caffe softmax源码解读

前向传播

template <typename Dtype>void SoftmaxLayer<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();  Dtype* scale_data = scale_.mutable_cpu_data();  int channels = bottom[0]->shape(softmax_axis_);  int dim = bottom[0]->count() / outer_num_; //dim表示要分类的类别数，count()得到的是总共的输入Blob数，outer_num_得到的是是每一类的Blob数  caffe_copy(bottom[0]->count(), bottom_data, top_data); //先将输入拷贝到输出缓冲区  // We need to subtract the max to avoid numerical issues, compute the exp,  // and then normalize，减去最大值，避免数值问题，计算指数，归一化  for (int i = 0; i < outer_num_; ++i) {    // 初始化scale_的data域为第一个平面，其中scale用来存放临时计算结果    caffe_copy(inner_num_, bottom_data + i * dim, scale_data);    for (int j = 0; j < channels; j++) {      for (int k = 0; k < inner_num_; k++) {        scale_data[k] = std::max(scale_data[k],            bottom_data[i * dim + j * inner_num_ + k]);      }    }    // 输出缓冲区减去最大值    //a_k = a_k - max(a_i)    // C = alpha . A . B + beta * C    // A为单位1矩阵    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_,        1, -1., sum_multiplier_.cpu_data(), scale_data, 1., top_data);    // exponentiation    caffe_exp<Dtype>(dim, top_data, top_data);    // sum after exp    caffe_cpu_gemv<Dtype>(CblasTrans, channels, inner_num_, 1.,        top_data, sum_multiplier_.cpu_data(), 0., scale_data);    // division    for (int j = 0; j < channels; j++) {      caffe_div(inner_num_, top_data, scale_data, top_data);      top_data += inner_num_;    }  }}

后向传播

template <typename Dtype>void SoftmaxLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,    const vector<bool>& propagate_down,    const vector<Blob<Dtype>*>& bottom) {    //获得data、diff指针  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 channels = top[0]->shape(softmax_axis_);  int dim = top[0]->count() / outer_num_;  caffe_copy(top[0]->count(), top_diff, bottom_diff); //先用top_diff初始化bottom_diff  for (int i = 0; i < outer_num_; ++i) {    // 计算top_diff和top_data的点积，然后从bottom_diff中减去该值    for (int k = 0; k < inner_num_; ++k) {      scale_data[k] = caffe_cpu_strided_dot<Dtype>(channels,          bottom_diff + i * dim + k, inner_num_,          top_data + i * dim + k, inner_num_);    }    // 减值    caffe_cpu_gemm<Dtype>(CblasNoTrans, CblasNoTrans, channels, inner_num_, 1,        -1., sum_multiplier_.cpu_data(), scale_data, 1., bottom_diff + i * dim);  }  // 逐点相乘  caffe_mul(top[0]->count(), bottom_diff, top_data, bottom_diff);}