에이
| 중단 | 호출 시 프로세스를 중단하려면 예외를 발생시킵니다. |
| 중단.옵션 | Abort 에 대한 선택적 속성 |
| Abs <T는 T번호를 확장합니다. > | 텐서의 절대값을 계산합니다. |
| AbstractDataBuffer <T> | |
| AbstractDataBufferWindow <B는 DataBuffer를 확장합니다 <?>> | |
| AbstractDenseNdArray <T, U는 NdArray <T>>를 확장합니다. | |
| AbstractNdArray <T, U는 NdArray <T>>를 확장합니다. | |
| AbstractTF_Buffer | |
| AbstractTF_그래프 | |
| AbstractTF_ImportGraphDefOptions | |
| AbstractTF_Session | |
| AbstractTF_SessionOptions | |
| AbstractTF_상태 | |
| AbstractTF_Tensor | |
| AbstractTFE_컨텍스트 | |
| AbstractTFE_ContextOptions | |
| AbstractTFE_Op | |
| AbstractTFE_TensorHandle | |
| AccumulateN <T는 TType을 확장합니다. > | 텐서 목록의 요소별 합계를 반환합니다. |
| 누산기적용그라디언트 | 지정된 누산기에 그라데이션을 적용합니다. |
| 누산기Num누적됨 | 지정된 누산기에서 집계된 그래디언트 수를 반환합니다. |
| AccumulatorSetGlobalStep | global_step에 대한 새 값으로 누산기를 업데이트합니다. |
| AccumulatorTakeGradient <T는 TType을 확장합니다.> | 주어진 ConditionalAccumulator에서 평균 기울기를 추출합니다. |
| Acos <T는 TType을 확장합니다. > | x 요소별로 acos를 계산합니다. |
| Acosh <T는 TType을 확장합니다> | x 요소별로 역쌍곡선 코사인을 계산합니다. |
| 활성화 <T는 T번호를 확장합니다> | 활성화를 위한 추상 기본 클래스 참고: 호출 메소드를 호출하기 전에 |
| 에이다델타 | Adadelta 알고리즘을 구현하는 최적화 프로그램입니다. |
| 아다그라드 | Adagrad 알고리즘을 구현하는 최적화 프로그램입니다. |
| AdaGradDA | Adagrad Dual-Averaging 알고리즘을 구현하는 최적화 프로그램입니다. |
| 아담 | Adam 알고리즘을 구현하는 최적화 프로그램입니다. |
| 아다맥스 | Adamax 알고리즘을 구현하는 최적화 프로그램입니다. |
| <T 확장 TType > 추가 | x + y 요소를 반환합니다. |
| AddManySparseToTensorsMap | `SparseTensorsMap`에 `N` 미니배치 `SparseTensor`를 추가하고 `N` 핸들을 반환합니다. |
| AddManySparseToTensorsMap.Options | AddManySparseToTensorsMap 의 선택적 속성 |
| AddN <T는 TType을 확장합니다. > | 모든 입력 텐서 요소를 현명하게 추가합니다. |
| AddSparseToTensorsMap | 핸들을 반환하는 `SparseTensorsMap`에 `SparseTensor`를 추가하세요. |
| AddSparseToTensorsMap.Options | AddSparseToTensorsMap 의 선택적 속성 |
| adjustContrast <T는 T번호를 확장합니다. > | 하나 이상의 이미지 대비를 조정합니다. |
| adjustHue <T는 T번호를 확장합니다.> | 하나 이상의 이미지의 색조를 조정합니다. |
| adjustSaturation <T는 T숫자를 확장합니다.> | 하나 이상의 이미지의 채도를 조정합니다. |
| 모두 | 텐서의 차원 전체에 걸쳐 요소의 "논리적 및"를 계산합니다. |
| 전체.옵션 | All 에 대한 선택적 속성 |
| 전체후보샘플러 | 학습된 유니그램 분포를 사용하여 후보 샘플링에 대한 레이블을 생성합니다. |
| AllCandidateSampler.Options | AllCandidateSampler 의 선택적 속성 |
| 할당설명 | Protobuf 유형 tensorflow.AllocationDescription |
| 할당설명.빌더 | Protobuf 유형 tensorflow.AllocationDescription |
| 할당설명또는 빌더 | |
| 할당설명Protos | |
| 할당기록 | An allocation/de-allocation operation performed by the allocator. |
| AllocationRecord.Builder | An allocation/de-allocation operation performed by the allocator. |
| 할당기록또는빌더 | |
| 할당자메모리사용됨 | Protobuf 유형 tensorflow.AllocatorMemoryUsed |
| AllocatorMemoryUsed.Builder | Protobuf 유형 tensorflow.AllocatorMemoryUsed |
| AllocatorMemoryUsedOrBuilder | |
| AllReduce <T는 Tnumber를 확장합니다.> | 동일한 유형과 모양의 여러 텐서를 상호 축소합니다. |
| AllReduce.옵션 | AllReduce 의 선택적 속성 |
| AllToAll <T는 TType을 확장합니다. > | TPU 복제본 간에 데이터를 교환하는 작업입니다. |
| 각도 <U는 T번호를 확장합니다.> | 복소수의 인수를 반환합니다. |
| 익명반복자 | 반복자 리소스의 컨테이너입니다. |
| 익명메모리캐시 | |
| 익명MultiDeviceIterator | 다중 장치 반복자 리소스에 대한 컨테이너입니다. |
| 익명RandomSeedGenerator | |
| 익명SeedGenerator | |
| 어느 | 텐서의 차원 전체에 걸쳐 요소의 "논리적 or"를 계산합니다. |
| 모든.옵션 | Any 에 대한 선택적 속성 |
| APIDef | Used to specify and override the default API & behavior in the generated code for client languages, from what you would get from the OpDef alone. |
| ApiDef.Arg | Protobuf 유형 tensorflow.ApiDef.Arg |
| ApiDef.Arg.Builder | Protobuf 유형 tensorflow.ApiDef.Arg |
| ApiDef.ArgOrBuilder | |
| ApiDef.Attr | Description of the graph-construction-time configuration of this Op. |
| ApiDef.Attr.Builder | Description of the graph-construction-time configuration of this Op. |
| ApiDef.AttrOrBuilder | |
| ApiDef.Builder | Used to specify and override the default API & behavior in the generated code for client languages, from what you would get from the OpDef alone. |
| ApiDef.Endpoint | If you specify any endpoint, this will replace all of the inherited endpoints. |
| ApiDef.Endpoint.Builder | If you specify any endpoint, this will replace all of the inherited endpoints. |
| ApiDef.EndpointOrBuilder | |
| ApiDef.Visibility | Protobuf 열거형 tensorflow.ApiDef.Visibility |
| ApiDefOrBuilder | |
| ApiDefProtos | |
| ApiDef | Protobuf 유형 tensorflow.ApiDefs |
| ApiDefs.Builder | Protobuf 유형 tensorflow.ApiDefs |
| ApiDefsOrBuilder | |
| ApplyAdadelta <T는 TType을 확장합니다. > | adadelta 체계에 따라 '*var'를 업데이트합니다. |
| ApplyAdadelta.옵션 | ApplyAdadelta 의 선택적 속성 |
| ApplyAdagrad <T는 TType을 확장합니다> | adagrad 체계에 따라 '*var'를 업데이트합니다. |
| ApplyAdagrad.옵션 | ApplyAdagrad 의 선택적 속성 |
| ApplyAdagradDa <T는 TType을 확장합니다> | 근위부 adagrad 체계에 따라 '*var'를 업데이트합니다. |
| ApplyAdagradDa.옵션 | ApplyAdagradDa 의 선택적 속성 |
| ApplyAdagradV2 <T는 TType을 확장합니다.> | adagrad 체계에 따라 '*var'를 업데이트합니다. |
| ApplyAdagradV2.옵션 | ApplyAdagradV2 의 선택적 속성 |
| ApplyAdam <T는 TType을 확장합니다> | Adam 알고리즘에 따라 '*var'를 업데이트합니다. |
| ApplyAdam.옵션 | ApplyAdam 의 선택적 속성 |
| ApplyAdaMax <T는 TType을 확장합니다. > | AdaMax 알고리즘에 따라 '*var'를 업데이트합니다. |
| ApplyAdaMax.옵션 | ApplyAdaMax 의 선택적 속성 |
| ApplyAddSign <T는 TType을 확장합니다. > | AddSign 업데이트에 따라 '*var'를 업데이트합니다. |
| ApplyAddSign.Options | ApplyAddSign 의 선택적 속성 |
| ApplyCenteredRmsProp <T는 TType을 확장합니다.> | 중심 RMSProp 알고리즘에 따라 '*var'를 업데이트합니다. |
| ApplyCenteredRmsProp.Options | ApplyCenteredRmsProp 의 선택적 속성 |
| ApplyFtrl <T는 TType을 확장합니다. > | Ftrl-proximal 체계에 따라 '*var'를 업데이트합니다. |
| ApplyFtrl.Options | ApplyFtrl 의 선택적 속성 |
| ApplyGradientDescent <T는 TType을 확장합니다. > | '*var'에서 'alpha' * 'delta'를 빼서 업데이트합니다. |
| ApplyGradientDescent.Options | ApplyGradientDescent 의 선택적 속성 |
| ApplyMomentum <T는 TType을 확장합니다.> | 모멘텀 체계에 따라 '*var'를 업데이트합니다. |
| ApplyMomentum.옵션 | ApplyMomentum 의 선택적 속성 |
| ApplyPowerSign <T는 TType을 확장합니다. > | AddSign 업데이트에 따라 '*var'를 업데이트합니다. |
| ApplyPowerSign.옵션 | ApplyPowerSign 의 선택적 속성 |
| ApplyProximalAdagrad <T는 TType을 확장합니다> | Adagrad 학습률을 사용하여 FOBOS에 따라 '*var' 및 '*accum'을 업데이트합니다. |
| ApplyProximalAdagrad.옵션 | ApplyProximalAdagrad 의 선택적 속성 |
| ApplyProximalGradientDescent <T는 TType을 확장합니다> | 고정 학습률을 사용하는 FOBOS 알고리즘으로 '*var'를 업데이트합니다. |
| ApplyProximalGradientDescent.Options | ApplyProximalGradientDescent 의 선택적 속성 |
| ApplyRmsProp <T는 TType을 확장합니다. > | RMSProp 알고리즘에 따라 '*var'를 업데이트합니다. |
| ApplyRmsProp.옵션 | ApplyRmsProp 의 선택적 속성 |
| 대략 같음 | abs(xy) < 허용오차 요소별 진리값을 반환합니다. |
| ApproximateEqual.Options | ApproximateEqual 의 선택적 속성 |
| ArgMax <V는 TNumber를 확장합니다. > | 텐서의 차원 전체에서 가장 큰 값을 가진 인덱스를 반환합니다. |
| ArgMin <V는 T번호를 확장합니다> | 텐서의 차원 전체에서 가장 작은 값을 가진 인덱스를 반환합니다. |
| Asin <T는 TType을 확장합니다.> | x 요소별로 삼각법 역사인을 계산합니다. |
| Asinh <T는 TType을 확장합니다.> | x 요소별로 역쌍곡사인을 계산합니다. |
| Assert카디널리티데이터세트 | |
| AssertNextDataset | 다음에 어떤 변환이 발생하는지 확인하는 변환입니다. |
| AssertNextDataset | |
| 주장하다 | 주어진 조건이 참인지 확인합니다. |
| AssertThat.Options | AssertThat 의 선택적 속성 |
| 자산 파일 정의 | An asset file def for a single file or a set of sharded files with the same name. |
| AssetFileDef.Builder | An asset file def for a single file or a set of sharded files with the same name. |
| AssetFileDefOrBuilder | |
| <T 확장 TType > 할당 | 'value'를 할당하여 'ref'를 업데이트합니다. |
| 할당.옵션 | Assign 에 대한 선택적 속성 |
| AssignAdd <T는 TType을 확장합니다. > | 'value'를 추가하여 'ref'를 업데이트합니다. |
| 할당추가.옵션 | AssignAdd 의 선택적 속성 |
| 할당AddVariableOp | 변수의 현재 값에 값을 추가합니다. |
| AssignSub <T는 TType을 확장합니다. > | 'value'를 빼서 'ref'를 업데이트합니다. |
| AssignSub.옵션 | AssignSub 의 선택적 속성 |
| AssignSubVariableOp | 변수의 현재 값에서 값을 뺍니다. |
| 할당변수작업 | 변수에 새 값을 할당합니다. |
| AsString | 주어진 텐서의 각 항목을 문자열로 변환합니다. |
| AsString.옵션 | AsString 의 선택적 속성 |
| Atan <T는 TType을 확장합니다.> | x 요소별로 삼각법 역탄젠트를 계산합니다. |
| Atan2 <T는 T번호를 확장합니다. > | 인수의 부호를 고려하여 `y/x`의 아크탄젠트를 요소별로 계산합니다. |
| Atanh <T는 TType을 확장합니다.> | x 요소별로 역쌍곡선 탄젠트를 계산합니다. |
| 속성값 | Protocol buffer representing the value for an attr used to configure an Op. |
| AttrValue.Builder | Protocol buffer representing the value for an attr used to configure an Op. |
| 속성값.목록값 | LINT.IfChange tensorflow.AttrValue.ListValue |
| AttrValue.ListValue.Builder | LINT.IfChange tensorflow.AttrValue.ListValue |
| AttrValue.ListValueOrBuilder | |
| AttrValue.ValueCase | |
| AttrValueOrBuilder | |
| AttrValueProtos | |
| 오디오스펙트로그램 | 시간 경과에 따른 오디오 데이터의 시각화를 생성합니다. |
| 오디오스펙트로그램.옵션 | AudioSpectrogram 의 선택적 속성 |
| 오디오요약 | 오디오와 함께 '요약' 프로토콜 버퍼를 출력합니다. |
| 오디오요약.옵션 | AudioSummary 의 선택적 속성 |
| 자동 병렬 옵션 | Protobuf 유형 tensorflow.AutoParallelOptions |
| AutoParallelOptions.Builder | Protobuf 유형 tensorflow.AutoParallelOptions |
| AutoParallelOptionsOrBuilder | |
| AutoShard데이터세트 | 입력 데이터 세트를 샤딩하는 데이터 세트를 생성합니다. |
| AutoShard데이터세트 | 입력 데이터 세트를 샤딩하는 데이터 세트를 생성합니다. |
| AutoShardDataset.옵션 | AutoShardDataset 의 선택적 속성 |
| AutoShardDataset.옵션 | AutoShardDataset 의 선택적 속성 |
| 사용 가능한 장치 정보 | Matches DeviceAttributes tensorflow.AvailableDeviceInfo |
| AvailableDeviceInfo.Builder | Matches DeviceAttributes tensorflow.AvailableDeviceInfo |
| AvailableDeviceInfoOrBuilder | |
| AvgPool <T는 T번호를 확장합니다. > | 입력에 대해 평균 풀링을 수행합니다. |
| AvgPool.옵션 | AvgPool 의 선택적 속성 |
| AvgPool3d <T는 T번호를 확장합니다. > | 입력에 대해 3D 평균 풀링을 수행합니다. |
| AvgPool3d.옵션 | AvgPool3d 의 선택적 속성 |
| AvgPool3dGrad <T는 TNumber를 확장합니다. > | 평균 풀링 함수의 기울기를 계산합니다. |
| AvgPool3dGrad.옵션 | AvgPool3dGrad 의 선택적 속성 |
| AvgPoolGrad <T는 TNumber를 확장합니다. > | 평균 풀링 함수의 기울기를 계산합니다. |
| AvgPoolGrad.옵션 | AvgPoolGrad 의 선택적 속성 |
비
| BandedTriangularSolve <T는 TType을 확장합니다.> | |
| BandedTriangularSolve.Options | BandedTriangularSolve 의 선택적 속성 |
| BandPart <T는 TType을 확장합니다. > | 각 가장 안쪽 행렬의 중앙 밴드 외부에 있는 모든 항목을 0으로 설정하는 텐서를 복사합니다. |
| 장벽 | 다양한 그래프 실행에서 지속되는 장벽을 정의합니다. |
| 장벽.옵션 | Barrier 의 선택적 속성 |
| 장벽닫기 | 주어진 장벽을 닫습니다. |
| BarrierClose.옵션 | BarrierClose 의 선택적 속성 |
| 장벽불완전한크기 | 주어진 장벽의 불완전한 요소 수를 계산합니다. |
| 장벽삽입많은 | 각 키에 대해 해당 값을 지정된 구성 요소에 할당합니다. |
| 배리어준비크기 | 주어진 장벽의 완전한 요소 수를 계산합니다. |
| 장벽가져다많은 | 장벽에서 주어진 수의 완성된 요소를 가져옵니다. |
| BarrierTakeMany.Options | BarrierTakeMany 의 선택적 속성 |
| BaseInitializer <T는 TType을 확장합니다.> | 모든 초기화 프로그램에 대한 추상 기본 클래스 |
| 일괄 | 모든 입력 텐서를 비결정적으로 일괄 처리합니다. |
| 배치.옵션 | Batch 의 선택적 속성 |
| BatchCholesky <T는 TNumber를 확장합니다> | |
| BatchCholeskyGrad <T는 TNumber를 확장합니다.> | |
| 배치 데이터세트 | |
| 배치 데이터세트 | `input_dataset`에서 `batch_size` 요소를 일괄 처리하는 데이터세트를 생성합니다. |
| BatchDataset.옵션 | BatchDataset 의 선택적 속성 |
| 일괄 Fft | |
| BatchFft2d | |
| BatchFft3d | |
| BatchIfft | |
| BatchIfft2d | |
| BatchIfft3d | |
| BatchMatMul <T는 TType을 확장합니다> | 두 개의 텐서 조각을 일괄적으로 곱합니다. |
| BatchMatMul.Options | BatchMatMul 의 선택적 속성 |
| BatchMatrixBandPart <T는 TType을 확장합니다.> | |
| BatchMatrixDeterminant <T는 TType을 확장합니다.> | |
| BatchMatrixDiag <T는 TType을 확장합니다.> | |
| BatchMatrixDiagPart <T는 TType을 확장합니다.> | |
| BatchMatrixInverse <T는 TNumber를 확장합니다.> | |
| BatchMatrixInverse.Options | BatchMatrixInverse 의 선택적 속성 |
| BatchMatrixSetDiag <T는 TType을 확장합니다.> | |
| BatchMatrixSolve <T는 TNumber를 확장합니다.> | |
| BatchMatrixSolve.Options | BatchMatrixSolve 의 선택적 속성 |
| BatchMatrixSolveLs <T는 TNumber를 확장합니다. > | |
| BatchMatrixSolveLs.Options | BatchMatrixSolveLs 의 선택적 속성 |
| BatchMatrixTriangularSolve <T는 TNumber를 확장합니다.> | |
| BatchMatrixTriangularSolve.Options | BatchMatrixTriangularSolve 의 선택적 속성 |
| BatchNormWithGlobalNormalization <T는 TType을 확장합니다.> | 일괄 정규화. |
| BatchNormWithGlobalNormalizationGrad <T는 TType을 확장합니다.> | 배치 정규화를 위한 기울기. |
| BatchSelfAdjointEig <T는 TNumber를 확장합니다. > | |
| BatchSelfAdjointEig.Options | BatchSelfAdjointEig 의 선택적 속성 |
| BatchSvd <T는 TType을 확장합니다. > | |
| BatchSvd.옵션 | BatchSvd 의 선택적 속성 |
| BatchToSpace <T는 TType을 확장합니다. > | T 유형의 4차원 텐서에 대한 BatchToSpace. |
| BatchToSpaceNd <T는 TType을 확장합니다. > | T 유형의 ND 텐서에 대한 BatchToSpace. |
| 벤치마크 항목 | Protobuf 유형 tensorflow.BenchmarkEntries |
| BenchmarkEntries.Builder | Protobuf 유형 tensorflow.BenchmarkEntries |
| 벤치마크항목 또는 빌더 | |
| 벤치마크 항목 | Each unit test or benchmark in a test or benchmark run provides some set of information. |
| BenchmarkEntry.Builder | Each unit test or benchmark in a test or benchmark run provides some set of information. |
| 벤치마크EntryOrBuilder | |
| BesselI0 <T는 T번호를 확장합니다. > | |
| BesselI0e <T는 T번호를 확장합니다. > | |
| BesselI1 <T는 Tnumber를 확장합니다. > | |
| BesselI1e <T는 Tnumber를 확장합니다. > | |
| BesselJ0 <T는 Tnumber를 확장합니다. > | |
| BesselJ1 <T는 Tnumber를 확장합니다. > | |
| BesselK0 <T는 T번호를 확장합니다. > | |
| BesselK0e <T는 T번호를 확장합니다. > | |
| BesselK1 <T는 Tnumber를 확장합니다. > | |
| BesselK1e <T는 Tnumber를 확장합니다. > | |
| BesselY0 <T는 Tnumber를 확장합니다. > | |
| BesselY1 <T는 Tnumber를 확장합니다. > | |
| Betainc <T는 T번호를 확장합니다> | 정규화된 불완전 베타 적분을 계산합니다 \\(I_x(a, b)\\). |
| BfcMemoryMapProtos | |
| Bfloat16레이아웃 | 32비트 부동 소수점을 16비트에서/으로 변환하여 가수를 7비트로 자르지만 동일한 편향으로 8비트 지수를 유지하는 데이터 레이아웃입니다. |
| BiasAdd <T는 TType을 확장합니다. > | '값'에 '편향'을 추가합니다. |
| 바이어스추가.옵션 | BiasAdd 의 선택적 속성 |
| BiasAddGrad <T는 TType을 확장합니다. > | "bias" 텐서의 "BiasAdd"에 대한 역방향 연산입니다. |
| BiasAddGrad.Options | BiasAddGrad 의 선택적 속성 |
| 바이너리크로센트로피 | 실제 레이블과 예측 레이블 간의 교차 엔트로피 손실을 계산합니다. |
| BinaryCrossentropy <T는 TNumber를 확장합니다.> | 실제 레이블과 예측 레이블 간의 이진 교차 엔트로피 손실을 계산하는 측정항목입니다. |
| Bincount <T는 Tnumber를 확장합니다.> | 정수 배열에서 각 값의 발생 횟수를 셉니다. |
| Bin요약 | Protobuf 유형 tensorflow.BinSummary |
| BinSummary.Builder | Protobuf 유형 tensorflow.BinSummary |
| BinSummaryOrBuilder | |
| 비트캐스트 <U는 TType을 확장합니다.> | 데이터를 복사하지 않고 한 유형에서 다른 유형으로 텐서를 비트캐스트합니다. |
| BitwiseAnd <T는 T번호를 확장합니다> | Elementwise는 `x`와 `y`의 비트별 AND를 계산합니다. |
| BitwiseOr <T는 T번호를 확장합니다. > | Elementwise는 `x`와 `y`의 비트별 OR을 계산합니다. |
| BitwiseXor <T는 Tnumber를 확장합니다.> | Elementwise는 `x`와 `y`의 비트별 XOR을 계산합니다. |
| BlockLSTM <T는 Tnumber를 확장합니다. > | 모든 시간 단계에 대해 LSTM 셀의 순방향 전파를 계산합니다. |
| BlockLSTM.옵션 | BlockLSTM 의 선택적 속성 |
| BlockLSTMMGrad <T는 TNumber를 확장합니다.> | 전체 시간 시퀀스에 대한 LSTM 셀 역전파를 계산합니다. |
| 부울데이터버퍼 | 부울의 DataBuffer . |
| BooleanDataLayout <S는 DataBuffer를 확장합니다 <?>> | 버퍼에 저장된 데이터를 부울로 변환하는 DataLayout 입니다. |
| 부울DenseNdArray | |
| 부울마스크 | |
| BooleanMask.Options | BooleanMask 의 선택적 속성 |
| 부울마스크업데이트 | |
| BooleanMaskUpdate.Options | BooleanMaskUpdate 의 선택적 속성 |
| 부울NdArray | 부울의 NdArray . |
| 부울 레이아웃 | 부울을 바이트에서/바이트로 변환하는 데이터 레이아웃입니다. |
| BoostedTreesAggregateStats | 배치에 대해 누적된 통계 요약을 집계합니다. |
| BoostedTrees버킷화 | 버킷 경계를 기준으로 각 기능을 버킷화합니다. |
| BoostedTreesCalculateBestFeatureSplit | 각 기능에 대한 이득을 계산하고 기능에 대해 가능한 최상의 분할 정보를 반환합니다. |
| BoostedTreesCalculateBestFeatureSplit.Options | BoostedTreesCalculateBestFeatureSplit 의 선택적 속성 |
| BoostedTreesCalculateBestFeatureSplitV2 | 각 기능에 대한 이득을 계산하고 각 노드에 대해 가능한 최상의 분할 정보를 반환합니다. |
| BoostedTreesCalculateBestGainsPerFeature | 각 기능에 대한 이득을 계산하고 기능에 대해 가능한 최상의 분할 정보를 반환합니다. |
| BoostedTrees센터바이어스 | 훈련 데이터(편향)에서 사전을 계산하고 첫 번째 노드를 로짓의 사전으로 채웁니다. |
| BoostedTreesCreateEnsemble | 트리 앙상블 모델을 생성하고 이에 대한 핸들을 반환합니다. |
| BoostedTreesCreateQuantileStreamResource | Quantile Streams에 대한 리소스를 생성합니다. |
| BoostedTreesCreateQuantileStreamResource.Options | BoostedTreesCreateQuantileStreamResource 에 대한 선택적 속성 |
| BoostedTreesDeserializeEnsemble | 직렬화된 트리 앙상블 구성을 역직렬화하고 현재 트리를 대체합니다. 앙상블. |
| BoostedTreesEnsembleResourceHandleOp | BoostedTreesEnsembleResource에 대한 핸들을 생성합니다. |
| BoostedTreesEnsembleResourceHandleOp.Options | BoostedTreesEnsembleResourceHandleOp 의 선택적 속성 |
| BoostedTrees예제디버그 출력 | 각 예제에 대한 디버깅/모델 해석 가능성 출력. |
| BoostedTreesFlushQuantile요약 | 각 분위수 스트림 리소스에서 분위수 요약을 플러시합니다. |
| BoostedTreesGetEnsembleStates | 나무 앙상블 리소스 스탬프 토큰, 나무 수 및 성장 통계를 검색합니다. |
| BoostedTreesMakeQuantile요약 | 배치에 대한 분위수의 요약을 작성합니다. |
| BoostedTreesMakeStats요약 | 배치에 대해 누적된 통계를 요약합니다. |
| BoostedTree예측 | 입력 인스턴스에서 여러 가산 회귀 앙상블 예측기를 실행하고 로짓을 계산합니다. |
| BoostedTreesQuantileStreamResourceAddSummaries | 각 분위수 스트림 리소스에 분위수 요약을 추가합니다. |
| BoostedTreesQuantileStreamResourceDeserialize | 버킷 경계와 준비 플래그를 현재 QuantileAccumulator로 역직렬화합니다. |
| BoostedTreesQuantileStreamResourceFlush | 분위수 스트림 리소스에 대한 요약을 플러시합니다. |
| BoostedTreesQuantileStreamResourceFlush.Options | BoostedTreesQuantileStreamResourceFlush 의 선택적 속성 |
| BoostedTreesQuantileStreamResourceGetBucketBoundaries | 누적된 요약을 기반으로 각 기능에 대한 버킷 경계를 생성합니다. |
| BoostedTreesQuantileStreamResourceHandleOp | BoostedTreesQuantileStreamResource에 대한 핸들을 생성합니다. |
| BoostedTreesQuantileStreamResourceHandleOp.Options | BoostedTreesQuantileStreamResourceHandleOp 의 선택적 속성 |
| BoostedTreesSerializeEnsemble | 트리 앙상블을 proto로 직렬화합니다. |
| BoostedTreesSparseAggregateStats | 배치에 대해 누적된 통계 요약을 집계합니다. |
| BoostedTreeSparseCalculateBestFeatureSplit | 각 기능에 대한 이득을 계산하고 기능에 대해 가능한 최상의 분할 정보를 반환합니다. |
| BoostedTreesSparseCalculateBestFeatureSplit.Options | BoostedTreesSparseCalculateBestFeatureSplit 의 선택적 속성 |
| BoostedTrees훈련예측 | 입력 인스턴스에서 여러 가산 회귀 앙상블 예측기를 실행하고 캐시된 로짓에 대한 업데이트를 계산합니다. |
| BoostedTreesUpdate앙상블 | 성장 중인 마지막 나무에 레이어를 추가하여 나무 앙상블을 업데이트합니다. 또는 새 트리를 시작하여. |
| BoostedTreesUpdateEnsembleV2 | 성장 중인 마지막 나무에 레이어를 추가하여 나무 앙상블을 업데이트합니다. 또는 새 트리를 시작하여. |
| BoostedTreesUpdateEnsembleV2.Options | BoostedTreesUpdateEnsembleV2 의 선택적 속성 |
| BoundedTensorSpecProto | A protobuf to represent tf.BoundedTensorSpec. |
| BoundedTensorSpecProto.Builder | A protobuf to represent tf.BoundedTensorSpec. |
| BoundedTensorSpecProtoOrBuilder | |
| BroadcastDynamicShape <T는 TNumber를 확장합니다.> | 브로드캐스트를 사용하여 s0 op s1의 모양을 반환합니다. |
| BroadcastGradientArgs <T는 TNumber를 확장합니다. > | 브로드캐스트를 사용하여 s0 op s1의 기울기를 계산하기 위한 감소 지수를 반환합니다. |
| BroadcastHelper <T는 TType을 확장합니다> | XLA 스타일 브로드캐스트 수행을 위한 도우미 연산자 이항 연산자에 대한 XLA의 브로드캐스팅 규칙을 사용하여 `lhs` 및 `rhs` 중 더 낮은 순위를 갖는 크기 1 차원을 추가하여 `lhs` 및 `rhs`를 동일한 순위로 브로드캐스트합니다. |
| BroadcastRecv <T는 TType을 확장합니다. > | 다른 장치에서 브로드캐스트된 텐서 값을 수신합니다. |
| BroadcastRecv.옵션 | BroadcastRecv 의 선택적 속성 |
| BroadcastSend <T는 TType을 확장합니다. > | 하나 이상의 다른 장치에 텐서 값을 브로드캐스트합니다. |
| BroadcastSend.옵션 | BroadcastSend 의 선택적 속성 |
| BroadcastTo <T는 TType을 확장합니다> | 호환 가능한 모양에 대한 배열을 브로드캐스트합니다. |
| 버킷화 | '경계'를 기준으로 '입력'을 버킷화합니다. |
| 빌드 구성 | Protobuf 유형 tensorflow.BuildConfiguration |
| BuildConfiguration.Builder | Protobuf 유형 tensorflow.BuildConfiguration |
| BuildConfigurationOrBuilder | |
| BundleEntryProto | Describes the metadata related to a checkpointed tensor. |
| BundleEntryProto.Builder | Describes the metadata related to a checkpointed tensor. |
| BundleEntryProtoOrBuilder | |
| BundleHeaderProto | Special header that is associated with a bundle. |
| BundleHeaderProto.Builder | Special header that is associated with a bundle. |
| BundleHeaderProto.Endianness | An enum indicating the endianness of the platform that produced this bundle. |
| BundleHeaderProtoOrBuilder | |
| 바이트데이터버퍼 | 바이트의 DataBuffer . |
| ByteDataLayout <S는 DataBuffer를 확장합니다 <?>> | 버퍼에 저장된 데이터를 바이트로 변환하는 DataLayout 입니다. |
| ByteDenseNdArray | |
| ByteNdArray | NdArray 바이트입니다. |
| ByteSequenceProvider <T> | ByteSequenceTensorBuffer 에 저장될 바이트 시퀀스를 생성합니다. |
| ByteSequenceTensorBuffer | 문자열 텐서 데이터를 저장하기 위한 버퍼입니다. |
| 바이트 목록 | Containers to hold repeated fundamental values. |
| BytesList.Builder | Containers to hold repeated fundamental values. |
| BytesListOrBuilder | |
| 바이트생산통계데이터세트 | StatsAggregator에 있는 'input_dataset'의 각 요소의 바이트 크기를 기록합니다. |
| 바이트생산통계데이터세트 | StatsAggregator에 있는 'input_dataset'의 각 요소의 바이트 크기를 기록합니다. |
기음
| 캐시데이터세트 | 'input_dataset'에서 요소를 캐시하는 데이터세트를 생성합니다. |
| 캐시데이터세트V2 | |
| 호출 가능 옵션 | Defines a subgraph in another `GraphDef` as a set of feed points and nodes to be fetched or executed. |
| CallableOptions.Builder | Defines a subgraph in another `GraphDef` as a set of feed points and nodes to be fetched or executed. |
| CallableOptionsOrBuilder | |
| 캐스트 <U는 TType을 확장합니다> | SrcT 유형의 x를 DstT의 y로 캐스트합니다. |
| Cast.옵션 | Cast 의 선택적 속성 |
| CastHelper | 피연산자를 캐스팅하기 위한 도우미 클래스 |
| 범주형 교차센트피 | 레이블과 예측 간의 교차엔트로피 손실을 계산합니다. |
| CategoricalCrossentropy <T는 TNumber를 확장합니다.> | 실제 레이블과 예측 레이블 간의 범주형 교차 엔트로피 손실을 계산하는 측정항목입니다. |
| 범주형 힌지 | 라벨과 예측 간의 범주형 힌지 손실을 계산합니다. |
| CategoricalHinge <T는 TNumber를 확장합니다.> | 라벨과 예측 간의 범주형 힌지 손실 측정항목을 계산하는 측정항목입니다. |
| Ceil <T는 T번호를 확장합니다> | x보다 작지 않은 요소별 가장 작은 정수를 반환합니다. |
| CheckNumerics <T는 T번호를 확장합니다. > | NaN, -Inf 및 +Inf 값에 대한 텐서를 확인합니다. |
| Cholesky <T는 TType을 확장합니다> | 하나 이상의 정사각 행렬에 대한 Cholesky 분해를 계산합니다. |
| CholeskyGrad <T는 TNumber를 확장합니다. > | Cholesky 알고리즘의 역방향 역전파 기울기를 계산합니다. |
| 가장 빠른 데이터 세트 선택 | |
| 가장 빠른 데이터 세트 선택 | |
| ClipByValue <T는 TType을 확장합니다. > | 텐서 값을 지정된 최소값과 최대값으로 자릅니다. |
| 닫기요약작성기 | |
| ClusterDef | Defines a TensorFlow cluster as a set of jobs. |
| ClusterDef.Builder | Defines a TensorFlow cluster as a set of jobs. |
| ClusterDefOrBuilder | |
| ClusterDevice필터 | Defines the device filters for jobs in a cluster. |
| ClusterDeviceFilters.Builder | Defines the device filters for jobs in a cluster. |
| ClusterDeviceFiltersOrBuilder | |
| ClusterOutput <T는 TType을 확장합니다. > | XLA 계산의 출력을 다른 소비자 그래프 노드에 연결하는 연산자입니다. |
| ClusterProtos | |
| 암호 | The canonical error codes for TensorFlow APIs. |
| 코드위치 | Code location information: A stack trace with host-name information. |
| CodeLocation.Builder | Code location information: A stack trace with host-name information. |
| 코드위치또는빌더 | |
| 컬렉션Def | CollectionDef should cover most collections. |
| CollectionDef.AnyList | AnyList is used for collecting Any protos. |
| CollectionDef.AnyList.Builder | AnyList is used for collecting Any protos. |
| CollectionDef.AnyListOrBuilder | |
| CollectionDef.Builder | CollectionDef should cover most collections. |
| CollectionDef.BytesList | BytesList is used for collecting strings and serialized protobufs. |
| CollectionDef.BytesList.Builder | BytesList is used for collecting strings and serialized protobufs. |
| CollectionDef.BytesListOrBuilder | |
| CollectionDef.FloatList | FloatList is used for collecting float values. |
| CollectionDef.FloatList.Builder | FloatList is used for collecting float values. |
| CollectionDef.FloatListOrBuilder | |
| CollectionDef.Int64List | Int64List is used for collecting int, int64 and long values. |
| CollectionDef.Int64List.Builder | Int64List is used for collecting int, int64 and long values. |
| CollectionDef.Int64ListOrBuilder | |
| CollectionDef.KindCase | |
| CollectionDef.NodeList | NodeList is used for collecting nodes in graph. |
| CollectionDef.NodeList.Builder | NodeList is used for collecting nodes in graph. |
| CollectionDef.NodeListOrBuilder | |
| 컬렉션DefOrBuilder | |
| CollectiveGather <T는 T번호를 확장합니다> | 동일한 유형과 모양의 여러 텐서를 상호 축적합니다. |
| CollectiveGather.옵션 | CollectiveGather 의 선택적 속성 |
| CollectivePermute <T는 TType을 확장합니다. > | 복제된 TPU 인스턴스 전체에서 텐서를 순열하는 작업입니다. |
| CombinedNonMaxSuppression | 점수의 내림차순으로 경계 상자의 하위 집합을 탐욕스럽게 선택합니다. 이 작업은 모든 클래스에서 배치당 입력에 대해 non_max_suppression을 수행합니다. |
| CombinedNonMaxSuppression.Options | CombinedNonMaxSuppression 의 선택적 속성 |
| 커밋 ID | Protobuf 유형 tensorflow.CommitId |
| 커밋Id.Builder | Protobuf 유형 tensorflow.CommitId |
| CommitId.KindCase | |
| 커밋IdOrBuilder | |
| CompareAndBitpack | 'input' 값을 'threshold'와 비교하고 결과 비트를 'uint8'로 압축합니다. |
| 편집결과 | TPU 컴파일 결과를 반환합니다. |
| 컴파일 성공 어설션 | 컴파일이 성공했다고 어설션합니다. |
| 복합 <U는 TType을 확장합니다.> | 두 개의 실수를 복소수로 변환합니다. |
| ComplexAbs <U는 TNumber를 확장합니다.> | 텐서의 복소 절대값을 계산합니다. |
| 요소 압축 | 데이터 세트 요소를 압축합니다. |
| Compute_func_Pointer_TF_OpKernelContext | |
| ComputeAccidentalHits | true_labels와 일치하는 samplingd_candidates의 위치 ID를 계산합니다. |
| ComputeAccidentalHits.Options | ComputeAccidentalHits 의 선택적 속성 |
| ComputeBatchSize | 부분 배치가 없는 데이터 세트의 정적 배치 크기를 계산합니다. |
| Concat <T는 TType을 확장합니다> | 한 차원을 따라 텐서를 연결합니다. |
| 데이터세트 연결 | 'input_dataset'을 'another_dataset'와 연결하는 데이터세트를 생성합니다. |
| 콘크리트함수 | 입력 및 출력 서명이 있는 단일 함수로 호출할 수 있는 그래프입니다. |
| CondContextDef | Protocol buffer representing a CondContext object. |
| CondContextDef.Builder | Protocol buffer representing a CondContext object. |
| CondContextDefOrBuilder | |
| 조건부 누산기 | 그라디언트 집계를 위한 조건부 누산기입니다. |
| ConditionalAccumulator.Options | ConditionalAccumulator 의 선택적 속성 |
| 컨피그프로토 | Session configuration parameters. |
| ConfigProto.Builder | Session configuration parameters. |
| ConfigProto.Experimental | Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| ConfigProto.Experimental.Builder | Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| ConfigProto.Experimental.MlirBridgeRollout | An enum that describes the state of the MLIR bridge rollout. |
| ConfigProto.ExperimentalOrBuilder | |
| ConfigProtoOrBuilder | |
| ConfigProtos | |
| 분산TPU 구성 | 분산 TPU 시스템의 중앙 집중식 구조를 설정합니다. |
| 구성DistributedTPU.옵션 | ConfigureDistributedTPU 의 선택적 속성 |
| TPU임베딩 구성 | 분산 TPU 시스템에서 TPUEmbedding을 설정합니다. |
| Conj <T는 TType을 확장합니다. > | 복소수의 켤레 복소수를 반환합니다. |
| ConjugateTranspose <T는 TType을 확장합니다. > | 순열에 따라 x의 차원을 섞고 결과를 켤레화합니다. |
| 상수 <T는 TType을 확장합니다. > | 상수 값으로 텐서를 생성하는 초기화 프로그램입니다. |
| 상수 <T는 TType을 확장합니다. > | 상수 값을 생성하는 연산자입니다. |
| 강제 | 제약 조건의 기본 클래스입니다. |
| 소비MutexLock | 이 작업은 `MutexLock`에 의해 생성된 잠금을 사용합니다. |
| ControlFlowContextDef | Container for any kind of control flow context. |
| ControlFlowContextDef.Builder | Container for any kind of control flow context. |
| ControlFlowContextDef.CtxtCase | |
| ControlFlowContextDefOrBuilder | |
| ControlFlowProtos | |
| 제어트리거 | 아무것도 하지 않습니다. |
| Conv <T는 TType을 확장합니다. > | 다음 문서에 설명된 XLA ConvGeneralDilated 연산자를 래핑합니다. https://www.tensorflow.org/performance/xla/Operation_semantics#conv_convolution . |
| Conv2d <T는 Tnumber를 확장합니다. > | 4차원 '입력' 및 '필터' 텐서를 사용하여 2차원 컨벌루션을 계산합니다. |
| Conv2d.옵션 | Conv2d 의 선택적 속성 |
| Conv2dBackpropFilter <T는 TNumber를 확장합니다.> | 필터에 대한 컨볼루션의 기울기를 계산합니다. |
| Conv2dBackpropFilter.Options | Conv2dBackpropFilter 의 선택적 속성 |
| Conv2dBackpropInput <T는 TNumber를 확장합니다.> | 입력에 대한 컨볼루션의 기울기를 계산합니다. |
