diff --git a/fastNLP/action/action.py b/fastNLP/action/action.py
index ea12a37e..2bc08b75 100644
--- a/fastNLP/action/action.py
+++ b/fastNLP/action/action.py
@@ -67,5 +67,5 @@ class Batchifier(object):
             if len(batch) == self.batch_size:
                 yield batch
                 batch = []
-        if len(batch) < self.batch_size and self.drop_last is False:
+        if 0 < len(batch) < self.batch_size and self.drop_last is False:
             yield batch
diff --git a/fastNLP/action/trainer.py b/fastNLP/action/trainer.py
index e9c2b7bc..45049e9d 100644
--- a/fastNLP/action/trainer.py
+++ b/fastNLP/action/trainer.py
@@ -1,11 +1,11 @@
 import _pickle
+import os
+from datetime import timedelta
+from time import time
 
 import numpy as np
 import torch
 import torch.nn as nn
-import os
-from time import time
-from datetime import timedelta
 
 from fastNLP.action.action import Action
 from fastNLP.action.action import RandomSampler, Batchifier
@@ -77,16 +77,17 @@ class BaseTrainer(Action):
 
         # main training epochs
         iterations = len(data_train) // self.batch_size
+        self.define_optimizer()
+
         for epoch in range(1, self.n_epochs + 1):
 
             # turn on network training mode; define optimizer; prepare batch iterator
             self.mode(test=False)
-            self.define_optimizer()
             self.iterator = iter(Batchifier(RandomSampler(data_train), self.batch_size, drop_last=True))
 
             # training iterations in one epoch
             for step in range(iterations):
-                batch_x, batch_y = self.batchify(data_train)
+                batch_x, batch_y = self.batchify(data_train)  # pad ?
 
                 prediction = self.data_forward(network, batch_x)
 
@@ -212,7 +213,7 @@ class BaseTrainer(Action):
         max_length = max([len(x) for x in batch])
         for idx, sample in enumerate(batch):
             if len(sample) < max_length:
-                batch[idx] = sample + [fill * (max_length - len(sample))]
+                batch[idx] = sample + ([fill] * (max_length - len(sample)))
         return batch
 
     def best_eval_result(self, validator):