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C++卷积神经网络

news 文章来源：https://blog.csdn.net/qq_42877866/article/details/132513172 2025/2/12 0:41:04

C++卷积神经网络

#include"TP_NNW.h"
#include<iostream>
#pragma warning(disable:4996)
using namespace std;
using namespace mnist;float* SGD(Weight* W1, Weight& W5, Weight& Wo, float** X)
{Vector2 ve(28, 28);float* temp = new float[10];Vector2 Cout;float*** y1 = Conv(X, ve, Cout, W1, 20);for (int i = 0; i < 20; i++)for (int n = 0; n < Cout.height; n++)for (int m = 0; m < Cout.width; m++)y1[i][n][m] = ReLU(y1[i][n][m]);float*** y2 = y1;Vector2 Cout2;float*** y3 = Pool(y1, Cout, 20, Cout2);float* y4 = reshape(y3, Cout2, 20, true);float* v5 = dot(W5, y4);float* y5 = ReLU(v5, W5);float* v = dot(Wo, y5);float* y = Softmax(v, Wo);for (int i = 0; i < Wo.len.height; i++)temp[i] = y[i];return temp;
}
void trainSGD(Weight* W1, Weight& W5, Weight& Wo, FILE* fp, FILE* tp)
{Vector2 ve(28, 28);unsigned char* reader = new unsigned char[ve.height * ve.width];float** X = apply2(ve.height, ve.width);unsigned char hao;hot_one<char> D(10);Weight* momentum1 = new Weight[20];//动量Weight momentum5;Weight momentumo;Weight* dW1 = new Weight[20];//动量Weight dW5;Weight dWo;for (int i = 0; i < 20; i++)W1[0] >> momentum1[i];W5 >> momentum5;Wo >> momentumo;int N = 8000;//训练集取前8000个int bsize = 100;//100个纠正一次int b_len;int* blist = bList(bsize, N, &b_len);for (int batch = 0; batch < b_len; batch++){for (int i = 0; i < 20; i++)W1[0] >> dW1[i];W5 >> dW5;Wo >> dWo;int begins = blist[batch];for (int k = begins; k < begins + bsize && k < N; k++){::fread(reader, sizeof(unsigned char), ve.height * ve.width, fp);//读取图像Toshape2(X, reader, ve);//组合成二维数组Vector2 Cout;//储存卷积后数组的尺寸  20float*** y1 = Conv(X, ve, Cout, W1, 20);//卷积for (int i = 0; i < 20; i++)for (int n = 0; n < Cout.height; n++){for (int m = 0; m < Cout.width; m++){y1[i][n][m] = ReLU(y1[i][n][m]);//通过ReLU函数}}float*** y2 = y1;//给变量y2Vector2 Cout2;//记录池化后的尺寸   10float*** y3 = Pool(y1, Cout, 20, Cout2);//池化层float* y4 = reshape(y3, Cout2, 20, true);//作为神经元输入float* v5 = dot(W5, y4);//矩阵乘法float* y5 = ReLU(v5, W5);//ReLU函数float* v = dot(Wo, y5);//举证乘法float* y = Softmax(v, Wo);//soft分类::fread(&hao, sizeof(unsigned char), 1, tp);//读取标签D.re(hao);float* e = new float[10];for (int i = 0; i < 10; i++)e[i] = ((float)D.one[i]) - y[i];float* delta = e;float* e5 = FXCB_err(Wo, delta);float* delta5 = Delta2(y5, e5, W5);float* e4 = FXCB_err(W5, delta5);float*** e3 = Toshape3(e4, 20, Cout2);float*** e2 = apply3(20, Cout.height, Cout.width);Weight one(2, 2, ones);/*for (int i = 0; i < 20; i++){::printf("第%d层\n", i);for (int n = 0; n < Cout2.height; n++){for (int m = 0; m < Cout2.width; m++)::printf("%0.3f ", e3[i][n][m]);puts("");}}getchar();*/for (int i = 0; i < 20; i++)//---------------------------------kron(e2[i], Cout, e3[i], Cout2, one.WG, one.len);/*for (int i = 0; i < 20; i++){::printf("第%d层\n", i);for (int n = 0; n < Cout.height; n++){for (int m = 0; m < Cout.width; m++)::printf("%f ", e2[i][n][m]);puts("");}}getchar();*/float*** delta2 = apply3(20, Cout.height, Cout.width);for (int i = 0; i < 20; i++)for (int n = 0; n < Cout.height; n++)for (int m = 0; m < Cout.width; m++)delta2[i][n][m] = (y2[i][n][m] > 0) * e2[i][n][m];float*** delta_x = (float***)malloc(sizeof(float***) * 20);Vector2 t1;for (int i = 0; i < 20; i++)delta_x[i] = conv2(X, ve, delta2[i], Cout, &t1);for (int i = 0; i < 20; i++)for (int n = 0; n < t1.height; n++)for (int m = 0; m < t1.width; m++)dW1[i].WG[n][m] += delta_x[i][n][m];dW5.re(delta5, y4, 1);dWo.re(delta, y5, 1);Free3(delta_x, 20, t1.height);Free3(delta2, 20, Cout.height);one.release();Free3(e2, 20, Cout.height);Free3(e3, 20, Cout2.height);free(e4);free(delta5);free(e5);free(v5);delete e;free(y5);free(v);free(y);Free3(y1, 20, Cout.height);free(y4);}for (int i = 0; i < 20; i++)dW1[i] /= (bsize);dW5 /= (bsize);dWo /= (bsize);for (int i = 0; i < 20; i++)for (int n = 0; n < W1[0].len.height; n++)for (int m = 0; m < W1[0].len.width; m++){momentum1[i].WG[n][m] = ALPHA * dW1[i].WG[n][m] + BETA * momentum1[i].WG[n][m];W1[i].WG[n][m] += momentum1[i].WG[n][m];}for (int n = 0; n < W5.len.height; n++)for (int m = 0; m < W5.len.width; m++)momentum5.WG[n][m] = ALPHA * dW5.WG[n][m] + BETA * momentum5.WG[n][m];W5 += momentum5;for (int n = 0; n < Wo.len.height; n++)for (int m = 0; m < Wo.len.width; m++)momentumo.WG[n][m] = ALPHA * dWo.WG[n][m] + BETA * momentumo.WG[n][m];Wo += momentumo;}for (int i = 0; i < 20; i++){momentum1[i].release();dW1[i].release();}momentum5.release();momentumo.release();Free2(X, ve.height);free(blist);delete reader;D.release();dW5.release();dWo.release();return;
