Uses of Class
org.tensorflow.Output

Packages that use Output

Package	Description
org.tensorflow	Defines classes to build, save, load and execute TensorFlow models.
org.tensorflow.op
org.tensorflow.op.core

Uses of Output in org.tensorflow

Methods in org.tensorflow that return Output

Modifier and Type	Method and Description
Output<?>[]	Graph.addGradients(Output<?> y, Output<?>[] x) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., dy/dx_1, dy/dx_2...
Output<?>[]	Graph.addGradients(String prefix, Output<?>[] y, Output<?>[] x, Output<?>[] dx) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
Output<T>	Input.asOutput() Returns the symbolic handle of a tensor.
Output<T>	Output.asOutput()
Output<T>	Operand.asOutput() Returns the symbolic handle of a tensor.
<T> Output<T>	Operation.output(int idx) Returns a symbolic handle to one of the tensors produced by this operation.
Output<?>[]	Operation.outputList(int idx, int length) Returns symbolic handles to a list of tensors produced by this operation.

Methods in org.tensorflow with parameters of type Output

Modifier and Type	Method and Description
Output<?>[]	Graph.addGradients(Output<?> y, Output<?>[] x) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., dy/dx_1, dy/dx_2...
Output<?>[]	Graph.addGradients(Output<?> y, Output<?>[] x) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., dy/dx_1, dy/dx_2...
Output<?>[]	Graph.addGradients(String prefix, Output<?>[] y, Output<?>[] x, Output<?>[] dx) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
Output<?>[]	Graph.addGradients(String prefix, Output<?>[] y, Output<?>[] x, Output<?>[] dx) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
Output<?>[]	Graph.addGradients(String prefix, Output<?>[] y, Output<?>[] x, Output<?>[] dx) Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
OperationBuilder	OperationBuilder.addInput(Output<?> input) Returns the builder to create an operation.
OperationBuilder	OperationBuilder.addInputList(Output<?>[] inputs)
Session.Runner	Session.Runner.feed(Output<?> o, Tensor<?> t) Use t instead of the Tensor referred to by executing the operation referred to by output.
Session.Runner	Session.Runner.fetch(Output<?> output) Makes Session.Runner.run() return the Tensor referred to by output.

Uses of Output in org.tensorflow.op

Methods in org.tensorflow.op that return Output

Modifier and Type	Method and Description
static Output<?>[]	Operands.asOutputs(Iterable<? extends Operand<?>> inputs) Converts a list of Operand into an array of Output.

