Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
aliran tensor:: operasi:: SparseCrossHashed
#include <sparse_ops.h> Menghasilkan persilangan renggang dari daftar tensor renggang dan padat.
Ringkasan
Operasi ini mengambil dua daftar, satu dari 2D SparseTensor dan satu dari 2D Tensor , masing-masing mewakili fitur dari satu kolom fitur. Ini menghasilkan SparseTensor 2D dengan persilangan fitur-fitur ini secara batch.
Misalnya, jika masukannya adalah
inputs[0]: SparseTensor with shape = [2, 2]
[0, 0]: "a"
[1, 0]: "b"
[1, 1]: "c"
inputs[1]: SparseTensor with shape = [2, 1]
[0, 0]: "d"
[1, 0]: "e"
inputs[2]: Tensor [["f"], ["g"]]
maka outputnya adalah
shape = [2, 2]
[0, 0]: "a_X_d_X_f"
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
jika hashed_output=true maka outputnya akan menjadi
shape = [2, 2]
[0, 0]: FingerprintCat64(
Fingerprint64("f"), FingerprintCat64(
Fingerprint64("d"), Fingerprint64("a")))
[1, 0]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("b")))
[1, 1]: FingerprintCat64(
Fingerprint64("g"), FingerprintCat64(
Fingerprint64("e"), Fingerprint64("c"))) Argumen:
- ruang lingkup: Objek Lingkup
- indeks: 2-D. Indeks setiap masukan
SparseTensor . - nilai: 1-D. nilai setiap
SparseTensor . - bentuk: 1-D. Bentuk setiap
SparseTensor . - input_padat: 2-D. Kolom diwakili oleh
Tensor yang padat. - num_buckets: Digunakan jika hashed_output benar. keluaran = hashed_valuenum_buckets jika num_buckets > 0 jika tidak hash_value.
- strong_hash: boolean, jika benar, siphash dengan garam akan digunakan sebagai pengganti farmhash.
- garam: Tentukan garam yang akan digunakan oleh fungsi siphash.
Pengembalian:
-
Output keluaran_indeks: 2-D. Indeks dari gabungan SparseTensor . - Nilai keluaran
Output : 1-D. Nilai tidak kosong dari SparseTensor yang digabungkan atau di-hash. - Bentuk keluaran
Output : 1-D. Bentuk SparseTensor yang digabungkan.
Atribut publik
Fungsi publik
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 2025-07-27 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-27 UTC."],[],[],null,["# tensorflow::ops::SparseCrossHashed Class Reference\n\ntensorflow::ops::SparseCrossHashed\n==================================\n\n`#include \u003csparse_ops.h\u003e`\n\nGenerates sparse cross from a list of sparse and dense tensors.\n\nSummary\n-------\n\nThe op takes two lists, one of 2D `SparseTensor` and one of 2D [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor), each representing features of one feature column. It outputs a 2D `SparseTensor` with the batchwise crosses of these features.\n\nFor example, if the inputs are \n\n```text\ninputs[0]: SparseTensor with shape = [2, 2]\n[0, 0]: \"a\"\n[1, 0]: \"b\"\n[1, 1]: \"c\"\n\ninputs[1]: SparseTensor with shape = [2, 1]\n[0, 0]: \"d\"\n[1, 0]: \"e\"\n\ninputs[2]: Tensor [[\"f\"], [\"g\"]]\n```\n\n\u003cbr /\u003e\n\nthen the output will be \n\n```scdoc\nshape = [2, 2]\n[0, 0]: \"a_X_d_X_f\"\n[1, 0]: \"b_X_e_X_g\"\n[1, 1]: \"c_X_e_X_g\"\n```\n\n\u003cbr /\u003e\n\nif hashed_output=true then the output will be \n\n```text\nshape = [2, 2]\n[0, 0]: FingerprintCat64(\n Fingerprint64(\"f\"), FingerprintCat64(\n Fingerprint64(\"d\"), Fingerprint64(\"a\")))\n[1, 0]: FingerprintCat64(\n Fingerprint64(\"g\"), FingerprintCat64(\n Fingerprint64(\"e\"), Fingerprint64(\"b\")))\n[1, 1]: FingerprintCat64(\n Fingerprint64(\"g\"), FingerprintCat64(\n Fingerprint64(\"e\"), Fingerprint64(\"c\")))\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- indices: 2-D. Indices of each input `SparseTensor`.\n- values: 1-D. values of each `SparseTensor`.\n- shapes: 1-D. Shapes of each `SparseTensor`.\n- dense_inputs: 2-D. Columns represented by dense [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor).\n- num_buckets: It is used if hashed_output is true. output = hashed_valuenum_buckets if num_buckets \\\u003e 0 else hashed_value.\n- strong_hash: boolean, if true, siphash with salt will be used instead of farmhash.\n- salt: Specify the salt that will be used by the siphash function.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) output_indices: 2-D. Indices of the concatenated `SparseTensor`.\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) output_values: 1-D. Non-empty values of the concatenated or hashed `SparseTensor`.\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) output_shape: 1-D. Shape of the concatenated `SparseTensor`.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [SparseCrossHashed](#classtensorflow_1_1ops_1_1_sparse_cross_hashed_1a75df417d574408f2c120294be39de389)`(const ::`[tensorflow::Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::InputList](/versions/r2.3/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` indices, ::`[tensorflow::InputList](/versions/r2.3/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` values, ::`[tensorflow::InputList](/versions/r2.3/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` shapes, ::`[tensorflow::InputList](/versions/r2.3/api_docs/cc/class/tensorflow/input-list#classtensorflow_1_1_input_list)` dense_inputs, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` num_buckets, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` strong_hash, ::`[tensorflow::Input](/versions/r2.3/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` salt)` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_sparse_cross_hashed_1a68e99ac704684420839783001f5f37f4) | [Operation](/versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output_indices](#classtensorflow_1_1ops_1_1_sparse_cross_hashed_1a13ce1f14f64b18d1c495ccf725acf0bb) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [output_shape](#classtensorflow_1_1ops_1_1_sparse_cross_hashed_1a89578d009bb75ad63a153f89045c46a2) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n| [output_values](#classtensorflow_1_1ops_1_1_sparse_cross_hashed_1a6371e5ae28289305864042629e0b4fe5) | `::`[tensorflow::Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output_indices\n\n```scdoc\n::tensorflow::Output output_indices\n``` \n\n### output_shape\n\n```scdoc\n::tensorflow::Output output_shape\n``` \n\n### output_values\n\n```scdoc\n::tensorflow::Output output_values\n``` \n\nPublic functions\n----------------\n\n### SparseCrossHashed\n\n```gdscript\n SparseCrossHashed(\n const ::tensorflow::Scope & scope,\n ::tensorflow::InputList indices,\n ::tensorflow::InputList values,\n ::tensorflow::InputList shapes,\n ::tensorflow::InputList dense_inputs,\n ::tensorflow::Input num_buckets,\n ::tensorflow::Input strong_hash,\n ::tensorflow::Input salt\n)\n```"]]
