运行结果和单线程一致的【辣鸡】神经网络。
希望一会儿做完可视化，能让我发现到底哪里出问题了
居然比单线程的程序慢了100倍！
Main.cpp
#include "net.h"
void core();
int main()
{
auto t1 = std::chrono::system_clock::now();
double program=clock();
//doit3551();
//doit251();
core();
auto t2 = std::chrono::system_clock::now();
cout<<"程序一共运行时间: " << std::chrono::duration_cast(t2-t1).count()<<"ms"< 137 数1点个数
//0 1 2 3 -> 137 识别二进制
std::vectorin({0,40,80,120});
std::vectorout({160});
net_t<162> net("data.txt");
srand(0);
std::vectorinput0({0, 0, 0, 0}); //0
std::vectorinput1({0, 0, 0, 1}); //1
std::vectorinput2({0, 0, 1, 0}); //1
std::vectorinput3({0, 0, 1, 1}); //0
std::vectorinput4({0, 1, 0, 0}); //1
std::vectorinput5({0, 1, 0, 1}); //0
std::vectorinput6({0, 1, 1, 0}); //0
std::vectorinput7({0, 1, 1, 1}); //1
std::vectorinput8({1, 0, 0, 0}); //1
std::vectorinput9({1, 0, 0, 1}); //0
std::vectorinput10({1, 0, 1, 0}); //0
std::vectorinput11({1, 0, 1, 1}); //1
std::vectorinput12({1, 1, 0, 0}); //0
std::vectorinput13({1, 1, 0, 1}); //1
std::vectorinput14({1, 1, 1, 0}); //1
std::vectorinput15({1, 1, 1, 1}); //0
std::vectoroutput0({0.05});
std::vectoroutput1({0.1});
std::vectoroutput2({0.15});
std::vectoroutput3({0.2});
std::vectoroutput4({0.25});
std::vectoroutput5({0.3});
std::vectoroutput6({0.35});
std::vectoroutput7({0.4});
std::vectoroutput8({0.45});
std::vectoroutput9({0.5});
std::vectoroutput10({0.55});
std::vectoroutput11({0.6});
std::vectoroutput12({0.65});
std::vectoroutput13({0.7});
std::vectoroutput14({0.75});
std::vectoroutput15({0.8});
net.activation_way = "ReLU";
net.setIO(input1, output1, &in, &out);
ANN::rate = 0.01;
//net.outputNetwork();
//net.outputNetwork();
//net.train(input5, output5);
//net.train(input6, output6);
//net.train(input7, output7);
//net.testOutput(input8);
//net.testOutput(input15);
//return;
double error=0;
for (int i = 1;i<=20000;++i){
error = 0;
error += net.train(input0, output0);
error += net.train(input1, output1);
error += net.train(input2, output2);
error += net.train(input3, output3);
error += net.train(input4, output4);
error += net.train(input5, output5);
error += net.train(input6, output6);
error += net.train(input7, output7);
error += net.train(input8, output8);
error += net.train(input9, output9);
error += net.train(input10, output10);
error += net.train(input11, output11);
error += net.train(input12, output12);
error += net.train(input13, output13);
error += net.train(input14, output14);
error += net.train(input15, output15);
error/=16;
cout< net.h
#ifndef __net_h__
#define __net_h__
#include
//#include
#include
//#include
//#include
//#include
//#include
//#include "recordlog.h"
//#include
//#include "threadsafe_queue.h" //线程安全点queue
#include "thread_pool.h"
//#include
using std::cin;
using std::endl;
using std::cout;
#define pr(x) cout<<#x<<" = "< activation_way = "ReLU";
for (int i = 0; i < neuron_size; ++ i){
vector_map[i].clear();
tmp[i] = i;
neurons[i].number = i;
neurons[i].is_input = false;
neurons[i].is_output = false;
}
this -> output_number.clear();
this -> input_number.clear();
printf("神经元个数为 %d\n", neuron_size);
while (n--){
int s, t;
fscanf(file, "%d%d", &s, &t);
vector_map[s].push_back(t);
vector_map[t].push_back(s);
}
for (int i = 0; i < neuron_size; ++ i) //去重，可能有a->b b->这样点读入，所以要去一次重
{
std::sort(vector_map[i].begin(), vector_map[i].end());
vector_map[i].erase(unique(vector_map[i].begin(), vector_map[i].end()), vector_map[i].end());
}
fclose(file);
}
//初始化输入节点
void initInputNeuron(std::vector &input_num){
int sz = input_num.size(); //输入节点总数
input_weight.resize(sz); //输入节点到第一个节点点权重
for (int i = 0; i < sz; ++ i){
if (activation_way == "ReLU") input_weight[i] = 1;
else input_weight[i] = ANN::randomDouble(-1, 1);
neurons[input_num[i]].is_input = true;
}
}
//利用高度地图来构建地图
//TODO!!!!
