TheLazy.Dev
Portfolio GitHub

Blog

python |pytorch |hstack |columnstack

hstack and column_stack in PyTorch

hyperkai

Super Kai (Kazuya Ito)

Posted on July 14, 2024

hstack and column_stack in PyTorch

Buy Me a Coffee☕

*Memos:

hstack() can get the 1D or more D horizontally(column-wisely) stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below:

*Memos:

  • hstack() can be used with torch but not with a tensor.
  • The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool). *Basically, the size of tensors must be the same.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch

tensor1 = torch.tensor(2)
tensor2 = torch.tensor(7)
tensor3 = torch.tensor(4)

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([2, 7, 4])

tensor1 = torch.tensor([2, 7, 4])
tensor2 = torch.tensor([8, 3, 2])
tensor3 = torch.tensor([5, 0, 8])

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([2, 7, 4, 8, 3, 2, 5, 0, 8])

tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]])
tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]])
tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]])

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2, 7, 4, 5, 0, 8, 9, 4, 7],
#         [8, 3, 2, 3, 6, 1, 1, 0, 5]])

tensor1 = torch.tensor([[2., 7., 4.], [8., 3., 2.]])
tensor2 = torch.tensor([[5., 0., 8.], [3., 6., 1.]])
tensor3 = torch.tensor([[9., 4., 7.], [1., 0., 5.]])

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2., 7., 4., 5., 0., 8., 9., 4., 7.],
#         [8., 3., 2., 3., 6., 1., 1., 0., 5.]])

tensor1 = torch.tensor([[2.+0.j, 7.+0.j, 4.+0.j],
                        [8.+0.j, 3.+0.j, 2.+0.j]])
tensor2 = torch.tensor([[5.+0.j, 0.+0.j, 8.+0.j],
                        [3.+0.j, 6.+0.j, 1.+0.j]])
tensor3 = torch.tensor([[9.+0.j, 4.+0.j, 7.+0.j],
                        [1.+0.j, 0.+0.j, 5.+0.j]])
torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2.+0.j, 7.+0.j, 4.+0.j, 5.+0.j, 0.+0.j,
#          8.+0.j, 9.+0.j, 4.+0.j, 7.+0.j],
#         [8.+0.j, 3.+0.j, 2.+0.j, 3.+0.j, 6.+0.j,
#          1.+0.j, 1.+0.j, 0.+0.j, 5.+0.j]])

tensor1 = torch.tensor([[True, False, True], [False, True, False]])
tensor2 = torch.tensor([[False, True, False], [True, False, True]])
tensor3 = torch.tensor([[True, False, True], [False, True, False]])

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([[True, False, True, False, True, False, True, False, True],
#         [False, True, False, True, False, True, False, True, False]])

tensor1 = torch.tensor([[[2, 7, 4]]])
tensor2 = torch.tensor([])
tensor3 = torch.tensor([[[5, 0, 8]]])

torch.hstack(tensors=(tensor1, tensor2, tensor3))
# tensor([[[2., 7., 4.],
#          [5., 0., 8.]]])
Enter fullscreen mode Exit fullscreen mode

column_stack() can get the 2D or more D horizontally stacked tensor of zero or more elements from the one or more 0D or more D tensors of zero or more elements as shown below:

*Memos:

  • column_stack() can be used with torch but not with a tensor.
  • The 1st argument with torch is tensors(Required-Type:tuple or list of tensor of int, float, complex or bool). *Basically, the size of tensors must be the same.
  • There is out argument with torch(Optional-Default:None-Type:tensor): *Memos:
    • out= must be used.
    • My post explains out argument. 
import torch

tensor1 = torch.tensor(2)
tensor2 = torch.tensor(7)
tensor3 = torch.tensor(4)

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2, 7, 4]])

tensor1 = torch.tensor([2, 7, 4])
tensor2 = torch.tensor([8, 3, 2])
tensor3 = torch.tensor([5, 0, 8])

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2, 8, 5], [7, 3, 0], [4, 2, 8]])

tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]])
tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]])
tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]])

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2, 7, 4, 5, 0, 8, 9, 4, 7],
#         [8, 3, 2, 3, 6, 1, 1, 0, 5]])

tensor1 = torch.tensor([[2., 7., 4.], [8., 3., 2.]])
tensor2 = torch.tensor([[5., 0., 8.], [3., 6., 1.]])
tensor3 = torch.tensor([[9., 4., 7.], [1., 0., 5.]])

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2., 7., 4., 5., 0., 8., 9., 4., 7.],
#         [8., 3., 2., 3., 6., 1., 1., 0., 5.]])

tensor1 = torch.tensor([[2.+0.j, 7.+0.j, 4.+0.j],
                        [8.+0.j, 3.+0.j, 2.+0.j]])
tensor2 = torch.tensor([[5.+0.j, 0.+0.j, 8.+0.j],
                        [3.+0.j, 6.+0.j, 1.+0.j]])
tensor3 = torch.tensor([[9.+0.j, 4.+0.j, 7.+0.j],
                        [1.+0.j, 0.+0.j, 5.+0.j]])
torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[2.+0.j, 7.+0.j, 4.+0.j, 5.+0.j, 0.+0.j,
#          8.+0.j, 9.+0.j, 4.+0.j, 7.+0.j],
#         [8.+0.j, 3.+0.j, 2.+0.j, 3.+0.j, 6.+0.j,
#          1.+0.j, 1.+0.j, 0.+0.j, 5.+0.j]])

tensor1 = torch.tensor([[True, False, True], [False, True, False]])
tensor2 = torch.tensor([[False, True, False], [True, False, True]])
tensor3 = torch.tensor([[True, False, True], [False, True, False]])

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[True, False, True, False, True, False, True, False, True],
#         [False, True, False, True, False, True, False, True, False]])

tensor1 = torch.tensor([[]])
tensor2 = torch.tensor([8])
tensor3 = torch.tensor([[]])

torch.column_stack(tensors=(tensor1, tensor2, tensor3))
# tensor([[8.]])
Enter fullscreen mode Exit fullscreen mode
💖 💪 🙅 🚩
hyperkai
Super Kai (Kazuya Ito)

Posted on July 14, 2024

Join Our Newsletter. No Spam, Only the good stuff.

Sign up to receive the latest update from our blog.

Related

hstack and column_stack in PyTorch
python hstack and column_stack in PyTorch

July 14, 2024