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| アボート | 呼び出されたときに例外を発生させてプロセスを中止します。 |
| 中止オプション | Abortのオプション属性 |
| Abs <T extends TNumber > | テンソルの絶対値を計算します。 |
| 抽象データバッファ<T> | |
| AbstractDataBufferWindow <B extends DataBuffer <?>> | |
| AbstractDenseNdArray <T, U はNdArray <T>> を拡張します | |
| AbstractNdArray <T、U はNdArray <T>> を拡張します | |
| AbstractTF_Buffer | |
| 抽象TF_グラフ | |
| AbstractTF_ImportGraphDefOptions | |
| AbstractTF_Session | |
| AbstractTF_SessionOptions | |
| 抽象TF_ステータス | |
| AbstractTF_Tensor | |
| 抽象TFE_コンテキスト | |
| AbstractTFE_ContextOptions | |
| 抽象TFE_Op | |
| AbstractTFE_TensorHandle | |
| AccumulateN <T はTTypeを拡張 > | テンソルのリストの要素ごとの合計を返します。 |
| アキュムレータApplyGradient | 指定されたアキュムレータに勾配を適用します。 |
| アキュムレータNumAccumulated | 指定されたアキュムレータに集約された勾配の数を返します。 |
| アキュムレータセットグローバルステップ | アキュムレータを global_step の新しい値で更新します。 |
| AccumulatorTakeGradient <T extends TType > | 指定された ConditionalAccumulator の平均勾配を抽出します。 |
| Acos <T はTTypeを拡張 > | x の acos を要素ごとに計算します。 |
| Acosh <T はTTypeを拡張 > | x の逆双曲線余弦を要素ごとに計算します。 |
| アクティベーション<T extends TNumber > | アクティベーションの抽象基本クラス 注: |
| エイダデルタ | Adadelta アルゴリズムを実装するオプティマイザー。 |
| アダグラード | Adagrad アルゴリズムを実装するオプティマイザー。 |
| アダグラッドDA | Adagrad Dual-Averaging アルゴリズムを実装するオプティマイザー。 |
| アダム | Adam アルゴリズムを実装するオプティマイザー。 |
| アダマックス | Adamax アルゴリズムを実装するオプティマイザー。 |
| <T extends TType >を追加します | x + y を要素ごとに返します。 |
| AddManySparseToTensorsMap | 「N」ミニバッチ「SparseTensor」を「SparseTensorsMap」に追加し、「N」ハンドルを返します。 |
| AddManySparseToTensorsMap.Options | AddManySparseToTensorsMapのオプションの属性 |
| AddN <T はTTypeを拡張 > | すべての入力テンソルを要素ごとに追加します。 |
| SparseToTensorsMap を追加 | `SparseTensor` を `SparseTensorsMap` に追加すると、そのハンドルが返されます。 |
| AddSparseToTensorsMap.Options | AddSparseToTensorsMapのオプションの属性 |
| AdjustContrast <T extends TNumber > | 1 つまたは複数の画像のコントラストを調整します。 |
| AdjustHue <T extends TNumber > | 1 つまたは複数の画像の色相を調整します。 |
| AdjustSaturation <T extends TNumber > | 1 つまたは複数の画像の彩度を調整します。 |
| 全て | テンソルの次元全体で要素の「論理積」を計算します。 |
| すべてのオプション | Allのオプションの属性 |
| すべての候補者サンプラー | 学習されたユニグラム分布を使用して候補サンプリングのラベルを生成します。 |
| AllCandidateSampler.Options | AllCandidateSamplerのオプションの属性 |
| 割り当ての説明 | Protobuf 型tensorflow.AllocationDescription |
| AllocationDescription.Builder | Protobuf 型tensorflow.AllocationDescription |
| 割り当ての説明またはビルダー | |
| 割り当て説明プロトス | |
| 割り当て記録 | An allocation/de-allocation operation performed by the allocator. |
| AllocationRecord.Builder | An allocation/de-allocation operation performed by the allocator. |
| 割り当てレコードまたはビルダー | |
| アロケータ使用済みメモリ | Protobuf 型tensorflow.AllocatorMemoryUsed |
| AllocatorMemory Used.Builder | Protobuf 型tensorflow.AllocatorMemoryUsed |
| アロケータメモリ使用済みまたはビルダー | |
| AllReduce <T extends TNumber > | 同じタイプと形状の複数のテンソルを相互に削減します。 |
| AllReduce.オプション | AllReduceのオプションの属性 |
| AllToAll <T extends TType > | TPU レプリカ間でデータを交換する Op。 |
| 角度<U はTNumberを延長 > | 複素数の引数を返します。 |
| 匿名反復子 | イテレータリソースのコンテナ。 |
| 匿名メモリキャッシュ | |
| AnonymousMultiDeviceIterator | マルチデバイス反復子リソースのコンテナー。 |
| 匿名ランダムシードジェネレーター | |
| 匿名シードジェネレーター | |
| どれでも | テンソルの次元にわたる要素の「論理和」を計算します。 |
| 任意のオプション | Anyのオプション属性 |
| アピデフ | 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 型tensorflow.ApiDef.Arg |
| ApiDef.Arg.Builder | Protobuf 型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.エンドポイント | 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 enum tensorflow.ApiDef.Visibility |
| ApiDefOrBuilder | |
| ApiDefProtos | |
| APIDefs | Protobuf 型tensorflow.ApiDefs |
| ApiDefs.Builder | Protobuf 型tensorflow.ApiDefs |
| APIDefsOrBuilder | |
| applyAdadelta <T extends TType > | adadelta スキームに従って「*var」を更新します。 |
| Adadelta.Options の適用 | ApplyAdadeltaのオプションの属性 |
| applyAdagrad <T はTTypeを拡張 > | adagrad スキームに従って「*var」を更新します。 |
| 適用Adagrad.オプション | ApplyAdagradのオプションの属性 |
| applyAdagradDa <T はTTypeを拡張 > | 近位の adagrad スキームに従って「*var」を更新します。 |
| AdagradDa.Options の適用 | ApplyAdagradDaのオプションの属性 |
| applyAdagradV2 <T はTTypeを拡張 > | adagrad スキームに従って「*var」を更新します。 |
| 適用AdagradV2.オプション | ApplyAdagradV2のオプションの属性 |
| applyAdam <T extends TType > | Adam アルゴリズムに従って「*var」を更新します。 |
| Adam.Options の適用 | ApplyAdamのオプションの属性 |
| applyAdaMax <T はTTypeを拡張 > | AdaMax アルゴリズムに従って「*var」を更新します。 |
| AdaMax.Options の適用 | ApplyAdaMaxのオプションの属性 |
| applyAddSign <T はTTypeを拡張 > | AddSign の更新に従って「*var」を更新します。 |
| applyAddSign.Options | ApplyAddSignのオプションの属性 |
| applyCenteredRmsProp <T はTTypeを拡張 > | 中心の RMSProp アルゴリズムに従って「*var」を更新します。 |
| applyCenteredRmsProp.Options | ApplyCenteredRmsPropのオプションの属性 |
| applyFtrl <T はTTypeを拡張 > | Ftrl-proximal スキームに従って「*var」を更新します。 |
| 適用Ftrl.オプション | ApplyFtrlのオプションの属性 |
| applyGradientDescent <T extends TType > | '*var' から 'alpha' * 'delta' を減算して更新します。 |
| ApplyGradientDescent.Options | ApplyGradientDescentのオプションの属性 |
| applyMomentum <T はTTypeを拡張 > | 運動量スキームに従って「*var」を更新します。 |
| モメンタムの適用オプション | ApplyMomentumのオプションの属性 |
| applyPowerSign <T はTTypeを拡張 > | AddSign の更新に従って「*var」を更新します。 |
| applyPowerSign.Options | ApplyPowerSignのオプションの属性 |
| applyProximalAdagrad <T はTTypeを拡張 > | Adagrad 学習率の FOBOS に従って「*var」と「*accum」を更新します。 |
| applyProximalAdagrad.Options | ApplyProximalAdagradのオプションの属性 |
| applyProximalGradientDescent <T extends TType > | 「*var」を固定学習率の FOBOS アルゴリズムとして更新します。 |
| applyProximalGradientDescent.Options | ApplyProximalGradientDescentのオプションの属性 |
| applyRmsProp <T はTTypeを拡張 > | RMSProp アルゴリズムに従って「*var」を更新します。 |
| applyRmsProp.Options | ApplyRmsPropのオプションの属性 |
| ほぼ等しい | abs(xy) < 許容誤差の要素ごとの真理値を返します。 |
| 近似等しいオプション | ApproximateEqualのオプションの属性 |
| ArgMax <V はTNumberを拡張 > | テンソルの次元全体で最大値を持つインデックスを返します。 |
| ArgMin <V はTNumberを拡張 > | テンソルの次元全体で最小値を持つインデックスを返します。 |
| Asin <T extends TType > | x の三角関数の逆サインを要素ごとに計算します。 |
| Asinh <T はTTypeを拡張 > | x の逆双曲線正弦を要素ごとに計算します。 |
| AssertCardinalityDataset | |
| AssertNextDataset | 次にどの変換が起こるかを表明する変換。 |
| AssertNextDataset | |
| アサートそれ | 指定された条件が true であることをアサートします。 |
| AssertThat.オプション | AssertThatのオプションの属性 |
| アセットファイル定義 | 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 extends TType > を割り当てます | 「value」を代入して「ref」を更新します。 |
| 割り当てオプション | Assignのオプション属性 |
| AssignAdd <T extends TType > | 「value」を追加して「ref」を更新します。 |
| 追加オプションの割り当て | AssignAddのオプションの属性 |
| AssignAddVariableOp | 変数の現在の値に値を追加します。 |
| AssignSub <T はTTypeを拡張 > | 'ref' から 'value' を減算して更新します。 |
| サブオプションの割り当て | AssignSubのオプションの属性 |
| AssignSubVariableOp | 変数の現在の値から値を減算します。 |
| 変数の割り当て操作 | 変数に新しい値を代入します。 |
| 文字列として | 指定されたテンソルの各エントリを文字列に変換します。 |
| AsString.Options | AsStringのオプションの属性 |
| Atan <T はTTypeを拡張 > | x の三角関数逆正接を要素ごとに計算します。 |
| Atan2 <T はTNumberを拡張 > | 引数の符号を考慮して、「y/x」の逆正接を要素ごとに計算します。 |
| アタン<T はTTypeを拡張 > | x の逆双曲線正接を要素ごとに計算します。 |
| 属性値 | 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. |
| AttrValue.ListValue | LINT.IfChangeProtobuf 型 tensorflow.AttrValue.ListValue |
| AttrValue.ListValue.Builder | LINT.IfChangeProtobuf 型 tensorflow.AttrValue.ListValue |
| AttrValue.ListValueOrBuilder | |
| AttrValue.ValueCase | |
| AttrValueOrBuilder | |
| 属性値プロトス | |
| オーディオスペクトログラム | 時間の経過に伴うオーディオ データの視覚化を生成します。 |
| AudioSpectrogram.オプション | AudioSpectrogramのオプションの属性 |
| オーディオ概要 | 音声付きの「概要」プロトコル バッファを出力します。 |
| Audiosummary.オプション | AudioSummaryのオプションの属性 |
| AutoParallelオプション | Protobuf 型tensorflow.AutoParallelOptions |
| AutoParallelOptions.Builder | Protobuf 型tensorflow.AutoParallelOptions |
| AutoParallelOptionsOrBuilder | |
| AutoShardDataset | 入力データセットをシャーディングするデータセットを作成します。 |
| AutoShardDataset | 入力データセットをシャーディングするデータセットを作成します。 |
| AutoShardDataset.Options | AutoShardDatasetのオプションの属性 |
| AutoShardDataset.Options | AutoShardDatasetのオプションの属性 |
| 利用可能なデバイス情報 | Matches DeviceAttributesProtobuf 型 tensorflow.AvailableDeviceInfo |
