View source on GitHub
|
Computes the eigenvalues of one or more matrices.
tf.eigvals(
tensor, name=None
)
Note: If your program backpropagates through this function, you should replace it with a call to tf.linalg.eig (possibly ignoring the second output) to avoid computing the eigen decomposition twice. This is because the eigenvectors are used to compute the gradient w.r.t. the eigenvalues. See _SelfAdjointEigV2Grad in linalg_grad.py.
tensor: Tensor of shape [..., N, N].name: string, optional name of the operation.e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N
eigenvalues of tensor[..., :, :].