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Specifications and Common Mistakes

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mindspore.ops.diag_embed

mindspore.ops.diag_embed(input, offset=0, dim1=- 2, dim2=- 1)

Creates a tensor with diagonals filled by input. The remaining elements are filled by 0. If the shape of input is $[x_0,x_1,...,x_{n-1},x_n]$, the output shape is: the vector obtained by inserting $x_n+|offset|$ into the vector $[x_0,x_1,...,x_{n-1}]$ at position dim1 and dim2.

Parameters

• input (Tensor) – Values to fill diagonal.

• offset (int, optional) –

  Offset of the diagonal. $offset=0$ refers to the main diagonal. Default: 0 .

  • If $offset>0$, fill the diagonals that are offset units upward from the main diagonal.

  • If $offset<0$, fill the diagonals that are |offset| units downward from the main diagonal.

• dim1 (int, optional) – The first dimension in input with respect to which to fill diagonal. Default: -2 .

• dim2 (int, optional) – The second dimension in input with respect to which to fill diagonal. Default: -1 .

Returns

Tensor, has the same dtype as input, but the shape of output is one dimension higher than the input.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If dtype of input is not supported.

• TypeError – If offset is not an int.

• TypeError – If dim1 or dim2 is not an int.

• ValueError – If the dimension of input is not 1D-6D.

• ValueError – If dim1 is not in range of [-len(input.shape) - 1, len(input.shape)].

• ValueError – If dim2 is not in range of [-len(input.shape) - 1, len(input.shape)].

• ValueError – If dim1 and dim2 are identical.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2,3,4]), mindspore.float32)
>>> output = ops.diag_embed(x)
>>> print(output)
[[2. 0. 0.]
 [0. 3. 0.]
 [0. 0. 4.]]