diff --git a/demo/image_classification/api_v2_train.py b/demo/image_classification/api_v2_train.py
index 0b4dc4d92982680a06cd95a58d3af0975e25b690..94bf0b5db48c5046ab02fea4994ce2aae09c641e 100644
--- a/demo/image_classification/api_v2_train.py
+++ b/demo/image_classification/api_v2_train.py
@@ -12,27 +12,41 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 
+import sys
+import paddle.v2 as paddle
 from api_v2_vgg import vgg_bn_drop
 from api_v2_resnet import resnet_cifar10
-import paddle.v2 as paddle
 
 
+# End batch and end pass event handler
 def event_handler(event):
     if isinstance(event, paddle.event.EndIteration):
         if event.batch_id % 100 == 0:
-            print "Pass %d, Batch %d, Cost %f" % (event.pass_id, event.batch_id,
-                                                  event.cost)
+            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
 
+    # PaddlePaddle init
     paddle.init(use_gpu=True, trainer_count=1)
 
     image = paddle.layer.data(
         name="image", type=paddle.data_type.dense_vector(datadim))
 
+    # Add neural network config
     # option 1. resnet
     net = resnet_cifar10(image, depth=32)
     # option 2. vgg
@@ -46,8 +60,10 @@ def main():
         name="label", type=paddle.data_type.integer_value(classdim))
     cost = paddle.layer.classification_cost(input=out, label=lbl)
 
+    # Create parameters
     parameters = paddle.parameters.create(cost)
 
+    # Create optimizer
     momentum_optimizer = paddle.optimizer.Momentum(
         momentum=0.9,
         regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
@@ -57,6 +73,7 @@ def main():
         learning_rate_schedule='discexp',
         batch_size=128)
 
+    # Create trainer
     trainer = paddle.trainer.SGD(cost=cost,
                                  parameters=parameters,
                                  update_equation=momentum_optimizer)