squeeze() can get the 0D or more D tensor of the zero or more elements whose zero or more dimensions are removed if the size is 1
from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
squeeze()
can be used with torch or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). - The 2nd argument with
torch
or the 1st or more arguments with a tensor aredim
(Optional-Type:int
,tuple
ofint
orlist
ofint
): *Memos:- Each number must be unique.
- It can remove the specific zero or more dimensions whose size is
1
. - If the size is not
1
, zero or more dimensions are not removed even if you set it.
import torch
my_tensor = torch.tensor([[[[0], [1]],
[[2], [3]],
[[4], [5]]]])
torch.squeeze(input=my_tensor)
my_tensor.squeeze()
torch.squeeze(input=my_tensor, dim=(0, 3))
my_tensor.squeeze(dim=(0, 3))
my_tensor.squeeze(0, 3)
torch.squeeze(input=my_tensor, dim=(0, 1, 3))
my_tensor.squeeze(dim=(0, 1, 3))
my_tensor.squeeze(0, 1, 3)
etc.
torch.squeeze(input=my_tensor, dim=(0, 1, 2, 3))
my_tensor.squeeze(dim=(0, 1, 2, 3))
my_tensor.squeeze(0, 1, 2, 3)
etc.
# tensor([[0, 1],
# [2, 3],
# [4, 5]])
torch.squeeze(input=my_tensor, dim=0)
torch.squeeze(input=my_tensor, dim=-4)
torch.squeeze(input=my_tensor, dim=(0,))
torch.squeeze(input=my_tensor, dim=(-4,))
torch.squeeze(input=my_tensor, dim=(0, 1))
torch.squeeze(input=my_tensor, dim=(0, 2))
torch.squeeze(input=my_tensor, dim=(0, -2))
torch.squeeze(input=my_tensor, dim=(0, -3))
torch.squeeze(input=my_tensor, dim=(1, 0))
etc.
torch.squeeze(input=my_tensor, dim=(0, 1, 2))
etc.
# tensor([[[0], [1]],
# [[2], [3]],
# [[4], [5]]])
torch.squeeze(input=my_tensor, dim=1)
torch.squeeze(input=my_tensor, dim=2)
torch.squeeze(input=my_tensor, dim=-2)
torch.squeeze(input=my_tensor, dim=-3)
torch.squeeze(input=my_tensor, dim=())
torch.squeeze(input=my_tensor, dim=(1,))
torch.squeeze(input=my_tensor, dim=(2,))
torch.squeeze(input=my_tensor, dim=(-2,))
torch.squeeze(input=my_tensor, dim=(-3,))
torch.squeeze(input=my_tensor, dim=(1, 2))
etc.
# tensor([[[[0], [1]],
# [[2], [3]],
# [[4], [5]]]])
torch.squeeze(input=my_tensor, dim=3)
torch.squeeze(input=my_tensor, dim=-1)
torch.squeeze(input=my_tensor, dim=(3,))
torch.squeeze(input=my_tensor, dim=(-1,))
torch.squeeze(input=my_tensor, dim=(1, 3))
torch.squeeze(input=my_tensor, dim=(1, -1))
torch.squeeze(input=my_tensor, dim=(2, 3))
torch.squeeze(input=my_tensor, dim=(2, -1))
torch.squeeze(input=my_tensor, dim=(3, 1))
etc.
torch.squeeze(input=my_tensor, dim=(1, 2, 3))
etc.
# tensor([[[0, 1],
# [2, 3],
# [4, 5]]])
my_tensor = torch.tensor([[[[0.], [1.]],
[[2.], [3.]],
[[4.], [5.]]]])
torch.squeeze(input=my_tensor)
# tensor([[0., 1.],
# [2., 3.],
# [4., 5.]])
my_tensor = torch.tensor([[[[0.+0.j], [1.+0.j]],
[[2.+0.j], [3.+0.j]],
[[4.+0.j], [5.+0.j]]]])
torch.squeeze(input=my_tensor)
# tensor([[0.+0.j, 1.+0.j],
# [2.+0.j, 3.+0.j],
# [4.+0.j, 5.+0.j]])
my_tensor = torch.tensor([[[[True], [False]],
[[False], [True]],
[[True], [False]]]])
torch.squeeze(input=my_tensor)
# tensor([[True, False],
# [False, True],
# [True, False]])
unsqueeze() can get the 1D or more D tensor of zero or more elements with additional dimension whose size is 1
from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
-
unsqueeze()
can be used withtorch
or a tensor. - The 1st argument(
input
) withtorch
or using a tensor(Required-Type:tensor
ofint
,float
,complex
orbool
). - The 2nd argument with
torch
or the 1st argument with a tensor isdim
(Required-Type:int
). *It can add the dimension whose size is1
to a specific position.
import torch
my_tensor = torch.tensor([[0, 1, 2],
[3, 4, 5],
[6, 7, 8],
[10, 11, 12]])
torch.unsqueeze(input=my_tensor, dim=0)
my_tensor.unsqueeze(dim=0)
torch.unsqueeze(input=my_tensor, dim=-3)
# tensor([[[0, 1, 2],
# [3, 4, 5],
# [6, 7, 8]
# [10, 11, 12]]])
torch.unsqueeze(input=my_tensor, dim=1)
torch.unsqueeze(input=my_tensor, dim=-2)
# tensor([[[0, 1, 2]],
# [[3, 4, 5]],
# [[6, 7, 8]]
# [[10, 11, 12]]])
torch.unsqueeze(input=my_tensor, dim=2)
torch.unsqueeze(input=my_tensor, dim=-1)
# tensor([[[0], [1], [2]],
# [[3], [4], [5]],
# [[6], [7], [8]],
# [[10], [11], [12]]])
torch.unsqueeze(input=my_tensor, dim=3)
torch.unsqueeze(input=my_tensor, dim=-1)
# 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., 1., 2.],
[3., 4., 5.],
[6., 7., 8.],
[10., 11., 12.]])
torch.unsqueeze(input=my_tensor, dim=0)
# tensor([[[0., 1., 2.],
# [3., 4., 5.],
# [6., 7., 8.],
# [10., 11., 12.]]])
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],
[10.+0.j, 11.+0.j, 12.+0.j]])
torch.unsqueeze(input=my_tensor, dim=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],
# [10.+0.j, 11.+0.j, 12.+0.j]]])
my_tensor = torch.tensor([[True, False, True],
[False, True, False],
[True, False, True],
[False, True, False]])
torch.unsqueeze(input=my_tensor, dim=0)
# tensor([[[True, False, True],
# [False, True, False],
# [True, False, True],
# [False, True, False]]])
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