Uses of Output in org.tensorflow.op.core

Methods in org.tensorflow.op.core that return Output

Modifier and Type	Method and Description
Output<T>	Softplus.activations()
Output<T>	Softsign.activations()
Output<T>	Elu.activations()
Output<U>	QuantizedRelu.activations() Has the same output shape as "features".
Output<T>	Relu.activations()
Output<T>	Relu6.activations()
Output<T>	Selu.activations()
Output<U>	QuantizedReluX.activations() Has the same output shape as "features".
Output<U>	QuantizedRelu6.activations() Has the same output shape as "features".
Output<U>	MaxPoolWithArgmax.argmax() 4-D.
Output<T>	BatchMatrixBandPart.asOutput()
Output<T>	Sinh.asOutput()
Output<T>	ReadVariableOp.asOutput()
Output<T>	MaxPool.asOutput()
Output<T>	ResizeNearestNeighbor.asOutput()
Output<T>	SparseApplyProximalGradientDescent.asOutput()
Output<String>	IteratorToStringHandle.asOutput()
Output<T>	FFT2D.asOutput()
Output<T>	AccumulateNV2.asOutput()
Output<T>	UnsortedSegmentMax.asOutput()
Output<Boolean>	QueueIsClosedV2.asOutput()
Output<T>	Relu6Grad.asOutput()
Output<String>	SerializeTensor.asOutput()
Output<U>	Imag.asOutput()
Output<T>	MaxPool3DGradGrad.asOutput()
Output<T>	InvGrad.asOutput()
Output<T>	BiasAdd.asOutput()
Output<T>	Slice.asOutput()
Output<T>	Softplus.asOutput()
Output<Object>	TensorListFromTensor.asOutput()
Output<T>	Multiply.asOutput()
Output<T>	BitwiseAnd.asOutput()
Output<Object>	BatchIFFT3D.asOutput()
Output<Object>	OptionalNone.asOutput()
Output<T>	NthElement.asOutput()
Output<T>	CholeskyGrad.asOutput()
Output<T>	Softsign.asOutput()
Output<T>	ApplyFtrlV2.asOutput()
Output<Object>	TensorSliceDataset.asOutput()
Output<T>	ReduceMean.asOutput()
Output<T>	Maximum.asOutput()
Output<Object>	RandomShuffleQueue.asOutput()
Output<Object>	MutexLock.asOutput()
Output<UInt8>	DecodeJpeg.asOutput()
Output<T>	ExtractImagePatches.asOutput()
Output<T>	RestoreSlice.asOutput()
Output<U>	RandomGamma.asOutput()
Output<T>	Conv3D.asOutput()
Output<T>	SparseApplyCenteredRMSProp.asOutput()
Output<T>	Sqrt.asOutput()
Output<Object>	SparseTensorSliceDataset.asOutput()
Output<String>	SparseConditionalAccumulator.asOutput()
Output<T>	AssignAdd.asOutput()
Output<T>	DataFormatVecPermute.asOutput()
Output<T>	TensorArrayPack.asOutput()
Output<T>	MatrixSetDiag.asOutput()
Output<T>	DepthwiseConv2dNativeBackpropInput.asOutput()
Output<T>	InplaceAdd.asOutput()
Output<T>	TileGrad.asOutput()
Output<T>	Cumsum.asOutput()
Output<T>	StridedSliceAssign.asOutput()
Output<T>	MaxPoolV2.asOutput()
Output<String>	RegexReplace.asOutput()
Output<Object>	WholeFileReader.asOutput()
Output<Object>	SummaryWriter.asOutput()
Output<T>	ScatterSub.asOutput()
Output<Float>	ExtractGlimpse.asOutput()
Output<String>	TensorSummary.asOutput()
Output<U>	OneHot.asOutput()
Output<T>	Tile.asOutput()
Output<T>	Unbatch.asOutput()
Output<T>	SegmentMin.asOutput()
Output<T>	SparseSegmentMeanWithNumSegments.asOutput()
Output<T>	Max.asOutput()
Output<Object>	IdentityReader.asOutput()
Output<Integer>	SetSize.asOutput()
Output<T>	PreventGradient.asOutput()
Output<T>	StopGradient.asOutput()
Output<Object>	IteratorV2.asOutput()
Output<T>	DebugGradientIdentity.asOutput()
Output<T>	Conv2D.asOutput()
Output<T>	Floor.asOutput()
Output<T>	Identity.asOutput()
Output<T>	TensorListElementShape.asOutput()
Output<T>	Placeholder.asOutput()
Output<T>	SpaceToDepth.asOutput()
Output<String>	EncodeProto.asOutput()
Output<Double>	Timestamp.asOutput()
Output<T>	LinSpace.asOutput()
Output<Object>	GetSessionHandleV2.asOutput()
Output<Object>	ShuffleAndRepeatDataset.asOutput()
Output<Integer>	QueueSize.asOutput()
Output<T>	SparseReduceMax.asOutput()
Output<T>	Log.asOutput()
Output<T>	Neg.asOutput()
Output<T>	Igammac.asOutput()
Output<Object>	EmptyTensorList.asOutput()
Output<T>	ScatterUpdate.asOutput()
Output<String>	RecordInput.asOutput()
Output<T>	UnsortedSegmentProd.asOutput()
Output<Object>	PrefetchDataset.asOutput()
Output<Boolean>	VarIsInitializedOp.asOutput()
Output<U>	RandomUniformInt.asOutput()
Output<Object>	BatchIFFT2D.asOutput()
Output<T>	Minimum.asOutput()
Output<T>	Abs.asOutput()
Output<T>	SparseApplyAdagrad.asOutput()
Output<T>	DeepCopy.asOutput()
Output<T>	Sub.asOutput()
Output<T>	Bincount.asOutput()
Output<Object>	FIFOQueue.asOutput()
Output<T>	SegmentProd.asOutput()
Output<T>	LRN.asOutput()
Output<Boolean>	IsFinite.asOutput()
Output<String>	AudioSummary.asOutput()
Output<T>	DynamicStitch.asOutput()
Output<T>	SparseApplyAdagradDA.asOutput()
Output<T>	SparseSegmentMeanGrad.asOutput()
Output<T>	SegmentMean.asOutput()
Output<T>	ScatterAdd.asOutput()
Output<Object>	StatsAggregatorHandle.asOutput()
Output<T>	Erf.asOutput()
Output<U>	TruncatedNormal.asOutput()
Output<T>	BatchMatrixDeterminant.asOutput()
Output<T>	BatchMatrixInverse.asOutput()
Output<U>	SparseTensorDenseMatMul.asOutput()
Output<Float>	FakeQuantWithMinMaxVarsPerChannel.asOutput()
Output<Object>	LatencyStatsDataset.asOutput()
Output<Object>	RangeDataset.asOutput()
Output<T>	ExtractJpegShape.asOutput()
Output<Long>	AddManySparseToTensorsMap.asOutput()
Output<T>	Const.asOutput()
Output<T>	Exit.asOutput()
Output<T>	Empty.asOutput()
Output<Boolean>	IsBoostedTreesEnsembleInitialized.asOutput()
Output<Float>	SparseMatMul.asOutput()
Output<T>	Lgamma.asOutput()
Output<Object>	SqlDataset.asOutput()
Output<T>	IgammaGradA.asOutput()
Output<Integer>	TensorArraySize.asOutput()
Output<String>	GetSessionHandle.asOutput()
Output<T>	RefNextIteration.asOutput()
Output<T>	Cholesky.asOutput()
Output<T>	Pow.asOutput()
Output<String>	HistogramSummary.asOutput()
Output<T>	Range.asOutput()
Output<Object>	BatchFFT3D.asOutput()
Output<T>	UnbatchGrad.asOutput()
Output<Integer>	TensorListLength.asOutput()
Output<T>	Mul.asOutput()
Output<T>	PlaceholderV2.asOutput()
Output<T>	SpaceToBatch.asOutput()
Output<T>	Add.asOutput()
Output<V>	ArgMin.asOutput()
Output<String>	Barrier.asOutput()
Output<U>	CudnnRNNParamsSize.asOutput()
Output<T>	MaxPoolGradGradV2.asOutput()
Output<T>	SparseDenseCwiseMul.asOutput()
Output<T>	SparseSegmentSumWithNumSegments.asOutput()
Output<T>	FloorDiv.asOutput()
Output<UInt8>	PopulationCount.asOutput()
Output<T>	ResizeBilinearGrad.asOutput()
Output<Boolean>	LessEqual.asOutput()
Output<V>	StatelessTruncatedNormal.asOutput()
Output<T>	SparseSegmentSqrtNGrad.asOutput()
Output<T>	Sign.asOutput()
Output<U>	RandomPoisson.asOutput()
Output<T>	ApplyAdagradDA.asOutput()
Output<Object>	MutableDenseHashTable.asOutput()
Output<U>	Cast.asOutput()
Output<T>	PadV2.asOutput()
Output<T>	FractionalAvgPoolGrad.asOutput()
Output<Boolean>	GreaterEqual.asOutput()
Output<T>	ExpandDims.asOutput()
Output<T>	BroadcastDynamicShape.asOutput()
Output<Object>	ShuffleDataset.asOutput()
Output<T>	InvertPermutation.asOutput()
Output<U>	MaxPool3DGrad.asOutput()
Output<T>	LogSoftmax.asOutput()
Output<T>	RefExit.asOutput()
Output<Object>	PaddedBatchDataset.asOutput()
Output<Float>	ResizeArea.asOutput()
Output<Boolean>	IsNan.asOutput()
Output<Object>	HashTable.asOutput()
Output<T>	ScatterDiv.asOutput()
Output<Boolean>	BoostedTreesCenterBias.asOutput()
Output<T>	Variable.asOutput()
Output<T>	Elu.asOutput()
Output<T>	SquaredDifference.asOutput()
Output<U>	RandomNormal.asOutput()
Output<UInt8>	DecodeAndCropJpeg.asOutput()
Output<T>	TanhGrad.asOutput()
Output<U>	SparseTensorDenseAdd.asOutput()
Output<String>	ShardedFilespec.asOutput()
Output<T>	MaxPoolGradV2.asOutput()
Output<T>	DepthwiseConv2dNative.asOutput()
Output<T>	SparseSliceGrad.asOutput()
Output<Boolean>	LogicalNot.asOutput()
Output<Boolean>	NotEqual.asOutput()
Output<T>	BatchSelfAdjointEig.asOutput()
Output<T>	DataFormatDimMap.asOutput()
Output<T>	GatherNd.asOutput()
Output<T>	Restore.asOutput()
Output<Object>	TensorListScatter.asOutput()
Output<T>	SigmoidGrad.asOutput()
Output<T>	BitwiseOr.asOutput()
Output<T>	SparseApplyRMSProp.asOutput()
Output<T>	RGBToHSV.asOutput()
Output<U>	SerializeManySparse.asOutput()
Output<T>	Tan.asOutput()
Output<T>	Prod.asOutput()
Output<T>	Betainc.asOutput()
Output<T>	Atan.asOutput()
Output<T>	Zeros.asOutput()
Output<T>	RefIdentity.asOutput()
Output<Object>	TextLineReader.asOutput()
Output<String>	ScalarSummary.asOutput()
Output<Float>	AdjustSaturation.asOutput()
Output<T>	IFFT2D.asOutput()
Output<Long>	StringToHashBucketStrong.asOutput()
Output<T>	UnravelIndex.asOutput()
Output<T>	Snapshot.asOutput()
Output<T>	DebugGradientRefIdentity.asOutput()
Output<Boolean>	Greater.asOutput()
Output<T>	ReduceMin.asOutput()
Output<T>	EluGrad.asOutput()
Output<Long>	AddSparseToTensorsMap.asOutput()
Output<Object>	MutableHashTableOfTensors.asOutput()
Output<Object>	OptimizeDataset.asOutput()
Output<Float>	ResizeBicubic.asOutput()
Output<T>	UnsortedSegmentMin.asOutput()
Output<T>	RefEnter.asOutput()
Output<String>	ConditionalAccumulator.asOutput()
Output<T>	RandomShuffle.asOutput()
Output<String>	DecodeJSONExample.asOutput()
Output<Object>	TensorDataset.asOutput()
Output<Integer>	NonMaxSuppressionV3.asOutput()
Output<T>	SoftsignGrad.asOutput()
Output<T>	ZerosLike.asOutput()
Output<Boolean>	LogicalAnd.asOutput()
Output<Integer>	NonMaxSuppression.asOutput()
Output<V>	RandomPoissonV2.asOutput()
Output<Object>	PaddingFIFOQueue.asOutput()
Output<String>	BigQueryReader.asOutput()
Output<T>	StridedSlice.asOutput()
Output<Object>	BoostedTreesEnsembleResourceHandleOp.asOutput()
Output<Boolean>	All.asOutput()
Output<String>	StatsAggregatorSummary.asOutput()
Output<T>	Div.asOutput()
Output<T>	BatchMatMul.asOutput()
Output<String>	EncodeWav.asOutput()
Output<T>	Softmax.asOutput()
Output<T>	AvgPoolGrad.asOutput()
Output<Object>	RFFT2D.asOutput()
Output<T>	Stack.asOutput()
Output<T>	MatrixBandPart.asOutput()
Output<T>	BroadcastTo.asOutput()
Output<String>	ReaderSerializeState.asOutput()
Output<Float>	FakeQuantWithMinMaxVars.asOutput()
Output<T>	Squeeze.asOutput()
Output<T>	ResizeNearestNeighborGrad.asOutput()
Output<Object>	BatchDatasetV2.asOutput()
Output<T>	IFFT3D.asOutput()
Output<Object>	PrependFromQueueAndPaddedBatchDataset.asOutput()
Output<U>	StridedSliceGrad.asOutput()
Output<Boolean>	QueueIsClosed.asOutput()
Output<T>	Transpose.asOutput()
Output<T>	ReluGrad.asOutput()
Output<Boolean>	RegexFullMatch.asOutput()
Output<T>	BatchMatrixSolve.asOutput()
Output<T>	Reciprocal.asOutput()
Output<T>	MatrixTriangularSolve.asOutput()
Output<T>	Acosh.asOutput()
Output<Object>	UnbatchDataset.asOutput()
Output<T>	AccumulatorTakeGradient.asOutput()
Output<T>	Relu.asOutput()
Output<T>	DepthToSpace.asOutput()
Output<T>	TruncateDiv.asOutput()
Output<T>	Gather.asOutput()
Output<Object>	FixedLengthRecordDataset.asOutput()
Output<T>	BatchCholesky.asOutput()
Output<String>	DecodeCompressed.asOutput()
Output<T>	BatchCholeskyGrad.asOutput()
Output<T>	Conj.asOutput()
Output<T>	Polygamma.asOutput()
Output<Float>	AdjustHue.asOutput()
Output<T>	Cos.asOutput()
Output<T>	Expm1.asOutput()
Output<T>	FFT.asOutput()
Output<Float>	IRFFT3D.asOutput()
Output<T>	BatchMatrixSolveLs.asOutput()
Output<T>	Atanh.asOutput()
Output<T>	BesselI1e.asOutput()
Output<T>	ScatterMin.asOutput()
Output<T>	PlaceholderWithDefault.asOutput()
Output<String>	Fact.asOutput()
Output<U>	RandomUniform.asOutput()
Output<String>	ShardedFilename.asOutput()
Output<U>	Angle.asOutput()
Output<Float>	TensorArraySplit.asOutput()
Output<Long>	SdcaFprint.asOutput()
Output<Long>	StringToHashBucketFast.asOutput()
Output<String>	Rpc.asOutput()
Output<T>	UnsortedSegmentSum.asOutput()
Output<Boolean>	ReduceAny.asOutput()
Output<T>	Assign.asOutput()
Output<T>	DepthwiseConv2dNativeBackpropFilter.asOutput()
Output<Object>	ConcatenateDataset.asOutput()
Output<UInt8>	DecodeBmp.asOutput()
Output<T>	Relu6.asOutput()
Output<Object>	BatchFFT2D.asOutput()
Output<T>	ApplyRMSProp.asOutput()
Output<T>	ResizeBicubicGrad.asOutput()
Output<T>	SoftplusGrad.asOutput()
Output<T>	LeftShift.asOutput()
Output<T>	ApplyPowerSign.asOutput()
Output<T>	Negate.asOutput()
Output<V>	StatelessRandomUniform.asOutput()
Output<T>	CollectiveReduce.asOutput()
Output<T>	AvgPool3D.asOutput()
Output<Object>	TensorListSetItem.asOutput()
Output<T>	Atan2.asOutput()
Output<T>	MaxPoolGradGradWithArgmax.asOutput()
Output<T>	Reshape.asOutput()
Output<Float>	BoostedTreesPredict.asOutput()
Output<T>	Round.asOutput()
Output<T>	Conv2DBackpropInput.asOutput()
Output<Object>	TensorListPushBackBatch.asOutput()
Output<String>	StringStrip.asOutput()
Output<U>	SerializeSparse.asOutput()
Output<T>	Square.asOutput()
Output<T>	Dilation2DBackpropFilter.asOutput()
Output<U>	Shape.asOutput()
Output<U>	Fill.asOutput()
Output<T>	MatrixLogarithm.asOutput()
Output<T>	MatrixDiagPart.asOutput()
Output<Long>	LookupTableSize.asOutput()
Output<String>	DatasetToGraph.asOutput()
Output<Integer>	StringLength.asOutput()
Output<T>	GuaranteeConst.asOutput()
Output<Boolean>	IsVariableInitialized.asOutput()
Output<T>	SparseApplyMomentum.asOutput()
Output<Object>	PriorityQueue.asOutput()
Output<T>	SpaceToBatchND.asOutput()
Output<Float>	CropAndResize.asOutput()
Output<T>	MatrixSolve.asOutput()
Output<Float>	AudioSpectrogram.asOutput()
Output<Boolean>	InTopK.asOutput()
Output<String>	ReadFile.asOutput()
Output<T>	DrawBoundingBoxes.asOutput()
Output<Object>	FilterByLastComponentDataset.asOutput()
Output<T>	DecodePng.asOutput()
Output<T>	Diag.asOutput()
Output<T>	Digamma.asOutput()
Output<T>	ApplyGradientDescent.asOutput()
Output<Object>	RepeatDataset.asOutput()
