[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.linalg.eigvalsh\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/linalg_ops.py#L465-L486) |\n\nComputes the eigenvalues of one or more self-adjoint matrices.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.linalg.eigvalsh`](https://www.tensorflow.org/api_docs/python/tf/linalg/eigvalsh), [`tf.compat.v1.self_adjoint_eigvals`](https://www.tensorflow.org/api_docs/python/tf/linalg/eigvalsh)\n\n\u003cbr /\u003e\n\n tf.linalg.eigvalsh(\n tensor, name=None\n )\n\n| **Note:** If your program backpropagates through this function, you should replace it with a call to tf.linalg.eigh (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.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|-----------------------------------------|\n| `tensor` | `Tensor` of shape `[..., N, N]`. |\n| `name` | string, optional name of the operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|-----|---------------------------------------------------------------------------------------------------------------|\n| `e` | Eigenvalues. Shape is `[..., N]`. The vector `e[..., :]` contains the `N` eigenvalues of `tensor[..., :, :]`. |\n\n\u003cbr /\u003e"]]
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