import torch
x = torch.tensor(3.0)
y = torch.tensor(2.0)
x + y, x * y, x / y, x ** y
(tensor(5.), tensor(6.), tensor(1.5000), tensor(9.))
x = torch.arange(4)
x
tensor([0, 1, 2, 3])
x[3]
tensor(3)
len(x)
4
x.shape
torch.Size([4])
# 5列4行
A = torch.arange(20).reshape(5,4)
A
tensor([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15],
[16, 17, 18, 19]])
A.T
# 行列转换
tensor([[ 0, 4, 8, 12, 16],
[ 1, 5, 9, 13, 17],
[ 2, 6, 10, 14, 18],
[ 3, 7, 11, 15, 19]])
B = torch.tensor([[1,2,3],[2,0,4],[3,4,5]])
B
tensor([[1, 2, 3],
[2, 0, 4],
[3, 4, 5]])
B = B.T
B
tensor([[1, 2, 3],
[2, 0, 4],
[3, 4, 5]])
X = torch.arange(24).reshape(2,3,4)
X
tensor([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
A = torch.arange(20, dtype=torch.float32).reshape(5, 4)
B = A.clone() # 通过分配新内存,将A的一个副本分配给B
A, A + B
(tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[12., 13., 14., 15.],
[16., 17., 18., 19.]]),
tensor([[ 0., 2., 4., 6.],
[ 8., 10., 12., 14.],
[16., 18., 20., 22.],
[24., 26., 28., 30.],
[32., 34., 36., 38.]]))
A * B
tensor([[ 0., 1., 4., 9.],
[ 16., 25., 36., 49.],
[ 64., 81., 100., 121.],
[144., 169., 196., 225.],
[256., 289., 324., 361.]])
a = 2
X = torch.arange(24).reshape(2, 3, 4)
a + X, (a * X).shape
(tensor([[[ 2, 3, 4, 5],
[ 6, 7, 8, 9],
[10, 11, 12, 13]],
[[14, 15, 16, 17],
[18, 19, 20, 21],
[22, 23, 24, 25]]]),
torch.Size([2, 3, 4]))
x = torch.arange(4, dtype=torch.float32)
x, x.sum()
(tensor([0., 1., 2., 3.]), tensor(6.))
A.shape, A.sum()
(torch.Size([5, 4]), tensor(190.))
A_sum_axis0 = A.sum(axis=0) #列累加
A_sum_axis0, A_sum_axis0.shape
(tensor([40., 45., 50., 55.]), torch.Size([4]))
A_sum_axis1 = A.sum(axis=1) #行累加
A_sum_axis1, A_sum_axis1.shape
(tensor([ 6., 22., 38., 54., 70.]), torch.Size([5]))
A.sum(axis=[0, 1]) # 结果和A.sum()相同
tensor(190.)
A.mean(), A.sum() / A.numel()
(tensor(9.5000), tensor(9.5000))
A.mean(axis=0), A.sum(axis=0) / A.shape[0]
(tensor([ 8., 9., 10., 11.]), tensor([ 8., 9., 10., 11.]))
sum_A = A.sum(axis=1, keepdims=True)
sum_A
tensor([[ 6.],
[22.],
[38.],
[54.],
[70.]])
A / sum_A
tensor([[0.0000, 0.1667, 0.3333, 0.5000],
[0.1818, 0.2273, 0.2727, 0.3182],
[0.2105, 0.2368, 0.2632, 0.2895],
[0.2222, 0.2407, 0.2593, 0.2778],
[0.2286, 0.2429, 0.2571, 0.2714]])
A.cumsum(axis=0)
tensor([[ 0., 1., 2., 3.],
[ 4., 6., 8., 10.],
[12., 15., 18., 21.],
[24., 28., 32., 36.],
[40., 45., 50., 55.]])
y = torch.ones(4, dtype = torch.float32)
x, y, torch.dot(x, y)
(tensor([0., 1., 2., 3.]), tensor([1., 1., 1., 1.]), tensor(6.))
torch.sum(x * y)
tensor(6.)
A.shape, x.shape, torch.mv(A, x)
(torch.Size([5, 4]), torch.Size([4]), tensor([ 14., 38., 62., 86., 110.]))
A
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[12., 13., 14., 15.],
[16., 17., 18., 19.]])
B = torch.ones(4, 3)
B
tensor([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])
Cij=∑k=1nAik⋅BkjC_{ij} = \sum_{k = 1}^{n} A_{ik} \cdot B_{kj}Cij=∑k=1nAik⋅Bkj
有点复杂 还好我脑瓜子灵活
B = torch.ones(4, 3)
torch.mm(A, B) #这个有点复杂
tensor([[ 6., 6., 6.],
[22., 22., 22.],
[38., 38., 38.],
[54., 54., 54.],
[70., 70., 70.]])