}
void trainSGD1(Weight* W1, Weight& W5, Weight& Wo, FILE* fp, FILE* tp)
{Vector2 ve(28, 28);unsigned char* reader = new unsigned char[ve.height * ve.width];float** X = apply2(ve.height, ve.width);unsigned char hao;hot_one<char> D(10);Weight* momentum1 = new Weight[20];//动量Weight momentum5;Weight momentumo;Weight* dW1 = new Weight[20];//动量Weight dW5;Weight dWo;for (int i = 0; i < 20; i++)W1[0] >> momentum1[i];W5 >> momentum5;Wo >> momentumo;int N = 108;//训练集取前8000个int bsize = 12;//100个纠正一次int b_len;int* blist = bList(bsize, N, &b_len);for (int batch = 0; batch < b_len; batch++){for (int i = 0; i < 20; i++)W1[0] >> dW1[i];W5 >> dW5;Wo >> dWo;int begins = blist[batch];for (int k = begins; k < begins + bsize && k < N; k++){::fread(reader, sizeof(unsigned char), ve.height * ve.width, fp);//读取图像Toshape2(X, reader, ve);//组合成二维数组Vector2 Cout;//储存卷积后数组的尺寸  20float*** y1 = Conv(X, ve, Cout, W1, 20);//卷积for (int i = 0; i < 20; i++)for (int n = 0; n < Cout.height; n++){for (int m = 0; m < Cout.width; m++){y1[i][n][m] = ReLU(y1[i][n][m]);//通过ReLU函数}}float*** y2 = y1;//给变量y2Vector2 Cout2;//记录池化后的尺寸   10float*** y3 = Pool(y1, Cout, 20, Cout2);//池化层float* y4 = reshape(y3, Cout2, 20, true);//作为神经元输入float* v5 = dot(W5, y4);//矩阵乘法float* y5 = ReLU(v5, W5);//ReLU函数float* v = dot(Wo, y5);//举证乘法float* y = Softmax(v, Wo);//soft分类::fread(&hao, sizeof(unsigned char), 1, tp);//读取标签D.re(hao);float* e = new float[10];for (int i = 0; i < 10; i++)e[i] = ((float)D.one[i]) - y[i];float* delta = e;float* e5 = FXCB_err(Wo, delta);float* delta5 = Delta2(y5, e5, W5);float* e4 = FXCB_err(W5, delta5);float*** e3 = Toshape3(e4, 20, Cout2);float*** e2 = apply3(20, Cout.height, Cout.width);Weight one(2, 2, ones);/*for (int i = 0; i < 20; i++){::printf("第%d层\n", i);for (int n = 0; n < Cout2.height; n++){for (int m = 0; m < Cout2.width; m++)::printf("%0.3f ", e3[i][n][m]);puts("");}}getchar();*/for (int i = 0; i < 20; i++)//---------------------------------kron(e2[i], Cout, e3[i], Cout2, one.WG, one.len);/*for (int i = 0; i < 20; i++){::printf("第%d层\n", i);for (int n = 0; n < Cout.height; n++){for (int m = 0; m < Cout.width; m++)::printf("%f ", e2[i][n][m]);puts("");}}getchar();*/float*** delta2 = apply3(20, Cout.height, Cout.width);for (int i = 0; i < 20; i++)for (int n = 0; n < Cout.height; n++)for (int m = 0; m < Cout.width; m++)delta2[i][n][m] = (y2[i][n][m] > 0) * e2[i][n][m];float*** delta_x = (float***)malloc(sizeof(float***) * 20);Vector2 t1;for (int i = 0; i < 20; i++)delta_x[i] = conv2(X, ve, delta2[i], Cout, &t1);for (int i = 0; i < 20; i++)for (int n = 0; n < t1.height; n++)for (int m = 0; m < t1.width; m++)dW1[i].WG[n][m] += delta_x[i][n][m];dW5.re(delta5, y4, 1);dWo.re(delta, y5, 1);Free3(delta_x, 20, t1.height);Free3(delta2, 20, Cout.height);one.release();Free3(e2, 20, Cout.height);Free3(e3, 20, Cout2.height);free(e4);free(delta5);free(e5);free(v5);delete e;free(y5);free(v);free(y);Free3(y1, 20, Cout.height);free(y4);}for (int i = 0; i < 20; i++)dW1[i] /= (bsize);dW5 /= (bsize);dWo /= (bsize);for (int i = 0; i < 20; i++)for (int n = 0; n < W1[0].len.height; n++)for (int m = 0; m < W1[0].len.width; m++){momentum1[i].WG[n][m] = ALPHA * dW1[i].WG[n][m] + BETA * momentum1[i].WG[n][m];W1[i].WG[n][m] += momentum1[i].WG[n][m];}for (int n = 0; n < W5.len.height; n++)for (int m = 0; m < W5.len.width; m++)momentum5.WG[n][m] = ALPHA * dW5.WG[n][m] + BETA * momentum5.WG[n][m];W5 += momentum5;for (int n = 0; n < Wo.len.height; n++)for (int m = 0; m < Wo.len.width; m++)momentumo.WG[n][m] = ALPHA * dWo.WG[n][m] + BETA * momentumo.WG[n][m];Wo += momentumo;}for (int i = 0; i < 20; i++){momentum1[i].release();dW1[i].release();}momentum5.release();momentumo.release();Free2(X, ve.height);free(blist);delete reader;D.release();dW5.release();dWo.release();return;
}
float rand1()
{float temp = (rand() % 20) / (float)10;if (temp < 0.0001)temp = 0.07;temp *= (rand() % 2 == 0) ? -1 : 1;return temp * 0.01;
}
float rand2()
{float temp = (rand() % 10) / (float)10;float ret = (2 * temp - 1) * sqrt(6) / sqrt(360 + 2000);if (ret < 0.0001 && ret>-0.0001)ret = 0.07;return ret;
}
float rand3()
{float temp = (rand() % 10) / (float)10;float ret = (2 * temp - 1) * sqrt(6) / sqrt(10 + 100);if (ret < 0.0001 && ret>-0.0001)ret = 0.07;return ret;
