slogdet

paddle.compat. slogdet ( x: Tensor, out: SlogdetResult | None = None ) → SlogdetResult [source]

(PyTorch Compatible API) Calculates the sign and natural logarithm of the absolute value of a square matrix’s or batches square matrices’ determinant. The determinant can be computed with sign * exp (logabsdet).

Supports input of float, double, complex64, complex128.

Notes

For matrices that have zero determinant, this returns (0, -inf).

2. For matrices with complex value, the \(abs(det)\) is the modulus of the determinant, and therefore \(sign = det / abs(det)\).

3. The return structure of this API has been revised from a single stacked Tensor of shape `[2, *]` (where index 0 was sign and index 1 was logabsdet) to a tuple of two independent Tensors `(sign, logabsdet)` (see PR #72505). This modification may cause incompatibility with models previously exported for inference that relied on the old return structure.

Parameters

x (Tensor) – the batch of matrices of size \((*, n, n)\) where math:* is one or more batch dimensions.
out (SlogdetResult, optional) – The tuple of output tensor, contains abs and logabsdet.

Returns

A tuple containing two Tensors: (sign, logabsdet). The first Tensor represents the signs of the determinants and the second Tensor represents the natural logarithms of the absolute values of the determinants. Each output Tensor has a shape of \((*)\), where \(*\) matches the batch dimensions of the input x.

Return type

SlogdetResult

Examples

           >>> import paddle
>>> x = paddle.to_tensor([[1., 0.], [0., 1.]])
>>> A = paddle.compat.slogdet(x)
>>> print(A.sign)
Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       1.)
>>> print(A.logabsdet)
Tensor(shape=[], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       0.)

          