Uses of Interface
org.tensorflow.op.Op

Packages that use Op

Package	Description
org.tensorflow.op
org.tensorflow.op.audio
org.tensorflow.op.bitwise
org.tensorflow.op.collective
org.tensorflow.op.core
org.tensorflow.op.data
org.tensorflow.op.dtypes
org.tensorflow.op.image
org.tensorflow.op.io
org.tensorflow.op.linalg
org.tensorflow.op.math
org.tensorflow.op.nn
org.tensorflow.op.quantization
org.tensorflow.op.random
org.tensorflow.op.signal
org.tensorflow.op.sparse
org.tensorflow.op.strings
org.tensorflow.op.summary
org.tensorflow.op.train

Uses of Op in org.tensorflow.op

Classes in org.tensorflow.op that implement Op

Modifier and Type	Class and Description
class	PrimitiveOp A base class for Op implementations that are backed by a single Operation.

Uses of Op in org.tensorflow.op.audio

Classes in org.tensorflow.op.audio that implement Op

Modifier and Type	Class and Description
class	AudioSpectrogram Produces a visualization of audio data over time.
class	DecodeWav Decode a 16-bit PCM WAV file to a float tensor.
class	EncodeWav Encode audio data using the WAV file format.
class	Mfcc Transforms a spectrogram into a form that's useful for speech recognition.

Uses of Op in org.tensorflow.op.bitwise

Classes in org.tensorflow.op.bitwise that implement Op

Modifier and Type	Class and Description
class	BitwiseAnd<T extends Number> Elementwise computes the bitwise AND of `x` and `y`.
class	BitwiseOr<T extends Number> Elementwise computes the bitwise OR of `x` and `y`.
class	BitwiseXor<T extends Number> Elementwise computes the bitwise XOR of `x` and `y`.
class	Invert<T extends Number> Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.
class	LeftShift<T extends Number> Elementwise computes the bitwise left-shift of `x` and `y`.
class	RightShift<T extends Number> Elementwise computes the bitwise right-shift of `x` and `y`.

Uses of Op in org.tensorflow.op.collective

Classes in org.tensorflow.op.collective that implement Op

Modifier and Type	Class and Description
class	AllReduce<T extends Number> Mutually reduces multiple tensors of identical type and shape.
class	BroadcastRecv<T extends Number> Receives a tensor value broadcast from another device.
class	BroadcastSend<T extends Number> Broadcasts a tensor value to one or more other devices.

