Remove plot in 1.0 book

01.fit_a_line/README.cn.md

@@ -183,27 +183,22 @@ 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, fluid.contrib.trainer.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.
@@ -228,7 +223,7 @@ 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)
 ```
 <div align="center">

01.fit_a_line/README.md

@@ -202,27 +202,22 @@ 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, fluid.contrib.trainer.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.
@@ -230,7 +225,7 @@ def event_handler_plot(event):
                 trainer.stop()
         step += 1
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         if event.epoch % 10 == 0:
             # We can save the trained parameters for the inferences later
             if params_dirname is not None:
@@ -249,7 +244,7 @@ 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

@@ -225,27 +225,22 @@ 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, fluid.contrib.trainer.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.
@@ -270,7 +265,7 @@ 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)
 ```
 <div align="center">

01.fit_a_line/index.html

@@ -244,27 +244,22 @@ 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, fluid.contrib.trainer.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.
@@ -272,7 +267,7 @@ def event_handler_plot(event):
                 trainer.stop()
         step += 1
 
-    if isinstance(event, fluid.contrib.trainer.EndEpochEvent):
+    if isinstance(event, EndEpochEvent):
         if event.epoch % 10 == 0:
             # We can save the trained parameters for the inferences later
             if params_dirname is not None:
@@ -291,7 +286,7 @@ 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

@@ -69,28 +69,24 @@ 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.
@@ -109,7 +105,7 @@ 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)