diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc
index ac98e49b1c205e30d04c7cda967f97f6613291c3..3cdcfd792355960698e7dea1f82d13b42f315901 100644
--- a/paddle/fluid/operators/activation_op.cc
+++ b/paddle/fluid/operators/activation_op.cc
@@ -560,6 +560,28 @@ $$out = \max(0, x) + \min(0, \alpha * (e^x - 1))$$
   }
 };
 
+class CELUOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "The input is a multi-dimensional Tensor. The data type is "
+             "float32 or float64.");
+    AddOutput("Out",
+              "The output is a multi-dimensional Tensor which has same "
+              "dimension and data type as the ``x``.");
+    AddAttr<float>("alpha", "The alpha value of CELU").SetDefault(1.0f);
+    AddComment(R"DOC(
+CELU Activation Operator.
+
+Applies the following element-wise computation on the input according to
+https://arxiv.org/abs/1704.07483.
+
+$$out = \max(0, x) + \min(0, \alpha * (e^(x/\alpha) - 1))$$
+
+)DOC");
+  }
+};
+
 class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -982,6 +1004,29 @@ class ELUDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
   }
 };
 
+// celu grad: dx=dy if y>0 else dy*(x/alpha).exp()
+// celu gradgrad: ddx=ddy if y>0 else ddy*(x/alpha).exp()/alpha
+template <typename T>
+class CELUDoubleGradMaker : public ::paddle::framework::SingleGradOpMaker<T> {
+ public:
+  using ::paddle::framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("celu_grad_grad");
+
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("DOut", this->Input(framework::GradVarName("Out")));
+    // X@GRAD@GRAD: ddx
+    op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
+    op->SetAttrMap(this->Attrs());
+
+    // Out@GRAD@GRAD: ddy
+    op->SetOutput("DX", this->InputGrad("X"));
+    op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
+  }
+};
+
 // sqrt Grad: dx = 0.5 * dy / y
 // sqrt GradGrad: ddy = 0.5 * ddx / y, dy = -1 * dx * ddx
 template <typename T>
@@ -1353,6 +1398,35 @@ REGISTER_OP_CPU_KERNEL(
 
