【笔记】Caffe全连接层源码（inner product layer）：区别于tensorflow中的fullyconnected_layer

本文主要是介绍【笔记】Caffe全连接层源码（inner product layer）：区别于tensorflow中的fullyconnected_layer，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

caffe的官方文档，介绍如下：

那么，它究竟做了什么那？
        假设conv2的输入是256*27*27,那么conv2的输出即50*22*22，conv2的输入即pool2的输入，pool2的输出为50*11*11，即ip1的输入,ip1的输出为500*1*1，那么pool2->ip1的参数个数是多少呢？这里就要理解好什么是fully_connected了，即wTx，x为列向量，w的长度与x相同。在本文的例子中x的维度为50*11*11，那么pool2->ip1的参数个数为500*50*11*11 。50*11*11即是一个有50个通道大小为11*11的图片，那么在做完全卷积的时候，需要把对所有通道一起作卷积，即把图片转化成一个50*11*11的向量上段话转自http://www.cnblogs.com/dupuleng/articles/4312149.html

        我们再来看一下它的头文件，其中有以下参数：

        同大部分layer层一样，它也必须实现setup、reshape、Forward_cpu、Backward_cpu。
* 其中，setup定义参数：M_样本个数、K_单个样本特征长度、N_全连接之后神经元的个数。
* Forward_cpu，主要是计算y=W’*x + b, X表示输入，y表示输出 。x为输入，维度 M_*K_ 、 y为输出，维度 M_*N_ 、W为权重，维度 N_*K_, W_diff权重的梯度维度也为N_*K_ 、 b为偏置，维度 N_*1_ 。
* Backward_cpu：反向传播就是在更新w、b，计算delta。

我们来具体看一下代码：

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/vision_layers.hpp"

namespace caffe {

/*
输入层：（M_, N_, 1, 1);
输出层： (M_, K_, 1, 1);
W矩阵：（N_,K_,1,1);
b矩阵：（N_,1,1,1);
M_样本个数，K_单个样本特征长度，N_全连接之后神经元的个数。
*/  
template <typename Dtype>
void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
   //通过读取配置proto文件获得输出神经元的个数及是否使用偏置项
  const int num_output = this->layer_param_.inner_product_param().num_output();
  bias_term_ = this->layer_param_.inner_product_param().bias_term();
  //全连接之后输出的神经元的个数
  N_ = num_output;

  //全连接层输出的Blob维数为样本的个数*输出神经元的个数*1*1（M*N）  
  //这里axis=1，即从C开始展开，即，CHW
  //输出：n_1 * (c_1 + c_2 + ... + c_K) * h * w
  const int axis = bottom[0]->CanonicalAxisIndex(
      this->layer_param_.inner_product_param().axis());
  // Dimensions starting from "axis" are "flattened" into a single
  // length K_ vector. For example, if bottom[0]'s shape is (N, C, H, W),
  // and axis == 1, N inner products with dimension CHW are performed.
  //表示单个样本的特征长度，
  K_ = bottom[0]->count(axis);
  // Check if we need to set up the weights
  if (this->blobs_.size() > 0) {
    LOG(INFO) << "Skipping parameter initialization";
  } else {
     //如果配置文件使用偏置项，则开辟2个Blob类智能指针，否则开辟一个
    if (bias_term_) {
      this->blobs_.resize(2);
    } else {
      this->blobs_.resize(1);
    }
    // Intialize the weight
    vector<int> weight_shape(2);
    //lobs_[0]指向权重矩阵，blobs_[1]指向偏置矩阵 ，全连接层，形状为N_*K_*1*1
    weight_shape[0] = N_;
    weight_shape[1] = K_;

    //新开辟一个Blob，指针返回给blobs_[0],weight_shape[2]为刚刚初始化的；  
    this->blobs_[0].reset(new Blob<Dtype>(weight_shape));

    // fill the weights
    //根据配置文件中的权重核（ weight_filler )的类型初始化填充权重矩阵blobs_[0];  
    shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>(
        this->layer_param_.inner_product_param().weight_filler()));
    weight_filler->Fill(this->blobs_[0].get());

