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mindspore.nn.MatrixSetDiag

class mindspore.nn.MatrixSetDiag

Modifies the batched diagonal part of a batched tensor.

Assume x has $k+1$ dimensions $[I,J,K,...,M,N]$ and diagonal has $k$ dimensions $[I,J,K,...,min(M,N)]$. Then the output is a tensor of rank $k+1$ with dimensions $[I,J,K,...,M,N]$ where:

$$output[i,j,k,...,m,n]=diagnoal[i,j,k,...,n]form==n$$

$$output[i,j,k,...,m,n]=x[i,j,k,...,m,n]form!=n$$

Inputs:

• x (Tensor) - The batched tensor. Rank k+1, where k >= 1. It can be one of the following data types: float32, float16, int32, int8, and uint8.

• diagonal (Tensor) - The diagonal values. Must have the same type as input x. Rank k, where k >= 1.

Outputs:

Tensor, has the same type and shape as input x.

Raises

• TypeError – If dtype of x or diagonal is not one of float32, float16, int32, int8 or uint8.

• ValueError – If length of shape of x is less than 2.

• ValueError – If x_shape[-2] < x_shape[-1] and x_shape[:-1] != diagonal_shape.

• ValueError – If x_shape[-2] >= x_shape[-1] and x_shape[:-2] + x_shape[-1:] != diagonal_shape.

Supported Platforms:

Ascend

Examples

>>> x = Tensor([[[-1, 0], [0, 1]], [[-1, 0], [0, 1]], [[-1, 0], [0, 1]]], mindspore.float32)
>>> diagonal = Tensor([[-1., 2.], [-1., 1.], [-1., 1.]], mindspore.float32)
>>> matrix_set_diag = nn.MatrixSetDiag()
>>> output = matrix_set_diag(x, diagonal)
>>> print(output)
[[[-1.  0.]
  [ 0.  2.]]
 [[-1.  0.]
  [ 0.  1.]]
 [[-1.  0.]
  [ 0.  1.]]]