Uses of Op in org.tensorflow.op.core

Classes in org.tensorflow.op.core that implement Op

Modifier and Type	Class and Description
class	Abort Raise a exception to abort the process when called.
class	All Computes the "logical and" of elements across dimensions of a tensor.
class	AllToAll<T> An Op to exchange data across TPU replicas.
class	AnonymousIteratorV2 A container for an iterator resource.
class	AnonymousMemoryCache
class	AnonymousMultiDeviceIterator A container for a multi device iterator resource.
class	AnonymousRandomSeedGenerator
class	Any Computes the "logical or" of elements across dimensions of a tensor.
class	ApplyAdagradV2<T> Update '*var' according to the adagrad scheme.
class	AssertNextDataset A transformation that asserts which transformations happen next.
class	AssertThat Asserts that the given condition is true.
class	Assign<T> Update 'ref' by assigning 'value' to it.
class	AssignAdd<T> Update 'ref' by adding 'value' to it.
class	AssignAddVariableOp Adds a value to the current value of a variable.
class	AssignSub<T> Update 'ref' by subtracting 'value' from it.
class	AssignSubVariableOp Subtracts a value from the current value of a variable.
class	AssignVariableOp Assigns a new value to a variable.
class	AutoShardDataset Creates a dataset that shards the input dataset.
class	Barrier Defines a barrier that persists across different graph executions.
class	BarrierClose Closes the given barrier.
class	BarrierIncompleteSize Computes the number of incomplete elements in the given barrier.
class	BarrierInsertMany For each key, assigns the respective value to the specified component.
class	BarrierReadySize Computes the number of complete elements in the given barrier.
class	BarrierTakeMany Takes the given number of completed elements from a barrier.
class	Batch Batches all input tensors nondeterministically.
class	BatchMatMulV2<T> Multiplies slices of two tensors in batches.
class	BatchToSpace<T> BatchToSpace for 4-D tensors of type T.
class	BatchToSpaceNd<T> BatchToSpace for N-D tensors of type T.
class	Bitcast<U> Bitcasts a tensor from one type to another without copying data.
class	BlockLSTM<T extends Number> Computes the LSTM cell forward propagation for all the time steps.
class	BlockLSTMGrad<T extends Number> Computes the LSTM cell backward propagation for the entire time sequence.
class	BlockLSTMGradV2<T extends Number> Computes the LSTM cell backward propagation for the entire time sequence.
class	BlockLSTMV2<T extends Number> Computes the LSTM cell forward propagation for all the time steps.
class	BoostedTreesAggregateStats Aggregates the summary of accumulated stats for the batch.
class	BoostedTreesBucketize Bucketize each feature based on bucket boundaries.
class	BoostedTreesCalculateBestFeatureSplit Calculates gains for each feature and returns the best possible split information for the feature.
class	BoostedTreesCalculateBestGainsPerFeature Calculates gains for each feature and returns the best possible split information for the feature.
class	BoostedTreesCenterBias Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior.
class	BoostedTreesCreateEnsemble Creates a tree ensemble model and returns a handle to it.
class	BoostedTreesCreateQuantileStreamResource Create the Resource for Quantile Streams.
class	BoostedTreesDeserializeEnsemble Deserializes a serialized tree ensemble config and replaces current tree
class	BoostedTreesEnsembleResourceHandleOp Creates a handle to a BoostedTreesEnsembleResource
class	BoostedTreesExampleDebugOutputs Debugging/model interpretability outputs for each example.
class	BoostedTreesFlushQuantileSummaries Flush the quantile summaries from each quantile stream resource.
class	BoostedTreesGetEnsembleStates Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
class	BoostedTreesMakeQuantileSummaries Makes the summary of quantiles for the batch.
class	BoostedTreesMakeStatsSummary Makes the summary of accumulated stats for the batch.
class	BoostedTreesPredict Runs multiple additive regression ensemble predictors on input instances and
class	BoostedTreesQuantileStreamResourceAddSummaries Add the quantile summaries to each quantile stream resource.
class	BoostedTreesQuantileStreamResourceDeserialize Deserialize bucket boundaries and ready flag into current QuantileAccumulator.
class	BoostedTreesQuantileStreamResourceFlush Flush the summaries for a quantile stream resource.
class	BoostedTreesQuantileStreamResourceGetBucketBoundaries Generate the bucket boundaries for each feature based on accumulated summaries.
class	BoostedTreesQuantileStreamResourceHandleOp Creates a handle to a BoostedTreesQuantileStreamResource.
class	BoostedTreesSerializeEnsemble Serializes the tree ensemble to a proto.
class	BoostedTreesSparseAggregateStats Aggregates the summary of accumulated stats for the batch.
class	BoostedTreesSparseCalculateBestFeatureSplit Calculates gains for each feature and returns the best possible split information for the feature.
class	BoostedTreesTrainingPredict Runs multiple additive regression ensemble predictors on input instances and
class	BoostedTreesUpdateEnsemble Updates the tree ensemble by either adding a layer to the last tree being grown
class	BoostedTreesUpdateEnsembleV2 Updates the tree ensemble by adding a layer to the last tree being grown
class	BroadcastDynamicShape<T extends Number> Return the shape of s0 op s1 with broadcast.
class	BroadcastGradientArgs<T extends Number> Return the reduction indices for computing gradients of s0 op s1 with broadcast.
class	BroadcastTo<T> Broadcast an array for a compatible shape.
class	Bucketize Bucketizes 'input' based on 'boundaries'.
class	CacheDatasetV2
class	ChooseFastestDataset
class	ClipByValue<T> Clips tensor values to a specified min and max.
class	CollectiveGather<T extends Number> Mutually accumulates multiple tensors of identical type and shape.
class	CollectivePermute<T> An Op to permute tensors across replicated TPU instances.
class	CombinedNonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,
class	Concat<T> Concatenates tensors along one dimension.
class	ConfigureDistributedTPU Sets up the centralized structures for a distributed TPU system.
class	ConfigureTPUEmbedding Sets up TPUEmbedding in a distributed TPU system.
class	Constant<T> An operator producing a constant value.
class	ConsumeMutexLock This op consumes a lock created by `MutexLock`.
class	ControlTrigger Does nothing.
class	CountUpTo<T extends Number> Increments 'ref' until it reaches 'limit'.
class	CrossReplicaSum<T extends Number> An Op to sum inputs across replicated TPU instances.
class	CSVDataset
class	CudnnRNNBackpropV3<T extends Number> Backprop step of CudnnRNNV3.
class	CudnnRNNCanonicalToParamsV2<T extends Number> Converts CudnnRNN params from canonical form to usable form.
class	CudnnRNNParamsToCanonicalV2<T extends Number> Retrieves CudnnRNN params in canonical form.
class	CudnnRNNV3<T extends Number> A RNN backed by cuDNN.
class	CumulativeLogsumexp<T extends Number> Compute the cumulative product of the tensor `x` along `axis`.
class	DatasetCardinality Returns the cardinality of `input_dataset`.
class	DatasetFromGraph Creates a dataset from the given `graph_def`.
class	DebugGradientIdentity<T> Identity op for gradient debugging.
class	DebugGradientRefIdentity<T> Identity op for gradient debugging.
class	DecodePaddedRaw<T extends Number> Reinterpret the bytes of a string as a vector of numbers.
class	DecodeProto The op extracts fields from a serialized protocol buffers message into tensors.
class	DeepCopy<T> Makes a copy of `x`.
class	DeleteIterator A container for an iterator resource.
class	DeleteMemoryCache
class	DeleteMultiDeviceIterator A container for an iterator resource.
class	DeleteRandomSeedGenerator
