Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
aliran tensor:: operasi:: BatchToSpaceND
#include <array_ops.h> BatchToSpace untuk tensor ND tipe T.
Ringkasan
Operasi ini membentuk ulang dimensi "batch" 0 menjadi M + 1 dimensi bentuk block_shape + [batch] , menyisipkan blok-blok ini kembali ke dalam kisi yang ditentukan oleh dimensi spasial [1, ..., M] , untuk mendapatkan hasil dengan peringkat yang sama dengan input. Dimensi spasial dari hasil antara ini kemudian secara opsional dipotong menurut crops untuk menghasilkan keluaran. Ini adalah kebalikan dari SpaceToBatch. Lihat di bawah untuk deskripsi yang tepat.
Argumen:
- ruang lingkup: Objek Lingkup
- input: ND dengan bentuk
input_shape = [batch] + spatial_shape + remaining_shape , dimana spasial_shape memiliki dimensi M. - block_shape: 1-D dengan bentuk
[M] , semua nilai harus >= 1. - tanaman: 2-D dengan bentuk
[M, 2] , semua nilai harus >= 0. crops[i] = [crop_start, crop_end] menentukan jumlah yang akan dipotong dari dimensi masukan i + 1 , yang sesuai dengan dimensi spasial i . Diperlukan crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1] .
Operasi ini setara dengan langkah-langkah berikut:
- Bentuk ulang
input untuk reshaped bentuk: [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape), input_shape[1], ..., input_shape[N-1]] - Ubah dimensi
reshaped untuk menghasilkan permuted bentuk [batch / prod(block_shape),input_shape[1], block_shape[0], ..., input_shape[M], block_shape[M-1],input_shape[M+1], ..., bentuk_masukan[N-1]] - Bentuk ulang
permuted untuk menghasilkan reshaped_permuted dari bentuk [batch / prod(block_shape),input_shape[1] * block_shape[0], ..., input_shape[M] * block_shape[M-1],input_shape[M+1], .. ., bentuk_masukan[N-1]] - Pangkas awal dan akhir dimensi
[1, ..., M] dari reshaped_permuted sesuai dengan crops untuk menghasilkan keluaran bentuk: [batch / prod(block_shape),input_shape[1] * block_shape[0] - crop[0, 0] - tanaman[0,1], ..., bentuk_input[M] * bentuk_blok[M-1] - tanaman[M-1,0] - tanaman[M-1,1],bentuk_input[M+1] , ..., bentuk_masukan[N-1]]
Beberapa contoh:
(1) Untuk masukan bentuk [4, 1, 1, 1] berikut, block_shape = [2, 2] , dan crops = [[0, 0], [0, 0]] :
[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
Tensor keluaran memiliki bentuk [1, 2, 2, 1] dan nilai:
x = [[[[1], [2]], [[3], [4]]]]
(2) Untuk input bentuk [4, 1, 1, 3] berikut, block_shape = [2, 2] , dan crops = [[0, 0], [0, 0]] :
[[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
Tensor keluaran memiliki bentuk [1, 2, 2, 3] dan nilai:
x = [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]] (3) Untuk masukan bentuk [4, 2, 2, 1] berikut, block_shape = [2, 2] , dan crops = [[0, 0], [0, 0]] :
x = [[[[1], [3]], [[9], [11]]],
[[[2], [4]], [[10], [12]]],
[[[5], [7]], [[13], [15]]],
[[[6], [8]], [[14], [16]]]] Tensor keluaran memiliki bentuk [1, 4, 4, 1] dan nilai:
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]],
[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]] (4) Untuk masukan bentuk [8, 1, 3, 1] berikut, block_shape = [2, 2] , dan crops = [[0, 0], [2, 0]] :
x = [[[[0], [1], [3]]], [[[0], [9], [11]]],
[[[0], [2], [4]]], [[[0], [10], [12]]],
[[[0], [5], [7]]], [[[0], [13], [15]]],
[[[0], [6], [8]]], [[[0], [14], [16]]]] Tensor keluaran memiliki bentuk [2, 2, 4, 1] dan nilai:
x = [[[[1], [2], [3], [4]],
[[5], [6], [7], [8]]],
[[[9], [10], [11], [12]],
[[13], [14], [15], [16]]]] Pengembalian:
Atribut publik
Fungsi publik
simpul
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Keluaran
operator::tensorflow::Output() const
Kecuali dinyatakan lain, konten di halaman ini dilisensikan berdasarkan Lisensi Creative Commons Attribution 4.0, sedangkan contoh kode dilisensikan berdasarkan Lisensi Apache 2.0. Untuk mengetahui informasi selengkapnya, lihat Kebijakan Situs Google Developers. Java adalah merek dagang terdaftar dari Oracle dan/atau afiliasinya.