| 利用可能なデバイス情報ビルダー | Matches DeviceAttributesProtobuf 型 tensorflow.AvailableDeviceInfo |
| 利用可能なデバイス情報またはビルダー | |
| AvgPool <T はTNumberを拡張 > | 入力に対して平均プーリングを実行します。 |
| AvgPool.オプション | AvgPoolのオプションの属性 |
| AvgPool3d <T はTNumberを拡張 > | 入力に対して 3D 平均プーリングを実行します。 |
| AvgPool3d.オプション | AvgPool3dのオプションの属性 |
| AvgPool3dGrad <T はTNumberを拡張 > | 平均プーリング関数の勾配を計算します。 |
| AvgPool3dGrad.オプション | AvgPool3dGradのオプションの属性 |
| AvgPoolGrad <T はTNumberを拡張 > | 平均プーリング関数の勾配を計算します。 |
| AvgPoolGrad.オプション | AvgPoolGradのオプションの属性 |
B
| BandedTriangularSolve <T extends TType > | |
| BandedTriangularSolve.Options | BandedTriangularSolveのオプションの属性 |
| BandPart <T はTTypeを拡張 > | 最も内側の各行列の中心バンドの外側をすべてゼロに設定するテンソルをコピーします。 |
| バリア | 異なるグラフ実行にわたって持続するバリアを定義します。 |
| バリアオプション | Barrierのオプション属性 |
| バリア閉じる | 指定されたバリアを閉じます。 |
| BarrierClose.オプション | BarrierCloseのオプションの属性 |
| バリア不完全サイズ | 指定されたバリア内の不完全な要素の数を計算します。 |
| バリア挿入多く | 各キーについて、指定されたコンポーネントにそれぞれの値を割り当てます。 |
| バリアレディサイズ | 指定されたバリア内の完全な要素の数を計算します。 |
| バリアテイクメニー | バリアから指定された数の完了した要素を取得します。 |
| BarrierTakeMany.オプション | BarrierTakeManyのオプションの属性 |
| BaseInitializer <T はTTypeを拡張 > | すべてのイニシャライザの抽象基本クラス |
| バッチ | すべての入力テンソルを非決定的にバッチ処理します。 |
| バッチオプション | Batchのオプションの属性 |
| BatchCholesky <T extends TNumber > | |
| BatchCholeskyGrad <T extends TNumber > | |
| バッチデータセット | |
| バッチデータセット | `input_dataset` から `batch_size` 要素をバッチ処理するデータセットを作成します。 |
| BatchDataset.Options | BatchDatasetのオプションの属性 |
| バッチFft | |
| バッチFft2d | |
| バッチFft3d | |
| バッチイフト | |
| バッチIfft2d | |
| バッチIfft3d | |
| BatchMatMul <T はTTypeを拡張 > | 2 つのテンソルのスライスをバッチで乗算します。 |
| BatchMatMul.オプション | BatchMatMulのオプションの属性 |
| BatchMatrixBandPart <T はTTypeを拡張 > | |
| BatchMatrixDeterminant <T extends TType > | |
| BatchMatrixDiag <T はTTypeを拡張 > | |
| BatchMatrixDiagPart <T はTTypeを拡張 > | |
| BatchMatrixInverse <T extends TNumber > | |
| BatchMatrixInverse.Options | BatchMatrixInverseのオプションの属性 |
| BatchMatrixSetDiag <T はTTypeを拡張 > | |
| BatchMatrixSolve <T extends TNumber > | |
| BatchMatrixSolve.オプション | BatchMatrixSolveのオプションの属性 |
| BatchMatrixSolveLs <T extends TNumber > | |
| BatchMatrixSolveLs.オプション | BatchMatrixSolveLsのオプションの属性 |
| BatchMatrixTriangularSolve <T extends TNumber > | |
| BatchMatrixTriangularSolve.Options | BatchMatrixTriangularSolveのオプションの属性 |
| BatchNormWithGlobalNormalization <T extends TType > | バッチ正規化。 |
| BatchNormWithGlobalNormalizationGrad <T extends TType > | バッチ正規化のための勾配。 |
| BatchSelfAdjointEig <T extends TNumber > | |
| BatchSelfAdjointEig.Options | BatchSelfAdjointEigのオプションの属性 |
| BatchSvd <T はTTypeを拡張 > | |
| BatchSvd.オプション | BatchSvdのオプションの属性 |
| BatchToSpace <T extends TType > | T 型の 4 次元テンソルの BatchToSpace。 |
| BatchToSpaceNd <T はTTypeを拡張 > | T 型の ND テンソルの BatchToSpace。 |
| ベンチマークエントリ | Protobuf 型tensorflow.BenchmarkEntries |
| BenchmarkEntries.Builder | Protobuf 型tensorflow.BenchmarkEntries |
| ベンチマークエントリまたはビルダー | |
| ベンチマークエントリー | 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. |
| BenchmarkEntryOrBuilder | |
| BesselI0 <T はTNumberを拡張 > | |
| BesselI0e <T はTNumberを拡張 > | |
| BesselI1 <T はTNumberを拡張 > | |
| BesselI1e <T はTNumberを拡張 > | |
| BesselJ0 <T はTNumberを拡張 > | |
| BesselJ1 <T はTNumberを拡張 > | |
| BesselK0 <T はTNumberを拡張 > | |
| BesselK0e <T はTNumberを拡張 > | |
| BesselK1 <T はTNumberを拡張 > | |
| BesselK1e <T はTNumberを拡張 > | |
| BesselY0 <T はTNumberを拡張 > | |
| BesselY1 <T はTNumberを拡張 > | |
| Betainc <T extends TNumber > | 正規化された不完全ベータ積分を計算します \\(I_x(a, b)\\)。 |
| Bfcメモリマッププロトス | |
| Bfloat16レイアウト | 32 ビット浮動小数点数と 16 ビット浮動小数点数を変換し、仮数部を 7 ビットに切り捨てますが、同じバイアスで 8 ビットの指数を保持するデータ レイアウト。 |
| BiasAdd <T extends TType > | 「値」に「バイアス」を追加します。 |
| BiasAdd.オプション | BiasAddのオプションの属性 |
| BiasAddGrad <T はTTypeを拡張 > | 「バイアス」テンソルに対する「BiasAdd」の逆方向操作。 |
| BiasAddGrad.Options | BiasAddGradのオプションの属性 |
| バイナリクロセントロピー | 真のラベルと予測されたラベル間のクロスエントロピー損失を計算します。 |
| BinaryCrossentropy <T extends TNumber > | 真のラベルと予測されたラベル間のバイナリ クロス エントロピー損失を計算するメトリック。 |
| ビンカウント<T extends TNumber > | 整数配列内の各値の出現数をカウントします。 |
| ビンの概要 | Protobuf 型tensorflow.BinSummary |
| Binsummary.Builder | Protobuf 型tensorflow.BinSummary |
| ビンの概要またはビルダー | |
| ビットキャスト<U はTTypeを拡張 > | データをコピーせずに、ある型から別の型にテンソルをビットキャストします。 |
| BitwiseAnd <T extends TNumber > | Elementwise は、「x」と「y」のビット単位の AND を計算します。 |
| BitwiseOr <T extends TNumber > | Elementwise は、「x」と「y」のビット単位の OR を計算します。 |
| BitwiseXor <T extends TNumber > | Elementwise は、「x」と「y」のビットごとの XOR を計算します。 |
| BlockLSTM <T はTNumberを拡張 > | すべてのタイム ステップについて LSTM セルの順方向伝播を計算します。 |
| BlockLSTM.オプション | BlockLSTMのオプションの属性 |
| BlockLSTMGrad <T extends TNumber > | 時系列全体に対する LSTM セルの逆方向伝播を計算します。 |
| ブールデータバッファ | ブール値のDataBuffer 。 |
| BooleanDataLayout <S extends DataBuffer <?>> | バッファーに格納されたデータをブール値に変換するDataLayout 。 |
| BooleanDenseNdArray | |
| ブールマスク | |
| BooleanMask.Options | BooleanMaskのオプションの属性 |
| ブールマスク更新 | |
| BooleanMaskUpdate.Options | BooleanMaskUpdateのオプションの属性 |
| ブール値NdArray | ブール値のNdArray 。 |
| ブールレイアウト | ブール値をバイトに変換するデータ レイアウト。 |
| BoostedTrees集計統計 | バッチの蓄積された統計の概要を集計します。 |
| ブーストツリーバケット化 | バケット境界に基づいて各機能をバケット化します。 |
| BoostedTrees計算BestFeature分割 | 各特徴のゲインを計算し、その特徴に対して可能な限り最適な分割情報を返します。 |
| BoostedTreesCalculateBestFeatureSplit.Options | BoostedTreesCalculateBestFeatureSplitのオプションの属性 |
| BoostedTrees計算BestFeatureSplitV2 | 各機能のゲインを計算し、各ノードについて可能な限り最適な分割情報を返します。 |
| BoostedTrees計算BestGainsPereture | 各特徴のゲインを計算し、その特徴に対して可能な限り最適な分割情報を返します。 |
| ブーストツリーセンターバイアス | トレーニング データから事前分布 (バイアス) を計算し、最初のノードにロジットの事前分布を入力します。 |
| BoostedTrees作成アンサンブル | ツリー アンサンブル モデルを作成し、そのモデルへのハンドルを返します。 |
| BoostedTreesCreateQuantileStreamResource | 分位数ストリームのリソースを作成します。 |
| BoostedTreesCreateQuantileStreamResource.Options | BoostedTreesCreateQuantileStreamResourceのオプションの属性 |
| BoostedTreesDeserializeアンサンブル | シリアル化されたツリー アンサンブル構成を逆シリアル化し、現在のツリーを置き換えます。 アンサンブル。 |
| BoostedTreesアンサンブルリソースハンドルOp | BoostedTreesEnsembleResource へのハンドルを作成します |
| BoostedTreesEnsembleResourceHandleOp.Options | BoostedTreesEnsembleResourceHandleOpのオプションの属性 |
| ブーストツリーの例デバッグ出力 | 各例のデバッグ/モデルの解釈可能性の出力。 |
| BoostedTreesFlushQuantileサマリー | 各分位ストリーム リソースから分位サマリーをフラッシュします。 |
| BoostedTreesGetEnsembleStates | ツリー アンサンブル リソース スタンプ トークン、ツリーの数、および成長統計を取得します。 |
| BoostedTreesMakeQuantile要約 | バッチの分位数の要約を作成します。 |
| BoostedTreesMakeStats概要 | バッチの蓄積された統計の概要を作成します。 |
| ブーストツリー予測 | 入力インスタンスに対して複数の加法回帰アンサンブル予測子を実行し、 ロジットを計算します。 |
| BoostedTreesQuantileStreamResourceAddSummaries | 分位値の要約を各分位値ストリーム リソースに追加します。 |
| BoostedTreesQuantileStreamResourceDeserialize | バケット境界と準備完了フラグを現在の QuantileAccumulator に逆シリアル化します。 |
| BoostedTreesQuantileStreamリソースフラッシュ | 分位点ストリーム リソースの概要をフラッシュします。 |
| BoostedTreesQuantileStreamResourceFlush.Options | BoostedTreesQuantileStreamResourceFlushのオプションの属性 |
| BoostedTreesQuantileStreamResourceGetBucketBoundaries | 蓄積されたサマリーに基づいて、各フィーチャのバケット境界を生成します。 |
| BoostedTreesQuantileStreamResourceHandleOp | BoostedTreesQuantileStreamResource へのハンドルを作成します。 |
| BoostedTreesQuantileStreamResourceHandleOp.Options | BoostedTreesQuantileStreamResourceHandleOpのオプションの属性 |
| BoostedTreesSerializeアンサンブル | ツリー アンサンブルをプロトにシリアル化します。 |
| BoostedTreesSparseAggregateStats | バッチの蓄積された統計の概要を集計します。 |
| ブーストツリースパース計算ベスト機能スプリット | 各特徴のゲインを計算し、その特徴に対して可能な限り最適な分割情報を返します。 |
| BoostedTreesSparseCalculateBestFeatureSplit.Options | BoostedTreesSparseCalculateBestFeatureSplitのオプション属性 |
| ブーストツリートレーニング予測 | 入力インスタンスに対して複数の加法回帰アンサンブル予測子を実行し、 キャッシュされたロジットの更新を計算します。 |
| BoostedTreesUpdateEnsemble | 成長している最後のツリーにレイヤーを追加することによって、ツリー アンサンブルを更新します。 または、新しいツリーを開始します。 |
| BoostedTreesUpdateEnsembleV2 | 成長している最後のツリーにレイヤーを追加して、ツリー アンサンブルを更新します。 または、新しいツリーを開始します。 |
| BoostedTreesUpdateEnsembleV2.オプション | BoostedTreesUpdateEnsembleV2のオプションの属性 |
| BoundedTensorSpecProto | A protobuf to represent tf.BoundedTensorSpec. |
| BoundedTensorSpecProto.Builder | A protobuf to represent tf.BoundedTensorSpec. |
| BoundedTensorSpecProtoOrBuilder | |
| BroadcastDynamicShape <T extends TNumber > | s0 op s1 の形状をブロードキャストで返します。 |
| BroadcastGradientArgs <T extends TNumber > | ブロードキャストで s0 op s1 の勾配を計算するためのリダクション インデックスを返します。 |
| BroadcastHelper <T はTTypeを拡張 > | XLA スタイルのブロードキャストを実行するためのヘルパー オペレーター 二項演算子に対する XLA のブロードキャスト ルールを使用して、'lhs' と 'rhs' のランクが低い方にサイズ 1 の次元を追加することにより、'lhs' と 'rhs' を同じランクにブロードキャストします。 |
| BroadcastRecv <T はTTypeを拡張 > | 別のデバイスからブロードキャストされたテンソル値を受信します。 |
| ブロードキャスト受信オプション | BroadcastRecvのオプションの属性 |
| BroadcastSend <T はTTypeを拡張 > | テンソル値を 1 つ以上の他のデバイスにブロードキャストします。 |
| BroadcastSend.オプション | BroadcastSendのオプションの属性 |
| BroadcastTo <T extends TType > | 互換性のある形状の配列をブロードキャストします。 |
| バケット化 | 「境界」に基づいて「入力」をバケット化します。 |
| ビルド構成 | Protobuf 型tensorflow.BuildConfiguration |
| BuildConfiguration.Builder | Protobuf 型tensorflow.BuildConfiguration |
| ビルド構成またはビルダー | |
| バンドルエントリープロト | Describes the metadata related to a checkpointed tensor. |
| BundleEntryProto.Builder | Describes the metadata related to a checkpointed tensor. |
| BundleEntryProtoOrBuilder | |
| バンドルヘッダープロト | 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. |
| バンドルヘッダープロトまたはビルダー | |
| バイトデータバッファ | バイトのDataBuffer 。 |
| ByteDataLayout <S extends DataBuffer <?>> | バッファーに格納されたデータをバイトに変換するDataLayout 。 |
| ByteDenseNdArray | |
| ByteNDArray | バイトのNdArray 。 |
| ByteSequenceProvider <T> | ByteSequenceTensorBufferに格納されるバイトのシーケンスを生成します。 |
| ByteSequenceTensorBuffer | 文字列テンソルデータを保存するためのバッファ。 |
| バイトリスト | Containers to hold repeated fundamental values. |
| BytesList.Builder | Containers to hold repeated fundamental values. |
| BytesListOrBuilder | |
| BytesProducedStatsDataset | StatsAggregator の `input_dataset` の各要素のバイト サイズを記録します。 |
| BytesProducedStatsDataset | StatsAggregator の `input_dataset` の各要素のバイト サイズを記録します。 |
C
| キャッシュデータセット | `input_dataset` から要素をキャッシュするデータセットを作成します。 |
| キャッシュデータセットV2 | |
| 呼び出し可能なオプション | 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 | |
| キャスト<U extends TType > | SrcT 型の x を DstT の y にキャストします。 |
| キャストオプション | Castのオプションの属性 |
| キャストヘルパー | オペランドをキャストするためのヘルパー クラス |
| カテゴリクロセントロピー | ラベルと予測の間のクロスエントロピー損失を計算します。 |
| CategoricalCrossentropy <T extends TNumber > | 真のラベルと予測されたラベル間のカテゴリカルなクロスエントロピー損失を計算するメトリック。 |
| カテゴリカルヒンジ | ラベルと予測の間のカテゴリカル ヒンジ損失を計算します。 |
| CategoricalHinge <T extends TNumber > | ラベルと予測の間のカテゴリカル ヒンジ損失メトリックを計算するメトリック。 |
| Ceil <T extends TNumber > | x 以上の要素ごとの最小の整数を返します。 |
| CheckNumerics <T extends TNumber > | テンソルの NaN、-Inf、+Inf 値をチェックします。 |
| Cholesky <T はTTypeを拡張 > | 1 つ以上の正方行列のコレスキー分解を計算します。 |
| CholeskyGrad <T extends TNumber > | コレスキー アルゴリズムの逆伝搬モードの逆伝播勾配を計算します。 |
| 最速のデータセットを選択してください | |
| 最速のデータセットを選択してください | |
| ClipByValue <T はTTypeを拡張 > | テンソル値を指定された最小値と最大値にクリップします。 |
| 閉じる概要ライター | |
| クラスター定義 | Defines a TensorFlow cluster as a set of jobs. |
| ClusterDef.Builder | Defines a TensorFlow cluster as a set of jobs. |
| ClusterDefOrBuilder | |
| クラスターデバイスフィルター | Defines the device filters for jobs in a cluster. |
| ClusterDeviceFilters.Builder | Defines the device filters for jobs in a cluster. |
| ClusterDeviceFiltersOrBuilder | |
| ClusterOutput <T extends TType > | XLA 計算の出力を他のコンシューマー グラフ ノードに接続する演算子。 |
| クラスタープロトス | |
| コード | The canonical error codes for TensorFlow APIs. |
| コードの場所 | Code location information: A stack trace with host-name information. |
| コードロケーションビルダー | Code location information: A stack trace with host-name information. |
| コード場所またはビルダー | |
| コレクション定義 | 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 | |
| コレクション定義ビルダー | 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 | |
| コレクションDef.KindCase | |
| CollectionDef.NodeList | NodeList is used for collecting nodes in graph. |
| CollectionDef.NodeList.Builder | NodeList is used for collecting nodes in graph. |
| CollectionDef.NodeListOrBuilder | |
| コレクション定義またはビルダー | |
| CollectiveGather <T extends TNumber > | 同じタイプと形状の複数のテンソルを相互に蓄積します。 |
| CollectiveGather.オプション | CollectiveGatherのオプションの属性 |
| CollectivePermute <T extends TType > | レプリケートされた TPU インスタンス全体でテンソルを並べ替える Op。 |
| 複合非最大抑制 | スコアの降順で境界ボックスのサブセットを貪欲に選択します。 この操作は、すべてのクラスにわたって、バッチごとの入力に対して non_max_suppression を実行します。 |
| CombinedNonMaxSuppression.Options | CombinedNonMaxSuppressionのオプションの属性 |
| コミットID | Protobuf 型tensorflow.CommitId |
| CommitId.Builder | Protobuf 型tensorflow.CommitId |
| CommitId.KindCase | |
| コミットIdOrBuilder | |
| 比較とビットパック | 「input」の値を「threshold」と比較し、結果のビットを「uint8」にパックします。 |
| コンパイル結果 | TPU コンパイルの結果を返します。 |
| コンパイル成功アサート | コンパイルが成功したことをアサートします。 |
| 複合<U はTTypeを拡張 > | 2 つの実数を複素数に変換します。 |
| ComplexAbs <U はTNumberを拡張 > | テンソルの複素絶対値を計算します。 |
| 要素の圧縮 | データセット要素を圧縮します。 |
| Compute_func_Pointer_TF_OpKernelContext | |
| 偶発的ヒットを計算する | true_labels に一致する、sampled_candidates 内の位置の ID を計算します。 |
| ComputeAccidentalHits.Options | ComputeAccidentalHitsのオプションの属性 |
| バッチサイズの計算 | 部分的なバッチを除いたデータセットの静的なバッチ サイズを計算します。 |
| Concat <T extends TType > | テンソルを 1 次元に沿って連結します。 |
| データセットを連結する | 「input_dataset」と「another_dataset」を連結したデータセットを作成します。 |
| 具体的な関数 | 入力および出力のシグネチャを使用して、単一の関数として呼び出すことができるグラフ。 |
| CondContextDef | Protocol buffer representing a CondContext object. |
| CondContextDef.Builder | Protocol buffer representing a CondContext object. |
| CondContextDefOrBuilder | |
| 条件付きアキュムレータ | 勾配を集約するための条件付きアキュムレータ。 |
| ConditionalAccumulator.オプション | ConditionalAccumulatorのオプションの属性 |
| コンフィグプロト | 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 | |
| 構成プロトス | |
| 分散型 TPU の構成 | 分散 TPU システムの集中構造をセットアップします。 |
| DistributedTPU.Options の構成 | ConfigureDistributedTPUのオプションの属性 |
| TPU埋め込みの構成 | 分散 TPU システムで TPUEmbedding をセットアップします。 |
| Conj <T extends TType > | 複素数の複素共役を返します。 |
| ConjugateTranspose <T extends TType > | 順列に従って x の次元をシャッフルし、結果を共役させます。 |
| 定数<T extends TType > | 定数値を持つテンソルを生成するイニシャライザ。 |
| 定数<T extends TType > | 定数値を生成する演算子。 |
| 制約 | 制約の基本クラス。 |
| MutexLock の消費 | この操作は、「MutexLock」によって作成されたロックを消費します。 |
| ControlFlowContextDef | Container for any kind of control flow context. |
| ControlFlowContextDef.Builder | Container for any kind of control flow context. |
| ControlFlowContextDef.CtxtCase | |
| ControlFlowContextDefOrBuilder | |
| コントロールフロープロトス | |
| コントロールトリガー | 何もしません。 |
| Conv <T extends TType > | XLA ConvGeneralDirated 演算子をラップします。ドキュメントは次のとおりです。 https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution 。 |
| Conv2d <T はTNumberを拡張 > | 4 次元の「入力」テンソルと「フィルター」テンソルを指定して 2 次元の畳み込みを計算します。 |
| Conv2d.オプション | Conv2dのオプションの属性 |
| Conv2dBackpropFilter <T extends TNumber > | フィルターに関する畳み込みの勾配を計算します。 |
| Conv2dBackpropFilter.Options | Conv2dBackpropFilterのオプションの属性 |
| Conv2dBackpropInput <T extends TNumber > | 入力に対する畳み込みの勾配を計算します。 |
| Conv2dBackpropInput.Options | Conv2dBackpropInputのオプションの属性 |
| Conv3d <T はTNumberを拡張 > | 5 次元の「入力」テンソルと「フィルター」テンソルを指定して 3 次元畳み込みを計算します。 |
| Conv3d.オプション | Conv3dのオプションの属性 |
| Conv3dBackpropFilter <T extends TNumber > | フィルターに関する 3-D 畳み込みの勾配を計算します。 |
| Conv3dBackpropFilter.Options | Conv3dBackpropFilterのオプションの属性 |
| Conv3dBackpropInput <U はTNumberを拡張 > | 入力に対する 3-D 畳み込みの勾配を計算します。 |
| Conv3dBackpropInput.Options | Conv3dBackpropInputのオプションの属性 |
| コピー<T extends TType > | CPU から CPU へ、または GPU から GPU へテンソルをコピーします。 |
| コピー.オプション | Copyのオプション属性 |
| CopyHost <T はTTypeを拡張 > | テンソルをホストにコピーします。 |
| コピーホストのオプション | CopyHostのオプションの属性 |
| Cos <T はTTypeを拡張 > | x の cos を要素ごとに計算します。 |
| Cosh <T はTTypeを拡張 > | x の双曲線余弦を要素ごとに計算します。 |
