cuda从零开始手搓PB神经网络

#ifndef __BP_NETWORK_HPP__
#define __BP_NETWORK_HPP__
#include "matrix.hpp"
#include "mat.hpp"
#include "update_methods.hpp"template<typename activate_type, typename val_type_, template<typename> class update_type_tpl, typename init_type, int input_num_, int output_num_, int ... remain_layer>
struct bp_network
{constexpr static int input_num = input_num_;constexpr static int output_num = output_num_;using val_type = val_type_;using input_type = mat<input_num, 1, val_type>;using input_t_type = mat<1, input_num, val_type>;using output_type = mat<output_num, 1, val_type>;using weight_type = mat<output_num, input_num, val_type>;using forward_func = typename func_pair<activate_type>::forward_func;using backward_func = typename func_pair<activate_type>::backward_func;using next_node_type = typename bp_network<activate_type, val_type, update_type_tpl, init_type, output_num, remain_layer...>;using term_output_type = typename next_node_type::term_output_type;weight_type weight;update_type_tpl<weight_type> weight_update_method;output_type bias;update_type_tpl<output_type> bias_update_method;input_type pre_input;output_type pre_func_input;next_node_type next_node;bp_network():weight_update_method(), bias_update_method(){weight.template reset<init_type>();bias.template reset<init_type>();next_node = bp_network<activate_type, val_type, update_type_tpl, init_type, output_num, remain_layer...>();}auto forward(input_type& input){output_type curr_output;pre_input = input;auto temp = weight.dot(input);pre_func_input = temp + bias;curr_output = pre_func_input.template activate<forward_func>();return next_node.forward(curr_output);}auto backward(term_output_type& delta, val_type lr){output_type curr_delta = next_node.backward(delta, lr);curr_delta = pre_func_input.template activate<backward_func>() * curr_delta;auto ret = weight.t_dot(curr_delta);// 更新参数weight_type delta_weight = curr_delta.dot(pre_input.t());weight = weight_update_method.update(weight, delta_weight);bias = bias_update_method.update(bias, curr_delta);return ret;}   // 更新惯性量void update_inert(){weight_update_method.update_inert();bias_update_method.update_inert();next_node.update_inert();}void print(){weight.print();printf("-----------------\n");bias.print();printf("=================\n");next_node.print();}
};template<typename activate_type, typename val_type_, template<typename> class update_type_tpl, typename init_type, int input_num_, int output_num_>
struct bp_network<activate_type, val_type_, update_type_tpl, init_type, input_num_, output_num_>
{constexpr static int input_num = input_num_;constexpr static int output_num = output_num_;using val_type = val_type_;using input_type = mat<input_num, 1, val_type>;using input_t_type = mat<1, input_num, val_type>;using output_type = mat<output_num, 1, val_type>;using weight_type = mat<output_num, input_num, val_type>;using forward_func = typename func_pair<activate_type>::forward_func;using backward_func = typename func_pair<activate_type>::backward_func;using term_output_type = typename output_type;using weight_update_type = typename update_type_tpl<weight_type>;using bias_update_type = typename update_type_tpl<output_type>;weight_type weight;weight_update_type weight_update;output_type bias;bias_update_type bias_update;output_type pre_func_input;input_type pre_input;bp_network():weight_update(), bias_update(){weight.template reset<init_type>();bias.template reset<init_type>();}auto forward(input_type& input){pre_input = input;auto temp = weight.dot(input);pre_func_input = temp + bias;return pre_func_input.template activate<forward_func>();}auto backward(output_type& delta, val_type lr){output_type curr_delta = pre_func_input.template activate<backward_func>() * delta;auto ret = weight.t_dot(curr_delta);// 更新参数weight_type delta_weight = curr_delta.dot(pre_input.t());weight = weight_update.update(weight, delta_weight);bias = bias_update.update(bias, curr_delta);return ret;}void update_inert(){weight_update.update_inert();bias_update.update_inert();}void print(){weight.print();printf("-----------------\n");bias.print();printf("*****************\n");}
};#endif

下面实验一下我们的bp神经网络。

#include <chrono>
#include <thread>
#include "matrix.hpp"
#include "bp_network.hpp"
int main()
{constexpr int row_num = 32;constexpr int adj_num = 32;constexpr int col_num = 32;/*matrix_device_proxy<row_num, adj_num, double> A;eyes(A(), 2.0);matrix_device_proxy<adj_num, col_num, double> B;eyes(B(), 1.0);matrix_device_proxy<row_num, col_num, double> C;mat_dot<sigmoid>(A(), B(), C());print(type_cast(C()));auto A = mat<row_num, adj_num, double>::eyes(2.0);auto B = mat<adj_num, col_num, double>::eyes(1.0);auto C = A.dot(B);C = C + 1.0;C = sqrtl(C);C = C - 2.0;C = C * 3.0;C = C / 4.0;C.print();std::cout << "---------- D ----------" << std::endl;auto D = mat<row_num, col_num, double>::xavier_gaussian();D.print();std::cout << "---------- E ----------" << std::endl;auto E = mat<row_num, col_num, double>::xavier_mean();E.print();std::cout << "---------- F ----------" << std::endl;auto F = mat<row_num, col_num, double>::he_gaussian();F.print();std::cout << "---------- G ----------" << std::endl;auto G = mat<row_num, col_num, double>::he_mean();G.print();*/bp_network<sigmoid, double, nadam, xavier_gaussian_type, row_num, adj_num, col_num> node;auto input = mat<row_num, 1, double>::ones(0.2);auto expect = mat<col_num, 1, double>::ones(0.4);int times = 8000;int update_inert_times = 100;int step = times / update_inert_times;// 计时开始auto start = std::chrono::high_resolution_clock::now();for (int i = 0; i < times; ++i){auto output = node.forward(input);auto delta = (output - expect);node.backward(delta, 0.001);if (i == times - 1){output.t().print();}if (i % step == 0 && i != 0){node.update_inert();}}// 计时结束// 获取结束时间点auto end = std::chrono::high_resolution_clock::now();// 计算持续时间std::chrono::duration<double> duration = end - start;// 输出执行时间std::cout << "Execution time: " << duration.count() << " seconds" << std::endl;//node.print();cudaDeviceReset();return 0;
}

以上代码有个学习率lr没有地方设置哈，将来优化，见谅。执行结果如下：

可以看出，经过8000次的训练，这个使用sigmoid激活函数、NAdam优化、Xavier-Gaussian初始化的323232的PB能够将误差缩减到0.0001这个量级，而训练时间仅为8.54秒。还是相当给力的。
虽然这对于我的工作没有任何关系，但是我还是想搞一下。毕竟“越是没用的知识就越有用，越是有用的东西就越没用”。