Terakhir diperbarui pada 2025-07-26 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::BatchToSpaceND Class Reference\n\ntensorflow::ops::BatchToSpaceND\n===============================\n\n`#include \u003carray_ops.h\u003e`\n\n[BatchToSpace](/versions/r1.15/api_docs/cc/class/tensorflow/ops/batch-to-space#classtensorflow_1_1ops_1_1_batch_to_space) for N-D tensors of type T.\n\nSummary\n-------\n\nThis operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of shape `block_shape + [batch]`, interleaves these blocks back into the grid defined by the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as the input. The spatial dimensions of this intermediate result are then optionally cropped according to `crops` to produce the output. This is the reverse of SpaceToBatch. See below for a precise description.\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, where spatial_shape has M dimensions.\n- block_shape: 1-D with shape `[M]`, all values must be \\\u003e= 1.\n- crops: 2-D with shape `[M, 2]`, all values must be \\\u003e= 0. `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input dimension `i + 1`, which corresponds to spatial dimension `i`. It is required that `crop_start[i] + crop_end[i] \u003c= block_shape[i] * input_shape[i + 1]`.\n\n\u003cbr /\u003e\n\nThis operation is equivalent to the following steps:\n\n\n1. Reshape `input` to `reshaped` of shape: \\[block_shape\\[0\\], ..., block_shape\\[M-1\\], batch / prod(block_shape), input_shape\\[1\\], ..., input_shape\\[N-1\\]\\]\n2. Permute dimensions of `reshaped` to produce `permuted` of shape \\[batch / prod(block_shape),input_shape\\[1\\], block_shape\\[0\\], ..., input_shape\\[M\\], block_shape\\[M-1\\],input_shape\\[M+1\\], ..., input_shape\\[N-1\\]\\]\n3. Reshape `permuted` to produce `reshaped_permuted` of shape \\[batch / prod(block_shape),input_shape\\[1\\] \\* block_shape\\[0\\], ..., input_shape\\[M\\] \\* block_shape\\[M-1\\],input_shape\\[M+1\\], ..., input_shape\\[N-1\\]\\]\n4. Crop the start and end of dimensions `[1, ..., M]` of `reshaped_permuted` according to `crops` to produce the output of shape: \\[batch / prod(block_shape),input_shape\\[1\\] \\* block_shape\\[0\\] - crops\\[0,0\\] - crops\\[0,1\\], ..., input_shape\\[M\\] \\* block_shape\\[M-1\\] - crops\\[M-1,0\\] - crops\\[M-1,1\\],input_shape\\[M+1\\], ..., input_shape\\[N-1\\]\\]\n\n\u003cbr /\u003e\n\nSome examples:\n\n(1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n\n```text\n[[[[1]]], [[[2]]], [[[3]]], [[[4]]]]\n```\n\n\u003cbr /\u003e\n\nThe output tensor has shape `[1, 2, 2, 1]` and value:\n\n\n```text\nx = [[[[1], [2]], [[3], [4]]]]\n```\n\n\u003cbr /\u003e\n\n(2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n\n```text\n[[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]\n```\n\n\u003cbr /\u003e\n\nThe output tensor has shape `[1, 2, 2, 3]` and value:\n\n\n```text\nx = [[[[1, 2, 3], [4, 5, 6]],\n [[7, 8, 9], [10, 11, 12]]]]\n```\n\n\u003cbr /\u003e\n\n(3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:\n\n\n```text\nx = [[[[1], [3]], [[9], [11]]],\n [[[2], [4]], [[10], [12]]],\n [[[5], [7]], [[13], [15]]],\n [[[6], [8]], [[14], [16]]]]\n```\n\n\u003cbr /\u003e\n\nThe output tensor has shape `[1, 4, 4, 1]` and value:\n\n\n```text\nx = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]],\n [[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n```\n\n\u003cbr /\u003e\n\n(4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`:\n\n\n```text\nx = [[[[0], [1], [3]]], [[[0], [9], [11]]],\n [[[0], [2], [4]]], [[[0], [10], [12]]],\n [[[0], [5], [7]]], [[[0], [13], [15]]],\n [[[0], [6], [8]]], [[[0], [14], [16]]]]\n```\n\n\u003cbr /\u003e\n\nThe output tensor has shape `[2, 2, 4, 1]` and value:\n\n\n```text\nx = [[[[1], [2], [3], [4]],\n [[5], [6], [7], [8]]],\n [[[9], [10], [11], [12]],\n [[13], [14], [15], [16]]]]\n```\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): The output tensor.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [BatchToSpaceND](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1ae9fc7cf839b67ec1692eb9dbd13dab3f)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` input, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` block_shape, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` crops)` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1a1e8d19aed27a8ba75041200ee25a7310) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1a2f9a5258c2d37ba9ce71c6ebfe2f754d) | `::`[tensorflow::Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|------------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1a8c320b154abac62302b289161e5aa745)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1a94adde19cfddf4d1109cceff401543c8)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_batch_to_space_n_d_1a17e07f190557e6565111355cc159b528)`() 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### BatchToSpaceND\n\n```gdscript\n BatchToSpaceND(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input block_shape,\n ::tensorflow::Input crops\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```"]]
