mxnet系列 全连接层代码阅读

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<TBlob> &in_data,
                       const std::vector<OpReqType> &req,
                       const std::vector<TBlob> &out_data,
                       const std::vector<TBlob> &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<xpu> *s = ctx.get_stream<xpu>();
#if defined(__CUDACC__)
    CHECK_EQ(s->blas_handle_ownership_, Stream<xpu>::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<xpu, 2, DType> data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>( //输入
        Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s);
    Tensor<xpu, 2, DType> wmat = in_data[fullc::kWeight].get<xpu, 2, DType>(s); //权重
    Tensor<xpu, 2, DType> out = out_data[fullc::kOut].get_with_shape<xpu, 2, DType>( //输出
        Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s);
    out = dot(data, wmat.T()); //点乘
    if (!param_.no_bias) {
      Tensor<xpu, 1, DType> bias = in_data[fullc::kBias].get<xpu, 1, DType>(s);
      out += repmat(bias, data.size(0));
    }
  }

  virtual void Backward(const OpContext &ctx,
                        const std::vector<TBlob> &out_grad,
                        const std::vector<TBlob> &in_data,
                        const std::vector<TBlob> &out_data,
                        const std::vector<OpReqType> &req,
                        const std::vector<TBlob> &in_grad,
                        const std::vector<TBlob> &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<xpu> *s = ctx.get_stream<xpu>();
    const TShape& ishape = in_data[fullc::kData].shape_;
    const TShape& oshape = out_grad[fullc::kOut].shape_;

    Tensor<xpu, 2, DType> data = in_data[fullc::kData].get_with_shape<xpu, 2, DType>( //输入
        Shape2(ishape[0], ishape.ProdShape(1, ishape.ndim())), s);
    Tensor<xpu, 2, DType> wmat = in_data[fullc::kWeight].get<xpu, 2, DType>(s);       //权重
    Tensor<xpu, 2, DType> grad = out_grad[fullc::kOut].get_with_shape<xpu, 2, DType>( //梯度
        Shape2(oshape[0], oshape.ProdShape(1, oshape.ndim())), s);

#if defined(__CUDACC__)
    CHECK_EQ(s->blas_handle_ownership_, Stream<xpu>::OwnHandle)
        << "Must init CuBLAS handle in stream";
#endif
    //  backprop
    CHECK_NE(req[fullc::kWeight], kWriteInplace) << "cannot write weight inplace";
    // gradient of weight
    Tensor<xpu, 2, DType> gwmat = in_grad[fullc::kWeight].get<xpu, 2, DType>(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<xpu, 1, DType> gbias = in_grad[fullc::kBias].get<xpu, 1, DType>(s);//偏置梯度
      Assign(gbias, req[fullc::kBias], sum_rows(grad));
    }
    // gradient of data
    Tensor<xpu, 2, DType> gdata = in_grad[fullc::kData].get_with_shape<xpu, 2, DType>( //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
  }