diff --git a/paddle_hub/__init__.py b/paddle_hub/__init__.py
index 3003edf1d583a0a370ea29602dd1196e4b4dbee1..656ebccb0f8daa6533c5d476f732aedea16a76bc 100644
--- a/paddle_hub/__init__.py
+++ b/paddle_hub/__init__.py
@@ -26,3 +26,4 @@ from .tools.logger import logger
 from .tools.paddle_helper import connect_program
 from .io.type import DataType
 from .hub_server import default_hub_server
+from .finetune.task import append_mlp_classifier
diff --git a/paddle_hub/finetune/task.py b/paddle_hub/finetune/task.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1f347618654ae253f955fae27a2f155e61acf69
--- /dev/null
+++ b/paddle_hub/finetune/task.py
@@ -0,0 +1,53 @@
+#   Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import paddle.fluid as fluid
+
+__all__ = ['append_mlp_classifier']
+
+
+def append_mlp_classifier(feature, label, num_classes=2, hidden_units=None):
+    cls_feats = fluid.layers.dropout(
+        x=feature, dropout_prob=0.1, dropout_implementation="upscale_in_train")
+
+    # append fully connected layer according to hidden_units
+    if hidden_units != None:
+        for n_hidden in hidden_units:
+            cls_feats = fluid.layers.fc(input=cls_feats, size=n_hidden)
+
+    logits = fluid.layers.fc(
+        input=cls_feats,
+        size=num_classes,
+        param_attr=fluid.ParamAttr(
+            name="cls_out_w",
+            initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+        bias_attr=fluid.ParamAttr(
+            name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
+
+    ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
+        logits=logits, label=label, return_softmax=True)
+    loss = fluid.layers.mean(x=ce_loss)
+
+    num_example = fluid.layers.create_tensor(dtype='int64')
+    accuracy = fluid.layers.accuracy(
+        input=probs, label=label, total=num_example)
+
+    # TODO: encapsulate to Task
+    return loss, probs, accuracy, num_example
+
+
+class Task(object):
+    def __init__(self):
+        pass