 /* ========================================================================== */
 
+/* ========================    celu  register     ============================
+ */
+REGISTER_OPERATOR(
+    celu, ops::ActivationOp, ops::CELUOpMaker, ops::ActivationOpInferVarType,
+    ops::ActivationGradOpMaker<ops::CELUGradFunctor<float>::FwdDeps(),
+                               paddle::framework::OpDesc>,
+    ops::ActivationGradOpMaker<ops::CELUGradFunctor<float>::FwdDeps(),
+                               paddle::imperative::OpBase>,
+    ops::ActFwdInplaceInferer);
+REGISTER_OPERATOR(celu_grad, ops::ActivationOpGrad,
+                  ops::ActivationGradOpInplaceInferer,
+                  ops::CELUDoubleGradMaker<paddle::framework::OpDesc>,
+                  ops::CELUDoubleGradMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(
+    celu_grad_grad,
+    ops::ActivationOpDoubleGrad<ops::CELUGradFunctor<float>::FwdDeps()>,
+    ops::ActivationDoubleGradOpInplaceInferer);
+
+REGISTER_ACTIVATION_CPU_KERNEL(celu, CELU, CELUFunctor, CELUGradFunctor);
+REGISTER_OP_CPU_KERNEL(
+    celu_grad_grad, ops::CELUDoubleGradKernel<plat::CPUDeviceContext,
+                                              ops::CELUGradGradFunctor<float>>,
+    ops::CELUDoubleGradKernel<plat::CPUDeviceContext,
+                              ops::CELUGradGradFunctor<double>>,
+    ops::CELUDoubleGradKernel<plat::CPUDeviceContext,
+                              ops::CELUGradGradFunctor<plat::float16>>);
+
+/* ========================================================================== */
+
 /* ===========================   sqrt register  ============================= */
 REGISTER_OPERATOR(
     sqrt, ops::ActivationOp, ops::SqrtOpMaker, ops::ActivationOpInferVarType,
diff --git a/paddle/fluid/operators/activation_op.cu b/paddle/fluid/operators/activation_op.cu
index f330f2d7e87ba711f8cd138a00f921be6086acd7..d83a63015cfe5b16422fe236254778056b287072 100644
--- a/paddle/fluid/operators/activation_op.cu
+++ b/paddle/fluid/operators/activation_op.cu
@@ -1202,6 +1202,59 @@ struct CudaELUGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
+template <typename T>
+struct CudaCELUFunctor : public BaseActivationFunctor<T> {
+  using CT = typename details::MPTypeTrait<T>::Type;
+  CT zero = static_cast<CT>(0.0f);
+  CT one = static_cast<CT>(1.0f);
+  float alpha;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+
+  // celu(x) = max(0, x) + min(0, alpha * (exp(x/alpha) - 1))
+  __device__ __forceinline__ T operator()(const T& arg_x) const {
+    CT x = static_cast<CT>(arg_x);
+    CT temp = static_cast<CT>(alpha) * (exp(x / static_cast<CT>(alpha)) - one);
+    CT res = (x > zero ? x : zero) + (temp > zero ? zero : temp);
+    return static_cast<T>(res);
+  }
+};
+
+template <typename T>
+struct CudaCELUGradFunctor : public BaseActivationFunctor<T> {
+  using MPType = typename details::MPTypeTrait<T>::Type;
+  MPType zero = static_cast<MPType>(0.0f);
+  MPType one = static_cast<MPType>(1.0f);
+  float alpha;
+
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+
+  // dx = dout, if alpha > 0 and x > 0
+  // dx = dout * (x/alpha).exp(), if alpha > 0 and x <= 0
+  // dx = dout , if alpha < 0 and x > 0
+  // dx = dout * (x/alpha).exp(), if alpha < 0 and x <=0
+  __device__ __forceinline__ T operator()(const T& arg_dout,
+                                          const T& arg_x) const {
+    MPType dout = static_cast<MPType>(arg_dout);
+    MPType x = static_cast<MPType>(arg_x);
+    MPType a = static_cast<MPType>(alpha);
+    MPType temp_a_pos = static_cast<MPType>(alpha > 0.0f);
+    MPType temp_a_neg = static_cast<MPType>(alpha <= 0.0f);
+    MPType temp_x_pos = static_cast<MPType>(x > zero);
+    MPType temp_x_neg = static_cast<MPType>(x <= zero);
+    return static_cast<T>(
+        dout *
+        (temp_a_pos * temp_x_pos + temp_a_pos * temp_x_neg * exp(x / a) +
+         temp_a_neg * temp_x_pos + exp(x / a) * temp_a_neg * temp_x_neg));
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
+};
+
 template <typename DeviceContext, typename Functor>
 class ActivationCudaKernel
     : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
@@ -1341,6 +1394,19 @@ REGISTER_OP_CUDA_KERNEL(
                              ops::ELUGradGradFunctor<plat::float16>>);
 /* ========================================================================== */
 
