|
|
#include <stdio.h>
|
|
|
#include <math.h>
|
|
|
#include <stdlib.h>
|
|
|
#include <time.h>
|
|
|
|
|
|
|
|
|
#define INNODE 2 // 输入层神经元个数
|
|
|
#define HIDENODE 10 // 隐藏层神经元个数
|
|
|
#define OUTNODE 1 // 输出层神经元个数
|
|
|
|
|
|
|
|
|
/**
|
|
|
* 步长（学习率）
|
|
|
*/
|
|
|
double StudyRate = 1.6;
|
|
|
|
|
|
/**
|
|
|
* 允许最大误差
|
|
|
*/
|
|
|
double threshold = 1e-4;
|
|
|
|
|
|
/**
|
|
|
* 最大迭代次数
|
|
|
*/
|
|
|
int mostTimes = 1e6;
|
|
|
|
|
|
/**
|
|
|
* 训练集大小
|
|
|
*/
|
|
|
int trainSize = 0;
|
|
|
|
|
|
/**
|
|
|
* 测试集大小
|
|
|
*/
|
|
|
int testSize = 0;
|
|
|
|
|
|
/**
|
|
|
* 样本
|
|
|
*/
|
|
|
typedef struct Sample {
|
|
|
double out[30][OUTNODE]; // 输出
|
|
|
double in[30][INNODE]; // 输入
|
|
|
}Sample;
|
|
|
|
|
|
|
|
|
/**
|
|
|
* 神经元结点
|
|
|
*/
|
|
|
typedef struct Node {
|
|
|
double value; // 当前神经元结点输出的值
|
|
|
double bias; // 当前神经元结点偏偏置值
|
|
|
double bias_delta; // 当前神经元结点偏置值的修正值
|
|
|
double* weight; // 当前神经元结点向下一层结点传播的权值
|
|
|
double* weight_delta; // 当前神经元结点向下一层结点传播的权值的修正值
|
|
|
}Node;
|
|
|
|
|
|
/**
|
|
|
* 输入层
|
|
|
*/
|
|
|
Node inputLayer[INNODE];
|
|
|
/**
|
|
|
* 隐藏层
|
|
|
*/
|
|
|
Node hideLayer[HIDENODE];
|
|
|
/**
|
|
|
* 输出层
|
|
|
*/
|
|
|
Node outLayer[OUTNODE];
|
|
|
|
|
|
|
|
|
|
|
|
double Max(double a, double b) {
|
|
|
return a > b ? a : b;
|
|
|
}
|
|
|
|
|
|
/**
|
|
|
* 激活函数sigmoid
|
|
|
* @param x 输入值
|
|
|
* @return 输出值
|
|
|
*/
|
|
|
|
|
|
double sigmoid(double x) {
|
|
|
//请补全sigmod函数的计算结果
|
|
|
return 1 / (1 + exp(-x));
|
|
|
}
|
|
|
|
|
|
|
|
|
/**
|
|
|
* 读取训练集
|
|
|
* @param filename 文件名
|
|
|
* @return 训练集
|
|
|
*/
|
|
|
Sample* getTrainData(const char* filename) {
|
|
|
Sample* result = (Sample*)malloc(sizeof(Sample));
|
|
|
FILE* file = fopen(filename, "r");
|
|
|
if (file != NULL) {
|
|
|
int count = 0;
|
|
|
while (fscanf(file, "%lf %lf %lf", &result->in[count][0], &result->in[count][1], &result->out[count][0]) != EOF) {
|
|
|
++count;
|
|
|
}
|
|
|
trainSize = count;
|
|
|
printf("%s The file has been successfully read!\n", filename);
|
|
|
fclose(file);
|
|
|
return result;
|
|
|
}
|
|
|
else {
|
|
|
fclose(file);
|
|
|
printf("%s Encountered an error while opening the file!\n\a", filename);
|
|
|
return NULL;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/**
|
|
|
* 读取测试集
|
|
|
* @param filename 文件名
|
|
|
* @return 测试集
|
|
|
*/
|
|
|
Sample* getTestData(const char* filename) {
|
|
|
/*在内存中分配足够的空间来存储一个Sample结构，并将指向该内存块的指针存储在result变量中*/
|
|
|
Sample* result = (Sample*)malloc(sizeof(Sample));
|
|
|
FILE* file = fopen(filename, "r");//打开文件
|
|
|
if (file != NULL) {
|
|
|
// 初始化一个整数变量count，用于跟踪读取的数据行数；
|
|
|
int count = 0;
|
|
|
/*利用while循环从测试集文件中逐行读取两个浮点数，直到读取到文件末尾。
|
|
|
每次成功读取一行数据后，递增count；*/
|
|
|
while (fscanf(file, "%3f %3f", &result->in[count][0], &result->in[count][1]) != EOF) {
|
|
|
++count;
|
|
|
}
|
|
|
//将最终的count的值存储在名为testSize的全局变量中，以便后续使用
