diff --git a/fluid/DeepASR/tools/profile.py b/fluid/DeepASR/tools/profile.py
index cb0227c33a25b1c38977f8485237f13d0351c36f..9d0b47694273345357726ad51062d01ff01b120d 100644
--- a/fluid/DeepASR/tools/profile.py
+++ b/fluid/DeepASR/tools/profile.py
@@ -169,7 +169,8 @@ def profile(args):
             outs = exe.run(fluid.default_main_program(),
                            feed={"feature": feature_t,
                                  "label": label_t},
-                           fetch_list=[avg_cost, accuracy],
+                           fetch_list=[avg_cost, accuracy]
+                           if args.print_train_acc else [],
                            return_numpy=False)
 
             if args.print_train_acc:
diff --git a/fluid/DeepASR/train.py b/fluid/DeepASR/train.py
index 9856dad7d56b47bf14c32a7d0ca0ec10b8ecf88f..b5d2239e94dd5ddcd79d0245e4b980d6cf5bfbf4 100644
--- a/fluid/DeepASR/train.py
+++ b/fluid/DeepASR/train.py
@@ -216,16 +216,17 @@ def train(args):
             label_t.set(labels, place)
             label_t.set_lod([lod])
 
-            cost, acc = exe.run(fluid.default_main_program(),
-                                feed={"feature": feature_t,
-                                      "label": label_t},
-                                fetch_list=[avg_cost, accuracy],
-                                return_numpy=False)
+            to_print = batch_id > 0 and (batch_id % args.print_per_batches == 0)
+            outs = exe.run(fluid.default_main_program(),
+                           feed={"feature": feature_t,
+                                 "label": label_t},
+                           fetch_list=[avg_cost, accuracy] if to_print else [],
+                           return_numpy=False)
 
-            if batch_id > 0 and (batch_id % args.print_per_batches == 0):
+            if to_print:
                 print("\nBatch %d, train cost: %f, train acc: %f" %
-                      (batch_id, lodtensor_to_ndarray(cost)[0],
-                       lodtensor_to_ndarray(acc)[0]))
+                      (batch_id, lodtensor_to_ndarray(outs[0])[0],
+                       lodtensor_to_ndarray(outs[1])[0]))
                 # save the latest checkpoint
                 if args.checkpoints != '':
                     model_path = os.path.join(args.checkpoints,