hstack and column_stack in PyTorch

Buy Me a Coffee☕

*Memos:

• My post explains stack().
• My post explains vstack() and dstack().
• My post explains cat().

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.]]])

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.]])