| コサイン類似度 | ラベルと予測の間のコサイン類似度を計算します。 |
| CosineSimilarity <T extends TNumber > | ラベルと予測の間のコサイン類似性メトリックを計算するメトリック。 |
| コストグラフ定義 | Protobuf 型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 型tensorflow.CostGraphDef |
| CostGraphDef.Node | Protobuf 型tensorflow.CostGraphDef.Node |
| CostGraphDef.Node.Builder | Protobuf 型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 | |
| コストグラフ定義またはビルダー | |
| コストグラフプロトス | |
| CountUpTo <T extends TNumber > | 「limit」に達するまで「ref」をインクリメントします。 |
| CPU情報 | Protobuf 型tensorflow.CPUInfo |
| CPU情報ビルダー | Protobuf 型tensorflow.CPUInfo |
| CPU情報またはビルダー | |
| Create_func_TF_OpKernelConstruction | |
| 概要DbWriterの作成 | |
| サマリーファイルライターの作成 | |
| 切り抜きとサイズ変更 | 入力画像テンソルからクロップを抽出し、サイズを変更します。 |
| CropAndResize.Options | CropAndResizeのオプションの属性 |
| GradBoxの切り抜きとサイズ変更 | 入力ボックスのテンソルに対する Crop_and_resize オペレーションの勾配を計算します。 |
| CropAndResizeGradBoxes.Options | CropAndResizeGradBoxesのオプションの属性 |
| CropAndResizeGradImage <T extends TNumber > | 入力イメージ テンソルに対する Crop_and_resize オペレーションの勾配を計算します。 |
| CropAndResizeGradImage.Options | CropAndResizeGradImageのオプションの属性 |
| Cross <T extends TNumber > | ペアごとの外積を計算します。 |
| CrossReplicaSum <T extends TNumber > | レプリケートされた TPU インスタンス全体の入力を合計する Op。 |
| CSRSparseMatrixComponents <T はTTypeを拡張 > | CSR コンポーネントをバッチ `index` で読み取ります。 |
| CSRSparseMatrixToDense <T extends TType > | (おそらくバッチ処理された) CSRSparseMatrix を密に変換します。 |
| CSRSparseMatrixToSparseTensor <T extends TType > | (おそらくバッチ処理された) CSRSparesMatrix を SparseTensor に変換します。 |
| CSVデータセット | |
| CSVデータセット | |
| CSVデータセットV2 | |
| CtcBeamSearchDecoder <T はTNumberを拡張 > | 入力で指定されたロジットに対してビーム検索デコードを実行します。 |
| CtcBeamSearchDecoder.オプション | CtcBeamSearchDecoderのオプションの属性 |
| CtcGreedyDecoder <T はTNumberを拡張 > | 入力で指定されたロジットに対して貪欲なデコードを実行します。 |
| CtcGreedyDecoder.オプション | CtcGreedyDecoderのオプションの属性 |
| CtcLoss <T はTNumberを拡張 > | 各バッチエントリの CTC 損失 (対数確率) を計算します。 |
| CtcLoss.オプション | CtcLossのオプションの属性 |
| CTCLossV2 | 各バッチエントリの CTC 損失 (対数確率) を計算します。 |
| CTCLossV2.オプション | CTCLossV2のオプションの属性 |
| CudnnRNN <T はTNumberを拡張 > | cuDNN によってサポートされる RNN。 |
| CudnnRNN.オプション | CudnnRNNのオプションの属性 |
| CudnnRNNBackprop <T はTNumberを拡張 > | CudnnRNNV3 のバックプロップ ステップ。 |
| CudnnRNNBackprop.Options | CudnnRNNBackpropのオプションの属性 |
| CudnnRNNCanonicalToParams <T extends TNumber > | CudnnRNN パラメータを正規形式から使用可能な形式に変換します。 |
| CudnnRNNCanonicalToParams.Options | CudnnRNNCanonicalToParamsのオプションの属性 |
| CudnnRnnParamsSize <U はTNumberを拡張 > | Cudnn RNN モデルで使用できる重みのサイズを計算します。 |
| CudnnRnnParamsSize.Options | CudnnRnnParamsSizeのオプションの属性 |
| CudnnRNNParamsToCanonical <T extends TNumber > | CudnnRNN パラメータを正規形式で取得します。 |
| CudnnRNNParamsToCanonical.Options | CudnnRNNParamsToCanonicalのオプション属性 |
| Cumprod <T はTTypeを拡張 > | `axis` に沿ったテンソル `x` の累積積を計算します。 |
| Cumprod.オプション | Cumprodのオプションの属性 |
| Cumsum <T extends TType > | `axis` に沿ったテンソル `x` の累積和を計算します。 |
| 合計オプション | Cumsumのオプションの属性 |
| CumulativeLogsumexp <T extends TNumber > | `axis` に沿ったテンソル `x` の累積積を計算します。 |
| CumulativeLogsumexp.オプション | CumulativeLogsumexpのオプションの属性 |
D
| データバッファ<T> | 特定のタイプのデータのコンテナー。 |
| データバッファアダプターファクトリー | データバッファアダプターの工場。 |
| データバッファ | DataBufferインスタンスを作成するためのヘルパー クラス。 |
| DataBufferWindow <B extends DataBuffer <?>> | DataBufferの一部を表示するための変更可能なコンテナー。 |
| データクラス | Protobuf enum tensorflow.DataClass |
| DataFormatDimMap <T extends TNumber > | 指定された宛先データ形式でディメンション インデックスを返します。 ソースデータ形式。 |
| DataFormatDimMap.Options | DataFormatDimMapのオプションの属性 |
| DataFormatVecPermute <T extends TNumber > | 入力テンソルを `src_format` から `dst_format` に並べ替えます。 |
| DataFormatVecPermute.Options | DataFormatVecPermuteのオプション属性 |
| datalayout <sはdatabuffer <？>、t>を拡張します | バッファに保存されたデータを特定のタイプに変換します。 |
| Datalayouts | 線形代数計算で頻繁に使用されるデータ形式のデータDataLayoutインスタンスを公開します。 |
| DataServiceDataset | |
| DataServicedataset.options | DataServiceDatasetのオプションの属性 |
| データセット | 独立した要素の潜在的に大きなリスト（サンプル）を表し、これらの要素全体で反復と変換を実行できるようにします。 |
| DataSetCardinality | 「input_dataset」のカーディナリティを返します。 |
| DataSetCardinality | 「input_dataset」のカーディナリティを返します。 |
| データセットフロムグラフ | 指定された `graph_def`からデータセットを作成します。 |
| DataTiterator | tf.data datsetを介した反復の状態を表します。 |
| DataSetoPtional | オプションは、データセットの終了に到達したときに失敗する可能性のあるデータセットgetNext操作の結果を表します。 |
| データセットグラフ | 「input_dataset」を表すシリアル化されたgraphdefを返します。 |
| DataSettograph.options | DatasetToGraphのオプションの属性 |
| DataSettosingLeelement | 特定のデータセットから単一要素を出力します。 |
| DataSettotFrecord | Trecord形式を使用して、指定されたデータセットを指定されたファイルに書き込みます。 |
| DataSettotFrecord | Trecord形式を使用して、指定されたデータセットを指定されたファイルに書き込みます。 |
| DataStorageVisitor <r> | DataBufferインスタンスのバッキングストレージにアクセスしてください。 |
| データタイプ | (== suppress_warning documentation-presence ==) LINT.IfChangeprotobuf enum tensorflow.DataType |
| dawsn <tはtnumber >を拡張します | |
| deallocator_pointer_long_pointer | |
| 討論 | An Event related to the debugging of a TensorFlow program. |
| debugevent.builder | An Event related to the debugging of a TensorFlow program. |
| debugevent.whatcase | |
| debugeventorbuilder | |
| DebugeventProtos | |
| DebuggedDevice | 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. |
| DebuggedDeviceOrbuilder | |
| デバッググラフ | A debugger-instrumented graph. |
| DebuggedGraph.Builder | A debugger-instrumented graph. |
| debuggedgraphorbuilder | |
| DebuggedSourceFile | protobufタイプtensorflow.DebuggedSourceFile |
| debuggedsourcefile.builder | protobufタイプtensorflow.DebuggedSourceFile |
| DebuggedSourceFileorBuilder | |
| DebuggedSourceFiles | protobufタイプtensorflow.DebuggedSourceFiles |
| debuggedsourcefiles.builder | protobufタイプtensorflow.DebuggedSourceFiles |
| DebuggedSourceFilesOrbuilder | |
| debuggradientidentity <t extens ttype > | グラデーションデバッグのIDOP。 |
| debuggradientRefidentity <t ttype > | グラデーションデバッグのIDOP。 |
| debugidentity <tはttype >を拡張します | デバッグID v2 op。 |
| debugidentity.options | DebugIdentityのオプションの属性 |
| Debugmetadata | Metadata about the debugger and the debugged TensorFlow program. |
| debugmetadata.builder | Metadata about the debugger and the debugged TensorFlow program. |
| debugmetadataorbuilder | |
| debugnancount | デバッグナンバリューカウンターop。 |
| debugnancount.options | DebugNanCountのオプションの属性 |
| debugnumericssummary <uはtnumber >を拡張します | デバッグ数値概要v2 op。 |
| debugnumericssummary.options | DebugNumericsSummaryのオプションの属性 |
| debugoptions | Options for initializing DebuggerState in TensorFlow Debugger (tfdbg). |
| debugoptions.builder | Options for initializing DebuggerState in TensorFlow Debugger (tfdbg). |
| debugoptionsorbuilder | |
| debugprotos | |
| debugtensorwatch | Option for watching a node in TensorFlow Debugger (tfdbg). |
| debugtensorwatch.builder | Option for watching a node in TensorFlow Debugger (tfdbg). |
| debugtensorwatchorbuilder | |
| decodeandcropjpeg | JPEGエンコード画像をUINT8テンソルにデコードしてトリミングします。 |
| decodeandcropjpeg.options | DecodeAndCropJpegのオプション属性 |
| decodebase64 | WebセーフBase64エンコード文字列をデコードします。 |
| decodebmp | BMPエンコード画像の最初のフレームをUINT8テンソルにデコードします。 |
| decodebmp.options | DecodeBmpのオプション属性 |
| デコードコンプライド | 文字列を減圧します。 |
| decodecompressed.options | DecodeCompressedのオプションの属性 |
| decodecsv | CSVレコードをテンソルに変換します。 |
| decodecsv.options | DecodeCsvのオプションの属性 |
| DecodeGif | GIFエンコード画像のフレームをUINT8テンソルにデコードします。 |
| decodeimage <t endocs tnumber > | decode_bmp、decode_gif、decode_jpeg、decode_pngの関数。 |
| decodeimage.options | DecodeImageのオプションの属性 |
| decodejpeg | JPEGエンコード画像をUINT8テンソルにデコードします。 |
| decodejpeg.options | DecodeJpegのオプション属性 |
| decodejsonexample | JSONエンコードの例レコードをバイナリプロトコルバッファ文字列に変換します。 |
| decodepaddedraw <tはtnumber >を拡張します | 文字列のバイトを数字のベクトルとして再解釈します。 |
| decodepaddedraw.options | DecodePaddedRawのオプションの属性 |
