diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc
index fe0c87bc570825014222807cb90a3bb341b44e8e..13f23a681989fe27d7634489aadc4b3f9103468b 100644
--- a/paddle/framework/pybind.cc
+++ b/paddle/framework/pybind.cc
@@ -275,6 +275,13 @@ All parameter, weight, gradient are variables in Paddle.
               const std::shared_ptr<operators::NetOp> &net) -> void {
              self.set_stepnet(net);
            });
+
+  rnn.def("backward", [](const operators::RecurrentOp &forwardOp,
+                         const std::unordered_set<std::string> &no_grad_vars) {
+    const auto &op = *static_cast<const OperatorBase *>(&forwardOp);
+    return Backward(op, no_grad_vars);
+  });
+
   ExposeOperator(rnn);
 
   m.def("unique_integer", UniqueIntegerGenerator);
diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc
index 78ce0ba3c0fa4fe380e49a848c2434fe593cd00b..8c7300a3508c436f8dd4342d18c083984c2f04d6 100644
--- a/paddle/operators/recurrent_op.cc
+++ b/paddle/operators/recurrent_op.cc
@@ -77,7 +77,6 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
   // Now all variables in scope must be created outside of op.
   PADDLE_ENFORCE_NOT_NULL(stepnet_);
   PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs");
-  PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs");
 
   if (seq_len_ > step_scopes->size()) {
     for (size_t i = step_scopes->size(); i < seq_len_; ++i) {
diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py
index 501cf6110ff745b8a6022b463bc9cc3a70145c60..b6d8131be98d5897d6bb674fd9b97ced0a2df3d3 100644
--- a/python/paddle/v2/framework/tests/gradient_checker.py
+++ b/python/paddle/v2/framework/tests/gradient_checker.py
@@ -29,13 +29,13 @@ def get_numeric_gradient(op,
                          local_scope=None):
     """
     Get Numeric Gradient for an operator's input.
-    
-    :param op: C++ operator instance, could be an network 
-    :param input_values: The input variables. Should be an dictionary, key is 
-    variable name. Value is numpy array.
-    :param output_name: The final output variable name. 
+
+    :param op: C++ operator instance, could be an network
+    :param input_values: The input variables. Should be an dictionary, key is
+    variable name. Value is numpy array
+    :param output_name: The final output variable name.
     :param input_to_check: The input variable need to get gradient.
-    :param delta: The perturbation value for numeric gradient method. The 
+    :param delta: The perturbation value for numeric gradient method. The
     smaller delta is, the more accurate result will get. But if that delta is
      too small, it could occur numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py
index 3d4a34d8d713ff1beeeba8ac48ad95176f7a29f2..0da56c1ad9ae299207f10a1827d1f9f970bf6ad9 100644
--- a/python/paddle/v2/framework/tests/test_recurrent_op.py
+++ b/python/paddle/v2/framework/tests/test_recurrent_op.py
@@ -3,6 +3,7 @@ import paddle.v2.framework.core as core
 import unittest
 import numpy as np
 from paddle.v2.framework.op import Operator, RecurrentOp
+from gradient_checker import GradientChecker
 
 
 def py_sigmoid(x):
@@ -69,7 +70,7 @@ def create_tensor(scope, name, shape, np_data):
     return tensor
 
 
-class TestRecurrentOp(unittest.TestCase):
+class RecurrentOpTest(unittest.TestCase):
     '''
     Test RNNOp
 
@@ -164,5 +165,42 @@ class TestRecurrentOp(unittest.TestCase):
         self.assertEqual(pd_output.shape, py_output.shape)
 
 
+class RecurrentGradientOpTest(unittest.TestCase):
+    def create_forward_op(self):
+        self.forward_op = RecurrentOp(
+            # inputs
+            inlinks=["x"],
+            boot_memories=["h_boot"],
+            step_net="stepnet",
+            # outputs
+            outlinks=["h"],
+            step_scopes="step_scopes",
+            # attributes
+            inlink_alias=["x@alias"],
+            outlink_alias=["h@alias"],
+            pre_memories=["h@pre"],
+            memories=["h@alias"])
+
+        # create a stepnet for RNN
+        stepnet = core.Net.create()
+        x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx")
+        h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh")
+        sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum")
+        sig_op = Operator("sigmoid", X="sum", Y="h@alias")
+
+        for op in [x_fc_op, h_fc_op, sum_op, sig_op]:
+            stepnet.add_op(op)
+        stepnet.complete_add_op(True)
+        self.forward_op.set_stepnet(stepnet)
+
+    def create_gradient_op(self):
+        a = set()
+        backward_op = core.RecurrentOp.backward(self.forward_op, a)
+
+    def test_grad(self):
+        self.create_forward_op()
+        self.create_gradient_op()
+
+
 if __name__ == '__main__':
     unittest.main()