Package org.tensorflow.op.core

Class Summary

Class	Description
Abort	Raise a exception to abort the process when called.
Abort.Options	Optional attributes for Abort
Abs<T extends Number>	Computes the absolute value of a tensor.
AccumulateNV2<T>	Returns the element-wise sum of a list of tensors.
AccumulatorApplyGradient	Applies a gradient to a given accumulator.
AccumulatorNumAccumulated	Returns the number of gradients aggregated in the given accumulators.
AccumulatorSetGlobalStep	Updates the accumulator with a new value for global_step.
AccumulatorTakeGradient<T>	Extracts the average gradient in the given ConditionalAccumulator.
Acos<T>	Computes acos of x element-wise.
Acosh<T>	Computes inverse hyperbolic cosine of x element-wise.
Add<T>	Returns x + y element-wise.
AddManySparseToTensorsMap	Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
AddManySparseToTensorsMap.Options	Optional attributes for AddManySparseToTensorsMap
AddN<T>	Add all input tensors element wise.
AddSparseToTensorsMap	Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
AddSparseToTensorsMap.Options	Optional attributes for AddSparseToTensorsMap
AddV2<T>	Returns x + y element-wise.
AdjustContrast	Adjust the contrast of one or more images.
AdjustHue	Adjust the hue of one or more images.
AdjustSaturation	Adjust the saturation of one or more images.
All	Computes the "logical and" of elements across dimensions of a tensor.
All.Options	Optional attributes for All
AllCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
AllCandidateSampler.Options	Optional attributes for AllCandidateSampler
Angle<U extends Number>	Returns the argument of a complex number.
AnonymousIterator	A container for an iterator resource.
Any	Computes the "logical or" of elements across dimensions of a tensor.
Any.Options	Optional attributes for Any
ApplyAdadelta<T>	Update '*var' according to the adadelta scheme.
ApplyAdadelta.Options	Optional attributes for ApplyAdadelta
ApplyAdagrad<T>	Update '*var' according to the adagrad scheme.
ApplyAdagrad.Options	Optional attributes for ApplyAdagrad
ApplyAdagradDA<T>	Update '*var' according to the proximal adagrad scheme.
ApplyAdagradDA.Options	Optional attributes for ApplyAdagradDA
ApplyAdam<T>	Update '*var' according to the Adam algorithm.
ApplyAdam.Options	Optional attributes for ApplyAdam
ApplyAdaMax<T>	Update '*var' according to the AdaMax algorithm.
ApplyAdaMax.Options	Optional attributes for ApplyAdaMax
ApplyAddSign<T>	Update '*var' according to the AddSign update.
ApplyAddSign.Options	Optional attributes for ApplyAddSign
ApplyCenteredRMSProp<T>	Update '*var' according to the centered RMSProp algorithm.
ApplyCenteredRMSProp.Options	Optional attributes for ApplyCenteredRMSProp
ApplyFtrl<T>	Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrl.Options	Optional attributes for ApplyFtrl
ApplyFtrlV2<T>	Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrlV2.Options	Optional attributes for ApplyFtrlV2
ApplyGradientDescent<T>	Update '*var' by subtracting 'alpha' * 'delta' from it.
ApplyGradientDescent.Options	Optional attributes for ApplyGradientDescent
ApplyMomentum<T>	Update '*var' according to the momentum scheme.
ApplyMomentum.Options	Optional attributes for ApplyMomentum
ApplyPowerSign<T>	Update '*var' according to the AddSign update.
ApplyPowerSign.Options	Optional attributes for ApplyPowerSign
ApplyProximalAdagrad<T>	Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
ApplyProximalAdagrad.Options	Optional attributes for ApplyProximalAdagrad
ApplyProximalGradientDescent<T>	Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyProximalGradientDescent.Options	Optional attributes for ApplyProximalGradientDescent
ApplyRMSProp<T>	Update '*var' according to the RMSProp algorithm.
ApplyRMSProp.Options	Optional attributes for ApplyRMSProp
ApproximateEqual	Returns the truth value of abs(x-y) < tolerance element-wise.
ApproximateEqual.Options	Optional attributes for ApproximateEqual
ArgMax<V extends Number>	Returns the index with the largest value across dimensions of a tensor.
ArgMin<V extends Number>	Returns the index with the smallest value across dimensions of a tensor.
Asin<T>	Computes asin of x element-wise.
Asinh<T>	Computes inverse hyperbolic sine of x element-wise.
Assert	Asserts that the given condition is true.
Assert.Options	Optional attributes for Assert
Assign<T>	Update 'ref' by assigning 'value' to it.
Assign.Options	Optional attributes for Assign
AssignAdd<T>	Update 'ref' by adding 'value' to it.
AssignAdd.Options	Optional attributes for AssignAdd
AssignAddVariableOp	Adds a value to the current value of a variable.
AssignSub<T>	Update 'ref' by subtracting 'value' from it.
AssignSub.Options	Optional attributes for AssignSub
AssignSubVariableOp	Subtracts a value from the current value of a variable.
AssignVariableOp	Assigns a new value to a variable.
AsString	Converts each entry in the given tensor to strings.
AsString.Options	Optional attributes for AsString
Atan<T>	Computes atan of x element-wise.
Atan2<T extends Number>	Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
Atanh<T>	Computes inverse hyperbolic tangent of x element-wise.
AudioSpectrogram	Produces a visualization of audio data over time.
AudioSpectrogram.Options	Optional attributes for AudioSpectrogram
AudioSummary	Outputs a `Summary` protocol buffer with audio.
AudioSummary.Options	Optional attributes for AudioSummary
AvgPool<T extends Number>	Performs average pooling on the input.
AvgPool.Options	Optional attributes for AvgPool
AvgPool3D<T extends Number>	Performs 3D average pooling on the input.
AvgPool3D.Options	Optional attributes for AvgPool3D
AvgPool3DGrad<T extends Number>	Computes gradients of average pooling function.
AvgPool3DGrad.Options	Optional attributes for AvgPool3DGrad
AvgPoolGrad<T extends Number>	Computes gradients of the average pooling function.
AvgPoolGrad.Options	Optional attributes for AvgPoolGrad
Barrier	Defines a barrier that persists across different graph executions.
Barrier.Options	Optional attributes for Barrier
BarrierClose	Closes the given barrier.
BarrierClose.Options	Optional attributes for BarrierClose
BarrierIncompleteSize	Computes the number of incomplete elements in the given barrier.
BarrierInsertMany	For each key, assigns the respective value to the specified component.
BarrierReadySize	Computes the number of complete elements in the given barrier.
BarrierTakeMany	Takes the given number of completed elements from a barrier.
BarrierTakeMany.Options	Optional attributes for BarrierTakeMany
Batch	Batches all input tensors nondeterministically.
Batch.Options	Optional attributes for Batch
BatchCholesky<T extends Number>
BatchCholeskyGrad<T extends Number>
BatchDataset	Creates a dataset that batches `batch_size` elements from `input_dataset`.
BatchDatasetV2	Creates a dataset that batches `batch_size` elements from `input_dataset`.
BatchFFT
BatchFFT2D
BatchFFT3D
BatchIFFT
BatchIFFT2D
BatchIFFT3D
BatchMatMul<T>	Multiplies slices of two tensors in batches.
BatchMatMul.Options	Optional attributes for BatchMatMul
BatchMatrixBandPart<T>
BatchMatrixDeterminant<T>
BatchMatrixDiag<T>
BatchMatrixDiagPart<T>
BatchMatrixInverse<T extends Number>
BatchMatrixInverse.Options	Optional attributes for BatchMatrixInverse
BatchMatrixSetDiag<T>
BatchMatrixSolve<T extends Number>
BatchMatrixSolve.Options	Optional attributes for BatchMatrixSolve
BatchMatrixSolveLs<T extends Number>
BatchMatrixSolveLs.Options	Optional attributes for BatchMatrixSolveLs
BatchMatrixTriangularSolve<T extends Number>
BatchMatrixTriangularSolve.Options	Optional attributes for BatchMatrixTriangularSolve
BatchNormWithGlobalNormalization<T>	Batch normalization.
BatchNormWithGlobalNormalizationGrad<T>	Gradients for batch normalization.
BatchSelfAdjointEig<T extends Number>
BatchSelfAdjointEigV2<T extends Number>
BatchSelfAdjointEigV2.Options	Optional attributes for BatchSelfAdjointEigV2
BatchSvd<T>
BatchSvd.Options	Optional attributes for BatchSvd
BatchToSpace<T>	BatchToSpace for 4-D tensors of type T.
BatchToSpaceND<T>	BatchToSpace for N-D tensors of type T.
BesselI0e<T extends Number>	Computes the Bessel i0e function of `x` element-wise.
BesselI1e<T extends Number>	Computes the Bessel i1e function of `x` element-wise.