|
|
|
testSize = count;
|
|
|
printf("%s The file has been successfully read!\n", filename);
|
|
|
fclose(file);
|
|
|
//返回result
|
|
|
return result;
|
|
|
}
|
|
|
else {
|
|
|
fclose(file);
|
|
|
printf("%s Encountered an error while opening the file!\n\a", filename);
|
|
|
return NULL;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/**
|
|
|
* 打印样本
|
|
|
* @param data 要打印的样本
|
|
|
* @param size 样本大小
|
|
|
*/
|
|
|
void printData(Sample* data, int size) {
|
|
|
if (data != NULL) {
|
|
|
int count = 0;
|
|
|
while (count<size) {
|
|
|
printf("%f %f %f\n", &data->in[count][0], &data->in[count][1], &data->out[count][0]);
|
|
|
count++;
|
|
|
}
|
|
|
}
|
|
|
else {
|
|
|
printf("sample is empty!\n");
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/**
|
|
|
* 初始化函数
|
|
|
*/
|
|
|
void init() {
|
|
|
// 设置时间戳为生成随机序列的种子
|
|
|
srand(time(NULL));
|
|
|
|
|
|
// 输入层的初始化
|
|
|
for (int i = 0; i < INNODE; ++i) {
|
|
|
inputLayer[i].weight = (double*)malloc(sizeof(double) * HIDENODE);
|
|
|
inputLayer[i].weight_delta = (double*)malloc(sizeof(double) * HIDENODE);
|
|
|
inputLayer[i].bias = 0.0;
|
|
|
inputLayer[i].bias_delta = 0.0;
|
|
|
}
|
|
|
|
|
|
// 输出层权值初始化
|
|
|
for (int i = 0; i < INNODE; ++i) {
|
|
|
for (int j = 0; j < HIDENODE; ++j) {
|
|
|
inputLayer[i].weight[j] = rand() % 10000 / (double)10000 * 2 - 1.0;
|
|
|
inputLayer[i].weight_delta[j] = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
|
|
|
// 初始化隐藏层结点
|
|
|
for (int i = 0; i < HIDENODE; ++i) {
|
|
|
/*为隐藏层节点 i 分配了一个 double 类型的数组，用于存储该节点向下一层节点传播的权重。
|
|
|
使用malloc 函数在堆内存中分配足够的空间，以存储 OUTNODE 个 double 类型的权重值。
|
|
|
*/
|
|
|
hideLayer[i].weight =
|
|
|
/*为隐藏层节点 i 分配了一个用于存储权重修正值的数组。这个数组将在神经网络的训练过程中用于存储权重的更新值。
|
|
|
使用malloc 函数在堆内存中分配足够的空间，以存储 OUTNODE 个 double 类型的权重值。
|
|
|
*/
|
|
|
hideLayer[i].weight_delta =
|
|
|
/*为隐藏层节点 i 初始化了一个随机的偏置值。
|
|
|
这个值通常是一个在 -1.0 到 1.0 之间的随机数，用于调整该节点的激活函数的阈值。
|
|
|
*/
|
|
|
hideLayer[i].bias =
|
|
|
/*初始化了隐藏层节点 i 的偏置值修正值，初始值为0.0。*/
|
|
|
hideLayer[i].bias_delta = 0.0;
|
|
|
}
|
|
|
|
|
|
// 初始化隐藏层权值
|
|
|
for (int i = 0; i < HIDENODE; ++i) {
|
|
|
for (int j = 0; j < OUTNODE; ++j) {
|
|
|
hideLayer[i].weight[j] = rand() % 10000 / (double)10000 * 2 - 1.0;
|
|
|
hideLayer[i].weight_delta[j] = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
for (int i = 0; i < OUTNODE; ++i) {
|
|
|
outLayer[i].bias = rand() % 10000 / (double)10000 * 2 - 1.0;
|
|
|
outLayer[i].bias_delta = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/**
|
|
|
* 重置修正值
|
|
|
*/
|
|
|
void resetDelta() {
|
|
|
for (int i = 0; i < INNODE; ++i) {
|
|
|
for (int j = 0; j < HIDENODE; ++j) {
|
|
|
inputLayer[i].weight_delta[j] = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
for (int i = 0; i < HIDENODE; ++i) {
|
|
|
hideLayer[i].bias_delta = 0.0;
|
|
|
for (int j = 0; j < OUTNODE; ++j) {
|
|
|
