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aliran tensor:: operasi:: MatriksDiagV2
#include <array_ops.h> Mengembalikan tensor diagonal kumpulan dengan nilai diagonal kumpulan tertentu.
Ringkasan
Mengembalikan tensor dengan konten diagonal sebagai k[0] -th hingga k[1] -th diagonal dari sebuah matriks, dengan semua yang lain diisi dengan padding . num_rows dan num_cols menentukan dimensi matriks keluaran terdalam. Jika keduanya tidak ditentukan, op mengasumsikan matriks terdalam berbentuk persegi dan menyimpulkan ukurannya dari k dan dimensi diagonal terdalam. Jika hanya salah satu saja yang ditentukan, operasi mengasumsikan nilai yang tidak ditentukan adalah yang terkecil berdasarkan kriteria lainnya.
Misalkan diagonal mempunyai r dimensi [I, J, ..., L, M, N] . Tensor keluaran memiliki peringkat r+1 dengan bentuk [I, J, ..., L, M, num_rows, num_cols] ketika hanya satu diagonal yang diberikan ( k adalah bilangan bulat atau k[0] == k[1] ) . Jika tidak, ia memiliki peringkat r dengan bentuk [I, J, ..., L, num_rows, num_cols] .
Dimensi diagonal terdalam kedua mempunyai arti ganda. Jika k adalah skalar atau k[0] == k[1] , M adalah bagian dari ukuran kumpulan [I, J, ..., M], dan tensor keluarannya adalah:
output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper
padding_value ; otherwise Jika tidak, M diperlakukan sebagai jumlah diagonal untuk matriks dalam kumpulan yang sama ( M = k[1]-k[0]+1 ), dan tensor keluarannya adalah:
output[i, j, ..., l, m, n]
= diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] <= d <= k[1]
padding_value ; otherwise di mana
d = n - m ,
diag_index = k[1] - d , dan
index_in_diag = n - max(d, 0) .
Misalnya:
# The main diagonal.
diagonal = np.array([[1, 2, 3, 4], # Input shape: (2, 4)
[5, 6, 7, 8]])
tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0], # Output shape: (2, 4, 4)
[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]]]# A superdiagonal (per batch).
diagonal = np.array([[1, 2, 3], # Input shape: (2, 3)
[4, 5, 6]])
tf.matrix_diag(diagonal, k = 1)
==> [[[0, 1, 0, 0], # Output shape: (2, 4, 4)
[0, 0, 2, 0],
[0, 0, 0, 3],
[0, 0, 0, 0]],
[[0, 4, 0, 0],
[0, 0, 5, 0],
[0, 0, 0, 6],
[0, 0, 0, 0]]]# A band of diagonals.
diagonals = np.array([[[1, 2, 3], # Input shape: (2, 2, 3)
[4, 5, 0]],
[[6, 7, 9],
[9, 1, 0]]])
tf.matrix_diag(diagonals, k = (-1, 0))
==> [[[1, 0, 0], # Output shape: (2, 3, 3)
[4, 2, 0],
[0, 5, 3]],
[[6, 0, 0],
[9, 7, 0],
[0, 1, 9]]]# Rectangular matrix.
diagonal = np.array([1, 2]) # Input shape: (2)
tf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)
==> [[0, 0, 0, 0], # Output shape: (3, 4)
[1, 0, 0, 0],
[0, 2, 0, 0]]# Rectangular matrix with inferred num_cols and padding_value = 9.
tf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)
==> [[9, 9], # Output shape: (3, 2)
[1, 9],
[9, 2]] Argumen:
- ruang lingkup: Objek Lingkup
- diagonal: Peringkat
r , di mana r >= 1 - k: Offset diagonal. Nilai positif berarti superdiagonal, 0 mengacu pada diagonal utama, dan nilai negatif berarti subdiagonal.
k dapat berupa bilangan bulat tunggal (untuk satu diagonal) atau sepasang bilangan bulat yang menentukan ujung rendah dan tinggi dari pita matriks. k[0] tidak boleh lebih besar dari k[1] . - num_rows: Jumlah baris matriks keluaran. Jika tidak tersedia, operasi mengasumsikan matriks keluaran adalah matriks persegi dan menyimpulkan ukuran matriks dari k dan dimensi
diagonal terdalam. - num_cols: Jumlah kolom matriks keluaran. Jika tidak tersedia, operasi mengasumsikan matriks keluaran adalah matriks persegi dan menyimpulkan ukuran matriks dari k dan dimensi
diagonal terdalam. - padding_value: Angka untuk mengisi area di luar pita diagonal yang ditentukan. Standarnya adalah 0.
Pengembalian:
-
Output : Memiliki peringkat r+1 ketika k adalah bilangan bulat atau k[0] == k[1] , peringkat r sebaliknya.