    // If necessary, intiialize and fill the bias term
    //填充偏置矩阵blobs_[1]，每个输出单元对应一个偏置，共N_个  
    if (bias_term_) {
      vector<int> bias_shape(1, N_);
      this->blobs_[1].reset(new Blob<Dtype>(bias_shape));
      shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>(
          this->layer_param_.inner_product_param().bias_filler()));
      bias_filler->Fill(this->blobs_[1].get());
    }
  }  // parameter initialization
  this->param_propagate_down_.resize(this->blobs_.size(), true);
}

template <typename Dtype>

//一批次处理多个样本，在每一批次中权重矩阵与偏置矩阵是不变的
void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  // Figure out the dimensions
  const int axis = bottom[0]->CanonicalAxisIndex(
      this->layer_param_.inner_product_param().axis());
  const int new_K = bottom[0]->count(axis);
  CHECK_EQ(K_, new_K)
      << "Input size incompatible with inner product parameters.";
  // The first "axis" dimensions are independent inner products; the total
  // number of these is M_, the product over these dimensions.
  M_ = bottom[0]->count(0, axis);
  // The top shape will be the bottom shape with the flattened axes dropped,
  // and replaced by a single axis with dimension num_output (N_).
  vector<int> top_shape = bottom[0]->shape();
  top_shape.resize(axis + 1);
  top_shape[axis] = N_;
  top[0]->Reshape(top_shape);
  // Set up the bias multiplier
  if (bias_term_) {
    vector<int> bias_shape(1, M_);
    bias_multiplier_.Reshape(bias_shape);
    caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data());
  }
}
// 计算y=W'*x + b, X表示输入，y表示输出  
//  x为输入，维度 M_*K_    
//  y为输出，维度 M_*N_    
//  W为权重，维度 N_*K_, W_diff权重的梯度维度也为N_*K_  
//  b为偏置，维度 N_*1_  
template <typename Dtype>
void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = top[0]->mutable_cpu_data();
  const Dtype* weight = this->blobs_[0]->cpu_data();//内存中的权重矩阵是N*K

  //caffe_cpu_gemm, C←αA×B+βC,前两个参数控制A,B是否转置    
  //其中A(bottom_data)维度是M_xK_，B(weight')维度是K_xN_，C(top_data)维度为M_xN_   
  //最终 y = X*W', 维度为 M_xN_  
  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, N_, K_, (Dtype)1.,
      bottom_data, weight, (Dtype)0., top_data);

  // 表示y= y + b (bias_multiplier维度为M_*1, b为1*N_(b实际上是N_*1，但是存储方式与1*N_等价，
  // top_data为M_*N_)  
  // 实际是相当于将b复制成了M_*N_的矩阵，类似matlab的repmat(b, [M_, 1])，然后和top_data相加  
  if (bias_term_) {
    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1.,
        bias_multiplier_.cpu_data(),
        this->blobs_[1]->cpu_data(), (Dtype)1., top_data);
  }
}

template <typename Dtype>
void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  //data传递的是数据，diff传递的是梯度，top_diff的维度是N*M，每一列代表一个样本的error term
  if (this->param_propagate_down_[0]) {
    const Dtype* top_diff = top[0]->cpu_diff();
    const Dtype* bottom_data = bottom[0]->cpu_data();

    // Gradient with respect to weight    
    //A(top_diff'):N_*M_, B(bottom_data):M_*K_, C(W_diff)：N_*K_    
    //W_diff = top_diff' * bottom_data  
    caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1.,
        top_diff, bottom_data, (Dtype)0., this->blobs_[0]->mutable_cpu_diff());
  }
  if (bias_term_ && this->param_propagate_down_[1]) {
    const Dtype* top_diff = top[0]->cpu_diff();

    // Gradient with respect to bias  
    // top_diff(M_*N_), bias_multiplier(M_*1), b_diff(N_1)  
    // b_diff = top_diff' * bias_multiplier, 注意和gemm接口的区别  
    caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff,
        bias_multiplier_.cpu_data(), (Dtype)0.,
        this->blobs_[1]->mutable_cpu_diff());
  }
  if (propagate_down[0]) {
    const Dtype* top_diff = top[0]->cpu_diff();
    // Gradient with respect to bottom data  
    // A(top_diff) M_*N_ , B(weight) N_*K_, C(bottom_diff) M_*K_  
    // bottom_diff = top_diff * weight  
    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1.,
        top_diff, this->blobs_[0]->cpu_data(), (Dtype)0.,
        bottom[0]->mutable_cpu_diff());
  }
}