hideLayer[i].weight_delta[j] = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
for (int i = 0; i < OUTNODE; ++i) {
|
|
|
outLayer[i].bias_delta = 0.0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
|
|
|
int main_bp() {
|
|
|
// 初始化
|
|
|
init();
|
|
|
// 获取训练集
|
|
|
Sample* trainSample = getTrainData("TrainData.txt");
|
|
|
//打印训练集
|
|
|
printData(trainSample, trainSize);
|
|
|
|
|
|
|
|
|
for (int trainTime = 0; trainTime < mostTimes; ++trainTime) {
|
|
|
// 重置梯度信息
|
|
|
resetDelta();
|
|
|
|
|
|
// 当前训练最大误差
|
|
|
double error_max = 0.0;
|
|
|
|
|
|
// 开始训练（累计bp）
|
|
|
for (int currentTrainSample_Pos = 0; currentTrainSample_Pos < trainSize; ++currentTrainSample_Pos) {
|
|
|
|
|
|
// 输入自变量
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
inputLayer[inputLayer_Pos].value = trainSample->in[currentTrainSample_Pos][inputLayer_Pos];
|
|
|
}
|
|
|
|
|
|
/** ----- 开始正向传播 ----- */
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
sum += inputLayer[inputLayer_Pos].value * inputLayer[inputLayer_Pos].weight[hideLayer_Pos];
|
|
|
}
|
|
|
|
|
|
sum -= hideLayer[hideLayer_Pos].bias;
|
|
|
hideLayer[hideLayer_Pos].value = sigmoid(sum);
|
|
|
}
|
|
|
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
/*计算每一个隐藏层节点的value和权值的乘积，相加得到sum
|
|
|
|
|
|
*/
|
|
|
}
|
|
|
/*更新sum，使sum减去偏置值;
|
|
|
|
|
|
*/
|
|
|
/*利用sigmod函数对得到的sum进行激活，把激活后的结果赋值给对应的输出层节点value(outLayer[outLayer_Pos].value)。
|
|
|
|
|
|
*/
|
|
|
}
|
|
|
|
|
|
|
|
|
/** ----- 计算误差 ----- */
|
|
|
double error = 0.0;
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
double temp = fabs(outLayer[outLayer_Pos].value - trainSample->out[currentTrainSample_Pos][outLayer_Pos]);
|
|
|
// 损失函数
|
|
|
error += temp * temp / 2.0;
|
|
|
}
|
|
|
|
|
|
error_max = Max(error_max, error);
|
|
|
|
|
|
|
|
|
/** ----- 反向传播 ----- */
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
double bias_delta = -(trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
|
|
|
* outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value);
|
|
|
outLayer[outLayer_Pos].bias_delta += bias_delta;
|
|
|
}
|
|
|
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
double weight_delta = (trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
|
|
|
* outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
|
|
|
* hideLayer[hideLayer_Pos].value;
|
|
|
hideLayer[hideLayer_Pos].weight_delta[outLayer_Pos] += weight_delta;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
//
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
sum += -(trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
|
|
|
* outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
|
|
|
* hideLayer[hideLayer_Pos].weight[outLayer_Pos];
|
|
|
}
|
|
|
hideLayer[hideLayer_Pos].bias_delta += sum * hideLayer[hideLayer_Pos].value * (1.0 - hideLayer[hideLayer_Pos].value);
|
|
|
}
|
|
|
|
|
|
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