class	DeleteSessionTensor Delete the tensor specified by its handle in the session.
class	DestroyResourceOp Deletes the resource specified by the handle.
class	DestroyTemporaryVariable<T> Destroys the temporary variable and returns its final value.
class	DirectedInterleaveDataset A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
class	DrawBoundingBoxesV2<T extends Number> Draw bounding boxes on a batch of images.
class	DynamicPartition<T> Partitions `data` into `num_partitions` tensors using indices from `partitions`.
class	DynamicStitch<T> Interleave the values from the `data` tensors into a single tensor.
class	EditDistance Computes the (possibly normalized) Levenshtein Edit Distance.
class	Einsum<T> Tensor contraction according to Einstein summation convention.
class	Empty<T> Creates a tensor with the given shape.
class	EmptyTensorList Creates and returns an empty tensor list.
class	EncodeProto The op serializes protobuf messages provided in the input tensors.
class	EnqueueTPUEmbeddingIntegerBatch An op that enqueues a list of input batch tensors to TPUEmbedding.
class	EnqueueTPUEmbeddingSparseBatch An op that enqueues TPUEmbedding input indices from a SparseTensor.
class	EnqueueTPUEmbeddingSparseTensorBatch Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
class	EnsureShape<T> Ensures that the tensor's shape matches the expected shape.
class	Enter<T> Creates or finds a child frame, and makes `data` available to the child frame.
class	EuclideanNorm<T> Computes the euclidean norm of elements across dimensions of a tensor.
class	Exit<T> Exits the current frame to its parent frame.
class	ExpandDims<T> Inserts a dimension of 1 into a tensor's shape.
class	ExperimentalAutoShardDataset Creates a dataset that shards the input dataset.
class	ExperimentalBytesProducedStatsDataset Records the bytes size of each element of `input_dataset` in a StatsAggregator.
class	ExperimentalChooseFastestDataset
class	ExperimentalDatasetCardinality Returns the cardinality of `input_dataset`.
class	ExperimentalDatasetToTFRecord Writes the given dataset to the given file using the TFRecord format.
class	ExperimentalDenseToSparseBatchDataset Creates a dataset that batches input elements into a SparseTensor.
class	ExperimentalLatencyStatsDataset Records the latency of producing `input_dataset` elements in a StatsAggregator.
class	ExperimentalMatchingFilesDataset
class	ExperimentalMaxIntraOpParallelismDataset Creates a dataset that overrides the maximum intra-op parallelism.
class	ExperimentalParseExampleDataset Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
class	ExperimentalPrivateThreadPoolDataset Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	ExperimentalRandomDataset Creates a Dataset that returns pseudorandom numbers.
class	ExperimentalRebatchDataset Creates a dataset that changes the batch size.
class	ExperimentalSetStatsAggregatorDataset
class	ExperimentalSlidingWindowDataset Creates a dataset that passes a sliding window over `input_dataset`.
class	ExperimentalSqlDataset Creates a dataset that executes a SQL query and emits rows of the result set.
class	ExperimentalStatsAggregatorHandle Creates a statistics manager resource.
class	ExperimentalStatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.
class	ExperimentalUnbatchDataset A dataset that splits the elements of its input into multiple elements.
class	ExtractVolumePatches<T extends Number> Extract `patches` from `input` and put them in the "depth" output dimension.
class	Fill<U> Creates a tensor filled with a scalar value.
class	Fingerprint Generates fingerprint values.
class	FusedBatchNormGradV3<T extends Number,U extends Number> Gradient for batch normalization.
class	FusedBatchNormV3<T extends Number,U extends Number> Batch normalization.
class	Gather<T> Gather slices from `params` axis `axis` according to `indices`.
class	GatherNd<T> Gather slices from `params` into a Tensor with shape specified by `indices`.
class	GetSessionHandle Store the input tensor in the state of the current session.
class	GetSessionTensor<T> Get the value of the tensor specified by its handle.
class	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...
class	GRUBlockCell<T extends Number> Computes the GRU cell forward propagation for 1 time step.
class	GRUBlockCellGrad<T extends Number> Computes the GRU cell back-propagation for 1 time step.
class	GuaranteeConst<T> Gives a guarantee to the TF runtime that the input tensor is a constant.
class	HashTable Creates a non-initialized hash table.
class	HistogramFixedWidth<U extends Number> Return histogram of values.
class	Identity<T> Return a tensor with the same shape and contents as the input tensor or value.
class	IdentityN Returns a list of tensors with the same shapes and contents as the input
class	IgnoreErrorsDataset Creates a dataset that contains the elements of `input_dataset` ignoring errors.
class	ImmutableConst<T> Returns immutable tensor from memory region.
class	InfeedDequeue<T> A placeholder op for a value that will be fed into the computation.
class	InfeedDequeueTuple Fetches multiple values from infeed as an XLA tuple.
class	InfeedEnqueue An op which feeds a single Tensor value into the computation.
class	InfeedEnqueuePrelinearizedBuffer An op which enqueues prelinearized buffer into TPU infeed.
class	InfeedEnqueueTuple Feeds multiple Tensor values into the computation as an XLA tuple.
class	InitializeTable Table initializer that takes two tensors for keys and values respectively.
class	InitializeTableFromTextFile Initializes a table from a text file.
class	InplaceAdd<T> Adds v into specified rows of x.
class	InplaceSub<T> Subtracts `v` into specified rows of `x`.
class	InplaceUpdate<T> Updates specified rows with values in `v`.
class	IsBoostedTreesEnsembleInitialized Checks whether a tree ensemble has been initialized.
class	IsBoostedTreesQuantileStreamResourceInitialized Checks whether a quantile stream has been initialized.
class	IsVariableInitialized Checks whether a tensor has been initialized.
class	IteratorGetDevice Returns the name of the device on which `resource` has been placed.
class	KMC2ChainInitialization Returns the index of a data point that should be added to the seed set.
class	KmeansPlusPlusInitialization Selects num_to_sample rows of input using the KMeans++ criterion.
class	LinSpace<T extends Number> Generates values in an interval.
class	LMDBDataset
class	LoadTPUEmbeddingAdadeltaParameters Load Adadelta embedding parameters.
class	LoadTPUEmbeddingAdadeltaParametersGradAccumDebug Load Adadelta parameters with debug support.
class	LoadTPUEmbeddingAdagradParameters Load Adagrad embedding parameters.
class	LoadTPUEmbeddingAdagradParametersGradAccumDebug Load Adagrad embedding parameters with debug support.
class	LoadTPUEmbeddingADAMParameters Load ADAM embedding parameters.
class	LoadTPUEmbeddingADAMParametersGradAccumDebug Load ADAM embedding parameters with debug support.
class	LoadTPUEmbeddingCenteredRMSPropParameters Load centered RMSProp embedding parameters.
class	LoadTPUEmbeddingFTRLParameters Load FTRL embedding parameters.
class	LoadTPUEmbeddingFTRLParametersGradAccumDebug Load FTRL embedding parameters with debug support.
class	LoadTPUEmbeddingMDLAdagradLightParameters Load MDL Adagrad Light embedding parameters.
class	LoadTPUEmbeddingMomentumParameters Load Momentum embedding parameters.
class	LoadTPUEmbeddingMomentumParametersGradAccumDebug Load Momentum embedding parameters with debug support.
class	LoadTPUEmbeddingProximalAdagradParameters Load proximal Adagrad embedding parameters.
class	LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug Load proximal Adagrad embedding parameters with debug support.
class	LoadTPUEmbeddingRMSPropParameters Load RMSProp embedding parameters.
