Pytorch Tensor

import torch

torch.manual_seed(1)

<torch._C.Generator at 0x7fd3e757fa30>

x = torch.rand((4, 3), dtype=torch.float32)
print(x)

tensor([[0.7576, 0.2793, 0.4031],
        [0.7347, 0.0293, 0.7999],
        [0.3971, 0.7544, 0.5695],
        [0.4388, 0.6387, 0.5247]])

print(x.shape)

rows, columns = x.shape
rows, columns

torch.Size([4, 3])

(4, 3)

x.T, x.t()

(tensor([[0.7576, 0.7347, 0.3971, 0.4388],
         [0.2793, 0.0293, 0.7544, 0.6387],
         [0.4031, 0.7999, 0.5695, 0.5247]]),
 tensor([[0.7576, 0.7347, 0.3971, 0.4388],
         [0.2793, 0.0293, 0.7544, 0.6387],
         [0.4031, 0.7999, 0.5695, 0.5247]]))

x + x

tensor([[1.5153, 0.5586, 0.8061],
        [1.4694, 0.0586, 1.5997],
        [0.7943, 1.5087, 1.1390],
        [0.8776, 1.2774, 1.0493]])

x @ x.T

tensor([[0.8145, 0.8872, 0.7411, 0.7223],
        [0.8872, 1.1804, 0.7694, 0.7607],
        [0.7411, 0.7694, 1.0511, 0.9549],
        [0.7223, 0.7607, 0.9549, 0.8757]])

x * x

tensor([[0.5740, 0.0780, 0.1625],
        [0.5398, 0.0009, 0.6398],
        [0.1577, 0.5691, 0.3243],
        [0.1925, 0.4079, 0.2753]])

u, s, vT = torch.svd(x)

u, s, vT

(tensor([[-0.4645,  0.2591,  0.8391],
         [-0.5298,  0.6808, -0.5055],
         [-0.5164, -0.5608, -0.1963],
         [-0.4866, -0.3935, -0.0422]]),
 tensor([1.8458, 0.6668, 0.2649]),
 tensor([[-0.6284,  0.4516,  0.6334],
         [-0.4581, -0.8729,  0.1679],
         [-0.6287,  0.1847, -0.7554]]))