diff --git a/fluid/PaddleRec/tagspace/cluster_train.py b/fluid/PaddleRec/tagspace/cluster_train.py
index ee29bb3214145fec0b900e972ea7a5bb813cdd87..96cdf615ed9e6673159db5163f33f431d5c6e9bc 100644
--- a/fluid/PaddleRec/tagspace/cluster_train.py
+++ b/fluid/PaddleRec/tagspace/cluster_train.py
@@ -71,7 +71,7 @@ def train():
         buffer_size=batch_size*100, is_train=True)
     """ train network """
     # Train program
-    text, pos_tag, neg_tag, avg_cost, correct, cos_pos = net.network(vocab_text_size, vocab_tag_size, neg_size=neg_size)
+    avg_cost, correct, cos_pos = net.network(vocab_text_size, vocab_tag_size, neg_size=neg_size)
 
     # Optimization to minimize lost
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=args.base_lr)
diff --git a/fluid/PaddleRec/tagspace/net.py b/fluid/PaddleRec/tagspace/net.py
index 20aa1a311c643e9a7502557a02e8a900468e96ca..cbafb1701c47a3c40d15ce118304c5ff3b8d7b9a 100644
--- a/fluid/PaddleRec/tagspace/net.py
+++ b/fluid/PaddleRec/tagspace/net.py
@@ -46,4 +46,4 @@ def network(vocab_text_size, vocab_tag_size, emb_dim=10, hid_dim=1000, win_size=
     avg_cost = nn.mean(loss_part3)
     less = tensor.cast(cf.less_than(cos_neg, cos_pos), dtype='float32')
     correct = nn.reduce_sum(less)
-    return text, pos_tag, neg_tag, avg_cost, correct, cos_pos
+    return avg_cost, correct, cos_pos
\ No newline at end of file
diff --git a/fluid/PaddleRec/tagspace/train.py b/fluid/PaddleRec/tagspace/train.py
index 8b7bdf5d0b2e4346afffd6676936819048f5ebfd..69992154750dfdc3ed5e0b9433338a366d7cd3c7 100644
--- a/fluid/PaddleRec/tagspace/train.py
+++ b/fluid/PaddleRec/tagspace/train.py
@@ -64,7 +64,7 @@ def train():
         buffer_size=batch_size*100, is_train=True)
     """ train network """
     # Train program
-    text, pos_tag, neg_tag, avg_cost, correct, cos_pos = net.network(vocab_text_size, vocab_tag_size, neg_size=neg_size)
+    avg_cost, correct, cos_pos = net.network(vocab_text_size, vocab_tag_size, neg_size=neg_size)
 
     # Optimization to minimize lost
     sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=args.base_lr)