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aliran tensor:: operasi:: Buat ember
#include <math_ops.h> Mengelompokkan 'masukan' berdasarkan 'batas'.
Ringkasan
Misalnya, jika masukannya berupa batasan = [0, 10, 100] masukan = [[-5, 10000] [150, 10] [5, 100]]
maka keluarannya adalah keluaran = [[0, 3] [3, 2] [1, 3]]
Argumen:
- ruang lingkup: Objek Lingkup
- input: Segala bentuk Tensor berisi tipe int atau float.
- batas: Daftar pelampung yang diurutkan memberikan batas keranjang.
Pengembalian:
-
Output : Bentuknya sama dengan 'input', setiap nilai input diganti dengan indeks bucket.
(numpy) Setara dengan np.digitize.
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 2025-07-26 UTC.
[null,null,["Terakhir diperbarui pada 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::Bucketize Class Reference\n\ntensorflow::ops::Bucketize\n==========================\n\n`#include \u003cmath_ops.h\u003e`\n\nBucketizes 'input' based on 'boundaries'.\n\nSummary\n-------\n\nFor example, if the inputs are boundaries = \\[0, 10, 100\\] input = \\[\\[-5, 10000\\] \\[150, 10\\] \\[5, 100\\]\\]\n\nthen the output will be output = \\[\\[0, 3\\] \\[3, 2\\] \\[1, 3\\]\\]\n\nArguments:\n\n- scope: A [Scope](/versions/r2.3/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- input: [Any](/versions/r2.3/api_docs/cc/class/tensorflow/ops/any#classtensorflow_1_1ops_1_1_any) shape of [Tensor](/versions/r2.3/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) contains with int or float type.\n- boundaries: A sorted list of floats gives the boundary of the buckets.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r2.3/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): Same shape with 'input', each value of input replaced with bucket index.\n\n\u003cbr /\u003e\n\n(numpy) Equivalent to np.digitize.\n\n| ### Constructors and Destructors ||\n|---|---|\n| [Bucketize](#classtensorflow_1_1ops_1_1_bucketize_1a104987760896f84594d21a17738a6fe1)`(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)` input, const gtl::ArraySlice\u003c float \u003e & boundaries)` ||\n\n| ### Public attributes ||\n|---------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_bucketize_1a11d9d7e39578db3e3dfaf2ef9213ae34) | [Operation](/versions/r2.3/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_bucketize_1aa111bb19d459f3f26ae8f03297739125) | `::`[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_bucketize_1af82e929e268a0301d7ce4c41480a19e4)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_bucketize_1a7d66691237f8de46ab0c52782419cf53)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_bucketize_1aa3f697a162b180d9aa7847cb7d22dc3e)`() 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### Bucketize\n\n```gdscript\n Bucketize(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n const gtl::ArraySlice\u003c float \u003e & boundaries\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```"]]
