diff --git a/paddlehub/finetune/task.py b/paddlehub/finetune/task.py
index b88a94ead63d66a9aeac830956ab239e730aeac2..06d284ab5800330afb0da75b67509bae396f399e 100644
--- a/paddlehub/finetune/task.py
+++ b/paddlehub/finetune/task.py
@@ -163,7 +163,7 @@ class BasicTask(object):
         with fluid.program_guard(self.env.main_program,
                                  self._base_startup_program):
             with fluid.unique_name.guard(self.env.UNG):
-                self.env.output = self._build_net()
+                self.env.outputs = self._build_net()
                 if self.is_train_phase or self.is_test_phase:
                     self.env.label = self._add_label()
                     self.env.loss = self._add_loss()
@@ -200,8 +200,11 @@ class BasicTask(object):
             self.env.main_program = t_program
             self.env.loss = self.env.main_program.global_block().vars[
                 self.env.loss.name]
-            self.env.output = self.env.main_program.global_block().vars[
-                self.env.output.name]
+            outputs_name = [var.name for var in self.env.outputs]
+            self.env.outputs = [
+                self.env.main_program.global_block().vars[name]
+                for name in outputs_name
+            ]
             metrics_name = [var.name for var in self.env.metrics]
             self.env.metrics = [
                 self.env.main_program.global_block().vars[name]
@@ -344,10 +347,10 @@ class BasicTask(object):
         return self.env.label
 
     @property
-    def output(self):
+    def outputs(self):
         if not self.env.is_inititalized:
             self._build_env()
-        return self.env.output
+        return self.env.outputs
 
     @property
     def metrics(self):
@@ -378,7 +381,7 @@ class BasicTask(object):
     def fetch_list(self):
         if self.is_train_phase or self.is_test_phase:
             return [metric.name for metric in self.metrics] + [self.loss.name]
-        return [self.output.name]
+        return [output.name for output in self.outputs]
 
     def _build_env_start_event(self):
         pass
@@ -611,18 +614,18 @@ class ClassifierTask(BasicTask):
                 name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
             act="softmax")
 
-        return logits
+        return [logits]
 
     def _add_label(self):
         return fluid.layers.data(name="label", dtype="int64", shape=[1])
 
     def _add_loss(self):
         ce_loss = fluid.layers.cross_entropy(
-            input=self.output, label=self.label)
+            input=self.outputs[0], label=self.label)
         return fluid.layers.mean(x=ce_loss)
 
     def _add_metrics(self):
-        return [fluid.layers.accuracy(input=self.output, label=self.label)]
+        return [fluid.layers.accuracy(input=self.outputs[0], label=self.label)]
 
     def _build_env_end_event(self):
         with self.log_writer.mode(self.phase) as logw:
@@ -720,7 +723,7 @@ class TextClassifierTask(ClassifierTask):
                 name="cls_out_b", initializer=fluid.initializer.Constant(0.)),
             act="softmax")
 
-        return logits
+        return [logits]
 
 
 class SequenceLabelTask(BasicTask):
@@ -773,7 +776,7 @@ class SequenceLabelTask(BasicTask):
         logits = fluid.layers.flatten(logits, axis=2)
         logits = fluid.layers.softmax(logits)
         self.num_labels = logits.shape[1]
-        return logits
+        return [logits]
 
     def _add_label(self):
         label = fluid.layers.data(
@@ -782,7 +785,8 @@ class SequenceLabelTask(BasicTask):
 
     def _add_loss(self):
         labels = fluid.layers.flatten(self.label, axis=2)
-        ce_loss = fluid.layers.cross_entropy(input=self.output, label=labels)
+        ce_loss = fluid.layers.cross_entropy(
+            input=self.outputs[0], label=labels)
         loss = fluid.layers.mean(x=ce_loss)
         return loss
 
@@ -865,7 +869,7 @@ class SequenceLabelTask(BasicTask):
             return [metric.name for metric in self.metrics] + [self.loss.name]
         elif self.is_predict_phase:
             return [self.ret_infers.name] + [self.seq_len.name]
-        return [self.output.name]
+        return [output.name for output in self.outputs]
 
 
 class MultiLabelClassifierTask(ClassifierTask):
@@ -928,7 +932,7 @@ class MultiLabelClassifierTask(ClassifierTask):
         label_split = fluid.layers.split(self.label, self.num_classes, dim=-1)
         total_loss = fluid.layers.fill_constant(
             shape=[1], value=0.0, dtype='float64')
-        for index, probs in enumerate(self.output):
+        for index, probs in enumerate(self.outputs):
             ce_loss = fluid.layers.cross_entropy(
                 input=probs, label=label_split[index])
             total_loss += fluid.layers.reduce_sum(ce_loss)
@@ -939,7 +943,7 @@ class MultiLabelClassifierTask(ClassifierTask):
         label_split = fluid.layers.split(self.label, self.num_classes, dim=-1)
         # metrics change to auc of every class
         eval_list = []
-        for index, probs in enumerate(self.output):
+        for index, probs in enumerate(self.outputs):
             current_auc, _, _ = fluid.layers.auc(
                 input=probs, label=label_split[index])
             eval_list.append(current_auc)
@@ -1015,4 +1019,4 @@ class MultiLabelClassifierTask(ClassifierTask):
     def fetch_list(self):
         if self.is_train_phase or self.is_test_phase:
             return [metric.name for metric in self.metrics] + [self.loss.name]
-        return self.output
+        return self.outputs