| Conv2dBackpropInput.Options | Conv2dBackpropInput 의 선택적 속성 |
| Conv3d <T는 Tnumber를 확장합니다. > | 5차원 '입력' 및 '필터' 텐서를 사용하여 3차원 컨볼루션을 계산합니다. |
| Conv3d.옵션 | Conv3d 의 선택적 속성 |
| Conv3dBackpropFilter <T는 TNumber를 확장합니다.> | 필터에 대한 3차원 컨벌루션의 기울기를 계산합니다. |
| Conv3dBackpropFilter.Options | Conv3dBackpropFilter 의 선택적 속성 |
| Conv3dBackpropInput <U는 TNumber를 확장합니다.> | 입력에 대한 3차원 컨벌루션의 기울기를 계산합니다. |
| Conv3dBackpropInput.Options | Conv3dBackpropInput 의 선택적 속성 |
| 복사 <T는 TType을 확장합니다> | CPU에서 CPU로 또는 GPU에서 GPU로 텐서를 복사합니다. |
| 복사.옵션 | Copy 에 대한 선택적 속성 |
| CopyHost <T는 TType을 확장합니다. > | 텐서를 호스트에 복사합니다. |
| CopyHost.옵션 | CopyHost 의 선택적 속성 |
| Cos <T는 TType을 확장합니다. > | x 요소별로 cos를 계산합니다. |
| Cosh <T는 TType을 확장합니다.> | x 요소의 쌍곡선 코사인을 계산합니다. |
| 코사인 유사성 | 라벨과 예측 간의 코사인 유사성을 계산합니다. |
| 코사인 유사성 <T는 T숫자를 확장함> | 라벨과 예측 간의 코사인 유사성 측정항목을 계산하는 측정항목입니다. |
| 비용 그래프 정의 | Protobuf 유형 tensorflow.CostGraphDef |
| CostGraphDef.AggregatedCost | Total cost of this graph, typically used for balancing decisions. |
| CostGraphDef.AggregatedCost.Builder | Total cost of this graph, typically used for balancing decisions. |
| CostGraphDef.AggregatedCostOrBuilder | |
| CostGraphDef.Builder | Protobuf 유형 tensorflow.CostGraphDef |
| CostGraphDef.Node | Protobuf 유형 tensorflow.CostGraphDef.Node |
| CostGraphDef.Node.Builder | Protobuf 유형 tensorflow.CostGraphDef.Node |
| CostGraphDef.Node.InputInfo | Inputs of this node. |
| CostGraphDef.Node.InputInfo.Builder | Inputs of this node. |
| CostGraphDef.Node.InputInfoOrBuilder | |
| CostGraphDef.Node.OutputInfo | Outputs of this node. |
| CostGraphDef.Node.OutputInfo.Builder | Outputs of this node. |
| CostGraphDef.Node.OutputInfoOrBuilder | |
| CostGraphDef.NodeOrBuilder | |
| CostGraphDefOrBuilder | |
| 비용 그래프Protos | |
| CountUpTo <T는 T번호를 확장합니다. > | 'limit'에 도달할 때까지 'ref'를 증가시킵니다. |
| CPU정보 | Protobuf 유형 tensorflow.CPUInfo |
| CPUInfo.Builder | Protobuf 유형 tensorflow.CPUInfo |
| CPU정보 또는 빌더 | |
| Create_func_TF_OpKernelConstruction | |
| CreateSummaryDbWriter | |
| CreateSummaryFileWriter | |
| 자르기 및 크기 조정 | 입력 이미지 텐서에서 자르기를 추출하고 크기를 조정합니다. |
| 자르기및크기조정.옵션 | CropAndResize 의 선택적 속성 |
| 자르기및크기조정GradBoxes | 입력 상자 텐서에 대한 자르기 및 크기 조정 작업의 기울기를 계산합니다. |
| CropAndResizeGradBoxes.Options | CropAndResizeGradBoxes 의 선택적 속성 |
| CropAndResizeGradImage <T는 T숫자를 확장함> | 입력 이미지 텐서에 대한 Crop_and_resize 작업의 기울기를 계산합니다. |
| CropAndResizeGradImage.Options | CropAndResizeGradImage 의 선택적 속성 |
| 교차 <T는 T번호를 확장합니다> | 쌍별 교차곱을 계산합니다. |
| CrossReplicaSum <T는 TNumber를 확장합니다.> | 복제된 TPU 인스턴스 전체에서 입력을 합산하는 작업입니다. |
| CSRSparseMatrixComponents <T는 TType을 확장합니다.> | 배치 '인덱스'에서 CSR 구성 요소를 읽습니다. |
| CSRSparseMatrixToDense <T는 TType을 확장합니다.> | (아마도 일괄 처리된) CSRSparseMatrix를 고밀도로 변환합니다. |
| CSRSparseMatrixToSparseTensor <T는 TType을 확장합니다.> | (아마도 일괄 처리된) CSRSparesMatrix를 SparseTensor로 변환합니다. |
| CSV데이터세트 | |
| CSV데이터세트 | |
| CSV데이터세트V2 | |
| CtcBeamSearchDecoder <T는 TNumber를 확장합니다.> | 입력에 제공된 로짓에 대해 빔 검색 디코딩을 수행합니다. |
| CtcBeamSearchDecoder.Options | CtcBeamSearchDecoder 의 선택적 속성 |
| CtcGreedyDecoder <T는 TNumber를 확장합니다.> | 입력에 제공된 로짓에 대해 그리디 디코딩을 수행합니다. |
| CtcGreedyDecoder.옵션 | CtcGreedyDecoder 의 선택적 속성 |
| CtcLoss <T는 T번호를 확장합니다. > | 각 배치 항목에 대한 CTC 손실(로그 확률)을 계산합니다. |
| CtcLoss.옵션 | CtcLoss 의 선택적 속성 |
| CTCLossV2 | 각 배치 항목에 대한 CTC 손실(로그 확률)을 계산합니다. |
| CTCLossV2.옵션 | CTCLossV2 의 선택적 속성 |
| CudnnRNN <T는 T번호를 확장합니다. > | cuDNN이 지원하는 RNN입니다. |
| CudnnRNN.옵션 | CudnnRNN 의 선택적 속성 |
| CudnnRNNBackprop <T는 TNumber를 확장합니다.> | CudnnRNNV3의 역전파 단계. |
| CudnnRNNBackprop.옵션 | CudnnRNNBackprop 의 선택적 속성 |
| CudnnRNNCanonicalToParams <T는 TNumber를 확장합니다. > | CudnnRNN 매개변수를 표준 형식에서 사용 가능한 형식으로 변환합니다. |
| CudnnRNNCanonicalToParams.Options | CudnnRNNCanonicalToParams 의 선택적 속성 |
| CudnnRnnParamsSize <U는 TNumber를 확장합니다.> | Cudnn RNN 모델에서 사용할 수 있는 가중치의 크기를 계산합니다. |
| CudnnRnnParamsSize.Options | CudnnRnnParamsSize 의 선택적 속성 |
| CudnnRNNParamsToCanonical <T는 TNumber를 확장합니다.> | 표준 형식으로 CudnnRNN 매개변수를 검색합니다. |
| CudnnRNNParamsToCanonical.Options | CudnnRNNParamsToCanonical 의 선택적 속성 |
| Cumprod <T는 TType을 확장합니다.> | '축'을 따라 텐서 'x'의 누적 곱을 계산합니다. |
| Cumprod.옵션 | Cumprod 의 선택적 속성 |
| Cumsum <T는 TType을 확장합니다.> | `축`을 따라 텐서 `x`의 누적 합계를 계산합니다. |
| 누적.옵션 | Cumsum 의 선택적 속성 |
| CumulativeLogsumexp <T는 TNumber를 확장합니다.> | '축'을 따라 텐서 'x'의 누적 곱을 계산합니다. |
| CumulativeLogsumexp.Options | CumulativeLogsumexp 의 선택적 속성 |
디
| 데이터버퍼 <T> | 특정 유형의 데이터가 담긴 컨테이너입니다. |
| DataBufferAdapterFactory | 데이터 버퍼 어댑터 공장. |
| 데이터버퍼 | DataBuffer 인스턴스를 생성하기 위한 도우미 클래스입니다. |
| DataBufferWindow <B는 DataBuffer를 확장합니다 <?>> | DataBuffer 의 일부를 보기 위한 변경 가능한 컨테이너입니다. |
| 데이터클래스 | Protobuf 열거형 tensorflow.DataClass |
| DataFormatDimMap <T는 TNumber를 확장합니다. > | 지정된 대상 데이터 형식의 차원 인덱스를 반환합니다. 소스 데이터 형식. |
| DataFormatDimMap.옵션 | DataFormatDimMap 의 선택적 속성 |
| DataFormatVecPermute <T는 TNumber를 확장합니다. > | 입력 텐서를 `src_format`에서 `dst_format`으로 치환합니다. |
| DataFormatVecPermute.Options | DataFormatVecPermute 의 선택적 속성 |
| DataLayout <S는 DataBuffer <?>, T>를 확장합니다. | 버퍼에 저장된 데이터를 주어진 유형으로 변환합니다. |
| DatalAyouts | 선형 대수 계산에서 자주 사용되는 데이터 형식의 DataLayout 인스턴스를 노출시킵니다. |
| DataServicedAtAset | |
| DataServicedAtaset.Options | DataServiceDataset 의 선택적 속성 |
| 데이터 세트 | 잠재적으로 큰 독립 요소 (샘플) 목록을 나타내며, 이러한 요소에서 반복 및 변환을 수행 할 수 있습니다. |
| DataSetCardInality | 'input_dataset'의 카디널리티를 반환합니다. |
| DataSetCardInality | 'input_dataset'의 카디널리티를 반환합니다. |
| DataSetfromgraph | 주어진`graph_def`에서 데이터 세트를 만듭니다. |
| DataSetiterator | tf.data datset을 통한 반복 상태를 나타냅니다. |
| DataSetOptional | 선택 사항은 데이터 세트의 끝에 도달했을 때 실패 할 수있는 데이터 세트 getNext 작업의 결과를 나타냅니다. |
| DataSettograph | 'input_dataset'을 나타내는 직렬화 된 GraphDef를 반환합니다. |
| DataSettograph.Options | DatasetToGraph 의 선택적 특성 |
| DataSettingesingElement | 주어진 데이터 세트에서 단일 요소를 출력합니다. |
| DataSettotFrecord | 주어진 데이터 세트를 tfrecord 형식을 사용하여 주어진 파일에 씁니다. |
| DataSettotFrecord | 주어진 데이터 세트를 tfrecord 형식을 사용하여 주어진 파일에 씁니다. |
| DatastorageVisitor <R> | DataBuffer 인스턴스의 백업 스토리지를 방문하십시오. |
| 데이터 유형 | (== suppress_warning documentation-presence ==) LINT.IfChange tensorflow.DataType |
| dawsn <t는 tnumber >를 확장합니다 | |
| DealLocator_pointer_long_pointer | |
| Debugent | An Event related to the debugging of a TensorFlow program. |
| Debugent.builder | An Event related to the debugging of a TensorFlow program. |
| Debugvent.hatcase | |
| DebugentorBuilder | |
| DebugnventProtos | |
| 디버그 드 기보 | A device on which ops and/or tensors are instrumented by the debugger. |
| DebuggedDevice.builder | A device on which ops and/or tensors are instrumented by the debugger. |
| 디버그 드리 사무소 빌더 | |
| 디버그 그라프 | A debugger-instrumented graph. |
| DebuggedGraph.Builder | A debugger-instrumented graph. |
| 디버그 그라 그래버 빌더 | |
| 디버깅 소스 파일 | Protobuf 유형 tensorflow.DebuggedSourceFile |
| DebuggedSourcefile.builder | Protobuf 유형 tensorflow.DebuggedSourceFile |
| 디버그 소시 파일러 빌더 | |
| 디버깅 소스 파일 | Protobuf 유형 tensorflow.DebuggedSourceFiles |
| DebuggedSourcefiles.builder | Protobuf 유형 tensorflow.DebuggedSourceFiles |
| 디버깅 된 소시 파일 소르 빌더 | |
| 디버그 그라디언트 <t는 ttype >을 확장합니다 | 그라디언트 디버깅에 대한 ID OP. |
| DebuggradientRefidentity <t는 ttype >을 확장합니다 | 그라디언트 디버깅에 대한 ID OP. |
| Debugidentity <t는 ttype >를 확장합니다 | 디버그 아이덴티티 v2 op. |
| Debugidentity.Options | DebugIdentity 에 대한 선택적 속성 |
| 디버그 메타 데이터 | Metadata about the debugger and the debugged TensorFlow program. |
| debugmetadata.builder | Metadata about the debugger and the debugged TensorFlow program. |
| DebugmetadataorBuilder | |
| Debugnancount | Debug Nan Value Counter Op. |
| Debugnancount.options | DebugNanCount 의 선택적 속성 |
| debugnumericssummary <u는 tnumber >를 확장합니다 | 디버그 숫자 요약 v2 op. |
| debugnumericssummary.options | DebugNumericsSummary 의 선택적 속성 |
| 디버그 작업 | Options for initializing DebuggerState in TensorFlow Debugger (tfdbg). |
| 디버그 옵션. 빌더 | Options for initializing DebuggerState in TensorFlow Debugger (tfdbg). |
| Debugoptionsorbuilder | |
| 디버그 프로토스 | |
| DebugensorWatch | Option for watching a node in TensorFlow Debugger (tfdbg). |
| DebugensorWatch.Builder | Option for watching a node in TensorFlow Debugger (tfdbg). |
| DebugtensorWatchorBuilder | |
| Decodeandcropjpeg | JPEG 인코딩 된 이미지를 UINT8 텐서로 디코딩하고 자릅니다. |
| decodeandecropjpeg.options | DecodeAndCropJpeg 의 선택적 속성 |
| decodebase64 | 웹-안전베이스 64에 인코딩 된 문자열을 디코딩합니다. |
| decodebmp | BMP 인코딩 된 이미지의 첫 번째 프레임을 UINT8 텐서로 디코딩하십시오. |
| decodebmp.options | DecodeBmp 의 선택적 속성 |
| 디코드 응축 | 줄을 압축합니다. |
| decodecompressed.options | DecodeCompressed 의 선택적 속성 |
| decodecsv | CSV 레코드를 텐서로 변환합니다. |
| decodecsv.options | DecodeCsv 의 선택적 속성 |
| decodegif | GIF 인코딩 된 이미지의 프레임을 UINT8 텐서로 디코딩하십시오. |
| decodeimage <t는 tnumber >를 연장합니다 | decode_bmp, decode_gif, decode_jpeg 및 decode_png의 함수. |
| decodeimage.options | DecodeImage 의 선택적 속성 |
| decodejpeg | JPEG 인코딩 된 이미지를 UINT8 텐서로 디코딩하십시오. |
| decodejpeg.options | DecodeJpeg 의 선택적 속성 |
| decodejsonexample | JSON- 인코딩 된 예제 레코드를 이진 프로토콜 버퍼 스트링으로 변환합니다. |
| decodepaddedraw <t는 tnumber >를 확장합니다 | 문자열의 바이트를 숫자의 벡터로 재 해석하십시오. |
| DecodepaddedRaw.Options | DecodePaddedRaw 의 선택적 속성 |
| decodepng <t는 tnumber >를 확장합니다 | PNG- 인코딩 된 이미지를 UINT8 또는 UINT16 텐서로 디코딩하십시오. |
| decodepng.options | DecodePng 의 선택적 속성 |
| Decodeproto | OP는 직렬화 된 프로토콜 버퍼 메시지에서 텐서로 필드를 추출합니다. |
| decodeproto.options | DecodeProto 의 선택적 속성 |
| Decoderaw <t는 ttype >를 확장합니다 | 문자열의 바이트를 숫자의 벡터로 재 해석하십시오. |
| Decoderaw.options | DecodeRaw 의 선택적 속성 |
| Decodewav | 16 비트 PCM WAV 파일을 플로트 텐서로 디코딩하십시오. |
| decodewav.options | DecodeWav 의 선택적 속성 |
| DeepCopy <t는 ttype >를 연장합니다 | `x`의 사본을 만듭니다. |
| delete_func_pointer | |
| DELETEITERATOR | 반복 자원 용 컨테이너. |
| deletememoryCache | |
| deletemultideviceiterator | 반복 자원 용 컨테이너. |
| deleterandomseedgenerator | |
| deleteseedgenerator | |
| deletesessionTensor | 세션에서 손잡이로 지정된 텐서를 삭제하십시오. |
| DenseBincount <u는 tnumber >를 확장합니다 | 정수 배열에서 각 값의 발생 수를 계산합니다. |
| DENSEBINCOUNT.OPTIONS | DenseBincount 의 선택적 속성 |
| densecountsparseoutput <u는 tnumber >를 확장합니다 | tf.tensor 입력에 대한 희소 출력 빈 계산을 수행합니다. |
| densecountsparseoutput.options | DenseCountSparseOutput 의 선택적 속성 |
| DensendArray <t> | |
| densetocsrsparsematrix | 밀도가 높은 텐서를 CSRSPARSEMATRIX로 변환합니다. |
| DensetodenseSetOperation <t extends ttype > | 2 'tensor` 입력의 마지막 치수를 따라 설정 작업을 적용합니다. |
| DensetodenseSetOperation. options | DenseToDenseSetOperation 의 선택적 속성 |
| DensetosparsebatchDataset | 입력 요소를 SparSetensor에 배치하는 데이터 세트를 만듭니다. |
| DensetosparsebatchDataset | 입력 요소를 SparSetensor에 배치하는 데이터 세트를 만듭니다. |
| DensetoSparseseToperation <t extends ttype > | `tensor`와`sparsetensor '의 마지막 치수를 따라 설정된 작동을 적용합니다. |
| DensetoSparseseToperation.Options | DenseToSparseSetOperation 의 선택적 속성 |
| Depthtospace <t는 ttype >를 확장합니다 | T 형 텐서에 대한 깊이. |
| Depthtospace.options | DepthToSpace 에 대한 선택적 속성 |
| DecThwiseconv2dnative <t는 tnumber >를 확장합니다 | 4D`입력 '및`필터'텐서가 주어진 2D 깊이 컨볼 루션을 계산합니다. |
| Depthwiseconv2dnative.options | DepthwiseConv2dNative 의 선택적 속성 |
| DecThwiseconv2dnativeBackPropFilter <t는 tnumber >를 확장합니다 | 필터와 관련하여 깊이 컨볼 루션의 그라디언트를 계산합니다. |
| DecThwiseconv2dnativeBackPropFilter.Options | DepthwiseConv2dNativeBackpropFilter 의 선택적 속성 |
| DecThwiseconv2dnativeBackPropinput <t는 tnumber >를 확장합니다 | 입력과 관련하여 깊이 컨볼 루션의 그라디언트를 계산합니다. |
| DecThwiseconv2dnativeBackPropinput.Options | DepthwiseConv2dNativeBackpropInput 의 선택적 속성 |
| dequantize <u는 tnumber >를 확장하십시오 | '입력'텐서를 플로트 또는 bfloat16 텐서에 묻습니다. |
| 다문기 | 포장 된 UINT32 입력을 가져 와서 입력을 UINT8로 포장을 풀어야합니다. 장치에서의 수확. |
| dequantize.options | Dequantize 의 선택적 속성 |
| deserializeiterator | 주어진 변형 텐서를 반복자로 변환하고 주어진 자원에 저장합니다. |
| deserializemanysparse <t는 ttype >을 확장합니다 | 직렬화 된 미니 배트에서`sparsetensors '를 제거하고 연결합니다. |
| deserializesparse <u는 ttype >을 확장합니다 | `sparsetensor` 객체를 제조하십시오. |
| DestroveResourceop | 핸들에 의해 지정된 리소스를 삭제합니다. |
| DestroveResourceop.options | DestroyResourceOp 의 선택적 속성 |
| 파괴 temporaryvariable <t extends ttype > | 임시 변수를 파괴하고 최종 값을 반환합니다. |
| det <t는 ttype >를 확장합니다 | 하나 이상의 사각형 행렬의 결정 요인을 계산합니다. |
| deviceattributes | protobuf type tensorflow.DeviceAttributes |
| deviceattributes.builder | protobuf type tensorflow.DeviceAttributes |
| DeviceattributesorBuilder | |
| Deviceattributesprotos | |
| DeviceFiltersProtos | |
| DeviceIndex | OP가 실행하는 장치의 인덱스를 반환하십시오. |
| devicelocality | protobuf type tensorflow.DeviceLocality |
| devicelocality .Builder | protobuf type tensorflow.DeviceLocality |
| devicelocality orbuilder | |
| DeviceProperties | protobuf type tensorflow.DeviceProperties |
| DeviceProperties.builder | protobuf type tensorflow.DeviceProperties |
| DevicePropertiesorBuilder | |
| DevicePropertiesProtos | |
| DeviceSpec | 텐서 플로 장치에 대한 (아마도 부분적으로) 사양을 나타냅니다. |
| devicespec.builder | DeviceSpec 클래스를 구축하기위한 빌더 클래스. |
| devicespec.deviceType | |
| Devicestepstats | Protobuf 유형 tensorflow.DeviceStepStats |
| devicestepstats.builder | Protobuf 유형 tensorflow.DeviceStepStats |
| devicestepstatsorBuilder | |
| DictValue | Represents a Python dict keyed by `str`. |
| dictvalue.builder | Represents a Python dict keyed by `str`. |
| DictValueorBuilder | |
| digamma <t는 tnumber >를 연장합니다 | Lgamma의 미분 인 PSI를 계산합니다 (절대 값의 로그 `gamma (x)`), 요소 별. |
| Dilation2d <t는 tnumber >를 연장합니다 | 4-D`입력 '및 3D`필터'텐서의 회색조 팽창을 계산합니다. |
| Dilation2dbackPropfilter <t는 tnumber >를 확장합니다 | 필터에 대한 형태 학적 2-D 팽창의 기울기를 계산합니다. |
| Dilation2dbackPropinput <t는 tnumber >를 확장합니다 | 입력에 대한 형태 학적 2-D 팽창의 기울기를 계산합니다. |
| 차원 | |
| 치수 공간 | |
| DirectedInterleAvedataset | 고정 된 'n'데이터 세트 목록에서 '인터리베이타 세트'를 대체합니다. |
| DirectedInterleAvedataset | 고정 된 'n'데이터 세트 목록에서 '인터리베이타 세트'를 대체합니다. |
| div <t는 ttype >을 확장합니다 | x / y 요소로 반환합니다. |
| divnonan <t는 ttype >을 확장합니다 | 분모가 0 인 경우 0을 반환합니다. |
| 도트 <t는 ttype >을 확장합니다 | 문서화 된 XLA DotGeneral Operator를 랩핑합니다 https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral. |
| Doubledatabuffer | 복식의 DataBuffer . |
| Doubledatalayout <s는 databuffer <? >>를 확장합니다 | 버퍼에 저장된 데이터를 복식으로 변환하는 DataLayout . |
| DoubledEnsendArray | |
| Doublendarray | 복식의 NdArray . |
| DrawBoundingboxes <t는 tnumber >를 확장합니다 | 이미지의 배치에 경계 상자를 그립니다. |
| DummyiterationCounter | |
| dummymemoryCache | |
| 더미 시드 게이터 | |
| DynamicPartition <t extends ttype > | `data '는`partitions'의 인덱스를 사용하여`num_partitions '텐서에 칸막이입니다. |
| DynamicSlice <t는 ttype >를 확장합니다 | 문서화 된 XLA DynamicSlice 연산자를 랩핑합니다 https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice. |
| DynamicStitch <t는 ttype >을 확장합니다 | '데이터'텐서의 값을 단일 텐서에 intrea하십시오. |
| DynamicupDatesLice <t는 ttype >을 확장합니다 | 문서화 된 XLA DynamicupDatesLice 연산자를 랩핑합니다 https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice. |
이자형
| 독수리 | 텐서 플로 작동을 간절히 실행하기위한 환경. |
| eagersession.deviceplacementPolicy | 주어진 장치에서 작업을 실행하려고 할 때 행동하는 방법을 제어하지만 일부 입력 텐서는 해당 장치에 없습니다. |
| eagersession. options | |
| editdistance | (정규화 된) Levenshtein 편집 거리를 계산합니다. |
| editdistance.options | EditDistance 의 선택적 속성 |
| eig <u는 ttype >을 확장합니다 | 하나 이상의 사각형 행렬의 고유 분해를 계산합니다. |
| eig.options | Eig 의 선택적 속성 |
| Einsum <t는 ttype >를 확장합니다 | 아인슈타인 합산 협약에 따른 텐서 수축. |
| Einsum <t는 ttype >를 확장합니다 | 2 개의 입력과 1 개의 출력이있는 기본 Einsum OP를 지원하는 OP. |
| elu <t는 tnumber >를 확장합니다 | 지수 선형을 계산합니다.`exp (feature) -1` if <0,`feations '는 그렇지 않으면. |
| elu <t는 tfloating >을 확장합니다 | 지수 선형 단위. |
| Elugrad <t는 tnumber >를 연장합니다 | 지수 선형 (ELU) 작업을위한 그라디언트를 계산합니다. |
| 임베딩 활성화 | TPU 임베딩의 분화를 가능하게하는 OP. |
| 비어 <t는 ttype >를 확장합니다 | 주어진 모양으로 텐서를 만듭니다. |
| empty.options | Empty 선택적 속성 |
| emptytensorlist | 빈 텐서 목록을 생성하고 반환합니다. |
| emptytensormap | 빈 텐서 맵을 생성하고 반환합니다. |
| Encodebase64 | 문자열을 Web-Safe Base64 형식으로 인코딩합니다. |
| Encodebase64.options | EncodeBase64 의 선택적 속성 |
| encodejpeg | jpeg-encode 이미지. |
| encodejpeg.options | EncodeJpeg 의 선택적 속성 |
| encodejpegvariablequality | JPEG는 제공된 압축 품질로 입력 이미지를 인코딩합니다. |
| encodepng | PNG-encode 이미지. |
| encodepng.options | EncodePng 의 선택적 속성 |
| encoproto | OP는 입력 텐서에 제공된 Protobuf 메시지를 직렬화합니다. |
| encodeproto.options | EncodeProto 토의 선택적 속성 |
| encodewav | WAV 파일 형식을 사용하여 오디오 데이터를 인코딩합니다. |
| 엔드 포인트 | @Operator 로 주석이 달린 클래스의 메소드를 표시하는 데 사용되는 주석은 엔드 포인트를 ERROR(Ops/org.tensorflow.op.Ops Ops) 또는 그 그룹 중 하나로 생성해야합니다. |
| enqueuetpuembeddingintegerbatch | 입력 배치 텐서 목록을 tpuembedding에 넣는 OP. |
| enqueuetpuembeddingintegerbatch.options | EnqueueTPUEmbeddingIntegerBatch 의 선택적 속성 |
| enqueuetpuembeddingRaggedTensorbatch | tf.nn.embedding_lookup ()을 사용하는 코드 포팅이 완화됩니다. |
| enqueuetpuembeddingraggedTensorbatch.options | EnqueueTPUEmbeddingRaggedTensorBatch 의 선택적 속성 |
| enqueuetpuembeddingsparsebatch | sparsetensor의 tpuembedding 입력 지수를 흡수하는 OP. |
| enqueuetpuembeddingsparsebatch.options | EnqueueTPUEmbeddingSparseBatch 의 선택적 속성 |
| enqueuetpuembeddingsparsetensorbatch | tf.nn.embedding_lookup_sparse ()를 사용하는 코드 포팅이 완화됩니다. |
| enqueuetpuembeddingsparsetensorbatch.options | EnqueueTPUEmbeddingSparseTensorBatch 의 선택적 속성 |
| <t는 ttype >를 확장하는지 확인합니다 | 텐서의 모양이 예상 모양과 일치하도록합니다. |
| <t extends ttype >을 입력하십시오 | 자식 프레임을 생성하거나 찾아서 '데이터'를 어린이 프레임에 사용할 수있게합니다. |
| ENTER.OPTIONS | Enter 의 선택적 속성 |
| EntryValue | Protobuf 유형 tensorflow.EntryValue |
| EntryValue.builder | Protobuf 유형 tensorflow.EntryValue |
| EntryValue.kindcase | |
| EntryValueOrBuilder | |
| 동일한 | 요소 별 (x == y)의 진실 값을 반환합니다. |
| 평등 | Equal 속성 |
| erf <t는 tnumber >를 확장합니다 | `x` emelems-의 가우스 오류 함수를 계산합니다. |
| erfc <t는 tnumber >를 확장합니다 | `x` emelt-swise의 보완 오류 기능을 계산합니다. |
| erfinv <t는 tnumber >를 확장합니다 | |
| 오류 코드 | |
| ErrorCodeSprotos | |
| euclideannorm <t는 ttype >를 확장합니다 | 텐서의 치수에 걸쳐 유클리드 요소의 표준을 계산합니다. |
| euclideannorm.options | EuclideanNorm 의 선택적 속성 |
| 이벤트 | Protocol buffer representing an event that happened during the execution of a Brain model. |
| 이벤트 빌더 | Protocol buffer representing an event that happened during the execution of a Brain model. |
| 이벤트. 무슨 | |
| EventorBuilder | |
| EventProtos | |
| 예 | Protobuf 유형 tensorflow.Example |
| 예제 빌더 | Protobuf 유형 tensorflow.Example |
| exampleArbuilder | |
| exampleparserconfiguration | Protobuf 유형 tensorflow.ExampleParserConfiguration |
| exampleparserconfiguration.builder | Protobuf 유형 tensorflow.ExampleParserConfiguration |
| exampleparserconfigurationorbuilder | |
| exampleparserconfigurationProtos | |
| exampleProtos | |
| 실행하다 | TPU 장치에서 TPU 프로그램을로드하고 실행하는 OP. |
| ExecuteAndupDateVariables | 옵션 인 내장 변수 업데이트가있는 프로그램을 실행하는 OP. |
| 실행 | Data relating to the eager execution of an op or a Graph. |
| 실행. 빌더 | Data relating to the eager execution of an op or a Graph. |
| 실행 환경 | 텐서 플로 Operation 을 생성하고 실행하기위한 환경을 정의합니다. |
| ExecutionEnvironment.types | |
| ExecutionorBuilder | |
| 종료 <t는 ttype >를 확장합니다 | 현재 프레임을 모래 프레임으로 종료합니다. |
| exp <t는 ttype >를 확장합니다 | x 요소 단위의 지수를 계산합니다. |
| expanddims <t는 ttype >를 확장합니다 | 1의 치수를 텐서 모양에 삽입합니다. |
| Expint <t는 tnumber >를 확장합니다 | |
| expm1 <t는 ttype >를 확장합니다 | `exp (x) -1` emect -wise를 계산합니다. |
| 지수 <t는 tfloating >을 확장합니다 | 지수 활성화 기능. |
| ExtractGlimpse | 입력 텐서에서 엿볼 수 있습니다. |
| ExtractGlimpse.Options | ExtractGlimpse 의 선택적 속성 |
| extractimagepatches <t extends ttype > | '이미지'에서 '패치'를 추출하고 "깊이"출력 차원에 넣습니다. |
| extractjpegshape <t extends tnumber > | JPEG 인코딩 된 이미지의 모양 정보를 추출하십시오. |
| ExtractVolumepatches <t는 tnumber >를 확장합니다 | `입력 '에서`패치'를 추출하고` "깊이"`출력 차원에 넣으십시오. |
에프
| 사실 | 팩토리 노트에 대한 사실을 출력하십시오. |
| 가짜 quithminmaxargs | '입력'텐서를 가짜로 정의하고 같은 유형의 '출력'텐서에 플로트를 입력하십시오. |
| 가짜 quithminmaxargs.options | FakeQuantWithMinMaxArgs 의 선택적 속성 |
| 가짜 quithminmaxargsgradient | 가짜 quantwithminmaxargs 작업을위한 그라디언트를 계산합니다. |
| 가짜 quithminmaxargsgradient.options | FakeQuantWithMinMaxArgsGradient 의 선택적 속성 |
| 가짜 quithminmaxvars | 전역 플로트 스칼라를 통한 유형의 '입력'텐서를 가짜 정의 글로벌 플로트 스칼라`min '을 통해'입력 '유형 플로트의'입력 '텐서를'입력 '과 같은 모양의'출력 '텐서를 가짜로 정의하십시오. |
| 가짜 quithminmaxvars.options | FakeQuantWithMinMaxVars 의 선택적 속성 |
| 가짜 quithminmaxvarsgradient | 가짜 quantwithminmaxvars 작업을위한 그라디언트를 계산합니다. |
| 가짜 quithminmaxvarsgradient.options | FakeQuantWithMinMaxVarsGradient 의 선택적 속성 |
| 가짜 QuithMinMaxVarsperChannel | 가짜-채널 당 플로트를 통한 유형의 '입력'텐서를 정의하십시오. 채널 당 플로트 유형의 '입력'텐서와 모양 중 하나의``입력 '텐서를 가짜로 정의합니다. Min`과````````````````````````` "텐서가``min`와 'max'. |
| 가짜 quithminmaxvarsperchannel.options | FakeQuantWithMinMaxVarsPerChannel 의 선택적 속성 |
| 가짜 quithminmaxvarsperchannel gradient | 가짜 quantwithminmaxvarsperChannel 작동을위한 그라디언트를 계산합니다. |
| 가짜 QuithminMaxVarsperChannel gradient.Options | FakeQuantWithMinMaxVarsPerChannelGradient 의 선택적 속성 |
| FASTELEMENTEDECTENCE <T, U 확장 NDARRAY <T >> | 요소를 반복 할 때 동일한 NdArray 인스턴스를 재활용하는 시퀀스 |
| 특징 | Containers for non-sequential data. |
| feature.builder | Containers for non-sequential data. |
| feature.kindcase | |
| FeatureConfiguration | Protobuf Type tensorflow.FeatureConfiguration |
| featureconfiguration.builder | Protobuf Type tensorflow.FeatureConfiguration |
| featureconfiguration.configcase | |
| featureconfigurationorbuilder | |
| 피처리스트 | Containers for sequential data. |
| featurelist. 빌더 | Containers for sequential data. |
| FeaturelistorBuilder | |
| 기능가 | protobuf type tensorflow.FeatureLists |
| Fircurelists.builder | protobuf type tensorflow.FeatureLists |
| featureListsorbuilder | |
| feactionorBuilder | |
| FeatureProtos | |
| 특징 | Protobuf Type tensorflow.Features |
| 특징 빌더 | Protobuf Type tensorflow.Features |
| featuateorbuilder | |
| fft <t는 ttype >를 확장합니다 | 빠른 푸리에 변환. |
| fft2d <t는 ttype >를 확장합니다 | 2D 빠른 푸리에 변환. |
| fft3d <t는 ttype >을 확장합니다 | 3D 빠른 푸리에 변환. |
| FIFOQUEUE | 첫 번째 순서로 요소를 생성하는 대기열. |
| fifoqueue.options | FifoQueue 의 선택적 속성 |
| 채우기 <u 확장 ttype > | 스칼라 값으로 채워진 텐서를 만듭니다. |
| FilterByLastComponentDataset | 마지막 구성 요소에서 'input_dataset'의 첫 번째 구성 요소의 요소를 포함하는 데이터 세트를 만듭니다. |
| 지문 | 지문 값을 생성합니다. |
| FixedLenfeatureProto | protobuf type tensorflow.FixedLenFeatureProto |
| FixedLenfeatureProto.builder | protobuf type tensorflow.FixedLenFeatureProto |
| FixedLenfeatureProtoorBuilder | |
| FixedLengthrecordDataset | |
| FixedLengthreCordReader | 파일에서 고정 길이 레코드를 출력하는 리더. |
| FixedLengthrecordReader.Options | FixedLengthRecordReader 의 선택적 속성 |
| FIXEGRAMCANDIDATESAMPLER | 학습 된 유니그램 배포로 후보 샘플링에 대한 레이블을 생성합니다. |
| fixedunigramcandidatesampler.options | FixedUnigramCandidateSampler 의 선택적 속성 |
| float16layout | IEEE-754 하프-프레시션 플로팅 포인트 사양에 따라 32 비트 플로트를/에서 16 비트로 변환하는 데이터 레이아웃. |
| FloatDatabuffer | 플로트의 DataBuffer . |
| FloatDatalAyout <s는 databuffer <? >>를 확장합니다 | 버퍼에 저장된 데이터를 플로트로 변환하는 DataLayout . |
| floatdensendArray | |
| 플로트리스트 | protobuf type tensorflow.FloatList |
| floatlist.builder | protobuf type tensorflow.FloatList |
| FloatListorBuilder | |
| floatndarray | 플로트의 NdArray . |
| 바닥 <t는 tnumber >를 연장합니다 | x보다 크지 않은 요소에서 가장 큰 정수를 반환합니다. |
| Floordiv <t는 ttype >를 확장합니다 | x // y 요소 별을 반환합니다. |
| Floormod <t는 tnumber >를 연장합니다 | 요소 별 분열을 반환합니다. |
| FlushSummarywriter | |
| fractionalavgpool <t는 tnumber >를 확장합니다 | 입력에서 분수 평균 풀링을 수행합니다. |
| fractionalavgpool.options | FractionalAvgPool 의 선택적 속성 |
| fractionalavgpoolgrad <t는 tnumber >를 확장합니다 | Fractionalavgpool 함수의 기울기를 계산합니다. |
| Fractionalavgpoolgrad.Options | FractionalAvgPoolGrad 의 선택적 속성 |
| fractionalmaxpool <t는 tnumber >를 확장합니다 | 입력에서 분수 최대 풀링을 수행합니다. |
| fractionalmaxpool.options | FractionalMaxPool 의 선택적 속성 |
| fractionalmaxpoolgrad <t는 tnumber >를 확장합니다 | fractionalmaxpool 함수의 기울기를 계산합니다. |
| fractionalmaxpoolgrad.options | FractionalMaxPoolGrad 의 선택적 속성 |
| FresnelCos <t는 tnumber >를 확장합니다 | |
| Fresnelsin <t는 tnumber >를 확장합니다 | |
| ftrl | Ftrl 알고리즘을 구현하는 Optimizer. |
| functionDef | A function can be instantiated when the runtime can bind every attr with a value. |
| functionDef.argattrs | Attributes for function arguments. |
| functionDef.argattrs.builder | Attributes for function arguments. |
| functionDef.argattrSorBuilder | |
| functionDef.builder | A function can be instantiated when the runtime can bind every attr with a value. |
| FunctionDeflibrary | A library is a set of named functions. |
| functionDeflibrary.builder | A library is a set of named functions. |
| FunctionDeflibraryorBuilder | |
| functionDeforBuilder | |
| FunctionProtos | |
| functionspec | Represents `FunctionSpec` used in `Function`. |
| functionspec.builder | Represents `FunctionSpec` used in `Function`. |
| functionspec.experimentalCompile | Whether the function should be compiled by XLA. |
| 함수 specorBuilder | |
| fusedbatchnorm <t는 tnumber , u는 tnumber를 확장합니다>> | 배치 정규화. |
| Fusedbatchnorm.options | FusedBatchNorm 의 선택적 속성 |
| Fusedbatchnormgrad <t는 tnumber , u는 tnumber를 확장합니다> | 배치 정규화를위한 구배. |
| 퓨즈 배치 노르트 그레이드 옵션 | FusedBatchNormGrad 의 선택적 속성 |
| fusedPadConv2d <t는 tnumber >를 확장합니다 | 컨볼 루션 중에 전처리로 패딩을 수행합니다. |
| FusedResizeAndPadConv2d <T는 tnumber >를 확장합니다 | 컨볼 루션 중에 전처리로 크기 조정 및 패딩을 수행합니다. |
| FusedResizeAndPadConv2d.Options | FusedResizeAndPadConv2d 의 선택적 속성 |
G
| 수집 <t extends tnumber > | 동일한 유형과 모양의 여러 텐서를 상호 축적합니다. |
| 수집 <t extends ttype > | 'indices'에 따라 'Params'Axis` 축`axis '에서 슬라이스를 수집하십시오. |
| 수집 <t extends ttype > | 문서화 된 XLA 수집 연산자를 랩핑합니다 https://www.tensorflow.org/xla/operation_semantics#gather |
| 수집 | Gather 위한 선택적 속성 |
| 수집 | Gather 위한 선택적 속성 |
| gathernd <t는 ttype >를 확장합니다 | 'Params'에서 'indices'로 지정된 모양의 텐서로 슬라이스를 수집하십시오. |
| gatherv2 <t는 tnumber >를 확장합니다 | 동일한 유형과 모양의 여러 텐서를 상호 축적합니다. |
| gatherv2.options | GatherV2 의 선택적 속성 |
| 바운드 바운드 박스 프로 포스를 생성합니다 | 이 OP는 주어진 경계 박스 (bbox_deltas)에서 관심 영역을 생성합니다. OP는 상단`pre_nms_topn` 스코어링 상자를 선택하고, 앵커와 관련하여 해독하고, 'nms_threshold` min_size '. |
