diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt
index d0fe5b4635174fa0f74658509c4e8ca58a1d056a..059a6bba84cfb0c1f6cbbba3c88d589b52dc5592 100644
--- a/paddle/operators/CMakeLists.txt
+++ b/paddle/operators/CMakeLists.txt
@@ -184,6 +184,7 @@ set(DEPS_OPS
     sequence_softmax_op
     sum_op
     pool_op
+    maxout_op
     pool_with_index_op
     conv_op
     conv_transpose_op
@@ -210,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor)
 op_library(adagrad_op DEPS selected_rows_functor)
 op_library(conv_op DEPS vol2col)
 op_library(pool_op DEPS pooling)
+op_library(maxout_op DEPS maxouting)
 op_library(pool_with_index_op DEPS pooling)
 op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
 op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt
index 002b68fecf4f1e294387357f0346d9926a2b2b5a..3017f133afc5d4dcd484c78b44591a876ab4d667 100644
--- a/paddle/operators/math/CMakeLists.txt
+++ b/paddle/operators/math/CMakeLists.txt
@@ -14,6 +14,7 @@ if(WITH_GPU)
     nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
     nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
     nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
+    nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
 else()
     cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
     cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
@@ -26,6 +27,7 @@ else()
     cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
     cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
     cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
+    cc_library(maxouting SRCS maxouting.cc DEPS device_context)
 endif()
 
 cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
diff --git a/paddle/operators/math/maxouting.cc b/paddle/operators/math/maxouting.cc
new file mode 100644
index 0000000000000000000000000000000000000000..e5168ce7afd4139475afa6edd5999b9974407c9b
--- /dev/null
+++ b/paddle/operators/math/maxouting.cc
@@ -0,0 +1,106 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/math/maxouting.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+// All tensors are in NCHW format, and the groups must be greater than 1
+template <typename T>
+class MaxOutFunctor<platform::CPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * output,
+                  int groups) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    int fea_size = input_height * input_width;
+    // c_size means the output size of each sample
+    int c_size = fea_size * output_channels;
+    const T* input_data = input.data<T>();
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+
+    for (int i = 0; i < batch_size; ++i) {
+      int new_bindex =  c_size * i;
+      for (int c = 0; c < output_channels; ++c) {
+        int new_cindex = fea_size * c;
+        for (int f = 0; f < fea_size; ++f) {
+          T ele = static_cast<T>(-FLT_MAX);
+          for (int ph = 0; ph < groups; ++ph) {
+            T x = input_data[(new_bindex + new_cindex) * groups
+              + ph * fea_size + f];
+            ele = ele > x ? ele : x;
+          }
+          output_data[(new_bindex+new_cindex+f)] = ele;
+        }
+      }
+    }
+  }
+};
+
+
+
+template <class T>
+class MaxOutGradFunctor<platform::CPUPlace, T> {
+public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad,
+                  int groups) {
+    const int batch_size = input.dims()[0];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output.dims()[1];
+    int fea_size = input_height * input_width;
+    const T* input_data = input.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+
+    for (int i = 0; i < batch_size; ++i) {
+      int blen = fea_size * output_channels * i;
+      for (int c = 0; c < output_channels; ++c) {
+        int clen = fea_size * c;
+        for (int f = 0; f < fea_size; ++f) {
+          int input_idx0 = (blen + clen) * groups + f;
+          bool continue_match = true;
+          int output_idx = blen + clen + f;
+          for (int g = 0; g < groups && continue_match; ++g) {