| decodepng <t tnumber >を拡張します | PNGエンコード画像をUINT8またはUINT16テンソルにデコードします。 |
| decodepng.options | DecodePngのオプションの属性 |
| デコデプロト | OPは、シリアル化プロトコルバッファーメッセージからテンソルにフィールドを抽出します。 |
| decodeproto.options | DecodeProtoのオプションの属性 |
| decoderaw <tはttype >を拡張します | 文字列のバイトを数字のベクトルとして再解釈します。 |
| decoderaw.options | DecodeRawのオプションの属性 |
| decodewav | 16ビットPCM WAVファイルをフロートテンソルにデコードします。 |
| decodewav.options | DecodeWavのオプションの属性 |
| deepcopy <t extends ttype > | `x`のコピーを作成します。 |
| delete_func_pointer | |
| deleteiterator | イテレーターリソース用のコンテナ。 |
| deletememorycache | |
| deletemultideviceiterator | イテレーターリソース用のコンテナ。 |
| deleterandomseedgenerator | |
| deleteseedGenerator | |
| deletesessionTensor | セッション内のハンドルで指定されたテンソルを削除します。 |
| DenseBincount <uはtnumber >を拡張します | 整数配列内の各値の発生数をカウントします。 |
| DenseBincount.options | DenseBincountのオプション属性 |
| densecountsparseoutput <u extends tnumber > | tf.tensor入力のためにスパース出力ビンカウントを実行します。 |
| densecountsparseoutput.options | DenseCountSparseOutputのオプションの属性 |
| Densendarray <T> | |
| densetocsrsparsematrix | 密なテンソルを（おそらくバッチ付き）CSRSParsematrixに変換します。 |
| DensetodenseSetoperation <T拡張TTYPE > | 2つの「テンソル」入力の最後の次元に沿って設定操作を適用します。 |
| DensetodenseSetoperation.options | DenseToDenseSetOperationのオプションの属性 |
| densetosparsebatchdataset | 入力要素をSparsetEnsorにバッチバッチするデータセットを作成します。 |
| densetosparsebatchdataset | 入力要素をSparsetEnsorにバッチバッチするデータセットを作成します。 |
| densetosparseToperation <t ttype > | 「テンソル」と「sparsetensor」の最後の次元に沿って設定操作を適用します。 |
| DensetosparseToperation.options | DenseToSparseSetOperationのオプションの属性 |
| DEPTHTASE <T拡張TTYPE > | タイプTのテンソルの深さの宇宙。 |
| depthTospace.options | DepthToSpaceのオプションの属性 |
| depthwiseconv2dnative <tはtnumber >を拡張します | 4-D「入力」と「フィルター」テンソルを与えられた2次元深度畳み込みを計算します。 |
| depthwiseconv2dnative.options | DepthwiseConv2dNativeのオプション属性 |
| depthwiseconv2dnativebackpropfilter <t tnumber > | フィルターに対して深さごとの畳み込みの勾配を計算します。 |
| depthwiseconv2dnativebackpropfilter.options | DepthwiseConv2dNativeBackpropFilterのオプション属性 |
| depthwiseconv2dnativebackpropinput <t tnumber > | 入力に対する深さごとの畳み込みの勾配を計算します。 |
| depthwiseconv2dnativebackpropinput.options | DepthwiseConv2dNativeBackpropInputのオプション属性 |
| dequantize <uはtnumber >を拡張します | 「入力」テンソルをフロートまたはBFLOAT16テンソルに定量化します。 |
| 不安定 | 梱包されたUINT32入力を取り、UINT8に入力を開梱して行う デバイス上のデコンティング。 |
| dequantize.options | Dequantizeのオプションの属性 |
| Deserializeiterator | 指定されたバリアントテンソルをイテレーターに変換し、指定されたリソースに保存します。 |
| DeserializeManysParse <t ttype > | シリアル化されたミニバッチから「sparsetEnsors」を脱色し、連結します。 |
| DeserializeSparse <uはttype >を拡張します | 「sparsetEnsor」オブジェクトをaserializeします。 |
| DestroyResourceop | ハンドルで指定されたリソースを削除します。 |
| DestroyResourceop.options | DestroyResourceOpのオプションの属性 |
| DesistTemporaryVariaible <t拡張ttype > | 一時的な変数を破壊し、最終的な値を返します。 |
| ttype >を拡張します | 1つ以上の正方形マトリックスの決定要因を計算します。 |
| DeviceAttributes | protobufタイプtensorflow.DeviceAttributes |
| DeviceAttributes.Builder | protobufタイプtensorflow.DeviceAttributes |
| DeviceAttributeSorbuilder | |
| DeviceAttributesprotos | |
| devicefiltersprotos | |
| DeviceIndex | OPが実行されるデバイスのインデックスを返します。 |
| 逸脱性 | Protobuf Type tensorflow.DeviceLocality |
| DeviceLocality.Builder | Protobuf Type tensorflow.DeviceLocality |
| DeviceLocalityOrbuilder | |
| DeviceProperties | protobufタイプtensorflow.DeviceProperties |
| DeviceProperties.Builder | protobufタイプtensorflow.DeviceProperties |
| DevicePropertiesOrbuilder | |
| devicepropertiesprotos | |
| DevicesPec | Tensorflowデバイスの（おそらく部分的な）仕様を表します。 |
| devicespec.builder | DeviceSpecクラスの構築用ビルダークラス。 |
| devicespec.deviceType | |
| devicestepstats | protobufタイプtensorflow.DeviceStepStats |
| devicestepstats.builder | protobufタイプtensorflow.DeviceStepStats |
| devicestepstatsorbuilder | |
| dictvalue | Represents a Python dict keyed by `str`. |
| dictvalue.builder | Represents a Python dict keyed by `str`. |
| dictvalueorbuilder | |
| digamma <tはtnumber >を拡張します | lgammaの導関数であるpsiを計算します（の絶対値のログ `ガンマ（x）`）、要素に関して。 |
| 拡張2d <tはtnumber >を拡張します | 4-d `input`および3-d`フィルター`テンソルのグレースケール拡張を計算します。 |
| 拡張2dbackPropfilter <t tnumber > | フィルターに対する形態学的2-D拡張の勾配を計算します。 |
| 拡張2dbackpropinput <tはtnumber >を拡張します | 入力に対する形態学的2-D拡張の勾配を計算します。 |
| 寸法 | |
| dimensionalspace | |
| directedinterleavedataset | `n`データセットの固定リストにある「interleavedataset」の代替。 |
| directedinterleavedataset | `n`データセットの固定リストにある「interleavedataset」の代替。 |
| div <t ttype >を拡張します | x / y要素を返します。 |
| divnonan <t ttype >を拡張します | 分母がゼロの場合は0を返します。 |
| dot <t ttype >を伸ばします | で文書化されたXLA Dotgeneralオペレーターをラップします https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral。 |
| doubledatabuffer | ダブルのDataBuffer 。 |
| doubledatalayout <sはdatabuffer <？>>を拡張します | バッファに保存されているデータをダブルに変換するDataLayout 。 |
| doubledensendarray | |
| doubledray | ダブルのNdArray 。 |
| drawboundingboxes <tはtnumber >を拡張します | 画像のバッチに境界ボックスを描きます。 |
| DummyiterationCounter | |
| dummymemorycache | |
| DummySeedGenerator | |
| dynamicPartition <t extends ttype > | 「パーティション」のインデックスを使用して、「データ」を「num_partitions」テンソルに分割します。 |
| dynamicslice <t extends ttype > | 文書化されたXLA DynamicsLiceオペレーターをラップします https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice。 |
| dynamicStitch <t extends ttype > | 「データ」テンソルから値を単一のテンソルにインターリーブします。 |
| dynamicUpdateslice <t ttype > | 文書化されたXLA DynamicUpDatesLiceオペレーターをラップします https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice。 |
E
| エイジャーセッション | Tensorflow操作を熱心に実行するための環境。 |
| eagersession.deviceplacementpolicy | 特定のデバイスで操作を実行しようとするときに行動する方法を制御しますが、いくつかの入力テンソルはそのデバイスにありません。 |
| eagersession.options | |
| 編集 | （おそらく正規化された）levenshtein編集距離を計算します。 |
| editdistance.options | EditDistanceためのオプションの属性 |
| eig <uはttype >を拡張します | 1つ以上の正方形のマトリックスの固有分解を計算します。 |
| eig.options | Eigのオプションの属性 |
| einsum <tはttype >を伸ばします | アインシュタイン合計条約によるテンソル収縮。 |
| einsum <tはttype >を伸ばします | 2つの入力と1つの出力を備えたBasic Einsum OPをサポートするOP。 |
| elu <tはtnumber >を拡張します | 指数線形を計算します： `exp（feature）-1` if <0、` feations`それ以外の場合。 |
| elu <tはtfloating >を拡張します | 指数線形ユニット。 |
| Elugrad <tはtnumber >を拡張します | 指数線形（ELU）操作の勾配を計算します。 |
| 埋め込みアクティブ | TPU埋め込みの差別化を可能にするOP。 |
| 空の<t ttype >を伸ばします | 指定された形状のテンソルを作成します。 |
| empty.options | Emptyのオプションの属性 |
| emptyTensOrlist | 空のテンソルリストを作成して返します。 |
| emptytensormap | 空のテンソルマップを作成して返します。 |
| EncodeBase64 | 文字列をWebセーフBase64形式にエンコードします。 |
| EncodeBase64.options | EncodeBase64のオプション属性 |
| encodejpeg | jpeg-encode画像。 |
| encodejpeg.options | EncodeJpegのオプション属性 |
| encodejpegvariablequality | JPEGは、提供された圧縮品質で入力画像をエンコードします。 |
| encodepng | png-encode画像。 |
| encodepng.options | EncodePngのオプションの属性 |
| encodeproto | OPは、入力テンソルで提供されるプロトブフメッセージをシリアル化します。 |
| encodeproto.options | EncodeProtoのオプションの属性 |
| encodewav | WAVファイル形式を使用してオーディオデータをエンコードします。 |
| 終点 | 注釈は、 @Operatorで注釈されたクラスのメソッドをマークするために使用されます。@operatorは、エンドポイントをERROR(Ops/org.tensorflow.op.Ops Ops)またはそのグループの1つに生成する必要があります。 |
| enqueetpuembedingintegerbatch | 入力バッチテンソルのリストをtpuembedingに描写するOP。 |
| enqueetpuembeddingintegerbatch.options | EnqueueTPUEmbeddingIntegerBatchのオプション属性 |
| enqueetpuembeddingraggedtensorbatch | tf.nn.embedding_lookup（）を使用するコードの移植を容易にします。 |
| enqueetpuembeddingraggedtensorbatch.options | EnqueueTPUEmbeddingRaggedTensorBatchのオプション属性 |
| enqueetpuembeddingsparsebatch | sparsetensorから入力インデックスをtpuembeddingするOP。 |
| enqueetpuembeddingsparsebatch.options | EnqueueTPUEmbeddingSparseBatchのオプション属性 |
| enqueetpuembedingsparsetEnsorbatch | tf.nn.embedding_lookup_sparse（）を使用するコードの移植を容易にします。 |
| enqueetpuembeddingsparsetensorbatch.options | EnqueueTPUEmbeddingSparseTensorBatchのオプション属性 |
| ttype >を保証します | テンソルの形状が予想される形状と一致するようにします。 |
| <t extends ttype >を入力します | チャイルドフレームを作成または見つけ、子フレームで「データ」を利用できるようにします。 |
| Enter.options | Enterのオプションの属性 |