| 생성 BoundingboxProposals.Options를 생성합니다 | GenerateBoundingBoxProposals 의 선택적 속성 |
| 생성 Vocabremapping | 새롭고 오래된 어휘 파일로가는 길이 주어지면 다시 매핑 텐서를 반환합니다. 길이`num_new_vocab`는`remapping [i]`가 새로운 어휘에 해당하는 기존 어휘의 행 번호를 포함합니다 (`new_vocab_offset`에서 시작) 및 'num_new_vocab'entities '또는`- 1` 만약 새로운 어휘에 들어가면``I '가 오래된 어휘에 있지 않습니다. |
| 생성 Vocabremapping.options | GenerateVocabRemapping 의 선택적 속성 |
| getsessionhandle | 입력 텐서를 현재 세션 상태에 저장하십시오. |
| getSessionTensor <t extends ttype > | 손잡이로 지정된 텐서의 값을 얻으십시오. |
| Glorot <t는 tfloating >을 확장합니다 | Xavier 이니셜 라이저라고도하는 Glorot Initializer. |
| gpuinfo | protobuf type tensorflow.GPUInfo |
| gpuinfo.builder | protobuf type tensorflow.GPUInfo |
| gpuinfoorbuilder | |
| gpuoptions | protobuf type tensorflow.GPUOptions |
| gpuoptions.builder | protobuf type tensorflow.GPUOptions |
| gpuoptions.experimental | protobuf type tensorflow.GPUOptions.Experimental |
| gpuoptions.experimental.builder | protobuf type tensorflow.GPUOptions.Experimental |
| gpuoptions.experimental.virtualDevices | Configuration for breaking down a visible GPU into multiple "virtual" devices. |
| gpuoptions.experimental.virtualdevices.builder | Configuration for breaking down a visible GPU into multiple "virtual" devices. |
| gpuoptions.experimental.virtualDevicesOrBuilder | |
| gpuoptions.experimentalorBuilder | |
| gpuoptionsorbuilder | |
| GradientDef | GradientDef defines the gradient function of a function defined in a function library. |
| GradientDef.Builder | GradientDef defines the gradient function of a function defined in a function library. |
| GradientDeforBuilder | |
| GradientDescent | 기본 확률 론적 구배 하강 최적화. |
| 그라디언트 | y s wrt x s의 합의 편도함수를 계산하는 연산을 추가합니다. 즉, d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2... |
| 그라데이션.옵션 | Gradients 의 선택적 속성 |
| 그래프 | 텐서 플로 계산을 나타내는 데이터 흐름 그래프. |
| 그래프 | while 루프를위한 조건부 또는 신체 하위 그래프를 구축하기 위해 BuildSubgraph 메소드를 무시하는 추상 클래스를 인스턴스화하는 데 사용됩니다. |
| GraphDebuginfo | protobuf type tensorflow.GraphDebugInfo |
| GraphDebuginfo.builder | protobuf type tensorflow.GraphDebugInfo |
| Graphdebuginfo.filelinecol | This represents a file/line location in the source code. |
| Graphdebuginfo.filelinecol.builder | This represents a file/line location in the source code. |
| Graphdebuginfo.FilelineColorBuilder | |
| GraphDebuginfo.StackTrace | This represents a stack trace which is a ordered list of `FileLineCol`. |
| GraphDebuginfo.stacktrace.builder | This represents a stack trace which is a ordered list of `FileLineCol`. |
| GraphDebuginfo.StacktraceorBuilder | |
| GraphDebuginfoorBuilder | |
| GraphDebuginfopropotos | |
| GraphDef | Represents the graph of operations tensorflow.GraphDef |
| GraphDef.Builder | Represents the graph of operations tensorflow.GraphDef |
| GraphDeforBuilder | |
| GraphExecutionTrace | Data relating to an execution of a Graph (e.g., an eager execution of a FuncGraph). |
| GraphExecutionTrace.Builder | Data relating to an execution of a Graph (e.g., an eager execution of a FuncGraph). |
| GraphExecutionTraceorBuilder | |
| Graphopcreation | The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2). |
| Graphopcreation.builder | The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2). |
| GraphopcreationorBuilder | |
| 그래프 포옹 | Graph 에 노드로 추가 된 Operation 구현. |
| 그래프 탑승 구매자 | Graph 에 GraphOperation 을 추가하기위한 OperationBuilder . |
| 그래프 탑 | protobuf type tensorflow.GraphOptions |
| 그래프 탑. 빌더 | protobuf type tensorflow.GraphOptions |
| Graphoppionsorbuilder | |
| 그래프 프로토스 | |
| GraphTransferconstnodeinfo | protobuf type tensorflow.GraphTransferConstNodeInfo |
| GraphTransferconstnodeinfo.builder | protobuf type tensorflow.GraphTransferConstNodeInfo |
| GraphTransferconstnodeinfoorBuilder | |
| GraphTransfergraphinputnodeinfo | protobuf type tensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransfergraphinputnodeinfo.builder | protobuf type tensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransfergraphinputnodeinfoorBuilder | |
| GraphTransfergraphOutputNodeInfo | protobuf type tensorflow.GraphTransferGraphOutputNodeInfo |
| GraphTransfergraphOutputNodeInfo.Builder | protobuf type tensorflow.GraphTransferGraphOutputNodeInfo |
| GraphTransfergraphOutPutNodeInfoorBuilder | |
| GraphTransferinfo | Protocol buffer representing a handle to a tensorflow resource. |
| GraphTransferinfo.builder | Protocol buffer representing a handle to a tensorflow resource. |
| GraphTransferinfo.destination | protobuf enum tensorflow.GraphTransferInfo.Destination |
| GraphTransferinfoorBuilder | |
| GraphTransferinfopropoto | |
| GraphTransfernodeInfo | protobuf type tensorflow.GraphTransferNodeInfo |
| GraphTransfernodeinfo.builder | protobuf type tensorflow.GraphTransferNodeInfo |
| GraphTransfernodeinfoorBuilder | |
| GraphTransfernodeInput | protobuf type tensorflow.GraphTransferNodeInput |
| GraphTransfernodeInput.builder | protobuf type tensorflow.GraphTransferNodeInput |
| GraphTransfernodeInputInfo | protobuf type tensorflow.GraphTransferNodeInputInfo |
| GraphTransfernodeInputInfo.Builder | protobuf type tensorflow.GraphTransferNodeInputInfo |
| GraphTransfernodeInputinfoorBuilder | |
| GraphTransfernodeInPutorBuilder | |
| GraphTransferNodeOutputInfo | protobuf type tensorflow.GraphTransferNodeOutputInfo |
| GraphTransferNodeOutputInfo.Builder | protobuf type tensorflow.GraphTransferNodeOutputInfo |
| GraphTransferNodeOutPutInfoorBuilder | |
| 보다 큰 | 요소 별 (x> y)의 진실 값을 반환합니다. |
| 더 크게 | 요소 별 (x> = y)의 진실 값을 반환합니다. |
| Groblockcell <t는 tnumber >를 확장합니다 | GRU 셀 포워드 전파를 1 단계로 계산합니다. |
| grublockcellgrad <t는 tnumber >를 확장합니다 | Gru Cell Back-Propagation을 1 번 단계로 계산합니다. |
| 보증인 <t는 ttype >를 확장합니다 | TF 런타임에 입력 텐서가 일정하다는 것을 보장합니다. |
시간
| Hardsigmoid <t는 tfloating >을 확장합니다 | 하드 시그 모이 드 활성화. |
| 해시 가능 | 비 초기화 해시 테이블을 만듭니다. |
| Hashtable.options | HashTable 의 선택적 속성 |
| 그는 tfloating >을 연장합니다 | 그는 이니셜 라이저. |
| 도우미 | 여러 작업을 추가하거나 수행하고 그 중 하나를 반환하는 핵심 방법에 대한 컨테이너 클래스. |
| 돌쩌귀 | 레이블과 예측 사이의 힌지 손실을 계산합니다. |
| 힌지 <t는 tnumber >를 연장합니다 | 레이블과 예측 사이의 힌지 손실 메트릭을 계산하는 메트릭. |
| histogramfixedWidth <u는 tnumber >를 확장합니다 | 값의 히스토그램을 반환합니다. |
| 히스토그램 프로토 | Serialization format for histogram module in core/lib/histogram/histogram.h tensorflow.HistogramProto |
| HistogramProto.Builder | Serialization format for histogram module in core/lib/histogram/histogram.h tensorflow.HistogramProto |
| HistogramprotoorBuilder | |
| 히스토그램 | 히스토그램으로 '요약'프로토콜 버퍼를 출력합니다. |
| hsvtorgb <t는 tnumber >를 확장합니다 | 하나 이상의 이미지를 HSV에서 RGB로 변환합니다. |
| 허버 | 레이블과 예측 사이의 Huber 손실을 계산합니다. |
나
| 정체성 <t는 tfloating을 확장합니다> | Identity Matrix를 생성하는 이니셜 라이저. |
| 정체성 <t는 ttype >을 확장합니다 | 입력 텐서 또는 값과 동일한 모양과 내용의 텐서를 반환하십시오. |
| 신원 | 입력과 동일한 모양과 내용을 가진 텐서 목록을 반환합니다. 텐서. |
| IdentityReader | 대기열 작업을 열쇠와 가치로 출력하는 독자. |
| IdentityReader.options | IdentityReader 의 선택적 속성 |
| ifft <t는 ttype >을 확장합니다 | 반대 빠른 푸리에 변환. |
| ifft2d <t는 ttype >를 확장합니다 | 역 2D 빠른 푸리에 변환. |
| ifft3d <t는 ttype >를 확장합니다 | 역 3D 빠른 푸리에 변환. |
| Igamma <t는 tnumber >를 연장합니다 | 낮은 정규화 불완전한 감마 기능`p (a, x)`을 계산하십시오. |
| igammac <t는 tnumber >를 연장합니다 | 상단 정규화되지 않은 불완전한 감마 기능`q (a, x)`을 계산하십시오. |
| Igammagrada <t는 tnumber >를 확장합니다 | `Igamma (a, x)`wrt` a`의 그라디언트를 계산합니다. |
| incoreRrorsDataset | 오류를 무시하는 'input_dataset'의 요소가 포함 된 데이터 세트를 만듭니다. |
| incoreRrorsDataset | 오류를 무시하는 'input_dataset'의 요소가 포함 된 데이터 세트를 만듭니다. |
| INGORERRORSDATASET.OPTIONS | IgnoreErrorsDataset 의 선택적 속성 |
| INGORERRORSDATASET.OPTIONS | IgnoreErrorsDataset 의 선택적 속성 |
| 불법 인식 | 대상 배열의 순위로 인해 작업을 완료 할 수없는 경우 예외가 발생합니다. |
| imag <u는 tnumber >를 확장합니다 | 복소수의 가상 부분을 반환합니다. |
| ImageProjectiveTransformv2 <t는 tnumber >를 확장합니다 | 주어진 변환을 각 이미지에 적용합니다. |
| ImageProjectiveTransformv2.options | ImageProjectiveTransformV2 의 선택적 속성 |
| ImageProjectiveTransformv3 <t는 tnumber >를 확장합니다 | 주어진 변환을 각 이미지에 적용합니다. |
| ImageProjectiveTransformv3.Options | ImageProjectiveTransformV3 의 선택적 속성 |
| imagesUmmary | 이미지와 함께 '요약'프로토콜 버퍼를 출력합니다. |
| imagesUmmary.options | ImageSummary 의 선택적 속성 |
| EmutableConst <t는 ttype >을 확장합니다 | 메모리 영역에서 불변의 텐서를 반환합니다. |
| importevent | |
| 색인 | N- 차원 배열에서보기를 슬라이싱하는 데 사용되는 색인. |
| 인덱스 위치에 있습니다 | |
| IndexedPositionIterator.coordsLongConsumer | |
| 지수 | 인덱스 Index 개체를위한 도우미 클래스. |
| infeeddequeue <t는 ttype >을 확장합니다 | 계산에 공급되는 값에 대한 자리 표시 자 OP. |
| infeeddequeuetuple | Infeed에서 XLA 튜플로 여러 값을 가져옵니다. |
| 인간 큐 | 단일 텐서 값을 계산에 공급하는 OP. |
| infeedenqueue.options | InfeedEnqueue 의 선택적 속성 |
| infeedenqueueprelinearizedbuffer | TPU 인프레드로 전신 버퍼를 흡수하는 OP. |
| infeedenqueueprelinearizedbuffer.options | InfeedEnqueuePrelinearizedBuffer 의 선택적 속성 |
| infeedenqueuetuple | 여러 텐서 값을 XLA 튜플로 계산에 공급합니다. |
| infeedenqueuetuple.options | InfeedEnqueueTuple 의 선택적 속성 |
| 이니 | |
| 이니셜 라이저 <t는 ttype >을 확장합니다 | 이니셜 라이저의 인터페이스 |
| 초기화 가능 | 키와 값에 대해 각각 두 개의 텐서를 사용하는 테이블 이니셜 라이저. |
| 초기화 가능한 경우 dataSet | |
| initializetableFromTextFile | 텍스트 파일에서 테이블을 초기화합니다. |
| InitializetableFromTextFile.Options | InitializeTableFromTextFile 의 선택적 속성 |
| inplaceadd <t extends ttype > | x의 지정된 행에 V를 추가합니다. |
| inplacesub <t는 ttype >를 확장합니다 | `v`를`x '의 지정된 행으로 빼냅니다. |
| inplaceupdate <t는 ttype >을 확장합니다 | 값 'V'로 지정된 행 'I'업데이트. |
| int64List | Protobuf 유형 tensorflow.Int64List |
| int64list.builder | Protobuf 유형 tensorflow.Int64List |
| int64ListorBuilder | |
| intdatabuffer | Ints의 DataBuffer . |
| intdatalayout <s는 databuffer <? >>를 확장합니다 | 버퍼에 저장된 데이터를 ints로 변환하는 DataLayout . |
| intdensendArray | |
| 상호 연결 링크 | Protobuf Type tensorflow.InterconnectLink |
| interconnectlink.builder | Protobuf Type tensorflow.InterconnectLink |
| 인터커넥트 링크어 빌더 | |
| intndarray | 정수의 NdArray . |
| intopk | 대상이 상단`k` 예측에 있는지 여부를 말합니다. |
| inv <t는 ttype >을 확장합니다 | Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes). |
| Inv.Options | Optional attributes for Inv |
| Invert <T extends TNumber > | Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010. |
| InvertPermutation <T extends TNumber > | Computes the inverse permutation of a tensor. |
| InvGrad <T extends TType > | Computes the gradient for the inverse of `x` wrt its input. |
| Irfft <U extends TNumber > | Inverse real-valued fast Fourier transform. |
| Irfft2d <U extends TNumber > | Inverse 2D real-valued fast Fourier transform. |
| Irfft3d <U extends TNumber > | Inverse 3D real-valued fast Fourier transform. |
| IsBoostedTreesEnsembleInitialized | Checks whether a tree ensemble has been initialized. |
| IsBoostedTreesQuantileStreamResourceInitialized | Checks whether a quantile stream has been initialized. |
| IsFinite | Returns which elements of x are finite. |
| IsInf | Returns which elements of x are Inf. |
| IsNan | Returns which elements of x are NaN. |
| IsotonicRegression <U extends TNumber > | Solves a batch of isotonic regression problems. |
| IsVariableInitialized | Checks whether a tensor has been initialized. |
| Iterator | |
| IteratorFromStringHandle | |
| IteratorFromStringHandle.Options | Optional attributes for IteratorFromStringHandle |
| IteratorGetDevice | Returns the name of the device on which `resource` has been placed. |
| IteratorGetDevice | Returns the name of the device on which `resource` has been placed. |
| IteratorGetNext | Gets the next output from the given iterator . |
| IteratorGetNextAsOptional | Gets the next output from the given iterator as an Optional variant. |
| IteratorGetNextSync | Gets the next output from the given iterator. |
| IteratorToStringHandle | Converts the given `resource_handle` representing an iterator to a string. |
제이
| JobDef | Defines a single job in a TensorFlow cluster. |
| JobDef.Builder | Defines a single job in a TensorFlow cluster. |
| JobDefOrBuilder | |
| JobDeviceFilters | Defines the device filters for tasks in a job. |
| JobDeviceFilters.Builder | Defines the device filters for tasks in a job. |
| JobDeviceFiltersOrBuilder | |
| 가입하다 | Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator). |
| Join.Options | Optional attributes for Join |
케이
| KernelDef | Protobuf type tensorflow.KernelDef |
| KernelDef.AttrConstraint | Protobuf type tensorflow.KernelDef.AttrConstraint |
| KernelDef.AttrConstraint.Builder | Protobuf type tensorflow.KernelDef.AttrConstraint |
| KernelDef.AttrConstraintOrBuilder | |
| KernelDef.Builder | Protobuf type tensorflow.KernelDef |
| KernelDefOrBuilder | |
| KernelDefProtos | |
| KernelList | A collection of KernelDefs tensorflow.KernelList |
| KernelList.Builder | A collection of KernelDefs tensorflow.KernelList |
| KernelListOrBuilder | |
| KeyValueSort <T extends TNumber , U extends TType > | Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort . |
| KLDivergence | Computes Kullback-Leibler divergence loss between labels and predictions. |
| KLDivergence <T extends TNumber > | A metric that computes the Kullback-Leibler divergence loss metric between labels and predictions. |
| KMC2ChainInitialization | Returns the index of a data point that should be added to the seed set. |
| KmeansPlusPlusInitialization | Selects num_to_sample rows of input using the KMeans++ criterion. |
| KthOrderStatistic | Computes the Kth order statistic of a data set. |
엘
| L2Loss <T extends TNumber > | L2 Loss. |
| LatencyStatsDataset | Records the latency of producing `input_dataset` elements in a StatsAggregator. |
| LatencyStatsDataset | Records the latency of producing `input_dataset` elements in a StatsAggregator. |
| LeakyRelu <T extends TNumber > | Computes rectified linear: `max(features, features * alpha)`. |
| LeakyRelu.Options | Optional attributes for LeakyRelu |
| LeakyReluGrad <T extends TNumber > | Computes rectified linear gradients for a LeakyRelu operation. |
| LeakyReluGrad.Options | Optional attributes for LeakyReluGrad |
| LearnedUnigramCandidateSampler | Generates labels for candidate sampling with a learned unigram distribution. |
| LearnedUnigramCandidateSampler.Options | Optional attributes for LearnedUnigramCandidateSampler |
| LeCun <T extends TFloating > | LeCun normal initializer. |
| LeftShift <T extends TNumber > | Elementwise computes the bitwise left-shift of `x` and `y`. |
| 더 적은 | Returns the truth value of (x < y) element-wise. |
| LessEqual | Returns the truth value of (x <= y) element-wise. |
| Lgamma <T extends TNumber > | Computes the log of the absolute value of `Gamma(x)` element-wise. |
| Linear <U extends TNumber > | Linear activation function (pass-through). |
| LinSpace <T extends TNumber > | Generates values in an interval. |
| Listener_BytePointer | |
| Listener_String | |
| ListValue | Represents a Python list. |
| ListValue.Builder | Represents a Python list. |
| ListValueOrBuilder | |
| LMDBDataset | Creates a dataset that emits the key-value pairs in one or more LMDB files. |
| LmdbDataset | |
| LmdbReader | A Reader that outputs the records from a LMDB file. |
| LmdbReader.Options | Optional attributes for LmdbReader |
| LoadAndRemapMatrix | Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint at `ckpt_path` and potentially reorders its rows and columns using the specified remappings. |
| LoadAndRemapMatrix.Options | Optional attributes for LoadAndRemapMatrix |
| LoadTPUEmbeddingAdadeltaParameters | Load Adadelta embedding parameters. |
| LoadTPUEmbeddingAdadeltaParameters.Options | Optional attributes for LoadTPUEmbeddingAdadeltaParameters |
| LoadTPUEmbeddingAdadeltaParametersGradAccumDebug | Load Adadelta parameters with debug support. |
| LoadTPUEmbeddingAdadeltaParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingAdadeltaParametersGradAccumDebug |
| LoadTPUEmbeddingAdagradParameters | Load Adagrad embedding parameters. |
| LoadTPUEmbeddingAdagradParameters.Options | Optional attributes for LoadTPUEmbeddingAdagradParameters |
| LoadTPUEmbeddingAdagradParametersGradAccumDebug | Load Adagrad embedding parameters with debug support. |
| LoadTPUEmbeddingAdagradParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingAdagradParametersGradAccumDebug |
| LoadTPUEmbeddingADAMParameters | Load ADAM embedding parameters. |
| LoadTPUEmbeddingADAMParameters.Options | Optional attributes for LoadTPUEmbeddingADAMParameters |
| LoadTPUEmbeddingADAMParametersGradAccumDebug | Load ADAM embedding parameters with debug support. |
| LoadTPUEmbeddingADAMParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingADAMParametersGradAccumDebug |
| LoadTPUEmbeddingCenteredRMSPropParameters | Load centered RMSProp embedding parameters. |
| LoadTPUEmbeddingCenteredRMSPropParameters.Options | Optional attributes for LoadTPUEmbeddingCenteredRMSPropParameters |
| LoadTPUEmbeddingFTRLParameters | Load FTRL embedding parameters. |
| LoadTPUEmbeddingFTRLParameters.Options | Optional attributes for LoadTPUEmbeddingFTRLParameters |
| LoadTPUEmbeddingFTRLParametersGradAccumDebug | Load FTRL embedding parameters with debug support. |
| LoadTPUEmbeddingFTRLParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingFTRLParametersGradAccumDebug |
| LoadTPUEmbeddingMDLAdagradLightParameters | Load MDL Adagrad Light embedding parameters. |
| LoadTPUEmbeddingMDLAdagradLightParameters.Options | Optional attributes for LoadTPUEmbeddingMDLAdagradLightParameters |
| LoadTPUEmbeddingMomentumParameters | Load Momentum embedding parameters. |
| LoadTPUEmbeddingMomentumParameters.Options | Optional attributes for LoadTPUEmbeddingMomentumParameters |
| LoadTPUEmbeddingMomentumParametersGradAccumDebug | Load Momentum embedding parameters with debug support. |
| LoadTPUEmbeddingMomentumParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingMomentumParametersGradAccumDebug |
| LoadTPUEmbeddingProximalAdagradParameters | Load proximal Adagrad embedding parameters. |
| LoadTPUEmbeddingProximalAdagradParameters.Options | Optional attributes for LoadTPUEmbeddingProximalAdagradParameters |
| LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug | Load proximal Adagrad embedding parameters with debug support. |
| LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug |
| LoadTPUEmbeddingProximalYogiParameters | |
| LoadTPUEmbeddingProximalYogiParameters.Options | Optional attributes for LoadTPUEmbeddingProximalYogiParameters |
| LoadTPUEmbeddingProximalYogiParametersGradAccumDebug | |
| LoadTPUEmbeddingProximalYogiParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingProximalYogiParametersGradAccumDebug |
| LoadTPUEmbeddingRMSPropParameters | Load RMSProp embedding parameters. |
| LoadTPUEmbeddingRMSPropParameters.Options | Optional attributes for LoadTPUEmbeddingRMSPropParameters |
| LoadTPUEmbeddingRMSPropParametersGradAccumDebug | Load RMSProp embedding parameters with debug support. |
| LoadTPUEmbeddingRMSPropParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingRMSPropParametersGradAccumDebug |
| LoadTPUEmbeddingStochasticGradientDescentParameters | Load SGD embedding parameters. |
| LoadTPUEmbeddingStochasticGradientDescentParameters.Options | Optional attributes for LoadTPUEmbeddingStochasticGradientDescentParameters |
| LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug | Load SGD embedding parameters. |
| LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug.Options | Optional attributes for LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug |
| LocalLinks | Protobuf type tensorflow.LocalLinks |
| LocalLinks.Builder | Protobuf type tensorflow.LocalLinks |
| LocalLinksOrBuilder | |
| LocalResponseNormalization <T extends TNumber > | Local Response Normalization. |
| LocalResponseNormalization.Options | Optional attributes for LocalResponseNormalization |
| LocalResponseNormalizationGrad <T extends TNumber > | Gradients for Local Response Normalization. |
| LocalResponseNormalizationGrad.Options | Optional attributes for LocalResponseNormalizationGrad |
| Log <T extends TType > | Computes natural logarithm of x element-wise. |
| Log1p <T extends TType > | Computes natural logarithm of (1 + x) element-wise. |
| LogCosh | 계산 예측 오류의 쌍곡선 코사인 로그를 계산합니다. |
| LogCoshError <T extends TNumber > | A metric that computes the logarithm of the hyperbolic cosine of the prediction error metric between labels and predictions. |
| LogicalAnd | Returns the truth value of x AND y element-wise. |
| LogicalNot | Returns the truth value of `NOT x` element-wise. |
| LogicalOr | Returns the truth value of x OR y element-wise. |
| LogMatrixDeterminant <T extends TType > | Computes the sign and the log of the absolute value of the determinant of one or more square matrices. |
| LogMemoryProtos | |
| LogMessage | Protocol buffer used for logging messages to the events file. |
| LogMessage.Builder | Protocol buffer used for logging messages to the events file. |
| LogMessage.Level | Protobuf enum tensorflow.LogMessage.Level |
| LogMessageOrBuilder | |
| LogSoftmax <T extends TNumber > | Computes log softmax activations. |
| LogUniformCandidateSampler | Generates labels for candidate sampling with a log-uniform distribution. |
| LogUniformCandidateSampler.Options | Optional attributes for LogUniformCandidateSampler |
| LongDataBuffer | A DataBuffer of longs. |
| LongDataLayout <S extends DataBuffer <?>> | A DataLayout that converts data stored in a buffer to longs. |
| LongDenseNdArray | |
| LongNdArray | An NdArray of longs. |
| LookupTableExport <T extends TType , U extends TType > | Outputs all keys and values in the table. |
| LookupTableFind <U extends TType > | Looks up keys in a table, outputs the corresponding values. |
| LookupTableImport | Replaces the contents of the table with the specified keys and values. |
| LookupTableInsert | Updates the table to associates keys with values. |
| LookupTableRemove | Removes keys and its associated values from a table. |
| LookupTableSize | Computes the number of elements in the given table. |
| LoopCond | Forwards the input to the output. |
| 손실 | |
| 사상자 수 | Built-in loss functions. |
| LossesHelper | These are helper methods for Losses and Metrics and will be module private when Java modularity is applied to TensorFlow Java. |
| LossMetric <T extends TNumber > | Interface for Metrics that wrap Loss functions. |
| LossTuple <T extends TNumber > | A helper class for loss methods to return labels, target, and sampleWeights |
| 낮추다 | Converts all uppercase characters into their respective lowercase replacements. |
| Lower.Options | Optional attributes for Lower |
| LowerBound <U extends TNumber > | Applies lower_bound(sorted_search_values, values) along each row. |
| LSTMBlockCell <T extends TNumber > | Computes the LSTM cell forward propagation for 1 time step. |
| LSTMBlockCell.Options | Optional attributes for LSTMBlockCell |
| LSTMBlockCellGrad <T extends TNumber > | Computes the LSTM cell backward propagation for 1 timestep. |
| Lu <T extends TType , U extends TNumber > | Computes the LU decomposition of one or more square matrices. |
중
| MachineConfiguration | Protobuf type tensorflow.MachineConfiguration |
| MachineConfiguration.Builder | Protobuf type tensorflow.MachineConfiguration |
| MachineConfigurationOrBuilder | |
| MakeIterator | Makes a new iterator from the given `dataset` and stores it in `iterator`. |
| MakeUnique | Make all elements in the non-Batch dimension unique, but \"close\" to their initial value. |
| MapClear | Op removes all elements in the underlying container. |
| MapClear.Options | Optional attributes for MapClear |
| MapDataset | |
| MapIncompleteSize | Op returns the number of incomplete elements in the underlying container. |
| MapIncompleteSize.Options | Optional attributes for MapIncompleteSize |
| MapIterator | |
| MapOptional | |
| MapPeek | Op peeks at the values at the specified key. |
| MapPeek.Options | Optional attributes for MapPeek |
| MapSize | Op returns the number of elements in the underlying container. |
| MapSize.Options | Optional attributes for MapSize |
| MapStage | Stage (key, values) in the underlying container which behaves like a hashtable. |
| MapStage.Options | Optional attributes for MapStage |
| MapUnstage | Op removes and returns the values associated with the key from the underlying container. |
| MapUnstage.Options | Optional attributes for MapUnstage |
| MapUnstageNoKey | Op removes and returns a random (key, value) from the underlying container. |
| MapUnstageNoKey.Options | Optional attributes for MapUnstageNoKey |
| MatchingFiles | Returns the set of files matching one or more glob patterns. |
| MatchingFilesDataset | |
| MatchingFilesDataset | |
| MatMul <T extends TType > | Multiply the matrix "a" by the matrix "b". |
| MatMul.Options | Optional attributes for MatMul |
| MatrixDiag <T extends TType > | Returns a batched diagonal tensor with given batched diagonal values. |
| MatrixDiagPart <T extends TType > | Returns the batched diagonal part of a batched tensor. |
| MatrixDiagPartV3 <T extends TType > | Returns the batched diagonal part of a batched tensor. |
| MatrixDiagPartV3.Options | Optional attributes for MatrixDiagPartV3 |
| MatrixDiagV3 <T extends TType > | Returns a batched diagonal tensor with given batched diagonal values. |
| MatrixDiagV3.Options | Optional attributes for MatrixDiagV3 |
