diff --git a/dygraph/ptb_lm/ptb_dy.py b/dygraph/ptb_lm/ptb_dy.py
index d96e4ed3563e721434221d3ef981ce8625e726ed..befa4ad643ea346eb13e024954d97ad47129aee1 100644
--- a/dygraph/ptb_lm/ptb_dy.py
+++ b/dygraph/ptb_lm/ptb_dy.py
@@ -326,7 +326,7 @@ def train_ptb_lm():
             boundaries=bd, values=lr_arr), parameter_list=ptb_model.parameters())
 
         def eval(model, data):
-            print("begion to eval")
+            print("begin to eval")
             total_loss = 0.0
             iters = 0.0
             init_hidden_data = np.zeros(
@@ -404,10 +404,18 @@ def train_ptb_lm():
                           (epoch_id, batch_id, ppl[0],
                            sgd._global_learning_rate().numpy(), out_loss))
 
-            print("one ecpoh finished", epoch_id)
+            print("one epoch finished", epoch_id)
             print("time cost ", time.time() - start_time)
             ppl = np.exp(total_loss / iters)
             print("-- Epoch:[%d]; ppl: %.5f" % (epoch_id, ppl[0]))
+
+            if batch_size <= 20 and epoch_id == 0 and ppl[0] > 1000:
+                # for bad init, after first epoch, the loss is over 1000
+                # no more need to continue
+                print("Parameters are randomly initialized and not good this time because the loss is over 1000 after the first epoch.")
+                print("Abort this training process and please start again.")
+                return 
+
             if args.ce:
                 print("kpis\ttrain_ppl\t%0.3f" % ppl[0])
             save_model_dir = os.path.join(args.save_model_dir,