| EntryValue | protobufタイプtensorflow.EntryValue |
| EntryValue.Builder | protobufタイプtensorflow.EntryValue |
| EntryValue.kindcase | |
| EntryValueorBuilder | |
| 等しい | （x == y）要素ごとの真理値を返します。 |
| equal.options | Equalの属性 |
| erf <tはtnumber >を拡張します | `x`要素の場合のガウスエラー関数を計算します。 |
| erfc <tはtnumber >を拡張します | `x`要素ごとの相補的なエラー関数を計算します。 |
| erfinv <tはtnumber >を拡張します | |
| エラーコード | |
| errorcodesprotos | |
| euclideannorm <t ttype > | テンソルの寸法にわたって要素のユークリッドの規範を計算します。 |
| euclideannorm.options | EuclideanNormのオプションの属性 |
| イベント | Protocol buffer representing an event that happened during the execution of a Brain model. |
| event.builder | Protocol buffer representing an event that happened during the execution of a Brain model. |
| event.whatcase | |
| eventorbuilder | |
| eventProtos | |
| 例 | protobufタイプtensorflow.Example |
| example.builder | protobufタイプtensorflow.Example |
| Exampleorbuilder | |
| emplerserconfiguration | protobufタイプtensorflow.ExampleParserConfiguration |
| emplerserconfiguration.builder | protobufタイプtensorflow.ExampleParserConfiguration |
| emplerserconfigurationorbuilder | |
| emplerserconfigurationprotos | |
| ExampleProtos | |
| 実行する | TPUデバイスでTPUプログラムをロードおよび実行するOP。 |
| execueandupdateVariables | オプションのインプレース変数更新を備えたプログラムを実行するOP。 |
| 実行 | Data relating to the eager execution of an op or a Graph. |
| execution.builder | Data relating to the eager execution of an op or a Graph. |
| executionEnvironment | Tensorflow Operation作成および実行するための環境を定義します。 |
| executionEnvironment.types | |
| executionorbuilder | |
| exit <t ttype >を伸ばします | 現在のフレームを親フレームに終了します。 |
| exp <t ttype >を拡張します | x要素の指数を計算します。 |
| ExpandDims <t拡張TType > | 1の寸法をテンソルの形状に挿入します。 |
| expint <tはtnumber >を拡張します | |
| expm1 <tはttype >を拡張します | `exp（x）-1`要素ごとに計算します。 |
| 指数<tはtfloating >を拡張します | 指数アクティブ化関数。 |
| 抽出グリンプ | 入力テンソルから垣間見ることを抽出します。 |
| extractglimpse.options | ExtractGlimpseのオプションの属性 |
| extractimagePatches <t ttype > | 「画像」から「パッチ」を抽出し、それらを「深さ」の出力寸法に入れます。 |
| extractjpegshape <tはtnumber >を拡張します | JPEGエンコード画像の形状情報を抽出します。 |
| extractvolumepatches <tはtnumber >を拡張します | 「入力」から「パッチ」を抽出し、「「深さ」」の出力寸法にそれらを置きます。 |
F
| 事実 | 要因に関する事実を出力します。 |
| fakequantwithminmaxargs | 「入力」テンソルを偽物化し、同じタイプの「出力」テンソルにフロートを入力します。 |
| fakequantwithminmaxargs.options | FakeQuantWithMinMaxArgsのオプションの属性 |
| fakequantwithminmaxargsgradient | fakequantwithminmaxargs操作の勾配を計算します。 |
| fakequantwithminmaxargsgradient.options | FakeQuantWithMinMaxArgsGradientのオプションの属性 |
| fakequantwithminmaxvars | グローバルフロートスカラーを介してフロートのタイプフロートの「入力」テンソルを偽物化する 偽のフロートスカラーを介して「inputs」タイプフロートの「入力」テンソルをquantizeします。 |
| fakequantwithminmaxvars.options | FakeQuantWithMinMaxVarsのオプションの属性 |
| fakequantwithminmaxvarsgradient | fakequantwithminmaxvars操作の勾配を計算します。 |
| fakequantwithminmaxvarsgradient.options | FakeQuantWithMinMaxVarsGradientのオプションの属性 |
| fakequantwithminmaxvarsperchannel | チャンネルごとのフロートを介してフロートのタイプフロートの「入力」テンソルを偽物化する 偽 - チャンネルごとの型フロートの「入力」テンソルとシェイプの1つをQuantizeします： `[d]`、 `[b、d]` `[b、h、w、d]`形状のmin`と `max` [d]`から「出力」テンソル「入力」と同じ形状。 |
| fakequantwithminmaxvarsperchannel.options | FakeQuantWithMinMaxVarsPerChannelのオプション属性 |
| fakequantwithminmaxvarsperchannelgradient | fakequantwithminmaxvarsperchannel操作の勾配を計算します。 |
| fakequantwithminminmaxvarsperchannelgradient.options | FakeQuantWithMinMaxVarsPerChannelGradientのオプション属性 |
| ファステルメントシーケンス<t、uはndarrayを拡張します<t >> | その要素を反復するときに同じNdArrayインスタンスをリサイクルするシーケンス |
| 特徴 | Containers for non-sequential data. |
| feature.builder | Containers for non-sequential data. |
| feature.kindcase | |
| featureconfiguration | protobufタイプtensorflow.FeatureConfiguration |
| featureconfiguration.builder | protobufタイプtensorflow.FeatureConfiguration |
| featureconfiguration.configcase | |
| featureconfigurationorbuilder | |
| 長編主義者 | Containers for sequential data. |
| featurelist.builder | Containers for sequential data. |
| featurelistorbuilder | |
| 長編主義者 | Protobuf Type tensorflow.FeatureLists |
| featurelists.builder | Protobuf Type tensorflow.FeatureLists |
| featurelistSorbuilder | |
| featureorbuilder | |
| featureprotos | |
| 特徴 | Protobuf Type tensorflow.Features |
| feature.builder | Protobuf Type tensorflow.Features |
| featureSorbuilder | |
| fft <tはttype >を拡張します | 高速フーリエ変換。 |
| fft2d <tはttype >を拡張します | 2D高速フーリエ変換。 |
| fft3d <t extends ttype > | 3D高速フーリエ変換。 |
| fifoqueue | ファーストインファーストアウト順序で要素を生成するキュー。 |
| fifoqueue.options | FifoQueueのオプションの属性 |
| fill <uはttype >を拡張します | スカラー値で満たされたテンソルを作成します。 |
| filterbylastComponentDataset | 最後のコンポーネントにtrueを持つ「input_dataset」の最初のコンポーネントの要素を含むデータセットを作成します。 |
| 指紋 | 指紋値を生成します。 |
| fixedlenfeatureproto | Protobuf Type tensorflow.FixedLenFeatureProto |
| fixedlenfeatureproto.builder | Protobuf Type tensorflow.FixedLenFeatureProto |
| sixtlenfeatureprotoorbuilder | |
| sixedlengtorddataset | |
| sixtlengthrecordreader | ファイルから固定長のレコードを出力する読者。 |
| sixtlengthrecordreader.options | FixedLengthRecordReaderのオプション属性 |
| sixedunigramcandidateSampler | 学習したユニグラム分布を使用して、候補サンプリングのラベルを生成します。 |
| sixtunigramcandidateSampler.options | FixedUnigramCandidateSamplerのオプションの属性 |
| float16layout | IEEE-754ハーフエシジョンフローティングポイント仕様に、32ビットフロートを16ビットから16ビットに変換するデータレイアウト。 |
| floatdatabuffer | フロートのDataBuffer 。 |
| floatdatalayout <sはdatabuffer <？>>を拡張します | フロートにバッファに保存されているデータを変換するDataLayout 。 |
| floatdensendarray | |
| フロートリスト | protobufタイプtensorflow.FloatList |
| floatlist.builder | protobufタイプtensorflow.FloatList |
| floatlistorbuilder | |
| floatndarray | フロートのNdArray 。 |
| フロア<tはtnumber >を伸ばします | xより大きくない要素ごとの最大整数を返します。 |
| floordiv <tはttype >を拡張します | x // y要素ごとに返します。 |
| floormod <tはtnumber >を拡張します | 要素ごとの分割の残りを返します。 |
| flushsummarywriter | |
| fractionalavgpool <tはtnumber >を拡張します | 入力上の分数平均プーリングを実行します。 |
| fractionalavgpool.options | FractionalAvgPoolのオプションの属性 |
| fractionalavgpoolgrad <t tnumber > | FractionAlavgpool関数の勾配を計算します。 |
| fractionalavgpoolgrad.options | FractionalAvgPoolGradのオプション属性 |
| fractionalmaxpool <tはtnumber >を拡張します | 入力で部分的な最大プーリングを実行します。 |
| fractionalmaxpool.options | FractionalMaxPoolのオプションの属性 |
| fractionalmaxpoolgrad <t tnumber > | FractionalMaxpool関数の勾配を計算します。 |
| fractionalmaxpoolgrad.options | FractionalMaxPoolGradのオプション属性 |
| Frennelcos <tはtnumber >を拡張します | |
| fresnelsin <tはtnumber >を拡張します | |
| ftrl | FTRLアルゴリズムを実装するオプティマイザー。 |
| 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.experimmatalcompile | Whether the function should be compiled by XLA. |
| functionspecorbuilder | |
| FusedBatchNorm <Tはtnumberを拡張し、uはtnumberを拡張します> | バッチ正規化。 |
| FusedBatchNorm.options | FusedBatchNormのオプション属性 |
| fusedbatchnormgrad <t tnumberを拡張し、uはtnumberを拡張します> | バッチ正規化の勾配。 |
| FusedBatchNormgrad.options | FusedBatchNormGradのオプション属性 |
| FusedPadConv2d <T拡張tnumber > | 畳み込み中にプリプロースとしてパディングを実行します。 |
| fusedResizeandpadconv2d <t tnumber > | 畳み込み中に、再処理としてサイズとパディングを実行します。 |
| fusedResizeandpadconv2d.options | FusedResizeAndPadConv2dのオプションの属性 |
G
| 収集<t tnumber >を拡張します | 同一のタイプと形状の複数のテンソルを相互に蓄積します。 |
| 収集<t type >を拡張します | 「インデックス」に従って「パラメージ」軸から「軸」からスライスを収集します。 |
| 収集<t type >を拡張します | 文書化されたXLA収集オペレーターをラップします https://www.tensorflow.org/xla/operation_semantics#gather |
| gracking.options | Gatherのオプションの属性 |
| gracking.options | Gatherのオプションの属性 |
| gathernd <tはttype >を拡張します | 「インデックス」で指定された形状のある「パラマ」からテンソルにスライスを収集します。 |
| grachv2 <tはtnumber >を拡張します | 同一のタイプと形状の複数のテンソルを相互に蓄積します。 |
| grackingv2.options | GatherV2のオプションの属性 |
| GeneateBoundingBoxProposals | このOPは、ARXIV：1506.01497のEQ.2に従って、指定された境界ボックス（bbox_deltas）エンコードされたWRTアンカーから関心のある領域を生成します。 OPは、上部の「pre_nms_topn`スコアリングボックスを選択し、アンカーに関してそれらを解読し、「nms_threshold」よりも高い重なりのボックスに非最大抑制を適用します。 min_size`。 |
