// 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
神经网络caffe框架源码解析--softmax_layer.cpp类代码研究,布布扣,bubuko.com
神经网络caffe框架源码解析--softmax_layer.cpp类代码研究
原文:http://blog.csdn.net/lingerlanlan/article/details/32700431