}void train()
{FILE* fp = fopen("t10k-images.idx3-ubyte", "rb");FILE* tp = fopen("t10k-labels.idx1-ubyte", "rb");int rdint;::fread(&rdint, sizeof(int), 1, fp);::printf("训练集幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集数量：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集高度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集宽度：%d\n", ReverseInt(rdint));int start1 = ftell(fp);::fread(&rdint, sizeof(int), 1, tp);::printf("标签幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签数量：%d\n", ReverseInt(rdint));int start2 = ftell(tp);Weight* W1 = new Weight[20];WD(W1, 9, 9, 20, rand1);Weight W5(100, 2000, rand2);Weight Wo(10, W5.len.height, rand3);for (int k = 0; k < 3; k++){trainSGD(W1, W5, Wo, fp, tp);fseek(fp, start1, 0);fseek(tp, start2, 0);::printf("第%d次训练结束\n", k + 1);}fclose(fp);fclose(tp);fp = fopen("mnist_Weight.acp", "wb");for (int i = 0; i < 20; i++)W1[i].save(fp);W5.save(fp);Wo.save(fp);fclose(fp);::printf("训练完成");getchar();
}
void train1()
{FILE* fp = fopen("out_img.acp", "rb");FILE* tp = fopen("out_label.acp", "rb");int rdint;::fread(&rdint, sizeof(int), 1, fp);::printf("训练集幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集数量：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集高度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集宽度：%d\n", ReverseInt(rdint));int start1 = ftell(fp);::fread(&rdint, sizeof(int), 1, tp);::printf("标签幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签数量：%d\n", ReverseInt(rdint));int start2 = ftell(tp);Weight* W1 = new Weight[20];WD(W1, 9, 9, 20, rand1);Weight W5(100, 2000, rand2);Weight Wo(10, W5.len.height, rand3);for (int k = 0; k < 1000; k++){trainSGD1(W1, W5, Wo, fp, tp);fseek(fp, start1, 0);fseek(tp, start2, 0);::printf("第%d次训练结束\n", k + 1);}fclose(fp);fclose(tp);fp = fopen("mnist_Weight.acp", "wb");for (int i = 0; i < 20; i++)W1[i].save(fp);W5.save(fp);Wo.save(fp);fclose(fp);::printf("训练完成");getchar();
}
void test()
{FILE* fp = fopen("mnist_Weight.acp", "rb");Weight* W1 = new Weight[20];WD(W1, 9, 9, 20, rand1);Weight W5(100, 2000, rand1);Weight Wo(10, W5.len.height, rand1);for (int i = 0; i < 20; i++)W1[i].load(fp);W5.load(fp);Wo.load(fp);fclose(fp);fp = fopen("t10k-images.idx3-ubyte", "rb");FILE* tp = fopen("t10k-labels.idx1-ubyte", "rb");int rdint;::fread(&rdint, sizeof(int), 1, fp);::printf("训练集幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集数量：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集高度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集宽度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签数量：%d\n", ReverseInt(rdint));unsigned char* res = new unsigned char[28 * 28];float** X = apply2(28, 28);unsigned char biaoqian;Vector2 t2828 = Vector2(28, 28);for (int i = 0; i < 50; i++){::fread(res, sizeof(unsigned char), 28 * 28, fp);Toshape2(X, res, 28, 28);print(X, t2828);float* h = SGD(W1, W5, Wo, X);//带入神经网络int c = -1;for (int i = 0; i < 10; i++){if (h[i] > 0.85){c = i;break;}}::fread(&biaoqian, sizeof(unsigned char), 1, tp);::printf("正确结果应当为“%d”,      神经网络识别为“%d”   \n", biaoqian, c);}
}
void sb()
{Weight* W1;Weight W5(100, 2000, rand2);Weight Wo(10, W5.len.height, rand3);//::printf("加载权重完毕\n");Vector2 out;char path[256];for (int r = 0; r < 4; r++){sprintf(path, "acp%d.png", r);float** img = Get_data_by_Mat(path, out);//print(img, out);float* h = SGD(W1, W5, Wo, img);//带入神经网络int c = -1;float x = 0;for (int i = 0; i < 10; i++){if (h[i] > 0.85 && h[i] > x){x = h[i];c = i;}}::printf("%d ", c);Free2(img, out.height);free(h);remove(path);}puts("");
}void sb(char* path)
{Weight* W1 = new Weight[20];Weight W5(100, 2000, rand2);Weight Wo(10, W5.len.height, rand3);FILE* fp = fopen("mnist_Weight.acp", "rb");puts("开始加载权重");WD(W1, 9, 9, 20, rand1);for (int i = 0; i < 20; i++)W1[i].load(fp);W5.load(fp);Wo.load(fp);fclose(fp);::printf("加载权重完毕\n");Vector2 out;float** img = Get_data_by_Mat(path, out);printf("图像载入完毕");//print(img, out);float* h = SGD(W1, W5, Wo, img);//带入神经网络int c = -1;float max = -1;for (int i = 0; i < 10; i++){::printf("%f\n", h[i]);/*if (h[i] > 0.65 && h[i] > x){x = h[i];c = i;}*/if (max< h[i]){max = h[i];c = i;}}::printf("神经网络认为它是数字-->%d   相似度为：%f", c, max);Free2(img, out.height);free(h);}
bool thank(int x1,int x2, int y1, int y2, int z1, int z2 )
{int dis = 0;int xx = (x1 - x2);dis += xx * xx;xx = (y1 - y2);dis += xx * xx;xx = (z1 - z2);dis += xx * xx;dis = (int)sqrt(dis);if (dis < 100)return true;return false;
}
void qg(char* path)