+/* ======================== celu register  ============================ */
+REGISTER_ACTIVATION_CUDA_KERNEL(celu, CELU, CudaCELUFunctor,
+                                CudaCELUGradFunctor);
+
+REGISTER_OP_CUDA_KERNEL(
+    celu_grad_grad, ops::CELUDoubleGradKernel<plat::CUDADeviceContext,
+                                              ops::CELUGradGradFunctor<float>>,
+    ops::CELUDoubleGradKernel<plat::CUDADeviceContext,
+                              ops::CELUGradGradFunctor<double>>,
+    ops::CELUDoubleGradKernel<plat::CUDADeviceContext,
+                              ops::CELUGradGradFunctor<plat::float16>>);
+/* ========================================================================== */
+
 /* ===========================    relu register  ============================ */
 #ifdef PADDLE_WITH_HIP
 REGISTER_ACTIVATION_CUDA_KERNEL(relu, Relu, CudaReluFunctor,
diff --git a/paddle/fluid/operators/activation_op.h b/paddle/fluid/operators/activation_op.h
index 4f26cb095c5a72b455933d8ab244b0dcb3d05232..a6240c038b110050a0db4e46111e7163d5ade43e 100644
--- a/paddle/fluid/operators/activation_op.h
+++ b/paddle/fluid/operators/activation_op.h
@@ -1389,6 +1389,51 @@ struct ELUGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
+template <typename T>
+struct CELUFunctor : public BaseActivationFunctor<T> {
+  float alpha;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+
+  template <typename Device, typename X, typename Out>
+  void operator()(Device d, X x, Out out) const {
+    out.device(d) =
+        (x < static_cast<T>(0))
+            .select(static_cast<T>(alpha) *
+                        ((x / static_cast<T>(alpha)).exp() - static_cast<T>(1)),
+                    x);
+  }
+};
+
+template <typename T>
+struct CELUGradFunctor : public BaseActivationFunctor<T> {
+  float alpha;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+  template <typename Device, typename X, typename Out, typename dOut,
+            typename dX>
+  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
+    auto temp_a_pos = static_cast<T>(alpha > 0);
+    auto temp_a_neg = static_cast<T>(alpha <= 0);
+    auto temp_x_pos = (x > static_cast<T>(0)).template cast<T>();
+    auto temp_x_neg = (x <= static_cast<T>(0)).template cast<T>();
+
+    // dx = dout, if alpha > 0 and x > 0
+    // dx = dout * (x/alpha).exp(), if alpha > 0 and x <= 0
+    // dx = dout , if alpha < 0 and x > 0
+    // dx = dout * (x/alpha).exp(), if alpha < 0 and x <=0
+    dx.device(d) =
+        dout * temp_a_pos * temp_x_pos +
+        dout * (x / static_cast<T>(alpha)).exp() * temp_a_pos * temp_x_neg +
+        dout * temp_a_neg * temp_x_pos +
+        dout * (x / static_cast<T>(alpha)).exp() * temp_a_neg * temp_x_neg;
+  }
+
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
+};
+
 // FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5198
 template <typename T>
 struct PowFunctor : public BaseActivationFunctor<T> {
@@ -1775,6 +1820,45 @@ struct ELUGradGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
 };
 
+template <typename T>
+struct CELUGradGradFunctor : public BaseActivationFunctor<T> {
+  float alpha;
+  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
+    return {{"alpha", &alpha}};
+  }
+  template <typename Device>
+  void operator()(const Device& dev, const framework::Tensor* X,
+                  const framework::Tensor* ddX, framework::Tensor* ddOut,
+                  const framework::Tensor* dOut, framework::Tensor* dX) const {
+    auto* d = dev.eigen_device();
+    auto ddx = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(ddX, "Input", "DDX", "CELUGradGrad"));
+    auto x = framework::EigenVector<T>::Flatten(
+        GET_DATA_SAFELY(X, "Input", "X", "CELUGradGrad"));
+
+    if (dX) {
+      auto dx = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(dX, "Output", "DX", "CELUGradGrad"));
+      auto dout = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(dOut, "Output", "DOut", "CELUGradGrad"));
+      dx.device(*d) = ddx * dout / static_cast<T>(alpha) *
+                      (x / static_cast<T>(alpha)).exp() *
+                      (x <= static_cast<T>(0)).template cast<T>();
+    }
+
+    if (ddOut) {
+      auto ddout = framework::EigenVector<T>::Flatten(
+          GET_DATA_SAFELY(ddOut, "Output", "DDOut", "CELUGradGrad"));
+      ddout.device(*d) = ddx *
+                         ((x > static_cast<T>(0)).template cast<T>() +
+                          (x / static_cast<T>(alpha)).exp() *
+                              (x <= static_cast<T>(0)).template cast<T>())
+                             .template cast<T>();
+    }
+  }
+  static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
+};
+
 template <typename T>
 struct SqrtGradGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device>
@@ -2107,6 +2191,33 @@ class ELUDoubleGradKernel
   }
 };
 