Output<UInt8>	CompareAndBitpack.asOutput()
Output<T>	RefSelect.asOutput()
Output<T>	BiasAddGrad.asOutput()
Output<T>	ReverseSequence.asOutput()
Output<U>	HistogramFixedWidth.asOutput()
Output<T>	GetSessionTensor.asOutput()
Output<T>	Min.asOutput()
Output<T>	Cross.asOutput()
Output<Boolean>	LoopCond.asOutput()
Output<T>	Sin.asOutput()
Output<Integer>	BarrierReadySize.asOutput()
Output<String>	EncodeJpeg.asOutput()
Output<T>	ApplyAdam.asOutput()
Output<Float>	IRFFT.asOutput()
Output<T>	TruncateMod.asOutput()
Output<T>	Concat.asOutput()
Output<Long>	ReaderNumRecordsProduced.asOutput()
Output<String>	TensorSummaryV2.asOutput()
Output<Object>	SinkDataset.asOutput()
Output<T>	QuantizeAndDequantizeV3.asOutput()
Output<String>	ReduceJoin.asOutput()
Output<T>	Asin.asOutput()
Output<T>	ApplyAdagrad.asOutput()
Output<Float>	Mfcc.asOutput()
Output<Boolean>	Less.asOutput()
Output<U>	Multinomial.asOutput()
Output<T>	BatchMatrixSetDiag.asOutput()
Output<T>	Conv3DBackpropInput.asOutput()
Output<T>	Dilation2D.asOutput()
Output<T>	BatchToSpaceND.asOutput()
Output<Integer>	Bucketize.asOutput()
Output<UInt8>	DecodeGif.asOutput()
Output<Object>	TensorListReserve.asOutput()
Output<T>	AddV2.asOutput()
Output<T>	CudnnRNNCanonicalToParams.asOutput()
Output<Integer>	MapIncompleteSize.asOutput()
Output<T>	QuantizeAndDequantizeV2.asOutput()
Output<T>	ScatterNdNonAliasingAdd.asOutput()
Output<Object>	BatchIFFT.asOutput()
Output<T>	SparseSegmentMean.asOutput()
Output<Float>	FakeQuantWithMinMaxArgsGradient.asOutput()
Output<T>	AssignSub.asOutput()
Output<T>	MirrorPadGrad.asOutput()
Output<T>	SegmentSum.asOutput()
Output<T>	MaxPoolGradWithArgmax.asOutput()
Output<Float>	TensorArrayWrite.asOutput()
Output<T>	Enter.asOutput()
Output<T>	SqrtGrad.asOutput()
Output<Object>	TensorListPushBack.asOutput()
Output<T>	ReciprocalGrad.asOutput()
Output<T>	ApplyProximalAdagrad.asOutput()
Output<T>	ParallelDynamicStitch.asOutput()
Output<T>	GatherV2.asOutput()
Output<T>	InplaceUpdate.asOutput()
Output<Boolean>	InTopKV2.asOutput()
Output<T>	Subtract.asOutput()
Output<T>	Dilation2DBackpropInput.asOutput()
Output<Object>	TFRecordReader.asOutput()
Output<T>	Mod.asOutput()
Output<T>	CollectiveBcastRecv.asOutput()
Output<T>	SparseApplyProximalAdagrad.asOutput()
Output<Float>	EditDistance.asOutput()
Output<T>	Roll.asOutput()
Output<T>	RandomGammaGrad.asOutput()
Output<Object>	ParseExampleDataset.asOutput()
Output<U>	Conv3DBackpropInputV2.asOutput()
Output<T>	Sigmoid.asOutput()
Output<U>	Real.asOutput()
Output<T>	Cosh.asOutput()
Output<T>	MatrixDiag.asOutput()
Output<T>	MaxPoolGrad.asOutput()
Output<T>	DivNoNan.asOutput()
Output<T>	DiagPart.asOutput()
Output<Object>	MutexV2.asOutput()
Output<T>	BatchToSpace.asOutput()
Output<Boolean>	ApproximateEqual.asOutput()
Output<T>	SparseApplyFtrlV2.asOutput()
Output<Long>	ReaderNumWorkUnitsCompleted.asOutput()
Output<Object>	Iterator.asOutput()
Output<T>	Tanh.asOutput()
Output<T>	SparseSegmentSqrtNWithNumSegments.asOutput()
Output<T>	Acos.asOutput()
Output<Integer>	Rank.asOutput()
Output<T>	Print.asOutput()
Output<Float>	IRFFT2D.asOutput()
Output<T>	BatchMatrixDiagPart.asOutput()
Output<T>	Rint.asOutput()
Output<Float>	CropAndResizeGradBoxes.asOutput()
Output<T>	ApplyProximalGradientDescent.asOutput()
Output<T>	DestroyTemporaryVariable.asOutput()
Output<T>	SegmentMax.asOutput()
Output<Object>	BatchDataset.asOutput()
Output<T>	SparseApplyFtrl.asOutput()
Output<Object>	BatchFFT.asOutput()
Output<T>	RsqrtGrad.asOutput()
Output<T>	ScatterNdUpdate.asOutput()
Output<T>	Conv3DBackpropFilter.asOutput()
Output<T>	SparseDenseCwiseAdd.asOutput()
Output<Object>	MutableHashTable.asOutput()
Output<Boolean>	ReduceAll.asOutput()
Output<Float>	LoadAndRemapMatrix.asOutput()
Output<Float>	ResizeBilinear.asOutput()
Output<Integer>	MapSize.asOutput()
Output<T>	FFT3D.asOutput()
Output<String>	StringJoin.asOutput()
Output<Object>	TakeDataset.asOutput()
Output<U>	Bitcast.asOutput()
Output<Boolean>	IsInf.asOutput()
Output<T>	ApplyAddSign.asOutput()
Output<String>	EncodeBase64.asOutput()
Output<T>	BatchMatrixDiag.asOutput()
Output<T>	FusedResizeAndPadConv2D.asOutput()
Output<T>	DecodeRaw.asOutput()
Output<T>	Invert.asOutput()
Output<T>	NextIteration.asOutput()
Output<String>	Substr.asOutput()
Output<Float>	AdjustContrast.asOutput()
Output<Long>	StringToHashBucket.asOutput()
Output<U>	LookupTableFind.asOutput()
Output<T>	SeluGrad.asOutput()
Output<T>	BatchMatrixTriangularSolve.asOutput()
Output<T>	ParseTensor.asOutput()
Output<Object>	AnonymousIterator.asOutput()
Output<Object>	TextLineDataset.asOutput()
Output<T>	LRNGrad.asOutput()
Output<Long>	Where.asOutput()
Output<T>	Selu.asOutput()
Output<T>	BesselI0e.asOutput()
Output<Object>	BytesProducedStatsDataset.asOutput()
Output<T>	InplaceSub.asOutput()
Output<Object>	RFFT3D.asOutput()
Output<T>	AvgPool3DGrad.asOutput()
Output<T>	Mean.asOutput()
Output<T>	SparseSoftmax.asOutput()
Output<Object>	SerializeIterator.asOutput()
Output<Object>	SetStatsAggregatorDataset.asOutput()
Output<Object>	CacheDataset.asOutput()
Output<V>	StatelessMultinomial.asOutput()
Output<T>	CountUpTo.asOutput()
Output<String>	AsString.asOutput()
Output<Object>	SlideDataset.asOutput()
Output<T>	TensorListGetItem.asOutput()
Output<Boolean>	Equal.asOutput()
Output<U>	ResourceGather.asOutput()
Output<Object>	PaddedBatchDatasetV2.asOutput()
Output<T>	AvgPool.asOutput()
Output<T>	SparseSegmentSqrtN.asOutput()
Output<T>	ApplyFtrl.asOutput()
Output<T>	ReduceMax.asOutput()
Output<Object>	TensorListConcatLists.asOutput()
Output<T>	ScatterMax.asOutput()
Output<T>	QuantizeAndDequantize.asOutput()
Output<Integer>	OrderedMapIncompleteSize.asOutput()
Output<String>	MatchingFiles.asOutput()
Output<T>	Erfc.asOutput()
Output<Integer>	NonMaxSuppressionWithOverlaps.asOutput()
Output<T>	Pad.asOutput()
Output<T>	Igamma.asOutput()
Output<U>	Complex.asOutput()
Output<T>	EnsureShape.asOutput()
Output<T>	Constant.asOutput()
Output<Object>	VarHandleOp.asOutput()
Output<T>	BatchNormWithGlobalNormalization.asOutput()
Output<T>	SparseReduceSum.asOutput()
Output<Float>	Dequantize.asOutput()
Output<T>	ApplyAdaMax.asOutput()
Output<Float>	TensorArrayScatter.asOutput()
Output<T>	Cumprod.asOutput()
Output<String>	BoostedTreesExampleDebugOutputs.asOutput()
Output<T>	MaxPool3D.asOutput()
Output<U>	SparseToDense.asOutput()
Output<T>	Log1p.asOutput()
Output<T>	ReduceProd.asOutput()
Output<String>	EncodePng.asOutput()
Output<U>	ScatterNd.asOutput()
Output<String>	ImageSummary.asOutput()
Output<T>	MirrorPad.asOutput()
Output<T>	ConjugateTranspose.asOutput()
Output<T>	FractionalMaxPoolGrad.asOutput()
Output<T>	Rsqrt.asOutput()
Output<U>	Size.asOutput()
Output<Object>	RandomDataset.asOutput()
Output<T>	ScatterMul.asOutput()
Output<V>	ArgMax.asOutput()
Output<T>	RealDiv.asOutput()
Output<T>	Inv.asOutput()
Output<T>	SparseSegmentSum.asOutput()
Output<T>	MatrixInverse.asOutput()
Output<Integer>	NonMaxSuppressionV2.asOutput()
Output<Object>	IteratorGetNextAsOptional.asOutput()
Output<T>	Asinh.asOutput()
Output<T>	Ceil.asOutput()
Output<T>	Conv3DBackpropFilterV2.asOutput()
Output<T>	ClipByValue.asOutput()
Output<Integer>	BarrierIncompleteSize.asOutput()
Output<T>	TensorArrayGather.asOutput()
Output<Object>	ZipDataset.asOutput()
Output<T>	Where3.asOutput()
Output<T>	RightShift.asOutput()
Output<T>	ReduceSum.asOutput()
Output<Object>	IteratorFromStringHandleV2.asOutput()
Output<T>	TensorArrayRead.asOutput()
Output<T>	CropAndResizeGradImage.asOutput()
Output<T>	MatrixDeterminant.asOutput()
Output<T>	MaxPoolGradGrad.asOutput()
Output<Object>	IteratorFromStringHandle.asOutput()
Output<T>	ApplyAdadelta.asOutput()
Output<Integer>	StageSize.asOutput()
Output<T>	Conv2DBackpropFilter.asOutput()
Output<Boolean>	Any.asOutput()
Output<Object>	DenseToSparseBatchDataset.asOutput()
Output<T>	RandomCrop.asOutput()
Output<T>	MatrixSolveLs.asOutput()
Output<T>	ApplyMomentum.asOutput()
Output<T>	HSVToRGB.asOutput()
Output<T>	VariableShape.asOutput()
Output<T>	SparseApplyAdadelta.asOutput()
Output<T>	Zeta.asOutput()
Output<Float>	FakeQuantWithMinMaxArgs.asOutput()
Output<Float>	TensorArrayUnpack.asOutput()
Output<T>	TensorListGather.asOutput()
Output<String>	GenerateBigQueryReaderPartitions.asOutput()
Output<Object>	FixedLengthRecordReader.asOutput()
Output<T>	MatMul.asOutput()
Output<Object>	OptionalFromValue.asOutput()
Output<T>	ImmutableConst.asOutput()
Output<V>	StatelessRandomNormal.asOutput()
Output<T>	Sum.asOutput()
Output<T>	StringToNumber.asOutput()
Output<T>	FusedPadConv2D.asOutput()
Output<T>	OnesLike.asOutput()
Output<Boolean>	OptionalHasValue.asOutput()
Output<T>	FloorMod.asOutput()
Output<T>	ParallelConcat.asOutput()
Output<Integer>	AccumulatorNumAccumulated.asOutput()
Output<T>	ApplyCenteredRMSProp.asOutput()
Output<String>	MergeSummary.asOutput()
Output<T>	TemporaryVariable.asOutput()
Output<U>	ParameterizedTruncatedNormal.asOutput()
Output<T>	Reverse.asOutput()
Output<T>	CheckNumerics.asOutput()
Output<Boolean>	LogicalOr.asOutput()
Output<String>	StaticRegexReplace.asOutput()
Output<Object>	RFFT.asOutput()
Output<Integer>	OrderedMapSize.asOutput()
Output<T>	SparseDenseCwiseDiv.asOutput()
Output<T>	Exp.asOutput()
Output<Object>	SkipDataset.asOutput()
Output<U>	ComplexAbs.asOutput()
Output<T>	AddN.asOutput()
Output<Float>	BoostedTreesMakeStatsSummary.asOutput()
Output<T>	ScatterNdSub.asOutput()
Output<T>	BitwiseXor.asOutput()
Output<String>	LMDBReader.asOutput()
Output<Object>	WindowDataset.asOutput()
Output<T>	L2Loss.asOutput()
Output<T>	ResourceCountUpTo.asOutput()
Output<T>	IFFT.asOutput()
Output<T>	TensorListStack.asOutput()
Output<T>	CollectiveBcastSend.asOutput()
Output<String>	DecodeBase64.asOutput()
Output<Object>	TFRecordDataset.asOutput()
Output<T>	ScatterNdAdd.asOutput()
Output<Float>	DecodeWav.audio() 2-D with shape `[length, channels]`.
Output<T>	SparseAddGrad.aValGrad() 1-D with shape `[nnz(A)]`.
Output<T>	AccumulatorTakeGradient.average() The average of the accumulated gradients.
Output<T>	SoftmaxCrossEntropyWithLogits.backprop() backpropagated gradients (batch_size x num_classes matrix).
Output<T>	SparseSoftmaxCrossEntropyWithLogits.backprop() backpropagated gradients (batch_size x num_classes matrix).
Output<T>	Relu6Grad.backprops() The gradients: `gradients * (features > 0) * (features < 6)`.
Output<T>	EluGrad.backprops() The gradients: `gradients * (outputs + 1)` if outputs < 0, `gradients` otherwise.
Output<T>	SoftsignGrad.backprops() The gradients: `gradients / (1 + abs(features)) ** 2`.
Output<T>	ReluGrad.backprops() `gradients * (features > 0)`.
Output<T>	SoftplusGrad.backprops() The gradients: `gradients / (1 + exp(-features))`.
Output<Float>	FakeQuantWithMinMaxArgsGradient.backprops() Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: `gradients * (inputs >= min && inputs <= max)`.
Output<T>	SeluGrad.backprops() The gradients: `gradients * (outputs + scale * alpha)` if outputs < 0, `scale * gradients` otherwise.
Output<Float>	FakeQuantWithMinMaxVarsGradient.backpropsWrtInput() Backpropagated gradients w.r.t.
Output<Float>	FakeQuantWithMinMaxVarsPerChannelGradient.backpropsWrtInput() Backpropagated gradients w.r.t.
Output<Float>	FakeQuantWithMinMaxVarsGradient.backpropWrtMax() Backpropagated gradients w.r.t.
Output<Float>	FakeQuantWithMinMaxVarsPerChannelGradient.backpropWrtMax() Backpropagated gradients w.r.t.
Output<Float>	FakeQuantWithMinMaxVarsGradient.backpropWrtMin() Backpropagated gradients w.r.t.
Output<Float>	FakeQuantWithMinMaxVarsPerChannelGradient.backpropWrtMin() Backpropagated gradients w.r.t.
Output<T>	BatchMatrixBandPart.band()
Output<T>	MatrixBandPart.band() Rank `k` tensor of the same shape as input.
Output<T>	UnbatchGrad.batchedGrad()
Output<Long>	Batch.batchIndex()
Output<T>	FusedBatchNorm.batchMean() A 1D Tensor for the computed batch mean, to be used by TensorFlow to compute the running mean.
Output<U>	FusedBatchNormV2.batchMean() A 1D Tensor for the computed batch mean, to be used by TensorFlow to compute the running mean.
Output<T>	FusedBatchNorm.batchVariance() A 1D Tensor for the computed batch variance, to be used by TensorFlow to compute the running variance.
Output<U>	FusedBatchNormV2.batchVariance() A 1D Tensor for the computed batch variance, to be used by TensorFlow to compute the running variance.
Output<Float>	SampleDistortedBoundingBoxV2.bboxes() 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Output<Float>	SampleDistortedBoundingBox.bboxes() 3-D with shape `[1, 1, 4]` containing the distorted bounding box.
Output<T>	SampleDistortedBoundingBoxV2.begin() 1-D, containing `[offset_height, offset_width, 0]`.
Output<T>	SampleDistortedBoundingBox.begin() 1-D, containing `[offset_height, offset_width, 0]`.
Output<String>	DecodeJSONExample.binaryExamples() Each string is a binary Example protocol buffer corresponding to the respective element of `json_examples`.
Output<T>	Bincount.bins() 1D `Tensor` with length equal to `size`.
Output<T>	SparseAddGrad.bValGrad() 1-D with shape `[nnz(B)]`.
Output<String>	EncodeProto.bytes() Tensor of serialized protos with shape `batch_shape`.
Output<Long>	FractionalAvgPool.colPoolingSequence() column pooling sequence, needed to calculate gradient.
Output<Long>	FractionalMaxPool.colPoolingSequence() column pooling sequence, needed to calculate gradient.
Output<String>	EncodeWav.contents() 0-D.
