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#include <ops.h> 이니셜라이저를 사용 하면 간단한 기본 상수 및 다차원 배열을 나타내는 중첩된 이니셜라이저 목록과 같은 다양한 종류의 C++ 상수에서 입력 객체를 생성할 수 있습니다.
요약
초기화 생성자는 모두 템플릿이므로 앞서 언급한 종류의 C++ 상수를 사용하여 초기화 생성자를 생성할 수 있습니다. 초기화 프로그램은 생성된 값을 Tensor 객체에 저장합니다.
생성자와 소멸자 |
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Initializer (const T & v)
산술 유형 또는 문자열로 변환할 수 있는 유형의 스칼라 값으로 구성합니다(예: |
Initializer (const Tensor & t)
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Initializer (const T & v, const TensorShape & shape)
스칼라 값과 명시적 모양으로 구성합니다. |
Initializer (const std::initializer_list< T > & v)
스칼라의 초기화 목록(1차원 텐서)에서 구성합니다. |
Initializer (const std::initializer_list< T > & v, const TensorShape & shape)
스칼라의 이니셜라이저 목록과 명시적 모양으로 구성합니다. |
Initializer (const std::initializer_list< Initializer > & v)
중첩된 초기화 목록에서 다차원 텐서를 구성합니다. |
공개 속성
공공 기능
달리 명시되지 않는 한 이 페이지의 콘텐츠에는 Creative Commons Attribution 4.0 라이선스에 따라 라이선스가 부여되며, 코드 샘플에는 Apache 2.0 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 Google Developers 사이트 정책을 참조하세요. 자바는 Oracle 및/또는 Oracle 계열사의 등록 상표입니다.
최종 업데이트: 2025-07-26(UTC)
[null,null,["최종 업데이트: 2025-07-26(UTC)"],[],[],null,["# tensorflow::Input::Initializer Struct Reference\n\ntensorflow::Input::Initializer\n==============================\n\n`#include \u003cops.h\u003e`\n\n[Initializer](/versions/r1.15/api_docs/cc/struct/tensorflow/input/initializer#structtensorflow_1_1_input_1_1_initializer) enables constructing an [Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input) object from various kinds of C++ constants such as simple primitive constants and nested initializer lists representing a multi-dimensional array.\n\nSummary\n-------\n\n[Initializer](/versions/r1.15/api_docs/cc/struct/tensorflow/input/initializer#structtensorflow_1_1_input_1_1_initializer) constructors are all templates, so the aforementioned kinds of C++ constants can be used to construct an [Initializer](/versions/r1.15/api_docs/cc/struct/tensorflow/input/initializer#structtensorflow_1_1_input_1_1_initializer). [Initializer](/versions/r1.15/api_docs/cc/struct/tensorflow/input/initializer#structtensorflow_1_1_input_1_1_initializer) stores the value it got constructed with in a [Tensor](/versions/r1.15/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) object.\n\n| ### Constructors and Destructors ||\n|---|---|\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1ade60a4fdcfa9a530604fbf39d3b5be12)`(const T & v)` Construct from a scalar value of an arithmetic type or a type that can be converted to a string (eg. ||\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1a9314222b3303dcf97314a4bcbcaa94ad)`(const `[Tensor](/versions/r1.15/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor)` & t)` ||\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1ab77d0712180868a7311936ca9a034835)`(const T & v, const TensorShape & shape)` Construct from a scalar value and an explicit shape. ||\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1a91bd52431434dc5358ae8aa39070fe5f)`(const std::initializer_list\u003c T \u003e & v)` Construct from a initializer list of scalars (a one-dimensional tensor). ||\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1a3f572c2835a2310e2d5c28138e69ae76)`(const std::initializer_list\u003c T \u003e & v, const TensorShape & shape)` Construct from a initializer list of scalars and an explicit shape. ||\n| [Initializer](#structtensorflow_1_1_input_1_1_initializer_1a8099f954da757c77ac7d8e1c32df88ce)`(const std::initializer_list\u003c `[Initializer](/versions/r1.15/api_docs/cc/struct/tensorflow/input/initializer#structtensorflow_1_1_input_1_1_initializer)` \u003e & v)` Construct a multi-dimensional tensor from a nested initializer list. ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------|\n| [status](#structtensorflow_1_1_input_1_1_initializer_1af0ab9526e575fd7d4b9d5f7dbabcb7e4) | [Status](/versions/r1.15/api_docs/cc/class/tensorflow/status#classtensorflow_1_1_status) |\n| [tensor](#structtensorflow_1_1_input_1_1_initializer_1a7b520438780dc80f0162a480a3cadb74) | [Tensor](/versions/r1.15/api_docs/cc/class/tensorflow/tensor#classtensorflow_1_1_tensor) |\n\n| ### Public functions ||\n|-----------------------------------------------------------------------------------------------------|---------------|\n| [AsTensorProto](#structtensorflow_1_1_input_1_1_initializer_1a6b1e360b983fec2140b756971fe7699d)`()` | `TensorProto` |\n\nPublic attributes\n-----------------\n\n### status\n\n```text\nStatus tensorflow::Input::Initializer::status\n``` \n\n### tensor\n\n```text\nTensor tensorflow::Input::Initializer::tensor\n``` \n\nPublic functions\n----------------\n\n### AsTensorProto\n\n```text\nTensorProto tensorflow::Input::Initializer::AsTensorProto()\n``` \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const T & v\n)\n``` \nConstruct from a scalar value of an arithmetic type or a type that can be converted to a string (eg.\n\na string literal). \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const Tensor & t\n)\n``` \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const T & v,\n const TensorShape & shape\n)\n``` \nConstruct from a scalar value and an explicit shape. \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const std::initializer_list\u003c T \u003e & v\n)\n``` \nConstruct from a initializer list of scalars (a one-dimensional tensor). \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const std::initializer_list\u003c T \u003e & v,\n const TensorShape & shape\n)\n``` \nConstruct from a initializer list of scalars and an explicit shape. \n\n### Initializer\n\n```gdscript\n tensorflow::Input::Initializer::Initializer(\n const std::initializer_list\u003c Initializer \u003e & v\n)\n``` \nConstruct a multi-dimensional tensor from a nested initializer list.\n\nNote that C++ syntax allows nesting of arbitrarily typed initializer lists, so such invalid initializers cannot be disallowed at compile time. This function performs checks to make sure that the nested initializer list is indeed a valid multi-dimensional tensor."]]