{::printf(path);::printf("识别为：");//Mat img = imread(path);CImage img;img.Load(path);//Vec3b yes = Vec3b(204, 198, 204);CImage sav;// = Mat(120, 80, CV_8UC3);sav.Create(120, 80, 24);ResizeCImage(img, img.GetWidth() * 10, img.GetHeight() * 10);int XS = img.GetBPP() / 8;int pitch = img.GetPitch();//resize(img, img, Size(img.cols * 10, img.rows * 10));unsigned char* rgb = (unsigned char*)img.GetBits();for (int i = 0; i < img.GetHeight(); i++)for (int j = 0; j < img.GetWidth(); j++){//Vec3b rgb = img.at<Vec3b>(i, j);int x1= *(rgb + (j * XS) + (i * pitch) + 0);int y1 = *(rgb + (j * XS) + (i * pitch) + 1);int z1 = *(rgb + (j * XS) + (i * pitch) + 2);if (thank(x1, 204, y1, 198, z1, 204)){*(rgb + (j * XS) + (i * pitch) + 0) = 255;*(rgb + (j * XS) + (i * pitch) + 1) = 255;*(rgb + (j * XS) + (i * pitch) + 2) = 255;//img.at<Vec3b>(i, j) = Vec3b(255, 255, 255);}/*elseimg.at<Vec3b>(i, j) = Vec3b(0, 0, 0);*/}/*char p[256];for (int k = 0; k < 4; k++){sprintf(p, "acp%d.png", k);for (int i = 35 + (k * 80); i < 115 + (k * 80); i++)for (int j = 30; j < 150; j++)sav.at<Vec3b>(j - 30, i - (35 + (k * 80))) = img.at<Vec3b>(j, i);imwrite(p, sav);}img.release();sav.release();*/sb();
}
void test1()
{FILE* fp = fopen("mnist_Weight.acp", "rb");Weight* W1 = new Weight[20];WD(W1, 9, 9, 20, rand1);Weight W5(100, 2000, rand1);Weight Wo(10, W5.len.height, rand1);for (int i = 0; i < 20; i++)W1[i].load(fp);W5.load(fp);Wo.load(fp);fclose(fp);fp = fopen("out_img.acp", "rb");FILE* tp = fopen("out_label.acp", "rb");int rdint;::fread(&rdint, sizeof(int), 1, fp);::printf("训练集幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集数量：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集高度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, fp);::printf("训练集宽度：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签幻数：%d\n", ReverseInt(rdint));::fread(&rdint, sizeof(int), 1, tp);::printf("标签数量：%d\n", ReverseInt(rdint));unsigned char* res = new unsigned char[28 * 28];float** X = apply2(28, 28);unsigned char biaoqian;Vector2 t2828 = Vector2(28, 28);for (int i = 0; i < 10; i++){::fread(res, sizeof(unsigned char), 28 * 28, fp);Toshape2(X, res, 28, 28);print(X, t2828);float* h = SGD(W1, W5, Wo, X);//带入神经网络int c = -1;for (int i = 0; i < 10; i++){if (h[i] > 0.85){c = i;break;}}::fread(&biaoqian, sizeof(unsigned char), 1, tp);::printf("正确结果应当为“%d”,      神经网络识别为“%d”   \n", biaoqian, c);}
}
void main(int argc, char** argv)
{//train();//请先调用这个训练，训练结束后就可以直接加载权重了if (argc > 1){sb(argv[1]);getchar();}
}

#include"TP_NNW.h"
#include<iostream>
#pragma warning(disable:4996)
void Weight::apply(int H, int W)
{fz = true;this->len.height = H;this->len.width = W;this->WG = apply2(H, W);//申请内存for (int i = 0; i < H; i++)for (int j = 0; j < W; j++)this->WG[i][j] = Get_rand();//得到随机值
}void Weight::apply(int H, int W, float(*def)())
{fz = true;this->len.height = H;this->len.width = W;this->WG = apply2(H, W);for (int i = 0; i < H; i++)for (int j = 0; j < W; j++)this->WG[i][j] = def();
}Weight::~Weight()
{this->release();
}Weight::Weight(int H/*高度*/, int W/*宽度*/)
{W = W <= 0 ? 1 : W;//防止出现0和负数H = H <= 0 ? 1 : H;//防止出现0和负数fz = true;this->apply(H, W);
}Weight::Weight(int H/*高度*/, int W/*宽度*/, float(*def)())
{W = W <= 0 ? 1 : W;H = H <= 0 ? 1 : H;fz = true;this->apply(H, W, def);
}void Weight::re(float* delta, float* inp, float alpha)
{for (int i = 0; i < this->len.height; i++){for (int j = 0; j < this->len.width; j++)this->WG[i][j] += alpha * delta[i] * inp[j];}
}void Weight::save(FILE* fp)
{for (int i = 0; i < this->len.height; i++)for (int j = 0; j < this->len.width; j++)fwrite(&this->WG[i][j], sizeof(float), 1, fp);
}void Weight::load(FILE* fp)
{for (int i = 0; i < this->len.height; i++)for (int j = 0; j < this->len.width; j++)fread(&this->WG[i][j], sizeof(float), 1, fp);
}void Weight::release()
{if (this->fz){Free2(this->WG, this->len.height);//free(this->WG);}this->fz = false;
}
void Weight::operator >> (Weight& temp)
{temp.release();//free(temp.WG);temp.apply(this->len.height, this->len.width, zeros);
}
void Weight::operator+=(Weight& temp)
{for (int i = 0; i < this->len.height; i++)for (int j = 0; j < this->len.width; j++)this->WG[i][j] += temp.WG[i][j];
}//void Weight::operator/=(int & temp)
//{
//	for (int i = 0; i < this->len.height; i++)
//		for (int j = 0; j < this->len.width; j++)
//			this->WG[i][j] /= temp;
//}void Weight::operator/=(int temp)
{for (int i = 0; i < this->len.height; i++)for (int j = 0; j < this->len.width; j++)this->WG[i][j] /= temp;