#ifdef CPU_ONLY
STUB_GPU(InnerProductLayer);
#endif

INSTANTIATE_CLASS(InnerProductLayer);
REGISTER_LAYER_CLASS(InnerProduct);

}  // namespace caffe

    

这里有三点要介绍一下：
（1）axis的理解，这里，在官方文档中介绍如下：
这里写图片描述
在这里，axis=1，也就是说，我们从c开始暂开，这里n代表的是样本个数。
（2）这里涉及到一个函数，caffe_cpu_gemm，它的实现如下：

void caffe_cpu_gemm<float>(const CBLAS_TRANSPOSE TransA,
  const CBLAS_TRANSPOSE TransB, const int M, const int N, const int K,
  const float alpha, const float* A, const float* B, const float beta,
    float* C) {
  int lda = (TransA == CblasNoTrans) ? K : M;
  int ldb = (TransB == CblasNoTrans) ? N : K;
  cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B,
      ldb, beta, C, N);
}

        也就是矩阵相乘后相加，功能： C(vetor)←alpba*AB+ beta*C. This function multiplies A * C (after transposing A, if needed) and multiplies the resulting matrix by alpha. It then multiplies vector C by beta. It stores the sum of these two products in vector C.
（3）caffe_cpu_gemv，是矩阵相乘，功能： C(vetor)←alpba*AB+ beta*C. This function multiplies A * C (after transposing A, if needed) and multiplies the resulting matrix by alpha. It then multiplies vector C by beta. It stores the sum of these two products in vector C.

附：

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 vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); int M_; # 样本个数 int K_; # 特征维度 int N_; # 输出神经元个数 bool bias_term_; Blob<Dtype> bias_multiplier_; bool transpose_; ///< if true, assume transposed weights }; } // namespace caffe #endif // CAFFE_INNER_PRODUCT_LAYER_HPP_

