ValueError: index conventions not satisfied permute_indices ( permutation ) ¶
ValueError: index conventions not satisfied sage: a # non-word-constituent Traceback (most recent call last). ValueError: index conventions not satisfied: repeated indices of same type sage: ( a * a ) # multiple indices group of the same type Traceback (most recent call last). Personally, I will consider 2D tensor as matrix, 3D tensor as a list of matrix, 4D tensor as a list of cubic. But when it comes to higher dimension, I find it really hard to think. However, PyTorch does it slightly differently than what many people are used to from e.g. We can also permute a tensor with new dimension using Tensor.permute(). For example, a tensor with dimension 2, 3 can be permuted to 3, 2.
It doesnt make a copy of the original tensor. It returns a view of the input tensor with its dimension permuted. When in 2D dimension, the permute operation is easy to understand, it is just a transpose of a matrix. It plans to implement swapaxes as an alternative transposition mechanism, so swapaxes and permute would work on both PyTorch tensors and NumPy-like arrays (and. Transposing and permuting tensors are a common thing to do. torch.permute() method is used to perform a permute operation on a PyTorch tensor. ValueError: index conventions not satisfied sage: a # repeated indices of the same type Traceback (most recent call last). Hello, I am always confused about the permute operation on tensors whose dim are greater than 2. dtype ( torch.dtype, optional) the desired data type of returned tensor. out ( Tensor, optional) the output tensor.
#Tensor permute generator#
generator ( torch.Generator, optional) a pseudorandom number generator for sampling. ValueError: index conventions not satisfied sage: a # unbalanced parenthis Traceback (most recent call last). Returns a random permutation of integers from 0 to n - 1. tensornpP np.transpose(tensornp, (2,1,3,4,0)) tensorptP tensorpt.permute(2,1,3,4,0) tensortfP tf.transpose(tensortf, (2,1,3,4,0)) This is how we. In this case we have to use the tensor.permute () attribute with PyTorch. Sage: a )' ] # nested symmetries Traceback (most recent call last). anspose supports only swapping of two axes and not more.
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