}void Weight::operator<<(Weight& temp)
{Free2(this->WG, this->len.height);this->len.height = temp.len.height;this->len.width = temp.len.width;this->WG = temp.WG;
}void WD(Weight* WGS, int H, int W, int len)
{for (int i = 0; i < len; i++){WGS[i].apply(H, W);}
}
void WD(Weight* WGS, int H, int W, int len, float(*def)())
{for (int i = 0; i < len; i++){WGS[i].apply(H, W, def);}
}
float zeros()
{return 0;
}void print(float* y, int y_len)
{for (int i = 0; i < y_len; i++){printf("%0.2f ", y[i]);//printf("%d ", y[i]>0?1:0);}puts("");
}void print(float* y, Vector2& vec)
{print(y, vec.height);
}void print(float** y, Vector2& vec)
{for (int i = 0; i < vec.height; i++)print(y[i], vec.width);
}void print(char* y, int y_len)
{for (int i = 0; i < y_len; i++){printf("%d ", y[i]);}puts("");
}void print(char** y, Vector2& vec)
{for (int i = 0; i < vec.height; i++)print(y[i], vec.width);
}void print(Weight& w)
{print(w.WG, w.len);
}void print(Weight* w, int len)
{for (int i = 0; i < len; i++){printf("\n第%d层\n", i + 1);print(w[i]);}
}float** apply2(int H, int W)
{float** temp = (float**)malloc(sizeof(float**) * H);for (int i = 0; i < H; i++)temp[i] = (float*)malloc(sizeof(float*) * W);return temp;
}float*** apply3(int P, int H/*高度*/, int W/*宽度*/)
{float*** temp = (float***)malloc(sizeof(float***) * P);for (int i = 0; i < P; i++)temp[i] = apply2(H, W);return temp;
}char** apply2_char(int H, int W)
{char** temp = (char**)malloc(sizeof(float**) * H);for (int i = 0; i < H; i++)temp[i] = (char*)malloc(sizeof(float*) * W);return temp;
}
float ones()
{return 1;
}
float*** Conv(float** X, Vector2& inp, Vector2& out, Weight* W, int W_len)
{out.height = inp.height - W[0].len.height + 1;out.width = inp.width - W[0].len.width + 1;float*** temp = (float***)malloc(sizeof(float***) * W_len);for (int k = 0; k < W_len; k++)temp[k] = conv2(X, inp, W[k].WG, W[0].len);return temp;
}
float*** Pool(float*** y, Vector2& inp, int P, Vector2& out)
{int h = inp.height / 2, w = inp.width / 2;out.height = h;out.width = w;float*** temp = apply3(P, h, w);float** filter = apply2(2, 2);for (int i = 0; i < 2; i++)for (int j = 0; j < 2; j++)filter[i][j] = 0.25;for (int k = 0; k < P; k++){Vector2 len;Vector2 t22 = Vector2(2, 2);float** img = conv2(y[k], inp, filter, t22, &len);for (int i = 0; i < h; i++)for (int j = 0; j < w; j++)temp[k][i][j] = img[i * 2][j * 2];Free2(img, len.height);}Free2(filter, 2);return temp;
}
float* apply1(int H)
{float* temp = (float*)malloc(sizeof(float*) * H);return temp;
}char* apply1_char(int H)
{char* temp = (char*)malloc(sizeof(char*) * H);return temp;
}float Get_rand()
{float temp = (float)(rand() % 10) / (float)10;return rand() % 2 == 0 ? temp : -temp;
}float Sigmoid(float x)
{return 1 / (1 + exp(-x));
}float* Sigmoid(float* x, Weight& w)
{return Sigmoid(x, w.len.height);
}float* Sigmoid(float* x, int height)
{float* y = (float*)malloc(sizeof(float*) * height);for (int i = 0; i < height; i++)y[i] = Sigmoid(x[i]);return y;
}float ReLU(float x)
{return x > 0 ? x : 0;
}float* ReLU(float* x, Weight& w)
{return ReLU(x, w.len.height);
}float* ReLU(float* x, int height)
{float* y = (float*)malloc(sizeof(float*) * height);for (int i = 0; i < height; i++)y[i] = ReLU(x[i]);return y;
}float* Softmax(float* x, Weight& w)
{return Softmax(x, w.len.height);
}float dsigmoid(float x)
{return x * (1 - x);
}float* Softmax(float* x, int height)
{float* t = new float[height];float* ex = new float[height];float sum = 0;for (int i = 0; i < height; i++){ex[i] = exp(x[i]);sum += ex[i];}for (int i = 0; i < height; i++){t[i] = ex[i] / sum;}delete ex;return t;
}float* FXCB_err(Weight& w, float* delta)
{float* temp = (float*)malloc(sizeof(float*) * w.len.width);for (int i = 0; i < w.len.width; i++)temp[i] = 0;for (int i = 0; i < w.len.width; i++)for (int j = 0; j < w.len.height; j++)temp[i] += w.WG[j][i] * delta[j];return temp;
}float* Delta1(float* y, float* e, Weight& w)
{float* temp = (float*)malloc(sizeof(float*) * w.len.height);for (int i = 0; i < w.len.height; i++)temp[i] = y[i] * (1 - y[i]) * e[i];return temp;
}float* Delta2(float* v, float* e, Weight& w)
{float* temp = (float*)malloc(sizeof(float*) * w.len.height);for (int i = 0; i < w.len.height; i++)temp[i] = v[i] > 0 ? e[i] : 0;return temp;
}float* dot(Weight& W, float* inp, int* len)
{float* temp = (float*)malloc(sizeof(float*) * W.len.height);for (int i = 0; i < W.len.height; i++)temp[i] = 0;for (int i = 0; i < W.len.height; i++){for (int j = 0; j < W.len.width; j++)temp[i] += (W.WG[i][j] * inp[j]);}if (len != NULL)*len = W.len.height;return temp;
