diff --git a/docs/source/tutorials/tutorial_9_seq_labeling.rst b/docs/source/tutorials/tutorial_9_seq_labeling.rst
index 60bc1440..0272fc1c 100644
--- a/docs/source/tutorials/tutorial_9_seq_labeling.rst
+++ b/docs/source/tutorials/tutorial_9_seq_labeling.rst
@@ -146,7 +146,7 @@ fastNLP的数据载入主要是由Loader与Pipe两个基类衔接完成的，您
                   target_vocab=data_bundle.get_vocab('target'))
 
     from fastNLP import SpanFPreRecMetric
-    from torch import Adam
+    from torch.optim import Adam
     from fastNLP import LossInForward
     metric = SpanFPreRecMetric(tag_vocab=data_bundle.get_vocab('target'))
     optimizer = Adam(model.parameters(), lr=2e-5)
diff --git a/fastNLP/core/trainer.py b/fastNLP/core/trainer.py
index a39362e2..721478f7 100644
--- a/fastNLP/core/trainer.py
+++ b/fastNLP/core/trainer.py
@@ -666,8 +666,8 @@ class Trainer(object):
                     # edit prediction
                     self.callback_manager.on_loss_begin(batch_y, prediction)
                     loss = self._compute_loss(prediction, batch_y).mean()
-                    avg_loss += loss.item()
                     loss = loss / self.update_every
+                    avg_loss += loss.item()
 
                     # Is loss NaN or inf? requires_grad = False
                     self.callback_manager.on_backward_begin(loss)
diff --git a/reproduction/text_classification/train_char_cnn.py b/reproduction/text_classification/train_char_cnn.py
index 55d830e6..a4a97dc4 100644
--- a/reproduction/text_classification/train_char_cnn.py
+++ b/reproduction/text_classification/train_char_cnn.py
@@ -94,11 +94,7 @@ ops=Config
 # print('RNG SEED: {}'.format(ops.seed))
 
 
-##1.task相关信息：利用dataloader载入dataInfo
-#dataloader=SST2Loader()
-#dataloader=IMDBLoader()
-# dataloader=YelpLoader(fine_grained=True)
-# datainfo=dataloader.process(ops.datapath,char_level_op=True,split_dev_op=False)
+##1.task相关信息：利用dataloader载入DataBundle
 char_vocab=ops.char_cnn_config["alphabet"]["en"]["lower"]["alphabet"]
 ops.number_of_characters=len(char_vocab)
 ops.embedding_dim=ops.number_of_characters
@@ -155,10 +151,8 @@ for dataset in data_bundle.datasets.values():
 # print(data_bundle.datasets['train'][0]['chars'])
 # print(data_bundle.datasets['train'][0]['raw_words'])
 
-data_bundle.datasets['train'].set_input('chars')
-data_bundle.datasets['test'].set_input('chars')
-data_bundle.datasets['train'].set_target('target')
-data_bundle.datasets['test'].set_target('target')
+data_bundle.set_input('chars')
+data_bundle.set_target('target')
 
 ##2. 定义/组装模型，这里可以随意，就如果是fastNLP封装好的，类似CNNText就直接用初始化调用就好了，这里只是给出一个伪框架表示占位，在这里建立符合fastNLP输入输出规范的model
 class ModelFactory(nn.Module):
@@ -180,8 +174,7 @@ class ModelFactory(nn.Module):
         return {C.OUTPUT:None}
 
 ## 2.或直接复用fastNLP的模型
-#vocab=datainfo.vocabs['words']
-vocab_label=data_bundle.vocabs['target']
+vocab_label=data_bundle.get_vocab('target')
 '''
 # emded_char=CNNCharEmbedding(vocab)
 # embed_word = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50', requires_grad=True)
@@ -199,7 +192,7 @@ embedding=nn.Embedding(num_embeddings=len(char_vocab)+1,embedding_dim=len(char_v
 for para in embedding.parameters():
     para.requires_grad=False
 #CNNText太过于简单
-#model=CNNText(init_embed=embedding, num_classes=ops.num_classes)
+#model=CNNText(embed=embedding, num_classes=ops.num_classes)
 model=CharacterLevelCNN(ops,embedding)
 
 ## 3. 声明loss,metric,optimizer