Output<String>	ReadFile.contents()
Output<String>	EncodeJpeg.contents() 0-D.
Output<String>	EncodePng.contents() 0-D.
Output<Boolean>	BoostedTreesCenterBias.continueCentering() Bool, whether to continue bias centering.
Output<V>	UniqueWithCountsV2.count() A 1-D Tensor.
Output<U>	UniqueWithCounts.count() 1-D.
Output<Float>	CropAndResize.crops() A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`.
Output<Integer>	Skipgram.currentEpoch() The current epoch number.
Output<T>	CollectiveReduce.data()
Output<T>	CollectiveBcastRecv.data()
Output<T>	CollectiveBcastSend.data()
Output<T>	BatchNormWithGlobalNormalizationGrad.db() 1D backprop tensor for beta.
Output<T>	SparseFillEmptyRowsGrad.dDefaultValue() 0-D.
Output<Long>	CTCGreedyDecoder.decodedIndices() Indices matrix, size `(total_decoded_outputs x 2)`, of a `SparseTensor`.
Output<Long>	CTCGreedyDecoder.decodedShape() Shape vector, size `(2)`, of the decoded SparseTensor.
Output<Long>	CTCGreedyDecoder.decodedValues() Values vector, size: `(total_decoded_outputs)`, of a `SparseTensor`.
Output<U>	SparseToDense.dense() Dense output tensor of shape `output_shape`.
Output<T>	BatchNormWithGlobalNormalizationGrad.dg() 1D backprop tensor for gamma.
Output<T>	MatrixDiagPart.diagonal() The extracted diagonal(s) having shape `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`.
Output<T>	DiagPart.diagonal() The extracted diagonal.
Output<T>	BatchMatrixDiagPart.diagonal()
Output<T>	BatchNormWithGlobalNormalizationGrad.dm() 1D backprop tensor for mean.
Output<T>	BatchNormWithGlobalNormalizationGrad.dv() 1D backprop tensor for variance.
Output<T>	SparseFillEmptyRowsGrad.dValues() 1-D.
Output<T>	BatchNormWithGlobalNormalizationGrad.dx() 4D backprop tensor for input.
<T> Output<T>	Gradients.dy(int index) Returns a symbolic handle to one of the gradient operation output
Output<T>	SelfAdjointEig.e() Eigenvalues.
Output<T>	BatchSelfAdjointEigV2.e()
Output<T>	TensorListElementShape.elementShape()
Output<Boolean>	SparseFillEmptyRows.emptyRowIndicator() 1-D.
Output<Integer>	Skipgram.examples() A vector of word ids.
Output<String>	BoostedTreesExampleDebugOutputs.examplesDebugOutputsSerialized() Output rank 1 Tensor containing a proto serialized as a string for each example.
Output<String>	Fact.fact()
Output<String>	ShardedFilespec.filename()
Output<String>	ShardedFilename.filename()
Output<String>	MatchingFiles.filenames() A vector of matching filenames.
Output<T>	Dilation2DBackpropFilter.filterBackprop() 3-D with shape `[filter_height, filter_width, depth]`.
Output<Float>	TensorArray.flow() A scalar used to control gradient flow.
Output<Float>	TensorArraySplit.flowOut() A float scalar that enforces proper chaining of operations.
Output<Float>	TensorArrayWrite.flowOut() A float scalar that enforces proper chaining of operations.
Output<Float>	TensorArrayGradWithShape.flowOut()
Output<Float>	TensorArrayScatter.flowOut() A float scalar that enforces proper chaining of operations.
Output<Float>	TensorArrayUnpack.flowOut()
Output<Float>	TensorArrayGrad.flowOut()
Output<Float>	ExtractGlimpse.glimpse() A tensor representing the glimpses `[batch_size, glimpse_height, glimpse_width, channels]`.
Output<?>	TensorArrayGradWithShape.gradHandle()
Output<?>	TensorArrayGrad.gradHandle()
Output<Float>	CTCLoss.gradient() The gradient of `loss`.
Output<String>	DatasetToGraph.graph() The graph representation of the dataset (as serialized GraphDef).
Output<?>	TensorSliceDataset.handle()
Output<?>	RandomShuffleQueue.handle() The handle to the queue.
Output<?>	SparseTensorSliceDataset.handle()
Output<String>	SparseConditionalAccumulator.handle() The handle to the accumulator.
Output<?>	IteratorV2.handle()
Output<?>	GetSessionHandleV2.handle() The handle for the tensor stored in the session state, represented as a ResourceHandle object.
Output<?>	ShuffleAndRepeatDataset.handle()
Output<?>	EmptyTensorList.handle()
Output<?>	PrefetchDataset.handle()
Output<?>	FIFOQueue.handle() The handle to the queue.
Output<?>	StatsAggregatorHandle.handle()
Output<?>	LatencyStatsDataset.handle()
Output<?>	RangeDataset.handle()
Output<?>	SqlDataset.handle()
Output<String>	GetSessionHandle.handle() The handle for the tensor stored in the session state, represented as a string.
Output<String>	Barrier.handle() The handle to the barrier.
Output<?>	ShuffleDataset.handle()
Output<?>	PaddedBatchDataset.handle()
Output<?>	OptimizeDataset.handle()
Output<String>	ConditionalAccumulator.handle() The handle to the accumulator.
Output<?>	TensorDataset.handle()
Output<?>	PaddingFIFOQueue.handle() The handle to the queue.
Output<?>	BatchDatasetV2.handle()
Output<?>	PrependFromQueueAndPaddedBatchDataset.handle()
Output<?>	UnbatchDataset.handle()
Output<?>	FixedLengthRecordDataset.handle()
Output<?>	ConcatenateDataset.handle()
Output<?>	PriorityQueue.handle() The handle to the queue.
Output<?>	RepeatDataset.handle()
Output<?>	SinkDataset.handle()
Output<?>	TensorListReserve.handle()
Output<?>	ParseExampleDataset.handle()
Output<?>	Iterator.handle() A handle to the iterator that can be passed to a "MakeIterator" or "IteratorGetNext" op.
Output<?>	TensorArray.handle() The handle to the TensorArray.
Output<?>	BatchDataset.handle()
Output<?>	TakeDataset.handle()
Output<?>	AnonymousIterator.handle() A handle to the iterator that can be passed to a "MakeIterator" or "IteratorGetNext" op.
Output<?>	TextLineDataset.handle()
Output<?>	BytesProducedStatsDataset.handle()
Output<?>	SetStatsAggregatorDataset.handle()
Output<?>	CacheDataset.handle()
Output<?>	SlideDataset.handle()
Output<?>	PaddedBatchDatasetV2.handle()
Output<?>	RandomDataset.handle()
Output<?>	ZipDataset.handle()
Output<?>	DenseToSparseBatchDataset.handle()
Output<?>	SkipDataset.handle()
Output<?>	WindowDataset.handle()
Output<?>	TFRecordDataset.handle()
Output<Boolean>	OptionalHasValue.hasValue()
Output<?>	CudnnRNNV2.hostReserved()
Output<Long>	Batch.id()
Output<Long>	ComputeAccidentalHits.ids() A vector of IDs of positions in sampled_candidates that match a true_label for the row with the corresponding index in indices.
Output<V>	UniqueWithCountsV2.idx() A 1-D Tensor.
Output<V>	UniqueV2.idx() A 1-D Tensor.
Output<U>	SetDiff1D.idx() 1-D.
Output<U>	UniqueWithCounts.idx() 1-D.
Output<U>	Unique.idx() 1-D.
Output<UInt8>	DecodeJpeg.image() 3-D with shape `[height, width, channels]`..
Output<UInt8>	DecodeAndCropJpeg.image() 3-D with shape `[height, width, channels]`..
Output<UInt8>	DecodeBmp.image() 3-D with shape `[height, width, channels]`.
Output<T>	DecodePng.image() 3-D with shape `[height, width, channels]`.
Output<UInt8>	DecodeGif.image() 4-D with shape `[num_frames, height, width, 3]`.
Output<T>	ExtractJpegShape.imageShape() 1-D.
Output<T>	Dilation2DBackpropInput.inBackprop() 4-D with shape `[batch, in_height, in_width, depth]`.
Output<Long>	Where.index()
Output<Long>	StringSplit.indices() A dense matrix of int64 representing the indices of the sparse tensor.
Output<Long>	BarrierTakeMany.indices() A one-dimensional tensor of indices, with length num_elems.
Output<Integer>	TopK.indices() The indices of `values` within the last dimension of `input`.
Output<Long>	SparseAccumulatorTakeGradient.indices() Indices of the average of the accumulated sparse gradients.
Output<Long>	StringSplitV2.indices()
Output<Integer>	ComputeAccidentalHits.indices() A vector of indices corresponding to rows of true_candidates.
Output<T>	CudnnRNNBackprop.inputBackprop()
Output<T>	CudnnRNNBackpropV2.inputBackprop()
Output<T>	CudnnRNNBackprop.inputCBackprop()
Output<T>	CudnnRNNBackpropV2.inputCBackprop()
Output<T>	CudnnRNNBackprop.inputHBackprop()
Output<T>	CudnnRNNBackpropV2.inputHBackprop()
Output<Boolean>	QueueIsClosedV2.isClosed()
Output<Boolean>	QueueIsClosed.isClosed()
Output<Boolean>	VarIsInitializedOp.isInitialized() a scalar boolean which is true if the variable has been initialized.
Output<Boolean>	IsBoostedTreesEnsembleInitialized.isInitialized() output boolean on whether it is initialized or not.
Output<Boolean>	IsVariableInitialized.isInitialized()
Output<T>	TensorListGetItem.item()
Output<Long>	OrderedMapUnstageNoKey.key()
Output<Long>	MapUnstageNoKey.key()
Output<String>	ReaderRead.key() A scalar.
Output<String>	BarrierTakeMany.keys() A one-dimensional tensor of keys, with length num_elements.
Output<T>	LookupTableExport.keys() Vector of all keys present in the table.
Output<String>	ReaderReadUpTo.keys() A 1-D tensor.
Output<Integer>	Skipgram.labels() A vector of word ids.
Output<Integer>	BoostedTreesGetEnsembleStates.lastLayerNodesRange() Rank size 2 tensor that contains start and end ids of the nodes in the latest layer.
Output<Integer>	TensorListLength.length()
Output<Long>	TensorArrayConcat.lengths() A vector of the row sizes of the original T elements in the value output.
Output<T>	LogMatrixDeterminant.logAbsDeterminant() The logs of the absolute values of the determinants of the N input matrices.
Output<Float>	BoostedTreesPredict.logits() Output rank 2 Tensor containing logits for each example.
Output<Float>	CTCBeamSearchDecoder.logProbability() A matrix, shaped: `(batch_size x top_paths)`.
Output<Float>	CTCGreedyDecoder.logProbability() Matrix, size `(batch_size x 1)`, containing sequence log-probabilities.
Output<T>	LogSoftmax.logsoftmax() Same shape as `logits`.
Output<T>	SoftmaxCrossEntropyWithLogits.loss() Per example loss (batch_size vector).
Output<Float>	CTCLoss.loss() A vector (batch) containing log-probabilities.
Output<T>	SparseSoftmaxCrossEntropyWithLogits.loss() Per example loss (batch_size vector).
Output<Float>	QuantizedRelu.maxActivations() The float value that the highest quantized value represents.
Output<Float>	QuantizedReluX.maxActivations() The float value that the highest quantized value represents.
Output<Float>	QuantizedRelu6.maxActivations() The float value that the highest quantized value represents.
Output<Float>	QuantizedMatMul.maxOut() The float value that the highest quantized output value represents.
Output<Float>	QuantizedBiasAdd.maxOut() The float value that the highest quantized output value represents.
Output<Float>	QuantizedMaxPool.maxOutput() The float value that the highest quantized output value represents.
Output<Float>	QuantizedAvgPool.maxOutput() The float value that the highest quantized output value represents.
Output<Float>	QuantizedConv2D.maxOutput() The float value that the highest quantized output value represents.
Output<Float>	QuantizedAdd.maxZ() The float value that the highest quantized output value represents.
Output<Float>	QuantizedMul.maxZ() The float value that the highest quantized output value represents.
Output<T>	DynamicStitch.merged()
Output<T>	ParallelDynamicStitch.merged()
Output<Float>	QuantizedRelu.minActivations() The float value that the lowest quantized value represents.
Output<Float>	QuantizedReluX.minActivations() The float value that the lowest quantized value represents.
Output<Float>	QuantizedRelu6.minActivations() The float value that the lowest quantized value represents.
Output<Float>	QuantizedMatMul.minOut() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedBiasAdd.minOut() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedMaxPool.minOutput() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedAvgPool.minOutput() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedConv2D.minOutput() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedAdd.minZ() The float value that the lowest quantized output value represents.
Output<Float>	QuantizedMul.minZ() The float value that the lowest quantized output value represents.
Output<?>	MutexLock.mutexLock() A tensor that keeps a shared pointer to a lock on the mutex; when the Tensor is destroyed, the use count on the shared pointer is decreased by 1.
Output<Integer>	BoostedTreesTrainingPredict.nodeIds() Rank 1 Tensor containing new node ids in the new tree_ids.
Output<Integer>	AccumulatorNumAccumulated.numAccumulated() The number of gradients aggregated in the given accumulator.
Output<Integer>	BoostedTreesGetEnsembleStates.numAttemptedLayers() The number of layers we attempted to build (but not necessarily succeeded).
Output<Integer>	BoostedTreesGetEnsembleStates.numFinalizedTrees() The number of trees that were finished successfully.
Output<Integer>	GenerateVocabRemapping.numPresent() Number of new vocab entries found in old vocab.
Output<Integer>	BoostedTreesGetEnsembleStates.numTrees() The number of trees in the tree ensemble resource.
Output<U>	FusedBatchNormGradV2.offsetBackprop() A 1D Tensor for the gradient with respect to offset.
Output<T>	FusedBatchNormGrad.offsetBackprop() A 1D Tensor for the gradient with respect to offset.
Output<?>	OptionalNone.optional()
Output<?>	IteratorGetNextAsOptional.optional()
Output<?>	OptionalFromValue.optional()
Output<T>	SparseApplyProximalGradientDescent.out() Same as "var".
Output<T>	ApplyFtrlV2.out() Same as "var".
Output<T>	SparseApplyCenteredRMSProp.out() Same as "var".
Output<T>	Cumsum.out()
Output<T>	SparseApplyAdagrad.out() Same as "var".
Output<T>	SparseApplyAdagradDA.out() Same as "var".
Output<T>	ApplyAdagradDA.out() Same as "var".
Output<T>	SparseApplyRMSProp.out() Same as "var".
Output<T>	SetDiff1D.out() 1-D.
Output<T>	ApplyRMSProp.out() Same as "var".
Output<T>	ApplyPowerSign.out() Same as "var".
Output<T>	SparseApplyMomentum.out() Same as "var".