}char* randperm(int max, int count)
{char* temp = new char[count] {0};for (int i = 0; i < count; i++){while (1){char t = rand() % max;bool nothave = true;for (int j = 0; j < i; j++)if (t == temp[j]){nothave = false;break;}if (nothave){temp[i] = t;break;}}}return temp;
}void Dropout(float* y, float ratio, Weight& w)
{float* ym = new float[w.len.height] {0};float round = w.len.height * (1 - ratio);int num = (round - (float)(int)round >= 0.5f ? (int)round + 1 : (int)round);char* idx = randperm(w.len.height, num);for (int i = 0; i < num; i++){ym[idx[i]] = (1 / (1 - ratio));}for (int i = 0; i < w.len.height; i++){y[i] *= ym[i];}delete idx;delete ym;
}float** conv2(float** x, Vector2& x_len, float** fiter, Vector2& fiter_len, Vector2* out_len, int flag, int distance, int fill)
{switch (flag){case Valid:return VALID(x, x_len.height, x_len.width, fiter, fiter_len.height, fiter_len.width, distance, out_len);case Same:return SAME(x, x_len.height, x_len.width, fiter, fiter_len.height, fiter_len.width, distance, fill, out_len);}return nullptr;
}float** VALID(float** x, int x_h, int x_w, float** fiter, int fiter_h, int fiter_w, int distance, Vector2* out_len)
{int h = VALID_out_len(x_h, fiter_h, distance);int w = VALID_out_len(x_w, fiter_w, distance);float** temp = apply2(h, w);float** t = fiter;if (out_len != NULL){out_len->height = h;out_len->width = w;}for (int i = 0; i < x_h + 1 - fiter_h; i += distance)for (int j = 0; j < x_w + 1 - fiter_w; j += distance){float count = 0;for (int n = i; n < i + fiter_h; n++)for (int m = j; m < j + fiter_w; m++){if (n >= x_h || m >= x_w)continue;count += (x[n][m] * t[n - i][m - j]);}temp[(i / distance)][(j / distance)] = count;}//free(t);return temp;
}float** SAME(float** x, int x_h, int x_w, float** fiter, int fiter_h, int fiter_w, int distance, int fill, Vector2* out_len)
{return nullptr;
}int VALID_out_len(int x_len, int fiter_len, int distance)
{float temp = (float)(x_len - fiter_len) / (float)distance;int t = temp - (int)((float)temp) >= 0.5 ? (int)temp + 1 : (int)temp;t++;return t;
}void show_Weight(Weight& W)
{for (int i = 0; i < W.len.height; i++){for (int j = 0; j < W.len.width; j++){printf("%0.3f ", W.WG[i][j]);}puts("");}
}
void rot90(Weight& x)
{int h = x.len.width, w = x.len.height;x.WG = rot90(x.WG, x.len, true);x.len.width = w;x.len.height = h;
}
float** rot90(float** x, Vector2& x_len, bool release)
{float** temp = apply2(x_len.width, x_len.height);for (int i = 0; i < x_len.height; i++)for (int j = 0; j < x_len.width; j++){temp[x_len.width - 1 - j][i] = x[i][j];}if (release){Free2(x, x_len.height);//free(x);}return temp;
}float** rot180(float** x, Vector2& x_len, bool release)
{float** temp = apply2(x_len.height, x_len.width);for (int i = 0; i < x_len.height; i++){for (int j = 0; j < x_len.width; j++){temp[x_len.height - 1 - i][x_len.width - 1 - j] = x[i][j];}}if (release){Free2(x, x_len.height);//free(x);}return temp;
}
void ResizeCImage(CImage& image, int newWidth, int newHeight) {// 创建新的CImage对象，并设置大小CImage resizedImage;resizedImage.Create(newWidth, newHeight, image.GetBPP());// 使用Gdiplus::Graphics将原始图像绘制到新图像上，并进行缩放SetStretchBltMode(resizedImage.GetDC(), HALFTONE);image.StretchBlt(resizedImage.GetDC(), 0, 0, newWidth, newHeight);// 完成绘制后，释放新图像的设备上下文resizedImage.ReleaseDC();// 将结果拷贝回原始的CImage对象image.Destroy();image.Attach(resizedImage.Detach());resizedImage.Destroy();
}
float** Get_data_by_Mat(char* filepath, Vector2& out_len)
{CImage mat;// = cv::imread(filepath, 0);//cv::resize(mat, mat, cv::Size(28, 28));mat.Load(filepath);ResizeCImage(mat, 28, 28);/*cv::imshow("tt", mat);cv::waitKey(0);*/out_len.height = mat.GetHeight();out_len.width = mat.GetWidth();float** temp = apply2(mat.GetHeight(), mat.GetWidth());unsigned char* rgb = (unsigned char*)mat.GetBits();int pitch = mat.GetPitch();int hui = 0;int XS = mat.GetBPP()/8;for (int i = 0; i < out_len.height; i++)for (int j = 0; j < out_len.width; j++){hui = 0;for (int kkk = 0; kkk < 3; kkk++){hui += *(rgb + (j * XS) + (i * pitch) + kkk);}hui /= 3;temp[i][j] = ((float)hui / (float)255);//temp[i][j] = 1 - temp[i][j];}mat.Destroy();return temp;
}char** Get_data_by_Mat_char(char* filepath, Vector2& out_len, int threshold)
