神经网络caffe框架源码解析--softmax_layer.cpp类代码研究

// Copyright 2013 Yangqing Jia
//
#include <algorithm>
#include <vector>

#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/util/math_functions.hpp"

using std::max;

namespace caffe {

/**
 * 建立softmax网络层
 */
template <typename Dtype>
void SoftmaxLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
      vector<Blob<Dtype>*>* top) {
  CHECK_EQ(bottom.size(), 1) << "Softmax Layer takes a single blob as input.";
  CHECK_EQ(top->size(), 1) << "Softmax Layer takes a single blob as output.";
  //输出分配空间
  (*top)[0]->Reshape(bottom[0]->num(), bottom[0]->channels(),
      bottom[0]->height(), bottom[0]->width());
  //sum_multiplier_这里都是1，用于辅助计算，可以看作一个行向量，或者行数为1的矩阵
  sum_multiplier_.Reshape(1, bottom[0]->channels(),
      bottom[0]->height(), bottom[0]->width());
  Dtype* multiplier_data = sum_multiplier_.mutable_cpu_data();
  for (int i = 0; i < sum_multiplier_.count(); ++i) {
    multiplier_data[i] = 1.;
  }
  //临时变量scale_分配空间，大小为num,可以看作一个列向量
  scale_.Reshape(bottom[0]->num(), 1, 1, 1);
}

template <typename Dtype>
void SoftmaxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
    vector<Blob<Dtype>*>* top) {
  const Dtype* bottom_data = bottom[0]->cpu_data();
  Dtype* top_data = (*top)[0]->mutable_cpu_data();
  Dtype* scale_data = scale_.mutable_cpu_data();
  //把输出看成是num层，每层dim个元素
  int num = bottom[0]->num();
  int dim = bottom[0]->count() / bottom[0]->num();
  memcpy(top_data, bottom_data, sizeof(Dtype) * bottom[0]->count());
  // we need to subtract the max to avoid numerical issues, compute the exp,
  // and then normalize.
  //找出每一层的最大值
  for (int i = 0; i < num; ++i) {
    scale_data[i] = bottom_data[i*dim];
    for (int j = 0; j < dim; ++j) {
      scale_data[i] = max(scale_data[i], bottom_data[i * dim + j]);
    }
  }
  // subtraction  通过矩阵相乘的方式来计算，有num层的top_data，每层元素减去该层的最大值。太巧妙了
  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
    scale_data, sum_multiplier_.cpu_data(), 1., top_data);
  // C = alpha*op( A )*op( B ) + beta*C

  // Perform exponentiation 计算自然对数
  caffe_exp<Dtype>(num * dim, top_data, top_data);
  // sum after exp 每一层各自求和放到scale_data中
  caffe_cpu_gemv<Dtype>(CblasNoTrans, num, dim, 1., top_data,
      sum_multiplier_.cpu_data(), 0., scale_data);
  // Do division 每一层各自除以该层的和
  for (int i = 0; i < num; ++i) {
    caffe_scal<Dtype>(dim, Dtype(1.) / scale_data[i], top_data + i * dim);
  }
}

template <typename Dtype>
Dtype SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const bool propagate_down,
    vector<Blob<Dtype>*>* bottom) {
  const Dtype* top_diff = top[0]->cpu_diff();
  const Dtype* top_data = top[0]->cpu_data();
  Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
  Dtype* scale_data = scale_.mutable_cpu_data();
  int num = top[0]->num();
  int dim = top[0]->count() / top[0]->num();
  memcpy(bottom_diff, top_diff, sizeof(Dtype) * top[0]->count());
  // Compute inner1d(top_diff, top_data) and subtract them from the bottom diff
  for (int i = 0; i < num; ++i) {
    scale_data[i] = caffe_cpu_dot<Dtype>(dim, top_diff + i * dim,
        top_data + i * dim);//每一层，top_diff和top_data计算内积
  }
  // subtraction  每一层bottom_diff的元素减去该层的对应的内积
  caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, num, dim, 1, -1.,
      scale_data, sum_multiplier_.cpu_data(), 1., bottom_diff);
  // elementwise multiplication 元素各自相乘
  caffe_mul<Dtype>(top[0]->count(), bottom_diff, top_data, bottom_diff);
  return Dtype(0);
}

INSTANTIATE_CLASS(SoftmaxLayer);

}  // namespace caffe

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