| generateboundingboxProposals.options | GenerateBoundingBoxProposalsのオプションの属性 |
| 生成されたvocabremapping | 新しい語彙ファイルと古い語彙ファイルへのパスが与えられた場合、の再マッピングテンソルを返します 長さ `num_new_vocab`、ここで`再マッピング[i] `には、新しい語彙の行` i`に対応する古い語彙の行番号が含まれています（new_vocab_offset`から始まります。 1`新しい語彙のエントリ「I」が古い語彙にない場合。 |
| generatevocabremapping.options | GenerateVocabRemappingのオプションの属性 |
| GetSessionHandle | 現在のセッションの状態に入力テンソルを保存します。 |
| getSessionTensor <t extends ttype > | ハンドルで指定されたテンソルの値を取得します。 |
| Glorot <tはtfloating >を拡張します | Xavier Initializerとも呼ばれるGlorot Initializer。 |
| gpuinfo | protobufタイプtensorflow.GPUInfo |
| gpuinfo.builder | protobufタイプtensorflow.GPUInfo |
| gpuinfoorbuilder | |
| gpuoptions | protobufタイプtensorflow.GPUOptions |
| gpuoptions.builder | protobufタイプtensorflow.GPUOptions |
| gpuoptions.experimpal | protobufタイプtensorflow.GPUOptions.Experimental |
| gpuoptions.experimental.builder | protobufタイプ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.experimematalorbuilder | |
| 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 | |
| グラデーションデセント | 基本的な確率勾配降下オプティマイザー。 |
| 勾配 | 操作を追加して、 y s wrt x s、ie、 d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2... |
| gradients.options | Gradientsのオプションの属性 |
| グラフ | Tensorflow計算を表すデータフローグラフ。 |
| graph.whilesubgraphbuilder | BuildSubgraphメソッドをオーバーライドする抽象クラスをインスタンス化するために使用され、しばらくループする条件付きまたはボディサブグラフを構築します。 |
| graphdebuginfo | protobufタイプtensorflow.GraphDebugInfo |
| graphdebuginfo.builder | protobufタイプ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 | |
| graphdebuginfoprotos | |
| graphdef | Represents the graph of operationsprotobufタイプ tensorflow.GraphDef |
| graphdef.builder | Represents the graph of operationsprotobufタイプ 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 | |
| グラフェペレーション | Graphにノードとして追加されたOperationの実装。 |
| GraphOperationBuilder | GraphOperationをGraphに追加するためのOperationBuilder 。 |
| Graphoptions | protobufタイプtensorflow.GraphOptions |
| graphoptions.builder | protobufタイプtensorflow.GraphOptions |
| graphoptionsorbuilder | |
| GraphProtos | |
| GraphTransferConstNodeInfo | protobufタイプtensorflow.GraphTransferConstNodeInfo |
| GraphTransferConstNodeInfo.Builder | protobufタイプtensorflow.GraphTransferConstNodeInfo |
| GraphTransferConstNodeInfoorBuilder | |
| GraphTransfergraphinputNodeInfo | protobufタイプtensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransfergraphinputnodeinfo.Builder | protobufタイプtensorflow.GraphTransferGraphInputNodeInfo |
| GraphTransfergraphinputNodeInfoorBuilder | |
| GraphTransfergraphOutputNodeInfo | protobufタイプtensorflow.GraphTransferGraphOutputNodeInfo |
| GraphTransfergraphOutputNodeInfo.Builder | protobufタイプ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 | |
| グラフトランスフェリンフォプロト | |
| graphtransfernodeinfo | protobufタイプtensorflow.GraphTransferNodeInfo |
| GraphTransfernodeInfo.Builder | protobufタイプtensorflow.GraphTransferNodeInfo |
| GraphTransfernodeInfoorBuilder | |
| GraphTransfernodeInput | protobufタイプtensorflow.GraphTransferNodeInput |
| GraphTransfernodeInput.Builder | protobufタイプtensorflow.GraphTransferNodeInput |
| GraphTransfernodeInputinfo | protobufタイプtensorflow.GraphTransferNodeInputInfo |
| GraphTransfernodeInputinfo.Builder | protobufタイプtensorflow.GraphTransferNodeInputInfo |
| GraphTransfernodeInputinFoorBuilder | |
| GraphTransfernodeInputorBuilder | |
| GraphTransfernodeOutputinfo | protobufタイプtensorflow.GraphTransferNodeOutputInfo |
| GraphTransfernodeOutputinfo.Builder | protobufタイプtensorflow.GraphTransferNodeOutputInfo |
| GraphTransfernodeOutputinFoorBuilder | |
| グレーター | 要素ごとに（x> y）の真理値を返します。 |
| より等しい | （x> = y）要素ごとの真理値を返します。 |
| grublockcell <tはtnumber >を拡張します | 1回のステップでGRUセルフォワード伝播を計算します。 |
| grublockcellgrad <t tnumber > | 1回のステップでGRUセルバックプロパゲーションを計算します。 |
| 保証<t ttype >を拡張します | 入力テンソルが定数であることをTFランタイムに保証します。 |
H
| hardsigmoid <tはtfloating >を拡張します | ハードシグモイドの活性化。 |
| ハッシュテーブル | 非初期化ハッシュテーブルを作成します。 |
| hashtable.options | HashTableのオプションの属性 |
| 彼はtfloating >を拡張します | 彼は初期化された。 |
| ヘルパー | いくつかの操作を追加または実行し、そのうちの1つを返すコアメソッドのコンテナクラス。 |
| ヒンジ | ラベルと予測の間のヒンジの損失を計算します。 |
| ヒンジ<tはtnumber >を拡張します | ラベルと予測の間のヒンジ損失メトリックを計算するメトリック。 |
| histogramfixedwidth <uはtnumber >を拡張します | 値のヒストグラムを返します。 |
| histogramproto | Serialization format for histogram module in core/lib/histogram/histogram.hprotobufタイプ tensorflow.HistogramProto |
| histogramproto.builder | Serialization format for histogram module in core/lib/histogram/histogram.hprotobufタイプ tensorflow.HistogramProto |
| histogramprotoorbuilder | |
| ヒストグラムサマリー | ヒストグラムで「概要」プロトコルバッファーを出力します。 |
| hsvtorgb <tはtnumber >を拡張します | HSVからRGBに1つ以上の画像を変換します。 |
| フーバー | ラベルと予測の間のハーバーの損失を計算します。 |
私
| アイデンティティ<tはtfloating >を拡張します | アイデンティティマトリックスを生成する初期イザー。 |
| ID <t拡張ttype > | 入力テンソルまたは値と同じ形状と内容のテンソルを返します。 |
| IDN | 入力と同じ形状と内容を持つテンソルのリストを返します テンソル。 |
| IdentityReader | キーと値の両方としてキューに登録された作業を出力する読者。 |
| Identityreader.options | IdentityReaderのオプションの属性 |
| ifft <tはttype >を拡張します | 逆高速フーリエ変換。 |
| ifft2d <tはttype >を拡張します | 逆2D高速フーリエ変換。 |
| ifft3d <tはttype >を拡張します | 逆3D高速フーリエ変換。 |
| igamma <tはtnumber >を拡張します | 低い正規化された不完全なガンマ関数 `p（a、x）`を計算します。 |
| igammac <tはtnumber >を拡張します | 上部の正規化された不完全なガンマ関数 `q（a、x）`を計算します。 |
| igammagrada <t endocs tnumber > | `igamma（a、x）` wrt `a`の勾配を計算します。 |
| Ingroreerrorsdataset | エラーを無視する「input_dataset」の要素を含むデータセットを作成します。 |
| Ingroreerrorsdataset | エラーを無視する「input_dataset」の要素を含むデータセットを作成します。 |
| Ingreerrorsdataset.options | IgnoreErrorsDatasetのオプションの属性 |
| Ingreerrorsdataset.options | IgnoreErrorsDatasetのオプションの属性 |
| Illegalrankexception | ターゲット配列のランクのために操作が完了できない場合にスローされる例外。 |
| イメージ<uはtnumber >を拡張します | 複雑な数の想像上の部分を返します。 |
| ImageProjectiveTransFormv2 <T extends tnumber > | 指定された変換を各画像に適用します。 |
| ImageProjectiveTransFormv2.options | ImageProjectiveTransformV2のオプション属性 |
| ImageProjectiveTransFormv3 <T拡張TNumber > | 指定された変換を各画像に適用します。 |
| ImageProjectiveTransFormv3.options | ImageProjectiveTransformV3のオプション属性 |
| Imagesummary | 画像を使用して「概要」プロトコルバッファーを出力します。 |
| Imagesummary.options | ImageSummaryのオプションの属性 |
| Immutableconst <t ttype >を拡張します | メモリ領域から不変のテンソルを返します。 |
| ImportEvent | |
| 索引 | n次元配列からビューをスライスするために使用されるインデックス。 |
| IndexedPositionIterator | |
| indexedpositionterator.coordslongConsumer | |
| 指数 | インスタンス化されたIndexオブジェクトのヘルパークラス。 |
| infeeddequeue <t extends ttype > | 計算に供給される値のプレースホルダーOP。 |
| Infeeddequeuetuple | XLAタプルとしてのInfeedからの複数の値を取得します。 |
| Infeedenqueue | 単一のテンソル値を計算に供給するOP。 |
| Infeedenqueue.options | InfeedEnqueueのオプションの属性 |
| INFEEDENQUEUEPRELINEARIZEDBUFFER | TPU Infeedに予測バッファーをエンキングするOP。 |
| InfeedenqueueprelinearizedBuffer.options | InfeedEnqueuePrelinearizedBufferのオプション属性 |
| Infeedenqueuetuple | XLAタプルとして複数のテンソル値を計算に供給します。 |
| infeedenqueuetuple.options | InfeedEnqueueTupleのオプションの属性 |
| init | |
| InitialIzer <T拡張TTYPE > | 初期化器用のインターフェイス |
| 初期化可能 | それぞれキーと値に2つのテンソルを採取するテーブルイニシャルイザー。 |
| initializetablefromdataset | |
| 初期化可能なFromTextFile | テキストファイルからテーブルを初期化します。 |
| fromizetablefromtextfile.options | InitializeTableFromTextFile可能な属性のオプション属性 |
| inplaceadd <t ttype >を拡張します | vを指定されたxの行に追加します。 |
| inplacesub <t ttype >を拡張します | `v`を` x`の指定された行に減算します。 |
| inplaceUpdate <t ttype >を拡張します | 値「V」で指定された行「I」を更新します。 |
| int64List | protobufタイプtensorflow.Int64List |
| int64list.builder | protobufタイプtensorflow.Int64List |
| int64listorbuilder | |
| intdatabuffer | INTSのDataBuffer 。 |
| intdatalayout <sはdatabuffer <？>>を拡張します | バッファーに保存されているデータをINTに変換するDataLayout 。 |
| intdensendarray | |
| Interconnectlink | Protobuf type tensorflow.InterconnectLink |
| InterconnectLink.Builder | Protobuf type tensorflow.InterconnectLink |
