mindspore.nn.MatrixDiag

class mindspore.nn.MatrixDiag

Returns a batched diagonal tensor with a given batched diagonal values.

Assume x has \(k\) dimensions \([I, J, K, ..., N]\), then the output is a tensor of rank \(k+1\) with dimensions \([I, J, K, ..., N, N]\) where: \(output[i, j, k, ..., m, n] = 1\{m=n\} * x[i, j, k, ..., n]\)

Inputs:

• x (Tensor) - The diagonal values. It can be one of the following data types: float32, float16, int32, int8, and uint8. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.

Outputs:

Tensor, has the same type as input x. The shape must be x.shape + (x.shape[-1], ).

Raises

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

Supported Platforms:

Ascend

Examples

>>> x = Tensor(np.array([1, -1]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2,)
>>> print(output)
[[ 1.  0.]
 [ 0. -1.]]
>>> print(output.shape)
(2, 2)
>>> x = Tensor(np.array([[1, -1], [1, -1]]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2, 2)
>>> print(output)
[[[ 1.  0.]
  [ 0. -1.]]
 [[ 1.  0.]
  [ 0. -1.]]]
>>> print(output.shape)
(2, 2, 2)
>>> x = Tensor(np.array([[1, -1, 1], [1, -1, 1]]), mindspore.float32)
>>> matrix_diag = nn.MatrixDiag()
>>> output = matrix_diag(x)
>>> print(x.shape)
(2, 3)
>>> print(output)
[[[ 1.  0.  0.]
  [ 0. -1.  0.]
  [ 0.  0.  1.]
 [[ 1.  0.  0.]
  [ 0. -1.  0.]
  [ 0.  0.  1.]]]
>>> print(output.shape)
(2, 3, 3)