Caffe源码解读: Softmax_loss_Layer的前向与反向传播

来源：互联网发布：linux 拷贝文件夹空格编辑：程序博客网时间：2024/05/05 18:34

1,前向传播

分为两步：1，计算softmax概率prob_data，直接使用softmaxlayer的forward函数；

2，计算loss，采用交叉熵，即每个第i类数据的loss为-log(prob(i))。

template <typename Dtype>void SoftmaxWithLossLayer<Dtype>::Forward_cpu(    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  // The forward pass computes the softmax prob values.  softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);//直接使用softmax_layer->forward()  const Dtype* prob_data = prob_.cpu_data();                     //概率数据  const Dtype* label = bottom[1]->cpu_data();                    //真实标签  int dim = prob_.count() / outer_num_;  int count = 0;  Dtype loss = 0;  for (int i = 0; i < outer_num_; ++i) {    for (int j = 0; j < inner_num_; j++) {      const int label_value = static_cast<int>(label[i * inner_num_ + j]);      if (has_ignore_label_ && label_value == ignore_label_) {        continue;      }      DCHECK_GE(label_value, 0);      DCHECK_LT(label_value, prob_.shape(softmax_axis_));      loss -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],                           Dtype(FLT_MIN)));  //每个数据i的损失为-log(prob(i))      ++count;    }  }  //loss除去样本总数，得到每个样本的loss  top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);  if (top.size() == 2) {    top[1]->ShareData(prob_);  }}

2,反向传播

template <typename Dtype>void SoftmaxWithLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,    const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {  if (propagate_down[1]) {    LOG(FATAL) << this->type()               << " Layer cannot backpropagate to label inputs.";  }  if (propagate_down[0]) {    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();    const Dtype* prob_data = prob_.cpu_data();    caffe_copy(prob_.count(), prob_data, bottom_diff);//把概率数据复制到bottom_diff    const Dtype* label = bottom[1]->cpu_data();       //获得标签数据    int dim = prob_.count() / outer_num_;    int count = 0;    for (int i = 0; i < outer_num_; ++i) {      for (int j = 0; j < inner_num_; ++j) {        const int label_value = static_cast<int>(label[i * inner_num_ + j]);//获得真实标签        if (has_ignore_label_ && label_value == ignore_label_) {  //如果忽略标签，则偏导数为0          for (int c = 0; c < bottom[0]->shape(softmax_axis_); ++c) {            bottom_diff[i * dim + c * inner_num_ + j] = 0;          }        } else {          //计算当前概率密度与理想概率密度之差（label位对应的理想概率密度为1，其他为0，故不计算）          bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;          ++count;        }      }    }    // Scale gradient//缩放    Dtype loss_weight = top[0]->cpu_diff()[0] /                        get_normalizer(normalization_, count);    caffe_scal(prob_.count(), loss_weight, bottom_diff);  }}

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