LibTorch之张量操作与线性回归

L i b T o r c h 之张量操作与线性回归 LibTorch之张量操作与线性回归 LibTorch之张量操作与线性回归

pytorch到libtorch，一般就是[]到{}的变化

• 一 张量初始化
• 二 深浅拷贝
• 三 C++常用数据类型转换Tensor型变量
• 四 张量维度变换
• 五 截取张量
• 六 张量的四则运算
• 七 获取张量的大小
• 8.
• 9.
• 0.

一 张量初始化 1.torch::zeros

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::zeros({ 2,3 });
	cout << b << endl;

	system("pause");
	return 0;
}

2.torch::ones

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::ones({ 2,3 });
	cout << b << endl;
	system("pause");
	return 0;
}

3.torch::eye

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::eye(3);
	cout << b << endl;
	system("pause");
	return 0;
}

4.torch::full

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::full({ 2,3 }, 999);
	cout << b << endl;
	system("pause");
	return 0;
}

5.torch::tensor

#include 
using namespace std;
int main()
{
	torch::Tensor 	b = torch::tensor({ {1,2,3},{4,5,6} });
	cout << b << endl;

	system("pause");
	return 0;
}

6.torch::rand(产生0-1之间的随机值)

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::rand({ 2,3 });
	cout << b << endl;

	system("pause");
	return 0;
}

7.torch::randn(取正态分布服从N(0,1)的随机值)

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::randn({ 2,3 });
	cout << b << endl;

	system("pause");
	return 0;
}

8.torch::randint（取[min,max)的随机整型数值）

#include 
using namespace std;
int main()
{
	torch::Tensor b = torch::randint(1, 10, { 2,3 });
	cout << b << endl;

	system("pause");
	return 0;
}

二 深浅拷贝 1.浅拷贝

torch::Tensor b1 = torch::zeros({3,4});
// 浅拷贝
torch::Tensor b2 =b1;
torch::Tensor b2 = torch::Tensor(b1);

#include 
using namespace std;
int main()
{

	torch::Tensor b1 = torch::zeros({ 2,3 });
	cout << b1 << endl;
	torch::Tensor b2 = b1;
	//cout << b2 << endl;
	//torch::Tensor b2 = torch::Tensor(b1);
	cout << b2 << endl;
	// 
	b2[0][0] = 888;
	cout << "b1:" << endl;
	cout << b1 << endl;
	system("pause");
	return 0;
}

2.深拷贝

torch::Tensor b1 = torch::zeros({3,4});
torch::Tensor b2 = b.clone();

#include 
using namespace std;
int main()
{

	torch::Tensor b1 = torch::zeros({ 2,3 });
	cout << "b1:" << endl;
	cout << b1 << endl;
	torch::Tensor b2 = b1.clone();;
	b2[0][0] = 888;
	cout << "b2:" << endl;
	cout << b2 << endl;
	cout << "b1:" << endl;
	cout << b1 << endl;
	system("pause");
	return 0;
}

同大小张量

b2 = torch::zeros_like(b1);
b2 = torch::ones_like(b1);
b2 = torch::rand_like(b1,torch::kFloat);

#include 
using namespace std;
int main()
{

	torch::Tensor b1 = torch::zeros({ 2,3 });

	torch::Tensor b2 = torch::zeros_like(b1);
	cout << b2 << endl;
	b2 = torch::ones_like(b1);
	cout << b2 << endl;
	b2 = torch::rand_like(b1, torch::kFloat);
	cout << b2 << endl;

	system("pause");
	return 0;
}

三 C++常用数据类型转换Tensor型变量 1.数组

#include 
using namespace std;
int main()
{

	int a[10] = { 1,2,3,4,5,6 };
    b = torch::from_blob(a, { 6 }, torch::kInt);
	cout << b << endl;
   
	system("pause");
	return 0;
}

float data[] = { 1, 2, 3,
                 4, 5, 6 };
torch::Tensor f = torch::from_blob(data, {2, 3});

2.向量

#include 
using namespace std;
int main()
{

	std::vector a = { 1,2,3,4,5,6 };
    // 方式1
	torch::Tensor b= torch::tensor(a);
	cout << b << endl;
	// 方式2
	b = torch::from_blob(a.data(), { 2 }, torch::kFloat);
	cout << b << endl;

	system("pause");
	return 0;
}

四 张量维度变换 1.拼接：torch::cat 2.堆叠：torch::stack 3.unsqueeze

    torch::Tensor a = torch::rand({2,3});
    std::cout< 
五 截取张量
 
六 张量的四则运算
 
七 获取张量的大小
 
#include 
using namespace std;
int main()
{

	torch::Tensor rgb = torch::randn({ 1, 3, 6, 6 });
	cout << rgb << endl;
	cout << rgb.sizes() << endl;

	system("pause");
	return 0;
}
 
 

 

 

 
https://blog.csdn.net/Flag_ing/article/details/109628297 
https://www.nmxjp.com/w/59/54216/0.html 
https://pytorch.org/cppdocs/notes/tensor_creation.html