diff --git a/fluid/neural_machine_translation/transformer/train.py b/fluid/neural_machine_translation/transformer/train.py
index 684cc6f3987f2afe9c6a21636c1bee6406a32c47..b6dace622e4a6fca29cab62b4bc5fe8884817581 100644
--- a/fluid/neural_machine_translation/transformer/train.py
+++ b/fluid/neural_machine_translation/transformer/train.py
@@ -1,4 +1,5 @@
 import os
+import time
 import numpy as np
 
 import paddle
@@ -159,6 +160,7 @@ def main():
                                    ModelHyperParams.d_model), place)
 
     for pass_id in xrange(TrainTaskConfig.pass_num):
+        pass_start_time = time.time()
         for batch_id, data in enumerate(train_data()):
             # The current program desc is coupled with batch_size, thus all
             # mini-batches must have the same number of instances currently.
@@ -175,15 +177,17 @@ def main():
                            fetch_list=[sum_cost, avg_cost],
                            use_program_cache=True)
             sum_cost_val, avg_cost_val = np.array(outs[0]), np.array(outs[1])
-            print("pass_id = " + str(pass_id) + " batch = " + str(batch_id) +
-                  " sum_cost = " + str(sum_cost_val) + " avg_cost = " + str(
-                      avg_cost_val) + " ppl = " + str(
-                          np.exp([min(avg_cost_val[0], 100)])))
+            print("epoch: %d, batch: %d,  sum loss: %f, avg loss: %f, ppl: %f" %
+                  (pass_id, batch_id, sum_cost_val, avg_cost_val,
+                   np.exp([min(avg_cost_val[0], 100)])))
         # Validate and save the model for inference.
         val_sum_cost, val_avg_cost = test(exe)
-        print("pass_id = " + str(pass_id) + " val_sum_cost = " + str(
-            val_sum_cost) + " val_avg_cost = " + str(val_avg_cost) +
-              " val_ppl = " + str(np.exp(min(val_avg_cost, 100))))
+        pass_end_time = time.time()
+        time_consumed = pass_end_time - pass_start_time
+        print(
+            "epoch: %d, val sum loss: %f, val avg loss: %f, val ppl: %f, consumed %fs"
+            % (pass_id, val_sum_cost, val_avg_cost,
+               np.exp([min(val_avg_cost, 100)]), time_consumed))
         fluid.io.save_inference_model(
             os.path.join(TrainTaskConfig.model_dir,
                          "pass_" + str(pass_id) + ".infer.model"),