tf.raw_ops.MatrixDiagPartV3
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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.MatrixDiagPartV3
tf.raw_ops.MatrixDiagPartV3(
input, k, padding_value, align='RIGHT_LEFT', name=None
)
Returns a tensor with the k[0]
-th to k[1]
-th diagonals of the batched
input
.
Assume input
has r
dimensions [I, J, ..., L, M, N]
.
Let max_diag_len
be the maximum length among all diagonals to be extracted,
max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
Let num_diags
be the number of diagonals to extract,
num_diags = k[1] - k[0] + 1
.
If num_diags == 1
, the output tensor is of rank r - 1
with shape
[I, J, ..., L, max_diag_len]
and values:
diagonal[i, j, ..., l, n]
= input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
padding_value ; otherwise.
where y = max(-k[1], 0)
, x = max(k[1], 0)
.
Otherwise, the output tensor has rank r
with dimensions
[I, J, ..., L, num_diags, max_diag_len]
with values:
diagonal[i, j, ..., l, m, n]
= input[i, j, ..., l, n+y, n+x] ; if 0 <= n+y < M and 0 <= n+x < N,
padding_value ; otherwise.
where d = k[1] - m
, y = max(-d, 0) - offset
, and x = max(d, 0) - offset
.
offset
is zero except when the alignment of the diagonal is to the right.
offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT}
and `d >= 0`) or
(`align` in {LEFT_RIGHT, RIGHT_RIGHT}
and `d <= 0`)
0 ; otherwise
where diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))
.
The input must be at least a matrix.
For example:
input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4)
[5, 6, 7, 8],
[9, 8, 7, 6]],
[[5, 4, 3, 2],
[1, 2, 3, 4],
[5, 6, 7, 8]]])
# A main diagonal from each batch.
tf.matrix_diag_part(input) ==> [[1, 6, 7], # Output shape: (2, 3)
[5, 2, 7]]
# A superdiagonal from each batch.
tf.matrix_diag_part(input, k = 1)
==> [[2, 7, 6], # Output shape: (2, 3)
[4, 3, 8]]
# A band from each batch.
tf.matrix_diag_part(input, k = (-1, 2))
==> [[[0, 3, 8], # Output shape: (2, 4, 3)
[2, 7, 6],
[1, 6, 7],
[5, 8, 0]],
[[0, 3, 4],
[4, 3, 8],
[5, 2, 7],
[1, 6, 0]]]
# LEFT_RIGHT alignment.
tf.matrix_diag_part(input, k = (-1, 2), align="LEFT_RIGHT")
==> [[[3, 8, 0], # Output shape: (2, 4, 3)
[2, 7, 6],
[1, 6, 7],
[0, 5, 8]],
[[3, 4, 0],
[4, 3, 8],
[5, 2, 7],
[0, 1, 6]]]
# max_diag_len can be shorter than the main diagonal.
tf.matrix_diag_part(input, k = (-2, -1))
==> [[[5, 8],
[9, 0]],
[[1, 6],
[5, 0]]]
# padding_value = 9
tf.matrix_diag_part(input, k = (1, 3), padding_value = 9)
==> [[[9, 9, 4], # Output shape: (2, 3, 3)
[9, 3, 8],
[2, 7, 6]],
[[9, 9, 2],
[9, 3, 4],
[4, 3, 8]]]
Args |
input
|
A Tensor . Rank r tensor where r >= 2 .
|
k
|
A Tensor of type int32 .
Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main
diagonal, and negative value means subdiagonals. k can be a single integer
(for a single diagonal) or a pair of integers specifying the low and high ends
of a matrix band. k[0] must not be larger than k[1] .
|
padding_value
|
A Tensor . Must have the same type as input .
The value to fill the area outside the specified diagonal band with.
Default is 0.
|
align
|
An optional string from: "LEFT_RIGHT", "RIGHT_LEFT", "LEFT_LEFT", "RIGHT_RIGHT" . Defaults to "RIGHT_LEFT" .
Some diagonals are shorter than max_diag_len and need to be padded. align is
a string specifying how superdiagonals and subdiagonals should be aligned,
respectively. There are four possible alignments: "RIGHT_LEFT" (default),
"LEFT_RIGHT", "LEFT_LEFT", and "RIGHT_RIGHT". "RIGHT_LEFT" aligns superdiagonals
to the right (left-pads the row) and subdiagonals to the left (right-pads the
row). It is the packing format LAPACK uses. cuSPARSE uses "LEFT_RIGHT", which is
the opposite alignment.
