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텐서플로우:: 작전:: ArgMin
#include <math_ops.h>
텐서의 차원 전체에서 가장 작은 값을 가진 인덱스를 반환합니다.
요약
동점인 경우 반환 값의 동일성은 보장되지 않습니다.
용법:
import tensorflow as tf
a = [1, 10, 26.9, 2.8, 166.32, 62.3]
b = tf.math.argmin(input = a)
c = tf.keras.backend.eval(b)
# c = 0
# here a[0] = 1 which is the smallest element of a across axis 0
인수:
- 범위: 범위 개체
- 차원: int32 또는 int64,
[-rank(input), rank(input))
범위에 있어야 합니다. 축소할 입력 텐서 의 차원을 설명합니다. 벡터의 경우 차원 = 0을 사용합니다.
보고:
공개 속성
공공 기능
마디
::tensorflow::Node * node() const
operator::tensorflow::Input() const
연산자::텐서플로우::출력
operator::tensorflow::Output() const
공개 정적 함수
출력 유형
Attrs OutputType(
DataType x
)
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최종 업데이트: 2025-07-26(UTC)
[null,null,["최종 업데이트: 2025-07-26(UTC)"],[],[],null,["# tensorflow::ops::ArgMin Class Reference\n\ntensorflow::ops::ArgMin\n=======================\n\n`#include \u003cmath_ops.h\u003e`\n\nReturns the index with the smallest value across dimensions of a tensor.\n\nSummary\n-------\n\nNote that in case of ties the identity of the return value is not guaranteed.\n\nUsage: \n\n```python\n import tensorflow as tf\n a = [1, 10, 26.9, 2.8, 166.32, 62.3]\n b = tf.math.argmin(input = a)\n c = tf.keras.backend.eval(b)\n # c = 0\n # here a[0] = 1 which is the smallest element of a across axis 0\n \n```\n\n\u003cbr /\u003e\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- dimension: int32 or int64, must be in the range `[-rank(input), rank(input))`. Describes which dimension of the input [Tensor](/versions/r1.15/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) to reduce across. For vectors, use dimension = 0.\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| [ArgMin](#classtensorflow_1_1ops_1_1_arg_min_1a168791dd65474f6516ead5c14c228809)`(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)` dimension)` ||\n| [ArgMin](#classtensorflow_1_1ops_1_1_arg_min_1a585efda09c698305c3b4d4bd52e2ef89)`(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)` dimension, const `[ArgMin::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/arg-min/attrs#structtensorflow_1_1ops_1_1_arg_min_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|-------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_arg_min_1a2f56dc97fc445cb193387875c3d85750) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_arg_min_1adf842d8733fb5c05cf8773604ce4e39c) | `::`[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_arg_min_1aaa10eba6321c4a8bc4d7cf58a21e3328)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_arg_min_1a03587f44a8e9ecd6778a4a68e0fcf506)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_arg_min_1a191191cb11c666a611ba6e2bf95c15b7)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|----------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|\n| [OutputType](#classtensorflow_1_1ops_1_1_arg_min_1a164b1adce713e1a74f370b6fd657626f)`(DataType x)` | [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/arg-min/attrs#structtensorflow_1_1ops_1_1_arg_min_1_1_attrs) |\n\n| ### Structs ||\n|---------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::ArgMin::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/arg-min/attrs) | Optional attribute setters for [ArgMin](/versions/r1.15/api_docs/cc/class/tensorflow/ops/arg-min#classtensorflow_1_1ops_1_1_arg_min). |\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### ArgMin\n\n```gdscript\n ArgMin(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input dimension\n)\n``` \n\n### ArgMin\n\n```gdscript\n ArgMin(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input input,\n ::tensorflow::Input dimension,\n const ArgMin::Attrs & attrs\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``` \n\nPublic static functions\n-----------------------\n\n### OutputType\n\n```text\nAttrs OutputType(\n DataType x\n)\n```"]]