class	LoadTPUEmbeddingRMSPropParametersGradAccumDebug Load RMSProp embedding parameters with debug support.
class	LoadTPUEmbeddingStochasticGradientDescentParameters Load SGD embedding parameters.
class	LookupTableExport<T,U> Outputs all keys and values in the table.
class	LookupTableFind<U> Looks up keys in a table, outputs the corresponding values.
class	LookupTableImport Replaces the contents of the table with the specified keys and values.
class	LookupTableInsert Updates the table to associates keys with values.
class	LookupTableRemove Removes keys and its associated values from a table.
class	LookupTableSize Computes the number of elements in the given table.
class	LoopCond Forwards the input to the output.
class	LowerBound<U extends Number> Applies lower_bound(sorted_search_values, values) along each row.
class	LSTMBlockCell<T extends Number> Computes the LSTM cell forward propagation for 1 time step.
class	LSTMBlockCellGrad<T extends Number> Computes the LSTM cell backward propagation for 1 timestep.
class	Lu<T,U extends Number> Computes the LU decomposition of one or more square matrices.
class	MapClear Op removes all elements in the underlying container.
class	MapIncompleteSize Op returns the number of incomplete elements in the underlying container.
class	MapPeek Op peeks at the values at the specified key.
class	MapSize Op returns the number of elements in the underlying container.
class	MapStage Stage (key, values) in the underlying container which behaves like a hashtable.
class	MapUnstage Op removes and returns the values associated with the key
class	MapUnstageNoKey Op removes and returns a random (key, value)
class	MatrixDiagPartV2<T> Returns the batched diagonal part of a batched tensor.
class	MatrixDiagV2<T> Returns a batched diagonal tensor with given batched diagonal values.
class	MatrixSetDiagV2<T> Returns a batched matrix tensor with new batched diagonal values.
class	Max<T> Computes the maximum of elements across dimensions of a tensor.
class	MaxIntraOpParallelismDataset Creates a dataset that overrides the maximum intra-op parallelism.
class	Merge<T> Forwards the value of an available tensor from `inputs` to `output`.
class	Min<T> Computes the minimum of elements across dimensions of a tensor.
class	MirrorPad<T> Pads a tensor with mirrored values.
class	MirrorPadGrad<T> Gradient op for `MirrorPad` op.
class	MulNoNan<T> Returns x * y element-wise.
class	MutableDenseHashTable Creates an empty hash table that uses tensors as the backing store.
class	MutableHashTable Creates an empty hash table.
class	MutableHashTableOfTensors Creates an empty hash table.
class	Mutex Creates a Mutex resource that can be locked by `MutexLock`.
class	MutexLock Locks a mutex resource.
class	NcclAllReduce<T extends Number> Outputs a tensor containing the reduction across all input tensors.
class	NcclBroadcast<T extends Number> Sends `input` to all devices that are connected to the output.
class	NcclReduce<T extends Number> Reduces `input` from `num_devices` using `reduction` to a single device.
class	NearestNeighbors Selects the k nearest centers for each point.
class	NextAfter<T extends Number> Returns the next representable value of `x1` in the direction of `x2`, element-wise.
class	NextIteration<T> Makes its input available to the next iteration.
class	NonDeterministicInts<U> Non-deterministically generates some integers.
class	NonMaxSuppressionV5<T extends Number> Greedily selects a subset of bounding boxes in descending order of score,
class	NonSerializableDataset
class	NoOp Does nothing.
class	OneHot<U> Returns a one-hot tensor.
class	OnesLike<T> Returns a tensor of ones with the same shape and type as x.
class	OrderedMapClear Op removes all elements in the underlying container.
class	OrderedMapIncompleteSize Op returns the number of incomplete elements in the underlying container.
class	OrderedMapPeek Op peeks at the values at the specified key.
class	OrderedMapSize Op returns the number of elements in the underlying container.
class	OrderedMapStage Stage (key, values) in the underlying container which behaves like a ordered
class	OrderedMapUnstage Op removes and returns the values associated with the key
class	OrderedMapUnstageNoKey Op removes and returns the (key, value) element with the smallest
class	OutfeedDequeue<T> Retrieves a single tensor from the computation outfeed.
class	OutfeedDequeueTuple Retrieve multiple values from the computation outfeed.
class	OutfeedEnqueue Enqueue a Tensor on the computation outfeed.
class	OutfeedEnqueueTuple Enqueue multiple Tensor values on the computation outfeed.
class	Pad<T> Pads a tensor.
class	ParallelConcat<T> Concatenates a list of `N` tensors along the first dimension.
class	ParallelDynamicStitch<T> Interleave the values from the `data` tensors into a single tensor.
class	Placeholder<T> A placeholder op for a value that will be fed into the computation.
class	PlaceholderWithDefault<T> A placeholder op that passes through `input` when its output is not fed.
class	Prelinearize An op which linearizes one Tensor value to an opaque variant tensor.
class	PrelinearizeTuple An op which linearizes multiple Tensor values to an opaque variant tensor.
class	Print Prints a string scalar.
class	PrivateThreadPoolDataset Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	Prod<T> Computes the product of elements across dimensions of a tensor.
class	QuantizedConcat<T> Concatenates quantized tensors along one dimension.
class	QuantizedConcatV2<T>
class	QuantizedConv2DAndRelu<V>
class	QuantizedConv2DAndReluAndRequantize<V>
class	QuantizedConv2DAndRequantize<V>
class	QuantizedConv2DPerChannel<V> Computes QuantizedConv2D per channel.
class	QuantizedConv2DWithBias<V>
class	QuantizedConv2DWithBiasAndRelu<V>
class	QuantizedConv2DWithBiasAndReluAndRequantize<W>
class	QuantizedConv2DWithBiasAndRequantize<W>
class	QuantizedConv2DWithBiasSignedSumAndReluAndRequantize<X>
class	QuantizedConv2DWithBiasSumAndRelu<V>
class	QuantizedConv2DWithBiasSumAndReluAndRequantize<X>
class	QuantizedDepthwiseConv2D<V> Computes quantized depthwise Conv2D.
class	QuantizedDepthwiseConv2DWithBias<V> Computes quantized depthwise Conv2D with Bias.
class	QuantizedDepthwiseConv2DWithBiasAndRelu<V> Computes quantized depthwise Conv2D with Bias and Relu.
class	QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize<W> Computes quantized depthwise Conv2D with Bias, Relu and Requantize.
class	QuantizedMatMulWithBias<W> Performs a quantized matrix multiplication of `a` by the matrix `b` with bias add.
class	QuantizedMatMulWithBiasAndRelu<V> Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion.
class	QuantizedMatMulWithBiasAndReluAndRequantize<W> Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu and requantize fusion.
class	QuantizedReshape<T> Reshapes a quantized tensor as per the Reshape op.
class	RaggedGather<T extends Number,U> Gather ragged slices from `params` axis `0` according to `indices`.
class	RaggedRange<U extends Number,T extends Number> Returns a `RaggedTensor` containing the specified sequences of numbers.
class	RaggedTensorFromVariant<U extends Number,T> Decodes a `variant` Tensor into a `RaggedTensor`.
class	RaggedTensorToSparse<U> Converts a `RaggedTensor` into a `SparseTensor` with the same values.
class	RaggedTensorToTensor<U> Create a dense tensor from a ragged tensor, possibly altering its shape.
class	RaggedTensorToVariant Encodes a `RaggedTensor` into a `variant` Tensor.
class	Range<T extends Number> Creates a sequence of numbers.
class	Rank Returns the rank of a tensor.
class	ReadVariableOp<T> Reads the value of a variable.
class	RebatchDataset Creates a dataset that changes the batch size.
class	RecvTPUEmbeddingActivations An op that receives embedding activations on the TPU.
class	ReduceAll Computes the "logical and" of elements across dimensions of a tensor.