| MatrixLogarithm <T extends TType > | Computes the matrix logarithm of one or more square matrices: \\(log(exp(A)) = A\\) This op is only defined for complex matrices. |
| MatrixSetDiag <T extends TType > | Returns a batched matrix tensor with new batched diagonal values. |
| MatrixSetDiag.Options | Optional attributes for MatrixSetDiag |
| MatrixSolveLs <T extends TType > | Solves one or more linear least-squares problems. |
| MatrixSolveLs.Options | Optional attributes for MatrixSolveLs |
| Max <T extends TType > | Computes the maximum of elements across dimensions of a tensor. |
| Max.Options | Optional attributes for Max |
| Maximum <T extends TNumber > | Returns the max of x and y (ie |
| MaxIntraOpParallelismDataset | Creates a dataset that overrides the maximum intra-op parallelism. |
| MaxIntraOpParallelismDataset | Creates a dataset that overrides the maximum intra-op parallelism. |
| MaxNorm | Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value. |
| MaxPool <T extends TType > | Performs max pooling on the input. |
| MaxPool.Options | Optional attributes for MaxPool |
| MaxPool3d <T extends TNumber > | Performs 3D max pooling on the input. |
| MaxPool3d.Options | Optional attributes for MaxPool3d |
| MaxPool3dGrad <U extends TNumber > | Computes gradients of 3D max pooling function. |
| MaxPool3dGrad.Options | Optional attributes for MaxPool3dGrad |
| MaxPool3dGradGrad <T extends TNumber > | Computes second-order gradients of the maxpooling function. |
| MaxPool3dGradGrad.Options | Optional attributes for MaxPool3dGradGrad |
| MaxPoolGrad <T extends TNumber > | Computes gradients of the maxpooling function. |
| MaxPoolGrad.Options | Optional attributes for MaxPoolGrad |
| MaxPoolGradGrad <T extends TNumber > | Computes second-order gradients of the maxpooling function. |
| MaxPoolGradGrad.Options | Optional attributes for MaxPoolGradGrad |
| MaxPoolGradGradWithArgmax <T extends TNumber > | Computes second-order gradients of the maxpooling function. |
| MaxPoolGradGradWithArgmax.Options | Optional attributes for MaxPoolGradGradWithArgmax |
| MaxPoolGradWithArgmax <T extends TNumber > | Computes gradients of the maxpooling function. |
| MaxPoolGradWithArgmax.Options | Optional attributes for MaxPoolGradWithArgmax |
| MaxPoolWithArgmax <T extends TNumber , U extends TNumber > | Performs max pooling on the input and outputs both max values and indices. |
| MaxPoolWithArgmax.Options | Optional attributes for MaxPoolWithArgmax |
| Mean <T extends TNumber > | A metric that that implements a weighted mean WEIGHTED_MEAN |
| Mean <T extends TType > | Computes the mean of elements across dimensions of a tensor. |
| Mean.Options | Optional attributes for Mean |
| MeanAbsoluteError | 라벨과 예측 간의 절대차의 평균을 계산합니다. |
| MeanAbsoluteError <T extends TNumber > | A metric that computes the mean of absolute difference between labels and predictions. |
| MeanAbsolutePercentageError | Computes the mean absolute percentage error between labels and predictions. |
| MeanAbsolutePercentageError <T extends TNumber > | A metric that computes the mean of absolute difference between labels and predictions. |
| MeanMetricWrapper <T extends TNumber > | A class that bridges a stateless loss function with the Mean metric using a reduction of WEIGHTED_MEAN . |
| MeanSquaredError | Computes the mean of squares of errors between labels and predictions. |
| MeanSquaredError <T extends TNumber > | A metric that computes the mean of absolute difference between labels and predictions. |
| MeanSquaredLogarithmicError | Computes the mean squared logarithmic errors between labels and predictions. |
| MeanSquaredLogarithmicError <T extends TNumber > | A metric that computes the mean of absolute difference between labels and predictions. |
| MemAllocatorStats | Some of the data from AllocatorStats tensorflow.MemAllocatorStats |
| MemAllocatorStats.Builder | Some of the data from AllocatorStats tensorflow.MemAllocatorStats |
| MemAllocatorStatsOrBuilder | |
| MemChunk | Protobuf type tensorflow.MemChunk |
| MemChunk.Builder | Protobuf type tensorflow.MemChunk |
| MemChunkOrBuilder | |
| MemmappedFileSystemDirectory | A directory of regions in a memmapped file. |
| MemmappedFileSystemDirectory.Builder | A directory of regions in a memmapped file. |
| MemmappedFileSystemDirectoryElement | A message that describes one region of memmapped file. |
| MemmappedFileSystemDirectoryElement.Builder | A message that describes one region of memmapped file. |
| MemmappedFileSystemDirectoryElementOrBuilder | |
| MemmappedFileSystemDirectoryOrBuilder | |
| MemmappedFileSystemProtos | |
| MemoryDump | Protobuf type tensorflow.MemoryDump |
| MemoryDump.Builder | Protobuf type tensorflow.MemoryDump |
| MemoryDumpOrBuilder | |
| MemoryInfo | Protobuf type tensorflow.MemoryInfo |
| MemoryInfo.Builder | Protobuf type tensorflow.MemoryInfo |
| MemoryInfoOrBuilder | |
| MemoryLogRawAllocation | Protobuf type tensorflow.MemoryLogRawAllocation |
| MemoryLogRawAllocation.Builder | Protobuf type tensorflow.MemoryLogRawAllocation |
| MemoryLogRawAllocationOrBuilder | |
| MemoryLogRawDeallocation | Protobuf type tensorflow.MemoryLogRawDeallocation |
| MemoryLogRawDeallocation.Builder | Protobuf type tensorflow.MemoryLogRawDeallocation |
| MemoryLogRawDeallocationOrBuilder | |
| MemoryLogStep | Protobuf type tensorflow.MemoryLogStep |
| MemoryLogStep.Builder | Protobuf type tensorflow.MemoryLogStep |
| MemoryLogStepOrBuilder | |
| MemoryLogTensorAllocation | Protobuf type tensorflow.MemoryLogTensorAllocation |
| MemoryLogTensorAllocation.Builder | Protobuf type tensorflow.MemoryLogTensorAllocation |
| MemoryLogTensorAllocationOrBuilder | |
| MemoryLogTensorDeallocation | Protobuf type tensorflow.MemoryLogTensorDeallocation |
| MemoryLogTensorDeallocation.Builder | Protobuf type tensorflow.MemoryLogTensorDeallocation |
| MemoryLogTensorDeallocationOrBuilder | |
| MemoryLogTensorOutput | Protobuf type tensorflow.MemoryLogTensorOutput |
| MemoryLogTensorOutput.Builder | Protobuf type tensorflow.MemoryLogTensorOutput |
| MemoryLogTensorOutputOrBuilder | |
| MemoryStats | For memory tracking. |
| MemoryStats.Builder | For memory tracking. |
| MemoryStatsOrBuilder | |
| Merge <T extends TType > | Forwards the value of an available tensor from `inputs` to `output`. |
| MergeSummary | Merges summaries. |
| MergeV2Checkpoints | V2 format specific: merges the metadata files of sharded checkpoints. |
| MergeV2Checkpoints.Options | Optional attributes for MergeV2Checkpoints |
| MetaGraphDef | NOTE: This protocol buffer is evolving, and will go through revisions in the coming months. |
| MetaGraphDef.Builder | NOTE: This protocol buffer is evolving, and will go through revisions in the coming months. |
| MetaGraphDef.MetaInfoDef | Meta information regarding the graph to be exported. |
| MetaGraphDef.MetaInfoDef.Builder | Meta information regarding the graph to be exported. |
| MetaGraphDef.MetaInfoDefOrBuilder | |
| MetaGraphDefOrBuilder | |
| MetaGraphProtos | |
| Metric <T extends TNumber > | Base class for Metrics |
| MetricEntry | Protobuf type tensorflow.MetricEntry |
| MetricEntry.Builder | Protobuf type tensorflow.MetricEntry |
| MetricEntryOrBuilder | |
| MetricReduction | Defines the different types of metric reductions |
| 측정항목 | Helper class with built-in metrics functions. |
| MetricsHelper | These are helper methods for Metrics and will be module private when Java modularity is applied to TensorFlow Java. |
| Mfcc | Transforms a spectrogram into a form that's useful for speech recognition. |
| Mfcc.Options | Optional attributes for Mfcc |
| Min <T extends TType > | Computes the minimum of elements across dimensions of a tensor. |
| Min.Options | Optional attributes for Min |
| Minimum <T extends TNumber > | Returns the min of x and y (ie |
| MinMaxNorm | Constrains the weights to have the norm between a lower bound and an upper bound. |
| MirrorPad <T extends TType > | Pads a tensor with mirrored values. |
| MirrorPadGrad <T extends TType > | Gradient op for `MirrorPad` op. |
| MiscDataBufferFactory | Factory of miscellaneous data buffers |
| MlirPassthroughOp | Wraps an arbitrary MLIR computation expressed as a module with a main() function. |
| Mod <T extends TNumber > | Returns element-wise remainder of division. |
| ModelDataset | Identity transformation that models performance. |
| ModelDataset.Options | Optional attributes for ModelDataset |
| 기세 | Stochastic gradient descent plus momentum, either nesterov or traditional. |
| Mul <T extends TType > | Returns x * y element-wise. |
| MulNoNan <T extends TType > | Returns x * y element-wise. |
| MultiDeviceIterator | Creates a MultiDeviceIterator resource. |
| MultiDeviceIteratorFromStringHandle | Generates a MultiDeviceIterator resource from its provided string handle. |
| MultiDeviceIteratorFromStringHandle.Options | Optional attributes for MultiDeviceIteratorFromStringHandle |
| MultiDeviceIteratorGetNextFromShard | Gets next element for the provided shard number. |
| MultiDeviceIteratorInit | Initializes the multi device iterator with the given dataset. |
| MultiDeviceIteratorToStringHandle | Produces a string handle for the given MultiDeviceIterator. |
| Multinomial <U extends TNumber > | Draws samples from a multinomial distribution. |
| Multinomial.Options | Optional attributes for Multinomial |
| MutableDenseHashTable | Creates an empty hash table that uses tensors as the backing store. |
| MutableDenseHashTable.Options | Optional attributes for MutableDenseHashTable |
| MutableHashTable | Creates an empty hash table. |
| MutableHashTable.Options | Optional attributes for MutableHashTable |
| MutableHashTableOfTensors | Creates an empty hash table. |
| MutableHashTableOfTensors.Options | Optional attributes for MutableHashTableOfTensors |
| Mutex | Creates a Mutex resource that can be locked by `MutexLock`. |
| Mutex.Options | Optional attributes for Mutex |
| MutexLock | Locks a mutex resource. |
N
| Nadam | Nadam Optimizer that implements the NAdam algorithm. |
| NameAttrList | A list of attr names and their values. |
| NameAttrList.Builder | A list of attr names and their values. |
| NameAttrListOrBuilder | |
| NamedDevice | Protobuf type tensorflow.NamedDevice |
| NamedDevice.Builder | Protobuf type tensorflow.NamedDevice |
| NamedDeviceOrBuilder | |
| NamedTensorProto | A pair of tensor name and tensor values. |
| NamedTensorProto.Builder | A pair of tensor name and tensor values. |
| NamedTensorProtoOrBuilder | |
| NamedTensorProtos | |
| NamedTupleValue | Represents Python's namedtuple. |
| NamedTupleValue.Builder | Represents Python's namedtuple. |
| NamedTupleValueOrBuilder | |
| NcclAllReduce <T extends TNumber > | Outputs a tensor containing the reduction across all input tensors. |
| NcclAllReduce <T extends TNumber > | Outputs a tensor containing the reduction across all input tensors. |
| NcclBroadcast <T extends TNumber > | Sends `input` to all devices that are connected to the output. |
| NcclBroadcast <T extends TNumber > | Sends `input` to all devices that are connected to the output. |
| NcclReduce <T extends TNumber > | Reduces `input` from `num_devices` using `reduction` to a single device. |
| NcclReduce <T extends TNumber > | Reduces `input` from `num_devices` using `reduction` to a single device. |
| NdArray <T> | A data structure of N-dimensions. |
| NdArrays | Utility class for instantiating NdArray objects. |
| NdArraySequence <T extends NdArray <?>> | A sequence of elements of an N-dimensional array. |
| Ndtri <T extends TNumber > | |
| NearestNeighbors | Selects the k nearest centers for each point. |
| Neg <T extends TType > | Computes numerical negative value element-wise. |
| NegTrain | Training via negative sampling. |
| NextAfter <T extends TNumber > | Returns the next representable value of `x1` in the direction of `x2`, element-wise. |
| NextIteration <T extends TType > | Makes its input available to the next iteration. |
| NioDataBufferFactory | Factory of JDK NIO-based data buffers |
| NodeDef | Protobuf type tensorflow.NodeDef |
| NodeDef.Builder | Protobuf type tensorflow.NodeDef |
| NodeDef.ExperimentalDebugInfo | Protobuf type tensorflow.NodeDef.ExperimentalDebugInfo |
| NodeDef.ExperimentalDebugInfo.Builder | Protobuf type tensorflow.NodeDef.ExperimentalDebugInfo |
| NodeDef.ExperimentalDebugInfoOrBuilder | |
| NodeDefOrBuilder | |
| NodeExecStats | Time/size stats recorded for a single execution of a graph node. |
| NodeExecStats.Builder | Time/size stats recorded for a single execution of a graph node. |
| NodeExecStatsOrBuilder | |
| NodeOutput | Output sizes recorded for a single execution of a graph node. |
| NodeOutput.Builder | Output sizes recorded for a single execution of a graph node. |
| NodeOutputOrBuilder | |
| NodeProto | |
| NonDeterministicInts <U extends TType > | Non-deterministically generates some integers. |
| NoneValue | Represents None. |
| NoneValue.Builder | Represents None. |
| NoneValueOrBuilder | |
| NonMaxSuppression <T extends TNumber > | Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. |
| NonMaxSuppression.Options | Optional attributes for NonMaxSuppression |
| NonMaxSuppressionWithOverlaps | Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high overlaps with previously selected boxes. |
| NonNeg | Constrains the weights to be non-negative. |
| NonSerializableDataset | |
| NonSerializableDataset | |
| NoOp | Does nothing. |
| NotBroadcastableException | Exception that indicates that static shapes are not able to broadcast among each other during arithmetic operations. |
| NotEqual | Returns the truth value of (x != y) element-wise. |
| NotEqual.Options | Optional attributes for NotEqual |
| NthElement <T extends TNumber > | Finds values of the `n`-th order statistic for the last dimension. |
| NthElement.Options | Optional attributes for NthElement |
영형
| OneHot <U extends TType > | Returns a one-hot tensor. |
| OneHot.Options | Optional attributes for OneHot |
| Ones <T extends TType > | Initializer that generates tensors initialized to 1. |
| Ones <T extends TType > | An operator creating a constant initialized with ones of the shape given by `dims`. |
| OnesLike <T extends TType > | Returns a tensor of ones with the same shape and type as x. |
| 작전 | A logical unit of computation. |
| OpDef | Defines an operation. |
| OpDef.ArgDef | For describing inputs and outputs. |
| OpDef.ArgDef.Builder | For describing inputs and outputs. |
| OpDef.ArgDefOrBuilder | |
| OpDef.AttrDef | Description of the graph-construction-time configuration of this Op. |
| OpDef.AttrDef.Builder | Description of the graph-construction-time configuration of this Op. |
| OpDef.AttrDefOrBuilder | |
| OpDef.Builder | Defines an operation. |
| OpDefOrBuilder | |
| OpDefProtos | |
| OpDeprecation | Information about version-dependent deprecation of an op tensorflow.OpDeprecation |
| OpDeprecation.Builder | Information about version-dependent deprecation of an op tensorflow.OpDeprecation |
| OpDeprecationOrBuilder | |
| Operand <T extends TType > | Interface implemented by operands of a TensorFlow operation. |
| Operands | Utilities for manipulating operand related types and lists. |
| 작업 | Performs computation on Tensors. |
| OperationBuilder | A builder for Operation s. |
| 연산자 | Annotation used by classes to make TensorFlow operations conveniently accessible via org.tensorflow.op.Ops or one of its groups. |
| OpList | A collection of OpDefs tensorflow.OpList |
| OpList.Builder | A collection of OpDefs tensorflow.OpList |
| OpListOrBuilder | |
| OptimizeDataset | Creates a dataset by applying optimizations to `input_dataset`. |
| OptimizeDataset.Options | Optional attributes for OptimizeDataset |
| OptimizeDatasetV2 | Creates a dataset by applying related optimizations to `input_dataset`. |
| OptimizeDatasetV2.Options | Optional attributes for OptimizeDatasetV2 |
| Optimizer | Base class for gradient optimizers. |
| Optimizer.GradAndVar <T extends TType > | A class that holds a paired gradient and variable. |
| Optimizer.Options | Optional attributes for Optimizer |
| OptimizerOptions | Options passed to the graph optimizer tensorflow.OptimizerOptions |
| OptimizerOptions.Builder | Options passed to the graph optimizer tensorflow.OptimizerOptions |
| OptimizerOptions.GlobalJitLevel | Control the use of the compiler/jit. |
| OptimizerOptions.Level | Optimization level tensorflow.OptimizerOptions.Level |
| OptimizerOptionsOrBuilder | |
| Optimizers | Enumerator used to create a new Optimizer with default parameters. |
| OptionalFromValue | Constructs an Optional variant from a tuple of tensors. |
| OptionalGetValue | Returns the value stored in an Optional variant or raises an error if none exists. |
| OptionalHasValue | Returns true if and only if the given Optional variant has a value. |
| OptionalNone | Creates an Optional variant with no value. |
| OrderedMapClear | Op removes all elements in the underlying container. |
| OrderedMapClear.Options | Optional attributes for OrderedMapClear |
| OrderedMapIncompleteSize | Op returns the number of incomplete elements in the underlying container. |
| OrderedMapIncompleteSize.Options | Optional attributes for OrderedMapIncompleteSize |
| OrderedMapPeek | Op peeks at the values at the specified key. |
| OrderedMapPeek.Options | Optional attributes for OrderedMapPeek |
| OrderedMapSize | Op returns the number of elements in the underlying container. |
| OrderedMapSize.Options | Optional attributes for OrderedMapSize |
| OrderedMapStage | Stage (key, values) in the underlying container which behaves like a ordered associative container. |
| OrderedMapStage.Options | Optional attributes for OrderedMapStage |
| OrderedMapUnstage | Op removes and returns the values associated with the key from the underlying container. |
| OrderedMapUnstage.Options | Optional attributes for OrderedMapUnstage |
| OrderedMapUnstageNoKey | Op removes and returns the (key, value) element with the smallest key from the underlying container. |
| OrderedMapUnstageNoKey.Options | Optional attributes for OrderedMapUnstageNoKey |
| OrdinalSelector | A TPU core selector Op. |
| Orthogonal <T extends TFloating > | Initializer that generates an orthogonal matrix. |
| OutfeedDequeue <T extends TType > | Retrieves a single tensor from the computation outfeed. |
| OutfeedDequeue.Options | Optional attributes for OutfeedDequeue |
| OutfeedDequeueTuple | Retrieve multiple values from the computation outfeed. |
| OutfeedDequeueTuple.Options | Optional attributes for OutfeedDequeueTuple |
| OutfeedDequeueTupleV2 | Retrieve multiple values from the computation outfeed. |
| OutfeedDequeueV2 <T extends TType > | Retrieves a single tensor from the computation outfeed. |
| OutfeedEnqueue | Enqueue a Tensor on the computation outfeed. |
| OutfeedEnqueueTuple | Enqueue multiple Tensor values on the computation outfeed. |
| Output <T extends TType > | A symbolic handle to a tensor produced by an Operation . |
피
| Pad <T extends TType > | Pads a tensor. |
| Pad <T extends TType > | Wraps the XLA Pad operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#pad . |
| PaddedBatchDataset | Creates a dataset that batches and pads `batch_size` elements from the input. |
| PaddedBatchDataset.Options | Optional attributes for PaddedBatchDataset |
| PaddingFifoQueue | A queue that produces elements in first-in first-out order. |
| PaddingFifoQueue.Options | Optional attributes for PaddingFifoQueue |
| PairValue | Represents a (key, value) pair. |
| PairValue.Builder | Represents a (key, value) pair. |
| PairValueOrBuilder | |
| ParallelConcat <T extends TType > | Concatenates a list of `N` tensors along the first dimension. |
| ParallelDynamicStitch <T extends TType > | Interleave the values from the `data` tensors into a single tensor. |
| ParameterizedTruncatedNormal <U extends TNumber > | Outputs random values from a normal distribution. |
| ParameterizedTruncatedNormal.Options | Optional attributes for ParameterizedTruncatedNormal |
| ParseExample | Transforms a vector of tf.Example protos (as strings) into typed tensors. |
| ParseExampleDataset | Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features. |
| ParseExampleDataset.Options | Optional attributes for ParseExampleDataset |
| ParseSequenceExample | Transforms a vector of tf.io.SequenceExample protos (as strings) into typed tensors. |
| ParseSequenceExample.Options | Optional attributes for ParseSequenceExample |
| ParseSingleExample | Transforms a tf.Example proto (as a string) into typed tensors. |
| ParseSingleSequenceExample | Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. |
| ParseSingleSequenceExample.Options | Optional attributes for ParseSingleSequenceExample |
| ParseTensor <T extends TType > | Transforms a serialized tensorflow.TensorProto proto into a Tensor. |
| PartitionedInput <T extends TType > | An op that groups a list of partitioned inputs together. |
| PartitionedInput.Options | Optional attributes for PartitionedInput |
| PartitionedOutput <T extends TType > | An op that demultiplexes a tensor to be sharded by XLA to a list of partitioned outputs outside the XLA computation. |
| PartitionedOutput.Options | Optional attributes for PartitionedOutput |
| Placeholder <T extends TType > | A placeholder op for a value that will be fed into the computation. |
| Placeholder.Options | Optional attributes for Placeholder |
| PlaceholderWithDefault <T extends TType > | A placeholder op that passes through `input` when its output is not fed. |
| PlatformInfo | Protobuf type tensorflow.PlatformInfo |
| PlatformInfo.Builder | Protobuf type tensorflow.PlatformInfo |
| PlatformInfoOrBuilder | |
| Poisson | Computes the Poisson loss between labels and predictions. |
| Poisson <T extends TNumber > | A metric that computes the poisson loss metric between labels and predictions. |
| Polygamma <T extends TNumber > | Compute the polygamma function \\(\psi^{(n)}(x)\\). |
| PopulationCount | Computes element-wise population count (aka |
| PositionIterator | |
| Pow <T extends TType > | Computes the power of one value to another. |
| PrefetchDataset | Creates a dataset that asynchronously prefetches elements from `input_dataset`. |
| PrefetchDataset.Options | Optional attributes for PrefetchDataset |
| Prelinearize | An op which linearizes one Tensor value to an opaque variant tensor. |
| Prelinearize.Options | Optional attributes for Prelinearize |
| PrelinearizeTuple | An op which linearizes multiple Tensor values to an opaque variant tensor. |
| PrelinearizeTuple.Options | Optional attributes for PrelinearizeTuple |
| PreventGradient <T extends TType > | An identity op that triggers an error if a gradient is requested. |
| PreventGradient.Options | Optional attributes for PreventGradient |
| 인쇄 | Prints a string scalar. |
| Print.Options | Optional attributes for Print |
| PriorityQueue | A queue that produces elements sorted by the first component value. |
| PriorityQueue.Options | Optional attributes for PriorityQueue |
| PrivateThreadPoolDataset | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| PrivateThreadPoolDataset | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| Prod <T extends TType > | Computes the product of elements across dimensions of a tensor. |
| Prod.Options | Optional attributes for Prod |
| ProfileOptions | Next ID: 11 tensorflow.ProfileOptions |
| ProfileOptions.Builder | Next ID: 11 tensorflow.ProfileOptions |
| ProfileOptions.DeviceType | Protobuf enum tensorflow.ProfileOptions.DeviceType |
| ProfileOptionsOrBuilder | |
| ProfilerOptionsProtos |
큐
| Qr <T extends TType > | Computes the QR decompositions of one or more matrices. |
| Qr.Options | Optional attributes for Qr |
| Quantize <T extends TType > | Quantize the 'input' tensor of type float to 'output' tensor of type 'T'. |
| Quantize.Options | Optional attributes for Quantize |
| QuantizeAndDequantize <T extends TNumber > | Quantizes then dequantizes a tensor. |
| QuantizeAndDequantize.Options | Optional attributes for QuantizeAndDequantize |
| QuantizeAndDequantizeV3 <T extends TNumber > | Quantizes then dequantizes a tensor. |
| QuantizeAndDequantizeV3.Options | Optional attributes for QuantizeAndDequantizeV3 |
| QuantizeAndDequantizeV4 <T extends TNumber > | Returns the gradient of `quantization.QuantizeAndDequantizeV4`. |
| QuantizeAndDequantizeV4.Options | Optional attributes for QuantizeAndDequantizeV4 |
| QuantizeAndDequantizeV4Grad <T extends TNumber > | Returns the gradient of `QuantizeAndDequantizeV4`. |
| QuantizeAndDequantizeV4Grad.Options | Optional attributes for QuantizeAndDequantizeV4Grad |
| QuantizedAdd <V extends TType > | Returns x + y element-wise, working on quantized buffers. |
| QuantizedAvgPool <T extends TType > | Produces the average pool of the input tensor for quantized types. |
| QuantizedBatchNormWithGlobalNormalization <U extends TType > | Quantized Batch normalization. |