Atribut publik
Fungsi publik
simpul
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Keluaran
operator::tensorflow::Output() const
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Terakhir diperbarui pada 2025-07-27 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::MatrixDiagV2 Class Reference\n\ntensorflow::ops::MatrixDiagV2\n=============================\n\n`#include \u003carray_ops.h\u003e`\n\nReturns a batched diagonal tensor with given batched diagonal values.\n\nSummary\n-------\n\nReturns a tensor with the contents in `diagonal` as `k[0]`-th to `k[1]`-th diagonals of a matrix, with everything else padded with `padding`. `num_rows` and `num_cols` specify the dimension of the innermost matrix of the output. If both are not specified, the op assumes the innermost matrix is square and infers its size from `k` and the innermost dimension of `diagonal`. If only one of them is specified, the op assumes the unspecified value is the smallest possible based on other criteria.\n\nLet `diagonal` have `r` dimensions `[I, J, ..., L, M, N]`. The output tensor has rank `r+1` with shape `[I, J, ..., L, M, num_rows, num_cols]` when only one diagonal is given (`k` is an integer or `k[0] == k[1]`). Otherwise, it has rank `r` with shape `[I, J, ..., L, num_rows, num_cols]`.\n\nThe second innermost dimension of `diagonal` has double meaning. When `k` is scalar or `k[0] == k[1]`, `M` is part of the batch size \\[I, J, ..., M\\], and the output tensor is:\n\n\n```scdoc\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, n-max(d_upper, 0)] ; if n - m == d_upper\n padding_value ; otherwise\n```\n\n\u003cbr /\u003e\n\nOtherwise, `M` is treated as the number of diagonals for the matrix in the same batch (`M = k[1]-k[0]+1`), and the output tensor is:\n\n\u003cbr /\u003e\n\n```scdoc\noutput[i, j, ..., l, m, n]\n = diagonal[i, j, ..., l, diag_index, index_in_diag] ; if k[0] \u003c= d \u003c= k[1]\n padding_value ; otherwise\n```\nwhere `d = n - m`, `diag_index = k[1] - d`, and `index_in_diag = n - max(d, 0)`.\n\n\u003cbr /\u003e\n\nFor example:\n\n\n```scdoc\n# The main diagonal.\ndiagonal = np.array([[1, 2, 3, 4], # Input shape: (2, 4)\n [5, 6, 7, 8]])\ntf.matrix_diag(diagonal) ==\u003e [[[1, 0, 0, 0], # Output shape: (2, 4, 4)\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\n\u003cbr /\u003e\n\n\n```scdoc\n# A superdiagonal (per batch).\ndiagonal = np.array([[1, 2, 3], # Input shape: (2, 3)\n [4, 5, 6]])\ntf.matrix_diag(diagonal, k = 1)\n ==\u003e [[[0, 1, 0, 0], # Output shape: (2, 4, 4)\n [0, 0, 2, 0],\n [0, 0, 0, 3],\n [0, 0, 0, 0]],\n [[0, 4, 0, 0],\n [0, 0, 5, 0],\n [0, 0, 0, 6],\n [0, 0, 0, 0]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# A band of diagonals.\ndiagonals = np.array([[[1, 2, 3], # Input shape: (2, 2, 3)\n [4, 5, 0]],\n [[6, 7, 9],\n [9, 1, 0]]])\ntf.matrix_diag(diagonals, k = (-1, 0))\n ==\u003e [[[1, 0, 0], # Output shape: (2, 3, 3)\n [4, 2, 0],\n [0, 5, 3]],\n [[6, 0, 0],\n [9, 7, 0],\n [0, 1, 9]]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# Rectangular matrix.\ndiagonal = np.array([1, 2]) # Input shape: (2)\ntf.matrix_diag(diagonal, k = -1, num_rows = 3, num_cols = 4)\n ==\u003e [[0, 0, 0, 0], # Output shape: (3, 4)\n [1, 0, 0, 0],\n [0, 2, 0, 0]]\n```\n\n\u003cbr /\u003e\n\n\n```scdoc\n# Rectangular matrix with inferred num_cols and padding_value = 9.\ntf.matrix_diag(diagonal, k = -1, num_rows = 3, padding_value = 9)\n ==\u003e [[9, 9], # Output shape: (3, 2)\n [1, 9],\n [9, 2]]\n```\n\n\u003cbr /\u003e\n\nArguments:\n\n- scope: A [Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- diagonal: Rank `r`, where `r \u003e= 1`\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- num_rows: The number of rows of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of `diagonal`.\n- num_cols: The number of columns of the output matrix. If it is not provided, the op assumes the output matrix is a square matrix and infers the matrix size from k and the innermost dimension of `diagonal`.\n- padding_value: The number to fill the area outside the specified diagonal band with. Default is 0.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): Has rank `r+1` when `k` is an integer or `k[0] == k[1]`, rank `r` otherwise.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [MatrixDiagV2](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a0f4757e436efeaed1ba2bd9690367b7f)`(const ::`[tensorflow::Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` diagonal, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` k, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` num_rows, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` num_cols, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` padding_value)` ||\n\n| ### Public attributes ||\n|--------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a09bb44acf7362d90e9b70dfaa3ebd8fd) | [Operation](/versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a180f0e2069ba614f9a8bf4593d6b322a) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|--------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a5faf264fc6c643dfc49d1d5b00828973)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a8301ec1ffa503e41034e06b9fc8dfd93)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_matrix_diag_v2_1a96f20bc6c061cadeeb30e3986311fc2c)`() const ` | ` ` ` ` |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### MatrixDiagV2\n\n```gdscript\n MatrixDiagV2(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input diagonal,\n ::tensorflow::Input k,\n ::tensorflow::Input num_rows,\n ::tensorflow::Input num_cols,\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```"]]