{CImage mat;mat.Load(filepath);//cv::Mat mat = cv::imread(filepath, 0);out_len.height = mat.GetHeight();out_len.width = mat.GetWidth();char** temp = apply2_char(out_len.height, out_len.width);unsigned char* rgb = (unsigned char*)mat.GetBits();int pitch = mat.GetPitch();int hui = 0;int XS = mat.GetBPP() / 8;for (int i = 0; i < out_len.height; i++)for (int j = 0; j < out_len.width; j++){hui = 0;for (int kkk = 0; kkk < 3; kkk++){hui += *(rgb + (j * XS) + (i * pitch) + kkk);}hui /= 3;temp[i][j] = hui > threshold ? 0 : 1;}mat.Destroy();return temp;
}void Get_data_by_Mat(char* filepath, Weight& w)
{w.WG = Get_data_by_Mat(filepath, w.len);
}Weight Get_data_by_Mat(char* filepath)
{Weight temp;Get_data_by_Mat(filepath, temp);return temp;
}Vector2::Vector2()
{this->height = 0;this->width = 0;
}Vector2::Vector2(char height, int width)
{this->height = height;this->width = width;
}XML::XML(FILE* fp, char* name, int layer)
{this->fp = fp;this->name = name;this->layer = layer;
}void XML::showchild()
{char reader[500];while (fgets(reader, 500, this->fp)){int len = strlen(reader);int lay = 0;for (; lay < len; lay++){if (reader[lay] != '\t')break;}if (lay == this->layer){if (reader[lay + 1] == '/')continue;char show[500];memset(show, 0, 500);for (int i = lay + 1; i < len - 2; i++){if (reader[i] == '>')break;show[i - lay - 1] = reader[i];}puts(show);}}fseek(this->fp, 0, 0);
}void bit::operator=(int x)
{this->B = x;
}float* reshape(float** x, int h, int w)
{float* temp = (float*)malloc(sizeof(float*) * w * h);int count = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++){temp[count++] = x[i][j];}return temp;
}float* reshape(float** x, Vector2& x_len)
{return reshape(x, x_len.height, x_len.width);
}float* reshape(float*** x, Vector2& x_len, int P, bool releace)
{float* temp = apply1(x_len.height * x_len.width * P);int c = 0;for (int i = 0; i < P; i++)for (int n = 0; n < x_len.height; n++)for (int m = 0; m < x_len.width; m++)temp[c++] = x[i][n][m];if (releace)Free3(x, P, x_len.height);//free(x);return temp;
}int* bList(int distance, int max, int* out_len)
{int num = (max % distance != 0);int t = (int)(max / distance);t += num;if (out_len != NULL)*out_len = t;int* out = (int*)malloc(sizeof(int*) * t);for (int i = 0; i < t; i++){out[i] = i * distance;}return out;
}void Free2(float** x, int h)
{for (int i = 0; i < h; i++)free(x[i]);free(x);
}void Free3(float*** x, int p, int h)
{for (int i = 0; i < p; i++)for (int j = 0; j < h; j++)free(x[i][j]);for (int i = 0; i < p; i++)free(x[i]);free(x);
}void kron(float** out, Vector2& out_len, float** inp, Vector2& inp_len, float** filter, Vector2& filter_len)
{for (int i = 0; i < inp_len.height; i++)for (int j = 0; j < inp_len.width; j++){for (int n = i * 2; n < out_len.height && n < ((i * 2) + filter_len.height); n++)for (int m = (j * 2); m < ((j * 2) + filter_len.width) && m < out_len.width; m++){out[n][m] = inp[i][j] * filter[n - (i * 2)][m - (j * 2)] * 0.25;}}
}char** mnist::Toshape2(char* x, int h, int w)
{char** temp = apply2_char(h, w);int c = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++)temp[i][j] = x[c++];return temp;
}char** mnist::Toshape2(char* x, Vector2& x_len)
{return mnist::Toshape2(x, x_len.height, x_len.width);
}void mnist::Toshape2(char** out, char* x, int h, int w)
{int c = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++)out[i][j] = x[c++];
}void mnist::Toshape2(char** out, char* x, Vector2& x_len)
{mnist::Toshape2(out, x, x_len.height, x_len.width);
}float** mnist::Toshape2_F(char* x, int h, int w)
{float** temp = apply2(h, w);int c = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++)temp[i][j] = ((float)x[c++] / (float)255);return temp;
}float** mnist::Toshape2_F(char* x, Vector2& x_len)
{return mnist::Toshape2_F(x, x_len.height, x_len.width);
}void mnist::Toshape2(float** out, char* x, int h, int w)
{int c = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++)out[i][j] = ((float)x[c++] / (float)255);
}void mnist::Toshape2(float** out, char* x, Vector2& x_len)
{mnist::Toshape2(out, x, x_len.height, x_len.width);
}void mnist::Toshape2(float** out, unsigned char* x, int h, int w)
{int c = 0;for (int i = 0; i < h; i++)for (int j = 0; j < w; j++){out[i][j] = ((float)x[c++] / (float)255);}
}void mnist::Toshape2(float** out, unsigned char* x, Vector2& x_len)
{mnist::Toshape2(out, x, x_len.height, x_len.width);
}float*** mnist::Toshape3(float* x, int P, Vector2& x_len)
{float*** temp = apply3(P, x_len.height, x_len.width);int c = 0;for (int i = 0; i < P; i++)for (int j = 0; j < x_len.height; j++)for (int n = 0; n < x_len.width; n++)temp[i][j][n] = x[c++];return temp;
