permute()
This is used to reorder the tensor using row and column
Syntax: tensor.permute(a,b,c)
where
- tensor is the input tensor
- permute(1,2,0) is used to permute the tensor by row
- permute(2,1,0) is used to permute the tensor by column
Example: In this example, we are going to permute the tensor first by row and by column.
Python3
# import module import torch # create a tensor with 2 data # in 3 three elements each data = torch.tensor([[[10, 20, 30], [45, 67, 89]]]) # display print(data) # permute the tensor first by row print(data.permute(1, 2, 0)) # permute the tensor first by column print(data.permute(2, 1, 0)) |
Output:
tensor([[[10, 20, 30],
[45, 67, 89]]])
tensor([[[10],
[20],
[30]],
[[45],
[67],
[89]]])
tensor([[[10],
[45]],
[[20],
[67]],
[[30],
[89]]])
Tensor Operations in PyTorch
In this article, we will discuss tensor operations in PyTorch.
PyTorch is a scientific package used to perform operations on the given data like tensor in python. A Tensor is a collection of data like a numpy array. We can create a tensor using the tensor function:
Syntax: torch.tensor([[[element1,element2,.,element n],……,[element1,element2,.,element n]]])
where,
- torch is the module
- tensor is the function
- elements are the data
The Operations in PyTorch that are applied on tensor are: