diff --git a/fastNLP/core/dataset.py b/fastNLP/core/dataset.py
index 8375cf74..c8bd67e7 100644
--- a/fastNLP/core/dataset.py
+++ b/fastNLP/core/dataset.py
@@ -188,7 +188,8 @@ class DataSet(object):
             results.append(func(ins))
         if new_field_name is not None:
             self.add_field(new_field_name, results)
-        return results
+        else:
+            return results
 
 if __name__ == '__main__':
     from fastNLP.core.instance import Instance
diff --git a/reproduction/CNN-sentence_classification/model.py b/reproduction/CNN-sentence_classification/model.py
index 125e7bcc..870e7c4e 100644
--- a/reproduction/CNN-sentence_classification/model.py
+++ b/reproduction/CNN-sentence_classification/model.py
@@ -4,8 +4,8 @@ import torch.nn.functional as F
 
 
 class CNN_text(nn.Module):
-    def __init__(self, kernel_h=[3, 4, 5], kernel_num=100, embed_num=1000, embed_dim=300, dropout=0.5, L2_constrain=3,
-                 batchsize=50, pretrained_embeddings=None):
+    def __init__(self, kernel_h=[3, 4, 5], kernel_num=100, embed_num=1000, embed_dim=300, num_classes=2, dropout=0.5, L2_constrain=3,
+                 pretrained_embeddings=None):
         super(CNN_text, self).__init__()
 
         self.embedding = nn.Embedding(embed_num, embed_dim)
@@ -15,11 +15,11 @@ class CNN_text(nn.Module):
 
         # the network structure
         # Conv2d: input- N,C,H,W output- (50,100,62,1)
-        self.conv1 = nn.ModuleList([nn.Conv2d(1, 100, (K, 300)) for K in kernel_h])
-        self.fc1 = nn.Linear(300, 2)
+        self.conv1 = nn.ModuleList([nn.Conv2d(1, kernel_num, (K, embed_dim)) for K in kernel_h])
+        self.fc1 = nn.Linear(len(kernel_h)*kernel_num, num_classes)
 
     def max_pooling(self, x):
-        x = F.relu(conv(x)).squeeze(3)  # N,C,L - (50,100,62)
+        x = F.relu(self.conv1(x)).squeeze(3)  # N,C,L - (50,100,62)
         x = F.max_pool1d(x, x.size(2)).squeeze(2)
         # x.size(2)=62  squeeze: (50,100,1) -> (50,100)
         return x
@@ -33,3 +33,8 @@ class CNN_text(nn.Module):
         x = self.dropout(x)
         x = self.fc1(x)
         return x
+
+if __name__ == '__main__':
+    model = CNN_text(kernel_h=[1, 2, 3, 4],embed_num=3, embed_dim=2)
+    x = torch.LongTensor([[1, 2, 1, 2, 0]])
+    print(model(x))
\ No newline at end of file