Fix event_handler trainer

demo/image_classification/api_v2_train.py

@@ -18,24 +18,6 @@ from api_v2_vgg import vgg_bn_drop
 from api_v2_resnet import resnet_cifar10
 
 
-# End batch and end pass event handler
-def event_handler(event):
-    if isinstance(event, paddle.event.EndIteration):
-        if event.batch_id % 100 == 0:
-            print "\nPass %d, Batch %d, Cost %f, %s" % (
-                event.pass_id, event.batch_id, event.cost, event.metrics)
-        else:
-            sys.stdout.write('.')
-            sys.stdout.flush()
-    if isinstance(event, paddle.event.EndPass):
-        result = trainer.test(
-            reader=paddle.reader.batched(
-                paddle.dataset.cifar.test10(), batch_size=128),
-            reader_dict={'image': 0,
-                         'label': 1})
-        print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
-
-
 def main():
     datadim = 3 * 32 * 32
     classdim = 10
@@ -73,6 +55,23 @@ def main():
         learning_rate_schedule='discexp',
         batch_size=128)
 
+    # End batch and end pass event handler
+    def event_handler(event):
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 100 == 0:
+                print "\nPass %d, Batch %d, Cost %f, %s" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics)
+            else:
+                sys.stdout.write('.')
+                sys.stdout.flush()
+        if isinstance(event, paddle.event.EndPass):
+            result = trainer.test(
+                reader=paddle.reader.batched(
+                    paddle.dataset.cifar.test10(), batch_size=128),
+                reader_dict={'image': 0,
+                             'label': 1})
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
     # Create trainer
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,