Betainc<T extends Number>	Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
BiasAdd<T>	Adds `bias` to `value`.
BiasAdd.Options	Optional attributes for BiasAdd
BiasAddGrad<T>	The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddGrad.Options	Optional attributes for BiasAddGrad
Bincount<T extends Number>	Counts the number of occurrences of each value in an integer array.
Bitcast<U>	Bitcasts a tensor from one type to another without copying data.
BitwiseAnd<T extends Number>	Elementwise computes the bitwise AND of `x` and `y`.
BitwiseOr<T extends Number>	Elementwise computes the bitwise OR of `x` and `y`.
BitwiseXor<T extends Number>	Elementwise computes the bitwise XOR of `x` and `y`.
BoostedTreesBucketize	Bucketize each feature based on bucket boundaries.
BoostedTreesCalculateBestGainsPerFeature	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCenterBias	Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior.
BoostedTreesCreateEnsemble	Creates a tree ensemble model and returns a handle to it.
BoostedTreesCreateQuantileStreamResource	Create the Resource for Quantile Streams.
BoostedTreesCreateQuantileStreamResource.Options	Optional attributes for BoostedTreesCreateQuantileStreamResource
BoostedTreesDeserializeEnsemble	Deserializes a serialized tree ensemble config and replaces current tree
BoostedTreesEnsembleResourceHandleOp	Creates a handle to a BoostedTreesEnsembleResource
BoostedTreesEnsembleResourceHandleOp.Options	Optional attributes for BoostedTreesEnsembleResourceHandleOp
BoostedTreesExampleDebugOutputs	Debugging/model interpretability outputs for each example.
BoostedTreesGetEnsembleStates	Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
BoostedTreesMakeQuantileSummaries	Makes the summary of quantiles for the batch.
BoostedTreesMakeStatsSummary	Makes the summary of accumulated stats for the batch.
BoostedTreesPredict	Runs multiple additive regression ensemble predictors on input instances and
BoostedTreesQuantileStreamResourceAddSummaries	Add the quantile summaries to each quantile stream resource.
BoostedTreesQuantileStreamResourceFlush	Flush the summaries for a quantile stream resource.
BoostedTreesQuantileStreamResourceFlush.Options	Optional attributes for BoostedTreesQuantileStreamResourceFlush
BoostedTreesQuantileStreamResourceGetBucketBoundaries	Generate the bucket boundaries for each feature based on accumulated summaries.
BoostedTreesQuantileStreamResourceHandleOp	Creates a handle to a BoostedTreesQuantileStreamResource.
BoostedTreesQuantileStreamResourceHandleOp.Options	Optional attributes for BoostedTreesQuantileStreamResourceHandleOp
BoostedTreesSerializeEnsemble	Serializes the tree ensemble to a proto.
BoostedTreesTrainingPredict	Runs multiple additive regression ensemble predictors on input instances and
BoostedTreesUpdateEnsemble	Updates the tree ensemble by either adding a layer to the last tree being grown
BroadcastDynamicShape<T extends Number>	Return the shape of s0 op s1 with broadcast.
BroadcastGradientArgs<T extends Number>	Return the reduction indices for computing gradients of s0 op s1 with broadcast.
BroadcastTo<T>	Broadcast an array for a compatible shape.
Bucketize	Bucketizes 'input' based on 'boundaries'.
BytesProducedStatsDataset	Records the bytes size of each element of `input_dataset` in a StatsAggregator.
CacheDataset	Creates a dataset that caches elements from `input_dataset`.
Cast<U>	Cast x of type SrcT to y of DstT.
Cast.Options	Optional attributes for Cast
Ceil<T extends Number>	Returns element-wise smallest integer not less than x.
CheckNumerics<T extends Number>	Checks a tensor for NaN and Inf values.
Cholesky<T>	Computes the Cholesky decomposition of one or more square matrices.
CholeskyGrad<T extends Number>	Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
ClipByValue<T>	Clips tensor values to a specified min and max.
CloseSummaryWriter
CollectiveBcastRecv<T extends Number>	Receives a tensor value broadcast from another device.
CollectiveBcastSend<T extends Number>	Broadcasts a tensor value to one or more other devices.
CollectiveReduce<T extends Number>	Mutually reduces multiple tensors of identical type and shape.
CompareAndBitpack	Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
Complex<U>	Converts two real numbers to a complex number.
ComplexAbs<U extends Number>	Computes the complex absolute value of a tensor.
ComputeAccidentalHits	Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeAccidentalHits.Options	Optional attributes for ComputeAccidentalHits
Concat<T>	Concatenates tensors along one dimension.
ConcatenateDataset	Creates a dataset that concatenates `input_dataset` with `another_dataset`.
ConditionalAccumulator	A conditional accumulator for aggregating gradients.
ConditionalAccumulator.Options	Optional attributes for ConditionalAccumulator
Conj<T>	Returns the complex conjugate of a complex number.
ConjugateTranspose<T>	Shuffle dimensions of x according to a permutation and conjugate the result.
Const<T>	Returns a constant tensor.
Constant<T>	An operator producing a constant value.
ConsumeMutexLock	This op consumes a lock created by `MutexLock`.
ControlTrigger	Does nothing.
Conv2D<T extends Number>	Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2D.Options	Optional attributes for Conv2D
Conv2DBackpropFilter<T extends Number>	Computes the gradients of convolution with respect to the filter.
Conv2DBackpropFilter.Options	Optional attributes for Conv2DBackpropFilter
Conv2DBackpropInput<T extends Number>	Computes the gradients of convolution with respect to the input.
Conv2DBackpropInput.Options	Optional attributes for Conv2DBackpropInput
Conv3D<T extends Number>	Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3D.Options	Optional attributes for Conv3D
Conv3DBackpropFilter<T extends Number>	Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilter.Options	Optional attributes for Conv3DBackpropFilter
Conv3DBackpropFilterV2<T extends Number>	Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilterV2.Options	Optional attributes for Conv3DBackpropFilterV2
Conv3DBackpropInput<T extends Number>	Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInput.Options	Optional attributes for Conv3DBackpropInput
Conv3DBackpropInputV2<U extends Number>	Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInputV2.Options	Optional attributes for Conv3DBackpropInputV2
Cos<T>	Computes cos of x element-wise.
Cosh<T>	Computes hyperbolic cosine of x element-wise.
CountUpTo<T extends Number>	Increments 'ref' until it reaches 'limit'.
CreateSummaryDbWriter
CreateSummaryFileWriter
CropAndResize	Extracts crops from the input image tensor and resizes them.
CropAndResize.Options	Optional attributes for CropAndResize
CropAndResizeGradBoxes	Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradBoxes.Options	Optional attributes for CropAndResizeGradBoxes
CropAndResizeGradImage<T extends Number>	Computes the gradient of the crop_and_resize op wrt the input image tensor.
CropAndResizeGradImage.Options	Optional attributes for CropAndResizeGradImage
Cross<T extends Number>	Compute the pairwise cross product.
CTCBeamSearchDecoder	Performs beam search decoding on the logits given in input.
CTCBeamSearchDecoder.Options	Optional attributes for CTCBeamSearchDecoder
CTCGreedyDecoder	Performs greedy decoding on the logits given in inputs.
CTCGreedyDecoder.Options	Optional attributes for CTCGreedyDecoder
CTCLoss	Calculates the CTC Loss (log probability) for each batch entry.
CTCLoss.Options	Optional attributes for CTCLoss
CudnnRNN<T extends Number>	A RNN backed by cuDNN.
CudnnRNN.Options	Optional attributes for CudnnRNN
CudnnRNNBackprop<T extends Number>	Backprop step of CudnnRNN.
CudnnRNNBackprop.Options	Optional attributes for CudnnRNNBackprop
CudnnRNNBackpropV2<T extends Number>	Backprop step of CudnnRNN.
CudnnRNNBackpropV2.Options	Optional attributes for CudnnRNNBackpropV2
CudnnRNNCanonicalToParams<T extends Number>	Converts CudnnRNN params from canonical form to usable form.
CudnnRNNCanonicalToParams.Options	Optional attributes for CudnnRNNCanonicalToParams
CudnnRNNParamsSize<U extends Number>	Computes size of weights that can be used by a Cudnn RNN model.
CudnnRNNParamsSize.Options	Optional attributes for CudnnRNNParamsSize
CudnnRNNParamsToCanonical<T extends Number>	Retrieves CudnnRNN params in canonical form.
CudnnRNNParamsToCanonical.Options	Optional attributes for CudnnRNNParamsToCanonical
CudnnRNNV2<T extends Number>	A RNN backed by cuDNN.
CudnnRNNV2.Options	Optional attributes for CudnnRNNV2
Cumprod<T>	Compute the cumulative product of the tensor `x` along `axis`.