class	ReduceAny Computes the "logical or" of elements across dimensions of a tensor.
class	ReduceMax<T> Computes the maximum of elements across dimensions of a tensor.
class	ReduceMin<T> Computes the minimum of elements across dimensions of a tensor.
class	ReduceProd<T> Computes the product of elements across dimensions of a tensor.
class	ReduceSum<T> Computes the sum of elements across dimensions of a tensor.
class	RefEnter<T> Creates or finds a child frame, and makes `data` available to the child frame.
class	RefExit<T> Exits the current frame to its parent frame.
class	RefIdentity<T> Return the same ref tensor as the input ref tensor.
class	RefMerge<T> Forwards the value of an available tensor from `inputs` to `output`.
class	RefNextIteration<T> Makes its input available to the next iteration.
class	RefSelect<T> Forwards the `index`th element of `inputs` to `output`.
class	RefSwitch<T> Forwards the ref tensor `data` to the output port determined by `pred`.
class	RemoteFusedGraphExecute Execute a sub graph on a remote processor.
class	RequantizationRangePerChannel Computes requantization range per channel.
class	RequantizePerChannel<U> Requantizes input with min and max values known per channel.
class	Reshape<T> Reshapes a tensor.
class	ResourceAccumulatorApplyGradient Applies a gradient to a given accumulator.
class	ResourceAccumulatorNumAccumulated Returns the number of gradients aggregated in the given accumulators.
class	ResourceAccumulatorSetGlobalStep Updates the accumulator with a new value for global_step.
class	ResourceAccumulatorTakeGradient<T> Extracts the average gradient in the given ConditionalAccumulator.
class	ResourceApplyAdagradV2 Update '*var' according to the adagrad scheme.
class	ResourceApplyAdamWithAmsgrad Update '*var' according to the Adam algorithm.
class	ResourceApplyKerasMomentum Update '*var' according to the momentum scheme.
class	ResourceConditionalAccumulator A conditional accumulator for aggregating gradients.
class	ResourceCountUpTo<T extends Number> Increments variable pointed to by 'resource' until it reaches 'limit'.
class	ResourceGather<U> Gather slices from the variable pointed to by `resource` according to `indices`.
class	ResourceGatherNd<U>
class	ResourceScatterAdd Adds sparse updates to the variable referenced by `resource`.
class	ResourceScatterDiv Divides sparse updates into the variable referenced by `resource`.
class	ResourceScatterMax Reduces sparse updates into the variable referenced by `resource` using the `max` operation.
class	ResourceScatterMin Reduces sparse updates into the variable referenced by `resource` using the `min` operation.
class	ResourceScatterMul Multiplies sparse updates into the variable referenced by `resource`.
class	ResourceScatterNdAdd Applies sparse addition to individual values or slices in a Variable.
class	ResourceScatterNdSub Applies sparse subtraction to individual values or slices in a Variable.
class	ResourceScatterNdUpdate Applies sparse `updates` to individual values or slices within a given
class	ResourceScatterSub Subtracts sparse updates from the variable referenced by `resource`.
class	ResourceScatterUpdate Assigns sparse updates to the variable referenced by `resource`.
class	ResourceSparseApplyAdagradV2 Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
class	ResourceSparseApplyKerasMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
class	ResourceStridedSliceAssign Assign `value` to the sliced l-value reference of `ref`.
class	RetrieveTPUEmbeddingAdadeltaParameters Retrieve Adadelta embedding parameters.
class	RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug Retrieve Adadelta embedding parameters with debug support.
class	RetrieveTPUEmbeddingAdagradParameters Retrieve Adagrad embedding parameters.
class	RetrieveTPUEmbeddingAdagradParametersGradAccumDebug Retrieve Adagrad embedding parameters with debug support.
class	RetrieveTPUEmbeddingADAMParameters Retrieve ADAM embedding parameters.
class	RetrieveTPUEmbeddingADAMParametersGradAccumDebug Retrieve ADAM embedding parameters with debug support.
class	RetrieveTPUEmbeddingCenteredRMSPropParameters Retrieve centered RMSProp embedding parameters.
class	RetrieveTPUEmbeddingFTRLParameters Retrieve FTRL embedding parameters.
class	RetrieveTPUEmbeddingFTRLParametersGradAccumDebug Retrieve FTRL embedding parameters with debug support.
class	RetrieveTPUEmbeddingMDLAdagradLightParameters Retrieve MDL Adagrad Light embedding parameters.
class	RetrieveTPUEmbeddingMomentumParameters Retrieve Momentum embedding parameters.
class	RetrieveTPUEmbeddingMomentumParametersGradAccumDebug Retrieve Momentum embedding parameters with debug support.
class	RetrieveTPUEmbeddingProximalAdagradParameters Retrieve proximal Adagrad embedding parameters.
class	RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug Retrieve proximal Adagrad embedding parameters with debug support.
class	RetrieveTPUEmbeddingRMSPropParameters Retrieve RMSProp embedding parameters.
class	RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug Retrieve RMSProp embedding parameters with debug support.
class	RetrieveTPUEmbeddingStochasticGradientDescentParameters Retrieve SGD embedding parameters.
class	Reverse<T> Reverses specific dimensions of a tensor.
class	ReverseSequence<T> Reverses variable length slices.
class	RngSkip Advance the counter of a counter-based RNG.
class	Roll<T> Rolls the elements of a tensor along an axis.
class	Rpc Perform batches of RPC requests.
class	SamplingDataset Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
class	ScaleAndTranslate
class	ScaleAndTranslateGrad<T extends Number>
class	ScatterAdd<T> Adds sparse updates to a variable reference.
class	ScatterDiv<T> Divides a variable reference by sparse updates.
class	ScatterMax<T extends Number> Reduces sparse updates into a variable reference using the `max` operation.
class	ScatterMin<T extends Number> Reduces sparse updates into a variable reference using the `min` operation.
class	ScatterMul<T> Multiplies sparse updates into a variable reference.
class	ScatterNd<U> Scatter `updates` into a new tensor according to `indices`.
class	ScatterNdAdd<T> Applies sparse addition to individual values or slices in a Variable.
class	ScatterNdNonAliasingAdd<T> Applies sparse addition to `input` using individual values or slices
class	ScatterNdSub<T> Applies sparse subtraction to individual values or slices in a Variable.
class	ScatterNdUpdate<T> Applies sparse `updates` to individual values or slices within a given
class	ScatterSub<T> Subtracts sparse updates to a variable reference.
class	ScatterUpdate<T> Applies sparse updates to a variable reference.
class	SelectV2<T>
class	SendTPUEmbeddingGradients Performs gradient updates of embedding tables.
class	SetDiff1d<T,U extends Number> Computes the difference between two lists of numbers or strings.
class	SetSize Number of unique elements along last dimension of input `set`.
class	Shape<U extends Number> Returns the shape of a tensor.
class	ShapeN<U extends Number> Returns shape of tensors.
class	ShardDataset Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
class	ShuffleDatasetV2
class	ShutdownDistributedTPU Shuts down a running distributed TPU system.
class	Size<U extends Number> Returns the size of a tensor.
class	Skipgram Parses a text file and creates a batch of examples.
class	SleepDataset
class	Slice<T> Return a slice from 'input'.
class	SlidingWindowDataset Creates a dataset that passes a sliding window over `input_dataset`.
class	Snapshot<T> Returns a copy of the input tensor.
class	SnapshotDataset Creates a dataset that will write to / read from a snapshot.
class	SpaceToBatchNd<T> SpaceToBatch for N-D tensors of type T.
class	SparseApplyAdagradV2<T> Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