| QuantizedBiasAdd <V extends TType > | Adds Tensor 'bias' to Tensor 'input' for Quantized types. |
| QuantizedConcat <T extends TType > | Concatenates quantized tensors along one dimension. |
| QuantizedConv2d <V extends TType > | Computes a 2D convolution given quantized 4D input and filter tensors. |
| QuantizedConv2d.Options | Optional attributes for QuantizedConv2d |
| QuantizedConv2DAndRelu <V extends TType > | |
| QuantizedConv2DAndRelu.Options | Optional attributes for QuantizedConv2DAndRelu |
| QuantizedConv2DAndReluAndRequantize <V extends TType > | |
| QuantizedConv2DAndReluAndRequantize.Options | Optional attributes for QuantizedConv2DAndReluAndRequantize |
| QuantizedConv2DAndRequantize <V extends TType > | |
| QuantizedConv2DAndRequantize.Options | Optional attributes for QuantizedConv2DAndRequantize |
| QuantizedConv2DPerChannel <V extends TType > | Computes QuantizedConv2D per channel. |
| QuantizedConv2DPerChannel.Options | Optional attributes for QuantizedConv2DPerChannel |
| QuantizedConv2DWithBias <V extends TType > | |
| QuantizedConv2DWithBias.Options | Optional attributes for QuantizedConv2DWithBias |
| QuantizedConv2DWithBiasAndRelu <V extends TType > | |
| QuantizedConv2DWithBiasAndRelu.Options | Optional attributes for QuantizedConv2DWithBiasAndRelu |
| QuantizedConv2DWithBiasAndReluAndRequantize <W extends TType > | |
| QuantizedConv2DWithBiasAndReluAndRequantize.Options | Optional attributes for QuantizedConv2DWithBiasAndReluAndRequantize |
| QuantizedConv2DWithBiasAndRequantize <W extends TType > | |
| QuantizedConv2DWithBiasAndRequantize.Options | Optional attributes for QuantizedConv2DWithBiasAndRequantize |
| QuantizedConv2DWithBiasSignedSumAndReluAndRequantize <X extends TType > | |
| QuantizedConv2DWithBiasSignedSumAndReluAndRequantize.Options | Optional attributes for QuantizedConv2DWithBiasSignedSumAndReluAndRequantize |
| QuantizedConv2DWithBiasSumAndRelu <V extends TType > | |
| QuantizedConv2DWithBiasSumAndRelu.Options | Optional attributes for QuantizedConv2DWithBiasSumAndRelu |
| QuantizedConv2DWithBiasSumAndReluAndRequantize <X extends TType > | |
| QuantizedConv2DWithBiasSumAndReluAndRequantize.Options | Optional attributes for QuantizedConv2DWithBiasSumAndReluAndRequantize |
| QuantizedDepthwiseConv2D <V extends TType > | Computes quantized depthwise Conv2D. |
| QuantizedDepthwiseConv2D.Options | Optional attributes for QuantizedDepthwiseConv2D |
| QuantizedDepthwiseConv2DWithBias <V extends TType > | Computes quantized depthwise Conv2D with Bias. |
| QuantizedDepthwiseConv2DWithBias.Options | Optional attributes for QuantizedDepthwiseConv2DWithBias |
| QuantizedDepthwiseConv2DWithBiasAndRelu <V extends TType > | Computes quantized depthwise Conv2D with Bias and Relu. |
| QuantizedDepthwiseConv2DWithBiasAndRelu.Options | Optional attributes for QuantizedDepthwiseConv2DWithBiasAndRelu |
| QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize <W extends TType > | Computes quantized depthwise Conv2D with Bias, Relu and Requantize. |
| QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize.Options | Optional attributes for QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize |
| QuantizedInstanceNorm <T extends TType > | Quantized Instance normalization. |
| QuantizedInstanceNorm.Options | Optional attributes for QuantizedInstanceNorm |
| QuantizedMatMul <V extends TType > | Perform a quantized matrix multiplication of `a` by the matrix `b`. |
| QuantizedMatMul.Options | Optional attributes for QuantizedMatMul |
| QuantizedMatMulWithBias <W extends TType > | Performs a quantized matrix multiplication of `a` by the matrix `b` with bias add. |
| QuantizedMatMulWithBias.Options | Optional attributes for QuantizedMatMulWithBias |
| QuantizedMatMulWithBiasAndDequantize <W extends TNumber > | |
| QuantizedMatMulWithBiasAndDequantize.Options | Optional attributes for QuantizedMatMulWithBiasAndDequantize |
| QuantizedMatMulWithBiasAndRelu <V extends TType > | Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion. |
| QuantizedMatMulWithBiasAndRelu.Options | Optional attributes for QuantizedMatMulWithBiasAndRelu |
| QuantizedMatMulWithBiasAndReluAndRequantize <W extends TType > | Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu and requantize fusion. |
| QuantizedMatMulWithBiasAndReluAndRequantize.Options | Optional attributes for QuantizedMatMulWithBiasAndReluAndRequantize |
| QuantizedMatMulWithBiasAndRequantize <W extends TType > | |
| QuantizedMatMulWithBiasAndRequantize.Options | Optional attributes for QuantizedMatMulWithBiasAndRequantize |
| QuantizedMaxPool <T extends TType > | Produces the max pool of the input tensor for quantized types. |
| QuantizedMul <V extends TType > | Returns x * y element-wise, working on quantized buffers. |
| QuantizeDownAndShrinkRange <U extends TType > | Convert the quantized 'input' tensor into a lower-precision 'output', using the actual distribution of the values to maximize the usage of the lower bit depth and adjusting the output min and max ranges accordingly. |
| QuantizedRelu <U extends TType > | Computes Quantized Rectified Linear: `max(features, 0)` |
| QuantizedRelu6 <U extends TType > | Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` |
| QuantizedReluX <U extends TType > | Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)` |
| QuantizedReshape <T extends TType > | Reshapes a quantized tensor as per the Reshape op. |
| QuantizedResizeBilinear <T extends TType > | Resize quantized `images` to `size` using quantized bilinear interpolation. |
| QuantizedResizeBilinear.Options | Optional attributes for QuantizedResizeBilinear |
| QueueClose | Closes the given queue. |
| QueueClose.Options | Optional attributes for QueueClose |
| QueueDequeue | Dequeues a tuple of one or more tensors from the given queue. |
| QueueDequeue.Options | Optional attributes for QueueDequeue |
| QueueDequeueMany | Dequeues `n` tuples of one or more tensors from the given queue. |
| QueueDequeueMany.Options | Optional attributes for QueueDequeueMany |
| QueueDequeueUpTo | Dequeues `n` tuples of one or more tensors from the given queue. |
| QueueDequeueUpTo.Options | Optional attributes for QueueDequeueUpTo |
| QueueEnqueue | Enqueues a tuple of one or more tensors in the given queue. |
| QueueEnqueue.Options | Optional attributes for QueueEnqueue |
| QueueEnqueueMany | Enqueues zero or more tuples of one or more tensors in the given queue. |
| QueueEnqueueMany.Options | Optional attributes for QueueEnqueueMany |
| QueueIsClosed | Returns true if queue is closed. |
| QueueRunnerDef | Protocol buffer representing a QueueRunner. |
| QueueRunnerDef.Builder | Protocol buffer representing a QueueRunner. |
| QueueRunnerDefOrBuilder | |
| QueueRunnerProtos | |
| QueueSize | Computes the number of elements in the given queue. |
아르 자형
| RaggedBincount <U extends TNumber > | Counts the number of occurrences of each value in an integer array. |
| RaggedBincount.Options | Optional attributes for RaggedBincount |
| RaggedCountSparseOutput <U extends TNumber > | Performs sparse-output bin counting for a ragged tensor input. |
| RaggedCountSparseOutput.Options | Optional attributes for RaggedCountSparseOutput |
| RaggedCross <T extends TType , U extends TNumber > | Generates a feature cross from a list of tensors, and returns it as a RaggedTensor. |
| RaggedGather <T extends TNumber , U extends TType > | Gather ragged slices from `params` axis `0` according to `indices`. |
| RaggedRange <U extends TNumber , T extends TNumber > | Returns a `RaggedTensor` containing the specified sequences of numbers. |
| RaggedTensorFromVariant <U extends TNumber , T extends TType > | Decodes a `variant` Tensor into a `RaggedTensor`. |
| RaggedTensorToSparse <U extends TType > | Converts a `RaggedTensor` into a `SparseTensor` with the same values. |
| RaggedTensorToTensor <U extends TType > | Create a dense tensor from a ragged tensor, possibly altering its shape. |
| RaggedTensorToVariant | Encodes a `RaggedTensor` into a `variant` Tensor. |
| RaggedTensorToVariantGradient <U extends TType > | Helper used to compute the gradient for `RaggedTensorToVariant`. |
| RandomCrop <T extends TNumber > | Randomly crop `image`. |
| RandomCrop.Options | Optional attributes for RandomCrop |
| RandomDataset | Creates a Dataset that returns pseudorandom numbers. |
| RandomDataset | Creates a Dataset that returns pseudorandom numbers. |
| RandomGamma <U extends TNumber > | Outputs random values from the Gamma distribution(s) described by alpha. |
| RandomGamma.Options | Optional attributes for RandomGamma |
| RandomGammaGrad <T extends TNumber > | Computes the derivative of a Gamma random sample wrt |
| RandomNormal <T extends TFloating > | Initializer that generates tensors with a normal distribution. |
| RandomPoisson <V extends TNumber > | Outputs random values from the Poisson distribution(s) described by rate. |
| RandomPoisson.Options | Optional attributes for RandomPoisson |
| RandomShuffle <T extends TType > | Randomly shuffles a tensor along its first dimension. |
| RandomShuffle.Options | Optional attributes for RandomShuffle |
| RandomShuffleQueue | A queue that randomizes the order of elements. |
| RandomShuffleQueue.Options | Optional attributes for RandomShuffleQueue |
| RandomStandardNormal <U extends TNumber > | Outputs random values from a normal distribution. |
| RandomStandardNormal.Options | Optional attributes for RandomStandardNormal |
| RandomUniform <T extends TNumber > | Initializer that generates tensors with a uniform distribution. |
| RandomUniform <U extends TNumber > | Outputs random values from a uniform distribution. |
| RandomUniform.Options | Optional attributes for RandomUniform |
| RandomUniformInt <U extends TNumber > | Outputs random integers from a uniform distribution. |
| RandomUniformInt.Options | Optional attributes for RandomUniformInt |
| Range <T extends TNumber > | Creates a sequence of numbers. |
| RangeDataset | Creates a dataset with a range of values. |
| 계급 | Returns the rank of a tensor. |
| RawDataBufferFactory | Factory of raw data buffers |
| RawOp | A base class for Op implementations that are backed by a single Operation . |
| RawTensor | A tensor which memory has not been mapped to a data space directly accessible from the JVM. |
| ReaderBaseProtos | |
| ReaderBaseState | For serializing and restoring the state of ReaderBase, see reader_base.h for details. |
| ReaderBaseState.Builder | For serializing and restoring the state of ReaderBase, see reader_base.h for details. |
| ReaderBaseStateOrBuilder | |
| ReaderNumRecordsProduced | Returns the number of records this Reader has produced. |
| ReaderNumWorkUnitsCompleted | Returns the number of work units this Reader has finished processing. |
| ReaderRead | Returns the next record (key, value pair) produced by a Reader. |
| ReaderReadUpTo | Returns up to `num_records` (key, value) pairs produced by a Reader. |
| ReaderReset | Restore a Reader to its initial clean state. |
| ReaderRestoreState | Restore a reader to a previously saved state. |
| ReaderSerializeState | Produce a string tensor that encodes the state of a Reader. |
| ReadFile | Reads and outputs the entire contents of the input filename. |
| ReadVariableOp <T extends TType > | Reads the value of a variable. |
| Real <U extends TNumber > | Returns the real part of a complex number. |
| RealDiv <T extends TType > | Returns x / y element-wise for real types. |
| RebatchDataset | Creates a dataset that changes the batch size. |
| RebatchDataset | Creates a dataset that changes the batch size. |
| RebatchDataset.Options | Optional attributes for RebatchDataset |
| RebatchDataset.Options | Optional attributes for RebatchDataset |
| RebatchDatasetV2 | Creates a dataset that changes the batch size. |
| Reciprocal <T extends TType > | Computes the reciprocal of x element-wise. |
| ReciprocalGrad <T extends TType > | Computes the gradient for the inverse of `x` wrt its input. |
| RecordInput | Emits randomized records. |
| RecordInput.Options | Optional attributes for RecordInput |
| Recv <T extends TType > | Receives the named tensor from send_device on recv_device. |
| Recv <T extends TType > | Receives the named tensor from another XLA computation. |
| Recv.Options | Optional attributes for Recv |
| RecvTPUEmbeddingActivations | An op that receives embedding activations on the TPU. |
| Reduce <T extends TNumber > | Encapsulates metrics that perform a reduce operation on the metric values. |
| Reduce <T extends TNumber > | Mutually reduces multiple tensors of identical type and shape. |
| Reduce.Options | Optional attributes for Reduce |
| ReduceAll | Computes the "logical and" of elements across dimensions of a tensor. |
| ReduceAll.Options | Optional attributes for ReduceAll |
| ReduceAny | Computes the "logical or" of elements across dimensions of a tensor. |
| ReduceAny.Options | Optional attributes for ReduceAny |
| ReduceJoin | Joins a string Tensor across the given dimensions. |
| ReduceJoin.Options | Optional attributes for ReduceJoin |
| ReduceMax <T extends TType > | Computes the maximum of elements across dimensions of a tensor. |
| ReduceMax.Options | Optional attributes for ReduceMax |
| ReduceMin <T extends TType > | Computes the minimum of elements across dimensions of a tensor. |
| ReduceMin.Options | Optional attributes for ReduceMin |
| ReduceProd <T extends TType > | Computes the product of elements across dimensions of a tensor. |
| ReduceProd.Options | Optional attributes for ReduceProd |
| ReduceSum <T extends TType > | Computes the sum of elements across dimensions of a tensor. |
| ReduceSum.Options | Optional attributes for ReduceSum |
| ReduceV2 <T extends TNumber > | Mutually reduces multiple tensors of identical type and shape. |
| ReduceV2.Options | Optional attributes for ReduceV2 |
| 절감 | Type of Loss Reduction |
| RefEnter <T extends TType > | Creates or finds a child frame, and makes `data` available to the child frame. |
| RefEnter.Options | Optional attributes for RefEnter |
| RefExit <T extends TType > | Exits the current frame to its parent frame. |
| RefIdentity <T extends TType > | Return the same ref tensor as the input ref tensor. |
| RefMerge <T extends TType > | Forwards the value of an available tensor from `inputs` to `output`. |
| RefNextIteration <T extends TType > | Makes its input available to the next iteration. |
| RefSelect <T extends TType > | Forwards the `index`th element of `inputs` to `output`. |
| RefSwitch <T extends TType > | Forwards the ref tensor `data` to the output port determined by `pred`. |
| RegexFullMatch | Check if the input matches the regex pattern. |
| RegexReplace | Replaces matches of the `pattern` regular expression in `input` with the replacement string provided in `rewrite`. |
| RegexReplace.Options | Optional attributes for RegexReplace |
| RegisterDataset | Registers a dataset with the tf.data service. |
| RelativeDimensionalSpace | |
| Relu <T extends TType > | Computes rectified linear: `max(features, 0)`. |
| ReLU <T extends TNumber > | Rectified Linear Unit(ReLU) activation. |
| Relu6 <T extends TNumber > | Computes rectified linear 6: `min(max(features, 0), 6)`. |
| Relu6Grad <T extends TNumber > | Computes rectified linear 6 gradients for a Relu6 operation. |
| ReluGrad <T extends TNumber > | Computes rectified linear gradients for a Relu operation. |
| RemoteFusedGraphExecute | Execute a sub graph on a remote processor. |
| RemoteFusedGraphExecuteInfo | Protocol buffer representing a handle to a tensorflow resource. |
| RemoteFusedGraphExecuteInfo.Builder | Protocol buffer representing a handle to a tensorflow resource. |
| RemoteFusedGraphExecuteInfo.TensorShapeTypeProto | Protobuf type tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProto |
| RemoteFusedGraphExecuteInfo.TensorShapeTypeProto.Builder | Protobuf type tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProto |
| RemoteFusedGraphExecuteInfo.TensorShapeTypeProtoOrBuilder | |
| RemoteFusedGraphExecuteInfoOrBuilder | |
| RemoteFusedGraphExecuteInfoProto | |
| RemoteProfilerSessionManagerOptions | Options for remote profiler session manager. |
| RemoteProfilerSessionManagerOptions.Builder | Options for remote profiler session manager. |
| RemoteProfilerSessionManagerOptionsOrBuilder | |
| RemoteTensorHandle | Protobuf type tensorflow.eager.RemoteTensorHandle |
| RemoteTensorHandle.Builder | Protobuf type tensorflow.eager.RemoteTensorHandle |
| RemoteTensorHandleOrBuilder | |
| RemoteTensorHandleProtos | |
| RepeatDataset | Creates a dataset that emits the outputs of `input_dataset` `count` times. |
| ReplicaId | Replica ID. |
| ReplicatedInput <T extends TType > | Connects N inputs to an N-way replicated TPU computation. |
| ReplicatedInput.Options | Optional attributes for ReplicatedInput |
| ReplicatedOutput <T extends TType > | Connects N outputs from an N-way replicated TPU computation. |
| ReplicateMetadata | Metadata indicating how the TPU computation should be replicated. |
| ReplicateMetadata.Options | Optional attributes for ReplicateMetadata |
| RequantizationRange | Computes a range that covers the actual values present in a quantized tensor. |
| RequantizationRangePerChannel | Computes requantization range per channel. |
| Requantize <U extends TType > | Converts the quantized `input` tensor into a lower-precision `output`. |
| RequantizePerChannel <U extends TType > | Requantizes input with min and max values known per channel. |
| RequestedExitCode | Protobuf type tensorflow.RequestedExitCode |
| RequestedExitCode.Builder | Protobuf type tensorflow.RequestedExitCode |
| RequestedExitCodeOrBuilder | |
| Reshape <T extends TType > | Reshapes a tensor. |
| ResizeArea | Resize `images` to `size` using area interpolation. |
| ResizeArea.Options | Optional attributes for ResizeArea |
| ResizeBicubic | Resize `images` to `size` using bicubic interpolation. |
| ResizeBicubic.Options | Optional attributes for ResizeBicubic |
| ResizeBicubicGrad <T extends TNumber > | Computes the gradient of bicubic interpolation. |
| ResizeBicubicGrad.Options | Optional attributes for ResizeBicubicGrad |
| ResizeBilinear | Resize `images` to `size` using bilinear interpolation. |
| ResizeBilinear.Options | Optional attributes for ResizeBilinear |
| ResizeBilinearGrad <T extends TNumber > | Computes the gradient of bilinear interpolation. |
| ResizeBilinearGrad.Options | Optional attributes for ResizeBilinearGrad |
| ResizeNearestNeighbor <T extends TNumber > | Resize `images` to `size` using nearest neighbor interpolation. |
| ResizeNearestNeighbor.Options | Optional attributes for ResizeNearestNeighbor |
| ResizeNearestNeighborGrad <T extends TNumber > | Computes the gradient of nearest neighbor interpolation. |
| ResizeNearestNeighborGrad.Options | Optional attributes for ResizeNearestNeighborGrad |
| ResourceAccumulatorApplyGradient | Applies a gradient to a given accumulator. |
| ResourceAccumulatorNumAccumulated | Returns the number of gradients aggregated in the given accumulators. |
| ResourceAccumulatorSetGlobalStep | Updates the accumulator with a new value for global_step. |
| ResourceAccumulatorTakeGradient <T extends TType > | Extracts the average gradient in the given ConditionalAccumulator. |
| ResourceApplyAdadelta | Update '*var' according to the adadelta scheme. |
| ResourceApplyAdadelta.Options | Optional attributes for ResourceApplyAdadelta |
| ResourceApplyAdagrad | Update '*var' according to the adagrad scheme. |
| ResourceApplyAdagrad.Options | Optional attributes for ResourceApplyAdagrad |
| ResourceApplyAdagradDa | Update '*var' according to the proximal adagrad scheme. |
| ResourceApplyAdagradDa.Options | Optional attributes for ResourceApplyAdagradDa |
| ResourceApplyAdam | Update '*var' according to the Adam algorithm. |
| ResourceApplyAdam.Options | Optional attributes for ResourceApplyAdam |
| ResourceApplyAdaMax | Update '*var' according to the AdaMax algorithm. |
| ResourceApplyAdaMax.Options | Optional attributes for ResourceApplyAdaMax |
| ResourceApplyAdamWithAmsgrad | Update '*var' according to the Adam algorithm. |
| ResourceApplyAdamWithAmsgrad.Options | Optional attributes for ResourceApplyAdamWithAmsgrad |
| ResourceApplyAddSign | Update '*var' according to the AddSign update. |
| ResourceApplyAddSign.Options | Optional attributes for ResourceApplyAddSign |
| ResourceApplyCenteredRmsProp | Update '*var' according to the centered RMSProp algorithm. |
| ResourceApplyCenteredRmsProp.Options | Optional attributes for ResourceApplyCenteredRmsProp |
| ResourceApplyFtrl | Update '*var' according to the Ftrl-proximal scheme. |
| ResourceApplyFtrl.Options | Optional attributes for ResourceApplyFtrl |
| ResourceApplyGradientDescent | Update '*var' by subtracting 'alpha' * 'delta' from it. |
| ResourceApplyGradientDescent.Options | Optional attributes for ResourceApplyGradientDescent |
| ResourceApplyKerasMomentum | Update '*var' according to the momentum scheme. |
| ResourceApplyKerasMomentum.Options | Optional attributes for ResourceApplyKerasMomentum |
| ResourceApplyMomentum | Update '*var' according to the momentum scheme. |
| ResourceApplyMomentum.Options | Optional attributes for ResourceApplyMomentum |
| ResourceApplyPowerSign | Update '*var' according to the AddSign update. |
| ResourceApplyPowerSign.Options | Optional attributes for ResourceApplyPowerSign |
| ResourceApplyProximalAdagrad | Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. |
| ResourceApplyProximalAdagrad.Options | Optional attributes for ResourceApplyProximalAdagrad |
| ResourceApplyProximalGradientDescent | Update '*var' as FOBOS algorithm with fixed learning rate. |
| ResourceApplyProximalGradientDescent.Options | Optional attributes for ResourceApplyProximalGradientDescent |
| ResourceApplyRmsProp | Update '*var' according to the RMSProp algorithm. |
| ResourceApplyRmsProp.Options | Optional attributes for ResourceApplyRmsProp |
| ResourceConditionalAccumulator | A conditional accumulator for aggregating gradients. |
| ResourceConditionalAccumulator.Options | Optional attributes for ResourceConditionalAccumulator |
| ResourceCountUpTo <T extends TNumber > | Increments variable pointed to by 'resource' until it reaches 'limit'. |
| ResourceDtypeAndShape | Protobuf type tensorflow.eager.ResourceDtypeAndShape |
| ResourceDtypeAndShape.Builder | Protobuf type tensorflow.eager.ResourceDtypeAndShape |
| ResourceDtypeAndShapeOrBuilder | |
| ResourceGather <U extends TType > | Gather slices from the variable pointed to by `resource` according to `indices`. |
| ResourceGather.Options | Optional attributes for ResourceGather |
| ResourceGatherNd <U extends TType > | |
| ResourceHandle | |
| ResourceHandleProto | Protocol buffer representing a handle to a tensorflow resource. |
| ResourceHandleProto.Builder | Protocol buffer representing a handle to a tensorflow resource. |
| ResourceHandleProto.DtypeAndShape | Protocol buffer representing a pair of (data type, tensor shape). |
| ResourceHandleProto.DtypeAndShape.Builder | Protocol buffer representing a pair of (data type, tensor shape). |
| ResourceHandleProto.DtypeAndShapeOrBuilder | |
| ResourceHandleProtoOrBuilder | |
| ResourceScatterAdd | Adds sparse updates to the variable referenced by `resource`. |
| ResourceScatterDiv | Divides sparse updates into the variable referenced by `resource`. |
| ResourceScatterMax | Reduces sparse updates into the variable referenced by `resource` using the `max` operation. |
| ResourceScatterMin | Reduces sparse updates into the variable referenced by `resource` using the `min` operation. |
| ResourceScatterMul | Multiplies sparse updates into the variable referenced by `resource`. |
| ResourceScatterNdAdd | Applies sparse addition to individual values or slices in a Variable. |
| ResourceScatterNdAdd.Options | Optional attributes for ResourceScatterNdAdd |
| ResourceScatterNdMax | |
| ResourceScatterNdMax.Options | Optional attributes for ResourceScatterNdMax |
| ResourceScatterNdMin | |
| ResourceScatterNdMin.Options | Optional attributes for ResourceScatterNdMin |
| ResourceScatterNdSub | Applies sparse subtraction to individual values or slices in a Variable. |
| ResourceScatterNdSub.Options | Optional attributes for ResourceScatterNdSub |
| ResourceScatterNdUpdate | Applies sparse `updates` to individual values or slices within a given variable according to `indices`. |
| ResourceScatterNdUpdate.Options | Optional attributes for ResourceScatterNdUpdate |
| ResourceScatterSub | Subtracts sparse updates from the variable referenced by `resource`. |
| ResourceScatterUpdate | Assigns sparse updates to the variable referenced by `resource`. |
| ResourceSparseApplyAdadelta | var: Should be from a Variable(). |
| ResourceSparseApplyAdadelta.Options | Optional attributes for ResourceSparseApplyAdadelta |
| ResourceSparseApplyAdagrad | Update relevant entries in '*var' and '*accum' according to the adagrad scheme. |
