tf.linalg.tensor_diag_part
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Returns the diagonal part of the tensor.
tf.linalg.tensor_diag_part(
input, name=None
)
This operation returns a tensor with the diagonal part
of the input. The diagonal part is computed as follows:
Assume input has dimensions [D1,..., Dk, D1,..., Dk], then the output is a
tensor of rank k with dimensions [D1,..., Dk] where:
diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik].
For a rank 2 tensor, linalg.diag_part and linalg.tensor_diag_part
produce the same result. For rank 3 and higher, linalg.diag_part extracts
the diagonal of each inner-most matrix in the tensor. An example where
they differ is given below.
x = [[[[1111,1112],[1121,1122]],
[[1211,1212],[1221,1222]]],
[[[2111, 2112], [2121, 2122]],
[[2211, 2212], [2221, 2222]]]
]
tf.linalg.tensor_diag_part(x)
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[1111, 1212],
[2121, 2222]], dtype=int32)>
tf.linalg.diag_part(x).shape
TensorShape([2, 2, 2])
Args |
|---|
input
|
A Tensor with rank 2k.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor containing diagonals of input. Has the same type as input, and
rank k.
|
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Last updated 2024-04-26 UTC.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.tensor_diag_part\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/array_ops.py#L2354-L2396) |\n\nReturns the diagonal part of the tensor.\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.diag_part`](https://www.tensorflow.org/api_docs/python/tf/linalg/tensor_diag_part)\n\n\u003cbr /\u003e\n\n tf.linalg.tensor_diag_part(\n input, name=None\n )\n\nThis operation returns a tensor with the `diagonal` part\nof the `input`. The `diagonal` part is computed as follows:\n\nAssume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a\ntensor of rank `k` with dimensions `[D1,..., Dk]` where:\n\n`diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`.\n\nFor a rank 2 tensor, [`linalg.diag_part`](../../tf/linalg/diag_part) and [`linalg.tensor_diag_part`](../../tf/linalg/tensor_diag_part)\nproduce the same result. For rank 3 and higher, linalg.diag_part extracts\nthe diagonal of each inner-most matrix in the tensor. An example where\nthey differ is given below. \n\n x = [[[[1111,1112],[1121,1122]],\n [[1211,1212],[1221,1222]]],\n [[[2111, 2112], [2121, 2122]],\n [[2211, 2212], [2221, 2222]]]\n ]\n tf.linalg.tensor_diag_part(x)\n \u003ctf.Tensor: shape=(2, 2), dtype=int32, numpy=\n array([[1111, 1212],\n [2121, 2222]], dtype=int32)\u003e\n tf.linalg.diag_part(x).shape\n TensorShape([2, 2, 2])\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|--------------------------------------|\n| `input` | A `Tensor` with rank `2k`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A Tensor containing diagonals of `input`. Has the same type as `input`, and rank `k`. ||\n\n\u003cbr /\u003e"]]
View source on GitHub