class	Split<T> Splits a tensor into `num_split` tensors along one dimension.
class	SplitV<T> Splits a tensor into `num_split` tensors along one dimension.
class	Squeeze<T> Removes dimensions of size 1 from the shape of a tensor.
class	Stack<T> Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
class	Stage Stage values similar to a lightweight Enqueue.
class	StageClear Op removes all elements in the underlying container.
class	StagePeek Op peeks at the values at the specified index.
class	StageSize Op returns the number of elements in the underlying container.
class	StatefulRandomBinomial<V extends Number>
class	StatefulStandardNormal<U> Outputs random values from a normal distribution.
class	StatefulStandardNormalV2<U> Outputs random values from a normal distribution.
class	StatefulTruncatedNormal<U> Outputs random values from a truncated normal distribution.
class	StatefulUniform<U> Outputs random values from a uniform distribution.
class	StatefulUniformFullInt<U> Outputs random integers from a uniform distribution.
class	StatefulUniformInt<U> Outputs random integers from a uniform distribution.
class	StatsAggregatorHandleV2
class	StatsAggregatorSetSummaryWriter Set a summary_writer_interface to record statistics using given stats_aggregator.
class	StopGradient<T> Stops gradient computation.
class	StridedSlice<T> Return a strided slice from `input`.
class	StridedSliceAssign<T> Assign `value` to the sliced l-value reference of `ref`.
class	StridedSliceGrad<U> Returns the gradient of `StridedSlice`.
class	StringLower
class	StringNGrams<T extends Number> Creates ngrams from ragged string data.
class	StringUpper
class	Sum<T> Computes the sum of elements across dimensions of a tensor.
class	SwitchCond<T> Forwards `data` to the output port determined by `pred`.
class	TemporaryVariable<T> Returns a tensor that may be mutated, but only persists within a single step.
class	TensorArray An array of Tensors of given size.
class	TensorArrayClose Delete the TensorArray from its resource container.
class	TensorArrayConcat<T> Concat the elements from the TensorArray into value `value`.
class	TensorArrayGather<T> Gather specific elements from the TensorArray into output `value`.
class	TensorArrayGrad Creates a TensorArray for storing the gradients of values in the given handle.
class	TensorArrayGradWithShape Creates a TensorArray for storing multiple gradients of values in the given handle.
class	TensorArrayPack<T>
class	TensorArrayRead<T> Read an element from the TensorArray into output `value`.
class	TensorArrayScatter Scatter the data from the input value into specific TensorArray elements.
class	TensorArraySize Get the current size of the TensorArray.
class	TensorArraySplit Split the data from the input value into TensorArray elements.
class	TensorArrayUnpack
class	TensorArrayWrite Push an element onto the tensor_array.
class	TensorForestCreateTreeVariable Creates a tree resource and returns a handle to it.
class	TensorForestTreeDeserialize Deserializes a proto into the tree handle
class	TensorForestTreeIsInitializedOp Checks whether a tree has been initialized.
class	TensorForestTreePredict Output the logits for the given input data
class	TensorForestTreeResourceHandleOp Creates a handle to a TensorForestTreeResource
class	TensorForestTreeSerialize Serializes the tree handle to a proto
class	TensorForestTreeSize Get the number of nodes in a tree
class	TensorListConcat<T> Concats all tensors in the list along the 0th dimension.
class	TensorListConcatLists
class	TensorListConcatV2<U> Concats all tensors in the list along the 0th dimension.
class	TensorListElementShape<T extends Number> The shape of the elements of the given list, as a tensor.
class	TensorListFromTensor Creates a TensorList which, when stacked, has the value of `tensor`.
class	TensorListGather<T> Creates a Tensor by indexing into the TensorList.
class	TensorListGetItem<T>
class	TensorListLength Returns the number of tensors in the input tensor list.
class	TensorListPopBack<T> Returns the last element of the input list as well as a list with all but that element.
class	TensorListPushBack Returns a list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`.
class	TensorListPushBackBatch
class	TensorListReserve List of the given size with empty elements.
class	TensorListResize Resizes the list.
class	TensorListScatter Creates a TensorList by indexing into a Tensor.
class	TensorListScatterIntoExistingList Scatters tensor at indices in an input list.
class	TensorListScatterV2 Creates a TensorList by indexing into a Tensor.
class	TensorListSetItem
class	TensorListSplit Splits a tensor into a list.
class	TensorListStack<T> Stacks all tensors in the list.
class	TensorScatterAdd<T> Adds sparse `updates` to an existing tensor according to `indices`.
class	TensorScatterSub<T> Subtracts sparse `updates` from an existing tensor according to `indices`.
class	TensorScatterUpdate<T> Scatter `updates` into an existing tensor according to `indices`.
class	TensorStridedSliceUpdate<T> Assign `value` to the sliced l-value reference of `input`.
class	ThreadPoolDataset Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	ThreadPoolHandle Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	Tile<T> Constructs a tensor by tiling a given tensor.
class	Timestamp Provides the time since epoch in seconds.
class	TPUCompilationResult CompilationResultProto indicating the status of the TPU compilation.
class	TPUEmbeddingActivations An op enabling differentiation of TPU Embeddings.
class	TPUOrdinalSelector A TPU core selector Op.
class	TPUReplicatedInput<T> Connects N inputs to an N-way replicated TPU computation.
class	TPUReplicatedOutput<T> Connects outputs of an N-way replicated computation to N outputs.
class	TPUReplicateMetadata Metadata indicaitng how the TPU computation should be replicated.
class	TridiagonalMatMul<T> Calculate product with tridiagonal matrix.
class	TridiagonalSolve<T> Solves tridiagonal systems of equations.
class	TryRpc Perform batches of RPC requests.
class	Unbatch<T> Reverses the operation of Batch for a single output Tensor.
class	UnbatchGrad<T> Gradient of Unbatch.
class	UnicodeDecode<T extends Number> Decodes each string in `input` into a sequence of Unicode code points.
class	UnicodeEncode Encode a tensor of ints into unicode strings.
class	Unique<T,V extends Number> Finds unique elements along an axis of a tensor.
class	UniqueDataset Creates a dataset that contains the unique elements of `input_dataset`.
class	UniqueWithCounts<T,V extends Number> Finds unique elements along an axis of a tensor.
class	UnravelIndex<T extends Number> Converts an array of flat indices into a tuple of coordinate arrays.
class	UnsortedSegmentJoin Joins the elements of `inputs` based on `segment_ids`.
class	Unstack<T> Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
class	Unstage Op is similar to a lightweight Dequeue.
class	UnwrapDatasetVariant
class	UpperBound<U extends Number> Applies upper_bound(sorted_search_values, values) along each row.
class	VarHandleOp Creates a handle to a Variable resource.
class	Variable<T> Holds state in the form of a tensor that persists across steps.
class	VariableShape<T extends Number> Returns the shape of the variable pointed to by `resource`.
class	VarIsInitializedOp Checks whether a resource handle-based variable has been initialized.
class	Where Returns locations of nonzero / true values in a tensor.
class	Where3<T> Selects elements from `x` or `y`, depending on `condition`.
class	WorkerHeartbeat Worker heartbeat op.
class	WrapDatasetVariant
class	WriteRawProtoSummary
class	Zeros<T> An operator creating a constant initialized with zeros of the shape given by `dims`.
class	ZerosLike<T> Returns a tensor of zeros with the same shape and type as x.