+template <typename DeviceContext, typename Functor>
+class CELUDoubleGradKernel
+    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
+ public:
+  using T = typename Functor::ELEMENT_TYPE;
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    const framework::Tensor *X, *ddX, *dOut;
+    X = ddX = dOut = nullptr;
+    framework::Tensor *dX, *ddOut;
+    dX = ddOut = nullptr;
+
+    ExtractDoubleGradTensorWithInputDOut(ctx, &X, &ddX, &dX, &dOut, &ddOut);
+
+    if (dX) dX->mutable_data<T>(X->dims(), ctx.GetPlace());
+    if (ddOut) ddOut->mutable_data<T>(ctx.GetPlace());
+
+    auto& place = ctx.template device_context<DeviceContext>();
+
+    Functor functor;
+    auto attrs = functor.GetAttrs();
+    for (auto& attr : attrs) {
+      *attr.second = ctx.Attr<float>(attr.first);
+    }
+    functor(place, X, ddX, ddOut, dOut, dX);
+  }
+};
+
 template <typename DeviceContext, typename Functor>
 class SqrtDoubleGradKernel
     : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
diff --git a/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py b/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py
index 8f3353d1155f6fd7c0551842f55934d9c84e4043..c54f711c7ce12945dd8607b26302a9edf61f771d 100644
--- a/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py
+++ b/python/paddle/fluid/tests/unittests/test_activation_nn_grad.py
@@ -22,6 +22,7 @@ import paddle
 import paddle.fluid.layers as layers
 import paddle.fluid.core as core
 import gradient_checker
+import paddle.nn.functional as F
 
 from decorator_helper import prog_scope
 
@@ -168,6 +169,32 @@ class TestELUDoubleGradCheck(unittest.TestCase):
             self.func(p)
 
 
+class TestCELUDoubleGradCheck(unittest.TestCase):
+    @prog_scope()
+    def func(self, place):
+        shape = [2, 4, 4, 4]
+        eps = 1e-6
+        alpha = 0.2
+        dtype = np.float64
+        SEED = 0
+
+        x = layers.data('x', shape, False, dtype)
+        x.persistable = True
+
+        y = F.celu(x, alpha=alpha)
+        np.random.RandomState(SEED)
+        x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
+        gradient_checker.double_grad_check(
+            [x], y, x_init=x_arr, place=place, eps=eps)
+
+    def test_grad(self):
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(fluid.CUDAPlace(0))
+        for p in places:
+            self.func(p)
+
+
 class TestSqrtDoubleGradCheck(unittest.TestCase):
     @prog_scope()
     def func(self, place):
diff --git a/python/paddle/fluid/tests/unittests/test_activation_op.py b/python/paddle/fluid/tests/unittests/test_activation_op.py
index 346accac01cc70ced25c74b469a4101fd7e37696..b82dd631c6489079bebab65857e43e4e3baf8f92 100755
--- a/python/paddle/fluid/tests/unittests/test_activation_op.py
+++ b/python/paddle/fluid/tests/unittests/test_activation_op.py
@@ -1827,6 +1827,94 @@ class TestELUAPI(unittest.TestCase):
             self.elu(x_fp16)
 