4、inner_product_layer.cpp

#include <vector>

#include "caffe/filler.hpp"
#include "caffe/layers/inner_product_layer.hpp" #include "caffe/util/math_functions.hpp" namespace caffe { template <typename Dtype> void InnerProductLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { const int num_output = this->layer_param_.inner_product_param().num_output(); // 输出单元个数 bias_term_ = this->layer_param_.inner_product_param().bias_term(); transpose_ = this->layer_param_.inner_product_param().transpose(); N_ = num_output; const int axis = bottom[0]->CanonicalAxisIndex( this->layer_param_.inner_product_param().axis()); // axis 维数据被 flattened 为长度为 K_ 的向量 // 例如，bottom[0] - (N, C, H, W), axis=1, 则从 C 维开始，对维度CHW 进行 N 个内积. // 输出： N x (C1 + C2 + ... + CK) x H x W // 样本个数 x 输出单元个数 x 1 x 1 (M x N x 1 x 1) K_ = bottom[0]->count(axis); // Check if we need to set up the weights if (this->blobs_.size() > 0) { LOG(INFO) << "Skipping parameter initialization"; } else { if (bias_term_) { this->blobs_.resize(2); } else { this->blobs_.resize(1); } // 权重初始化 vector<int> weight_shape(2); if (transpose_) { // 权重矩阵是否进行转置 weight_shape[0] = K_; weight_shape[1] = N_; } else { weight_shape[0] = N_; weight_shape[1] = K_; } this->blobs_[0].reset(new Blob<Dtype>(weight_shape)); // 初始化权重 // blobs_[0]，N_ x K_ x 1 x 1 shared_ptr<Filler<Dtype> > weight_filler(GetFiller<Dtype>( this->layer_param_.inner_product_param().weight_filler())); weight_filler->Fill(this->blobs_[0].get()); // 如果有 bias 项，则初始化 // blobs_[1]，每个输出对应一个 bias，共 N_ 个. if (bias_term_) { vector<int> bias_shape(1, N_); this->blobs_[1].reset(new Blob<Dtype>(bias_shape)); shared_ptr<Filler<Dtype> > bias_filler(GetFiller<Dtype>( this->layer_param_.inner_product_param().bias_filler())); bias_filler->Fill(this->blobs_[1].get()); } } // 参数初始化 this->param_propagate_down_.resize(this->blobs_.size(), true); } template <typename Dtype> void InnerProductLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { // Figure out the dimensions const int axis = bottom[0]->CanonicalAxisIndex( this->layer_param_.inner_product_param().axis()); const int new_K = bottom[0]->count(axis); CHECK_EQ(K_, new_K) << "Input size incompatible with inner product parameters."; // The first "axis" dimensions are independent inner products; the total // number of these is M_, the product over these dimensions. M_ = bottom[0]->count(0, axis); // 样本数，batchsize // The top shape will be the bottom shape with the flattened axes dropped, // and replaced by a single axis with dimension num_output (N_). vector<int> top_shape = bottom[0]->shape(); top_shape.resize(axis + 1); top_shape[axis] = N_; top[0]->Reshape(top_shape); // Set up the bias multiplier if (bias_term_) { vector<int> bias_shape(1, M_); bias_multiplier_.Reshape(bias_shape); caffe_set(M_, Dtype(1), bias_multiplier_.mutable_cpu_data()); // 均设为 1 } } // 前向计算 // Y = W * x + b template <typename Dtype> void InnerProductLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { const Dtype* bottom_data = bottom[0]->cpu_data(); Dtype* top_data = top[0]->mutable_cpu_data(); const Dtype* weight = this->blobs_[0]->cpu_data(); caffe_cpu_gemm<Dtype>(CblasNoTrans, transpose_ ? CblasNoTrans : CblasTrans, M_, N_, K_, (Dtype)1., bottom_data, weight, (Dtype)0., top_data); if (bias_term_) { caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, N_, 1, (Dtype)1., bias_multiplier_.cpu_data(), this->blobs_[1]->cpu_data(), (Dtype)1., top_data); } } // 反向计算 template <typename Dtype> void InnerProductLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { if (this->param_propagate_down_[0]) { const Dtype* top_diff = top[0]->cpu_diff(); // top_diff: N x M, 每一列为一个样本的 error. const Dtype* bottom_data = bottom[0]->cpu_data(); // Gradient with respect to weight // 关于 weight 的梯度 if (transpose_) { caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, K_, N_, M_, (Dtype)1., bottom_data, top_diff, (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); } else { caffe_cpu_gemm<Dtype>(CblasTrans, CblasNoTrans, N_, K_, M_, (Dtype)1., top_diff, bottom_data, (Dtype)1., this->blobs_[0]->mutable_cpu_diff()); } } if (bias_term_ && this->param_propagate_down_[1]) { const Dtype* top_diff = top[0]->cpu_diff(); // Gradient with respect to bias caffe_cpu_gemv<Dtype>(CblasTrans, M_, N_, (Dtype)1., top_diff, bias_multiplier_.cpu_data(), (Dtype)1., this->blobs_[1]->mutable_cpu_diff()); } if (propagate_down[0]) { const Dtype* top_diff = top[0]->cpu_diff(); // Gradient with respect to bottom data if (transpose_) { caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasTrans, M_, K_, N_, (Dtype)1., top_diff, this->blobs_[0]->cpu_data(), (Dtype)0., bottom[0]->mutable_cpu_diff()); } else { caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, M_, K_, N_, (Dtype)1., top_diff, this->blobs_[0]->cpu_data(), (Dtype)0., bottom[0]->mutable_cpu_diff()); } } } #ifdef CPU_ONLY STUB_GPU(InnerProductLayer); #endif INSTANTIATE_CLASS(InnerProductLayer); REGISTER_LAYER_CLASS(InnerProduct); } // namespace caffe

这篇关于【笔记】Caffe全连接层源码（inner product layer）：区别于tensorflow中的fullyconnected_layer的文章就介绍到这儿，希望我们推荐的文章对大家有所帮助，也希望大家多多支持为之网！