Super Kai (Kazuya Ito)*Memos:
- My post explains adjoint(), mH and mT.
- My post explains reshape() and view().
- My post explains permute().
- My post explains movedim().
transpose() can get the 0D or more D transposed tensor of zero or more elements without losing data from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
transpose()can be used with torch or a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). - The 2nd argument with
torchor the 1st argument with a tensor isdim0(Required-Type:int). - The 3rd argument with
torchor the 2nd argument with a tensor isdim1(Required-Type:int). -
swapaxes() and swapdims() are the aliases of
transpose().
import torch
my_tensor = torch.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]]])
torch.transpose(input=my_tensor, dim0=0, dim1=0)
my_tensor.transpose(dim0=0, dim1=0)
torch.transpose(input=my_tensor, dim0=1, dim1=1)
torch.transpose(input=my_tensor, dim0=2, dim1=2)
torch.transpose(input=my_tensor, dim0=1, dim1=-2)
torch.transpose(input=my_tensor, dim0=2, dim1=-1)
torch.transpose(input=my_tensor, dim0=2, dim1=-2)
torch.transpose(input=my_tensor, dim0=-1, dim1=2)
torch.transpose(input=my_tensor, dim0=-2, dim1=1)
torch.transpose(input=my_tensor, dim0=-1, dim1=-1)
torch.transpose(input=my_tensor, dim0=-2, dim1=-2)
# 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]]])
torch.transpose(input=my_tensor, dim0=0, dim1=1)
torch.transpose(input=my_tensor, dim0=1, dim1=0)
torch.transpose(input=my_tensor, dim0=0, dim1=-2)
torch.transpose(input=my_tensor, dim0=-2, dim1=0)
# tensor([[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
# [[3, 4, 5], [ 9, 10, 11], [15, 16, 17], [21, 22, 23]]])
torch.transpose(input=my_tensor, dim0=0, dim1=2)
torch.transpose(input=my_tensor, dim0=2, dim1=0)
torch.transpose(input=my_tensor, dim0=0, dim1=-1)
torch.transpose(input=my_tensor, dim0=-1, dim1=0)
# tensor([[[0, 6, 12, 18], [3, 9, 15, 21]],
# [[1, 7, 13, 19], [4, 10, 16, 22]],
# [[2, 8, 14, 20], [5, 11, 17, 23]]])
torch.transpose(input=my_tensor, dim0=1, dim1=2)
torch.transpose(input=my_tensor, dim0=2, dim1=1)
torch.transpose(input=my_tensor, dim0=1, dim1=-1)
torch.transpose(input=my_tensor, dim0=-1, dim1=1)
torch.transpose(input=my_tensor, dim0=-1, dim1=-2)
torch.transpose(input=my_tensor, dim0=-2, dim1=-1)
torch.transpose(input=my_tensor, dim0=-2, dim1=2)
# tensor([[[0, 3], [1, 4], [2, 5]],
# [[6, 9], [7, 10], [8, 11]],
# [[12, 15], [13, 16], [14, 17]],
# [[18, 21], [19, 22], [20, 23]]])
my_tensor = torch.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.]]])
torch.transpose(input=my_tensor, dim0=0, dim1=0)
# 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.]]])
my_tensor = torch.tensor([[[0.+0.j, 1.+0.j, 2.+0.j],
[3.+0.j, 4.+0.j, 5.+0.j]],
[[6.+0.j, 7.+0.j, 8.+0.j],
[9.+0.j, 10.+0.j, 11.+0.j]],
[[12.+0.j, 13.+0.j, 14.+0.j],
[15.+0.j, 16.+0.j, 17.+0.j]],
[[18.+0.j, 19.+0.j, 20.+0.j],
[21.+0.j, 22.+0.j, 23.+0.j]]])
torch.transpose(input=my_tensor, dim0=0, dim1=0)
# tensor([[[0.+0.j, 1.+0.j, 2.+0.j],
# [3.+0.j, 4.+0.j, 5.+0.j]],
# [[6.+0.j, 7.+0.j, 8.+0.j],
# [9.+0.j, 10.+0.j, 11.+0.j]],
# [[12.+0.j, 13.+0.j, 14.+0.j],
# [15.+0.j, 16.+0.j, 17.+0.j]],
# [[18.+0.j, 19.+0.j, 20.+0.j],
# [21.+0.j, 22.+0.j, 23.+0.j]]])
my_tensor = torch.tensor([[[True, False, True], [True, False, True]],
[[False, True, False], [False, True, False]],
[[True, False, True], [True, False, True]],
[[False, True, False], [False, True, False]]])
torch.transpose(input=my_tensor, dim0=0, dim1=0)
# tensor([[[True, False, True], [True, False, True]],
# [[False, True, False], [False, True, False]],
# [[True, False, True], [True, False, True]],
# [[False, True, False], [False, True, False]]])
t() can get the 0D, 1D or 2D transposed tensor of zero or more elements without losing data from the 0D, 1D or 2D tensor of zero or more elements as shown below:
*Memos:
-
t()can be used withtorchor a tensor. - The 1st argument(
input) withtorchor using a tensor(Required-Type:tensorofint,float,complexorbool). -
t()is equivalent totranspose(input=my_tensor, dim0=0, dim1=1).
import torch
my_tensor = torch.tensor(0)
torch.t(input=my_tensor)
my_tensor.t()
# tensor(0)
my_tensor = torch.tensor([0, 1, 2])
torch.t(input=my_tensor)
# tensor([0, 1, 2])
my_tensor = torch.tensor([[0, 1, 2], [3, 4, 5],
[6, 7, 8], [9, 10, 11]])
torch.t(input=my_tensor)
# tensor([[0, 3, 6, 9],
# [1, 4, 7, 10],
# [2, 5, 8, 11]])
my_tensor = torch.tensor([[0., 1., 2.], [3., 4., 5.],
[6., 7., 8.], [9., 10., 11.]])
torch.t(input=my_tensor)
# tensor([[0., 3., 6., 9.],
# [1., 4., 7., 10.],
# [2., 5., 8., 11.]])
my_tensor = torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j],
[3.+0.j, 4.+0.j, 5.+0.j],
[6.+0.j, 7.+0.j, 8.+0.j],
[9.+0.j, 10.+0.j, 11.+0.j]])
torch.t(input=my_tensor)
# tensor([[0.+0.j, 3.+0.j, 6.+0.j, 9.+0.j],
# [1.+0.j, 4.+0.j, 7.+0.j, 10.+0.j],
# [2.+0.j, 5.+0.j, 8.+0.j, 11.+0.j]])
my_tensor = torch.tensor([[True, False, True], [True, False, True],
[False, True, False], [False, True, False]])
torch.t(input=my_tensor)
# tensor([[True, True, False, False],
# [False, False, True, True],
# [True, True, False, False]])
