PyTorch Tutorial 02 - Tensor Basics
教程Python代码如下：
import torch
import numpy as np
"""张量"""
#empty,空张量，值未初始化
x = torch.empty(1)
print(x)
x = torch.empty(3)
print(x)
x = torch.empty(2,3)
print(x)
x = torch.empty(2,2,3)
print(x)
#rand随机值张量
x = torch.rand(2,2)
print(x)
#zeros,0张量，这里x类型为float
x = torch.zeros(2,2)
print(x)
#ones,1张量
x = torch.ones(2,2)
print(x)
print(x.dtype)#这里x类型为float
#dtype,类型
x = torch.ones(2,2,dtype=torch.int)
print(x)
print(x.dtype)#这里x类型为int
x = torch.ones(2,2,dtype=torch.double)
print(x)
print(x.dtype)#这里x类型为double
#size(),大小
x = torch.ones(2,2,dtype=torch.double)
print(x.size())#torch.Size([2, 2])
#tensor
x = torch.tensor([2.5,0.1])
print(x)
"""加、减、乘运算"""
#加
x = torch.rand(2,2)
y = torch.rand(2,2)
print(x)
print(y)
z = x + y
print(z)
z = torch.add(x,y) #与z = x + y同
print(z)
print("z")
y.add_(x)
print(y)
print("y = y + x")
#减
x = torch.rand(2,2)
y = torch.rand(2,2)
print(x)
print(y)
z = x - y
z = torch.sub(x,y)
print(z)
print("z = x - y")
y.sub_(x)
print(y)
print("y = y - x")
#乘
x = torch.rand(2,2)
y = torch.rand(2,2)
print(x)
print(y)
z = x * y
z = torch.mul(x,y)
print(z)
print("z = x * y")
y.mul_(x)
print(y)
print("y = y * x")
#除
x = torch.rand(2,2)
y = torch.rand(2,2)
print(x)
print(y)
z = x / y
z = torch.div(x,y)
print(z)
print("z = x / y")
y.div_(x)
print(y)
print("y = y / x")
"""切片"""
x = torch.rand(5,3)
print(x)
print(x[0,:])#第一行
print(x[:,0])#第一列
print(x[0,0])#取某下标对应的元素
print(x[0,0].item())#item()的作用是从包含单个元素的张量中取出该元素的值，并保持元素类型不变，只有张量中只有一个元素时才能用item()方法
"""重塑"""
x = torch.rand(4,4)
print(x)
y = x.view(16)
print(y)
y = x.view(-1,8) #y = x.view(2,8),-1自动补齐
print(y)
print(y.size())
"""torch和numpy的转换"""
#torch转numpy
a = torch.ones(5)
print(a) #tensor([1., 1., 1., 1., 1.])
b = a.numpy()
print(b) #[1. 1. 1. 1. 1.]
print(type(b)) #<class 'numpy.ndarray'>
#a,b两个张量如果在CPU而不是GPU那么ab共享同一个位置，一个值改变另一个也会变,因为ab指向相同的内存位置
a.add_(1)
print(a) #tensor([2., 2., 2., 2., 2.])
print(b) #[2. 2. 2. 2. 2.]
#numpy转torch
a = np.ones(5)
print(a)
b = torch.from_numpy(a)
print(b)
a += 1
print(a)
print(b)
"""程序迁移至GPU"""
print("\n" + "程序迁移至GPU")
if torch.cuda.is_available():
device = torch.device("cuda")
#创建一个张量，并将其放在GPU上
x = torch.ones(5,device=device)
#先创建一个张量，再将其迁移到GPU上
y = torch.ones(5)
y = y.to(device)
z = x + y #这将会在GPU上执行，而且可能会快很多
print(z)
print("GPU z")
"""z.numpy()会报错，因为numpy()只能处理CPU张量，而不能处理GPU张量，所以不能把GPU张量转换回numpy，所以我们必须把其迁移回CPU"""
#将张量迁移回CPU
z = z.to("cpu")
print(z)
print("CPU z")
