diff --git a/paddle/fluid/operators/fc_op.cc b/paddle/fluid/operators/fc_op.cc
index a9ae1396db8d7dab0364779e506d5c0a3e2ff6ed..5fee30e146077acd1a72580dc8377464c5955775 100644
--- a/paddle/fluid/operators/fc_op.cc
+++ b/paddle/fluid/operators/fc_op.cc
@@ -30,21 +30,34 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
   auto w_dims = ctx->GetInputDim("W");
   std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
 
+  if (ctx->HasInput("Bias")) {
+    auto bias_dims = ctx->GetInputDim("Bias");
+    PADDLE_ENFORCE_EQ(bias_dims[0], 1, "The shape of Bias must be [1, dim].");
+    PADDLE_ENFORCE_EQ(bias_dims[1], framework::product(w_dims) / w_dims[0],
+                      "The shape of Bias must be [1, dim].");
+  }
   PADDLE_ENFORCE(in_dims.size() == 2 || in_dims.size() == 4,
                  "Fully Connected input should be 2-D or 4-D tensor.");
 
   PADDLE_ENFORCE(w_dims.size() == 2 || w_dims.size() == 4,
                  "Fully Connected input should be 2-D or 4-D tensor.");
 
+  PADDLE_ENFORCE_EQ(framework::product(w_dims) / w_dims[0],
+                    framework::product(in_dims) / in_dims[0],
+                    "Fully Connected input and weigth size do not match.");
+
   ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
   ctx->ShareLoD("Input", "Out");
 }
 
 framework::OpKernelType FCOp::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
-  framework::LibraryType library{framework::LibraryType::kMKLDNN};
-  framework::DataLayout layout{framework::DataLayout::kMKLDNN};
-
+  framework::LibraryType library = framework::LibraryType::kPlain;
+  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+  if (ctx.Attr<bool>("use_mkldnn");) {
+    library = framework::LibraryType::kMKLDNN;
+    layout = framework::DataLayout::kMKLDNN;
+  }
   return framework::OpKernelType(
       framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
       layout, library);
@@ -60,13 +73,22 @@ void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const {
   if (ctx->HasOutput(framework::GradVarName("W"))) {
     ctx->SetOutputDim(framework::GradVarName("W"), w_dims);
   }
+
+  if (ctx->HasInput("Bias")) {
+    auto bias_dims = ctx->GetInputDim("Bias");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias"));
+    ctx->SetOutputDim(framework::GradVarName("Bias"), bias_dims);
+  }
 }
 
 framework::OpKernelType FCOpGrad::GetExpectedKernelType(
     const framework::ExecutionContext& ctx) const {
-  framework::LibraryType library{framework::LibraryType::kMKLDNN};
-  framework::DataLayout layout{framework::DataLayout::kMKLDNN};
-
+  framework::LibraryType library = framework::LibraryType::kPlain;
+  framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+  if (ctx.Attr<bool>("use_mkldnn");) {
+    library = framework::LibraryType::kMKLDNN;
+    layout = framework::DataLayout::kMKLDNN;
+  }
   return framework::OpKernelType(
       framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
       layout, library);
@@ -75,12 +97,12 @@ framework::OpKernelType FCOpGrad::GetExpectedKernelType(
 void FCOpMaker::Make() {
   AddInput("Input", "(Tensor) The input tensor of fully connected operator. ");
   AddInput("W", "(Tensor), The second input tensor of fc op.");
+  AddInput("Bias", "(Tensor, optional) Bias vector with shape (1 x D")
+      .AsDispensable();
   AddOutput("Out", "(Tensor) The output tensor of fully connected operator. ");
   AddAttr<bool>("use_mkldnn",
                 "(bool, default false) Only used in mkldnn kernel")
       .SetDefault(false);
-  AddAttr<bool>("bias_attr", "(bool, default false) Only used in mkldnn kernel")
-      .SetDefault(false);
   AddComment(R"DOC(
   Fully Connected Operator.
 
@@ -94,9 +116,39 @@ void FCOpMaker::Make() {
 )DOC");
 }
 
+template <typename T>
+class FCOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+    auto& dev_ctx = ctx.template device_context<CPUDeviceContext>();
+    auto blas = math::GetBlas<CPUDeviceContext, T>(dev_ctx);
+    auto input = ctx.Input<Tensor>("Input");
+    auto w = ctx.Input<Tensor>("W");
+    auto b = ctx.Input<Tensor>("Bias");
+
+    const T* input_data = input->data<T>();
+    const T* w_data = w->data<T>();
+    auto output = ctx.Output<Tensor>("Out");
+    T* output_data = output->mutable_data<T>(ctx.GetPlace());
+
+    auto in_dims = ctx->GetInputDim("Input");
+    auto w_dims = ctx->GetInputDim("W");
+    std::vector<int64_t> output_shape({in_dims[0], w_dims[1]});
+
+    if (bias) {
+      const T* bias_data = bias->data<T>();
+    }
+  }
+};
+
 }  // namespace operators
 }  // namespace paddle
 
-REGISTER_OPERATOR(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker,
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(fc, ops::FCOp, ops::FCOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(fc_grad, paddle::operators::FCOpGrad);
+REGISTER_OPERATOR(fc_grad, ops::FCOpGrad);
+REGISTER_OP_CPU_KERNEL(fc, ops::FCMKLDNNOpKernel<float>,
+                       ops::FCMKLDNNOpKernel<double>);