Cumprod.Options	Optional attributes for Cumprod
Cumsum<T>	Compute the cumulative sum of the tensor `x` along `axis`.
Cumsum.Options	Optional attributes for Cumsum
DataFormatDimMap<T extends Number>	Returns the dimension index in the destination data format given the one in
DataFormatDimMap.Options	Optional attributes for DataFormatDimMap
DataFormatVecPermute<T extends Number>	Returns the permuted vector/tensor in the destination data format given the
DataFormatVecPermute.Options	Optional attributes for DataFormatVecPermute
DatasetToGraph	Returns a serialized GraphDef representing `input_dataset`.
DatasetToSingleElement	Outputs the single element from the given dataset.
DatasetToTFRecord	Writes the given dataset to the given file using the TFRecord format.
DebugGradientIdentity<T>	Identity op for gradient debugging.
DebugGradientRefIdentity<T>	Identity op for gradient debugging.
DecodeAndCropJpeg	Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeAndCropJpeg.Options	Optional attributes for DecodeAndCropJpeg
DecodeBase64	Decode web-safe base64-encoded strings.
DecodeBmp	Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeBmp.Options	Optional attributes for DecodeBmp
DecodeCompressed	Decompress strings.
DecodeCompressed.Options	Optional attributes for DecodeCompressed
DecodeCSV	Convert CSV records to tensors.
DecodeCSV.Options	Optional attributes for DecodeCSV
DecodeGif	Decode the first frame of a GIF-encoded image to a uint8 tensor.
DecodeJpeg	Decode a JPEG-encoded image to a uint8 tensor.
DecodeJpeg.Options	Optional attributes for DecodeJpeg
DecodeJSONExample	Convert JSON-encoded Example records to binary protocol buffer strings.
DecodePng<T extends Number>	Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodePng.Options	Optional attributes for DecodePng
DecodeProtoV2	The op extracts fields from a serialized protocol buffers message into tensors.
DecodeProtoV2.Options	Optional attributes for DecodeProtoV2
DecodeRaw<T extends Number>	Reinterpret the bytes of a string as a vector of numbers.
DecodeRaw.Options	Optional attributes for DecodeRaw
DecodeWav	Decode a 16-bit PCM WAV file to a float tensor.
DecodeWav.Options	Optional attributes for DecodeWav
DeepCopy<T>	Makes a copy of `x`.
DeleteSessionTensor	Delete the tensor specified by its handle in the session.
DenseToDenseSetOperation<T>	Applies set operation along last dimension of 2 `Tensor` inputs.
DenseToDenseSetOperation.Options	Optional attributes for DenseToDenseSetOperation
DenseToSparseBatchDataset	Creates a dataset that batches input elements into a SparseTensor.
DenseToSparseSetOperation<T>	Applies set operation along last dimension of `Tensor` and `SparseTensor`.
DenseToSparseSetOperation.Options	Optional attributes for DenseToSparseSetOperation
DepthToSpace<T>	DepthToSpace for tensors of type T.
DepthToSpace.Options	Optional attributes for DepthToSpace
DepthwiseConv2dNative<T extends Number>	Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNative.Options	Optional attributes for DepthwiseConv2dNative
DepthwiseConv2dNativeBackpropFilter<T extends Number>	Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropFilter.Options	Optional attributes for DepthwiseConv2dNativeBackpropFilter
DepthwiseConv2dNativeBackpropInput<T extends Number>	Computes the gradients of depthwise convolution with respect to the input.
DepthwiseConv2dNativeBackpropInput.Options	Optional attributes for DepthwiseConv2dNativeBackpropInput
Dequantize	Dequantize the 'input' tensor into a float Tensor.
Dequantize.Options	Optional attributes for Dequantize
DeserializeIterator	Converts the given variant tensor to an iterator and stores it in the given resource.
DeserializeManySparse<T>	Deserialize and concatenate `SparseTensors` from a serialized minibatch.
DeserializeSparse<U>	Deserialize `SparseTensor` objects.
DestroyResourceOp	Deletes the resource specified by the handle.
DestroyResourceOp.Options	Optional attributes for DestroyResourceOp
DestroyTemporaryVariable<T>	Destroys the temporary variable and returns its final value.
Diag<T>	Returns a diagonal tensor with a given diagonal values.
DiagPart<T>	Returns the diagonal part of the tensor.
Digamma<T extends Number>	Computes Psi, the derivative of Lgamma (the log of the absolute value of
Dilation2D<T extends Number>	Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
Dilation2DBackpropFilter<T extends Number>	Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2DBackpropInput<T extends Number>	Computes the gradient of morphological 2-D dilation with respect to the input.
Div<T>	Returns x / y element-wise.
DivNoNan<T extends Number>	Returns 0 if the denominator is zero.
DrawBoundingBoxes<T extends Number>	Draw bounding boxes on a batch of images.
DynamicPartition<T>	Partitions `data` into `num_partitions` tensors using indices from `partitions`.
DynamicStitch<T>	Interleave the values from the `data` tensors into a single tensor.
EagerPyFunc	Eagerly executes a python function to compute func(input)->output.
EditDistance	Computes the (possibly normalized) Levenshtein Edit Distance.
EditDistance.Options	Optional attributes for EditDistance
Elu<T extends Number>	Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
EluGrad<T extends Number>	Computes gradients for the exponential linear (Elu) operation.
Empty<T>	Creates a tensor with the given shape.
Empty.Options	Optional attributes for Empty
EmptyTensorList	Creates and returns an empty tensor list.
EncodeBase64	Encode strings into web-safe base64 format.
EncodeBase64.Options	Optional attributes for EncodeBase64
EncodeJpeg	JPEG-encode an image.
EncodeJpeg.Options	Optional attributes for EncodeJpeg
EncodePng	PNG-encode an image.
EncodePng.Options	Optional attributes for EncodePng
EncodeProto	The op serializes protobuf messages provided in the input tensors.
EncodeProto.Options	Optional attributes for EncodeProto
EncodeWav	Encode audio data using the WAV file format.
EnqueueInQueueDataset
EnsureShape<T>	Ensures that the tensor's shape matches the expected shape.
Enter<T>	Creates or finds a child frame, and makes `data` available to the child frame.
Enter.Options	Optional attributes for Enter
Equal	Returns the truth value of (x == y) element-wise.
Erf<T extends Number>	Computes the Gauss error function of `x` element-wise.
Erfc<T extends Number>	Computes the complementary error function of `x` element-wise.
Exit<T>	Exits the current frame to its parent frame.
Exp<T>	Computes exponential of x element-wise.
ExpandDims<T>	Inserts a dimension of 1 into a tensor's shape.
ExperimentalAssertNextDataset
ExperimentalCSVDataset
ExperimentalDirectedInterleaveDataset	A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
ExperimentalFunctionBufferingResourceGetNext	Gets the next element from a FunctionBufferingResource.
ExperimentalFunctionBufferingResourceReset	Resets the FunctionBufferingResource.
ExperimentalIdentityIndexedDataset
ExperimentalIgnoreErrorsDataset	Creates a dataset that contains the elements of `input_dataset` ignoring errors.
ExperimentalIndexedDatasetGet
ExperimentalIndexedDatasetMaterialize
ExperimentalIteratorGetDevice	Returns the name of the device on which `resource` has been placed.
ExperimentalLMDBDataset
ExperimentalMaterializedIndexDatasetHandle
ExperimentalThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ExperimentalThreadPoolHandle	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ExperimentalThreadPoolHandle.Options	Optional attributes for ExperimentalThreadPoolHandle
ExperimentalUniqueDataset	Creates a dataset that contains the unique elements of `input_dataset`.
Expm1<T>	Computes exponential of x - 1 element-wise.
ExtractGlimpse	Extracts a glimpse from the input tensor.
ExtractGlimpse.Options	Optional attributes for ExtractGlimpse
ExtractImagePatches<T extends Number>	Extract `patches` from `images` and put them in the "depth" output dimension.
ExtractJpegShape<T extends Number>	Extract the shape information of a JPEG-encoded image.
ExtractVolumePatches<T extends Number>	Extract `patches` from `input` and put them in the "depth" output dimension.
Fact	Output a fact about factorials.
FakeQuantWithMinMaxArgs	Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgs.Options	Optional attributes for FakeQuantWithMinMaxArgs
FakeQuantWithMinMaxArgsGradient	Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxArgsGradient.Options	Optional attributes for FakeQuantWithMinMaxArgsGradient
FakeQuantWithMinMaxVars	Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
FakeQuantWithMinMaxVars.Options	Optional attributes for FakeQuantWithMinMaxVars
FakeQuantWithMinMaxVarsGradient	Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsGradient.Options	Optional attributes for FakeQuantWithMinMaxVarsGradient
FakeQuantWithMinMaxVarsPerChannel	Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
FakeQuantWithMinMaxVarsPerChannel.Options	Optional attributes for FakeQuantWithMinMaxVarsPerChannel
FakeQuantWithMinMaxVarsPerChannelGradient	Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
FakeQuantWithMinMaxVarsPerChannelGradient.Options	Optional attributes for FakeQuantWithMinMaxVarsPerChannelGradient
FFT<T>	Fast Fourier transform.
