diff --git a/ctr/train.py b/ctr/train.py
index e808d17c3a3e38fe1970c95e5dad50db7ae1462b..7525c80295345e8e08b6ac6112edcdeb86b03eeb 100644
--- a/ctr/train.py
+++ b/ctr/train.py
@@ -23,7 +23,6 @@ paddle.init(use_gpu=False, trainer_count=11)
 # ==============================================================================
 #                    input layers
 # ==============================================================================
-
 dnn_merged_input = layer.data(
     name='dnn_input',
     type=paddle.data_type.sparse_binary_vector(data_meta_info['dnn_input']))
@@ -34,11 +33,10 @@ lr_merged_input = layer.data(
 
 click = paddle.layer.data(name='click', type=dtype.dense_vector(1))
 
+
 # ==============================================================================
 #                    network structure
 # ==============================================================================
-
-
 def build_dnn_submodel(dnn_layer_dims):
     dnn_embedding = layer.fc(input=dnn_merged_input, size=dnn_layer_dims[0])
     _input_layer = dnn_embedding
@@ -93,10 +91,10 @@ dataset = AvazuDataset(train_data_path, n_records_as_test=test_set_size)
 
 def event_handler(event):
     if isinstance(event, paddle.event.EndIteration):
+        num_samples = event.batch_id * batch_size
         if event.batch_id % 100 == 0:
             logging.warning("Pass %d, Samples %d, Cost %f" %
-                            (event.pass_id, event.batch_id * batch_size,
-                             event.cost))
+                            (event.pass_id, num_samples, event.cost))
 
         if event.batch_id % 1000 == 0:
             result = trainer.test(