tf.linalg.eig

Computes the eigen decomposition of a batch of matrices.

tf.linalg.eig( tensor, name=None )

Used in the notebooks

Used in the tutorials

The eigenvalues and eigenvectors for a non-Hermitian matrix in general are complex. The eigenvectors are not guaranteed to be linearly independent.

Computes the eigenvalues and right eigenvectors of the innermost N-by-N matrices in tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for i=0...N-1.

tensor Tensor of shape [..., N, N]. Only the lower triangular part of each inner inner matrix is referenced.
name string, optional name of the operation.

e Eigenvalues. Shape is [..., N]. The eigenvalues are not necessarily ordered.
v Eigenvectors. Shape is [..., N, N]. The columns of the inner most matrices contain eigenvectors of the corresponding matrices in tensor