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tensorflow::ops::MatrixDiagPartV2
#include <array_ops.h>
Returns the batched diagonal part of a batched tensor.
Summary
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), and
x = max(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 tridiagonal band from each batch.
tf.matrix_diag_part(input, k = (-1, 1))
==> [[[2, 7, 6], # Output shape: (2, 3, 3)
[1, 6, 7],
[5, 8, 0]],
[[4, 3, 8],
[5, 2, 7],
[1, 6, 0]]]
# Padding value = 9
tf.matrix_diag_part(input, k = (1, 3), padding_value = 9)
==> [[[4, 9, 9], # Output shape: (2, 3, 3)
[3, 8, 9],
[2, 7, 6]],
[[2, 9, 9],
[3, 4, 9],
[4, 3, 8]]]
Args:
- scope: A Scope object
- input: Rank
r tensor where r >= 2.
- k: 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: The value to fill the area outside the specified diagonal band with. Default is 0.
Returns:
Output: The extracted diagonal(s).
Public attributes
Public functions
node
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output
operator::tensorflow::Output() const
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Last updated 2023-10-06 UTC.
[null,null,["Last updated 2023-10-06 UTC."],[],[],null,["# tensorflow::ops::MatrixDiagPartV2 Class Reference\n\ntensorflow::ops::MatrixDiagPartV2\n=================================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns the batched diagonal part of a batched tensor.\n\nSummary\n-------\n\nReturns a tensor with the `k[0]`-th to `k[1]`-th diagonals of the batched `input`.\n\nAssume `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`.\n\nIf `num_diags == 1`, the output tensor is of rank `r - 1` with shape `[I, J, ..., L, max_diag_len]` and values:\n\n\u003cbr /\u003e\n\n```scdoc\ndiagonal[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\n\u003cbr /\u003e\n\nOtherwise, the output tensor has rank `r` with dimensions `[I, J, ..., L, num_diags, max_diag_len]` with values:\n\n\u003cbr /\u003e\n\n```scdoc\ndiagonal[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)`, and `x = max(d, 0)`.\n\n\u003cbr /\u003e\n\nThe input must be at least a matrix.\n\nFor example:\n\n\n```text\ninput = 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\n\u003cbr /\u003e\n\n\n```scdoc\n# A main diagonal from each batch.\ntf.matrix_diag_part(input) ==\u003e [[1, 6, 7], # Output shape: (2, 3)\n [5, 2, 7]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A superdiagonal from each batch.\ntf.matrix_diag_part(input, k = 1)\n ==\u003e [[2, 7, 6], # Output shape: (2, 3)\n [4, 3, 8]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A tridiagonal band from each batch.\ntf.matrix_diag_part(input, k = (-1, 1))\n ==\u003e [[[2, 7, 6], # Output shape: (2, 3, 3)\n [1, 6, 7],\n [5, 8, 0]],\n [[4, 3, 8],\n [5, 2, 7],\n [1, 6, 0]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# Padding value = 9\ntf.matrix_diag_part(input, k = (1, 3), padding_value = 9)\n ==\u003e [[[4, 9, 9], # Output shape: (2, 3, 3)\n [3, 8, 9],\n [2, 7, 6]],\n [[2, 9, 9],\n [3, 4, 9],\n [4, 3, 8]]]\n```\n\n\u003cbr /\u003e\n\nArgs:\n\n- scope: A [Scope](/versions/r2.14/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: Rank `r` tensor where `r \u003e= 2`.\n- k: 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: The value to fill the area outside the specified diagonal band with. Default is 0.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.14/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The extracted diagonal(s).\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [MatrixDiagPartV2](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1ad3de7ab4ab1196ff0eb0a0b9712563ef)`(const ::`[tensorflow::Scope](/versions/r2.14/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` k, ::`[tensorflow::Input](/versions/r2.14/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` padding_value)` ||\n\n| ### Public attributes ||\n|-------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [diagonal](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1a7a8892ae88249cf5f89b97544d71a59c) | `::`[tensorflow::Output](/versions/r2.14/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [operation](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1aefc836bb535eab5db669667a152eba42) | [Operation](/versions/r2.14/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n\n| ### Public functions ||\n|-------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1a0b20ceb05713921670ce29cf7671a152)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1acb91e8a485455813fcd8d9d3558c793b)`() const ` | |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_matrix_diag_part_v2_1a18e6d0e922c930c2880112f18f2ac011)`() const ` | |\n\nPublic attributes\n-----------------\n\n### diagonal\n\n```text\n::tensorflow::Output diagonal\n``` \n\n### operation\n\n```text\nOperation operation\n``` \n\nPublic functions\n----------------\n\n### MatrixDiagPartV2\n\n```gdscript\n MatrixDiagPartV2(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input k,\n ::tensorflow::Input padding_value\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n```"]]