| InterconnectLinkOrBuilder | |
| IntNdArray | An NdArray of integers. |
| InTopK | Says whether the targets are in the top `K` predictions. |
| Inv <T extends TType > | 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. |
J
| 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 | |
| 参加する | 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 |
K
| 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 KernelDefsProtobuf type tensorflow.KernelList |
| KernelList.Builder | A collection of KernelDefsProtobuf type 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. |
L
| 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`. |
| 少ない | 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 | Computes Computes the logarithm of the hyperbolic cosine of the prediction error. |
| 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. |
| 損失 | |
| 損失 | 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 |
| より低い | 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. |
M
| 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 | Computes the mean of absolute difference between labels and predictions. |
| 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 AllocatorStatsProtobuf type tensorflow.MemAllocatorStats |
| MemAllocatorStats.Builder | Some of the data from AllocatorStatsProtobuf type 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 |
| メトリクス | 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 |
| 勢い | 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 | 何もしません。 |
| 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 |
○
| 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. |
| オペ | 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 opProtobuf type tensorflow.OpDeprecation |
| OpDeprecation.Builder | Information about version-dependent deprecation of an opProtobuf type tensorflow.OpDeprecation |
| OpDeprecationOrBuilder | |
| Operand <T extends TType > | Interface implemented by operands of a TensorFlow operation. |
| Operands | Utilities for manipulating operand related types and lists. |
| 手術 | Performs computation on Tensors. |
| OperationBuilder | A builder for Operation s. |
| オペレーター | Annotation used by classes to make TensorFlow operations conveniently accessible via org.tensorflow.op.Ops or one of its groups. |
| OpList | A collection of OpDefsProtobuf type tensorflow.OpList |
| OpList.Builder | A collection of OpDefsProtobuf type 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 |
| オプティマイザ | 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 optimizerProtobuf type tensorflow.OptimizerOptions |
| OptimizerOptions.Builder | Options passed to the graph optimizerProtobuf type tensorflow.OptimizerOptions |
| OptimizerOptions.GlobalJitLevel | Control the use of the compiler/jit. |
| OptimizerOptions.Level | Optimization levelProtobuf enum 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 . |
P
| 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 |
| 印刷する | 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: 11Protobuf type tensorflow.ProfileOptions |
| ProfileOptions.Builder | Next ID: 11Protobuf type tensorflow.ProfileOptions |
| ProfileOptions.DeviceType | Protobuf enum tensorflow.ProfileOptions.DeviceType |
| ProfileOptionsOrBuilder | |
| ProfilerOptionsProtos |
Q
| 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. |
R
| 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. |
| ランク | 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 |
| 削減 | 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 |
| 復元する | 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 parametersProtobuf type tensorflow.RewriterConfig.CustomGraphOptimizer |
| RewriterConfig.CustomGraphOptimizer.Builder | Message to describe custom graph optimizer and its parametersProtobuf type 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?Protobuf enum tensorflow.RunOptions.TraceLevel |
| RunOptionsOrBuilder |
S
| 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. |
| 保存 | 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 |
| 範囲 | 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 > | Computes the maximum along segments of a tensor. |
| 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 > | Computes the sum along segments of a tensor. |
| 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. |
| 送信 | Sends the named tensor from send_device to recv_device. |
| 送信 | 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. |
| サーバ | 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 | |
| セッション | 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 | |
| 形 | The shape of a Tensor or NdArray . |
| Shape <U extends TNumber > | Returns the shape of a tensor. |
| Shape_inference_func_TF_ShapeInferenceContext_TF_Status | |
| 形をした | Any data container with a given Shape . |
| ShapeN <U extends TNumber > | Returns shape of tensors. |
| 形状 | 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. |
| サイン | 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 > | Computes hyperbolic sine of x element-wise. |
| 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. |
| スナップショット | 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 | |
| ステージ | 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 |
| ストリップ | Strip leading and trailing whitespaces from the Tensor. |
| StructProtos | |
| StructuredValue | `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 | |
| StructuredValueOrBuilder | |
| 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 |
| まとめ | 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 dataProtobuf type tensorflow.SummaryDescription |
| SummaryDescription.Builder | Metadata associated with a series of Summary dataProtobuf type 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`. |
T
| 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 > | Computes tan of x element-wise. |
| 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 |
| テンソル | A statically typed multi-dimensional array. |
| テンソル | |
| 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 | Split the data from the input value into TensorArray elements. |
| 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`. |
| タイムスタンプ | 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. |
| TupleValue | Represents a Python tuple. |
| TupleValue.Builder | Represents a Python tuple. |
| TupleValueOrBuilder | |
| TypeSpecProto | Represents a tf.TypeSpecProtobuf type tensorflow.TypeSpecProto |
| TypeSpecProto.Builder | Represents a tf.TypeSpecProtobuf type tensorflow.TypeSpecProto |
| TypeSpecProto.TypeSpecClass | Protobuf enum tensorflow.TypeSpecProto.TypeSpecClass |
| TypeSpecProtoOrBuilder | |
| TypesProtos |
U
| 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 > | Computes the maximum along segments of a tensor. |
| 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 > | Computes the sum along segments of a tensor. |
| 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 | |
| アッパー | 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. |
V
| 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 |
W
| WatchdogConfig | Protobuf type tensorflow.WatchdogConfig |
| WatchdogConfig.Builder | Protobuf type tensorflow.WatchdogConfig |
| WatchdogConfigOrBuilder | |
| WeakPointerScope | A minimalist pointer scope only keeping weak references to its elements. |
| どこ | 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. |
×
| 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 |
Z
| 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`. |