FFT2D<T>	2D fast Fourier transform.
FFT3D<T>	3D fast Fourier transform.
FIFOQueue	A queue that produces elements in first-in first-out order.
FIFOQueue.Options	Optional attributes for FIFOQueue
Fill<U>	Creates a tensor filled with a scalar value.
FilterByLastComponentDataset	Creates a dataset containing elements of first component of `input_dataset` having true in the last component.
FixedLengthRecordDataset	Creates a dataset that emits the records from one or more binary files.
FixedLengthRecordReader	A Reader that outputs fixed-length records from a file.
FixedLengthRecordReader.Options	Optional attributes for FixedLengthRecordReader
FixedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
FixedUnigramCandidateSampler.Options	Optional attributes for FixedUnigramCandidateSampler
Floor<T extends Number>	Returns element-wise largest integer not greater than x.
FloorDiv<T>	Returns x // y element-wise.
FloorMod<T extends Number>	Returns element-wise remainder of division.
FlushSummaryWriter
FractionalAvgPool<T extends Number>	Performs fractional average pooling on the input.
FractionalAvgPool.Options	Optional attributes for FractionalAvgPool
FractionalAvgPoolGrad<T extends Number>	Computes gradient of the FractionalAvgPool function.
FractionalAvgPoolGrad.Options	Optional attributes for FractionalAvgPoolGrad
FractionalMaxPool<T extends Number>	Performs fractional max pooling on the input.
FractionalMaxPool.Options	Optional attributes for FractionalMaxPool
FractionalMaxPoolGrad<T extends Number>	Computes gradient of the FractionalMaxPool function.
FractionalMaxPoolGrad.Options	Optional attributes for FractionalMaxPoolGrad
FusedBatchNorm<T extends Number>	Batch normalization.
FusedBatchNorm.Options	Optional attributes for FusedBatchNorm
FusedBatchNormGrad<T extends Number>	Gradient for batch normalization.
FusedBatchNormGrad.Options	Optional attributes for FusedBatchNormGrad
FusedBatchNormGradV2<T extends Number,U extends Number>	Gradient for batch normalization.
FusedBatchNormGradV2.Options	Optional attributes for FusedBatchNormGradV2
FusedBatchNormV2<T extends Number,U extends Number>	Batch normalization.
FusedBatchNormV2.Options	Optional attributes for FusedBatchNormV2
FusedPadConv2D<T extends Number>	Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2D<T extends Number>	Performs a resize and padding as a preprocess during a convolution.
FusedResizeAndPadConv2D.Options	Optional attributes for FusedResizeAndPadConv2D
Gather<T>	Gather slices from `params` according to `indices`.
Gather.Options	Optional attributes for Gather
GatherNd<T>	Gather slices from `params` into a Tensor with shape specified by `indices`.
GatherV2<T>	Gather slices from `params` axis `axis` according to `indices`.
GenerateVocabRemapping	Given a path to new and old vocabulary files, returns a remapping Tensor of
GenerateVocabRemapping.Options	Optional attributes for GenerateVocabRemapping
GetSessionHandle	Store the input tensor in the state of the current session.
GetSessionHandleV2	Store the input tensor in the state of the current session.
GetSessionTensor<T>	Get the value of the tensor specified by its handle.
Gradients	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...
Gradients.Options	Optional attributes for Gradients
Greater	Returns the truth value of (x > y) element-wise.
GreaterEqual	Returns the truth value of (x >= y) element-wise.
GuaranteeConst<T>	Gives a guarantee to the TF runtime that the input tensor is a constant.
HashTable	Creates a non-initialized hash table.
HashTable.Options	Optional attributes for HashTable
HistogramFixedWidth<U extends Number>	Return histogram of values.
HistogramSummary	Outputs a `Summary` protocol buffer with a histogram.
HSVToRGB<T extends Number>	Convert one or more images from HSV to RGB.
Identity<T>	Return a tensor with the same shape and contents as the input tensor or value.
IdentityN	Returns a list of tensors with the same shapes and contents as the input
IdentityReader	A Reader that outputs the queued work as both the key and value.
IdentityReader.Options	Optional attributes for IdentityReader
IFFT<T>	Inverse fast Fourier transform.
IFFT2D<T>	Inverse 2D fast Fourier transform.
IFFT3D<T>	Inverse 3D fast Fourier transform.
Igamma<T extends Number>	Compute the lower regularized incomplete Gamma function `P(a, x)`.
Igammac<T extends Number>	Compute the upper regularized incomplete Gamma function `Q(a, x)`.
IgammaGradA<T extends Number>	Computes the gradient of `igamma(a, x)` wrt `a`.
Imag<U extends Number>	Returns the imaginary part of a complex number.
ImageSummary	Outputs a `Summary` protocol buffer with images.
ImageSummary.Options	Optional attributes for ImageSummary
ImmutableConst<T>	Returns immutable tensor from memory region.
ImportEvent
InitializeTable	Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromTextFile	Initializes a table from a text file.
InitializeTableFromTextFile.Options	Optional attributes for InitializeTableFromTextFile
InplaceAdd<T>	Adds v into specified rows of x.
InplaceSub<T>	Subtracts `v` into specified rows of `x`.
InplaceUpdate<T>	Updates specified rows with values in `v`.
InTopK	Says whether the targets are in the top `K` predictions.
InTopKV2	Says whether the targets are in the top `K` predictions.
Inv<T>	Computes the reciprocal of x element-wise.
Invert<T extends Number>	Flips all bits elementwise.
InvertPermutation<T extends Number>	Computes the inverse permutation of a tensor.
InvGrad<T>	Computes the gradient for the inverse of `x` wrt its input.
IRFFT	Inverse real-valued fast Fourier transform.
IRFFT2D	Inverse 2D real-valued fast Fourier transform.
IRFFT3D	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.
IsVariableInitialized	Checks whether a tensor has been initialized.
Iterator	A container for an iterator resource.
IteratorFromStringHandle	Converts the given string representing a handle to an iterator to a resource.
IteratorFromStringHandle.Options	Optional attributes for IteratorFromStringHandle
IteratorFromStringHandleV2
IteratorFromStringHandleV2.Options	Optional attributes for IteratorFromStringHandleV2
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.
IteratorV2
L2Loss<T extends Number>	L2 Loss.
LatencyStatsDataset	Records the latency of producing `input_dataset` elements in a StatsAggregator.
LearnedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
LearnedUnigramCandidateSampler.Options	Optional attributes for LearnedUnigramCandidateSampler
LeftShift<T extends Number>	Elementwise computes the bitwise left-shift of `x` and `y`.
Less	Returns the truth value of (x < y) element-wise.
LessEqual	Returns the truth value of (x <= y) element-wise.
Lgamma<T extends Number>	Computes the log of the absolute value of `Gamma(x)` element-wise.
LinSpace<T extends Number>	Generates values in an interval.
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
LoadAndRemapMatrix.Options	Optional attributes for LoadAndRemapMatrix
Log<T>	Computes natural logarithm of x element-wise.
Log1p<T>	Computes natural logarithm of (1 + x) element-wise.
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>	Computes the sign and the log of the absolute value of the determinant of
LogSoftmax<T extends Number>	Computes log softmax activations.
LogUniformCandidateSampler	Generates labels for candidate sampling with a log-uniform distribution.
LogUniformCandidateSampler.Options	Optional attributes for LogUniformCandidateSampler
LookupTableExport<T,U>	Outputs all keys and values in the table.
LookupTableFind<U>	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.
LookupTableSize	Computes the number of elements in the given table.
LoopCond	Forwards the input to the output.
LowerBound<U extends Number>	Applies lower_bound(sorted_search_values, values) along each row.
LRN<T extends Number>	Local Response Normalization.
LRN.Options	Optional attributes for LRN
LRNGrad<T extends Number>	Gradients for Local Response Normalization.
LRNGrad.Options	Optional attributes for LRNGrad
MakeIterator	Makes a new iterator from the given `dataset` and stores it in `iterator`.
MapClear	Op removes all elements in the underlying container.
MapClear.Options	Optional attributes for MapClear
MapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
MapIncompleteSize.Options	Optional attributes for MapIncompleteSize
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
MapUnstage.Options	Optional attributes for MapUnstage
MapUnstageNoKey	Op removes and returns a random (key, value)
MapUnstageNoKey.Options	Optional attributes for MapUnstageNoKey
MatchingFiles	Returns the set of files matching one or more glob patterns.
