C++卷积神经网络

C++卷积神经网络

#include"TP_NNW.h"
#include
#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  20
			float*** 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y2
			Vector2 Cout2;//记录池化后的尺寸   10
			float*** 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  20
			float*** 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y2
			Vector2 Cout2;//记录池化后的尺寸   10
			float*** 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(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(i, j) = Vec3b(255, 255, 255);
			}
			/*else
				img.at(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(j - 30, i - (35 + (k * 80))) = img.at(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
#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
#include
#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;
public:
	T* one;
	int num;
	int count;
	hot_one() { this->fz = false; }
	hot_one(int type_num, int set_num = 0)
	{
		type_num = type_num <= 0 ? 1 : type_num;
		if (set_num >= type_num)
			set_num = 0;
		this->count = type_num;
		this->fz = true;
		this->num = set_num;
		this->one = new T[type_num]{ 0 };
		this->one[set_num] = 1;
	}
	void re(int set_num)
	{
		this->one[num] = 0;
		this->num = set_num;
		this->one[this->num] = 1;
	}
	void release()
	{
		if (this->fz)delete one;
		this->fz = false;
	}
	~hot_one()
	{
		this->release();
	}
};
int* bList(int distance, int max, int* out_len);
void Free2(float** x, int h);
void Free3(float*** x, int p, int h);
void kron(float** out, Vector2& out_len, float** inp, Vector2& inp_len, float** filter,
	Vector2& filter_len);