diff --git a/paddle/operators/activation_op.cc b/paddle/operators/activation_op.cc
index c66d575d24bb6b410602c34965ab1db6bc81b41d..154c618e8e7c4650b7f22684d3357de9c52a416c 100644
--- a/paddle/operators/activation_op.cc
+++ b/paddle/operators/activation_op.cc
@@ -223,6 +223,51 @@ $y = |x|$
   }
 };
 
+class CeilOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CeilOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Ceil operator");
+    AddOutput("Y", "Output of Ceil operator");
+    AddComment(R"DOC(
+Ceil Activation Operator.
+
+$y = ceil(x)$
+
+)DOC");
+  }
+};
+
+class FloorOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  FloorOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Floor operator");
+    AddOutput("Y", "Output of Floor operator");
+    AddComment(R"DOC(
+Floor Activation Operator.
+
+$y = floor(x)$
+
+)DOC");
+  }
+};
+
+class RoundOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  RoundOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X", "Input of Round operator");
+    AddOutput("Y", "Output of Round operator");
+    AddComment(R"DOC(
+Round Activation Operator.
+
+$y = [x]$
+
+)DOC");
+  }
+};
+
 class ReciprocalOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ReciprocalOpMaker(framework::OpProto *proto,
@@ -493,6 +538,15 @@ REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
 REGISTER_OP(abs, ops::ActivationOp, ops::AbsOpMaker, abs_grad,
             ops::ActivationOpGrad);
 
+REGISTER_OP(ceil, ops::ActivationOp, ops::CeilOpMaker, ceil_grad,
+            ops::ActivationOpGrad);
+
+REGISTER_OP(floor, ops::ActivationOp, ops::FloorOpMaker, floor_grad,
+            ops::ActivationOpGrad);
+
+REGISTER_OP(round, ops::ActivationOp, ops::RoundOpMaker, round_grad,
+            ops::ActivationOpGrad);
+
 REGISTER_OP(reciprocal, ops::ActivationOp, ops::ReciprocalOpMaker,
             reciprocal_grad, ops::ActivationOpGrad);
 
diff --git a/paddle/operators/activation_op.h b/paddle/operators/activation_op.h
index ceb4b4e40b67473f42e67e3f02f8e012e1b1eb50..8cd3bfbbd3f8f3210f94aef3a1586c8295730c1d 100644
--- a/paddle/operators/activation_op.h
+++ b/paddle/operators/activation_op.h
@@ -283,6 +283,41 @@ struct SqrtGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
+// ceil(x) = ceiling(x)
+template <typename T>
+struct CeilFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = x.ceil();
+  }
+};
+
+template <typename T>
+struct ZeroGradFunctor : public BaseActivationFunctor<T> {
+  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) = static_cast<T>(0) / x;
+  }
+};
+
+// floor(x) = flooring(x)
+template <typename T>
+struct FloorFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = x.ceil();
+  }
+};
+
+// round(x) = [x]
+template <typename T>
+struct RoundFunctor : public BaseActivationFunctor<T> {
+  template <typename Device, typename X, typename Y>
+  void operator()(Device d, X x, Y y) const {
+    y.device(d) = x.round();
+  }
+};
+
 // abs(x) = |x|
 template <typename T>
 struct AbsFunctor : public BaseActivationFunctor<T> {
@@ -677,6 +712,9 @@ struct HardSigmoidGradFunctor : public BaseActivationFunctor<T> {
   __macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor);     \
   __macro(sqrt, SqrtFunctor, SqrtGradFunctor);                       \
   __macro(abs, AbsFunctor, AbsGradFunctor);                          \
+  __macro(ceil, CeilFunctor, ZeroGradFunctor);                       \
+  __macro(floor, FloorFunctor, ZeroGradFunctor);                     \
+  __macro(round, RoundFunctor, ZeroGradFunctor);                     \
   __macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor);     \
   __macro(log, LogFunctor, LogGradFunctor);                          \
   __macro(square, SquareFunctor, SquareGradFunctor);                 \
diff --git a/python/paddle/v2/fluid/tests/test_activation_op.py b/python/paddle/v2/fluid/tests/test_activation_op.py
index 7649e60a3833e34523d87cb963af3888c3cef65d..bd52bef2605874d26e880fb09e589891fc1934d5 100644
--- a/python/paddle/v2/fluid/tests/test_activation_op.py
+++ b/python/paddle/v2/fluid/tests/test_activation_op.py
@@ -152,6 +152,49 @@ class TestAbs(OpTest):
         self.check_grad(['X'], 'Y', max_relative_error=0.007)
 
 
+class TestCeil(OpTest):
+    def setUp(self):
+        self.op_type = "ceil"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        self.inputs = {'X': x}
+        self.outputs = {'Y': np.ceil(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestFloor(OpTest):
+    def setUp(self):
+        self.op_type = "floor"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        self.inputs = {'X': x}
+        # numpy floor need +1
+        self.outputs = {'Y': np.floor(self.inputs['X']) + 1.0}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
+class TestRound(OpTest):
+    def setUp(self):
+        self.op_type = "round"
+        x = np.random.uniform(-1, 1, [4, 4]).astype("float32")
+        self.inputs = {'X': x}
+        self.outputs = {'Y': np.round(self.inputs['X'])}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Y', max_relative_error=0.007)
+
+
 class TestRelu(OpTest):
     def setUp(self):
         self.op_type = "relu"