torch.det¶

torch.det(input) → Tensor¶

Calculates determinant of a square matrix or batches of square matrices.

Note

Backward through det() internally uses SVD results when input is not invertible. In this case, double backward through det() will be unstable in when input doesn’t have distinct singular values. See svd() for details.

Parameters

input (Tensor) – the input tensor of size (*, n, n) where * is zero or more batch dimensions.

Example:

>>> A = torch.randn(3, 3)
>>> torch.det(A)
tensor(3.7641)

>>> A = torch.randn(3, 2, 2)
>>> A
tensor([[[ 0.9254, -0.6213],
         [-0.5787,  1.6843]],

        [[ 0.3242, -0.9665],
         [ 0.4539, -0.0887]],

        [[ 1.1336, -0.4025],
         [-0.7089,  0.9032]]])
>>> A.det()
tensor([1.1990, 0.4099, 0.7386])