在caffe中，全连接层叫做"inner_product_layer"，区别于tensorflow中的fullyconnected_layer。

　

1、prototxt中的定义

layer {
  bottom: "fc7"
  top: "fc8"
  name: "fc8"
  type: "InnerProduct"
  param {  # 权重学习参数
    lr_mult: 10  # 学习率
    decay_mult: 1
  }
  param {  # bias 学习参数
    lr_mult: 20  # 一般情况，bias 学习率是权重学习率的两倍.
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1000  # 输出单元个数 
    weight_filler {  # 权重初始化方法
      type: "gaussian"
      std: 0.005
    }
    bias_filler {  # bias 初始化方法
      type: "constant"
      value: 0.1
    }
  }
}


2、caffe.proto中的定义
message LayerParameter {
    optional InnerProductParameter inner_product_param = 117;
}

message InnerProductParameter {
  optional uint32 num_output = 1;   // 网络层输出个数
  optional bool bias_term = 2 [default = true]; // 是否有 bias 项
  optional FillerParameter weight_filler = 3; // 权重weight filler 
  optional FillerParameter bias_filler = 4; // 偏置bias filler

  // 在第一个 axis 进行单个内积计算.
  // -1 表示最后一个 axis
  optional int32 axis = 5 [default = 1];
  //权重矩阵是否进行转置
  optional bool transpose = 6 [default = false];
}

3、inner_product_layer.hpp
#ifndef CAFFE_INNER_PRODUCT_LAYER_HPP_
#define CAFFE_INNER_PRODUCT_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

namespace caffe {

/** * @brief Also known as a "fully-connected" layer, computes an inner product * with a set of learned weights, and (optionally) adds biases. * * TODO(dox): thorough documentation for Forward, Backward, and proto params. */
template <typename Dtype>
class InnerProductLayer : public Layer<Dtype> {
 public:
  explicit InnerProductLayer(const LayerParameter& param)
      : Layer<Dtype>(param) {}
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  virtual inline const char* type() const { return "InnerProduct"; }
  virtual inline int ExactNumBottomBlobs() const { return 1; }
  virtual inline int ExactNumTopBlobs() const { return 1; }

 protected:
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const