean()
 
-        plot_cost.append(test_title, event.step, avg_cost_set[0])
-        plot_cost.plot()
-
         print("avg_cost: %s" % avg_cost)
-        print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
-                                                          float(avg_cost)))
 
-        if event.step == 20: # Adjust this number for accuracy
+        if float(avg_cost) < 4:  # Change this number to adjust accuracy
             trainer.save_params(params_dirname)
             trainer.stop()
+        else:
+            print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
+                                                          float(avg_cost)))
+            if math.isnan(float(avg_cost)):
+                sys.exit("got NaN loss, training failed.")
 ```
 
 ### 开始训练

05.recommender_system/README.md

@@ -415,30 +415,26 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 # Specify the directory path to save the parameters
 params_dirname = "recommender_system.inference.model"
 
-from paddle.utils import Ploter
-test_title = "Test cost"
-plot_cost = Ploter(test_title)
-
-
 def event_handler(event):
     if isinstance(event, EndStepEvent):
+        test_reader = paddle.batch(
+            paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
         # get avg cost
         avg_cost = np.array(avg_cost_set).mean()
 
-        plot_cost.append(test_title, event.step, avg_cost_set[0])
-        plot_cost.plot()
-
         print("avg_cost: %s" % avg_cost)
-        print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
-                                                          float(avg_cost)))
 
-        if event.step == 20: # Adjust this number for accuracy
+        if float(avg_cost) < 4:  # Change this number to adjust accuracy
             trainer.save_params(params_dirname)
             trainer.stop()
-
+        else:
+            print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
+                                                          float(avg_cost)))
+            if math.isnan(float(avg_cost)):
+                sys.exit("got NaN loss, training failed.")
 ```
 
 ### Training

05.recommender_system/index.cn.html

@@ -481,30 +481,26 @@ feed_order = [
 ```python
 # Specify the directory path to save the parameters
 params_dirname = "recommender_system.inference.model"
-
-from paddle.utils import Ploter
-test_title = "Test cost"
-plot_cost = Ploter(test_title)
-
-
 def event_handler(event):
     if isinstance(event, EndStepEvent):
+        test_reader = paddle.batch(
+            paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
         # get avg cost
         avg_cost = np.array(avg_cost_set).mean()
 
-        plot_cost.append(test_title, event.step, avg_cost_set[0])
-        plot_cost.plot()
-
         print("avg_cost: %s" % avg_cost)
-        print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
-                                                          float(avg_cost)))
 
-        if event.step == 20: # Adjust this number for accuracy
+        if float(avg_cost) < 4:  # Change this number to adjust accuracy
             trainer.save_params(params_dirname)
             trainer.stop()
+        else:
+            print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
+                                                          float(avg_cost)))
+            if math.isnan(float(avg_cost)):
+                sys.exit("got NaN loss, training failed.")
 ```
 
 ### 开始训练

05.recommender_system/index.html

@@ -457,30 +457,26 @@ For example, we can check the cost by `trainer.test` when `EndStepEvent` occurs
 # Specify the directory path to save the parameters
 params_dirname = "recommender_system.inference.model"
 
-from paddle.utils import Ploter
-test_title = "Test cost"
-plot_cost = Ploter(test_title)
-
-
 def event_handler(event):
     if isinstance(event, EndStepEvent):
+        test_reader = paddle.batch(
+            paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
         avg_cost_set = trainer.test(
             reader=test_reader, feed_order=feed_order)
 
         # get avg cost
         avg_cost = np.array(avg_cost_set).mean()
 
-        plot_cost.append(test_title, event.step, avg_cost_set[0])
-        plot_cost.plot()
-
         print("avg_cost: %s" % avg_cost)
-        print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
-                                                          float(avg_cost)))
 
-        if event.step == 20: # Adjust this number for accuracy
+        if float(avg_cost) < 4:  # Change this number to adjust accuracy
             trainer.save_params(params_dirname)
             trainer.stop()
-
+        else:
+            print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1,
+                                                          float(avg_cost)))
+            if math.isnan(float(avg_cost)):
+                sys.exit("got NaN loss, training failed.")
 ```
 
 ### Training