Tensor语法进阶

 1.  常用操作

1.1 GPU判断

# 判断是否有GPU，若有则进行部署
if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')
或
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device.type)
# cpu

# 进行部署
x = torch.tensor([1, 2, 3], device=device)
data = data.to(device)

1.2 tensor与Tensor的区别

x = torch.Tensor([1, 2])
# tensor([1., 2.])
print(x.type())
# torch.FloatTensor

y = torch.tensor([1, 2])
# tensor([1, 2])
print(y.type())
# torch.LongTensor

torch.tensor(5.6)
# tensor(5.6000)
torch.Tensor(5.6)
# TypeError: new(): data must be a sequence (got float)
torch.Tensor([5.6])
# tensor([5.6000])

2. 分布

2.1  指定分布的tensor

# 设定随机种子
torch.manual_seed(123)

# 值在0-1之间的均匀分布
y = torch.rand(5)
# tensor([0.3423, 0.2636, 0.8128, 0.1354, 0.3517])

# 均值为0，方差为1