refine the comments and the inference result format

01.fit_a_line/README.cn.md

@@ -179,7 +179,7 @@ feed_order=['x', 'y']
 除此之外，可以定义一个事件相应器来处理类似`打印训练进程`的事件：
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -190,11 +190,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: # record the test cost every 10 seconds
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -254,7 +254,9 @@ batch_size = 10
 tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
 
 results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
 ```
 
 ## 总结

01.fit_a_line/index.cn.html

@@ -221,7 +221,7 @@ feed_order=['x', 'y']
 除此之外，可以定义一个事件相应器来处理类似`打印训练进程`的事件：
 
 ```python
-# Specify the directory path to save the parameters
+# Specify the directory to save the parameters
 params_dirname = "fit_a_line.inference.model"
 
 # Plot data
@@ -232,11 +232,11 @@ plot_cost = Ploter(train_title, test_title)
 
 step = 0
 
-# event_handler to print training and testing info
+# event_handler prints training and testing info
 def event_handler_plot(event):
     global step
     if isinstance(event, fluid.EndStepEvent):
-        if event.step % 10 == 0: # every 10 batches, record a test cost
+        if event.step % 10 == 0: # record the test cost every 10 seconds
             test_metrics = trainer.test(
                 reader=test_reader, feed_order=feed_order)
 
@@ -296,7 +296,9 @@ batch_size = 10
 tensor_x = numpy.random.uniform(0, 10, [batch_size, 13]).astype("float32")
 
 results = inferencer.infer({'x': tensor_x})
-print("infer results: ", results[0])
+print("infer results: (House Price)")
+for k in range(0, batch_size-1):
+    print("%d. %f" % (k, results[0][k]))
 ```
 
 ## 总结