update print out,test=develop

01.fit_a_line/README.cn.md

@@ -155,7 +155,7 @@ use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 ```
 
-除此之外，还可以定义一个事件响应器来处理类似`打印训练进程`的事件：
+除此之外，还可以通过画图，来展现`训练进程`：
 
 ```python
 # Plot data
@@ -165,10 +165,6 @@ train_title = "Train cost"
 test_title = "Test cost"
 plot_cost = Ploter(train_title, test_title)
 
-
-def event_handler(title, loop_step, handler_val):
-    plot_cost.append(title, loop_step, handler_val)
-    plot_cost.plot()
 ```
 
 ### 创建训练过程
@@ -216,11 +212,13 @@ def train_loop(main_program):
                                       feed=feeder.feed(data_train),
                                       fetch_list=[avg_loss])
             if step % 10 == 0:  # record a train cost every 10 batches
-                event_handler(train_title, step, avg_loss_value[0])
+                plot_cost.append(train_title, step, avg_loss_value[0])
+                plot_cost.plot()
             if step % 100 == 0:  # record a test cost every 100 batches
                 test_metics = train_test(train_program=main_program,
                                          feeder=feeder_test)
-                event_handler(test_title, step, test_metics[0])
+                plot_cost.append(test_title, step, test_metics[0])
+                plot_cost.plot()
                 # If the accuracy is good enough, we can stop the training.
                 if test_metics[0] < 10.0:
                     return

01.fit_a_line/index.cn.html

@@ -197,7 +197,7 @@ use_cuda = False
 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
 ```
 
-除此之外，还可以定义一个事件响应器来处理类似`打印训练进程`的事件：
+除此之外，还可以通过画图，来展现`训练进程`：
 
 ```python
 # Plot data
@@ -207,10 +207,6 @@ train_title = "Train cost"
 test_title = "Test cost"
 plot_cost = Ploter(train_title, test_title)
 
-
-def event_handler(title, loop_step, handler_val):
-    plot_cost.append(title, loop_step, handler_val)
-    plot_cost.plot()
 ```
 
 ### 创建训练过程
@@ -258,11 +254,13 @@ def train_loop(main_program):
                                       feed=feeder.feed(data_train),
                                       fetch_list=[avg_loss])
             if step % 10 == 0:  # record a train cost every 10 batches
-                event_handler(train_title, step, avg_loss_value[0])
+                plot_cost.append(train_title, step, avg_loss_value[0])
+                plot_cost.plot()
             if step % 100 == 0:  # record a test cost every 100 batches
                 test_metics = train_test(train_program=main_program,
                                          feeder=feeder_test)
-                event_handler(test_title, step, test_metics[0])
+                plot_cost.append(test_title, step, test_metics[0])
+                plot_cost.plot()
                 # If the accuracy is good enough, we can stop the training.
                 if test_metics[0] < 10.0:
                     return

01.fit_a_line/train.py

@@ -21,11 +21,6 @@ import math
 import sys
 
 
-# event_handler prints training and testing info
-def event_handler(title, loop_step, handler_val):
-    print("%s, Step %d, Cost %f" % (title, loop_step, handler_val))
-
-
 def main():
 
     batch_size = 20
@@ -93,10 +88,13 @@ def main():
                     feed=feeder.feed(data_train),
                     fetch_list=[avg_loss])
                 if step % 10 == 0:  # record a train cost every 10 batches
-                    event_handler(train_title, step, avg_loss_value[0])
+                    print("%s, Step %d, Cost %f" %
+                          (train_title, step, avg_loss_value[0]))
+                if step % 100 == 0:  # record a test cost every 100 batches
                     test_metics = train_test(
                         program=test_program, feeder=feeder_test)
-                    event_handler(test_title, step, test_metics[0])
+                    print("%s, Step %d, Cost %f" %
+                          (test_title, step, test_metics[0]))
                     # If the accuracy is good enough, we can stop the training.
                     if test_metics[0] < 10.0:
                         return
@@ -107,8 +105,8 @@ def main():
                     sys.exit("got NaN loss, training failed.")
             if params_dirname is not None:
                 # We can save the trained parameters for the inferences later
-            fluid.io.save_inference_model(params_dirname, ['x'], [y_predict],
-                                          exe)
+                fluid.io.save_inference_model(params_dirname, ['x'],
+                                              [y_predict], exe)
 
     train_loop()