*My post explains hstack(), vstack(), dstack() and column_stack().
stack() can concatenate a sequence of 2 or more tensors as shown below:
import torch
tensor1 = torch.tensor(2) # The size is [].
tensor2 = torch.tensor(7) # The size is [].
tensor3 = torch.tensor(4) # The size is [].
torch.stack((tensor1, tensor2, tensor3))
# tensor([2, 7, 4])
# The size is [3].
tensor1 = torch.tensor([2, 7, 4]) # The size is [3].
tensor2 = torch.tensor([8, 3, 2]) # The size is [3].
tensor3 = torch.tensor([5, 0, 8]) # The size is [3].
torch.stack((tensor1, tensor2, tensor3))
# tensor([[2, 7, 4], [8, 3, 2], [5, 0, 8]])
# The size is [3, 3].
tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # The size is [2, 3].
tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]]) # The size is [2, 3].
tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]]) # The size is [2, 3].
torch.stack((tensor1, tensor2, tensor3))
# tensor([[[2, 7, 4], [8, 3, 2]],
# [[5, 0, 8], [3, 6, 1]],
# [[9, 4, 7], [1, 0, 5]]])
# The size is [3, 2, 3].
tensor1 = torch.tensor([[[2, 7, 4], [8, 3, 2]],
[[5, 0, 8], [3, 6, 1]]])
# The size is [2, 2, 3].
tensor2 = torch.tensor([[[9, 4, 7], [1, 0, 5]],
[[6, 7, 4], [2, 1, 9]]])
# The size is [2, 2, 3].
tensor3 = torch.tensor([[[1, 6, 3], [9, 6, 0]],
[[0, 8, 7], [3, 5, 2]]])
# The size is [2, 2, 3].
torch.stack((tensor1, tensor2, tensor3))
torch.stack((tensor1, tensor2, tensor3), 0)
# 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]]],
# [[[1, 6, 3], [9, 6, 0]],
# [[0, 8, 7], [3, 5, 2]]]])
# The size is [3, 2, 2, 3].
torch.stack((tensor1, tensor2, tensor3), 1)
torch.stack((tensor1, tensor2, tensor3), -3)
# tensor([[[[2, 7, 4], [8, 3, 2]],
# [[9, 4, 7], [1, 0, 5]],
# [[1, 6, 3], [9, 6, 0]]],
# [[[5, 0, 8], [3, 6, 1]],
# [[6, 7, 4], [2, 1, 9]],
# [[0, 8, 7], [3, 5, 2]]]])
# The size is [2, 3, 2, 3].
torch.stack((tensor1, tensor2, tensor3), 2)
torch.stack((tensor1, tensor2, tensor3), -2)
# tensor([[[[2, 7, 4], [9, 4, 7], [1, 6, 3]],
# [[8, 3, 2], [1, 0, 5], [9, 6, 0]]],
# [[[5, 0, 8], [6, 7, 4], [0, 8, 7]],
# [[3, 6, 1], [2, 1, 9], [3, 5, 2]]]])
# The size is [2, 2, 3, 3].
torch.stack((tensor1, tensor2, tensor3), 3)
torch.stack((tensor1, tensor2, tensor3), -1)
# tensor([[[[2, 9, 1], [7, 4, 6], [4, 7, 3]],
# [[8, 1, 9], [3, 0, 6], [2, 5, 0]]],
# [[[5, 6, 0], [0, 7, 8], [8, 4, 7]],
# [[3, 2, 3], [6, 1, 5], [1, 9, 2]]]])
# The size is [2, 2, 3, 3].
*Memos:
-
stack()can concatenate 0D or more D tensors. - The size of tensors must be the same.
- The 2nd argument is a dimension.
- If at least one tensor contains at least one floating-point number, the result is the tensor of floating-point numbers.
-
stack()can be called only from torch but not from a tensor.
cat() concatenate a sequence of seq 2 or more tensors as shown below:
import torch
tensor1 = torch.tensor([2, 7, 4]) # The size is [3].
tensor2 = torch.tensor([8, 3, 2]) # The size is [3].
tensor3 = torch.tensor([5, 0, 8]) # The size is [3].
torch.cat((tensor1, tensor2, tensor3))
# tensor([2, 7, 4, 8, 3, 2, 5, 0, 8])
# The size is [9].
tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # The size is [2, 3].
tensor2 = torch.tensor([[5, 0, 8], [3, 6, 1]]) # The size is [2, 3].
tensor3 = torch.tensor([[9, 4, 7], [1, 0, 5]]) # The size is [2, 3].
torch.cat((tensor1, tensor2, tensor3))
# tensor([[2, 7, 4],
# [8, 3, 2],
# [5, 0, 8],
# [3, 6, 1],
# [9, 4, 7],
# [1, 0, 5]])
# The size is [6, 3].
tensor1 = torch.tensor([[[2, 7, 4], [8, 3, 2]],
[[5, 0, 8], [3, 6, 1]]])
# The size is [2, 2, 3].
tensor2 = torch.tensor([[[9, 4, 7], [1, 0, 5]],
[[6, 7, 4], [2, 1, 9]]])
# The size is [2, 2, 3].
tensor3 = torch.tensor([[[1, 6, 3], [9, 6, 0]],
[[0, 8, 7], [3, 5, 2]]])
# The size is [2, 2, 3].
torch.cat((tensor1, tensor2, tensor3))
torch.cat((tensor1, tensor2, tensor3), 0)
torch.cat((tensor1, tensor2, tensor3), -3)
# 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]],
# [[1, 6, 3], [9, 6, 0]],
# [[0, 8, 7], [3, 5, 2]]])
# The size is [6, 2, 3].
torch.cat((tensor1, tensor2, tensor3), 1)
torch.cat((tensor1, tensor2, tensor3), -2)
# tensor([[[2, 7, 4],
# [8, 3, 2],
# [9, 4, 7],
# [1, 0, 5],
# [1, 6, 3],
# [9, 6, 0]],
# [[5, 0, 8],
# [3, 6, 1],
# [6, 7, 4],
# [2, 1, 9],
# [0, 8, 7],
# [3, 5, 2]]])
# The size is [2, 6, 3].
torch.cat((tensor1, tensor2, tensor3), 2)
torch.cat((tensor1, tensor2, tensor3), -1)
# tensor([[[2, 7, 4, 9, 4, 7, 1, 6, 3],
# [8, 3, 2, 1, 0, 5, 9, 6, 0]],
# [[5, 0, 8, 6, 7, 4, 0, 8, 7],
# [3, 6, 1, 2, 1, 9, 3, 5, 2]]])
# The size is [2, 2, 9].
*Memos:
-
cat()can concatenate 1D or more D tensors. - With
cat(), concatenation is possible with some tensors of different size but it is not possible with some tensors of different size. - The 2nd argument is a dimension.
- If at least one tensor contains at least one floating-point number, the result is the tensor of floating-point numbers.
-
cat()can be called only from torch but not from a tensor. -
cat()and concat() are the same because *concat()is the alias ofcat().
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