}int mnist::ReverseInt(int i)
{unsigned char ch1, ch2, ch3, ch4;ch1 = i & 255;ch2 = (i >> 8) & 255;ch3 = (i >> 16) & 255;ch4 = (i >> 24) & 255;return((int)ch1 << 24) + ((int)ch2 << 16) + ((int)ch3 << 8) + ch4;
}

#pragma once
#include<Windows.h>
#include<atlimage.h>
#define ALPHA 0.01
#define BETA 0.95
#define RATIO 0.2
void ResizeCImage(CImage& image, int newWidth, int newHeight);
struct bit
{unsigned B : 1;void operator=(int x);
};
enum Conv_flag
{Valid = 0,Same = 1
};
struct Vector2 {int height, width;Vector2();Vector2(char height, int width);
};
class Weight
{
private:void apply(int H/*高度*/, int W/*宽度*/);void apply(int H/*高度*/, int W/*宽度*/, float(*def)());
public:bool fz;Vector2 len;float** WG;~Weight();Weight() { fz = false; }Weight(int H/*高度*/, int W/*宽度*/);Weight(int H/*高度*/, int W/*宽度*/, float (*def)());void re(float* delta, float* inp, float alpha = ALPHA);void save(FILE* fp);void load(FILE* fp);void release();void operator>>(Weight& temp);void operator+=(Weight& temp);//void operator/=(int &temp);void operator/=(int temp);void operator<<(Weight& temp);void friend WD(Weight* WGS, int H, int W, int len);void friend WD(Weight* WGS, int H, int W, int len, float(*def)());
};
float zeros();
float ones();
float*** Pool(float*** y, Vector2& inp, int P, Vector2& out);//池化
float*** Conv(float** X, Vector2& inp, Vector2& out, Weight* W, int W_len);//卷积
void print(float* y, int y_len = 1);
void print(float* y, Vector2& vec);
void print(float** y, Vector2& vec);
void print(char* y, int y_len = 1);
void print(char** y, Vector2& vec);
void print(Weight& w);
void print(Weight* w, int len = 1);
float** apply2(int H/*高度*/, int W/*宽度*/);
float*** apply3(int P, int H/*高度*/, int W/*宽度*/);
char** apply2_char(int H/*高度*/, int W/*宽度*/);
float* apply1(int H);
char* apply1_char(int H);
float Get_rand();
float Sigmoid(float x);
float* Sigmoid(float* x, Weight& w);
float* Sigmoid(float* x, int height);
float ReLU(float x);
float* ReLU(float* x, Weight& w);
float* ReLU(float* x, int height);
float* Softmax(float* x, Weight& w);
float dsigmoid(float x);
float* Softmax(float* x, int height);
float* FXCB_err(Weight& w, float* delta);
float* Delta1(float* y, float* e, Weight& w);
float* Delta2(float* v, float* e, Weight& w);
float* dot(Weight& W/*权重*/, float* inp/*输入数据*/, int* len = NULL);
char* randperm(int max, int count);
void Dropout(float* y, float ratio, Weight& w);
float** conv2(float** x, Vector2& x_len, float** fiter, Vector2& fiter_len,Vector2* out_len = NULL, int flag = Valid, int distance = 1, int fill = 0);
float** VALID(float** x, int x_h, int x_w, float** fiter, int fiter_h,int fiter_w, int distance, Vector2* out_len = NULL);
float** SAME(float** x, int x_h, int x_w, float** fiter, int fiter_h,int fiter_w, int distance, int fill, Vector2* out_len = NULL);
int VALID_out_len(int x_len, int fiter_len, int distance);
void show_Weight(Weight& W);
void rot90(Weight& x);
float** rot90(float** x, Vector2& x_len, bool release = false);
float** rot180(float** x, Vector2& x_len, bool release = false);
float** Get_data_by_Mat(char* filepath, Vector2& out_len);
char** Get_data_by_Mat_char(char* filepath, Vector2& out_len, int threshold = 127);
void Get_data_by_Mat(char* filepath, Weight& w);
Weight Get_data_by_Mat(char* filepath);
float* reshape(float** x, int h, int w);
float* reshape(float** x, Vector2& x_len);
float* reshape(float*** x, Vector2& x_len, int P, bool releace = false);
namespace mnist
{char** Toshape2(char* x, int h, int w);char** Toshape2(char* x, Vector2& x_len);void Toshape2(char** out, char* x, int h, int w);void Toshape2(char** out, char* x, Vector2& x_len);float** Toshape2_F(char* x, int h, int w);float** Toshape2_F(char* x, Vector2& x_len);void Toshape2(float** out, char* x, int h, int w);void Toshape2(float** out, char* x, Vector2& x_len);void Toshape2(float** out, unsigned char* x, int h, int w);void Toshape2(float** out, unsigned char* x, Vector2& x_len);float*** Toshape3(float* x, int P, Vector2& x_len);int ReverseInt(int i);
}
struct XML
{char* name;FILE* fp;int layer;XML(FILE* fp, char* name, int layer);void showchild();
};
template<class T>
class hot_one
{bool fz;
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};
int* bList(int distance, int max, int* out_len);
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void Free3(float*** x, int p, int h);
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