Output<T>	ApplyGradientDescent.out() Same as "var".
Output<U>	HistogramFixedWidth.out() A 1-D `Tensor` holding histogram of values.
Output<T>	ApplyAdam.out() Same as "var".
Output<T>	ApplyAdagrad.out() Same as "var".
Output<V>	QuantizedMatMul.out()
Output<T>	ApplyProximalAdagrad.out() Same as "var".
Output<T>	SparseApplyProximalAdagrad.out() Same as "var".
Output<T>	SparseApplyFtrlV2.out() Same as "var".
Output<T>	ApplyProximalGradientDescent.out() Same as "var".
Output<T>	SparseApplyFtrl.out() Same as "var".
Output<T>	ApplyAddSign.out() Same as "var".
Output<T>	ApplyFtrl.out() Same as "var".
Output<U>	Complex.out()
Output<T>	ApplyAdaMax.out() Same as "var".
Output<T>	Cumprod.out()
Output<T>	ApplyAdadelta.out() Same as "var".
Output<T>	ApplyMomentum.out() Same as "var".
Output<T>	SparseApplyAdadelta.out() Same as "var".
Output<T>	ApplyCenteredRMSProp.out() Same as "var".
Output<Float>	SdcaOptimizer.outExampleStateData() a list of vectors containing the updated example state data.
Output<Float>	QuantizedResizeBilinear.outMax()
Output<Float>	QuantizedResizeBilinear.outMin()
Output<T>	MaxPool.output() The max pooled output tensor.
Output<T>	FFT2D.output() A complex64 tensor of the same shape as `input`.
Output<U>	QuantizeDownAndShrinkRange.output()
Output<U>	Requantize.output()
Output<T>	UnsortedSegmentMax.output() Has same shape as data, except for dimension 0 which has size `num_segments`.
Output<U>	Imag.output()
Output<T>	MaxPool3DGradGrad.output() Gradients of gradients w.r.t.
Output<T>	BiasAdd.output() Broadcasted sum of `value` and `bias`.
Output<T>	Slice.output()
Output<?>	BatchIFFT3D.output()
Output<T>	CholeskyGrad.output() Symmetrized version of df/dA .
Output<T>	ReduceMean.output() The reduced tensor.
Output<U>	RandomGamma.output() A tensor with shape `shape + shape(alpha)`.
Output<T>	Conv3D.output()
Output<T>	Merge.output() Will be set to the available input tensor.
Output<T>	MatrixSetDiag.output() Rank `k+1`, with `output.shape = input.shape`.
Output<T>	DepthwiseConv2dNativeBackpropInput.output() 4-D with shape according to `data_format`.
Output<T>	QuantizedMaxPool.output()
Output<T>	TileGrad.output()
Output<T>	MaxPoolV2.output() The max pooled output tensor.
Output<String>	RegexReplace.output() The text after applying pattern and rewrite.
Output<U>	OneHot.output() The one-hot tensor.
Output<T>	Tile.output()
Output<T>	SegmentMin.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	SparseSegmentMeanWithNumSegments.output() Has same shape as data, except for dimension 0 which has size `num_segments`.
Output<T>	Max.output() The reduced tensor.
Output<T>	PreventGradient.output() the same input tensor.
Output<T>	StopGradient.output()
Output<T>	DebugGradientIdentity.output()
Output<T>	Conv2D.output() A 4-D tensor.
Output<T>	Identity.output()
Output<T>	Placeholder.output() A placeholder tensor that must be replaced using the feed mechanism.
Output<T>	SpaceToDepth.output()
Output<T>	LinSpace.output() 1-D.
Output<T>	SparseReduceMax.output() `R-K`-D.
Output<T>	QuantizedAvgPool.output()
Output<T>	UnsortedSegmentProd.output() Has same shape as data, except for dimension 0 which has size `num_segments`.
Output<U>	RandomUniformInt.output() A tensor of the specified shape filled with uniform random integers.
Output<?>	BatchIFFT2D.output()
Output<T>	SegmentProd.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	LRN.output()
Output<T>	SparseSegmentMeanGrad.output()
Output<T>	SegmentMean.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<U>	TruncatedNormal.output() A tensor of the specified shape filled with random truncated normal values.
Output<T>	BatchMatrixDeterminant.output()
Output<T>	BatchMatrixInverse.output()
Output<T>	Const.output()
Output<T>	Exit.output() The same tensor as `data`.
Output<T>	Empty.output() A `Tensor` of type `T`.
Output<T>	RefMerge.output() Will be set to the available input tensor.
Output<T>	RefNextIteration.output() The same tensor as `data`.
Output<T>	Cholesky.output() Shape is `[..., M, M]`.
Output<T>	Range.output() 1-D.
Output<?>	BatchFFT3D.output()
Output<T>	PlaceholderV2.output() A placeholder tensor that must be replaced using the feed mechanism.
Output<T>	SpaceToBatch.output()
Output<V>	ArgMin.output()
Output<T>	MaxPoolGradGradV2.output() Gradients of gradients w.r.t.
Output<T>	SparseDenseCwiseMul.output() 1-D.
Output<T>	SparseSegmentSumWithNumSegments.output() Has same shape as data, except for dimension 0 which has size `num_segments`.
Output<T>	ResizeBilinearGrad.output() 4-D with shape `[batch, orig_height, orig_width, channels]`.
Output<V>	StatelessTruncatedNormal.output() Random values with specified shape.
Output<T>	SparseSegmentSqrtNGrad.output()
Output<U>	RandomPoisson.output()
Output<T>	PadV2.output()
Output<T>	FractionalAvgPoolGrad.output() 4-D.
Output<T>	ExpandDims.output() Contains the same data as `input`, but its shape has an additional dimension of size 1 added.
Output<U>	MaxPool3DGrad.output()
Output<T>	RefExit.output() The same tensor as `data`.
Output<U>	RandomNormal.output() A tensor of the specified shape filled with random normal values.
Output<U>	SparseTensorDenseAdd.output()
Output<T>	MaxPoolGradV2.output() Gradients w.r.t.
Output<T>	DepthwiseConv2dNative.output()
Output<T>	BatchSelfAdjointEig.output()
Output<T>	GatherNd.output() Values from `params` gathered from indices given by `indices`, with shape `indices.shape[:-1] + params.shape[indices.shape[-1]:]`.
Output<T>	RGBToHSV.output() `images` converted to HSV.
Output<T>	Prod.output() The reduced tensor.
Output<T>	RefIdentity.output()
Output<Float>	AdjustSaturation.output() The hue-adjusted image or images.
Output<T>	IFFT2D.output() A complex64 tensor of the same shape as `input`.
Output<Long>	StringToHashBucketStrong.output() A Tensor of the same shape as the input `string_tensor`.
Output<T>	UnravelIndex.output() An 2-D (or 1-D if indices is 0-D) tensor where each row has the same shape as the indices array.
Output<T>	Snapshot.output()
Output<T>	DebugGradientRefIdentity.output()
Output<T>	ReduceMin.output() The reduced tensor.
Output<T>	UnsortedSegmentMin.output() Has same shape as data, except for dimension 0 which has size `num_segments`.
Output<T>	RefEnter.output() The same tensor as `data`.
Output<T>	RandomShuffle.output() A tensor of same shape and type as `value`, shuffled along its first dimension.
Output<V>	RandomPoissonV2.output() A tensor with shape `shape + shape(rate)`.
Output<T>	StridedSlice.output()
Output<Boolean>	All.output() The reduced tensor.
Output<T>	BatchMatMul.output() 3-D or higher with shape `[..., r_o, c_o]`
Output<T>	CudnnRNNV2.output()
Output<T>	AvgPoolGrad.output() 4-D.
Output<?>	RFFT2D.output() A complex64 tensor of the same rank as `input`.
Output<T>	Stack.output() The packed tensor.
Output<T>	BroadcastTo.output() A Tensor.
Output<T>	Squeeze.output() Contains the same data as `input`, but has one or more dimensions of size 1 removed.
Output<T>	ResizeNearestNeighborGrad.output() 4-D with shape `[batch, orig_height, orig_width, channels]`.
Output<T>	IFFT3D.output() A complex64 tensor of the same shape as `input`.
Output<U>	StridedSliceGrad.output()
Output<Boolean>	RegexFullMatch.output() A bool tensor with the same shape as `input`.
Output<T>	BatchMatrixSolve.output()
Output<T>	MatrixTriangularSolve.output() Shape is `[..., M, K]`.
Output<T>	DepthToSpace.output()
Output<T>	Gather.output()
Output<T>	BatchCholesky.output()
Output<String>	DecodeCompressed.output() A Tensor with the same shape as input `bytes`, uncompressed from bytes.
Output<T>	BatchCholeskyGrad.output()
Output<T>	Conj.output()
Output<Float>	AdjustHue.output() The hue-adjusted image or images.
Output<T>	FFT.output() A complex64 tensor of the same shape as `input`.
Output<Float>	IRFFT3D.output() A float32 tensor of the same rank as `input`.
Output<T>	BatchMatrixSolveLs.output()
Output<T>	PlaceholderWithDefault.output() A placeholder tensor that defaults to `input` if it is not fed.
Output<U>	RandomUniform.output() A tensor of the specified shape filled with uniform random values.
Output<U>	Angle.output()
Output<Long>	SdcaFprint.output() a (N,2) shaped matrix where N is the number of elements in the input vector.
Output<Long>	StringToHashBucketFast.output() A Tensor of the same shape as the input `string_tensor`.
Output<T>	UnsortedSegmentSum.output() Has same shape as data, except for the first `segment_ids.rank` dimensions, which are replaced with a single dimension which has size `num_segments`.
Output<Boolean>	ReduceAny.output() The reduced tensor.
Output<T>	DepthwiseConv2dNativeBackpropFilter.output() 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`.
Output<T>	CudnnRNN.output()
Output<?>	BatchFFT2D.output()
Output<T>	ResizeBicubicGrad.output() 4-D with shape `[batch, orig_height, orig_width, channels]`.
Output<V>	StatelessRandomUniform.output() Random values with specified shape.
Output<T>	AvgPool3D.output() The average pooled output tensor.
Output<T>	MaxPoolGradGradWithArgmax.output() Gradients of gradients w.r.t.
Output<T>	Reshape.output()
Output<T>	Conv2DBackpropInput.output() 4-D with shape `[batch, in_height, in_width, in_channels]`.
Output<String>	StringStrip.output() A string `Tensor` of the same shape as the input.
Output<U>	Shape.output()
Output<U>	Fill.output()
Output<T>	MatrixLogarithm.output() Shape is `[..., M, M]`.
Output<Integer>	StringLength.output() Integer tensor that has the same shape as `input`.
Output<T>	GuaranteeConst.output()
Output<T>	SpaceToBatchND.output()
Output<T>	MatrixSolve.output() Shape is `[..., M, K]`.
Output<T>	DrawBoundingBoxes.output() 4-D with the same shape as `images`.
Output<?>	FilterByLastComponentDataset.output()
Output<T>	Diag.output()
Output<UInt8>	CompareAndBitpack.output() The bitpacked comparisons.
Output<T>	RefSelect.output() The forwarded tensor.
Output<T>	BiasAddGrad.output() 1-D with size the feature dimension of `out_backprop`.
Output<T>	ReverseSequence.output() The partially reversed input.
Output<T>	FractionalAvgPool.output() output tensor after fractional avg pooling.
Output<T>	Min.output() The reduced tensor.
Output<Boolean>	LoopCond.output() The same tensor as `input`.
Output<Float>	IRFFT.output() A float32 tensor of the same rank as `input`.
Output<T>	Concat.output() A `Tensor` with the concatenation of values stacked along the `concat_dim` dimension.
Output<T>	QuantizeAndDequantizeV3.output()
Output<String>	ReduceJoin.output() Has shape equal to that of the input with reduced dimensions removed or set to `1` depending on `keep_dims`.
Output<Float>	Mfcc.output()
Output<U>	Multinomial.output() 2-D Tensor with shape `[batch_size, num_samples]`.
Output<T>	BatchMatrixSetDiag.output()
Output<T>	Conv3DBackpropInput.output()
Output<T>	Dilation2D.output() 4-D with shape `[batch, out_height, out_width, depth]`.
Output<T>	BatchToSpaceND.output()
Output<Integer>	Bucketize.output() Same shape with 'input', each value of input replaced with bucket index.
Output<T>	QuantizeAndDequantizeV2.output()
Output<T>	ScatterNdNonAliasingAdd.output() A `Tensor` with the same shape as `input`, containing values of `input` updated with `updates`.
Output<?>	BatchIFFT.output()
Output<T>	SparseSegmentMean.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	MirrorPadGrad.output() The folded tensor.
Output<T>	SegmentSum.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	MaxPoolGradWithArgmax.output() Gradients w.r.t.
Output<T>	Enter.output() The same tensor as `data`.
Output<T>	GatherV2.output() Values from `params` gathered from indices given by `indices`, with shape `params.shape[:axis] + indices.shape + params.shape[axis + 1:]`.
Output<V>	QuantizedConv2D.output()
Output<Float>	EditDistance.output() A dense float tensor with rank R - 1.
Output<T>	Roll.output() Has the same shape and size as the input.
Output<T>	MaxPoolWithArgmax.output() The max pooled output tensor.
Output<T>	RandomGammaGrad.output()
Output<U>	Conv3DBackpropInputV2.output()
Output<U>	Real.output()
Output<T>	MatrixDiag.output() Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`.
Output<T>	MaxPoolGrad.output() Gradients w.r.t.
Output<T>	BatchToSpace.output() 4-D with shape `[batch, height, width, depth]`, where:
Output<T>	SparseSegmentSqrtNWithNumSegments.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<Integer>	Rank.output()
Output<T>	Print.output() = The unmodified `input` tensor
Output<Float>	IRFFT2D.output() A float32 tensor of the same rank as `input`.
Output<T>	QuantizedReshape.output()
Output<Float>	CropAndResizeGradBoxes.output() A 2-D tensor of shape `[num_boxes, 4]`.
Output<T>	SegmentMax.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<?>	BatchFFT.output()
Output<T>	Conv3DBackpropFilter.output()
Output<T>	SparseDenseCwiseAdd.output() 1-D.
Output<Boolean>	ReduceAll.output() The reduced tensor.
Output<T>	FFT3D.output() A complex64 tensor of the same shape as `input`.
Output<String>	StringJoin.output()
Output<U>	Bitcast.output()
Output<String>	EncodeBase64.output() Input strings encoded in base64.
Output<T>	BatchMatrixDiag.output()
Output<T>	FusedResizeAndPadConv2D.output()
Output<T>	DecodeRaw.output() A Tensor with one more dimension than the input `bytes`.
Output<T>	NextIteration.output() The same tensor as `data`.
Output<String>	Substr.output() Tensor of substrings