MatMul<T>	Multiply the matrix "a" by the matrix "b".
MatMul.Options	Optional attributes for MatMul
MatrixBandPart<T>	Copy a tensor setting everything outside a central band in each innermost matrix
MatrixDeterminant<T>	Computes the determinant of one or more square matrices.
MatrixDiag<T>	Returns a batched diagonal tensor with a given batched diagonal values.
MatrixDiagPart<T>	Returns the batched diagonal part of a batched tensor.
MatrixInverse<T>	Computes the inverse of one or more square invertible matrices or their
MatrixInverse.Options	Optional attributes for MatrixInverse
MatrixLogarithm<T>	Computes the matrix logarithm of one or more square matrices:
MatrixSetDiag<T>	Returns a batched matrix tensor with new batched diagonal values.
MatrixSolve<T>	Solves systems of linear equations.
MatrixSolve.Options	Optional attributes for MatrixSolve
MatrixSolveLs<T>	Solves one or more linear least-squares problems.
MatrixSolveLs.Options	Optional attributes for MatrixSolveLs
MatrixTriangularSolve<T>	Solves systems of linear equations with upper or lower triangular matrices by
MatrixTriangularSolve.Options	Optional attributes for MatrixTriangularSolve
Max<T>	Computes the maximum of elements across dimensions of a tensor.
Max.Options	Optional attributes for Max
Maximum<T extends Number>	Returns the max of x and y (i.e.
MaxPool<T>	Performs max pooling on the input.
MaxPool.Options	Optional attributes for MaxPool
MaxPool3D<T extends Number>	Performs 3D max pooling on the input.
MaxPool3D.Options	Optional attributes for MaxPool3D
MaxPool3DGrad<U extends Number>	Computes gradients of max pooling function.
MaxPool3DGrad.Options	Optional attributes for MaxPool3DGrad
MaxPool3DGradGrad<T extends Number>	Computes second-order gradients of the maxpooling function.
MaxPool3DGradGrad.Options	Optional attributes for MaxPool3DGradGrad
MaxPoolGrad<T extends Number>	Computes gradients of the maxpooling function.
MaxPoolGrad.Options	Optional attributes for MaxPoolGrad
MaxPoolGradGrad<T extends Number>	Computes second-order gradients of the maxpooling function.
MaxPoolGradGrad.Options	Optional attributes for MaxPoolGradGrad
MaxPoolGradGradV2<T extends Number>	Computes second-order gradients of the maxpooling function.
MaxPoolGradGradV2.Options	Optional attributes for MaxPoolGradGradV2
MaxPoolGradGradWithArgmax<T extends Number>	Computes second-order gradients of the maxpooling function.
MaxPoolGradV2<T extends Number>	Computes gradients of the maxpooling function.
MaxPoolGradV2.Options	Optional attributes for MaxPoolGradV2
MaxPoolGradWithArgmax<T extends Number>	Computes gradients of the maxpooling function.
MaxPoolV2<T>	Performs max pooling on the input.
MaxPoolV2.Options	Optional attributes for MaxPoolV2
MaxPoolWithArgmax<T extends Number,U extends Number>	Performs max pooling on the input and outputs both max values and indices.
Mean<T>	Computes the mean of elements across dimensions of a tensor.
Mean.Options	Optional attributes for Mean
Merge<T>	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
Mfcc	Transforms a spectrogram into a form that's useful for speech recognition.
Mfcc.Options	Optional attributes for Mfcc
Min<T>	Computes the minimum of elements across dimensions of a tensor.
Min.Options	Optional attributes for Min
Minimum<T extends Number>	Returns the min of x and y (i.e.
MirrorPad<T>	Pads a tensor with mirrored values.
MirrorPadGrad<T>	Gradient op for `MirrorPad` op.
Mod<T extends Number>	Returns element-wise remainder of division.
ModelDataset	Identity transformation that models performance.
Mul<T>	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 Number>	Draws samples from a multinomial distribution.
Multinomial.Options	Optional attributes for Multinomial
Multiply<T>	Returns x * y element-wise.
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
MutexLock	Locks a mutex resource.
MutexV2	Creates a Mutex resource that can be locked by `MutexLock`.
MutexV2.Options	Optional attributes for MutexV2
Neg<T>	Computes numerical negative value element-wise.
Negate<T>	Computes numerical negative value element-wise.
NegTrain	Training via negative sampling.
NextIteration<T>	Makes its input available to the next iteration.
NonMaxSuppression	Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppression.Options	Optional attributes for NonMaxSuppression
NonMaxSuppressionV2	Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV3	Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV4	Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV4.Options	Optional attributes for NonMaxSuppressionV4
NonMaxSuppressionWithOverlaps	Greedily selects a subset of bounding boxes in descending order of score,
NoOp	Does nothing.
NotEqual	Returns the truth value of (x != y) element-wise.
NthElement<T extends Number>	Finds values of the `n`-th order statistic for the last dimension.
NthElement.Options	Optional attributes for NthElement
OneHot<U>	Returns a one-hot tensor.
OneHot.Options	Optional attributes for OneHot
OnesLike<T>	Returns a tensor of ones with the same shape and type as x.
OptimizeDataset	Creates a dataset by applying optimizations to `input_dataset`.
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
OrderedMapStage.Options	Optional attributes for OrderedMapStage
OrderedMapUnstage	Op removes and returns the values associated with the key
OrderedMapUnstage.Options	Optional attributes for OrderedMapUnstage
OrderedMapUnstageNoKey	Op removes and returns the (key, value) element with the smallest
OrderedMapUnstageNoKey.Options	Optional attributes for OrderedMapUnstageNoKey
Pad<T>	Pads a tensor with zeros.
PaddedBatchDataset	Creates a dataset that batches and pads `batch_size` elements from the input.
PaddedBatchDatasetV2	Creates a dataset that batches and pads `batch_size` elements from the input.
PaddingFIFOQueue	A queue that produces elements in first-in first-out order.
PaddingFIFOQueue.Options	Optional attributes for PaddingFIFOQueue
PadV2<T>	Pads a tensor.
ParallelConcat<T>	Concatenates a list of `N` tensors along the first dimension.
ParallelDynamicStitch<T>	Interleave the values from the `data` tensors into a single tensor.
ParameterizedTruncatedNormal<U extends Number>	Outputs random values from a normal distribution.
ParameterizedTruncatedNormal.Options	Optional attributes for ParameterizedTruncatedNormal
ParseExample	Transforms a vector of brain.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.
ParseSequenceExample	Transforms a vector of brain.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>	Transforms a serialized tensorflow.TensorProto proto into a Tensor.
Placeholder<T>	A placeholder op for a value that will be fed into the computation.
Placeholder.Options	Optional attributes for Placeholder
PlaceholderV2<T>	A placeholder op for a value that will be fed into the computation.
PlaceholderWithDefault<T>	A placeholder op that passes through `input` when its output is not fed.
Polygamma<T extends Number>	Compute the polygamma function \\(\psi^{(n)}(x)\\).
PopulationCount	Computes element-wise population count (a.k.a.
Pow<T>	Computes the power of one value to another.
PrefetchDataset	Creates a dataset that asynchronously prefetches elements from `input_dataset`.
PrependFromQueueAndPaddedBatchDataset
PreventGradient<T>	An identity op that triggers an error if a gradient is requested.
PreventGradient.Options	Optional attributes for PreventGradient
Print<T>	Prints a list of tensors.
Print.Options	Optional attributes for Print
PrintV2	Prints a string scalar.
PrintV2.Options	Optional attributes for PrintV2
PriorityQueue	A queue that produces elements sorted by the first component value.
PriorityQueue.Options	Optional attributes for PriorityQueue
Prod<T>	Computes the product of elements across dimensions of a tensor.
Prod.Options	Optional attributes for Prod
Qr<T>	Computes the QR decompositions of one or more matrices.
Qr.Options	Optional attributes for Qr
QuantizeAndDequantize<T extends Number>	Use QuantizeAndDequantizeV2 instead.
QuantizeAndDequantize.Options	Optional attributes for QuantizeAndDequantize
QuantizeAndDequantizeV2<T extends Number>	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV2.Options	Optional attributes for QuantizeAndDequantizeV2
QuantizeAndDequantizeV3<T extends Number>	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3.Options	Optional attributes for QuantizeAndDequantizeV3
QuantizedAdd<V>	Returns x + y element-wise, working on quantized buffers.
QuantizedAvgPool<T>	Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization<U>	Quantized Batch normalization.
QuantizedBiasAdd<V>	Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConcat<T>	Concatenates quantized tensors along one dimension.
QuantizedConv2D<V>	Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedConv2D.Options	Optional attributes for QuantizedConv2D
QuantizedInstanceNorm<T>	Quantized Instance normalization.
