diff --git a/paddle/operators/activation_op.cc b/paddle/operators/activation_op.cc
index ced14a8923140ec6b08e3e6725a5780b61033daf..cba57ba57f5e03c7861897e177cc09aa513e5395 100644
--- a/paddle/operators/activation_op.cc
+++ b/paddle/operators/activation_op.cc
@@ -321,6 +321,23 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+template <typename AttrType>
+class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ThresholdedReluOpMaker(framework::OpProto *proto,
+                         framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of ThresholdedRelu operator");
+    AddOutput("Y", "Output of ThresholdedRelu operator");
+    AddComment(
+        "ThresholdedRelu activation operator, "
+        "thresholded_relu = x for x > threshold, "
+        "thresholded_relu = 0 otherwise.");
+    AddAttr<AttrType>("threshold", "The threshold location of activation")
+        .SetDefault(static_cast<AttrType>(1.0));
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -392,6 +409,10 @@ REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
 REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker<float>,
             hard_shrink_grad, ops::ActivationOpGrad);
 
+REGISTER_OP(thresholded_relu, ops::ActivationOp,
+            ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
+            ops::ActivationOpGrad);
+
 #define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor)        \
   REGISTER_OP_CPU_KERNEL(                                                      \
       act_type,                                                                \
diff --git a/paddle/operators/activation_op.h b/paddle/operators/activation_op.h
index f88c9c48eb9fcb779de5a99a45a832e582d76ab0..502c33be103c465c14f128be38ac62d029f1bfb9 100644
--- a/paddle/operators/activation_op.h
+++ b/paddle/operators/activation_op.h
@@ -590,6 +590,32 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+template <typename T>
+struct ThresholdedReluFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
+
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = (x > static_cast<T>(threshold)).template cast<T>() * x;
+  }
+};
+
+template <typename T>
+struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
+  float threshold;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"threshold", &threshold}};
+  }
+
+  template <typename Device, typename X, typename Y, typename dY, typename dX>
+  void operator()(Device d, X x, Y y, dY dy, dX dx) const {
+    dx.device(d) = dy * (x > static_cast<T>(threshold)).template cast<T>();
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -615,4 +641,5 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
   __macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor);    \
   __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \
   __macro(elu, ELUFunctor, ELUGradFunctor);                       \
-  __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor)
+  __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor); \
+  __macro(thresholded_relu, ThresholdedReluFunctor, ThresholdedReluGradFunctor);
diff --git a/python/paddle/v2/framework/tests/test_activation_op.py b/python/paddle/v2/framework/tests/test_activation_op.py
index a28c4431e1ae9230750247c0ed16c9aff37364fa..3acd00e35213981fce60504876af1861961ebe12 100644
--- a/python/paddle/v2/framework/tests/test_activation_op.py
+++ b/python/paddle/v2/framework/tests/test_activation_op.py
@@ -363,5 +363,26 @@ class TestSoftsign(OpTest):
         self.check_grad(['X'], 'Y', max_relative_error=0.007)
 
 
+class TestThresholdedRelu(OpTest):
+    def setUp(self):
+        self.op_type = "thresholded_relu"
+        threshold = 0.25
+        self.relative_error = 0.005
+        X = np.random.uniform(-1, 1, [11, 17]).astype("float32")
+
+        # Same reason as TestAbs
+        X[np.abs(X - threshold) < self.relative_error] = threshold + 0.2
+
+        self.inputs = {'X': X}
+        self.attrs = {'threshold': threshold}
+        self.outputs = {'Y': (X > threshold) * X}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=self.relative_error)
+
+
 if __name__ == "__main__":
     unittest.main()