atleast_1d() can get the view of the one or more 1D or more D tensors of zero or more elements by only changing one or more 0D tensors to one or more 1D tensors from the one or more 0D or more D tensors of zero or more elements as shown below:
*Memos:
-
atleast_1d()
can be used with torch but not with a tensor. - The 1st or more arguments with
torch
are*tensors
(Required-Type:tensor
ofint
,float
,complex
orbool
ortuple
or list oftensor
ofint
,float
,complex
orbool
): *Memos:- If setting more than one tensors, a tuple of tensors is returned otherwise a tensor is returned.
- Don't use any keyword like
*tensors=
,tensor
orinput
.
- Setting no arguments returns an empty tuple.
import torch
tensor0 = torch.tensor(2) # 0D tensor
torch.atleast_1d(tensor0)
# tensor([2])
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor3 = torch.tensor([[[2, 7, 4], [8, 3, 2]], # 3D tensor
[[5, 0, 8], [3, 6, 1]]])
tensor4 = torch.tensor([[[[2, 7, 4], [8, 3, 2]], # 4D tensor
[[5, 0, 8], [3, 6, 1]]],
[[[9, 4, 7], [1, 0, 5]],
[[6, 7, 4], [2, 1, 9]]]])
torch.atleast_1d(tensor0, tensor1, tensor2, tensor3, tensor4)
torch.atleast_1d((tensor0, tensor1, tensor2, tensor3, tensor4))
# (tensor([2]),
# tensor([2, 7, 4]),
# tensor([[2, 7, 4], [8, 3, 2]]),
# tensor([[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]]),
# tensor([[[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]],
# [[[9, 4, 7], [1, 0, 5]],
# [[6, 7, 4], [2, 1, 9]]]]))
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2., 7., 4.], # 2D tensor
[8., 3., 2.]])
tensor3 = torch.tensor([[[2.+0.j, 7.+0.j, 4.+0.j], # 3D tensor
[8.+0.j, 3.+0.j, 2.+0.j]],
[[5.+0.j, 0.+0.j, 8.+0.j],
[3.+0.j, 6.+0.j, 1.+0.j]]])
tensor4 = torch.tensor([[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]],
[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]]])
# 4D tensor
torch.atleast_1d(tensor0, tensor1, tensor2, tensor3, tensor4)
# (tensor([2]),
# tensor([2, 7, 4]),
# tensor([[2., 7., 4.],
# [8., 3., 2.]]),
# tensor([[[2.+0.j, 7.+0.j, 4.+0.j],
# [8.+0.j, 3.+0.j, 2.+0.j]],
# [[5.+0.j, 0.+0.j, 8.+0.j],
# [3.+0.j, 6.+0.j, 1.+0.j]]]),
# tensor([[[[True, False, True], [False, True, False]],
# [[True, False, True], [False, True, False]]],
# [[[True, False, True], [False, True, False]],
# [[True, False, True], [False, True, False]]]]))
torch.atleast_1d()
# ()
atleast_2d() can get the view of the one or more 2D or more D tensors of zero or more elements by only changing one or more 0D or 1D tensors to one or more 2D tensors from the one or more 0D or more D tensors of zero or more elements as shown below:
*Memos:
-
atleast_2d()
can be used withtorch
but not with a tensor. - The 1st or more arguments with
torch
are*tensors
(Required-Type:tensor
ofint
,float
,complex
orbool
ortuple
or list oftensor
ofint
,float
,complex
orbool
): *Memos:- If setting more than one tensors, a tuple of tensors is returned otherwise a tensor is returned.
- Don't use any keyword like
*tensors=
,tensor
orinput
.
- Setting no arguments returns an empty tuple.
import torch
tensor0 = torch.tensor(2) # 0D tensor
torch.atleast_2d(tensor0)
# tensor([[2]])
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor3 = torch.tensor([[[2, 7, 4], [8, 3, 2]], # 3D tensor
[[5, 0, 8], [3, 6, 1]]])
tensor4 = torch.tensor([[[[2, 7, 4], [8, 3, 2]], # 4D tensor
[[5, 0, 8], [3, 6, 1]]],
[[[9, 4, 7], [1, 0, 5]],
[[6, 7, 4], [2, 1, 9]]]])
torch.atleast_2d(tensor0, tensor1, tensor2, tensor3, tensor4)
torch.atleast_2d((tensor0, tensor1, tensor2, tensor3, tensor4))
# (tensor([[2]]),
# tensor([[2, 7, 4]]),
# tensor([[2, 7, 4], [8, 3, 2]]),
# tensor([[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]]),
# tensor([[[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]],
# [[[9, 4, 7], [1, 0, 5]],
# [[6, 7, 4], [2, 1, 9]]]]))
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2., 7., 4.], # 2D tensor
[8., 3., 2.]])