QuantizedInstanceNorm.Options	Optional attributes for QuantizedInstanceNorm
QuantizedMatMul<V>	Perform a quantized matrix multiplication of `a` by the matrix `b`.
QuantizedMatMul.Options	Optional attributes for QuantizedMatMul
QuantizedMaxPool<T>	Produces the max pool of the input tensor for quantized types.
QuantizedMul<V>	Returns x * y element-wise, working on quantized buffers.
QuantizeDownAndShrinkRange<U>	Convert the quantized 'input' tensor into a lower-precision 'output', using the
QuantizedRelu<U>	Computes Quantized Rectified Linear: `max(features, 0)`
QuantizedRelu6<U>	Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
QuantizedReluX<U>	Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
QuantizedReshape<T>	Reshapes a quantized tensor as per the Reshape op.
QuantizedResizeBilinear<T>	Resize quantized `images` to `size` using quantized bilinear interpolation.
QuantizedResizeBilinear.Options	Optional attributes for QuantizedResizeBilinear
QuantizeV2<T>	Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizeV2.Options	Optional attributes for QuantizeV2
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.
QueueIsClosedV2	Returns true if queue is closed.
QueueSize	Computes the number of elements in the given queue.
RandomCrop<T extends Number>	Randomly crop `image`.
RandomCrop.Options	Optional attributes for RandomCrop
RandomDataset	Creates a Dataset that returns pseudorandom numbers.
RandomGamma<U extends Number>	Outputs random values from the Gamma distribution(s) described by alpha.
RandomGamma.Options	Optional attributes for RandomGamma
RandomGammaGrad<T extends Number>	Computes the derivative of a Gamma random sample w.r.t.
RandomNormal<U extends Number>	Outputs random values from a normal distribution.
RandomNormal.Options	Optional attributes for RandomNormal
RandomPoisson<U extends Number>	Use RandomPoissonV2 instead.
RandomPoisson.Options	Optional attributes for RandomPoisson
RandomPoissonV2<V extends Number>	Outputs random values from the Poisson distribution(s) described by rate.
RandomPoissonV2.Options	Optional attributes for RandomPoissonV2
RandomShuffle<T>	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
RandomUniform<U extends Number>	Outputs random values from a uniform distribution.
RandomUniform.Options	Optional attributes for RandomUniform
RandomUniformInt<U extends Number>	Outputs random integers from a uniform distribution.
RandomUniformInt.Options	Optional attributes for RandomUniformInt
Range<T extends Number>	Creates a sequence of numbers.
RangeDataset	Creates a dataset with a range of values.
Rank	Returns the rank of a tensor.
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>	Reads the value of a variable.
Real<U extends Number>	Returns the real part of a complex number.
RealDiv<T>	Returns x / y element-wise for real types.
Reciprocal<T>	Computes the reciprocal of x element-wise.
ReciprocalGrad<T>	Computes the gradient for the inverse of `x` wrt its input.
RecordInput	Emits randomized records.
RecordInput.Options	Optional attributes for RecordInput
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>	Computes the maximum of elements across dimensions of a tensor.
ReduceMax.Options	Optional attributes for ReduceMax
ReduceMean<T>	Computes the mean of elements across dimensions of a tensor.
ReduceMean.Options	Optional attributes for ReduceMean
ReduceMin<T>	Computes the minimum of elements across dimensions of a tensor.
ReduceMin.Options	Optional attributes for ReduceMin
ReduceProd<T>	Computes the product of elements across dimensions of a tensor.
ReduceProd.Options	Optional attributes for ReduceProd
ReduceSum<T>	Computes the sum of elements across dimensions of a tensor.
ReduceSum.Options	Optional attributes for ReduceSum
RefEnter<T>	Creates or finds a child frame, and makes `data` available to the child frame.
RefEnter.Options	Optional attributes for RefEnter
RefExit<T>	Exits the current frame to its parent frame.
RefIdentity<T>	Return the same ref tensor as the input ref tensor.
RefMerge<T>	Forwards the value of an available tensor from `inputs` to `output`.
RefNextIteration<T>	Makes its input available to the next iteration.
RefSelect<T>	Forwards the `index`th element of `inputs` to `output`.
RefSwitch<T>	Forwards the ref tensor `data` to the output port determined by `pred`.
RegexFullMatch	Check if the input matches the regex pattern.
RegexReplace	Replaces the match of pattern in input with rewrite.
RegexReplace.Options	Optional attributes for RegexReplace
Relu<T>	Computes rectified linear: `max(features, 0)`.
Relu6<T extends Number>	Computes rectified linear 6: `min(max(features, 0), 6)`.
Relu6Grad<T extends Number>	Computes rectified linear 6 gradients for a Relu6 operation.
ReluGrad<T extends Number>	Computes rectified linear gradients for a Relu operation.
RemoteFusedGraphExecute	Execute a sub graph on a remote processor.
RepeatDataset	Creates a dataset that emits the outputs of `input_dataset` `count` times.
RequantizationRange	Given a quantized tensor described by (input, input_min, input_max), outputs a
Requantize<U>	Convert the quantized 'input' tensor into a lower-precision 'output', using the
Reshape<T>	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 Number>	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 Number>	Computes the gradient of bilinear interpolation.
ResizeBilinearGrad.Options	Optional attributes for ResizeBilinearGrad
ResizeNearestNeighbor<T extends Number>	Resize `images` to `size` using nearest neighbor interpolation.
ResizeNearestNeighbor.Options	Optional attributes for ResizeNearestNeighbor
ResizeNearestNeighborGrad<T extends Number>	Computes the gradient of nearest neighbor interpolation.
ResizeNearestNeighborGrad.Options	Optional attributes for ResizeNearestNeighborGrad
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
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
ResourceApplyFtrlV2	Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrlV2.Options	Optional attributes for ResourceApplyFtrlV2
ResourceApplyGradientDescent	Update '*var' by subtracting 'alpha' * 'delta' from it.
ResourceApplyGradientDescent.Options	Optional attributes for ResourceApplyGradientDescent
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
ResourceCountUpTo<T extends Number>	Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather<U>	Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGather.Options	Optional attributes for ResourceGather
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	Adds sparse `updates` to individual values or slices within a given
ResourceScatterNdAdd.Options	Optional attributes for ResourceScatterNdAdd
ResourceScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given
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
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
ResourceSparseApplyFtrlV2	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrlV2.Options	Optional attributes for ResourceSparseApplyFtrlV2
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
Restore<T>	Restores a tensor from checkpoint files.
Restore.Options	Optional attributes for Restore
RestoreSlice<T>	Restores a tensor from checkpoint files.
RestoreSlice.Options	Optional attributes for RestoreSlice
RestoreV2	Restores tensors from a V2 checkpoint.
Reverse<T>	Reverses specific dimensions of a tensor.
ReverseSequence<T>	Reverses variable length slices.
ReverseSequence.Options	Optional attributes for ReverseSequence
RFFT	Real-valued fast Fourier transform.
RFFT2D	2D real-valued fast Fourier transform.
RFFT3D	3D real-valued fast Fourier transform.
RGBToHSV<T extends Number>	Converts one or more images from RGB to HSV.
RightShift<T extends Number>	Elementwise computes the bitwise right-shift of `x` and `y`.
Rint<T extends Number>	Returns element-wise integer closest to x.
Roll<T>	Rolls the elements of a tensor along an axis.
Round<T>	Rounds the values of a tensor to the nearest integer, element-wise.
Rpc	Perform batches of RPC requests.
Rpc.Options	Optional attributes for Rpc
Rsqrt<T>	Computes reciprocal of square root of x element-wise.
RsqrtGrad<T>	Computes the gradient for the rsqrt of `x` wrt its input.
SampleDistortedBoundingBox<T extends Number>	Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBox.Options	Optional attributes for SampleDistortedBoundingBox
SampleDistortedBoundingBoxV2<T extends Number>	Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBoxV2.Options	Optional attributes for SampleDistortedBoundingBoxV2
Save	Saves the input tensors to disk.
SaveSlices	Saves input tensors slices to disk.
SaveV2	Saves tensors in V2 checkpoint format.
ScalarSummary	Outputs a `Summary` protocol buffer with scalar values.
ScatterAdd<T>	Adds sparse updates to a variable reference.
ScatterAdd.Options	Optional attributes for ScatterAdd
ScatterDiv<T>	Divides a variable reference by sparse updates.
ScatterDiv.Options	Optional attributes for ScatterDiv
ScatterMax<T extends Number>	Reduces sparse updates into a variable reference using the `max` operation.
ScatterMax.Options	Optional attributes for ScatterMax
ScatterMin<T extends Number>	Reduces sparse updates into a variable reference using the `min` operation.
ScatterMin.Options	Optional attributes for ScatterMin
ScatterMul<T>	Multiplies sparse updates into a variable reference.