//本次最大问题：某些点不到output，所以back操作点时候出了问题。
void buildNetwork()
{
auto build_map = [=](int from, int to){
if (activation_way == "ReLU"){
neurons[from].neuron_array.push_back(std::make_pair(&neurons[to], ANN::randomDouble(0.1,1)));
}
else{
neurons[from].neuron_array.push_back(std::make_pair(&neurons[to], ANN::randomDouble(-1,1)));
}
neurons[to].back_array.push_back(std::make_pair(&neurons[from], 999983)); //反向边，权重随便定了一个数字
};
for (int i = 0; i < neuron_size; ++ i){
for (auto curnode : vector_map[i]){
if (height[i] > height[curnode]){
build_map(i, curnode);
}
continue;
//TODO 考虑是否要增加网络复杂度
if (height[i] == height[curnode] && i < curnode)
{
build_map(i, curnode);
}
}
}
for (int i = 0; i < neuron_size; ++ i){
if (activation_way == "ReLU") neurons[i].theta = 0;
else neurons[i].theta = ANN::randomDouble(-1, 1);
neurons[i].in_degree = neurons[i].out_degree = 0;
}
auto cmp1 = [](auto a, auto b)->bool{return abool{return a>b;};
auto bfs = [&](const std::vector &st,const auto &cmp, const bool &flag)
{
static bool inqueue[neuron_size];
memset(inqueue, 0, sizeof(inqueue));
std::queueq;
for (auto x : st)
{
q.push(x);
inqueue[x] = true; //标记走过点
}
while (!q.empty())
{
int now = q.front();
q.pop();
int debug=0;
for (auto x : vector_map[now])
{
//if (x==120) debug=1;
if (debug) cout<<"-------------\n"< &input,
std::vector &output,
std::vector *input_num = NULL,
std::vector *output_num = NULL){
if (input.size() == 0){
//throws something TODO
return;
}
if (output.size() == 0){
//throws something TODO
return;
}
if (input_num && output_num)
{
output_number = *output_num;
input_number = *input_num;
if (input_num -> size() != input.size() || output_num -> size() != output.size())
{
//throws something TODO
return;
}
}
else
{
std::random_shuffle(tmp, tmp + neuron_size);
printf("output nodes are: ");
for (int i = 0; i < output.size(); ++ i){
output_number.push_back(tmp[i]);
printf("%d ",tmp[i]);
}
printf("\n");
printf("input nodes are:");
for (int i = output.size(); i < input.size() + output.size(); ++ i){
input_number.push_back(tmp[i]);
printf("%d ",tmp[i]);
}
printf("\n");
}
initInputNeuron(*input_num);
for (int i = 0; i < neuron_size; ++ i)
{
neurons[i].activation_way = activation_way;
neurons[i].out_degree = 0;
neurons[i].in_degree = 0;
}
for (int i = 0; i < output.size(); ++ i){
neurons[output_number[i]].is_output = true;
}
//下面代码是实现，给每个节点定义高度，从而实现构建DAG图
std::queueq[output.size() + input.size()];
memset(height, -1, sizeof(height));
int cnt=0;
//下面点代码，把所有点输入，输出节点塞进队列中
for (auto curnode : output_number){
q[cnt++].push(curnode);
height[curnode] = 0;
}
for (auto curnode : input_number){
q[cnt++].push(curnode);
height[curnode] = neuron_size;
}
//然后同时从两个方向做BFS,直到BFS停止工作.