|
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.MatrixDiagPartV3\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.MatrixDiagPartV3`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/MatrixDiagPartV3)\n\n\u003cbr /\u003e\n\n tf.raw_ops.MatrixDiagPartV3(\n input, k, padding_value, align='RIGHT_LEFT', name=None\n )\n\nReturns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched\n`input`.\n\nAssume `input` has `r` dimensions `[I, J, ..., L, M, N]`.\nLet `max_diag_len` be the maximum length among all diagonals to be extracted,\n`max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`\nLet `num_diags` be the number of diagonals to extract,\n`num_diags = k[1] - k[0] + 1`.\n\nIf `num_diags == 1`, the output tensor is of rank `r - 1` with shape\n`[I, J, ..., L, max_diag_len]` and values: \n\n diagonal[i, j, ..., l, n]\n = input[i, j, ..., l, n+y, n+x] ; if 0 \u003c= n+y \u003c M and 0 \u003c= n+x \u003c N,\n padding_value ; otherwise.\n\nwhere `y = max(-k[1], 0)`, `x = max(k[1], 0)`.\n\nOtherwise, the output tensor has rank `r` with dimensions\n`[I, J, ..., L, num_diags, max_diag_len]` with values: \n\n diagonal[i, j, ..., l, m, n]\n = input[i, j, ..., l, n+y, n+x] ; if 0 \u003c= n+y \u003c M and 0 \u003c= n+x \u003c N,\n padding_value ; otherwise.\n\nwhere `d = k[1] - m`, `y = max(-d, 0) - offset`, and `x = max(d, 0) - offset`.\n\n`offset` is zero except when the alignment of the diagonal is to the right. \n\n offset = max_diag_len - diag_len(d) ; if (`align` in {RIGHT_LEFT, RIGHT_RIGHT}\n and `d \u003e= 0`) or\n (`align` in {LEFT_RIGHT, RIGHT_RIGHT}\n and `d \u003c= 0`)\n 0 ; otherwise\n\nwhere `diag_len(d) = min(cols - max(d, 0), rows + min(d, 0))`.\n\nThe input must be at least a matrix.\n\n#### For example:\n\n input = np.array([[[1, 2, 3, 4], # Input shape: (2, 3, 4)\n [5, 6, 7, 8],\n [9, 8, 7, 6]],\n [[5, 4, 3, 2],\n [1, 2, 3, 4],\n [5, 6, 7, 8]]])\n\n # A main diagonal from each batch.\n tf.matrix_diag_part(input) ==\u003e [[1, 6, 7], # Output shape: (2, 3)\n [5, 2, 7]]\n\n # A superdiagonal from each batch.\n tf.matrix_diag_part(input, k = 1)\n ==\u003e [[2, 7, 6], # Output shape: (2, 3)\n [4, 3, 8]]\n\n # A band from each batch.\n tf.matrix_diag_part(input, k = (-1, 2))\n ==\u003e [[[0, 3, 8], # Output shape: (2, 4, 3)\n [2, 7, 6],\n [1, 6, 7],\n [5, 8, 0]],\n [[0, 3, 4],\n [4, 3, 8],\n [5, 2, 7],\n [1, 6, 0]]]\n\n # LEFT_RIGHT alignment.\n tf.matrix_diag_part(input, k = (-1, 2), align=\"LEFT_RIGHT\")\n ==\u003e [[[3, 8, 0], # Output shape: (2, 4, 3)\n [2, 7, 6],\n [1, 6, 7],\n [0, 5, 8]],\n [[3, 4, 0],\n [4, 3, 8],\n [5, 2, 7],\n [0, 1, 6]]]\n\n # max_diag_len can be shorter than the main diagonal.\n tf.matrix_diag_part(input, k = (-2, -1))\n ==\u003e [[[5, 8],\n [9, 0]],\n [[1, 6],\n [5, 0]]]\n\n # padding_value = 9\n tf.matrix_diag_part(input, k = (1, 3), padding_value = 9)\n ==\u003e [[[9, 9, 4], # Output shape: (2, 3, 3)\n [9, 3, 8],\n [2, 7, 6]],\n [[9, 9, 2],\n [9, 3, 4],\n [4, 3, 8]]]\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Rank `r` tensor where `r \u003e= 2`. |\n| `k` | A `Tensor` of type `int32`. Diagonal offset(s). Positive value means superdiagonal, 0 refers to the main diagonal, and negative value means subdiagonals. `k` can be a single integer (for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. `k[0]` must not be larger than `k[1]`. |\n| `padding_value` | A `Tensor`. Must have the same type as `input`. The value to fill the area outside the specified diagonal band with. Default is 0. |\n| `align` | An optional `string` from: `\"LEFT_RIGHT\", \"RIGHT_LEFT\", \"LEFT_LEFT\", \"RIGHT_RIGHT\"`. Defaults to `\"RIGHT_LEFT\"`. Some diagonals are shorter than `max_diag_len` and need to be padded. `align` is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. There are four possible alignments: \"RIGHT_LEFT\" (default), \"LEFT_RIGHT\", \"LEFT_LEFT\", and \"RIGHT_RIGHT\". \"RIGHT_LEFT\" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row). It is the packing format LAPACK uses. cuSPARSE uses \"LEFT_RIGHT\", which is the opposite alignment. |\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"]]