tensor3 = torch.tensor([[[2.+0.j, 7.+0.j, 4.+0.j], # 3D tensor
[8.+0.j, 3.+0.j, 2.+0.j]],
[[5.+0.j, 0.+0.j, 8.+0.j],
[3.+0.j, 6.+0.j, 1.+0.j]]])
tensor4 = torch.tensor([[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]],
[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]]])
# 4D tensor
torch.atleast_2d(tensor0, tensor1, tensor2, tensor3, tensor4)
# (tensor([[2]]),
# tensor([[2, 7, 4]]),
# tensor([[2., 7., 4.],
# [8., 3., 2.]]),
# tensor([[[2.+0.j, 7.+0.j, 4.+0.j],
# [8.+0.j, 3.+0.j, 2.+0.j]],
# [[5.+0.j, 0.+0.j, 8.+0.j],
# [3.+0.j, 6.+0.j, 1.+0.j]]]),
# tensor([[[[True, False, True], [False, True, False]],
# [[True, False, True], [False, True, False]]],
# [[[True, False, True], [False, True, False]],
# [[True, False, True], [False, True, False]]]]))
torch.atleast_2d()
# ()
atleast_3d() can get the view of the one or more 3D or more D tensors of zero or more elements by only changing one or more 0D, 1D or 2D tensors to one or more 3D tensors from the one or more 0D or more D tensors of zero or more elements as shown below:
*Memos:
-
atleast_3d()
can be used withtorch
but not with a tensor. - The 1st or more arguments with
torch
are*tensors
(Required-Type:tensor
ofint
,float
,complex
orbool
ortuple
or list oftensor
ofint
,float
,complex
orbool
): *Memos:- If setting more than one tensors, a tuple of tensors is returned otherwise a tensor is returned.
- Don't use any keyword like
*tensors=
,tensor
orinput
.
- Setting no arguments returns an empty tuple.
import torch
tensor0 = torch.tensor(2) # 0D tensor
torch.atleast_3d(tensor0)
# tensor([[[2]]])
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor3 = torch.tensor([[[2, 7, 4], [8, 3, 2]], # 3D tensor
[[5, 0, 8], [3, 6, 1]]])
tensor4 = torch.tensor([[[[2, 7, 4], [8, 3, 2]], # 4D tensor
[[5, 0, 8], [3, 6, 1]]],
[[[9, 4, 7], [1, 0, 5]],
[[6, 7, 4], [2, 1, 9]]]])
torch.atleast_3d(tensor0, tensor1, tensor2, tensor3, tensor4)
torch.atleast_3d((tensor0, tensor1, tensor2, tensor3, tensor4))
# (tensor([[[2]]]),
# tensor([[[2], [7], [4]]]),
# tensor([[[2], [7], [4]],
# [[8], [3], [2]]]),
# tensor([[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]]),
# tensor([[[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]]],
# [[[9, 4, 7], [1, 0, 5]],
# [[6, 7, 4], [2, 1, 9]]]]))
tensor0 = torch.tensor(2) # 0D tensor
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([[2., 7., 4.], # 2D tensor
[8., 3., 2.]])
tensor3 = torch.tensor([[[2.+0.j, 7.+0.j, 4.+0.j], # 3D tensor
[8.+0.j, 3.+0.j, 2.+0.j]],
[[5.+0.j, 0.+0.j, 8.+0.j],
[3.+0.j, 6.+0.j, 1.+0.j]]])
tensor4 = torch.tensor([[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]],
[[[True, False, True], [False, True, False]],
[[True, False, True], [False, True, False]]]])
# 4D tensor
torch.atleast_3d(tensor0, tensor1, tensor2, tensor3, tensor4)
# (tensor([[[2]]]),
# tensor([[[2], [7], [4]]]),
# tensor([[[2.], [7.], [4.]],
# [[8.], [3.], [2.]]]),
# tensor([[[2.+0.j, 7.+0.j, 4.+0.j],
# [8.+0.j, 3.+0.j, 2.+0.j]],
# [[5.+0.j, 0.+0.j, 8.+0.j],
# [3.+0.j, 6.+0.j, 1.+0.j]]]),
# tensor([[[[True, False, True], [False, True, False]],
# [[True, False, True], [False, True, False]]],
# [[[True, False, True], [False, True, False]],
torch.atleast_3d()
# ()
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