【caffe源碼研究】第三章：源碼篇(12) ：激活函數層

以ReLULayer爲例，比較簡單，直接上代碼
前向就是根據激活函數表達式計算，反向就是計算倒數。

#include <algorithm>
#include <vector>

#include "caffe/layers/relu_layer.hpp"

namespace caffe {

template <typename Dtype>
void ReLULayer<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 int count = bottom[0]->count();
  //negative_slope [默認值 0] : 當輸入爲x負數時，指定輸出爲negative_slope * x；默認值爲0. 
  //當輸入爲x正數時，指定輸出爲x；
  Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
  for (int i = 0; i < count; ++i) {
    top_data[i] = std::max(bottom_data[i], Dtype(0))
        + negative_slope * std::min(bottom_data[i], Dtype(0));
  }
}
}
*/
template <typename Dtype>
void ReLULayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  if (propagate_down[0]) {
    const Dtype* bottom_data = bottom[0]->cpu_data();
    const Dtype* top_diff = top[0]->cpu_diff();
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    const int count = bottom[0]->count();
    Dtype negative_slope = this->layer_param_.relu_param().negative_slope();
    for (int i = 0; i < count; ++i) {
　　　　//當輸入bottom_data大於０，則bottom_diff[i] = top_diff[i]
　　　　//當輸入bottom_data小於等於０，則bottom_diff[i] = top_diff[i] * negative_slope
      bottom_diff[i] = top_diff[i] * ((bottom_data[i] > 0)
          + negative_slope * (bottom_data[i] <= 0));
    }
  }
}


#ifdef CPU_ONLY
STUB_GPU(ReLULayer);
#endif

INSTANTIATE_CLASS(ReLULayer);

}  // namespace caffe