| ResourceSparseApplyAdagrad.Options | Optional attributes for ResourceSparseApplyAdagrad |
| ResourceSparseApplyAdagradDa | Update entries in '*var' and '*accum' according to the proximal adagrad scheme. |
| ResourceSparseApplyAdagradDa.Options | Optional attributes for ResourceSparseApplyAdagradDa |
| ResourceSparseApplyAdagradV2 | Update relevant entries in '*var' and '*accum' according to the adagrad scheme. |
| ResourceSparseApplyAdagradV2.Options | Optional attributes for ResourceSparseApplyAdagradV2 |
| ResourceSparseApplyCenteredRmsProp | Update '*var' according to the centered RMSProp algorithm. |
| ResourceSparseApplyCenteredRmsProp.Options | Optional attributes for ResourceSparseApplyCenteredRmsProp |
| ResourceSparseApplyFtrl | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| ResourceSparseApplyFtrl.Options | Optional attributes for ResourceSparseApplyFtrl |
| ResourceSparseApplyKerasMomentum | Update relevant entries in '*var' and '*accum' according to the momentum scheme. |
| ResourceSparseApplyKerasMomentum.Options | Optional attributes for ResourceSparseApplyKerasMomentum |
| ResourceSparseApplyMomentum | Update relevant entries in '*var' and '*accum' according to the momentum scheme. |
| ResourceSparseApplyMomentum.Options | Optional attributes for ResourceSparseApplyMomentum |
| ResourceSparseApplyProximalAdagrad | Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. |
| ResourceSparseApplyProximalAdagrad.Options | Optional attributes for ResourceSparseApplyProximalAdagrad |
| ResourceSparseApplyProximalGradientDescent | Sparse update '*var' as FOBOS algorithm with fixed learning rate. |
| ResourceSparseApplyProximalGradientDescent.Options | Optional attributes for ResourceSparseApplyProximalGradientDescent |
| ResourceSparseApplyRmsProp | Update '*var' according to the RMSProp algorithm. |
| ResourceSparseApplyRmsProp.Options | Optional attributes for ResourceSparseApplyRmsProp |
| ResourceStridedSliceAssign | Assign `value` to the sliced l-value reference of `ref`. |
| ResourceStridedSliceAssign.Options | Optional attributes for ResourceStridedSliceAssign |
| 복원하다 | Restores tensors from a V2 checkpoint. |
| RestoreSlice <T extends TType > | Restores a tensor from checkpoint files. |
| RestoreSlice.Options | Optional attributes for RestoreSlice |
| RetrieveTPUEmbeddingAdadeltaParameters | Retrieve Adadelta embedding parameters. |
| RetrieveTPUEmbeddingAdadeltaParameters.Options | Optional attributes for RetrieveTPUEmbeddingAdadeltaParameters |
| RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug | Retrieve Adadelta embedding parameters with debug support. |
| RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug |
| RetrieveTPUEmbeddingAdagradParameters | Retrieve Adagrad embedding parameters. |
| RetrieveTPUEmbeddingAdagradParameters.Options | Optional attributes for RetrieveTPUEmbeddingAdagradParameters |
| RetrieveTPUEmbeddingAdagradParametersGradAccumDebug | Retrieve Adagrad embedding parameters with debug support. |
| RetrieveTPUEmbeddingAdagradParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingAdagradParametersGradAccumDebug |
| RetrieveTPUEmbeddingADAMParameters | Retrieve ADAM embedding parameters. |
| RetrieveTPUEmbeddingADAMParameters.Options | Optional attributes for RetrieveTPUEmbeddingADAMParameters |
| RetrieveTPUEmbeddingADAMParametersGradAccumDebug | Retrieve ADAM embedding parameters with debug support. |
| RetrieveTPUEmbeddingADAMParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingADAMParametersGradAccumDebug |
| RetrieveTPUEmbeddingCenteredRMSPropParameters | Retrieve centered RMSProp embedding parameters. |
| RetrieveTPUEmbeddingCenteredRMSPropParameters.Options | Optional attributes for RetrieveTPUEmbeddingCenteredRMSPropParameters |
| RetrieveTPUEmbeddingFTRLParameters | Retrieve FTRL embedding parameters. |
| RetrieveTPUEmbeddingFTRLParameters.Options | Optional attributes for RetrieveTPUEmbeddingFTRLParameters |
| RetrieveTPUEmbeddingFTRLParametersGradAccumDebug | Retrieve FTRL embedding parameters with debug support. |
| RetrieveTPUEmbeddingFTRLParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingFTRLParametersGradAccumDebug |
| RetrieveTPUEmbeddingMDLAdagradLightParameters | Retrieve MDL Adagrad Light embedding parameters. |
| RetrieveTPUEmbeddingMDLAdagradLightParameters.Options | Optional attributes for RetrieveTPUEmbeddingMDLAdagradLightParameters |
| RetrieveTPUEmbeddingMomentumParameters | Retrieve Momentum embedding parameters. |
| RetrieveTPUEmbeddingMomentumParameters.Options | Optional attributes for RetrieveTPUEmbeddingMomentumParameters |
| RetrieveTPUEmbeddingMomentumParametersGradAccumDebug | Retrieve Momentum embedding parameters with debug support. |
| RetrieveTPUEmbeddingMomentumParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingMomentumParametersGradAccumDebug |
| RetrieveTPUEmbeddingProximalAdagradParameters | Retrieve proximal Adagrad embedding parameters. |
| RetrieveTPUEmbeddingProximalAdagradParameters.Options | Optional attributes for RetrieveTPUEmbeddingProximalAdagradParameters |
| RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug | Retrieve proximal Adagrad embedding parameters with debug support. |
| RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug |
| RetrieveTPUEmbeddingProximalYogiParameters | |
| RetrieveTPUEmbeddingProximalYogiParameters.Options | Optional attributes for RetrieveTPUEmbeddingProximalYogiParameters |
| RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug | |
| RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug |
| RetrieveTPUEmbeddingRMSPropParameters | Retrieve RMSProp embedding parameters. |
| RetrieveTPUEmbeddingRMSPropParameters.Options | Optional attributes for RetrieveTPUEmbeddingRMSPropParameters |
| RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug | Retrieve RMSProp embedding parameters with debug support. |
| RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug |
| RetrieveTPUEmbeddingStochasticGradientDescentParameters | Retrieve SGD embedding parameters. |
| RetrieveTPUEmbeddingStochasticGradientDescentParameters.Options | Optional attributes for RetrieveTPUEmbeddingStochasticGradientDescentParameters |
| RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug | Retrieve SGD embedding parameters with debug support. |
| RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug.Options | Optional attributes for RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug |
| Reverse <T extends TType > | Reverses specific dimensions of a tensor. |
| ReverseSequence <T extends TType > | Reverses variable length slices. |
| ReverseSequence.Options | Optional attributes for ReverseSequence |
| RewriterConfig | Graph rewriting is experimental and subject to change, not covered by any API stability guarantees. |
| RewriterConfig.Builder | Graph rewriting is experimental and subject to change, not covered by any API stability guarantees. |
| RewriterConfig.CpuLayout | Enum for layout conversion between NCHW and NHWC on CPU. |
| RewriterConfig.CustomGraphOptimizer | Message to describe custom graph optimizer and its parameters tensorflow.RewriterConfig.CustomGraphOptimizer |
| RewriterConfig.CustomGraphOptimizer.Builder | Message to describe custom graph optimizer and its parameters tensorflow.RewriterConfig.CustomGraphOptimizer |
| RewriterConfig.CustomGraphOptimizerOrBuilder | |
| RewriterConfig.MemOptType | Protobuf enum tensorflow.RewriterConfig.MemOptType |
| RewriterConfig.NumIterationsType | Enum controlling the number of times to run optimizers. |
| RewriterConfig.Toggle | Protobuf enum tensorflow.RewriterConfig.Toggle |
| RewriterConfigOrBuilder | |
| RewriterConfigProtos | |
| Rfft <U extends TType > | Real-valued fast Fourier transform. |
| Rfft2d <U extends TType > | 2D real-valued fast Fourier transform. |
| Rfft3d <U extends TType > | 3D real-valued fast Fourier transform. |
| RgbToHsv <T extends TNumber > | Converts one or more images from RGB to HSV. |
| RightShift <T extends TNumber > | Elementwise computes the bitwise right-shift of `x` and `y`. |
| Rint <T extends TNumber > | Returns element-wise integer closest to x. |
| RMSProp | Optimizer that implements the RMSProp algorithm. |
| RngReadAndSkip | Advance the counter of a counter-based RNG. |
| RngSkip | Advance the counter of a counter-based RNG. |
| Roll <T extends TType > | Rolls the elements of a tensor along an axis. |
| Round <T extends TType > | Rounds the values of a tensor to the nearest integer, element-wise. |
| Rpc | Perform batches of RPC requests. |
| Rpc.Options | Optional attributes for Rpc |
| RPCOptions | Protobuf type tensorflow.RPCOptions |
| RPCOptions.Builder | Protobuf type tensorflow.RPCOptions |
| RPCOptionsOrBuilder | |
| Rsqrt <T extends TType > | Computes reciprocal of square root of x element-wise. |
| RsqrtGrad <T extends TType > | Computes the gradient for the rsqrt of `x` wrt its input. |
| RunConfiguration | Run-specific items such as arguments to the test / benchmark. |
| RunConfiguration.Builder | Run-specific items such as arguments to the test / benchmark. |
| RunConfigurationOrBuilder | |
| RunMetadata | Metadata output (i.e., non-Tensor) for a single Run() call. |
| RunMetadata.Builder | Metadata output (i.e., non-Tensor) for a single Run() call. |
| RunMetadata.FunctionGraphs | Protobuf type tensorflow.RunMetadata.FunctionGraphs |
| RunMetadata.FunctionGraphs.Builder | Protobuf type tensorflow.RunMetadata.FunctionGraphs |
| RunMetadata.FunctionGraphsOrBuilder | |
| RunMetadataOrBuilder | |
| RunOptions | Options for a single Run() call. |
| RunOptions.Builder | Options for a single Run() call. |
| RunOptions.Experimental | Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| RunOptions.Experimental.Builder | Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| RunOptions.Experimental.RunHandlerPoolOptions | Options for run handler thread pool. |
| RunOptions.Experimental.RunHandlerPoolOptions.Builder | Options for run handler thread pool. |
| RunOptions.Experimental.RunHandlerPoolOptionsOrBuilder | |
| RunOptions.ExperimentalOrBuilder | |
| RunOptions.TraceLevel | TODO(pbar) Turn this into a TraceOptions proto which allows tracing to be controlled in a more orthogonal manner? tensorflow.RunOptions.TraceLevel |
| RunOptionsOrBuilder |
에스
| SampleDistortedBoundingBox <T extends TNumber > | Generate a single randomly distorted bounding box for an image. |
| SampleDistortedBoundingBox.Options | Optional attributes for SampleDistortedBoundingBox |
| SamplingDataset | Creates a dataset that takes a Bernoulli sample of the contents of another dataset. |
| 구하다 | Saves tensors in V2 checkpoint format. |
| SaveableObject | Protobuf type tensorflow.SaveableObject |
| SaveableObject.Builder | Protobuf type tensorflow.SaveableObject |
| SaveableObjectOrBuilder | |
| SavedAsset | A SavedAsset points to an asset in the MetaGraph. |
| SavedAsset.Builder | A SavedAsset points to an asset in the MetaGraph. |
| SavedAssetOrBuilder | |
| SavedBareConcreteFunction | Protobuf type tensorflow.SavedBareConcreteFunction |
| SavedBareConcreteFunction.Builder | Protobuf type tensorflow.SavedBareConcreteFunction |
| SavedBareConcreteFunctionOrBuilder | |
| SavedConcreteFunction | Stores low-level information about a concrete function. |
| SavedConcreteFunction.Builder | Stores low-level information about a concrete function. |
| SavedConcreteFunctionOrBuilder | |
| SavedConstant | Protobuf type tensorflow.SavedConstant |
| SavedConstant.Builder | Protobuf type tensorflow.SavedConstant |
| SavedConstantOrBuilder | |
| SavedFunction | A function with multiple signatures, possibly with non-Tensor arguments. |
| SavedFunction.Builder | A function with multiple signatures, possibly with non-Tensor arguments. |
| SavedFunctionOrBuilder | |
| SavedModel | SavedModel is the high level serialization format for TensorFlow Models. |
| SavedModel.Builder | SavedModel is the high level serialization format for TensorFlow Models. |
| SavedModelBundle | SavedModelBundle represents a model loaded from storage. |
| SavedModelBundle.Exporter | Options for exporting a SavedModel. |
| SavedModelBundle.Loader | Options for loading a SavedModel. |
| SavedModelOrBuilder | |
| SavedModelProtos | |
| SavedObject | Protobuf type tensorflow.SavedObject |
| SavedObject.Builder | Protobuf type tensorflow.SavedObject |
| SavedObject.KindCase | |
| SavedObjectGraph | Protobuf type tensorflow.SavedObjectGraph |
| SavedObjectGraph.Builder | Protobuf type tensorflow.SavedObjectGraph |
| SavedObjectGraphOrBuilder | |
| SavedObjectGraphProtos | |
| SavedObjectOrBuilder | |
| SavedResource | A SavedResource represents a TF object that holds state during its lifetime. |
| SavedResource.Builder | A SavedResource represents a TF object that holds state during its lifetime. |
| SavedResourceOrBuilder | |
| SavedSlice | Saved tensor slice: it stores the name of the tensors, the slice, and the raw data. |
| SavedSlice.Builder | Saved tensor slice: it stores the name of the tensors, the slice, and the raw data. |
| SavedSliceMeta | Metadata describing the set of slices of the same tensor saved in a checkpoint file. |
| SavedSliceMeta.Builder | Metadata describing the set of slices of the same tensor saved in a checkpoint file. |
| SavedSliceMetaOrBuilder | |
| SavedSliceOrBuilder | |
| SavedTensorSliceMeta | Metadata describing the set of tensor slices saved in a checkpoint file. |
| SavedTensorSliceMeta.Builder | Metadata describing the set of tensor slices saved in a checkpoint file. |
| SavedTensorSliceMetaOrBuilder | |
| SavedTensorSliceProtos | |
| SavedTensorSlices | Each record in a v3 checkpoint file is a serialized SavedTensorSlices message. |
| SavedTensorSlices.Builder | Each record in a v3 checkpoint file is a serialized SavedTensorSlices message. |
| SavedTensorSlicesOrBuilder | |
| SavedUserObject | A SavedUserObject is an object (in the object-oriented language of the TensorFlow program) of some user- or framework-defined class other than those handled specifically by the other kinds of SavedObjects. |
| SavedUserObject.Builder | A SavedUserObject is an object (in the object-oriented language of the TensorFlow program) of some user- or framework-defined class other than those handled specifically by the other kinds of SavedObjects. |
| SavedUserObjectOrBuilder | |
| SavedVariable | Represents a Variable that is initialized by loading the contents from the checkpoint. |
| SavedVariable.Builder | Represents a Variable that is initialized by loading the contents from the checkpoint. |
| SavedVariableOrBuilder | |
| SaverDef | Protocol buffer representing the configuration of a Saver. |
| SaverDef.Builder | Protocol buffer representing the configuration of a Saver. |
| SaverDef.CheckpointFormatVersion | A version number that identifies a different on-disk checkpoint format. |
| SaverDefOrBuilder | |
| SaverProtos | |
| SaveSliceInfoDef | Protobuf type tensorflow.SaveSliceInfoDef |
| SaveSliceInfoDef.Builder | Protobuf type tensorflow.SaveSliceInfoDef |
| SaveSliceInfoDefOrBuilder | |
| SaveSlices | Saves input tensors slices to disk. |
| ScalarSummary | Outputs a `Summary` protocol buffer with scalar values. |
| ScaleAndTranslate | |
| ScaleAndTranslate.Options | Optional attributes for ScaleAndTranslate |
| ScaleAndTranslateGrad <T extends TNumber > | |
| ScaleAndTranslateGrad.Options | Optional attributes for ScaleAndTranslateGrad |
| ScatterAdd <T extends TType > | Adds sparse updates to a variable reference. |
| ScatterAdd.Options | Optional attributes for ScatterAdd |
| ScatterDiv <T extends TType > | Divides a variable reference by sparse updates. |
| ScatterDiv.Options | Optional attributes for ScatterDiv |
| ScatterMax <T extends TNumber > | Reduces sparse updates into a variable reference using the `max` operation. |
| ScatterMax.Options | Optional attributes for ScatterMax |
| ScatterMin <T extends TNumber > | Reduces sparse updates into a variable reference using the `min` operation. |
| ScatterMin.Options | Optional attributes for ScatterMin |
| ScatterMul <T extends TType > | Multiplies sparse updates into a variable reference. |
| ScatterMul.Options | Optional attributes for ScatterMul |
| ScatterNd <U extends TType > | Scatter `updates` into a new tensor according to `indices`. |
| ScatterNdAdd <T extends TType > | Applies sparse addition to individual values or slices in a Variable. |
| ScatterNdAdd.Options | Optional attributes for ScatterNdAdd |
| ScatterNdMax <T extends TType > | Computes element-wise maximum. |
| ScatterNdMax.Options | Optional attributes for ScatterNdMax |
| ScatterNdMin <T extends TType > | Computes element-wise minimum. |
| ScatterNdMin.Options | Optional attributes for ScatterNdMin |
| ScatterNdNonAliasingAdd <T extends TType > | Applies sparse addition to `input` using individual values or slices from `updates` according to indices `indices`. |
| ScatterNdSub <T extends TType > | Applies sparse subtraction to individual values or slices in a Variable. |
| ScatterNdSub.Options | Optional attributes for ScatterNdSub |
| ScatterNdUpdate <T extends TType > | Applies sparse `updates` to individual values or slices within a given variable according to `indices`. |
| ScatterNdUpdate.Options | Optional attributes for ScatterNdUpdate |
| ScatterSub <T extends TType > | Subtracts sparse updates to a variable reference. |
| ScatterSub.Options | Optional attributes for ScatterSub |
| ScatterUpdate <T extends TType > | Applies sparse updates to a variable reference. |
| ScatterUpdate.Options | Optional attributes for ScatterUpdate |
| 범위 | Manages groups of related properties when creating Tensorflow Operations, such as a common name prefix. |
| ScopedAllocatorOptions | Protobuf type tensorflow.ScopedAllocatorOptions |
| ScopedAllocatorOptions.Builder | Protobuf type tensorflow.ScopedAllocatorOptions |
| ScopedAllocatorOptionsOrBuilder | |
| SdcaFprint | Computes fingerprints of the input strings. |
| SdcaOptimizer | Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for linear models with L1 + L2 regularization. |
| SdcaOptimizer.Options | Optional attributes for SdcaOptimizer |
| SdcaShrinkL1 | Applies L1 regularization shrink step on the parameters. |
| SegmentMax <T extends TNumber > | 텐서의 세그먼트를 따라 최대값을 계산합니다. |
| SegmentMean <T extends TType > | Computes the mean along segments of a tensor. |
| SegmentMin <T extends TNumber > | Computes the minimum along segments of a tensor. |
| SegmentProd <T extends TType > | Computes the product along segments of a tensor. |
| SegmentSum <T extends TType > | 텐서의 세그먼트를 따라 합계를 계산합니다. |
| Select <T extends TType > | |
| SelfAdjointEig <T extends TType > | Computes the eigen decomposition of one or more square self-adjoint matrices. |
| SelfAdjointEig <T extends TType > | Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported). |
| SelfAdjointEig.Options | Optional attributes for SelfAdjointEig |
| Selu <T extends TNumber > | Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise. |
| SELU <T extends TFloating > | Scaled Exponential Linear Unit (SELU). |
| SeluGrad <T extends TNumber > | Computes gradients for the scaled exponential linear (Selu) operation. |
| 보내다 | Sends the named tensor from send_device to recv_device. |
| 보내다 | Sends the named tensor to another XLA computation. |
| Send.Options | Optional attributes for Send |
| SendTPUEmbeddingGradients | Performs gradient updates of embedding tables. |
| SequenceExample | Protobuf type tensorflow.SequenceExample |
| SequenceExample.Builder | Protobuf type tensorflow.SequenceExample |
| SequenceExampleOrBuilder | |
| SerializeIterator | Converts the given `resource_handle` representing an iterator to a variant tensor. |
| SerializeIterator.Options | Optional attributes for SerializeIterator |
| SerializeManySparse <U extends TType > | Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object. |
| SerializeSparse <U extends TType > | Serialize a `SparseTensor` into a `[3]` `Tensor` object. |
| SerializeTensor | Transforms a Tensor into a serialized TensorProto proto. |
| 섬기는 사람 | An in-process TensorFlow server, for use in distributed training. |
| ServerDef | Defines the configuration of a single TensorFlow server. |
| ServerDef.Builder | Defines the configuration of a single TensorFlow server. |
| ServerDefOrBuilder | |
| ServerProtos | |
| ServiceConfig | |
| ServiceConfig.DispatcherConfig | Configuration for a tf.data service DispatchServer. |
| ServiceConfig.DispatcherConfig.Builder | Configuration for a tf.data service DispatchServer. |
| ServiceConfig.DispatcherConfigOrBuilder | |
| ServiceConfig.WorkerConfig | Configuration for a tf.data service WorkerServer. |
| ServiceConfig.WorkerConfig.Builder | Configuration for a tf.data service WorkerServer. |
| ServiceConfig.WorkerConfigOrBuilder | |
| 세션 | Driver for Graph execution. |
| Session.Run | Output tensors and metadata obtained when executing a session. |
| Session.Runner | Run Operation s and evaluate Tensors . |
| SessionLog | Protocol buffer used for logging session state. |
| SessionLog.Builder | Protocol buffer used for logging session state. |
| SessionLog.SessionStatus | Protobuf enum tensorflow.SessionLog.SessionStatus |
| SessionLogOrBuilder | |
| SessionMetadata | Metadata about the session. |
| SessionMetadata.Builder | Metadata about the session. |
| SessionMetadataOrBuilder | |
| SetDiff1d <T extends TType , U extends TNumber > | Computes the difference between two lists of numbers or strings. |
| SetSize | Number of unique elements along last dimension of input `set`. |
| SetSize.Options | Optional attributes for SetSize |
| SetsOps | Implementation of set operations |
| SetsOps.Operation | Enumeration containing the string operation values to be passed to the TensorFlow Sparse Ops function ERROR(/SparseOps#denseToDenseSetOperation) |
| SetStatsAggregatorDataset | |
| SetStatsAggregatorDataset | |
| 모양 | The shape of a Tensor or NdArray . |
| Shape <U extends TNumber > | Returns the shape of a tensor. |
| Shape_inference_func_TF_ShapeInferenceContext_TF_Status | |
| Shaped | Any data container with a given Shape . |
| ShapeN <U extends TNumber > | Returns shape of tensors. |
| Shapes | An operator providing methods on org.tensorflow.op.core.Shape tensors and 1d operands that represent the dimensions of a shape. |
| ShapeUtils | Various methods for processing with Shapes and Operands |
| ShardDataset | Creates a `Dataset` that includes only 1/`num_shards` of this dataset. |
| ShardDataset.Options | Optional attributes for ShardDataset |
| ShardedFilename | Generate a sharded filename. |
| ShardedFilespec | Generate a glob pattern matching all sharded file names. |
| Sharding <T extends TType > | An op which shards the input based on the given sharding attribute. |
| ShortDataBuffer | A DataBuffer of shorts. |
| ShortDataLayout <S extends DataBuffer <?>> | A DataLayout that converts data stored in a buffer to shorts. |
| ShortDenseNdArray | |
| ShortNdArray | An NdArray of shorts. |
| ShuffleAndRepeatDataset | |
| ShuffleAndRepeatDataset.Options | Optional attributes for ShuffleAndRepeatDataset |
| ShuffleDataset | |
| ShuffleDataset.Options | Optional attributes for ShuffleDataset |
| ShutdownDistributedTPU | Shuts down a running distributed TPU system. |
| Sigmoid <T extends TFloating > | Sigmoid activation. |
| Sigmoid <T extends TType > | Computes sigmoid of `x` element-wise. |
| SigmoidCrossEntropyWithLogits | |
| SigmoidGrad <T extends TType > | Computes the gradient of the sigmoid of `x` wrt its input. |
| Sign <T extends TType > | Returns an element-wise indication of the sign of a number. |
| 서명 | Describe the inputs and outputs of an executable entity, such as a ConcreteFunction , among other useful metadata. |
| Signature.Builder | Builds a new function signature. |
| Signature.TensorDescription | |
| SignatureDef | SignatureDef defines the signature of a computation supported by a TensorFlow graph. |
| SignatureDef.Builder | SignatureDef defines the signature of a computation supported by a TensorFlow graph. |
| SignatureDefOrBuilder | |
| Sin <T extends TType > | Computes sine of x element-wise. |
| SingleElementSequence <T, U extends NdArray <T>> | A sequence of one single element |
| Sinh <T extends TType > | x 요소의 쌍곡사인을 계산합니다. |
| Size <U extends TNumber > | Returns the size of a tensor. |
| SkipDataset | |
| SkipDataset | Creates a dataset that skips `count` elements from the `input_dataset`. |
| Skipgram | Parses a text file and creates a batch of examples. |
| Skipgram.Options | Optional attributes for Skipgram |
| SleepDataset | |
| SleepDataset | |
| Slice <T extends TType > | Return a slice from 'input'. |
| SlicingElementSequence <T, U extends NdArray <T>> | A sequence creating a new NdArray instance (slice) for each element of an iteration |
| SlidingWindowDataset | Creates a dataset that passes a sliding window over `input_dataset`. |