+              int input_idx = input_idx0 + fea_size * g;
+              if (input_data[input_idx] == output_data[output_idx]) {
+                input_grad_data[input_idx] += output_grad_data[output_idx];
+                continue_match = false;
+              }
+          }
+        }
+      }
+    }
+  }
+};
+
+template class MaxOutGradFunctor<platform::CPUPlace, float>;
+template class MaxOutGradFunctor<platform::CPUPlace, double>;
+template class MaxOutFunctor<platform::CPUPlace, float>;
+template class MaxOutFunctor<platform::CPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/maxouting.cu b/paddle/operators/math/maxouting.cu
new file mode 100644
index 0000000000000000000000000000000000000000..7c698577b8a8258a58ba9a2b6c675457b2458a5b
--- /dev/null
+++ b/paddle/operators/math/maxouting.cu
@@ -0,0 +1,154 @@
+/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/math/maxouting.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+__global__ void KernelMaxOut(const int nthreads, const T* input_data,
+                            const int channels,
+                             const int input_height, const int input_width,
+                             int groups, T* output_data ) {
+  const int size = input_height * input_width * channels / groups;
+  const int feat_len = input_height * input_width;
+  int index = blockIdx.x * blockDim.x + threadIdx.x;
+  int offset = blockDim.x * gridDim.x;
+  for (int i = index; i < nthreads; i += offset) {
+    int batch_idx = i / size;
+    int batch_offset = i % size;
+    int channel_idx = batch_offset / feat_len;
+    int feat_idx = batch_offset % feat_len;
+    int data_idx =
+      (batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
+    T ele = static_cast<T>(-FLT_MAX);
+    for (int g = 0; g < groups; ++g) {
+      T x = input_data[data_idx + g * feat_len];
+      ele = ele > x ? ele : x;
+    }
+    output_data[i] = ele;
+  }
+}
+template <typename T>
+__global__ void KernelMaxoutGrad(
+    const int nthreads, const T* input_data, const T* output_data,
+    const T* output_grad, T* input_grad, const int channels,
+    const int input_height, const int input_width, int groups) {
+    const int size = input_height * input_width * channels / groups;
+    const int feat_len = input_height * input_width;
+    int index = blockIdx.x * blockDim.x + threadIdx.x;
+    int offset = blockDim.x * gridDim.x;
+    for (int i = index; i < nthreads; i += offset) {
+      int batch_idx = i / size;
+      int batch_offset = i % size;
+      int channel_idx = batch_offset / feat_len;
+      int feat_idx = batch_offset % feat_len;
+      int data_idx =
+        (batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
+      int max_index = -1;
+      bool continue_match = true;
+      for (int g = 0; g < groups && continue_match; ++g) {
+        if (input_data[data_idx + g * feat_len] == output_data[i]) {
+          max_index = data_idx + g * feat_len;
+          continue_match = false;
+          break;
+        }
+      }
+      if (max_index != -1) {
+        input_grad[max_index] += output_grad[index];
+      }
+    }
+}
+/*
+ * All tensors are in NCHW format.
+ */
+template <typename T>
+class MaxOutFunctor<platform::GPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor * output,
+                  int groups) {
+    const int batch_size = input.dims()[0];
+    const int input_channels = input.dims()[1];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output->dims()[1];
+    const int output_height = output->dims()[2];
+    const int output_width = output->dims()[3];
+
+    const T* input_data = input.data<T>();
+    T* output_data = output->mutable_data<T>(context.GetPlace());
+    int nthreads =  output->numel();
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelMaxOut<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(nthreads, input_data, input_channels,
+                              input_height, input_width, groups,
+                              output_data);
+  }
+};
+/*
+ * All tensors are in NCHW format.