bool flag = true;
while (flag){
int cnt = 0;
flag = false;
for (auto curnode : output_number){
flag |= bfs(q[cnt++], 1);
}
for (auto curnode : input_number){
flag |= bfs(q[cnt++], -1);
}
}
buildNetwork();
}
bool bfs(std::queue &q, int delta){
if (q.empty()){
return false;
}
int h = height[q.front()];
while (!q.empty() && height[q.front()] == h){
int curnode = q.front();
q.pop();
for (auto nextnode : vector_map[curnode]){
if (height[nextnode] != -1){
continue;
}
height[nextnode] = h + delta;
q.push(nextnode);
}
}
return true;
}
void propagate(std::vector &input){
if (activation_way == "ReLU") //对于ReLU算法，暂且采用此方法，降低死亡率。
{
for (int i = 0; i < input_number.size(); ++i)
{
input_weight[i] = 1;
}
}
//初始化所有节点
for (int i = 0; i < neuron_size; ++ i){
neurons[i].gain = 0;
neurons[i].output = 0;
neurons[i].isInThreadPool = false; //默认肯定不在线程池中
neurons[i].count = neurons[i].in_degree;
}
for (int i = 0; i != input.size(); ++ i){
neuron_t &NODE = neurons[input_number[i]];
NODE.mutex.lock();
NODE_GAIN = NODE_GAIN + input_weight[i] * input[i];
NODE.isInThreadPool = true;
auto f = ANN::propagate_neuron;
pool.async(f, &NODE, &pool);
NODE.mutex.unlock();
}
//在这里等待所有线程完毕
pool.wait();
}
void back(std::vector &input, std::vector &output){
for (int i = 0; i < neuron_size; ++ i){
neurons[i].partial_derivative = 0;
neurons[i].count = neurons[i].out_degree;
neurons[i].isInThreadPool = false;
}
for (int i = 0; i != output.size(); ++ i)
{
neuron_t &NODE = neurons[output_number[i]];
NODE_PE = NODE_OUTPUT - output[i];
//cout<< NODE_OUTPUT<<" "<(f, &BACK_NODE, &pool);
}
BACK_NODE.mutex.unlock();
}
}
//在这里等待所有线程完毕
pool.wait();
for (int i = 0; i < input.size(); ++ i){
neuron_t &NODE = neurons[input_number[i]];
double tmp = input[i] * NODE_PE * D_NODE;
input_weight[i] -= tmp * ANN::rate;
}
}
double train(std::vector &input, std::vector &output){
propagate(input);
back(input, output);
double error=0;
for (int i = 0; i < output.size(); ++ i){
error += 0.5*pow((neurons[output_number[i]].output - output[i]), 2);
}
return error;
}
void outputNetwork(){
printf("---------------input nodes------------:\n");
for (int i = 0; i < input_number.size(); ++ i)
{
printf("[%d] weight:(%.7lf) \n", input_number[i], input_weight[i]);
}
printf("---------------other nodes------------\n");
printf("other nodes\n");
for (int i = 0; i < neuron_size; ++ i){
neuron_t &NODE = neurons[i];
printf("[%d] gain(%.7lf) theta(%.7lf) par_derivative(%.7lf) output(%.70lf) d(%.7lf) in_degree(%d) out_degree(%d) count(%d)\n",
NODE.number,
(double)NODE_GAIN,
NODE_THETA,
NODE_PE,
NODE_OUTPUT,
D_NODE,
NODE.out_degree,
NODE.in_degree,
(int)NODE.count);
for (auto nextnode : NODE.neuron_array){
printf(" -> %d (%.7lf)\n", nextnode.first -> number, nextnode.second);
}
}
printf("=============End====================\n");
}
void testOutput(std::vector &input)
{
propagate(input);
cout<<"output: ";
for (auto curnode : output_number)
