全连接操作（全连接层）也具有前向和反向。代码 解析如下

virtual void Forward(const OpContext &ctx,                       const std::vector
    
      &in_data,                       const std::vector
     
       &req,                       const std::vector
      
        &out_data,                       const std::vector
       
         &aux_args) {    using namespace mshadow;    using namespace mshadow::expr;    if (req[fullc::kOut] == kNullOp) return;    CHECK_EQ(req[fullc::kOut], kWriteTo);    size_t expected = param_.no_bias ? 2 : 3;    CHECK_EQ(in_data.size(), expected);    CHECK_EQ(out_data.size(), 1);    // TODO(bing): check the BLAS Handle, be careful    // maybe need blas handle from context    // TODO(bing): judge shape to remove flatten op    Stream
        
          *s = ctx.get_stream
         
          ();#if defined(__CUDACC__) CHECK_EQ(s->blas_handle_ownership_, Stream
          
           ::OwnHandle) << "Must init CuBLAS handle in stream";#endif // __CUDACC__ const TShape& ishape = in_data[fullc::kData].shape_; const TShape& oshape = out_data[fullc::kOut].shape_; Tensor
           
             data = in_data[fullc::kData].get_with_shape
            
             ( //输入 Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s); Tensor
             
               wmat = in_data[fullc::kWeight].get
              
               (s); //权重 Tensor
               
                 out = out_data[fullc::kOut].get_with_shape
                
                 ( //输出 Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s); out = dot(data, wmat.T()); //点乘 if (!param_.no_bias) { Tensor
                 
                   bias = in_data[fullc::kBias].get
                  
                   (s); out += repmat(bias, data.size(0)); } } virtual void Backward(const OpContext &ctx, const std::vector
                   
                     &out_grad, const std::vector
                    
                      &in_data, const std::vector
                     
                       &out_data, const std::vector
                      
                        &req, const std::vector
                       
                         &in_grad, const std::vector
                        
                          &aux_args) { using namespace mshadow; using namespace mshadow::expr; CHECK_EQ(out_grad.size(), 1); size_t expected = param_.no_bias ? 2 : 3; CHECK(in_data.size() == expected && in_grad.size() == expected); CHECK_EQ(req.size(), expected); // TODO(bing): check the BLAS Handle, be careful // maybe need blas handle from context Stream
                         
                           *s = ctx.get_stream
                          
                           (); const TShape& ishape = in_data[fullc::kData].shape_; const TShape& oshape = out_grad[fullc::kOut].shape_; Tensor
                           
                             data = in_data[fullc::kData].get_with_shape
                            
                             ( //输入 Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s); Tensor
                             
                               wmat = in_data[fullc::kWeight].get
                              
                               (s); //权重 Tensor
                               
                                 grad = out_grad[fullc::kOut].get_with_shape
                                
                                 ( //梯度 Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s);#if defined(__CUDACC__) CHECK_EQ(s->blas_handle_ownership_, Stream
                                 
                                  ::OwnHandle) << "Must init CuBLAS handle in stream";#endif // backprop CHECK_NE(req[fullc::kWeight], kWriteInplace) << "cannot write weight inplace"; // gradient of weight Tensor
                                  
                                    gwmat = in_grad[fullc::kWeight].get
                                   
                                    (s); //权重梯度 Assign(gwmat, req[fullc::kWeight], dot(grad.T(), data)); //求权重梯度 // gradient of bias if (!param_.no_bias) { Tensor
                                    
                                      gbias = in_grad[fullc::kBias].get
                                     
                                      (s);//偏置梯度 Assign(gbias, req[fullc::kBias], sum_rows(grad)); } // gradient of data Tensor
                                      
                                        gdata = in_grad[fullc::kData].get_with_shape
                                       
                                        ( //输入梯度 Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s); Assign(gdata, req[fullc::kData], dot(grad, wmat)); //求权重梯度 }