+ */
+template <typename T>
+class MaxOutGradFunctor<platform::GPUPlace, T> {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad,
+                  int groups) {
+    const int batch_size = input.dims()[0];
+    const int input_channels = input.dims()[1];
+    const int input_height = input.dims()[2];
+    const int input_width = input.dims()[3];
+    const int output_channels = output.dims()[1];
+    const int output_height = output.dims()[2];
+    const int output_width = output.dims()[3];
+
+    const T* input_data = input.data<T>();
+    const T* output_data = output.data<T>();
+    const T* output_grad_data = output_grad.data<T>();
+    T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
+    int nthreads =  output.numel();
+    int blocks = (nthreads + 1024 - 1) / 1024;
+    dim3 threads(1024, 1);
+    dim3 grid(blocks, 1);
+
+    KernelMaxoutGrad<
+        T><<<grid, threads, 0,
+             reinterpret_cast<const platform::CUDADeviceContext&>(context)
+                 .stream()>>>(
+        nthreads, input_data, output_data, output_grad_data, input_grad_data,
+        input_channels, input_height, input_width, groups);
+  }
+};
+
+template class MaxOutGradFunctor<platform::GPUPlace, float>;
+template class MaxOutGradFunctor<platform::GPUPlace, double>;
+
+template class MaxOutFunctor<platform::GPUPlace, float>;
+template class MaxOutFunctor<platform::GPUPlace, double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/maxouting.h b/paddle/operators/math/maxouting.h
new file mode 100644
index 0000000000000000000000000000000000000000..d4c9da38ab8f8d88ed461d805ae64a015db968c4
--- /dev/null
+++ b/paddle/operators/math/maxouting.h
@@ -0,0 +1,47 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+#include "paddle/framework/tensor.h"
+#include "paddle/platform/device_context.h"
+#include "paddle/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+#define FLT_MAX \
+    __FLT_MAX__
+
+template <typename Place, typename T>
+
+class MaxOutFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input, framework::Tensor * output,
+                  int groups);
+};
+
+template <typename Place, class T>
+class MaxOutGradFunctor {
+ public:
+  void operator()(const platform::DeviceContext& context,
+                  const framework::Tensor& input,
+                  framework::Tensor * input_grad,
+                  const framework::Tensor& output,
+                  const framework::Tensor& output_grad, int groups);
+};
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/maxout_op.cc b/paddle/operators/maxout_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..95467f2e69093906980d075b6a41c5d2934dd5a2
--- /dev/null
+++ b/paddle/operators/maxout_op.cc
@@ -0,0 +1,104 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+ *
+ * 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. */
+
+#include "paddle/operators/maxout_op.h"
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class MaxOutOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MaxOutOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+        "(Tensor) The input tensor of maxout operator. "
+        "The format of input tensor is NCHW. Where N is batch size, C is the "
+        "number of channels, H and W is the height and width of feature.");
+    AddOutput("Out",
+        "(Tensor) The output tensor of maxout operator."
+        "The format of output tensor is also NCHW."
+        "Where N is batch size, C is "
+        "the number of channels, H and W is the height and "
+        "width of feature.");
+    AddAttr<int>(
+        "groups",
+        R"DOC("Specifies how many groups the input tensor will be split"
+        "in the channel dimension. And the number of output channel is "
+        "the number of channels divided by groups.."
+        )DOC");
+    AddComment(R"DOC(
+        Assumed the input shape is (N, Ci, H, W).
+        The output shape is (N, Co, H, W). Then `Co = Ci / groups`.
