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How to find the transpose of a tensor in PyTorch?
To transpose a tensor, we need two dimensions to be transposed. If a tensor is 0-D or 1-D tensor, the transpose of the tensor is same as is. For a 2-D tensor, the transpose is computed using the two dimensions 0 and 1 as transpose(input, 0, 1).
Syntax
To find the transpose of a scalar, a vector or a matrix, we can apply the first syntax defined below.
And for any dimensional tensor, we can apply the second syntax.
For <= 2D tensors,
Tensor.t() torch.t(input)
For any dimensional tensor,
Tensor.transpose(dim0, dim1) or torch.transpose(input, dim0, dim1)
Parameters
input – It's a PyTorch tensor to be transposed.
dim0 – It's the first dimension to be transposed.
dim1 – It's the second dimension to be transposed.
Steps
Import the torch library. Make sure you have it already installed.
import torch
Create a PyTorch tensor and print the tensor. Here, we have created a 3×3 tensor.
t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("Tensor:
", t)Find the transpose of the defined tensor using any of the above defined syntax and optionally assign the value to a new variable.
transposedTensor = torch.transpose(t, 0, 1)
Print the transposed tensor.
print("Transposed Tensor:
", transposedTensor)Example 1
# Python program to find transpose of a 2D tensor # import torch library import torch # define a 2D tensor A = torch.rand(2,3) print(A) # compute the transpose of the above tensor print(A.t()) # or print(torch.t(A)) print(A.transpose(0, 1)) # or print(torch.transpose(A, 0, 1))
Output
tensor([[0.0676, 0.2984, 0.6766], [0.6200, 0.5874, 0.4150]]) tensor([[0.0676, 0.6200], [0.2984, 0.5874], [0.6766, 0.4150]]) tensor([[0.0676, 0.6200], [0.2984, 0.5874], [0.6766, 0.4150]])
Example 2
# Python program to find transpose of a 3D tensor
# import torch library
import torch
# create a 3D tensor
A = torch.tensor([[[1,2,3],[3,4,5]],
[[5,6,7],[1,2,2]],
[[1,2,4],[1,2,5]]])
print("Original Tensor A:
",A)
print("Size of tensor:",A.size())
# print(A.t()) --> Error
# compute the transpose of the tensor
transposeA = torch.transpose(A, 0,1)
# other way to compute the transpose
# transposeA = A.transpose(0,1)
print("Transposed Tensor:
",transposeA)
print("Size after transpose:",transposeA.size())Output
Original Tensor A: tensor([[[1, 2, 3], [3, 4, 5]], [[5, 6, 7], [1, 2, 2]], [[1, 2, 4], [1, 2, 5]]]) Size of tensor: torch.Size([3, 2, 3]) Transposed Tensor: tensor([[[1, 2, 3], [5, 6, 7], [1, 2, 4]], [[3, 4, 5], [1, 2, 2], [1, 2, 5]]]) Size after transpose: torch.Size([2, 3, 3])
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