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tensorflow::ops::MatrixSetDiagV2
#include <array_ops.h>
Returns a batched matrix tensor with new batched diagonal values.
Summary
Given input and diagonal, this operation returns a tensor with the same shape and values as input, except for the specified diagonals of the innermost matrices. These will be overwritten by the values in diagonal.
input has r+1 dimensions [I, J, ..., L, M, N]. When k is scalar or k[0] == k[1], diagonal has r dimensions [I, J, ..., L, max_diag_len]. Otherwise, it has r+1 dimensions [I, J, ..., L, num_diags, max_diag_len]. num_diags is the number of diagonals, num_diags = k[1] - k[0] + 1. max_diag_len is the longest diagonal in the range [k[0], k[1]], max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))
The output is a tensor of rank k+1 with dimensions [I, J, ..., L, M, N]. If k is scalar or k[0] == k[1]:
output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]
output[i, j, ..., l, m, n] ; otherwise
Otherwise,
output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, k[1]-d, n-max(d, 0)] ; if d_lower <= d <= d_upper
input[i, j, ..., l, m, n] ; otherwise where
d = n - m
For example:
# The main diagonal.
input = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4)
[7, 7, 7, 7],
[7, 7, 7, 7]],
[[7, 7, 7, 7],
[7, 7, 7, 7],
[7, 7, 7, 7]]])
diagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3)
[4, 5, 6]])
tf.matrix_diag(diagonal) ==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4)
[7, 2, 7, 7],
[7, 7, 3, 7]],
[[4, 7, 7, 7],
[7, 5, 7, 7],
[7, 7, 6, 7]]]
# A superdiagonal (per batch).
tf.matrix_diag(diagonal, k = 1)
==> [[[7, 1, 7, 7], # Output shape: (2, 3, 4)
[7, 7, 2, 7],
[7, 7, 7, 3]],
[[7, 4, 7, 7],
[7, 7, 5, 7],
[7, 7, 7, 6]]]
# A band of diagonals.
diagonals = np.array([[[1, 2, 3], # Diagonal shape: (2, 2, 3)
[4, 5, 0]],
[[6, 1, 2],
[3, 4, 0]]])
tf.matrix_diag(diagonals, k = (-1, 0))
==> [[[1, 7, 7, 7], # Output shape: (2, 3, 4)
[4, 2, 7, 7],
[0, 5, 3, 7]],
[[6, 7, 7, 7],
[3, 1, 7, 7],
[7, 4, 2, 7]]]
Arguments:
- scope: A Scope object
- input: Rank
r+1, where r >= 1.
- diagonal: Rank
r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k >= 1.
- 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].
Returns:
Output: Rank r+1, with output.shape = input.shape.
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 2020-04-20 UTC.
[null,null,["Last updated 2020-04-20 UTC."],[],[],null,["# tensorflow::ops::MatrixSetDiagV2 Class Reference\n\ntensorflow::ops::MatrixSetDiagV2\n================================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns a batched matrix tensor with new batched diagonal values.\n\nSummary\n-------\n\nGiven `input` and `diagonal`, this operation returns a tensor with the same shape and values as `input`, except for the specified diagonals of the innermost matrices. These will be overwritten by the values in `diagonal`.\n\n`input` has `r+1` dimensions `[I, J, ..., L, M, N]`. When `k` is scalar or `k[0] == k[1]`, `diagonal` has `r` dimensions `[I, J, ..., L, max_diag_len]`. Otherwise, it has `r+1` dimensions `[I, J, ..., L, num_diags, max_diag_len]`. `num_diags` is the number of diagonals, `num_diags = k[1] - k[0] + 1`. `max_diag_len` is the longest diagonal in the range `[k[0], k[1]]`, `max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`\n\nThe output is a tensor of rank `k+1` with dimensions `[I, J, ..., L, M, N]`. If `k` is scalar or `k[0] == k[1]`:\n\n\n```text\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, n-max(k[1], 0)] ; if n - m == k[1]\n output[i, j, ..., l, m, n] ; otherwise\n```\n\n\u003cbr /\u003e\n\nOtherwise,\n\n\u003cbr /\u003e\n\n```scdoc\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, k[1]-d, n-max(d, 0)] ; if d_lower \u003c= d \u003c= d_upper\n input[i, j, ..., l, m, n] ; otherwise\n```\nwhere `d = n - m`\n\n\u003cbr /\u003e\n\nFor example:\n\n\n```scdoc\n# The main diagonal.