 
+def celu(x, alpha):
+    out_ref = np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x / alpha) - 1))
+    return out_ref.astype(x.dtype)
+
+
+class TestCELU(TestActivation):
+    def setUp(self):
+        self.op_type = "celu"
+        self.init_dtype()
+
+        np.random.seed(1024)
+        x = np.random.uniform(-3, 3, [10, 12]).astype(self.dtype)
+        alpha = 1.5
+        out = celu(x, alpha)
+        self.inputs = {'X': x}
+        self.attrs = {'alpha': alpha}
+        self.outputs = {'Out': out}
+
+    def test_check_grad(self):
+        if self.dtype == np.float16:
+            return
+        self.check_grad(['X'], 'Out')
+
+
+class TestCELUAPI(unittest.TestCase):
+    # test paddle.nn.CELU, paddle.nn.functional.celu
+    def setUp(self):
+        np.random.seed(1024)
+        self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32')
+        self.place=paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+        self.executed_api()
+
+    def executed_api(self):
+        self.celu = F.celu
+
+    def test_static_api(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            x = paddle.fluid.data('X', [10, 12])
+            out1 = self.celu(x, 1.5)
+            m = paddle.nn.CELU(1.5)
+            out2 = m(x)
+            exe = paddle.static.Executor(self.place)
+            res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
+        out_ref = celu(self.x_np, 1.5)
+        for r in res:
+            self.assertEqual(np.allclose(out_ref, r), True)
+
+    def test_dygraph_api(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.x_np)
+        out1 = self.celu(x, 1.5)
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.CELU(1.5)
+        out2 = m(x)
+        out_ref = celu(self.x_np, 1.5)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+
+        out1 = self.celu(x, 0.2)
+        x = paddle.to_tensor(self.x_np)
+        m = paddle.nn.CELU(0.2)
+        out2 = m(x)
+        out_ref = celu(self.x_np, 0.2)
+        for r in [out1, out2]:
+            self.assertEqual(np.allclose(out_ref, r.numpy()), True)
+        paddle.enable_static()
+
+    def test_errors(self):
+        paddle.enable_static()
+        with paddle.static.program_guard(paddle.static.Program()):
+            # The input type must be Variable.
+            self.assertRaises(TypeError, self.celu, 1)
+            # The input dtype must be float16, float32, float64.
+            x_int32 = paddle.fluid.data(
+                name='x_int32', shape=[10, 12], dtype='int32')
+            self.assertRaises(TypeError, self.celu, x_int32)
+            # The alpha must be not equal 0
+            x_fp32 = paddle.fluid.data(
+                name='x_fp32', shape=[10, 12], dtype='float32')
+            self.assertRaises(ZeroDivisionError, F.celu, x_fp32, 0)
+            # support the input dtype is float16
+            x_fp16 = paddle.fluid.data(
+                name='x_fp16', shape=[10, 12], dtype='float16')
+            self.celu(x_fp16)
+
+
 class TestELUInplaceAPI(TestELUAPI):
     # test paddle.nn.functional.elu_
     def executed_api(self):
@@ -2791,6 +2879,7 @@ create_test_act_fp16_class(TestBRelu)
 create_test_act_fp16_class(TestRelu6)
 create_test_act_fp16_class(TestSoftRelu, grad_atol=0.85)
 create_test_act_fp16_class(TestELU)
+create_test_act_fp16_class(TestCELU)
 create_test_act_fp16_class(TestReciprocal)
 create_test_act_fp16_class(TestLog)
 if core.is_compiled_with_rocm():
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_layers.py b/python/paddle/fluid/tests/unittests/test_imperative_layers.py
index dc15566f85475c835fc4a5dec69099ff68fd9722..3561405ae090bd99ec03d25d6abe0f01f8b13e7a 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_layers.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_layers.py
@@ -22,6 +22,9 @@ class TestLayerPrint(unittest.TestCase):
         module = nn.ELU(0.2)
         self.assertEqual(str(module), 'ELU(alpha=0.2)')
 
+        module = nn.CELU(0.2)
+        self.assertEqual(str(module), 'CELU(alpha=0.2)')
+
         module = nn.GELU(True)
         self.assertEqual(str(module), 'GELU(approximate=True)')
 
diff --git a/python/paddle/nn/__init__.py b/python/paddle/nn/__init__.py
index 98444e69d0b1b3de16bacad06375e69e2af06860..064052c07695de8de8a5e97a357d03c85644934e 100644
--- a/python/paddle/nn/__init__.py
+++ b/python/paddle/nn/__init__.py
@@ -25,6 +25,7 @@ from .clip import ClipGradByNorm  # noqa: F401
 from .clip import ClipGradByValue  # noqa: F401
 from .decode import BeamSearchDecoder  # noqa: F401
 from .decode import dynamic_decode  # noqa: F401
+from .layer.activation import CELU  # noqa: F401
 from .layer.activation import ELU  # noqa: F401
 from .layer.activation import GELU  # noqa: F401
 from .layer.activation import Tanh  # noqa: F401
@@ -185,6 +186,7 @@ def weight_norm(*args):
 
 __all__ = [     #noqa
            'BatchNorm',
+           'CELU',
            'GroupNorm',
            'LayerNorm',
            'SpectralNorm',
diff --git a/python/paddle/nn/functional/__init__.py b/python/paddle/nn/functional/__init__.py
index 4151f25b94aff2ee005c6d759cf11e07db397f8e..1af53e0826be878d8734fa602160881225eff13d 100644
--- a/python/paddle/nn/functional/__init__.py
+++ b/python/paddle/nn/functional/__init__.py
@@ -15,6 +15,7 @@
 # TODO: import all neural network related api under this directory,
 # including layers, linear, conv, rnn etc.
 
