modify log for training and ce

fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py

@@ -178,8 +178,6 @@ def train_and_evaluate(train_reader,
     total_time = 0.0
     print("[%s] Start Training" % time.asctime(time.localtime(time.time())))
     for epoch_id in range(global_config.epoch_num):
-        # only for ce
-        epoch_idx = epoch_id + 1
 
         data_size, data_count, total_acc, total_cost = 0, 0, 0.0, 0.0
         batch_id = 0
@@ -189,8 +187,8 @@ def train_and_evaluate(train_reader,
                                               feed=feeder.feed(data),
                                               fetch_list=[cost, acc])
             data_size = len(data)
-            total_acc += data_size * avg_acc_np
-            total_cost += data_size * avg_cost_np
+            total_acc += data_size * avg_acc_np[0]
+            total_cost += data_size * avg_cost_np[0]
             data_count += data_size
             if batch_id % 100 == 0:
                 print("[%s] epoch_id: %d, batch_id: %d, cost: %f, acc: %f" % (
@@ -200,13 +198,11 @@ def train_and_evaluate(train_reader,
                     avg_cost_np,
                     avg_acc_np))
             batch_id += 1
-        
         avg_cost = total_cost / data_count
         avg_acc = total_acc / data_count
         epoch_end_time = time.time()
         total_time += epoch_end_time - epoch_begin_time
 
-        
         print("")
         print("[%s] epoch_id: %d, train_avg_cost: %f, train_avg_acc: %f, epoch_time_cost: %f" % (
             time.asctime( time.localtime(time.time())),
@@ -219,11 +215,11 @@ def train_and_evaluate(train_reader,
             #Other situations do not need to care about these logs.
             gpu_num = get_cards(args)
             print("kpis\teach_pass_duration_card%s\t%s" % \
-                  (gpu_num, total_time / epoch_idx))
+                  (gpu_num, total_time / (global_config.epoch_num)))
             print("kpis\ttrain_avg_cost_card%s\t%s" %
-                  (gpu_num, avg_cost[0]))
+                  (gpu_num, avg_cost))
             print("kpis\ttrain_avg_acc_card%s\t%s" %
-                  (gpu_num, avg_acc[0]))
+                  (gpu_num, avg_acc))
 
 
         epoch_model = global_config.save_dirname + "/" + "epoch" + str(epoch_id)