\ninput = np.array([[[7, 7, 7, 7], # Input shape: (2, 3, 4)\n [7, 7, 7, 7],\n [7, 7, 7, 7]],\n [[7, 7, 7, 7],\n [7, 7, 7, 7],\n [7, 7, 7, 7]]])\ndiagonal = np.array([[1, 2, 3], # Diagonal shape: (2, 3)\n [4, 5, 6]])\ntf.matrix_diag(diagonal) ==\u003e [[[1, 7, 7, 7], # Output shape: (2, 3, 4)\n [7, 2, 7, 7],\n [7, 7, 3, 7]],\n [[4, 7, 7, 7],\n [7, 5, 7, 7],\n [7, 7, 6, 7]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A superdiagonal (per batch).\ntf.matrix_diag(diagonal, k = 1)\n ==\u003e [[[7, 1, 7, 7], # Output shape: (2, 3, 4)\n [7, 7, 2, 7],\n [7, 7, 7, 3]],\n [[7, 4, 7, 7],\n [7, 7, 5, 7],\n [7, 7, 7, 6]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A band of diagonals.\ndiagonals = np.array([[[1, 2, 3], # Diagonal shape: (2, 2, 3)\n [4, 5, 0]],\n [[6, 1, 2],\n [3, 4, 0]]])\ntf.matrix_diag(diagonals, k = (-1, 0))\n ==\u003e [[[1, 7, 7, 7], # Output shape: (2, 3, 4)\n [4, 2, 7, 7],\n [0, 5, 3, 7]],\n [[6, 7, 7, 7],\n [3, 1, 7, 7],\n [7, 4, 2, 7]]]\n```\n\n\u003cbr /\u003e\n\n\n````gdscript\n \n Arguments:\n \n- scope: A /versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope object\n\n \n- input: Rank r+1, where r \u003e= 1.\n\n \n- diagonal: Rank r when k is an integer or k[0] == k[1]. Otherwise, it has rank r+1. k \u003e= 1.\n\n \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\n \n\n Returns:\n \n- /versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output: Rank r+1, with output.shape = input.shape. \n\n \n\n \n\n\n \n### Constructors and Destructors\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a438f858712bda6df180ee19d8f278bf4(const ::/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope & scope, ::/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input input, ::/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input diagonal, ::/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input k)\n \n\n \n\n\n \n\n\n \n### Public attributes\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a433c91a80772823c3acd4729a873900f\n \n\n \n\n /versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a390fc69019f7170f80f7c4c3acb12cee\n \n\n \n\n ::/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output\n \n\n \n\n\n \n\n\n \n### Public functions\n\n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1ab0f95dc9ddcb2221701f55c8caddcdb1() const \n \n\n \n\n ::tensorflow::Node *\n \n\n \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a333c742af8203776572da9009d8c0930() const \n \n\n \n\n `\n` \n`\n` \n\n\n\n #classtensorflow_1_1ops_1_1_matrix_set_diag_v2_1a0574bd8260d99f8d93fa3a0cb880f0fa() const \n \n\n \n\n `\n` \n`\n` \n\n\n Public attributes\n \n \n### operation\n\n\n \n```\nOperation operation\n```\n\n \n\n \n \n \n### output\n\n\n \n\n\n```text\n::tensorflow::Output output\n```\n\n \n\n \n Public functions\n \n \n### MatrixSetDiagV2\n\n\n \n\n\n```gdscript\n MatrixSetDiagV2(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input diagonal,\n ::tensorflow::Input k\n)\n```\n\n \n\n \n \n \n### node\n\n\n \n\n\n```gdscript\n::tensorflow::Node * node() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Input\n\n\n \n\n\n```gdscript\n operator::tensorflow::Input() const \n```\n\n \n\n \n \n \n### operator::tensorflow::Output\n\n\n \n\n\n```gdscript\n operator::tensorflow::Output() const \n```\n\n \n\n \n\n \n\n \n````"]]