{
printf("%.7lf ", (double)neurons[curnode].output);
}
cout< getTest(std::vector &input)
{
std::vector q;
for (auto curnode : output_number)
{
q.push_back(neurons[curnode].gain);
}
return move(q);
}
};
#endif
net.cpp
#include "net.h"
#include
#include
#include
#include
#include
#include
#include
#include "recordlog.h"
#include
#include "threadsafe_queue.h" //线程安全点queue
#include "thread_pool.h"
#include
double ANN::rate;
double ANN::sigmoid(double x)
{
return 1.0/(1.0 + exp(-x));
}
double ANN::line(double x)
{
return x;
}
double ANN::ReLU(double x)
{
if (x<=0) return 0;
return x;
}
double ANN::derivative(double x, const std::string &activation_way)
{
if (activation_way == "sigmoid"){
return sigmoid(x) * (1 - sigmoid(x));
}
if (activation_way == "ReLU"){
if (x<0) return 0;
return 1;
}
if (activation_way == "line"){
return 1;
}
cout<<"no activationFunction!"< output = ANN::activationFunction(NODE_GAIN, NODE_THETA, NODE_ACWAY);
for (auto &nextnode : NODE_ARRAY){
neuron_t &NEXT_NODE = *(nextnode.first);
NEXT_NODE.mutex.lock();
NEXT_NODE_GAIN = NEXT_NODE_GAIN + NODE_OUTPUT * NODE_TO_NEXTNODE_WEIGHT;
-- NEXT_NODE_COUNT; //后继节点点入度减少1
NEXT_NODE.mutex.unlock();
}
NODE.mutex.unlock();
}
void neuron_t::back(){
neuron_t &NODE = *this;
NODE.mutex.lock();
for (auto &nextnode : NODE_ARRAY){
neuron_t &NEXT_NODE = *(nextnode.first);
NODE_PE += NEXT_NODE_PE * NODE_TO_NEXTNODE_WEIGHT * D_NEXT_NODE;
}
for (auto &nextnode : NODE_ARRAY){
neuron_t &NEXT_NODE = *(nextnode.first);
NODE_TO_NEXTNODE_WEIGHT -= NODE_OUTPUT * D_NEXT_NODE * NEXT_NODE_PE * ANN::rate;
}
NODE_THETA -= NODE_PE * D_NODE * ANN::rate;
//前驱节点点出度减少1
for (auto &backnode : NODE_BACK_ARRAY)
{
neuron_t &BACK_NODE = *(backnode.first);
BACK_NODE.mutex.lock();
-- BACK_NODE_COUNT;
BACK_NODE.mutex.unlock();
}
NODE.mutex.unlock();
}
void ANN::__propagate(neuron_t* node, thread_pool* pool)
{
//printf("节点%d 开始进入propagate计算\n", node-> number);
node -> propagate();
neuron_t &NODE = *node;
NODE.mutex.lock();
for (auto &x : NODE_ARRAY)
{
neuron_t &NEXT_NODE = *(x.first);
NEXT_NODE.mutex.lock();
if (!NEXT_NODE_COUNT && !NEXT_NODE.isInThreadPool)
{
NEXT_NODE.isInThreadPool = true;
FNT f = __propagate;
pool -> async(f, &NEXT_NODE, pool);
}
NEXT_NODE.mutex.unlock();
}
NODE.mutex.unlock();
}
void ANN::__back(neuron_t* node, thread_pool* pool)
{
//printf("节点%d开始进入back计算\n", node -> number);
node -> back();
neuron_t &NODE = *node;
NODE.mutex.lock();
//printf("节点%d获得back锁\n", node -> number);
for (auto &x : NODE_BACK_ARRAY)
{
neuron_t &BACK_NODE = *(x.first);
BACK_NODE.mutex.lock();
if (!BACK_NODE_COUNT && !BACK_NODE.isInThreadPool)
{
BACK_NODE.isInThreadPool = true;
FNT f = __back;
pool -> async(f, &BACK_NODE, pool);
}
BACK_NODE.mutex.unlock();
}
NODE.mutex.unlock();
}
thread_pool.cpp
#include "thread_pool.h"
/**
* Constructs a thread pool with `num_threads` threads and an empty work queue.
