$tensor permute$

# since we permute the axes/dims, the shape changed from (2, 3) => (3, 2) The below example will make things clear: In : aten Whereas tensor.permute() is only used to swap the axes. The main advantage of the permute() function is that the size of a returned tensor is the same as the size of the original tensor, which means it remains. This can be viewed as tensors of shapes (6, 1), (1, 6) etc., # reshaping (or viewing) 2x3 matrix as a column vector of shape 6x1Īlternatively, it can also be reshaped or viewed as a row vector of shape (1, 6) as in: In : aten.view(-1, 6) Torch random permute umpd police chief aisd canvas melissa mack age zeos speakers soft engine mounts kubota b2401 specs umass medical school salary grades 2021. Parameters input ( Tensor) the input tensor. PyTorchs view function actually does what the name suggests. torch.permute PyTorch 1.12 documentation Table of Contents torch.permute torch.permute(input, dims) Tensor Returns a view of the original tensor input with its dimensions permuted. In general, the ith dimension of the output array is the dimension dimorder (i) from the input array. For example, permute (A, 2 1) switches the row and column dimensions of a matrix A. For example, our input tensor aten has the shape (2, 3). permute change the dimension order of Tensorflow tensor using tf. B permute (A,dimorder) rearranges the dimensions of an array in the order specified by the vector dimorder. Torch.view() reshapes the tensor to a different but compatible shape.

Tensor permute