| SlidingWindowDataset | Creates a dataset that passes a sliding window over `input_dataset`. |
| Snapshot <T extends TType > | Returns a copy of the input tensor. |
| 스냅 사진 | Protobuf type tensorflow.SnapShot |
| SnapShot.Builder | Protobuf type tensorflow.SnapShot |
| SnapshotMetadataRecord | This stores the metadata information present in each snapshot record. |
| SnapshotMetadataRecord.Builder | This stores the metadata information present in each snapshot record. |
| SnapshotMetadataRecordOrBuilder | |
| SnapShotOrBuilder | |
| SnapshotProtos | |
| SnapshotRecord | Each SnapshotRecord represents one batch of pre-processed input data. |
| SnapshotRecord.Builder | Each SnapshotRecord represents one batch of pre-processed input data. |
| SnapshotRecordOrBuilder | |
| SnapshotTensorMetadata | Metadata for all the tensors in a Snapshot Record. |
| SnapshotTensorMetadata.Builder | Metadata for all the tensors in a Snapshot Record. |
| SnapshotTensorMetadataOrBuilder | |
| SobolSample <T extends TNumber > | Generates points from the Sobol sequence. |
| Softmax <T extends TFloating > | Softmax converts a real vector to a vector of categorical probabilities. |
| Softmax <T extends TNumber > | Computes softmax activations. |
| SoftmaxCrossEntropyWithLogits | |
| SoftmaxCrossEntropyWithLogits <T extends TNumber > | Computes softmax cross entropy cost and gradients to backpropagate. |
| Softplus <T extends TFloating > | Softplus activation function, softplus(x) = log(exp(x) + 1) . |
| Softplus <T extends TNumber > | Computes softplus: `log(exp(features) + 1)`. |
| SoftplusGrad <T extends TNumber > | Computes softplus gradients for a softplus operation. |
| Softsign <T extends TFloating > | Softsign activation function, softsign(x) = x / (abs(x) + 1) . |
| Softsign <T extends TNumber > | Computes softsign: `features / (abs(features) + 1)`. |
| SoftsignGrad <T extends TNumber > | Computes softsign gradients for a softsign operation. |
| Solve <T extends TType > | Solves systems of linear equations. |
| Solve.Options | Optional attributes for Solve |
| Sort <T extends TType > | Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort . |
| SourceFile | Content of a source file involved in the execution of the debugged TensorFlow program. |
| SourceFile.Builder | Content of a source file involved in the execution of the debugged TensorFlow program. |
| SourceFileOrBuilder | |
| SpaceToBatch <T extends TType > | SpaceToBatch for 4-D tensors of type T. |
| SpaceToBatchNd <T extends TType > | SpaceToBatch for ND tensors of type T. |
| SpaceToDepth <T extends TType > | SpaceToDepth for tensors of type T. |
| SpaceToDepth.Options | Optional attributes for SpaceToDepth |
| SparseAccumulatorApplyGradient | Applies a sparse gradient to a given accumulator. |
| SparseAccumulatorTakeGradient <T extends TType > | Extracts the average sparse gradient in a SparseConditionalAccumulator. |
| SparseAdd <T extends TType > | Adds two `SparseTensor` objects to produce another `SparseTensor`. |
| SparseAddGrad <T extends TType > | The gradient operator for the SparseAdd op. |
| SparseApplyAdadelta <T extends TType > | var: Should be from a Variable(). |
| SparseApplyAdadelta.Options | Optional attributes for SparseApplyAdadelta |
| SparseApplyAdagrad <T extends TType > | Update relevant entries in '*var' and '*accum' according to the adagrad scheme. |
| SparseApplyAdagrad.Options | Optional attributes for SparseApplyAdagrad |
| SparseApplyAdagradDa <T extends TType > | Update entries in '*var' and '*accum' according to the proximal adagrad scheme. |
| SparseApplyAdagradDa.Options | Optional attributes for SparseApplyAdagradDa |
| SparseApplyCenteredRmsProp <T extends TType > | Update '*var' according to the centered RMSProp algorithm. |
| SparseApplyCenteredRmsProp.Options | Optional attributes for SparseApplyCenteredRmsProp |
| SparseApplyFtrl <T extends TType > | Update relevant entries in '*var' according to the Ftrl-proximal scheme. |
| SparseApplyFtrl.Options | Optional attributes for SparseApplyFtrl |
| SparseApplyMomentum <T extends TType > | Update relevant entries in '*var' and '*accum' according to the momentum scheme. |
| SparseApplyMomentum.Options | Optional attributes for SparseApplyMomentum |
| SparseApplyProximalAdagrad <T extends TType > | Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. |
| SparseApplyProximalAdagrad.Options | Optional attributes for SparseApplyProximalAdagrad |
| SparseApplyProximalGradientDescent <T extends TType > | Sparse update '*var' as FOBOS algorithm with fixed learning rate. |
| SparseApplyProximalGradientDescent.Options | Optional attributes for SparseApplyProximalGradientDescent |
| SparseApplyRmsProp <T extends TType > | Update '*var' according to the RMSProp algorithm. |
| SparseApplyRmsProp.Options | Optional attributes for SparseApplyRmsProp |
| SparseBincount <U extends TNumber > | Counts the number of occurrences of each value in an integer array. |
| SparseBincount.Options | Optional attributes for SparseBincount |
| SparseCategoricalCrossentropy | Computes the crossentropy loss between labels and predictions. |
| SparseCategoricalCrossentropy <T extends TNumber > | A metric that computes the sparse categorical cross-entropy loss between true labels and predicted labels. |
| SparseConcat <T extends TType > | Concatenates a list of `SparseTensor` along the specified dimension. |
| SparseConditionalAccumulator | A conditional accumulator for aggregating sparse gradients. |
| SparseConditionalAccumulator.Options | Optional attributes for SparseConditionalAccumulator |
| SparseCountSparseOutput <U extends TNumber > | Performs sparse-output bin counting for a sparse tensor input. |
| SparseCountSparseOutput.Options | Optional attributes for SparseCountSparseOutput |
| SparseCross | Generates sparse cross from a list of sparse and dense tensors. |
| SparseCrossHashed | Generates sparse cross from a list of sparse and dense tensors. |
| SparseDenseCwiseAdd <T extends TType > | Adds up a SparseTensor and a dense Tensor, using these special rules: (1) Broadcasts the dense side to have the same shape as the sparse side, if eligible; (2) Then, only the dense values pointed to by the indices of the SparseTensor participate in the cwise addition. |
| SparseDenseCwiseDiv <T extends TType > | Component-wise divides a SparseTensor by a dense Tensor. |
| SparseDenseCwiseMul <T extends TType > | Component-wise multiplies a SparseTensor by a dense Tensor. |
| SparseFillEmptyRows <T extends TType > | Fills empty rows in the input 2-D `SparseTensor` with a default value. |
| SparseFillEmptyRowsGrad <T extends TType > | The gradient of SparseFillEmptyRows. |
| SparseMatMul | Multiply matrix "a" by matrix "b". |
| SparseMatMul.Options | Optional attributes for SparseMatMul |
| SparseMatrixAdd | Sparse addition of two CSR matrices, C = alpha * A + beta * B. |
| SparseMatrixMatMul <T extends TType > | Matrix-multiplies a sparse matrix with a dense matrix. |
| SparseMatrixMatMul.Options | Optional attributes for SparseMatrixMatMul |
| SparseMatrixMul | Element-wise multiplication of a sparse matrix with a dense tensor. |
| SparseMatrixNNZ | Returns the number of nonzeroes of `sparse_matrix`. |
| SparseMatrixOrderingAMD | Computes the Approximate Minimum Degree (AMD) ordering of `input`. |
| SparseMatrixSoftmax | Calculates the softmax of a CSRSparseMatrix. |
| SparseMatrixSoftmaxGrad | Calculates the gradient of the SparseMatrixSoftmax op. |
| SparseMatrixSparseCholesky | Computes the sparse Cholesky decomposition of `input`. |
| SparseMatrixSparseMatMul | Sparse-matrix-multiplies two CSR matrices `a` and `b`. |
| SparseMatrixSparseMatMul.Options | Optional attributes for SparseMatrixSparseMatMul |
| SparseMatrixTranspose | Transposes the inner (matrix) dimensions of a CSRSparseMatrix. |
| SparseMatrixTranspose.Options | Optional attributes for SparseMatrixTranspose |
| SparseMatrixZeros | Creates an all-zeros CSRSparseMatrix with shape `dense_shape`. |
| SparseReduceMax <T extends TNumber > | Computes the max of elements across dimensions of a SparseTensor. |
| SparseReduceMax.Options | Optional attributes for SparseReduceMax |
| SparseReduceMaxSparse <T extends TNumber > | Computes the max of elements across dimensions of a SparseTensor. |
| SparseReduceMaxSparse.Options | Optional attributes for SparseReduceMaxSparse |
| SparseReduceSum <T extends TType > | Computes the sum of elements across dimensions of a SparseTensor. |
| SparseReduceSum.Options | Optional attributes for SparseReduceSum |
| SparseReduceSumSparse <T extends TType > | Computes the sum of elements across dimensions of a SparseTensor. |
| SparseReduceSumSparse.Options | Optional attributes for SparseReduceSumSparse |
| SparseReorder <T extends TType > | Reorders a SparseTensor into the canonical, row-major ordering. |
| SparseReshape | Reshapes a SparseTensor to represent values in a new dense shape. |
| SparseSegmentMean <T extends TNumber > | Computes the mean along sparse segments of a tensor. |
| SparseSegmentMeanGrad <T extends TNumber > | Computes gradients for SparseSegmentMean. |
| SparseSegmentMeanWithNumSegments <T extends TNumber > | Computes the mean along sparse segments of a tensor. |
| SparseSegmentSqrtN <T extends TNumber > | Computes the sum along sparse segments of a tensor divided by the sqrt of N. |
| SparseSegmentSqrtNGrad <T extends TNumber > | Computes gradients for SparseSegmentSqrtN. |
| SparseSegmentSqrtNWithNumSegments <T extends TNumber > | Computes the sum along sparse segments of a tensor divided by the sqrt of N. |
| SparseSegmentSum <T extends TNumber > | Computes the sum along sparse segments of a tensor. |
| SparseSegmentSumWithNumSegments <T extends TNumber > | Computes the sum along sparse segments of a tensor. |
| SparseSlice <T extends TType > | Slice a `SparseTensor` based on the `start` and `size`. |
| SparseSliceGrad <T extends TType > | The gradient operator for the SparseSlice op. |
| SparseSoftmax <T extends TNumber > | Applies softmax to a batched ND `SparseTensor`. |
| SparseSoftmaxCrossEntropyWithLogits | |
| SparseSoftmaxCrossEntropyWithLogits <T extends TNumber > | Computes softmax cross entropy cost and gradients to backpropagate. |
| SparseSparseMaximum <T extends TNumber > | Returns the element-wise max of two SparseTensors. |
| SparseSparseMinimum <T extends TType > | Returns the element-wise min of two SparseTensors. |
| SparseSplit <T extends TType > | Split a `SparseTensor` into `num_split` tensors along one dimension. |
| SparseTensorDenseAdd <U extends TType > | Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. |
| SparseTensorDenseMatMul <U extends TType > | Multiply SparseTensor (of rank 2) "A" by dense matrix "B". |
| SparseTensorDenseMatMul.Options | Optional attributes for SparseTensorDenseMatMul |
| SparseTensorSliceDataset | Creates a dataset that splits a SparseTensor into elements row-wise. |
| SparseTensorToCSRSparseMatrix | Converts a SparseTensor to a (possibly batched) CSRSparseMatrix. |
| SparseToDense <U extends TType > | Converts a sparse representation into a dense tensor. |
| SparseToDense.Options | Optional attributes for SparseToDense |
| SparseToSparseSetOperation <T extends TType > | Applies set operation along last dimension of 2 `SparseTensor` inputs. |
| SparseToSparseSetOperation.Options | Optional attributes for SparseToSparseSetOperation |
| SpecializedType | For identifying the underlying type of a variant. |
| Spence <T extends TNumber > | |
| Split <T extends TType > | Splits a tensor into `num_split` tensors along one dimension. |
| SplitV <T extends TType > | Splits a tensor into `num_split` tensors along one dimension. |
| SqlDataset | Creates a dataset that executes a SQL query and emits rows of the result set. |
| SqlDataset | Creates a dataset that executes a SQL query and emits rows of the result set. |
| Sqrt <T extends TType > | Computes square root of x element-wise. |
| SqrtGrad <T extends TType > | Computes the gradient for the sqrt of `x` wrt its input. |
| Sqrtm <T extends TType > | Computes the matrix square root of one or more square matrices: matmul(sqrtm(A), sqrtm(A)) = A The input matrix should be invertible. |
| Square <T extends TType > | Computes square of x element-wise. |
| SquaredDifference <T extends TType > | Returns conj(x - y)(x - y) element-wise. |
| SquaredHinge | Computes the squared hinge loss between labels and predictions. |
| SquaredHinge <T extends TNumber > | A metric that computes the squared hinge loss metric between labels and predictions. |
| Squeeze <T extends TType > | Removes dimensions of size 1 from the shape of a tensor. |
| Squeeze.Options | Optional attributes for Squeeze |
| Stack <T extends TType > | Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. |
| Stack.Options | Optional attributes for Stack |
| StackFrameWithId | A stack frame with ID. |
| StackFrameWithId.Builder | A stack frame with ID. |
| StackFrameWithIdOrBuilder | |
| 단계 | Stage values similar to a lightweight Enqueue. |
| Stage.Options | Optional attributes for Stage |
| StageClear | Op removes all elements in the underlying container. |
| StageClear.Options | Optional attributes for StageClear |
| StagePeek | Op peeks at the values at the specified index. |
| StagePeek.Options | Optional attributes for StagePeek |
| StageSize | Op returns the number of elements in the underlying container. |
| StageSize.Options | Optional attributes for StageSize |
| StatefulRandomBinomial <V extends TNumber > | |
| StatefulStandardNormal <U extends TType > | Outputs random values from a normal distribution. |
| StatefulTruncatedNormal <U extends TType > | Outputs random values from a truncated normal distribution. |
| StatefulUniform <U extends TType > | Outputs random values from a uniform distribution. |
| StatefulUniformFullInt <U extends TType > | Outputs random integers from a uniform distribution. |
| StatefulUniformInt <U extends TType > | Outputs random integers from a uniform distribution. |
| StatelessMultinomial <V extends TNumber > | Draws samples from a multinomial distribution. |
| StatelessParameterizedTruncatedNormal <V extends TNumber > | |
| StatelessRandomBinomial <W extends TNumber > | Outputs deterministic pseudorandom random numbers from a binomial distribution. |
| StatelessRandomGamma <V extends TNumber > | Outputs deterministic pseudorandom random numbers from a gamma distribution. |
| StatelessRandomGetKeyCounterAlg | Picks the best algorithm based on device, and scrambles seed into key and counter. |
| StatelessRandomNormal <V extends TNumber > | Outputs deterministic pseudorandom values from a normal distribution. |
| StatelessRandomNormalV2 <U extends TNumber > | Outputs deterministic pseudorandom values from a normal distribution. |
| StatelessRandomPoisson <W extends TNumber > | Outputs deterministic pseudorandom random numbers from a Poisson distribution. |
| StatelessRandomUniform <V extends TNumber > | Outputs deterministic pseudorandom random values from a uniform distribution. |
| StatelessRandomUniformFullInt <V extends TNumber > | Outputs deterministic pseudorandom random integers from a uniform distribution. |
| StatelessRandomUniformFullIntV2 <U extends TNumber > | Outputs deterministic pseudorandom random integers from a uniform distribution. |
| StatelessRandomUniformInt <V extends TNumber > | Outputs deterministic pseudorandom random integers from a uniform distribution. |
| StatelessRandomUniformIntV2 <U extends TNumber > | Outputs deterministic pseudorandom random integers from a uniform distribution. |
| StatelessRandomUniformV2 <U extends TNumber > | Outputs deterministic pseudorandom random values from a uniform distribution. |
| StatelessSampleDistortedBoundingBox <T extends TNumber > | Generate a randomly distorted bounding box for an image deterministically. |
| StatelessSampleDistortedBoundingBox.Options | Optional attributes for StatelessSampleDistortedBoundingBox |
| StatelessTruncatedNormal <V extends TNumber > | Outputs deterministic pseudorandom values from a truncated normal distribution. |
| StatelessTruncatedNormalV2 <U extends TNumber > | Outputs deterministic pseudorandom values from a truncated normal distribution. |
| StaticRegexFullMatch | Check if the input matches the regex pattern. |
| StaticRegexReplace | Replaces the match of pattern in input with rewrite. |
| StaticRegexReplace.Options | Optional attributes for StaticRegexReplace |
| StatsAggregatorHandle | Creates a statistics manager resource. |
| StatsAggregatorHandle | |
| StatsAggregatorHandle.Options | Optional attributes for StatsAggregatorHandle |
| StatsAggregatorHandle.Options | Optional attributes for StatsAggregatorHandle |
| StatsAggregatorSetSummaryWriter | Set a summary_writer_interface to record statistics using given stats_aggregator. |
| StatsAggregatorSummary | Produces a summary of any statistics recorded by the given statistics manager. |
| StatsAggregatorSummary | Produces a summary of any statistics recorded by the given statistics manager. |
| StdArrays | Utility class for working with NdArray instances mixed with standard Java arrays. |
| StepStats | Protobuf type tensorflow.StepStats |
| StepStats.Builder | Protobuf type tensorflow.StepStats |
| StepStatsOrBuilder | |
| StepStatsProtos | |
| StopGradient <T extends TType > | Stops gradient computation. |
| StridedSlice <T extends TType > | Return a strided slice from `input`. |
| StridedSlice.Options | Optional attributes for StridedSlice |
| StridedSliceAssign <T extends TType > | Assign `value` to the sliced l-value reference of `ref`. |
| StridedSliceAssign.Options | Optional attributes for StridedSliceAssign |
| StridedSliceGrad <U extends TType > | Returns the gradient of `StridedSlice`. |
| StridedSliceGrad.Options | Optional attributes for StridedSliceGrad |
| StridedSliceHelper | Helper endpoint methods for Python like indexing. |
| StringFormat | Formats a string template using a list of tensors. |
| StringFormat.Options | Optional attributes for StringFormat |
| StringLayout | Data layout that converts a String to/from a sequence of bytes applying a given charset. |
| StringLength | String lengths of `input`. |
| StringLength.Options | Optional attributes for StringLength |
| StringNGrams <T extends TNumber > | Creates ngrams from ragged string data. |
| StringSplit | Split elements of `source` based on `sep` into a `SparseTensor`. |
| StringSplit.Options | Optional attributes for StringSplit |
| 조각 | Strip leading and trailing whitespaces from the Tensor. |
| StructProtos | |
| 구조화된 가치 | `StructuredValue` represents a dynamically typed value representing various data structures that are inspired by Python data structures typically used in TensorFlow functions as inputs and outputs. |
| StructuredValue.Builder | `StructuredValue` represents a dynamically typed value representing various data structures that are inspired by Python data structures typically used in TensorFlow functions as inputs and outputs. |
| StructuredValue.KindCase | |
| 구조화된 값 또는 빌더 | |
| Sub <T extends TType > | Returns x - y element-wise. |
| Substr | Return substrings from `Tensor` of strings. |
| Substr.Options | Optional attributes for Substr |
| Sum <T extends TType > | Computes the sum of elements across dimensions of a tensor. |
| Sum.Options | Optional attributes for Sum |
| 요약 | A Summary is a set of named values to be displayed by the visualizer. |
| Summary.Audio | Protobuf type tensorflow.Summary.Audio |
| Summary.Audio.Builder | Protobuf type tensorflow.Summary.Audio |
| Summary.AudioOrBuilder | |
| Summary.Builder | A Summary is a set of named values to be displayed by the visualizer. |
| Summary.Image | Protobuf type tensorflow.Summary.Image |
| Summary.Image.Builder | Protobuf type tensorflow.Summary.Image |
| Summary.ImageOrBuilder | |
| Summary.Value | Protobuf type tensorflow.Summary.Value |
| Summary.Value.Builder | Protobuf type tensorflow.Summary.Value |
| Summary.Value.ValueCase | |
| Summary.ValueOrBuilder | |
| SummaryDescription | Metadata associated with a series of Summary data tensorflow.SummaryDescription |
| SummaryDescription.Builder | Metadata associated with a series of Summary data tensorflow.SummaryDescription |
| SummaryDescriptionOrBuilder | |
| SummaryMetadata | A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value. |
| SummaryMetadata.Builder | A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value. |
| SummaryMetadata.PluginData | Protobuf type tensorflow.SummaryMetadata.PluginData |
| SummaryMetadata.PluginData.Builder | Protobuf type tensorflow.SummaryMetadata.PluginData |
| SummaryMetadata.PluginDataOrBuilder | |
| SummaryMetadataOrBuilder | |
| SummaryOrBuilder | |
| SummaryProtos | |
| SummaryWriter | |
| SummaryWriter.Options | Optional attributes for SummaryWriter |
| Svd <T extends TType > | Computes the singular value decompositions of one or more matrices. |
| Svd <T extends TType > | Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported). |
| Svd.Options | Optional attributes for Svd |
| Swish <T extends TFloating > | Swish activation function. |
| SwitchCond <T extends TType > | Forwards `data` to the output port determined by `pred`. |
티
| TaggedRunMetadata | For logging the metadata output for a single session.run() call. |
| TaggedRunMetadata.Builder | For logging the metadata output for a single session.run() call. |
| TaggedRunMetadataOrBuilder | |
| TakeDataset | |
| TakeDataset | Creates a dataset that contains `count` elements from the `input_dataset`. |
| TakeManySparseFromTensorsMap <T extends TType > | Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. |
| TakeManySparseFromTensorsMap.Options | Optional attributes for TakeManySparseFromTensorsMap |
| Tan <T extends TType > | x 요소별로 tan을 계산합니다. |
| Tanh <T extends TFloating > | Hyperbolic tangent activation function. |
| Tanh <T extends TType > | Computes hyperbolic tangent of `x` element-wise. |
| TanhGrad <T extends TType > | Computes the gradient for the tanh of `x` wrt its input. |
| TaskDeviceFilters | Defines the device filters for a remote task. |
| TaskDeviceFilters.Builder | Defines the device filters for a remote task. |
| TaskDeviceFiltersOrBuilder | |
| TBfloat16 | Brain 16-bit float tensor type. |
| TBfloat16Mapper | Maps memory of DT_BFLOAT16 tensors to a n-dimensional data space. |
| TBool | Boolean tensor type. |
| TBoolMapper | Maps memory of DT_BOOL tensors to a n-dimensional data space. |
| TemporaryVariable <T extends TType > | Returns a tensor that may be mutated, but only persists within a single step. |
| TemporaryVariable.Options | Optional attributes for TemporaryVariable |
| Tensor | A statically typed multi-dimensional array. |
| Tensor | |
| TensorArray | An array of Tensors of given size. |
| TensorArray.Options | Optional attributes for TensorArray |
| TensorArrayClose | Delete the TensorArray from its resource container. |
| TensorArrayConcat <T extends TType > | Concat the elements from the TensorArray into value `value`. |
| TensorArrayConcat.Options | Optional attributes for TensorArrayConcat |
| TensorArrayGather <T extends TType > | Gather specific elements from the TensorArray into output `value`. |
| TensorArrayGather.Options | Optional attributes for TensorArrayGather |
| TensorArrayGrad | Creates a TensorArray for storing the gradients of values in the given handle. |
| TensorArrayGradWithShape | Creates a TensorArray for storing multiple gradients of values in the given handle. |
| TensorArrayPack <T extends TType > | |
| TensorArrayPack.Options | Optional attributes for TensorArrayPack |
| TensorArrayRead <T extends TType > | Read an element from the TensorArray into output `value`. |
| TensorArrayScatter | Scatter the data from the input value into specific TensorArray elements. |
| TensorArraySize | Get the current size of the TensorArray. |
| TensorArraySplit | 입력 값의 데이터를 TensorArray 요소로 분할합니다. |
| TensorArrayUnpack | |
| TensorArrayWrite | Push an element onto the tensor_array. |
| TensorBuffers | Maps native tensor memory into DataBuffers , allowing I/O operations from the JVM. |
| TensorBundleProtos | |
| TensorConnection | Defines a connection between two tensors in a `GraphDef`. |
| TensorConnection.Builder | Defines a connection between two tensors in a `GraphDef`. |
| TensorConnectionOrBuilder | |
| TensorDataset | Creates a dataset that emits `components` as a tuple of tensors once. |
| TensorDebugMode | Available modes for extracting debugging information from a Tensor. |
| TensorDescription | Protobuf type tensorflow.TensorDescription |
| TensorDescription.Builder | Protobuf type tensorflow.TensorDescription |
| TensorDescriptionOrBuilder | |
| TensorDescriptionProtos | |
| TensorDiag <T extends TType > | Returns a diagonal tensor with a given diagonal values. |
| TensorDiagPart <T extends TType > | Returns the diagonal part of the tensor. |
