tf.raw_ops.MatrixDiagPart
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Returns the batched diagonal part of a batched tensor.
View aliases
Compat aliases for migration
See
Migration guide for
more details.
tf.compat.v1.raw_ops.MatrixDiagPart
tf.raw_ops.MatrixDiagPart(
input, name=None
)
This operation returns a tensor with the diagonal part
of the batched input. The diagonal part is computed as follows:
Assume input has k dimensions [I, J, K, ..., M, N], then the output is a
tensor of rank k - 1 with dimensions [I, J, K, ..., min(M, N)] where:
diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n].
The input must be at least a matrix.
For example:
# 'input' is [[[1, 0, 0, 0]
[0, 2, 0, 0]
[0, 0, 3, 0]
[0, 0, 0, 4]],
[[5, 0, 0, 0]
[0, 6, 0, 0]
[0, 0, 7, 0]
[0, 0, 0, 8]]]
and input.shape = (2, 4, 4)
tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]]
which has shape (2, 4)
Args |
|---|
input
|
A Tensor. Rank k tensor where k >= 2.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as input.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.raw_ops.MatrixDiagPart\n\n\u003cbr /\u003e\n\nReturns the batched diagonal part of a batched 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.raw_ops.MatrixDiagPart`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixDiagPart)\n\n\u003cbr /\u003e\n\n tf.raw_ops.MatrixDiagPart(\n input, name=None\n )\n\nThis operation returns a tensor with the `diagonal` part\nof the batched `input`. The `diagonal` part is computed as follows:\n\nAssume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a\ntensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where:\n\n`diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`.\n\nThe input must be at least a matrix.\n\n#### For example:\n\n # 'input' is [[[1, 0, 0, 0]\n [0, 2, 0, 0]\n [0, 0, 3, 0]\n [0, 0, 0, 4]],\n [[5, 0, 0, 0]\n [0, 6, 0, 0]\n [0, 0, 7, 0]\n [0, 0, 0, 8]]]\n\n and input.shape = (2, 4, 4)\n\n tf.matrix_diag_part(input) ==\u003e [[1, 2, 3, 4], [5, 6, 7, 8]]\n\n which has shape (2, 4)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|---------|---------------------------------------------|\n| `input` | A `Tensor`. Rank `k` tensor where `k \u003e= 2`. |\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`. Has the same type as `input`. ||\n\n\u003cbr /\u003e"]]