Output<Float>	AdjustContrast.output() The contrast-adjusted image or images.
Output<Long>	StringToHashBucket.output() A Tensor of the same shape as the input `string_tensor`.
Output<T>	BatchMatrixTriangularSolve.output()
Output<T>	ParseTensor.output() A Tensor of type `out_type`.
Output<T>	LRNGrad.output() The gradients for LRN.
Output<?>	RFFT3D.output() A complex64 tensor of the same rank as `input`.
Output<T>	AvgPool3DGrad.output() The backprop for input.
Output<T>	Mean.output() The reduced tensor.
Output<T>	SparseSoftmax.output() 1-D.
Output<V>	StatelessMultinomial.output() 2-D Tensor with shape `[batch_size, num_samples]`.
Output<T>	CountUpTo.output() A copy of the input before increment.
Output<String>	AsString.output()
Output<U>	ResourceGather.output()
Output<T>	AvgPool.output() The average pooled output tensor.
Output<T>	SparseSegmentSqrtN.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	ReduceMax.output() The reduced tensor.
Output<?>	TensorListConcatLists.output()
Output<T>	QuantizeAndDequantize.output()
Output<T>	Pad.output()
Output<T>	EnsureShape.output() A tensor with the same shape and contents as the input tensor or value.
Output<T>	FractionalMaxPool.output() output tensor after fractional max pooling.
Output<T>	SparseReduceSum.output() `R-K`-D.
Output<Float>	Dequantize.output()
Output<T>	MaxPool3D.output() The max pooled output tensor.
Output<T>	ReduceProd.output() The reduced tensor.
Output<U>	ScatterNd.output() A new tensor with the given shape and updates applied according to the indices.
Output<T>	MirrorPad.output() The padded tensor.
Output<T>	FractionalMaxPoolGrad.output() 4-D.
Output<U>	Size.output()
Output<V>	ArgMax.output()
Output<T>	SparseSegmentSum.output() Has same shape as data, except for dimension 0 which has size `k`, the number of segments.
Output<T>	MatrixInverse.output() Shape is `[..., M, M]`.
Output<V>	QuantizedBiasAdd.output()
Output<T>	Conv3DBackpropFilterV2.output()
Output<T>	ClipByValue.output() A clipped `Tensor` with the same shape as input 't'.
Output<T>	Where3.output() = A `Tensor` with the same type and shape as `x` and `y`.
Output<T>	ReduceSum.output() The reduced tensor.
Output<T>	CropAndResizeGradImage.output() A 4-D tensor of shape `[batch, image_height, image_width, depth]`.
Output<T>	MatrixDeterminant.output() Shape is `[...]`.
Output<T>	MaxPoolGradGrad.output() Gradients of gradients w.r.t.
Output<T>	QuantizeV2.output() The quantized data produced from the float input.
Output<T>	Conv2DBackpropFilter.output() 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`.
Output<Boolean>	Any.output() The reduced tensor.
Output<T>	RandomCrop.output() 3-D of shape `[crop_height, crop_width, channels].`
Output<T>	MatrixSolveLs.output() Shape is `[..., N, K]`.
Output<T>	HSVToRGB.output() `images` converted to RGB.
Output<T>	VariableShape.output()
Output<V>	StatelessRandomNormal.output() Random values with specified shape.
Output<T>	Sum.output() The reduced tensor.
Output<T>	StringToNumber.output() A Tensor of the same shape as the input `string_tensor`.
Output<T>	FusedPadConv2D.output()
Output<T>	ParallelConcat.output() The concatenated tensor.
Output<U>	ParameterizedTruncatedNormal.output() A matrix of shape num_batches x samples_per_batch, filled with random truncated normal values using the parameters for each row.
Output<T>	Reverse.output() The same shape as `tensor`.
Output<T>	CheckNumerics.output()
Output<String>	StaticRegexReplace.output() The text after applying pattern and rewrite.
Output<?>	RFFT.output() A complex64 tensor of the same rank as `input`.
Output<T>	SparseDenseCwiseDiv.output() 1-D.
Output<T>	L2Loss.output() 0-D.
Output<T>	QuantizedConcat.output() A `Tensor` with the concatenation of values stacked along the `concat_dim` dimension.
Output<T>	ResourceCountUpTo.output() A copy of the input before increment.
Output<T>	IFFT.output() A complex64 tensor of the same shape as `input`.
Output<String>	DecodeBase64.output() Decoded strings.
Output<T>	CudnnRNNV2.outputC()
Output<T>	CudnnRNN.outputC()
Output<T>	RefSwitch.outputFalse() If `pred` is false, data will be forwarded to this output.
Output<T>	Switch.outputFalse() If `pred` is false, data will be forwarded to this output.
Output<T>	CudnnRNNV2.outputH()
Output<T>	CudnnRNN.outputH()
Output<?>	TensorListFromTensor.outputHandle()
Output<?>	TensorListScatter.outputHandle()
Output<?>	TensorListSetItem.outputHandle()
Output<?>	TensorListPopBack.outputHandle()
Output<?>	TensorListPushBack.outputHandle()
Output<?>	TensorListPushBackBatch.outputHandles()
Output<Long>	SparseReduceSumSparse.outputIndices()
Output<Long>	SparseFillEmptyRows.outputIndices()
Output<Long>	SparseReduceMaxSparse.outputIndices()
Output<Long>	SparseReorder.outputIndices() 2-D.
Output<Long>	SparseCross.outputIndices() 2-D.
Output<Long>	SparseSparseMaximum.outputIndices() 2-D.
Output<Long>	SparseConcat.outputIndices() 2-D.
Output<Long>	SparseReshape.outputIndices() 2-D.
Output<Long>	SparseSparseMinimum.outputIndices() 2-D.
Output<Long>	SparseSlice.outputIndices()
Output<Float>	LoadAndRemapMatrix.outputMatrix() Output matrix containing existing values loaded from the checkpoint, and with any missing values filled in from initializing_values.
Output<Float>	QuantizeDownAndShrinkRange.outputMax() The float value that the maximum quantized output value represents.
Output<Float>	Requantize.outputMax() The requested_output_max value is copied into this output.
Output<Float>	QuantizedReshape.outputMax() This value is copied from input_max.
Output<Float>	RequantizationRange.outputMax() the computed max output.
Output<Float>	QuantizeV2.outputMax() The actual maximum scalar value used for the output.
Output<Float>	QuantizedConcat.outputMax() The float value that the maximum quantized output value represents.
Output<Float>	QuantizeDownAndShrinkRange.outputMin() The float value that the minimum quantized output value represents.
Output<Float>	Requantize.outputMin() The requested_output_min value is copied into this output.
Output<Float>	QuantizedReshape.outputMin() This value is copied from input_min.
Output<Float>	RequantizationRange.outputMin() The computed min output.
Output<Float>	QuantizeV2.outputMin() The actual minimum scalar value used for the output.
Output<Float>	QuantizedConcat.outputMin() The float value that the minimum quantized output value represents.
Output<T>	AssignAdd.outputRef() = Same as "ref".
Output<T>	StridedSliceAssign.outputRef()
Output<T>	ScatterSub.outputRef() = Same as `ref`.
Output<T>	ScatterUpdate.outputRef() = Same as `ref`.
Output<T>	ScatterAdd.outputRef() = Same as `ref`.
Output<T>	ScatterDiv.outputRef() = Same as `ref`.
Output<T>	ScatterMin.outputRef() = Same as `ref`.
Output<T>	Assign.outputRef() = Same as "ref".
Output<T>	AssignSub.outputRef() = Same as "ref".
Output<T>	ScatterNdUpdate.outputRef() Same as ref.
Output<T>	ScatterMax.outputRef() = Same as `ref`.
Output<T>	ScatterMul.outputRef() = Same as `ref`.
Output<T>	ScatterNdSub.outputRef() Same as ref.
Output<T>	ScatterNdAdd.outputRef() Same as ref.
Output<Float>	FakeQuantWithMinMaxVarsPerChannel.outputs()
Output<Float>	FakeQuantWithMinMaxVars.outputs()
Output<Float>	FakeQuantWithMinMaxArgs.outputs()
Output<Long>	SparseReduceSumSparse.outputShape()
Output<Long>	SparseReduceMaxSparse.outputShape()
Output<Long>	SparseCross.outputShape() 1-D.
Output<Long>	SparseConcat.outputShape() 1-D.
Output<Long>	SparseReshape.outputShape() 1-D.
Output<Long>	SparseSlice.outputShape() A list of 1-D tensors represents the shape of the output sparse tensors.
Output<T>	RefSwitch.outputTrue() If `pred` is true, data will be forwarded to this output.
Output<T>	Switch.outputTrue() If `pred` is true, data will be forwarded to this output.
Output<T>	SparseReduceSumSparse.outputValues()
Output<T>	SparseFillEmptyRows.outputValues() 1-D.
Output<T>	SparseReduceMaxSparse.outputValues()
Output<T>	SparseReorder.outputValues() 1-D.
Output<T>	SparseCross.outputValues() 1-D.
Output<T>	SparseSparseMaximum.outputValues() 1-D.
Output<T>	SparseConcat.outputValues() 1-D.
Output<T>	SparseSparseMinimum.outputValues() 1-D.
Output<T>	SparseSlice.outputValues() A list of 1-D tensors represents the values of the output sparse tensors.
Output<T>	CudnnRNNCanonicalToParams.params()
Output<T>	CudnnRNNBackprop.paramsBackprop()
Output<T>	CudnnRNNBackpropV2.paramsBackprop()
Output<U>	CudnnRNNParamsSize.paramsSize()
Output<Float>	BoostedTreesTrainingPredict.partialLogits() Rank 2 Tensor containing logits update (with respect to cached values stored) for each example.
Output<String>	GenerateBigQueryReaderPartitions.partitions() Serialized table partitions.
Output<T>	ExtractImagePatches.patches() 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]` containing image patches with size `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension.
Output<Boolean>	InTopK.precision() Computed Precision at `k` as a `bool Tensor`.
Output<Boolean>	InTopKV2.precision() Computed precision at `k` as a `bool Tensor`.
Output<U>	SparseTensorDenseMatMul.product()
Output<Float>	SparseMatMul.product()
Output<T>	Cross.product() Pairwise cross product of the vectors in `a` and `b`.
Output<T>	MatMul.product()
Output<T>	Qr.q() Orthonormal basis for range of `a`.
Output<T>	Qr.r() Triangular factor.
Output<T>	BroadcastDynamicShape.r0()
Output<T>	BroadcastGradientArgs.r0()
Output<T>	BroadcastGradientArgs.r1()
Output<?>	WholeFileReader.readerHandle() The handle to reference the Reader.
Output<?>	IdentityReader.readerHandle() The handle to reference the Reader.
Output<?>	TextLineReader.readerHandle() The handle to reference the Reader.
Output<String>	BigQueryReader.readerHandle() The handle to reference the Reader.
Output<?>	TFRecordReader.readerHandle() The handle to reference the Reader.
Output<?>	FixedLengthRecordReader.readerHandle() The handle to reference the Reader.
Output<String>	LMDBReader.readerHandle() The handle to reference the Reader.
Output<String>	RecordInput.records() A tensor of shape [batch_size].
Output<Long>	ReaderNumRecordsProduced.recordsProduced()
Output<T>	Variable.ref() A reference to the variable tensor.
Output<T>	TemporaryVariable.ref() A reference to the variable tensor.
Output<Long>	GenerateVocabRemapping.remapping() A Tensor of length num_new_vocab where the element at index i is equal to the old ID that maps to the new ID i.
Output<T>	CudnnRNNV2.reserveSpace()
Output<T>	CudnnRNN.reserveSpace()
Output<T>	FusedBatchNorm.reserveSpace1() A 1D Tensor for the computed batch mean, to be reused in the gradient computation.
Output<U>	FusedBatchNormV2.reserveSpace1() A 1D Tensor for the computed batch mean, to be reused in the gradient computation.
Output<T>	FusedBatchNorm.reserveSpace2() A 1D Tensor for the computed batch variance (inverted variance in the cuDNN case), to be reused in the gradient computation.
Output<U>	FusedBatchNormV2.reserveSpace2() A 1D Tensor for the computed batch variance (inverted variance in the cuDNN case), to be reused in the gradient computation.
Output<U>	FusedBatchNormGradV2.reserveSpace3() Unused placeholder to match the mean input in FusedBatchNorm.
Output<T>	FusedBatchNormGrad.reserveSpace3() Unused placeholder to match the mean input in FusedBatchNorm.
Output<U>	FusedBatchNormGradV2.reserveSpace4() Unused placeholder to match the variance input in FusedBatchNorm.
Output<T>	FusedBatchNormGrad.reserveSpace4() Unused placeholder to match the variance input in FusedBatchNorm.
Output<T>	ResizeNearestNeighbor.resizedImages() 4-D with shape `[batch, new_height, new_width, channels]`.
Output<Float>	ResizeArea.resizedImages() 4-D with shape `[batch, new_height, new_width, channels]`.
Output<T>	QuantizedResizeBilinear.resizedImages() 4-D with shape `[batch, new_height, new_width, channels]`.
Output<Float>	ResizeBicubic.resizedImages() 4-D with shape `[batch, new_height, new_width, channels]`.
Output<Float>	ResizeBilinear.resizedImages() 4-D with shape `[batch, new_height, new_width, channels]`.
Output<?>	BoostedTreesEnsembleResourceHandleOp.resource()
Output<?>	MutexV2.resource() The mutex resource.
Output<?>	VarHandleOp.resource()
Output<?>	IteratorFromStringHandleV2.resourceHandle()
Output<?>	IteratorFromStringHandle.resourceHandle() A handle to an iterator resource.
Output<String>	TryRpc.response() Same shape as `request`.
Output<String>	Rpc.response() Same shape as `request`.
Output<T>	BatchNormWithGlobalNormalization.result()
Output<U>	QuantizedBatchNormWithGlobalNormalization.result()
Output<Long>	SparseToSparseSetOperation.resultIndices() 2D indices of a `SparseTensor`.
Output<Long>	DenseToSparseSetOperation.resultIndices() 2D indices of a `SparseTensor`.
Output<Long>	DenseToDenseSetOperation.resultIndices() 2D indices of a `SparseTensor`.
Output<Float>	QuantizedBatchNormWithGlobalNormalization.resultMax()
Output<Float>	QuantizedBatchNormWithGlobalNormalization.resultMin()
Output<Long>	SparseToSparseSetOperation.resultShape() 1D `Tensor` shape of a `SparseTensor`.
Output<Long>	DenseToSparseSetOperation.resultShape() 1D `Tensor` shape of a `SparseTensor`.
Output<Long>	DenseToDenseSetOperation.resultShape() 1D `Tensor` shape of a `SparseTensor`.
Output<T>	SparseToSparseSetOperation.resultValues() 1D values of a `SparseTensor`.
Output<T>	DenseToSparseSetOperation.resultValues() 1D values of a `SparseTensor`.