*/
thread_pool::thread_pool(unsigned int num_threads) : num_threads(num_threads){
task_mutex.lock();
init_threads();
task_mutex.unlock();
in_thread_number = 0;
}
/**
* Destructs a thread pool, waiting on tasks to finish.
*/
thread_pool::~thread_pool(){
task_mutex.lock();
join = true;
task_mutex.unlock();
for(auto i = threads.begin();i != threads.end();i++)
i->join();
threads.clear();
}
/**
* Creates threads for the thread pool.
*/
void thread_pool::init_threads(){
for(int i = 0;i < num_threads;i++){
std::function f = std::bind(&thread_pool::thread_func, this);
threads.push_back(std::move(std::thread(f)));
}
}
/**
* Manages thread execution. This is the function that threads actually run.
* It pulls a task out of the queue and executes it.
*/
void thread_pool::thread_func(){
for(;;){
++tot;
// Lock the queue.
task_mutex.lock();
/*
bool flag = task_mutex.try_lock();
if (!flag)
{
std::this_thread::yield();
continue;
}
*/
// If there's nothing to do and we're not ready to join, just
// yield.
if(tasks.empty() && !join){
task_mutex.unlock();
std::this_thread::yield();
continue;
}
// If there's tasks waiting, do one.
else if(!tasks.empty()){
// Get a task.
auto f = std::move(tasks.front());
++ in_thread_number;
tasks.pop_front();
// Unlock the queue.
task_mutex.unlock();
// Execute the async function.
f.get();
-- in_thread_number;
}
// If there's no tasks and we're ready to join, then exit the
// function (effectively joining).
else if(join){
task_mutex.unlock();
return;
}
}
}
thread_pool.h
#ifndef THREAD_POOL_H
#define THREAD_POOL_H
#include
#include
#include
#include
#include
#include
#include
class thread_pool{
public:
thread_pool(unsigned int);
~thread_pool();
int tot=0;
void wait()
{
while(true)
{
if (task_mutex.try_lock())
{
if (tasks.empty() && !in_thread_number)
{
task_mutex.unlock();
break;
}
task_mutex.unlock();
std::this_thread::yield();
}
else std::this_thread::yield();
}
}
template
std::future async(std::function f, Args... args){
typedef std::function F;
std::promise *p = new std::promise;
// Create a function to package as a task.
auto task_wrapper = [p](F&& f, Args... args){
f(args...);
p->set_value();
};
// Create a function to package as a future for the user to wait on.
auto ret_wrapper = [p](){
p->get_future().get();
// Clean up resources
delete p;
};
task_mutex.lock();
// Package the task wrapper into a function to execute as a task.
auto task = std::async(std::launch::deferred,
task_wrapper,
std::move(f),
args...);
// Push the task onto the work queue.
tasks.emplace_back(std::move(task));
task_mutex.unlock();
// Package the return wrapper into a function for user to call to wait for the task to
// complete and to get the result.
