caffe Sigmoid cross entropy loss 交叉熵损失函数

Sigmoid 交叉熵损失函数（Sigmoid Cross Entropy Loss）

官方： loss ,

输入：

1. 形状:   得分  , 这个层使用 sigmoid 函数   映射到概率分布 

2. 形状:  标签 

输出：

1. 形状：  计算公式: 

应用场景： 
预测目标概率分布

Parameters

bottom	input Blob vector (length 2)  the scores , which this layer maps to probability predictions  using the sigmoid function  (see SigmoidLayer).  the targets
top	output Blob vector (length 1)  the computed cross-entropy loss:

Computes the sigmoid cross-entropy loss error gradient w.r.t. the predictions.

Gradients cannot be computed with respect to the target inputs (bottom[1]), so this method ignores bottom[1] and requires !propagate_down[1], crashing if propagate_down[1] is set.

Parameters

top	output Blob vector (length 1), providing the error gradient with respect to the outputs  This Blob's diff will simply contain the loss_weight* , as  is the coefficient of this layer's output  in the overall Netloss ; hence . (*Assuming that this top Blob is not used as a bottom (input) by any other layer of the Net.)
propagate_down	see Layer::Backward. propagate_down[1] must be false as gradient computation with respect to the targets is not implemented.
bottom	input Blob vector (length 2)  the predictions ; Backward computes diff   the labels – ignored as we can't compute their error gradients

#include <algorithm>
#include <cfloat>
#include <vector>

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

// Computes the cross-entropy (logistic) loss ,it is often used for predicting targets interpreted 
// as probabilities. Detailed reference to the official document:
// http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1SigmoidCrossEntropyLossLayer.html


namespace caffe {

template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);
  sigmoid_bottom_vec_.clear();
  sigmoid_bottom_vec_.push_back(bottom[0]);
  sigmoid_top_vec_.clear();
  sigmoid_top_vec_.push_back(sigmoid_output_.get());
  sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
}

template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
      "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
  sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
}

template <typename Dtype>
void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  // The forward pass computes the sigmoid outputs.
  sigmoid_bottom_vec_[0] = bottom[0];
  sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
  // Compute the loss (negative log likelihood)
  const int count = bottom[0]->count();
  const int num = bottom[0]->num();
  // Stable version of loss computation from input data
  const Dtype* input_data = bottom[0]->cpu_data();
  const Dtype* target = bottom[1]->cpu_data();
  Dtype loss = 0;
  for (int i = 0; i < count; ++i) {
    loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
        log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
  }
  top[0]->mutable_cpu_data()[0] = loss / num;
}

template <typename Dtype>
void SigmoidCrossEntropyLossLayer<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]) {
    // First, compute the diff
    const int count = bottom[0]->count();
    const int num = bottom[0]->num();
    const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
    const Dtype* target = bottom[1]->cpu_data();
    Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
    caffe_sub(count, sigmoid_output_data, target, bottom_diff);
    // Scale down gradient
    const Dtype loss_weight = top[0]->cpu_diff()[0];
    caffe_scal(count, loss_weight / num, bottom_diff);
  }
}

#ifdef CPU_ONLY
STUB_GPU_BACKWARD(SigmoidCrossEntropyLossLayer, Backward);
#endif

INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
REGISTER_LAYER_CLASS(SigmoidCrossEntropyLoss);

}  // namespace caffe