Uses of Op in org.tensorflow.op.data

Classes in org.tensorflow.op.data that implement Op

Modifier and Type	Class and Description
class	AnonymousIterator A container for an iterator resource.
class	BatchDataset Creates a dataset that batches `batch_size` elements from `input_dataset`.
class	BytesProducedStatsDataset Records the bytes size of each element of `input_dataset` in a StatsAggregator.
class	CacheDataset Creates a dataset that caches elements from `input_dataset`.
class	ConcatenateDataset Creates a dataset that concatenates `input_dataset` with `another_dataset`.
class	DatasetToGraph Returns a serialized GraphDef representing `input_dataset`.
class	DatasetToSingleElement Outputs the single element from the given dataset.
class	DatasetToTfRecord Writes the given dataset to the given file using the TFRecord format.
class	DenseToSparseBatchDataset Creates a dataset that batches input elements into a SparseTensor.
class	DeserializeIterator Converts the given variant tensor to an iterator and stores it in the given resource.
class	ExperimentalAssertNextDataset
class	ExperimentalCsvDataset
class	ExperimentalDirectedInterleaveDataset A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
class	ExperimentalIgnoreErrorsDataset Creates a dataset that contains the elements of `input_dataset` ignoring errors.
class	ExperimentalIteratorGetDevice Returns the name of the device on which `resource` has been placed.
class	ExperimentalLmdbDataset
class	ExperimentalNonSerializableDataset
class	ExperimentalSleepDataset
class	ExperimentalThreadPoolDataset Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	ExperimentalThreadPoolHandle Creates a dataset that uses a custom thread pool to compute `input_dataset`.
class	ExperimentalUniqueDataset Creates a dataset that contains the unique elements of `input_dataset`.
class	FilterByLastComponentDataset Creates a dataset containing elements of first component of `input_dataset` having true in the last component.
class	FixedLengthRecordDataset
class	Iterator
class	IteratorFromStringHandle
class	IteratorGetNext Gets the next output from the given iterator .
class	IteratorGetNextAsOptional Gets the next output from the given iterator as an Optional variant.
class	IteratorGetNextSync Gets the next output from the given iterator.
class	IteratorToStringHandle Converts the given `resource_handle` representing an iterator to a string.
class	LatencyStatsDataset Records the latency of producing `input_dataset` elements in a StatsAggregator.
class	LeakyReluGrad<T extends Number> Computes rectified linear gradients for a LeakyRelu operation.
class	MakeIterator Makes a new iterator from the given `dataset` and stores it in `iterator`.
class	MatchingFilesDataset
class	ModelDataset Identity transformation that models performance.
class	MultiDeviceIterator Creates a MultiDeviceIterator resource.
class	MultiDeviceIteratorFromStringHandle Generates a MultiDeviceIterator resource from its provided string handle.
class	MultiDeviceIteratorGetNextFromShard Gets next element for the provided shard number.
class	MultiDeviceIteratorInit Initializes the multi device iterator with the given dataset.
class	MultiDeviceIteratorToStringHandle Produces a string handle for the given MultiDeviceIterator.
class	OptimizeDataset Creates a dataset by applying optimizations to `input_dataset`.
class	OptionalFromValue Constructs an Optional variant from a tuple of tensors.
class	OptionalGetValue Returns the value stored in an Optional variant or raises an error if none exists.
class	OptionalHasValue Returns true if and only if the given Optional variant has a value.
class	OptionalNone Creates an Optional variant with no value.
class	PaddedBatchDataset Creates a dataset that batches and pads `batch_size` elements from the input.
class	ParseExampleDataset Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
class	PrefetchDataset Creates a dataset that asynchronously prefetches elements from `input_dataset`.
class	RandomDataset Creates a Dataset that returns pseudorandom numbers.
class	RangeDataset Creates a dataset with a range of values.
class	RepeatDataset Creates a dataset that emits the outputs of `input_dataset` `count` times.
class	SerializeIterator Converts the given `resource_handle` representing an iterator to a variant tensor.
class	SetStatsAggregatorDataset
class	ShuffleAndRepeatDataset Creates a dataset that shuffles and repeats elements from `input_dataset`
class	ShuffleDataset Creates a dataset that shuffles elements from `input_dataset` pseudorandomly.
class	SkipDataset Creates a dataset that skips `count` elements from the `input_dataset`.
class	SparseTensorSliceDataset Creates a dataset that splits a SparseTensor into elements row-wise.
class	SqlDataset Creates a dataset that executes a SQL query and emits rows of the result set.
class	StatsAggregatorHandle Creates a statistics manager resource.
class	TakeDataset Creates a dataset that contains `count` elements from the `input_dataset`.
class	TensorDataset Creates a dataset that emits `components` as a tuple of tensors once.
class	TensorSliceDataset Creates a dataset that emits each dim-0 slice of `components` once.
class	TextLineDataset Creates a dataset that emits the lines of one or more text files.
class	TfRecordDataset Creates a dataset that emits the records from one or more TFRecord files.
class	UnbatchDataset A dataset that splits the elements of its input into multiple elements.
class	WindowDataset A dataset that creates window datasets from the input dataset.
class	ZipDataset Creates a dataset that zips together `input_datasets`.

Uses of Op in org.tensorflow.op.dtypes

Classes in org.tensorflow.op.dtypes that implement Op

Modifier and Type	Class and Description
class	AsString Converts each entry in the given tensor to strings.
class	Cast<U> Cast x of type SrcT to y of DstT.
class	Complex<U> Converts two real numbers to a complex number.

Uses of Op in org.tensorflow.op.image

Classes in org.tensorflow.op.image that implement Op

Modifier and Type	Class and Description
class	AdjustContrast<T extends Number> Adjust the contrast of one or more images.
class	AdjustHue<T extends Number> Adjust the hue of one or more images.
class	AdjustSaturation<T extends Number> Adjust the saturation of one or more images.
class	CropAndResize Extracts crops from the input image tensor and resizes them.
class	CropAndResizeGradBoxes Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
class	CropAndResizeGradImage<T extends Number> Computes the gradient of the crop_and_resize op wrt the input image tensor.
class	DecodeAndCropJpeg Decode and Crop a JPEG-encoded image to a uint8 tensor.
class	DecodeBmp Decode the first frame of a BMP-encoded image to a uint8 tensor.
class	DecodeGif Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
class	DecodeJpeg Decode a JPEG-encoded image to a uint8 tensor.
class	DecodePng<T extends Number> Decode a PNG-encoded image to a uint8 or uint16 tensor.
class	DrawBoundingBoxes<T extends Number> Draw bounding boxes on a batch of images.
class	EncodeJpeg JPEG-encode an image.
class	EncodeJpegVariableQuality JPEG encode input image with provided compression quality.
class	EncodePng PNG-encode an image.
class	ExtractGlimpse Extracts a glimpse from the input tensor.
class	ExtractImagePatches<T extends Number> Extract `patches` from `images` and put them in the "depth" output dimension.
class	ExtractJpegShape<T extends Number> Extract the shape information of a JPEG-encoded image.
class	HsvToRgb<T extends Number> Convert one or more images from HSV to RGB.
class	NonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,
class	NonMaxSuppressionWithOverlaps Greedily selects a subset of bounding boxes in descending order of score,
class	QuantizedResizeBilinear<T> Resize quantized `images` to `size` using quantized bilinear interpolation.
class	RandomCrop<T extends Number> Randomly crop `image`.
class	ResizeArea Resize `images` to `size` using area interpolation.
class	ResizeBicubic Resize `images` to `size` using bicubic interpolation.
class	ResizeBicubicGrad<T extends Number> Computes the gradient of bicubic interpolation.
class	ResizeBilinear Resize `images` to `size` using bilinear interpolation.
class	ResizeBilinearGrad<T extends Number> Computes the gradient of bilinear interpolation.
class	ResizeNearestNeighbor<T extends Number> Resize `images` to `size` using nearest neighbor interpolation.
class	ResizeNearestNeighborGrad<T extends Number> Computes the gradient of nearest neighbor interpolation.
class	RgbToHsv<T extends Number> Converts one or more images from RGB to HSV.
class	SampleDistortedBoundingBox<T extends Number> Generate a single randomly distorted bounding box for an image.