Output<T>	DenseToDenseSetOperation.resultValues() 1D values of a `SparseTensor`.
Output<Long>	SparseFillEmptyRows.reverseIndexMap() 1-D.
Output<Long>	FractionalAvgPool.rowPoolingSequence() row pooling sequence, needed to calculate gradient.
Output<Long>	FractionalMaxPool.rowPoolingSequence() row pooling sequence, needed to calculate gradient.
Output<T>	BatchSvd.s()
Output<T>	Svd.s() Singular values.
Output<Long>	LogUniformCandidateSampler.sampledCandidates() A vector of length num_sampled, in which each element is the ID of a sampled candidate.
Output<Long>	LearnedUnigramCandidateSampler.sampledCandidates() A vector of length num_sampled, in which each element is the ID of a sampled candidate.
Output<Long>	AllCandidateSampler.sampledCandidates() A vector of length num_sampled, in which each element is the ID of a sampled candidate.
Output<Long>	FixedUnigramCandidateSampler.sampledCandidates() A vector of length num_sampled, in which each element is the ID of a sampled candidate.
Output<Long>	UniformCandidateSampler.sampledCandidates() A vector of length num_sampled, in which each element is the ID of a sampled candidate.
Output<Float>	LogUniformCandidateSampler.sampledExpectedCount() A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates.
Output<Float>	LearnedUnigramCandidateSampler.sampledExpectedCount() A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates.
Output<Float>	AllCandidateSampler.sampledExpectedCount() A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates.
Output<Float>	FixedUnigramCandidateSampler.sampledExpectedCount() A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates.
Output<Float>	UniformCandidateSampler.sampledExpectedCount() A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates.
Output<Integer>	DecodeWav.sampleRate() Scalar holding the sample rate found in the WAV header.
Output<U>	FusedBatchNormGradV2.scaleBackprop() A 1D Tensor for the gradient with respect to scale.
Output<T>	FusedBatchNormGrad.scaleBackprop() A 1D Tensor for the gradient with respect to scale.
Output<Integer>	NonMaxSuppressionV4.selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<Integer>	NonMaxSuppressionV3.selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<Integer>	NonMaxSuppression.selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<Integer>	NonMaxSuppressionWithOverlaps.selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<Integer>	NonMaxSuppressionV2.selectedIndices() A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`.
Output<String>	SerializeTensor.serialized() A serialized TensorProto proto of the input tensor.
Output<?>	SerializeIterator.serialized() A variant tensor storing the state of the iterator contained in the resource.
Output<U>	SerializeManySparse.serializedSparse()
Output<U>	SerializeSparse.serializedSparse()
Output<Long>	StringSplit.shape() a length-2 vector of int64 representing the shape of the sparse tensor, where the first value is N and the second value is the maximum number of tokens in a single input entry.
Output<Long>	SparseAccumulatorTakeGradient.shape() Shape of the average of the accumulated sparse gradients.
Output<Long>	StringSplitV2.shape()
Output<T>	LogMatrixDeterminant.sign() The signs of the log determinants of the inputs.
Output<T>	SampleDistortedBoundingBoxV2.size() 1-D, containing `[target_height, target_width, -1]`.
Output<Integer>	SetSize.size() For `set` ranked `n`, this is a `Tensor` with rank `n-1`, and the same 1st `n-1` dimensions as `set`.
Output<Integer>	QueueSize.size() The number of elements in the given queue.
Output<Integer>	TensorArraySize.size() The current size of the TensorArray.
Output<T>	SampleDistortedBoundingBox.size() 1-D, containing `[target_height, target_width, -1]`.
Output<Long>	LookupTableSize.size() Scalar that contains number of elements in the table.
Output<Integer>	BarrierReadySize.size() The number of complete elements (i.e.
Output<Integer>	MapIncompleteSize.size()
Output<Integer>	MapSize.size()
Output<Integer>	OrderedMapIncompleteSize.size()
Output<Integer>	BarrierIncompleteSize.size() The number of incomplete elements (i.e.
Output<Integer>	StageSize.size()
Output<Integer>	OrderedMapSize.size()
Output<Integer>	DecodeProtoV2.sizes() Tensor of int32 with shape `[batch_shape, len(field_names)]`.
Output<T>	Softmax.softmax() Same shape as `logits`.
Output<Long>	AddSparseToTensorsMap.sparseHandle() 0-D.
Output<Long>	AddManySparseToTensorsMap.sparseHandles() 1-D.
Output<Long>	DeserializeSparse.sparseIndices()
Output<Long>	TakeManySparseFromTensorsMap.sparseIndices() 2-D.
Output<Long>	DeserializeManySparse.sparseIndices()
Output<Long>	DeserializeSparse.sparseShape()
Output<Long>	TakeManySparseFromTensorsMap.sparseShape() 1-D.
Output<Long>	DeserializeManySparse.sparseShape()
Output<U>	DeserializeSparse.sparseValues()
Output<T>	TakeManySparseFromTensorsMap.sparseValues() 1-D.
Output<T>	DeserializeManySparse.sparseValues()
Output<Float>	AudioSpectrogram.spectrogram() 3D representation of the audio frequencies as an image.
Output<Long>	BoostedTreesGetEnsembleStates.stampToken() Stamp token of the tree ensemble resource.
Output<Long>	BoostedTreesSerializeEnsemble.stampToken() Stamp token of the tree ensemble resource.
Output<String>	ReaderSerializeState.state()
Output<Float>	BoostedTreesMakeStatsSummary.statsSummary() output Rank 4 Tensor (shape=[#features, #splits, #buckets, 2]) containing accumulated stats put into the corresponding node and bucket.
Output<Integer>	TryRpc.statusCode() Same shape as `request`.
Output<String>	TryRpc.statusMessage() Same shape as `request`.
Output<String>	IteratorToStringHandle.stringHandle() A string representation of the given handle.
Output<T>	AccumulateNV2.sum()
Output<T>	AddN.sum()
Output<Long>	SparseAdd.sumIndices()
Output<String>	TensorSummary.summary()
Output<String>	AudioSummary.summary() Scalar.
Output<String>	HistogramSummary.summary() Scalar.
Output<String>	ScalarSummary.summary() Scalar.
Output<String>	StatsAggregatorSummary.summary()
Output<String>	TensorSummaryV2.summary()
Output<String>	ImageSummary.summary() Scalar.
Output<String>	MergeSummary.summary() Scalar.
Output<Long>	SparseAdd.sumShape()
Output<T>	SparseAdd.sumValues()
Output<?>	MutableDenseHashTable.tableHandle() Handle to a table.
Output<?>	HashTable.tableHandle() Handle to a table.
Output<?>	MutableHashTableOfTensors.tableHandle() Handle to a table.
Output<?>	MutableHashTable.tableHandle() Handle to a table.
Output<T>	RestoreSlice.tensor() The restored tensor.
Output<T>	Restore.tensor() The restored tensor.
Output<T>	TensorListPopBack.tensor()
Output<T>	ImmutableConst.tensor()
Output<T>	TensorListStack.tensor()
Output<Long>	Skipgram.totalWordsProcessed() The total number of words processed so far.
Output<String>	BoostedTreesSerializeEnsemble.treeEnsembleSerialized() Serialized proto of the ensemble.
Output<Integer>	BoostedTreesTrainingPredict.treeIds() Rank 1 Tensor containing new tree ids for each example.
Output<Float>	LogUniformCandidateSampler.trueExpectedCount() A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates.
Output<Float>	LearnedUnigramCandidateSampler.trueExpectedCount() A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates.
Output<Float>	AllCandidateSampler.trueExpectedCount() A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates.
Output<Float>	FixedUnigramCandidateSampler.trueExpectedCount() A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates.
Output<Float>	UniformCandidateSampler.trueExpectedCount() A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates.
Output<Double>	Timestamp.ts()
Output<T>	BatchSvd.u()
Output<T>	Svd.u() Left singular vectors.
Output<T>	Unbatch.unbatchedTensor()
Output<Long>	ReaderNumWorkUnitsCompleted.unitsCompleted()
Output<T>	SelfAdjointEig.v() Eigenvectors.
Output<T>	BatchSelfAdjointEigV2.v()
Output<T>	BatchSvd.v()
Output<T>	Svd.v() Left singular vectors.
Output<T>	SparseSliceGrad.valGrad() 1-D.
Output<Integer>	NonMaxSuppressionV4.validOutputs() A 0-D integer tensor representing the number of valid elements in `selected_indices`, with the valid elements appearing first.
Output<T>	ReadVariableOp.value()
Output<T>	TensorArrayPack.value()
Output<T>	GetSessionTensor.value() The tensor for the given handle.
Output<T>	DestroyTemporaryVariable.value()
Output<String>	ReaderRead.value() A scalar.
Output<T>	TensorArrayGather.value() All of the elements in the TensorArray, concatenated along a new axis (the new dimension 0).
Output<T>	TensorArrayRead.value() The tensor that is read from the TensorArray.
Output<T>	TensorArrayConcat.value() All of the elements in the TensorArray, concatenated along the first axis.
Output<Integer>	Merge.valueIndex() The index of the chosen input tensor in `inputs`.
Output<Integer>	RefMerge.valueIndex() The index of the chosen input tensor in `inputs`.
Output<T>	NthElement.values() The `n`-th order statistic along each last dimensional slice.
Output<String>	StringSplit.values() A vector of strings corresponding to the splited values.
Output<U>	LookupTableExport.values() Tensor of all values in the table.
Output<T>	TopK.values() The `k` largest elements along each last dimensional slice.
Output<String>	ReaderReadUpTo.values() A 1-D tensor.
Output<T>	SparseAccumulatorTakeGradient.values() Values of the average of the accumulated sparse gradients.
Output<U>	LookupTableFind.values() Same shape as `keys`.
Output<String>	StringSplitV2.values()
Output<T>	TensorListGather.values()
Output<Integer>	Skipgram.vocabFreq() Frequencies of words.
Output<String>	Skipgram.vocabWord() A vector of words in the corpus.
Output<Float>	ComputeAccidentalHits.weights() A vector of the same length as indices and ids, in which each element is -FLOAT_MAX.
Output<Long>	Skipgram.wordsPerEpoch() Number of words per epoch in the data file.
Output<?>	SummaryWriter.writer()
Output<T>	FusedBatchNormGradV2.xBackprop() A 4D Tensor for the gradient with respect to x.
Output<T>	FusedBatchNormGrad.xBackprop() A 4D Tensor for the gradient with respect to x.
Output<T>	Sinh.y()
Output<T>	Sqrt.y()
Output<T>	DataFormatVecPermute.y() Vector of size 4 or Tensor of shape (4, 2) in destination data format.
Output<T>	InplaceAdd.y() A `Tensor` of type T.
Output<T>	Floor.y()
Output<T>	Log.y()
Output<T>	Neg.y()
Output<T>	Abs.y()
Output<T>	DeepCopy.y() y: A `Tensor` of type `T`.
Output<Boolean>	IsFinite.y()
Output<T>	Erf.y()
Output<T>	Lgamma.y()
Output<T>	UniqueWithCountsV2.y() A `Tensor`.
Output<UInt8>	PopulationCount.y()
Output<T>	Sign.y()
Output<U>	Cast.y()
Output<T>	UniqueV2.y() A `Tensor`.
Output<T>	InvertPermutation.y() 1-D.
Output<Boolean>	IsNan.y()
Output<Boolean>	LogicalNot.y()
Output<T>	DataFormatDimMap.y() A Tensor with each element as a dimension index in destination data format.
Output<T>	Tan.y()
Output<T>	Atan.y()
Output<T>	ZerosLike.y() a tensor of the same shape and type as x but filled with zeros.
Output<T>	Transpose.y()
Output<T>	Reciprocal.y()
Output<T>	Acosh.y()
Output<T>	Cos.y()
Output<T>	Expm1.y()
Output<T>	Atanh.y()
Output<T>	BesselI1e.y()
Output<T>	Negate.y()
Output<T>	Round.y()
Output<T>	Square.y()
Output<T>	Digamma.y()
Output<T>	Sin.y()
Output<T>	Asin.y()
Output<T>	UniqueWithCounts.y() 1-D.
Output<T>	InplaceUpdate.y() A `Tensor` of type T.
Output<T>	Sigmoid.y()
Output<T>	Cosh.y()
Output<T>	Tanh.y()
Output<T>	Acos.y()
Output<T>	Rint.y()
Output<Boolean>	IsInf.y()
Output<T>	Invert.y()
Output<T>	BesselI0e.y()
Output<T>	InplaceSub.y() A `Tensor` of type T.
Output<T>	Unique.y() 1-D.
Output<T>	FusedBatchNorm.y() A 4D Tensor for output data.
Output<T>	Erfc.y()
Output<T>	Log1p.y()
Output<T>	ConjugateTranspose.y()
Output<T>	Rsqrt.y()
Output<T>	Inv.y()
Output<T>	Asinh.y()
Output<T>	Ceil.y()
Output<T>	FusedBatchNormV2.y() A 4D Tensor for output data.
Output<T>	QuantizedInstanceNorm.y() A 4D Tensor.
Output<T>	OnesLike.y() a tensor of the same shape and type as x but filled with ones.
Output<T>	Exp.y()
Output<U>	ComplexAbs.y()
Output<Float>	QuantizedInstanceNorm.yMax() The value represented by the highest quantized output.
Output<Float>	QuantizedInstanceNorm.yMin() The value represented by the lowest quantized output.
Output<T>	InvGrad.z()
Output<T>	Multiply.z()
Output<T>	BitwiseAnd.z()
Output<T>	Maximum.z()
Output<T>	Igammac.z()
Output<T>	Minimum.z()
Output<T>	Sub.z()
Output<T>	IgammaGradA.z()
Output<T>	Pow.z()
Output<T>	Mul.z()
Output<T>	Add.z()
Output<T>	FloorDiv.z()
Output<Boolean>	LessEqual.z()
Output<Boolean>	GreaterEqual.z()
Output<T>	SquaredDifference.z()
Output<T>	TanhGrad.z()
Output<V>	QuantizedAdd.z()
Output<Boolean>	NotEqual.z()
Output<T>	SigmoidGrad.z()
Output<T>	BitwiseOr.z()
Output<T>	Betainc.z()
Output<Boolean>	Greater.z()
Output<Boolean>	LogicalAnd.z()
Output<T>	Div.z()
Output<T>	TruncateDiv.z()
Output<T>	Polygamma.z()
Output<T>	LeftShift.z()
Output<T>	Atan2.z()
Output<T>	TruncateMod.z()
Output<Boolean>	Less.z()
Output<T>	AddV2.z()
Output<T>	SqrtGrad.z()
Output<T>	ReciprocalGrad.z()
Output<T>	Subtract.z()
Output<T>	Mod.z()
Output<V>	QuantizedMul.z()
Output<T>	DivNoNan.z()
Output<Boolean>	ApproximateEqual.z()
Output<T>	RsqrtGrad.z()
Output<Boolean>	Equal.z()
Output<T>	Igamma.z()
Output<T>	RealDiv.z()
Output<T>	RightShift.z()
Output<T>	Zeta.z()
Output<T>	FloorMod.z()
Output<Boolean>	LogicalOr.z()
Output<T>	BitwiseXor.z()