+from .activation import celu  # noqa: F401
 from .activation import elu  # noqa: F401
 from .activation import elu_  # noqa: F401
 from .activation import gelu  # noqa: F401
@@ -115,6 +116,7 @@ from ...fluid.layers import temporal_shift  # noqa: F401
 from .sparse_attention import sparse_attention
 
 __all__ = [     #noqa
+           'celu',
            'conv1d',
            'conv1d_transpose',
            'conv2d',
diff --git a/python/paddle/nn/functional/activation.py b/python/paddle/nn/functional/activation.py
index 67be64c01cbb8f25868967b66136e12ea7749d20..a39c00075a3de191a83b11df12287d6a9692debf 100644
--- a/python/paddle/nn/functional/activation.py
+++ b/python/paddle/nn/functional/activation.py
@@ -31,6 +31,50 @@ from paddle import _C_ops
 __all__ = []
 
 
+def celu(x, alpha=1.0, name=None):
+    r"""
+    celu activation.
+
+    .. math::
+
+        celu(x) = max(0, x) + min(0, \alpha * (e^{x/\alpha}-1))
+
+    Parameters:
+        x (Tensor): The input Tensor with data type float32, float64.
+        alpha (float, optional): The 'alpha' value of the CELU formulation. Default is 1.0.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        A Tensor with the same data type and shape as ``x`` .
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn.functional as F
+            x = paddle.to_tensor([[-1., 6.], [1., 15.6]])
+            out = F.celu(x, alpha=0.2)
+            # [[-0.19865242,  6.        ],
+            #  [ 1.        , 15.60000038]]
+    """
+    if alpha == 0:
+        raise ZeroDivisionError("alpha cannot be 0 for celu")
+
+    if in_dygraph_mode():
+        return _C_ops.celu(x, 'alpha', alpha)
+
+    check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'celu')
+    helper = LayerHelper("celu", **locals())
+    out = helper.create_variable_for_type_inference(x.dtype)
+    helper.append_op(
+        type='celu',
+        inputs={'X': x},
+        outputs={'Out': out},
+        attrs={'alpha': alpha})
+    return out
+
+
 def elu(x, alpha=1.0, name=None):
     r"""
     elu activation.
diff --git a/python/paddle/nn/layer/__init__.py b/python/paddle/nn/layer/__init__.py
index 074dfac5108f96860e84339c8ffd7b166d1d2de1..eb7535b16c6e1e7e19e3a1260d506e6996379aa3 100644
--- a/python/paddle/nn/layer/__init__.py
+++ b/python/paddle/nn/layer/__init__.py
@@ -18,6 +18,7 @@ from . import rnn  # noqa: F401
 from . import transformer  # noqa: F401
 from . import container  # noqa: F401
 
+from .activation import CELU  # noqa: F401
 from .activation import PReLU  # noqa: F401
 from .activation import ReLU  # noqa: F401
 from .activation import ReLU6  # noqa: F401
diff --git a/python/paddle/nn/layer/activation.py b/python/paddle/nn/layer/activation.py
index abfeff0641a472fe072471b6eeaaca468cda9ee0..cf0ac79ca8ff6ff228f11af442cf257eb60e7bfd 100644
--- a/python/paddle/nn/layer/activation.py
+++ b/python/paddle/nn/layer/activation.py
@@ -25,6 +25,48 @@ from paddle.nn import Layer
 __all__ = []
 
 
+class CELU(Layer):
+    r"""
+    CELU Activation.
+
+    .. math::
+    
+        CELU(x) = max(0, x) + min(0, \alpha * (e^{x/\alpha}-1))
+
+    Parameters:
+        alpha (float, optional): The 'alpha' value of the CELU formulation. Default is 1.0.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        - input: Tensor with any shape.
+        - output: Tensor with the same shape as input.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            
+            x = paddle.to_tensor([[-1. ,6.], [1., 15.6]])
+            m = paddle.nn.CELU(0.2)
+            out = m(x)
+            # [[-0.19865242,  6.        ],
+            #  [ 1.        , 15.60000038]]
+    """
+
+    def __init__(self, alpha=1.0, name=None):
+        super(CELU, self).__init__()
+        self._alpha = alpha
+        self._name = name
+
+    def forward(self, x):
+        return F.celu(x, self._alpha, self._name)
+
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'alpha={}{}'.format(self._alpha, name_str)
+
+
 class ELU(Layer):
     r"""
     ELU Activation.