tf.raw_ops.MatrixDiag
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Returns a batched diagonal tensor with a given batched diagonal values.
tf.raw_ops.MatrixDiag(
diagonal, name=None
)
Given a diagonal, this operation returns a tensor with the diagonal and
everything else padded with zeros. The diagonal is computed as follows:
Assume diagonal has k dimensions [I, J, K, ..., N], then the output is a
tensor of rank k+1 with dimensions [I, J, K, ..., N, N]` where:
output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n].
For example:
# 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]
and diagonal.shape = (2, 4)
tf.matrix_diag(diagonal) ==> [[[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]]]
which has shape (2, 4, 4)
Args |
|---|
diagonal
|
A Tensor. Rank k, where k >= 1.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as diagonal.
|
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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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.MatrixDiag\n\n\u003cbr /\u003e\n\nReturns a batched diagonal tensor with a given batched diagonal values.\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.MatrixDiag`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixDiag)\n\n\u003cbr /\u003e\n\n tf.raw_ops.MatrixDiag(\n diagonal, name=None\n )\n\nGiven a `diagonal`, this operation returns a tensor with the `diagonal` and\neverything else padded with zeros. The diagonal is computed as follows:\n\nAssume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a\ntensor of rank `k+1` with dimensions \\[I, J, K, ..., N, N\\]\\` where:\n\n`output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`.\n\n#### For example:\n\n # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]]\n\n and diagonal.shape = (2, 4)\n\n tf.matrix_diag(diagonal) ==\u003e [[[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 which has shape (2, 4, 4)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------|---------------------------------------|\n| `diagonal` | A `Tensor`. Rank `k`, where `k \u003e= 1`. |\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 `diagonal`. ||\n\n\u003cbr /\u003e"]]