ScatterMul.Options	Optional attributes for ScatterMul
ScatterNd<U>	Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd<T>	Applies sparse addition between `updates` and individual values or slices
ScatterNdAdd.Options	Optional attributes for ScatterNdAdd
ScatterNdNonAliasingAdd<T>	Applies sparse addition to `input` using individual values or slices
ScatterNdSub<T>	Applies sparse subtraction between `updates` and individual values or slices
ScatterNdSub.Options	Optional attributes for ScatterNdSub
ScatterNdUpdate<T>	Applies sparse `updates` to individual values or slices within a given
ScatterNdUpdate.Options	Optional attributes for ScatterNdUpdate
ScatterSub<T>	Subtracts sparse updates to a variable reference.
ScatterSub.Options	Optional attributes for ScatterSub
ScatterUpdate<T>	Applies sparse updates to a variable reference.
ScatterUpdate.Options	Optional attributes for ScatterUpdate
SdcaFprint	Computes fingerprints of the input strings.
SdcaOptimizer	Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaOptimizer.Options	Optional attributes for SdcaOptimizer
SdcaShrinkL1	Applies L1 regularization shrink step on the parameters.
SegmentMax<T extends Number>	Computes the maximum along segments of a tensor.
SegmentMean<T>	Computes the mean along segments of a tensor.
SegmentMin<T extends Number>	Computes the minimum along segments of a tensor.
SegmentProd<T>	Computes the product along segments of a tensor.
SegmentSum<T>	Computes the sum along segments of a tensor.
SelfAdjointEig<T>	Computes the eigen decomposition of one or more square self-adjoint matrices.
SelfAdjointEig.Options	Optional attributes for SelfAdjointEig
Selu<T extends Number>	Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
SeluGrad<T extends Number>	Computes gradients for the scaled exponential linear (Selu) operation.
SerializeIterator	Converts the given `resource_handle` representing an iterator to a variant tensor.
SerializeManySparse<U>	Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
SerializeSparse<U>	Serialize a `SparseTensor` into a `[3]` `Tensor` object.
SerializeTensor	Transforms a Tensor into a serialized TensorProto proto.
SetDiff1D<T,U extends Number>	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
SetStatsAggregatorDataset
Shape<U extends Number>	Returns the shape of a tensor.
ShapeN<U extends Number>	Returns shape of tensors.
ShardedFilename	Generate a sharded filename.
ShardedFilespec	Generate a glob pattern matching all sharded file names.
ShuffleAndRepeatDataset	Creates a dataset that shuffles and repeats elements from `input_dataset`
ShuffleDataset	Creates a dataset that shuffles elements from `input_dataset` pseudorandomly.
ShuffleDataset.Options	Optional attributes for ShuffleDataset
Sigmoid<T>	Computes sigmoid of `x` element-wise.
SigmoidGrad<T>	Computes the gradient of the sigmoid of `x` wrt its input.
Sign<T>	Returns an element-wise indication of the sign of a number.
Sin<T>	Computes sin of x element-wise.
Sinh<T>	Computes hyperbolic sine of x element-wise.
SinkDataset	A placeholder for input pipeline graph optimizations.
Size<U extends Number>	Returns the size of a tensor.
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
Slice<T>	Return a slice from 'input'.
SlideDataset	Creates a dataset that passes a sliding window over `input_dataset`.
Snapshot<T>	Returns a copy of the input tensor.
Softmax<T extends Number>	Computes softmax activations.
SoftmaxCrossEntropyWithLogits<T extends Number>	Computes softmax cross entropy cost and gradients to backpropagate.
Softplus<T extends Number>	Computes softplus: `log(exp(features) + 1)`.
SoftplusGrad<T extends Number>	Computes softplus gradients for a softplus operation.
Softsign<T extends Number>	Computes softsign: `features / (abs(features) + 1)`.
SoftsignGrad<T extends Number>	Computes softsign gradients for a softsign operation.
SpaceToBatch<T>	SpaceToBatch for 4-D tensors of type T.
SpaceToBatchND<T>	SpaceToBatch for N-D tensors of type T.
SpaceToDepth<T>	SpaceToDepth for tensors of type T.
SpaceToDepth.Options	Optional attributes for SpaceToDepth
SparseAccumulatorApplyGradient	Applies a sparse gradient to a given accumulator.
SparseAccumulatorTakeGradient<T>	Extracts the average sparse gradient in a SparseConditionalAccumulator.
SparseAdd<T>	Adds two `SparseTensor` objects to produce another `SparseTensor`.
SparseAddGrad<T>	The gradient operator for the SparseAdd op.
SparseApplyAdadelta<T>	var: Should be from a Variable().
SparseApplyAdadelta.Options	Optional attributes for SparseApplyAdadelta
SparseApplyAdagrad<T>	Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
SparseApplyAdagrad.Options	Optional attributes for SparseApplyAdagrad
SparseApplyAdagradDA<T>	Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
SparseApplyAdagradDA.Options	Optional attributes for SparseApplyAdagradDA
SparseApplyCenteredRMSProp<T>	Update '*var' according to the centered RMSProp algorithm.
SparseApplyCenteredRMSProp.Options	Optional attributes for SparseApplyCenteredRMSProp
SparseApplyFtrl<T>	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrl.Options	Optional attributes for SparseApplyFtrl
SparseApplyFtrlV2<T>	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrlV2.Options	Optional attributes for SparseApplyFtrlV2
SparseApplyMomentum<T>	Update relevant entries in '*var' and '*accum' according to the momentum scheme.
SparseApplyMomentum.Options	Optional attributes for SparseApplyMomentum
SparseApplyProximalAdagrad<T>	Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
SparseApplyProximalAdagrad.Options	Optional attributes for SparseApplyProximalAdagrad
SparseApplyProximalGradientDescent<T>	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyProximalGradientDescent.Options	Optional attributes for SparseApplyProximalGradientDescent
SparseApplyRMSProp<T>	Update '*var' according to the RMSProp algorithm.
SparseApplyRMSProp.Options	Optional attributes for SparseApplyRMSProp
SparseConcat<T>	Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator	A conditional accumulator for aggregating sparse gradients.
SparseConditionalAccumulator.Options	Optional attributes for SparseConditionalAccumulator
SparseCross<T>	Generates sparse cross from a list of sparse and dense tensors.
SparseDenseCwiseAdd<T>	Adds up a SparseTensor and a dense Tensor, using these special rules:
SparseDenseCwiseDiv<T>	Component-wise divides a SparseTensor by a dense Tensor.
SparseDenseCwiseMul<T>	Component-wise multiplies a SparseTensor by a dense Tensor.
SparseFillEmptyRows<T>	Fills empty rows in the input 2-D `SparseTensor` with a default value.
SparseFillEmptyRowsGrad<T>	The gradient of SparseFillEmptyRows.
SparseMatMul	Multiply matrix "a" by matrix "b".
SparseMatMul.Options	Optional attributes for SparseMatMul
SparseReduceMax<T extends Number>	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMax.Options	Optional attributes for SparseReduceMax
SparseReduceMaxSparse<T extends Number>	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse.Options	Optional attributes for SparseReduceMaxSparse
SparseReduceSum<T>	Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSum.Options	Optional attributes for SparseReduceSum
SparseReduceSumSparse<T>	Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse.Options	Optional attributes for SparseReduceSumSparse
SparseReorder<T>	Reorders a SparseTensor into the canonical, row-major ordering.
SparseReshape	Reshapes a SparseTensor to represent values in a new dense shape.
SparseSegmentMean<T extends Number>	Computes the mean along sparse segments of a tensor.
SparseSegmentMeanGrad<T extends Number>	Computes gradients for SparseSegmentMean.
SparseSegmentMeanWithNumSegments<T extends Number>	Computes the mean along sparse segments of a tensor.
SparseSegmentSqrtN<T extends Number>	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSqrtNGrad<T extends Number>	Computes gradients for SparseSegmentSqrtN.
SparseSegmentSqrtNWithNumSegments<T extends Number>	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSum<T extends Number>	Computes the sum along sparse segments of a tensor.
SparseSegmentSumWithNumSegments<T extends Number>	Computes the sum along sparse segments of a tensor.
SparseSlice<T>	Slice a `SparseTensor` based on the `start` and `size`.
SparseSliceGrad<T>	The gradient operator for the SparseSlice op.
SparseSoftmax<T extends Number>	Applies softmax to a batched N-D `SparseTensor`.
SparseSoftmaxCrossEntropyWithLogits<T extends Number>	Computes softmax cross entropy cost and gradients to backpropagate.
SparseSparseMaximum<T extends Number>	Returns the element-wise max of two SparseTensors.
SparseSparseMinimum<T>	Returns the element-wise min of two SparseTensors.
SparseSplit<T>	Split a `SparseTensor` into `num_split` tensors along one dimension.
