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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

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mindspore.mint.diag

mindspore.mint.diag(input, diagonal=0)

If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.

If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.

The argument diagonal controls which diagonal to consider:

• If diagonal = 0, it is the main diagonal.

• If diagonal > 0, it is above the main diagonal.

• If diagonal < 0, it is below the main diagonal.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• input (Tensor) – The input tensor.

• diagonal (int, optional) – the diagonal to consider. Defaults: 0.

Returns

Tensor, has the same dtype as the input, its shape is up to diagonal.

• If input shape is $(x_0)$ : then output shape is $(x_0+\left|diagonal\right|,x_0+\left|diagonal\right|)$ 2-D Tensor.

• If input shape is $(x_0,x_1)$ : then output shape is main diagonal to move $(\left|diagonal\right|)$ elements remains elements' length 1-D Tensor.

Raises

• TypeError – If input is not a Tensor.

• ValueError – If shape of input is not 1-D and 2-D.

Supported Platforms:

Ascend

Examples

>>> from mindspore import Tensor, mint
>>> input = Tensor([1, 2, 3, 4]).astype('int32')
>>> output = mint.diag(input)
>>> print(output)
[[1 0 0 0]
 [0 2 0 0]
 [0 0 3 0]
 [0 0 0 4]]