+
+       math:
+       y_{si+j} = \max_k x_{gsi + sk + j}
+       g = groups
+       s = input.size / num_channels
+       0 \le i < num_channels / groups
+       0 \le j < s
+       0 \le k < groups
+
+    Please refer to Paper:
+      - Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
+      - Multi-digit Number Recognition from Street View \
+        Imagery using Deep Convolutional Neural Networks: \
+        https://arxiv.org/pdf/1312.6082v4.pdf
+        )DOC");
+  }
+};
+
+
+class MaxOutOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MaxoutOp"
+                   "should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of MaxoutOp should not be null.");
+    auto in_x_dims = ctx->GetInputDim("X");
+    int groups = ctx->Attrs().Get<int>("groups");
+    // check groups > 1
+    PADDLE_ENFORCE_GT(
+        groups, 1,
+        "groups should be larger than 1 in maxoutop");
+    std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1] / groups});
+    output_shape.push_back(in_x_dims[2]);
+    output_shape.push_back(in_x_dims[3]);
+    ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  }
+};
+
+class MaxOutOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
+    "Input(X@GRAD) should not be null.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+};
+}    // namespace operators
+}    // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(maxout, ops::MaxOutOp, ops::MaxOutOpMaker, maxout_grad,
+                        ops::MaxOutOpGrad);
+REGISTER_OP_CPU_KERNEL(maxout, ops::MaxOutKernel<paddle::platform::CPUPlace,
+                       float>);
+REGISTER_OP_CPU_KERNEL(maxout_grad,
+                       ops::MaxOutGradKernel<paddle::platform::CPUPlace,
+                       float>);
diff --git a/paddle/operators/maxout_op.cu.cc b/paddle/operators/maxout_op.cu.cc
new file mode 100644
index 0000000000000000000000000000000000000000..a5823fba6848a0d42a743c90d7d683e3e4ae4422
--- /dev/null
+++ b/paddle/operators/maxout_op.cu.cc
@@ -0,0 +1,25 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#include "paddle/operators/maxout_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(maxout,
+                       ops::MaxOutKernel<paddle::platform::GPUPlace, float>,
+                       ops::MaxOutKernel<paddle::platform::GPUPlace, double>);
+REGISTER_OP_GPU_KERNEL(maxout_grad,
+                       ops::MaxOutGradKernel<paddle::platform::GPUPlace,
+                        float>,
+                       ops::MaxOutGradKernel<paddle::platform::GPUPlace,
+                        double>);
diff --git a/paddle/operators/maxout_op.h b/paddle/operators/maxout_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..c404cd16a9b2372ea4c6a17eb5ac82cf8f3bf27c
--- /dev/null
+++ b/paddle/operators/maxout_op.h
@@ -0,0 +1,62 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/operators/math/maxouting.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename Place, typename T>
+class MaxOutKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    Tensor* out = context.Output<Tensor>("Out");
+    int groups = context.template Attr<int>("groups");
+
+    math::MaxOutFunctor<Place, T> maxout_forward;
+    maxout_forward(context.device_context(), *in_x, out, groups);
+  }
+};
+
+template <typename Place, typename T>
+class MaxOutGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* in_x = context.Input<Tensor>("X");
+    const Tensor* out = context.Input<Tensor>("Out");
+    const Tensor* out_grad =
+        context.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
+    int groups = context.template Attr<int>("groups");
+    auto& device_ctx = context.device_context();
+    math::SetConstant<Place, T> zero;
+    if (in_x_grad) {
+      in_x_grad->mutable_data<T>(context.GetPlace());
+      zero(device_ctx, in_x_grad, static_cast<T>(0.0));
+      math::MaxOutGradFunctor<Place, T> maxout_backward;
+      maxout_backward(context.device_context(), *in_x, in_x_grad, *out,
+        *out_grad, groups);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_maxout_op.py b/python/paddle/v2/fluid/tests/test_maxout_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..05e42f315833cab5bc5272cbd2173ea8012ff7f5
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_maxout_op.py
@@ -0,0 +1,39 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def maxout_forward_naive(input, groups):
+    s0, s1, s2, s3 = input.shape
+    return np.ndarray([s0, s1 / groups, groups, s2, s3], \
+        buffer = input, dtype=input.dtype).max(axis=(2))
+
+
+class TestMaxOutOp(OpTest):
+    def setUp(self):
+        self.op_type = "maxout"
+        self.init_test_case()
+        input = np.random.random(self.shape).astype("float32")
+        output = self.MaxOut_forward_naive(input, self.groups).astype("float32")
+
+        self.inputs = {'X': input}
+        self.attrs = {'groups': self.groups}
+
+        self.outputs = {'Out': output.astype('float32')}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+    def init_test_case(self):
+        self.MaxOut_forward_naive = maxout_forward_naive
+        self.shape = [100, 6, 2, 2]
+        self.groups=2
+
+
+
+
+if __name__ == '__main__':
+    unittest.main()