| TensorFlow | Static utility methods describing the TensorFlow runtime. |
| tensorflow | |
| tensorflow | |
| TensorFlowException | Unchecked exception thrown by TensorFlow core classes |
| TensorForestCreateTreeVariable | Creates a tree resource and returns a handle to it. |
| TensorForestTreeDeserialize | Deserializes a proto into the tree handle |
| TensorForestTreeIsInitializedOp | Checks whether a tree has been initialized. |
| TensorForestTreePredict | Output the logits for the given input data |
| TensorForestTreeResourceHandleOp | Creates a handle to a TensorForestTreeResource |
| TensorForestTreeResourceHandleOp.Options | Optional attributes for TensorForestTreeResourceHandleOp |
| TensorForestTreeSerialize | Serializes the tree handle to a proto |
| TensorForestTreeSize | Get the number of nodes in a tree |
| TensorInfo | Information about a Tensor necessary for feeding or retrieval. |
| TensorInfo.Builder | Information about a Tensor necessary for feeding or retrieval. |
| TensorInfo.CompositeTensor | Generic encoding for composite tensors. |
| TensorInfo.CompositeTensor.Builder | Generic encoding for composite tensors. |
| TensorInfo.CompositeTensorOrBuilder | |
| TensorInfo.CooSparse | For sparse tensors, The COO encoding stores a triple of values, indices, and shape. |
| TensorInfo.CooSparse.Builder | For sparse tensors, The COO encoding stores a triple of values, indices, and shape. |
| TensorInfo.CooSparseOrBuilder | |
| TensorInfo.EncodingCase | |
| TensorInfoOrBuilder | |
| TensorListConcat <U extends TType > | Concats all tensors in the list along the 0th dimension. |
| TensorListConcatLists | |
| TensorListElementShape <T extends TNumber > | The shape of the elements of the given list, as a tensor. |
| TensorListFromTensor | Creates a TensorList which, when stacked, has the value of `tensor`. |
| TensorListGather <T extends TType > | Creates a Tensor by indexing into the TensorList. |
| TensorListGetItem <T extends TType > | |
| TensorListLength | Returns the number of tensors in the input tensor list. |
| TensorListPopBack <T extends TType > | Returns the last element of the input list as well as a list with all but that element. |
| TensorListPushBack | Returns a list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`. |
| TensorListPushBackBatch | |
| TensorListReserve | List of the given size with empty elements. |
| TensorListResize | Resizes the list. |
| TensorListScatter | Creates a TensorList by indexing into a Tensor. |
| TensorListScatterIntoExistingList | Scatters tensor at indices in an input list. |
| TensorListSetItem | |
| TensorListSplit | Splits a tensor into a list. |
| TensorListStack <T extends TType > | Stacks all tensors in the list. |
| TensorListStack.Options | Optional attributes for TensorListStack |
| TensorMapErase | Returns a tensor map with item from given key erased. |
| TensorMapHasKey | Returns whether the given key exists in the map. |
| TensorMapInsert | Returns a map that is the 'input_handle' with the given key-value pair inserted. |
| TensorMapLookup <U extends TType > | Returns the value from a given key in a tensor map. |
| TensorMapper <T extends TType > | Maps the native memory of a RawTensor to a n-dimensional typed data space accessible from the JVM. |
| TensorMapSize | Returns the number of tensors in the input tensor map. |
| TensorMapStackKeys <T extends TType > | Returns a Tensor stack of all keys in a tensor map. |
| TensorMetadata | Metadata for a single tensor in the Snapshot Record. |
| TensorMetadata.Builder | Metadata for a single tensor in the Snapshot Record. |
| TensorMetadataOrBuilder | |
| TensorProto | Protocol buffer representing a tensor. |
| TensorProto.Builder | Protocol buffer representing a tensor. |
| TensorProtoOrBuilder | |
| TensorProtos | |
| TensorScatterNdAdd <T extends TType > | Adds sparse `updates` to an existing tensor according to `indices`. |
| TensorScatterNdMax <T extends TType > | |
| TensorScatterNdMin <T extends TType > | |
| TensorScatterNdSub <T extends TType > | Subtracts sparse `updates` from an existing tensor according to `indices`. |
| TensorScatterNdUpdate <T extends TType > | Scatter `updates` into an existing tensor according to `indices`. |
| TensorShapeProto | Dimensions of a tensor. |
| TensorShapeProto.Builder | Dimensions of a tensor. |
| TensorShapeProto.Dim | One dimension of the tensor. |
| TensorShapeProto.Dim.Builder | One dimension of the tensor. |
| TensorShapeProto.DimOrBuilder | |
| TensorShapeProtoOrBuilder | |
| TensorShapeProtos | |
| TensorSliceDataset | |
| TensorSliceDataset | Creates a dataset that emits each dim-0 slice of `components` once. |
| TensorSliceProto | Can only be interpreted if you know the corresponding TensorShape. |
| TensorSliceProto.Builder | Can only be interpreted if you know the corresponding TensorShape. |
| TensorSliceProto.Extent | Extent of the slice in one dimension. |
| TensorSliceProto.Extent.Builder | Extent of the slice in one dimension. |
| TensorSliceProto.Extent.HasLengthCase | |
| TensorSliceProto.ExtentOrBuilder | |
| TensorSliceProtoOrBuilder | |
| TensorSliceProtos | |
| TensorSpecProto | A protobuf to represent tf.TensorSpec. |
| TensorSpecProto.Builder | A protobuf to represent tf.TensorSpec. |
| TensorSpecProtoOrBuilder | |
| TensorStridedSliceUpdate <T extends TType > | Assign `value` to the sliced l-value reference of `input`. |
| TensorStridedSliceUpdate.Options | Optional attributes for TensorStridedSliceUpdate |
| TensorSummary | Outputs a `Summary` protocol buffer with a tensor and per-plugin data. |
| TensorType | Annotation for all tensor types. |
| TensorTypeInfo <T extends TType > | Registered information about a tensor type. |
| TensorTypeRegistry | Repository of all registered tensor types. |
| TestLogProtos | |
| TestResults | The output of one benchmark / test run. |
| TestResults.BenchmarkType | The type of benchmark. |
| TestResults.Builder | The output of one benchmark / test run. |
| TestResultsOrBuilder | |
| TextLineDataset | |
| TextLineDataset | Creates a dataset that emits the lines of one or more text files. |
| TextLineReader | A Reader that outputs the lines of a file delimited by '\n'. |
| TextLineReader.Options | Optional attributes for TextLineReader |
| TF_AllocatorAttributes | |
| TF_ApiDefMap | |
| TF_AttrMetadata | |
| TF_Buffer | |
| TF_Buffer.Data_deallocator_Pointer_long | |
| TF_DeprecatedSession | |
| TF_DeviceList | |
| TF_DimensionHandle | |
| TF_Function | |
| TF_FunctionOptions | |
| TF_Graph | |
| TF_ImportGraphDefOptions | |
| TF_ImportGraphDefResults | |
| TF_Input | |
| TF_KernelBuilder | |
| TF_Library | |
| TF_OpDefinitionBuilder | |
| TF_Operation | |
| TF_OperationDescription | |
| TF_OpKernelConstruction | |
| TF_OpKernelContext | |
| TF_Output | |
| TF_Server | |
| TF_Session | |
| TF_SessionOptions | |
| TF_ShapeHandle | |
| TF_ShapeInferenceContext | |
| TF_Status | |
| TF_StringView | |
| TF_Tensor | |
| TF_TString | |
| TF_TString_Large | |
| TF_TString_Offset | |
| TF_TString_Raw | |
| TF_TString_Small | |
| TF_TString_Union | |
| TF_TString_View | |
| TF_WhileParams | |
| TFE_Context | |
| TFE_ContextOptions | |
| TFE_Op | |
| TFE_TensorDebugInfo | |
| TFE_TensorHandle | |
| TFFailedPreconditionException | |
| TFInvalidArgumentException | |
| TFloat16 | IEEE-754 half-precision 16-bit float tensor type. |
| TFloat16Mapper | Maps memory of DT_HALF tensors to a n-dimensional data space. |
| TFloat32 | IEEE-754 single-precision 32-bit float tensor type. |
| TFloat32Mapper | Maps memory of DT_FLOAT tensors to a n-dimensional data space. |
| TFloat64 | IEEE-754 double-precision 64-bit float tensor type. |
| TFloat64Mapper | Maps memory of DT_DOUBLE tensors to a n-dimensional data space. |
| TFloating | Common interface for all floating point tensors. |
| TFOutOfRangeException | |
| TFPermissionDeniedException | |
| TfRecordDataset | Creates a dataset that emits the records from one or more TFRecord files. |
| TFRecordDataset | |
| TfRecordReader | A Reader that outputs the records from a TensorFlow Records file. |
| TfRecordReader.Options | Optional attributes for TfRecordReader |
| TFResourceExhaustedException | |
| TFUnauthenticatedException | |
| TFUnimplementedException | |
| ThreadPoolDataset | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| ThreadPoolDataset | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| ThreadPoolHandle | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| ThreadPoolHandle | Creates a dataset that uses a custom thread pool to compute `input_dataset`. |
| ThreadPoolHandle.Options | Optional attributes for ThreadPoolHandle |
| ThreadPoolHandle.Options | Optional attributes for ThreadPoolHandle |
| ThreadPoolOptionProto | Protobuf type tensorflow.ThreadPoolOptionProto |
| ThreadPoolOptionProto.Builder | Protobuf type tensorflow.ThreadPoolOptionProto |
| ThreadPoolOptionProtoOrBuilder | |
| Tile <T extends TType > | Constructs a tensor by tiling a given tensor. |
| TileGrad <T extends TType > | Returns the gradient of `Tile`. |
| 타임스탬프 | Provides the time since epoch in seconds. |
| TInt32 | 32-bit signed integer tensor type. |
| TInt32Mapper | Maps memory of DT_INT32 tensors to a n-dimensional data space. |
| TInt64 | 64-bit signed integer tensor type. |
| TInt64Mapper | Maps memory of DT_INT64 tensors to a n-dimensional data space. |
| TIntegral | Common interface for all integral numeric tensors. |
| TNumber | Common interface for all numeric tensors. |
| ToBool | Converts a tensor to a scalar predicate. |
| ToHashBucket | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| ToHashBucketFast | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| ToHashBucketStrong | Converts each string in the input Tensor to its hash mod by a number of buckets. |
| ToNumber <T extends TNumber > | Converts each string in the input Tensor to the specified numeric type. |
| TopK <T extends TNumber > | Finds values and indices of the `k` largest elements for the last dimension. |
| TopK.Options | Optional attributes for TopK |
| TopKUnique | Returns the TopK unique values in the array in sorted order. |
| TopKWithUnique | Returns the TopK values in the array in sorted order. |
| TPUCompilationResult | Returns the result of a TPU compilation. |
| TPUEmbeddingActivations | An op enabling differentiation of TPU Embeddings. |
| TPUReplicatedInput <T extends TType > | Connects N inputs to an N-way replicated TPU computation. |
| TPUReplicatedInput.Options | Optional attributes for TPUReplicatedInput |
| TPUReplicatedOutput <T extends TType > | Connects N outputs from an N-way replicated TPU computation. |
| TPUReplicateMetadata | Metadata indicating how the TPU computation should be replicated. |
| TPUReplicateMetadata.Options | Optional attributes for TPUReplicateMetadata |
| TrackableObjectGraph | Protobuf type tensorflow.TrackableObjectGraph |
| TrackableObjectGraph.Builder | Protobuf type tensorflow.TrackableObjectGraph |
| TrackableObjectGraph.TrackableObject | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject |
| TrackableObjectGraph.TrackableObject.Builder | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject |
| TrackableObjectGraph.TrackableObject.ObjectReference | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference |
| TrackableObjectGraph.TrackableObject.ObjectReference.Builder | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference |
| TrackableObjectGraph.TrackableObject.ObjectReferenceOrBuilder | |
| TrackableObjectGraph.TrackableObject.SerializedTensor | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensor |
| TrackableObjectGraph.TrackableObject.SerializedTensor.Builder | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensor |
| TrackableObjectGraph.TrackableObject.SerializedTensorOrBuilder | |
| TrackableObjectGraph.TrackableObject.SlotVariableReference | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReference |
| TrackableObjectGraph.TrackableObject.SlotVariableReference.Builder | Protobuf type tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReference |
| TrackableObjectGraph.TrackableObject.SlotVariableReferenceOrBuilder | |
| TrackableObjectGraph.TrackableObjectOrBuilder | |
| TrackableObjectGraphOrBuilder | |
| TrackableObjectGraphProtos | |
| TransportOptions | |
| TransportOptions.RecvBufRespExtra | Extra data needed on a non-RDMA RecvBufResponse. |
| TransportOptions.RecvBufRespExtra.Builder | Extra data needed on a non-RDMA RecvBufResponse. |
| TransportOptions.RecvBufRespExtraOrBuilder | |
| Transpose <T extends TType > | Shuffle dimensions of x according to a permutation. |
| TriangularSolve <T extends TType > | Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. |
| TriangularSolve.Options | Optional attributes for TriangularSolve |
| TridiagonalMatMul <T extends TType > | Calculate product with tridiagonal matrix. |
| TridiagonalSolve <T extends TType > | Solves tridiagonal systems of equations. |
| TridiagonalSolve.Options | Optional attributes for TridiagonalSolve |
| TruncateDiv <T extends TType > | Returns x / y element-wise for integer types. |
| TruncatedNormal <T extends TFloating > | Initializer that generates a truncated normal distribution. |
| TruncatedNormal <U extends TNumber > | Outputs random values from a truncated normal distribution. |
| TruncatedNormal.Options | Optional attributes for TruncatedNormal |
| TruncateMod <T extends TNumber > | Returns element-wise remainder of division. |
| TryRpc | Perform batches of RPC requests. |
| TryRpc.Options | Optional attributes for TryRpc |
| TString | String type. |
| TStringInitializer <T> | Helper class for initializing a TString tensor. |
| TStringMapper | Maps memory of DT_STRING tensors to a n-dimensional data space. |
| TType | Common interface for all typed tensors. |
| TUint8 | 8-bit unsigned integer tensor type. |
| TUint8Mapper | Maps memory of DT_UINT8 tensors to a n-dimensional data space. |
| 튜플값 | Represents a Python tuple. |
| TupleValue.Builder | Represents a Python tuple. |
| TupleValueOrBuilder | |
| TypeSpecProto | Represents a tf.TypeSpec tensorflow.TypeSpecProto |
| TypeSpecProto.Builder | Represents a tf.TypeSpec tensorflow.TypeSpecProto |
| TypeSpecProto.TypeSpecClass | Protobuf enum tensorflow.TypeSpecProto.TypeSpecClass |
| TypeSpecProtoOrBuilder | |
| TypesProtos |
유
| Unbatch <T extends TType > | Reverses the operation of Batch for a single output Tensor. |
| Unbatch.Options | Optional attributes for Unbatch |
| UnbatchDataset | A dataset that splits the elements of its input into multiple elements. |
| UnbatchDataset | A dataset that splits the elements of its input into multiple elements. |
| UnbatchGrad <T extends TType > | Gradient of Unbatch. |
| UnbatchGrad.Options | Optional attributes for UnbatchGrad |
| UncompressElement | Uncompresses a compressed dataset element. |
| UnicodeDecode <T extends TNumber > | Decodes each string in `input` into a sequence of Unicode code points. |
| UnicodeDecode.Options | Optional attributes for UnicodeDecode |
| UnicodeDecodeWithOffsets <T extends TNumber > | Decodes each string in `input` into a sequence of Unicode code points. |
| UnicodeDecodeWithOffsets.Options | Optional attributes for UnicodeDecodeWithOffsets |
| UnicodeEncode | Encode a tensor of ints into unicode strings. |
| UnicodeEncode.Options | Optional attributes for UnicodeEncode |
| UnicodeScript | Determine the script codes of a given tensor of Unicode integer code points. |
| UnicodeTranscode | Transcode the input text from a source encoding to a destination encoding. |
| UnicodeTranscode.Options | Optional attributes for UnicodeTranscode |
| UniformCandidateSampler | Generates labels for candidate sampling with a uniform distribution. |
| UniformCandidateSampler.Options | Optional attributes for UniformCandidateSampler |
| Unique <T extends TType , V extends TNumber > | Finds unique elements along an axis of a tensor. |
| UniqueDataset | Creates a dataset that contains the unique elements of `input_dataset`. |
| UniqueDataset | Creates a dataset that contains the unique elements of `input_dataset`. |
| UniqueWithCounts <T extends TType , V extends TNumber > | Finds unique elements along an axis of a tensor. |
| UnitNorm | Constrains the weights to have unit norm. |
| UnravelIndex <T extends TNumber > | Converts an array of flat indices into a tuple of coordinate arrays. |
| UnsortedSegmentJoin | Joins the elements of `inputs` based on `segment_ids`. |
| UnsortedSegmentJoin.Options | Optional attributes for UnsortedSegmentJoin |
| UnsortedSegmentMax <T extends TNumber > | 텐서의 세그먼트를 따라 최대값을 계산합니다. |
| UnsortedSegmentMin <T extends TNumber > | Computes the minimum along segments of a tensor. |
| UnsortedSegmentProd <T extends TType > | Computes the product along segments of a tensor. |
| UnsortedSegmentSum <T extends TType > | 텐서의 세그먼트를 따라 합계를 계산합니다. |
| Unstack <T extends TType > | Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. |
| Unstack.Options | Optional attributes for Unstack |
| Unstage | Op is similar to a lightweight Dequeue. |
| Unstage.Options | Optional attributes for Unstage |
| UnwrapDatasetVariant | |
| 높은 | Converts all lowercase characters into their respective uppercase replacements. |
| Upper.Options | Optional attributes for Upper |
| UpperBound <U extends TNumber > | Applies upper_bound(sorted_search_values, values) along each row. |
다섯
| Validator | |
| Validator | |
| ValuesDef | Protocol buffer representing the values in ControlFlowContext. |
| ValuesDef.Builder | Protocol buffer representing the values in ControlFlowContext. |
| ValuesDefOrBuilder | |
| VarHandleOp | Creates a handle to a Variable resource. |
| VarHandleOp.Options | Optional attributes for VarHandleOp |
| Variable <T extends TType > | Holds state in the form of a tensor that persists across steps. |
| Variable.Options | Optional attributes for Variable |
| VariableAggregation | Indicates how a distributed variable will be aggregated. |
| VariableDef | Protocol buffer representing a Variable. |
| VariableDef.Builder | Protocol buffer representing a Variable. |
| VariableDefOrBuilder | |
| VariableProtos | |
| VariableShape <T extends TNumber > | Returns the shape of the variable pointed to by `resource`. |
| VariableSynchronization | Indicates when a distributed variable will be synced. |
| VarianceScaling <T extends TFloating > | Initializer capable of adapting its scale to the shape of weights tensors. |
| VarianceScaling.Distribution | The random distribution to use when initializing the values. |
| VarianceScaling.Mode | The mode to use for calculating the fan values. |
| VariantTensorDataProto | Protocol buffer representing the serialization format of DT_VARIANT tensors. |
| VariantTensorDataProto.Builder | Protocol buffer representing the serialization format of DT_VARIANT tensors. |
| VariantTensorDataProtoOrBuilder | |
| VarIsInitializedOp | Checks whether a resource handle-based variable has been initialized. |
| VarLenFeatureProto | Protobuf type tensorflow.VarLenFeatureProto |
| VarLenFeatureProto.Builder | Protobuf type tensorflow.VarLenFeatureProto |
| VarLenFeatureProtoOrBuilder | |
| VerifierConfig | The config for graph verifiers. |
| VerifierConfig.Builder | The config for graph verifiers. |
| VerifierConfig.Toggle | Protobuf enum tensorflow.VerifierConfig.Toggle |
| VerifierConfigOrBuilder | |
| VerifierConfigProtos | |
| VersionDef | Version information for a piece of serialized data There are different types of versions for each type of data (GraphDef, etc.), but they all have the same common shape described here. |
| VersionDef.Builder | Version information for a piece of serialized data There are different types of versions for each type of data (GraphDef, etc.), but they all have the same common shape described here. |
| VersionDefOrBuilder | |
| VersionsProtos |
여
| WatchdogConfig | Protobuf type tensorflow.WatchdogConfig |
| WatchdogConfig.Builder | Protobuf type tensorflow.WatchdogConfig |
| WatchdogConfigOrBuilder | |
| WeakPointerScope | A minimalist pointer scope only keeping weak references to its elements. |
| 어디 | Returns locations of nonzero / true values in a tensor. |
| WhileContextDef | Protocol buffer representing a WhileContext object. |
| WhileContextDef.Builder | Protocol buffer representing a WhileContext object. |
| WhileContextDefOrBuilder | |
| WholeFileReader | A Reader that outputs the entire contents of a file as a value. |
| WholeFileReader.Options | Optional attributes for WholeFileReader |
| WindowDataset | Combines (nests of) input elements into a dataset of (nests of) windows. |
| WorkerHealth | Current health status of a worker. |
| WorkerHeartbeat | Worker heartbeat op. |
| WorkerHeartbeatRequest | Protobuf type tensorflow.WorkerHeartbeatRequest |
| WorkerHeartbeatRequest.Builder | Protobuf type tensorflow.WorkerHeartbeatRequest |
| WorkerHeartbeatRequestOrBuilder | |
| WorkerHeartbeatResponse | Protobuf type tensorflow.WorkerHeartbeatResponse |
| WorkerHeartbeatResponse.Builder | Protobuf type tensorflow.WorkerHeartbeatResponse |
| WorkerHeartbeatResponseOrBuilder | |
| WorkerShutdownMode | Indicates the behavior of the worker when an internal error or shutdown signal is received. |
| WrapDatasetVariant | |
| WriteAudioSummary | Writes an audio summary. |
| WriteAudioSummary.Options | Optional attributes for WriteAudioSummary |
| WriteFile | Writes contents to the file at input filename. |
| WriteGraphSummary | Writes a graph summary. |
| WriteHistogramSummary | Writes a histogram summary. |
| WriteImageSummary | Writes an image summary. |
| WriteImageSummary.Options | Optional attributes for WriteImageSummary |
| WriteRawProtoSummary | Writes a serialized proto summary. |
| WriteScalarSummary | Writes a scalar summary. |
| WriteSummary | Writes a tensor summary. |
엑스
| Xdivy <T extends TType > | Returns 0 if x == 0, and x / y otherwise, elementwise. |
| XEvent | An XEvent is a trace event, optionally annotated with XStats. |
| XEvent.Builder | An XEvent is a trace event, optionally annotated with XStats. |
| XEvent.DataCase | |
| XEventMetadata | Metadata for an XEvent, corresponds to an event type and is shared by all XEvents with the same metadata_id. |
| XEventMetadata.Builder | Metadata for an XEvent, corresponds to an event type and is shared by all XEvents with the same metadata_id. |
| XEventMetadataOrBuilder | |
| XEventOrBuilder | |
| XlaRecvFromHost <T extends TType > | An op to receive a tensor from the host. |
| XlaSendToHost | An op to send a tensor to the host. |
| XlaSetBound | Set a bound for the given input value as a hint to Xla compiler, returns the same value. |
| XlaSpmdFullToShardShape <T extends TType > | An op used by XLA SPMD partitioner to switch from automatic partitioning to manual partitioning. |
| XlaSpmdShardToFullShape <T extends TType > | An op used by XLA SPMD partitioner to switch from manual partitioning to automatic partitioning. |
| XLine | An XLine is a timeline of trace events (XEvents). |
| XLine.Builder | An XLine is a timeline of trace events (XEvents). |
| XLineOrBuilder | |
| Xlog1py <T extends TType > | Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise. |
| Xlogy <T extends TType > | Returns 0 if x == 0, and x * log(y) otherwise, elementwise. |
| XPlane | An XPlane is a container of parallel timelines (XLines), generated by a profiling source or by post-processing one or more XPlanes. |
| XPlane.Builder | An XPlane is a container of parallel timelines (XLines), generated by a profiling source or by post-processing one or more XPlanes. |
| XPlaneOrBuilder | |
| XPlaneProtos | |
| XSpace | A container of parallel XPlanes, generated by one or more profiling sources. |
| XSpace.Builder | A container of parallel XPlanes, generated by one or more profiling sources. |
| XSpaceOrBuilder | |
| XStat | An XStat is a named value associated with an XEvent, e.g., a performance counter value, a metric computed by a formula applied over nested XEvents and XStats. |
| XStat.Builder | An XStat is a named value associated with an XEvent, e.g., a performance counter value, a metric computed by a formula applied over nested XEvents and XStats. |
| XStat.ValueCase | |
| XStatMetadata | Metadata for an XStat, corresponds to a stat type and is shared by all XStats with the same metadata_id. |
| XStatMetadata.Builder | Metadata for an XStat, corresponds to a stat type and is shared by all XStats with the same metadata_id. |
| XStatMetadataOrBuilder | |
| XStatOrBuilder |
지
| Zeros <T extends TType > | Creates an Initializer that sets all values to zero. |
| Zeros <T extends TType > | An operator creating a constant initialized with zeros of the shape given by `dims`. |
| ZerosLike <T extends TType > | Returns a tensor of zeros with the same shape and type as x. |
| Zeta <T extends TNumber > | Compute the Hurwitz zeta function \\(\zeta(x, q)\\). |
| ZipDataset | Creates a dataset that zips together `input_datasets`. |