sum += (trainSample->out[currentTrainSample_Pos][outLayer_Pos] - outLayer[outLayer_Pos].value)
|
|
|
* outLayer[outLayer_Pos].value * (1.0 - outLayer[outLayer_Pos].value)
|
|
|
* hideLayer[hideLayer_Pos].weight[outLayer_Pos];
|
|
|
}
|
|
|
inputLayer[inputLayer_Pos].weight_delta[hideLayer_Pos] += sum * hideLayer[hideLayer_Pos].value * (1.0 - hideLayer[hideLayer_Pos].value)
|
|
|
* inputLayer[inputLayer_Pos].value;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
// 判断误差是否达到允许误差范围
|
|
|
if (error_max < threshold) {
|
|
|
printf("\a Training completed!Total training count:%d, maximum error is:%f\n", trainTime + 1, error_max);
|
|
|
break;
|
|
|
}
|
|
|
|
|
|
// 误差无法接受，开始修正
|
|
|
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
inputLayer[inputLayer_Pos].weight[hideLayer_Pos] += StudyRate
|
|
|
* inputLayer[inputLayer_Pos].weight_delta[hideLayer_Pos] /
|
|
|
(double)trainSize;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
hideLayer[hideLayer_Pos].bias += StudyRate
|
|
|
* hideLayer[hideLayer_Pos].bias_delta / (double)trainSize;
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
hideLayer[hideLayer_Pos].weight[outLayer_Pos] += StudyRate
|
|
|
* hideLayer[hideLayer_Pos].weight_delta[outLayer_Pos] / (double)trainSize;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
outLayer[outLayer_Pos].bias += StudyRate
|
|
|
* outLayer[outLayer_Pos].bias_delta / (double)trainSize;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// 训练完成，读取测试集
|
|
|
Sample* testSample = getTestData("TestData.txt");
|
|
|
printf("The predicted results are as follows:\n");
|
|
|
for (int currentTestSample_Pos = 0; currentTestSample_Pos < testSize; ++currentTestSample_Pos) {
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
inputLayer[inputLayer_Pos].value = testSample->in[currentTestSample_Pos][inputLayer_Pos];
|
|
|
}
|
|
|
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int inputLayer_Pos = 0; inputLayer_Pos < INNODE; ++inputLayer_Pos) {
|
|
|
sum += inputLayer[inputLayer_Pos].value * inputLayer[inputLayer_Pos].weight[hideLayer_Pos];
|
|
|
}
|
|
|
sum -= hideLayer[hideLayer_Pos].bias;
|
|
|
hideLayer[hideLayer_Pos].value = sigmoid(sum);
|
|
|
}
|
|
|
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
double sum = 0.0;
|
|
|
for (int hideLayer_Pos = 0; hideLayer_Pos < HIDENODE; ++hideLayer_Pos) {
|
|
|
sum += hideLayer[hideLayer_Pos].value * hideLayer[hideLayer_Pos].weight[outLayer_Pos];
|
|
|
}
|
|
|
sum -= outLayer[outLayer_Pos].bias;
|
|
|
outLayer[outLayer_Pos].value = sigmoid(sum);
|
|
|
}
|
|
|
|
|
|
for (int outLayer_Pos = 0; outLayer_Pos < OUTNODE; ++outLayer_Pos) {
|
|
|
testSample->out[currentTestSample_Pos][outLayer_Pos] = outLayer[outLayer_Pos].value;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
printData(testSample, testSize);
|
|
|
|
|
|
return 0;
|
|
|
}
|