SparseTensorDenseAdd<U>	Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
SparseTensorDenseMatMul<U>	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.
SparseToDense<U>	Converts a sparse representation into a dense tensor.
SparseToDense.Options	Optional attributes for SparseToDense
SparseToSparseSetOperation<T>	Applies set operation along last dimension of 2 `SparseTensor` inputs.
SparseToSparseSetOperation.Options	Optional attributes for SparseToSparseSetOperation
Split<T>	Splits a tensor into `num_split` tensors along one dimension.
SplitV<T>	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.
Sqrt<T>	Computes square root of x element-wise.
SqrtGrad<T>	Computes the gradient for the sqrt of `x` wrt its input.
Square<T>	Computes square of x element-wise.
SquaredDifference<T>	Returns (x - y)(x - y) element-wise.
Squeeze<T>	Removes dimensions of size 1 from the shape of a tensor.
Squeeze.Options	Optional attributes for Squeeze
Stack<T>	Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Stack.Options	Optional attributes for Stack
Stage	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
StatelessMultinomial<V extends Number>	Draws samples from a multinomial distribution.
StatelessRandomNormal<V extends Number>	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomUniform<V extends Number>	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessTruncatedNormal<V extends Number>	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.Options	Optional attributes for StatsAggregatorHandle
StatsAggregatorSummary	Produces a summary of any statistics recorded by the given statistics manager.
StopGradient<T>	Stops gradient computation.
StridedSlice<T>	Return a strided slice from `input`.
StridedSlice.Options	Optional attributes for StridedSlice
StridedSliceAssign<T>	Assign `value` to the sliced l-value reference of `ref`.
StridedSliceAssign.Options	Optional attributes for StridedSliceAssign
StridedSliceGrad<U>	Returns the gradient of `StridedSlice`.
StridedSliceGrad.Options	Optional attributes for StridedSliceGrad
StringFormat	Formats a string template using a list of tensors.
StringFormat.Options	Optional attributes for StringFormat
StringJoin	Joins the strings in the given list of string tensors into one tensor;
StringJoin.Options	Optional attributes for StringJoin
StringLength	String lengths of `input`.
StringLength.Options	Optional attributes for StringLength
StringSplit	Split elements of `input` based on `delimiter` into a `SparseTensor`.
StringSplit.Options	Optional attributes for StringSplit
StringSplitV2	Split elements of `source` based on `sep` into a `SparseTensor`.
StringSplitV2.Options	Optional attributes for StringSplitV2
StringStrip	Strip leading and trailing whitespaces from the Tensor.
StringToHashBucket	Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToHashBucketFast	Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToHashBucketStrong	Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToNumber<T extends Number>	Converts each string in the input Tensor to the specified numeric type.
Sub<T>	Returns x - y element-wise.
Substr	Return substrings from `Tensor` of strings.
Subtract<T>	Returns x - y element-wise.
Sum<T>	Computes the sum of elements across dimensions of a tensor.
Sum.Options	Optional attributes for Sum
SummaryWriter
SummaryWriter.Options	Optional attributes for SummaryWriter
Svd<T>	Computes the singular value decompositions of one or more matrices.
Svd.Options	Optional attributes for Svd
Switch<T>	Forwards `data` to the output port determined by `pred`.
TakeDataset	Creates a dataset that contains `count` elements from the `input_dataset`.
TakeManySparseFromTensorsMap<T>	Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
TakeManySparseFromTensorsMap.Options	Optional attributes for TakeManySparseFromTensorsMap
Tan<T>	Computes tan of x element-wise.
Tanh<T>	Computes hyperbolic tangent of `x` element-wise.
TanhGrad<T>	Computes the gradient for the tanh of `x` wrt its input.
TemporaryVariable<T>	Returns a tensor that may be mutated, but only persists within a single step.
TemporaryVariable.Options	Optional attributes for TemporaryVariable
TensorArray	An array of Tensors of given size.
TensorArray.Options	Optional attributes for TensorArray
TensorArrayClose	Delete the TensorArray from its resource container.
TensorArrayConcat<T>	Concat the elements from the TensorArray into value `value`.
TensorArrayConcat.Options	Optional attributes for TensorArrayConcat
TensorArrayGather<T>	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>
TensorArrayPack.Options	Optional attributes for TensorArrayPack
TensorArrayRead<T>	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.
TensorDataset	Creates a dataset that emits `components` as a tuple of tensors once.
TensorListConcatLists
TensorListElementShape<T extends Number>	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>	Creates a Tensor by indexing into the TensorList.
TensorListGetItem<T>
TensorListLength	Returns the number of tensors in the input tensor list.
TensorListPopBack<T>	Returns the last element of the input list as well as a list with all but that element.
TensorListPushBack	Returns a list 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.
TensorListScatter	Creates a TensorList by indexing into a Tensor.
TensorListSetItem
TensorListStack<T>	Stacks all tensors in the list.
TensorListStack.Options	Optional attributes for TensorListStack
TensorSliceDataset	Creates a dataset that emits each dim-0 slice of `components` once.
TensorSummary	Outputs a `Summary` protocol buffer with a tensor.
TensorSummary.Options	Optional attributes for TensorSummary
TensorSummaryV2	Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
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
TFRecordDataset	Creates a dataset that emits the records from one or more TFRecord files.
TFRecordReader	A Reader that outputs the records from a TensorFlow Records file.
TFRecordReader.Options	Optional attributes for TFRecordReader
Tile<T>	Constructs a tensor by tiling a given tensor.
TileGrad<T>	Returns the gradient of `Tile`.
Timestamp	Provides the time since epoch in seconds.
TopK<T extends Number>	Finds values and indices of the `k` largest elements for the last dimension.
TopK.Options	Optional attributes for TopK
Transpose<T>	Shuffle dimensions of x according to a permutation.
TruncateDiv<T>	Returns x / y element-wise for integer types.
TruncatedNormal<U extends Number>	Outputs random values from a truncated normal distribution.
TruncatedNormal.Options	Optional attributes for TruncatedNormal
TruncateMod<T extends Number>	Returns element-wise remainder of division.
TryRpc	Perform batches of RPC requests.
TryRpc.Options	Optional attributes for TryRpc
Unbatch<T>	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.
UnbatchGrad<T>	Gradient of Unbatch.
UnbatchGrad.Options	Optional attributes for UnbatchGrad
UnicodeScript	Determine the script codes of a given tensor of Unicode integer code points.
UniformCandidateSampler	Generates labels for candidate sampling with a uniform distribution.
UniformCandidateSampler.Options	Optional attributes for UniformCandidateSampler
Unique<T,U extends Number>	Finds unique elements in a 1-D tensor.
UniqueV2<T,V extends Number>	Finds unique elements along an axis of a tensor.
UniqueWithCounts<T,U extends Number>	Finds unique elements in a 1-D tensor.
UniqueWithCountsV2<T,V extends Number>	Finds unique elements along an axis of a tensor.
UnravelIndex<T extends Number>	Converts a flat index or array of flat indices into a tuple of
UnsortedSegmentMax<T extends Number>	Computes the maximum along segments of a tensor.
UnsortedSegmentMin<T extends Number>	Computes the minimum along segments of a tensor.
UnsortedSegmentProd<T>	Computes the product along segments of a tensor.
UnsortedSegmentSum<T>	Computes the sum along segments of a tensor.
Unstack<T>	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
UpperBound<U extends Number>	Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp	Creates a handle to a Variable resource.
VarHandleOp.Options	Optional attributes for VarHandleOp
Variable<T>	Holds state in the form of a tensor that persists across steps.
Variable.Options	Optional attributes for Variable
VariableShape<T extends Number>	Returns the shape of the variable pointed to by `resource`.
VarIsInitializedOp	Checks whether a resource handle-based variable has been initialized.
Where	Returns locations of nonzero / true values in a tensor.
Where3<T>	Selects elements from `x` or `y`, depending on `condition`.
WholeFileReader	A Reader that outputs the entire contents of a file as a value.
WholeFileReader.Options	Optional attributes for WholeFileReader
WindowDataset	A dataset that creates window datasets from the input dataset.
WriteAudioSummary
WriteAudioSummary.Options	Optional attributes for WriteAudioSummary
WriteFile	Writes contents to the file at input filename.
WriteGraphSummary
WriteHistogramSummary
WriteImageSummary
WriteImageSummary.Options	Optional attributes for WriteImageSummary
WriteScalarSummary
WriteSummary
Xdivy<T>	Returns 0 if x == 0, and x / y otherwise, elementwise.
Xlogy<T>	Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
Zeros<T>	An operator creating a constant initialized with zeros of the shape given by `dims`.
ZerosLike<T>	Returns a tensor of zeros with the same shape and type as x.
Zeta<T extends Number>	Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
ZipDataset	Creates a dataset that zips together `input_datasets`.