return std::async(std::launch::deferred,
ret_wrapper);
}
protected:
void thread_func();
void init_threads();
private:
bool join = false;
unsigned int num_threads;
std::mutex task_mutex;
std::deque> tasks;
std::list threads;
std::atomic in_thread_number;
};
#endif // THREAD_POOL_HPP
Makefile
CXXFLAGS += -m64 \
-pipe \
-O2 \
-D_REENTRANT \
-W \
-fPIC \
-DQT_NO_DEBUG \
-DQT_NO_KEYWORDS \
-DQT_OPENGL_LIB \
-DQT_WIDGETS_LIB \
-DQT_GUI_LIB \
-DQT_XML_LIB \
-DQT_CORE_LIB \
-Wno-sign-compare \
-Wno-unused-result
INCPATH += -I. \
-isystem /usr/include/x86_64-linux-gnu/qt5 \
-isystem /usr/include/x86_64-linux-gnu/qt5/QtOpenGL \
-isystem /usr/include/x86_64-linux-gnu/qt5/QtWidgets \
-isystem /usr/include/x86_64-linux-gnu/qt5/QtGui \
-isystem /usr/include/x86_64-linux-gnu/qt5/QtXml \
-isystem /usr/include/x86_64-linux-gnu/qt5/QtCore \
-I/usr/lib/x86_64-linux-gnu/qt5/mkspecs/linux-g++-64 \
-I/usr/local/include/dbus-cxx-0.9/ \
-I/usr/include/dbus-1.0/ \
-I/usr/lib/x86_64-linux-gnu/dbus-1.0/include \
-I/usr/include/sigc++-2.0 \
-I/usr/lib/x86_64-linux-gnu/sigc++-2.0/include \
-I.
LIBS += -lQGLViewer-qt5 \
-lGL \
-lQt5OpenGL \
-lQt5Widgets \
-lQt5Gui \
-lQt5Xml \
-lQt5Core \
-lpthread \
-ljsoncpp \
-lQGLViewer-qt5 \
-ldbus-1 \
-lsigc-2.0 \
-lrt \
-ldbus-cxx \
-lpopt
objects = net.o thread_pool.o main.o
CXX = g++ $(CXXFLAGS) -std=c++14 $(INCPATH)
main : $(objects)
$(CXX) -o main $(objects) $(LIBS)
main.o : net.h main.cpp
$(CXX) -c main.cpp
net.o : net.cpp thread_pool.h net.h
$(CXX) -c net.cpp
thread_pool.o : thread_pool.h thread_pool.cpp
$(CXX) -c thread_pool.cpp
clean :
rm *.o
data.txt
972
0 1
0 8
0 101
0 114
0 65
0 44
1 120
1 0
1 148
1 114
1 79
1 101
2 109
2 8
2 144
2 52
2 65
2 83
3 77
3 7
3 22
3 100
3 26
3 96
4 61
4 103
4 142
4 117
4 137
4 25
5 150
5 40
5 90
5 66
5 105
5 10
6 125
6 80
6 37
6 120
6 161
6 148
7 77
7 3
7 58
7 100
7 11
7 21
8 44
8 65
8 83
8 0
8 2
8 114
9 49
9 153
9 118
9 151
9 124
9 24
10 111
10 55
10 112
10 40
10 66
10 5
11 115
11 139
11 21
11 7
11 19
11 58
12 50
12 110
12 91
12 78
12 99
12 15
13 76
13 39
13 62
13 89
13 38
13 63
14 97
14 94
14 156
14 90
14 141
14 150
15 96
15 110
15 71
15 99
15 22
15 12
16 23
16 158
16 72
16 79
16 101
16 84
17 84
17 60
17 80
17 161
17 29
17 155
18 68
18 122
18 20
18 136
18 61
18 121
19 115
19 27
19 116
19 139
19 112
19 11
20 122
20 18
20 106
20 52
20 137
20 61
21 125
21 11
21 148
21 100
21 139
21 7
22 100
22 37
22 96
22 3
22 15
22 71
23 158
23 44
23 16
23 101
23 142
23 103
24 49
24 42
24 151
24 113
24 9
24 140
25 138
25 117
25 133
25 30
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