Computes the eigen decomposition of a batch of matrices.

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

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.

Args | |
---|---|

`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. |

Returns | |
---|---|

`e` | Eigenvalues. Shape is `[..., N]` . Sorted in non-decreasing order. |

`v` | Eigenvectors. Shape is `[..., N, N]` . The columns of the inner most matrices contain eigenvectors of the corresponding matrices in `tensor` |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/linalg/eig