Methods in org.tensorflow.op.core that return types with arguments of type Output

Modifier and Type	Method and Description
List<Output<?>>	Batch.batchedTensors()
List<Output<T>>	CudnnRNNParamsToCanonical.biases()
List<Output<?>>	QueueDequeue.components() One or more tensors that were dequeued as a tuple.
List<Output<?>>	IteratorGetNextSync.components()
List<Output<?>>	QueueDequeueMany.components() One or more tensors that were dequeued as a tuple.
List<Output<?>>	IteratorGetNext.components()
List<Output<?>>	OptionalGetValue.components()
List<Output<?>>	QueueDequeueUpTo.components() One or more tensors that were dequeued as a tuple.
List<Output<?>>	DatasetToSingleElement.components() The components of the single element of `input`.
List<Output<?>>	ParseSingleSequenceExample.contextDenseValues()
List<Output<?>>	ParseSequenceExample.contextDenseValues()
List<Output<Long>>	ParseSingleSequenceExample.contextSparseIndices()
List<Output<Long>>	ParseSequenceExample.contextSparseIndices()
List<Output<Long>>	ParseSingleSequenceExample.contextSparseShapes()
List<Output<Long>>	ParseSequenceExample.contextSparseShapes()
List<Output<?>>	ParseSingleSequenceExample.contextSparseValues()
List<Output<?>>	ParseSequenceExample.contextSparseValues()
List<Output<Long>>	CTCBeamSearchDecoder.decodedIndices() A list (length: top_paths) of indices matrices.
List<Output<Long>>	CTCBeamSearchDecoder.decodedShape() A list (length: top_paths) of shape vector.
List<Output<Long>>	CTCBeamSearchDecoder.decodedValues() A list (length: top_paths) of values vectors.
List<Output<?>>	ParseExample.denseValues()
List<Output<?>>	ParseSingleExample.denseValues()
List<Output<?>>	Gradients.dy() Partial derivatives of ys w.r.t.
List<Output<Long>>	ParseSequenceExample.featureListDenseLengths()
List<Output<?>>	ParseSingleSequenceExample.featureListDenseValues()
List<Output<?>>	ParseSequenceExample.featureListDenseValues()
List<Output<Long>>	ParseSingleSequenceExample.featureListSparseIndices()
List<Output<Long>>	ParseSequenceExample.featureListSparseIndices()
List<Output<Long>>	ParseSingleSequenceExample.featureListSparseShapes()
List<Output<Long>>	ParseSequenceExample.featureListSparseShapes()
List<Output<?>>	ParseSingleSequenceExample.featureListSparseValues()
List<Output<?>>	ParseSequenceExample.featureListSparseValues()
List<Output<Float>>	BoostedTreesCalculateBestGainsPerFeature.gainsList() An output list of Rank 1 tensors indicating the best gains for each feature to split for certain nodes.
List<Output<Float>>	BoostedTreesCalculateBestGainsPerFeature.leftNodeContribsList() A list of Rank 2 tensors indicating the contribution of the left nodes when branching from parent nodes (given by the tensor element in the output node_ids_list) to the left direction by the given threshold for each feature.
List<Output<Integer>>	BoostedTreesCalculateBestGainsPerFeature.nodeIdsList() An output list of Rank 1 tensors indicating possible split node ids for each feature.
List<Output<Float>>	SdcaOptimizer.outDeltaDenseWeights() a list of vectors where the values are the delta weights associated with a dense feature group.
List<Output<Float>>	SdcaOptimizer.outDeltaSparseWeights() a list of vectors where each value is the delta weights associated with a sparse feature group.
List<Output<T>>	SplitV.output() Tensors whose shape matches that of `value` except along `axis`, where their sizes are `size_splits[i]`.
List<Output<T>>	Unstack.output() The list of tensors unpacked from `value`.
List<Output<?>>	EagerPyFunc.output()
List<Output<?>>	IdentityN.output()
List<Output<U>>	ShapeN.output()
List<Output<?>>	DecodeCSV.output() Each tensor will have the same shape as records.
List<Output<T>>	Split.output() They are identically shaped tensors, whose shape matches that of `value` except along `axis`, where their sizes are `values.shape[split_dim] / num_split`.
List<Output<Long>>	SparseSplit.outputIndices()
List<Output<T>>	DynamicPartition.outputs()
List<Output<?>>	RemoteFusedGraphExecute.outputs() Arbitrary number of tensors with arbitrary data types
List<Output<Long>>	SparseSplit.outputShape() A list of 1-D tensors represents the shape of the output sparse tensors.
List<Output<T>>	SparseSplit.outputValues() A list of 1-D tensors represents the values of the output sparse tensors.
List<Output<Float>>	BoostedTreesCalculateBestGainsPerFeature.rightNodeContribsList() A list of Rank 2 tensors, with the same shape/conditions as left_node_contribs_list, but just that the value is for the right node.
List<Output<Long>>	ParseExample.sparseIndices()
List<Output<Long>>	ParseSingleExample.sparseIndices()
List<Output<Long>>	ParseExample.sparseShapes()
List<Output<Long>>	ParseSingleExample.sparseShapes()
List<Output<?>>	ParseExample.sparseValues()
List<Output<?>>	ParseSingleExample.sparseValues()
List<Output<?>>	RestoreV2.tensors() shape {N}.
List<Output<Integer>>	BoostedTreesCalculateBestGainsPerFeature.thresholdsList() An output list of Rank 1 tensors indicating the bucket id to compare with (as a threshold) for split in each node.
List<Output<?>>	OrderedMapUnstage.values()
List<Output<?>>	OrderedMapPeek.values()
List<Output<?>>	Unstage.values()
List<Output<?>>	DecodeProtoV2.values() List of tensors containing values for the corresponding field.
List<Output<?>>	MapUnstage.values()
List<Output<?>>	MapPeek.values()
List<Output<?>>	OrderedMapUnstageNoKey.values()
List<Output<?>>	StagePeek.values()
List<Output<?>>	BarrierTakeMany.values() One any-dimensional tensor per component in a barrier element.
List<Output<?>>	MapUnstageNoKey.values()
List<Output<T>>	CudnnRNNParamsToCanonical.weights()