Uses of Op in org.tensorflow.op.io

Classes in org.tensorflow.op.io that implement Op

Modifier and Type	Class and Description
class	DecodeBase64 Decode web-safe base64-encoded strings.
class	DecodeCompressed Decompress strings.
class	DecodeCsv Convert CSV records to tensors.
class	DecodeJsonExample Convert JSON-encoded Example records to binary protocol buffer strings.
class	DecodeRaw<T> Reinterpret the bytes of a string as a vector of numbers.
class	DeserializeManySparse<T> Deserialize and concatenate `SparseTensors` from a serialized minibatch.
class	EncodeBase64 Encode strings into web-safe base64 format.
class	FifoQueue A queue that produces elements in first-in first-out order.
class	FixedLengthRecordReader A Reader that outputs fixed-length records from a file.
class	IdentityReader A Reader that outputs the queued work as both the key and value.
class	LmdbReader A Reader that outputs the records from a LMDB file.
class	MatchingFiles Returns the set of files matching one or more glob patterns.
class	PaddingFifoQueue A queue that produces elements in first-in first-out order.
class	ParseExample Transforms a vector of brain.Example protos (as strings) into typed tensors.
class	ParseSequenceExample Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
class	ParseSingleExample Transforms a tf.Example proto (as a string) into typed tensors.
class	ParseSingleSequenceExample Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
class	ParseTensor<T> Transforms a serialized tensorflow.TensorProto proto into a Tensor.
class	PriorityQueue A queue that produces elements sorted by the first component value.
class	QueueClose Closes the given queue.
class	QueueDequeue Dequeues a tuple of one or more tensors from the given queue.
class	QueueDequeueMany Dequeues `n` tuples of one or more tensors from the given queue.
class	QueueDequeueUpTo Dequeues `n` tuples of one or more tensors from the given queue.
class	QueueEnqueue Enqueues a tuple of one or more tensors in the given queue.
class	QueueEnqueueMany Enqueues zero or more tuples of one or more tensors in the given queue.
class	QueueIsClosed Returns true if queue is closed.
class	QueueSize Computes the number of elements in the given queue.
class	RandomShuffleQueue A queue that randomizes the order of elements.
class	ReaderNumRecordsProduced Returns the number of records this Reader has produced.
class	ReaderNumWorkUnitsCompleted Returns the number of work units this Reader has finished processing.
class	ReaderRead Returns the next record (key, value pair) produced by a Reader.
class	ReaderReadUpTo Returns up to `num_records` (key, value) pairs produced by a Reader.
class	ReaderReset Restore a Reader to its initial clean state.
class	ReaderRestoreState Restore a reader to a previously saved state.
class	ReaderSerializeState Produce a string tensor that encodes the state of a Reader.
class	ReadFile Reads and outputs the entire contents of the input filename.
class	SerializeManySparse<U> Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
class	SerializeSparse<U> Serialize a `SparseTensor` into a `[3]` `Tensor` object.
class	SerializeTensor Transforms a Tensor into a serialized TensorProto proto.
class	ShardedFilename Generate a sharded filename.
class	ShardedFilespec Generate a glob pattern matching all sharded file names.
class	TextLineReader A Reader that outputs the lines of a file delimited by '\n'.
class	TfRecordReader A Reader that outputs the records from a TensorFlow Records file.
class	WholeFileReader A Reader that outputs the entire contents of a file as a value.
class	WriteFile Writes contents to the file at input filename.

Uses of Op in org.tensorflow.op.linalg

Classes in org.tensorflow.op.linalg that implement Op

Modifier and Type	Class and Description
class	BandPart<T> Copy a tensor setting everything outside a central band in each innermost matrix
class	BatchCholesky<T extends Number>
class	BatchCholeskyGrad<T extends Number>
class	BatchMatMul<T> Multiplies slices of two tensors in batches.
class	BatchMatrixBandPart<T>
class	BatchMatrixDeterminant<T>
class	BatchMatrixDiag<T>
class	BatchMatrixDiagPart<T>
class	BatchMatrixInverse<T extends Number>
class	BatchMatrixSetDiag<T>
class	BatchMatrixSolve<T extends Number>
class	BatchMatrixSolveLs<T extends Number>
class	BatchMatrixTriangularSolve<T extends Number>
class	BatchSelfAdjointEig<T extends Number>
class	BatchSvd<T>
class	Cholesky<T> Computes the Cholesky decomposition of one or more square matrices.
class	CholeskyGrad<T extends Number> Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
class	ConjugateTranspose<T> Shuffle dimensions of x according to a permutation and conjugate the result.
class	Cross<T extends Number> Compute the pairwise cross product.
class	Det<T> Computes the determinant of one or more square matrices.
class	Diag<T> Returns a batched diagonal tensor with a given batched diagonal values.
class	DiagPart<T> Returns the batched diagonal part of a batched tensor.
class	Inv<T> Computes the inverse of one or more square invertible matrices or their
class	LoadAndRemapMatrix Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
class	LogMatrixDeterminant<T> Computes the sign and the log of the absolute value of the determinant of
class	MatMul<T> Multiply the matrix "a" by the matrix "b".
class	MatrixLogarithm<T> Computes the matrix logarithm of one or more square matrices:
class	MatrixSolveLs<T> Solves one or more linear least-squares problems.
class	Qr<T> Computes the QR decompositions of one or more matrices.
class	QuantizedMatMul<V> Perform a quantized matrix multiplication of `a` by the matrix `b`.
class	SelfAdjointEig<T> Computes the eigen decomposition of one or more square self-adjoint matrices.
class	SetDiag<T> Returns a batched matrix tensor with new batched diagonal values.
class	Solve<T> Solves systems of linear equations.
class	Sqrtm<T> Computes the matrix square root of one or more square matrices:
class	Svd<T> Computes the singular value decompositions of one or more matrices.
class	TensorDiag<T> Returns a diagonal tensor with a given diagonal values.
class	TensorDiagPart<T> Returns the diagonal part of the tensor.
class	Transpose<T> Shuffle dimensions of x according to a permutation.
class	TriangularSolve<T> Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.

Uses of Op in org.tensorflow.op.math

Classes in org.tensorflow.op.math that implement Op

Modifier and Type	Class and Description
class	Abs<T extends Number> Computes the absolute value of a tensor.
class	AccumulateN<T> Returns the element-wise sum of a list of tensors.
class	Acos<T> Computes acos of x element-wise.
class	Acosh<T> Computes inverse hyperbolic cosine of x element-wise.
class	Add<T> Returns x + y element-wise.
class	AddN<T> Add all input tensors element wise.
class	Angle<U extends Number> Returns the argument of a complex number.
class	ApproximateEqual Returns the truth value of abs(x-y) < tolerance element-wise.
class	ArgMax<V extends Number> Returns the index with the largest value across dimensions of a tensor.
class	ArgMin<V extends Number> Returns the index with the smallest value across dimensions of a tensor.
class	Asin<T> Computes the trignometric inverse sine of x element-wise.
class	Asinh<T> Computes inverse hyperbolic sine of x element-wise.
class	Atan<T> Computes the trignometric inverse tangent of x element-wise.
class	Atan2<T extends Number> Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
class	Atanh<T> Computes inverse hyperbolic tangent of x element-wise.
class	BesselI0e<T extends Number> Computes the Bessel i0e function of `x` element-wise.
class	BesselI1e<T extends Number> Computes the Bessel i1e function of `x` element-wise.
class	Betainc<T extends Number> Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
class	Bincount<T extends Number> Counts the number of occurrences of each value in an integer array.
class	Ceil<T extends Number> Returns element-wise smallest integer not less than x.
class	CheckNumerics<T extends Number> Checks a tensor for NaN and Inf values.
class	CompareAndBitpack Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
class	ComplexAbs<U extends Number> Computes the complex absolute value of a tensor.
class	Conj<T> Returns the complex conjugate of a complex number.
class	Cos<T> Computes cos of x element-wise.
class	Cosh<T> Computes hyperbolic cosine of x element-wise.
class	Cumprod<T> Compute the cumulative product of the tensor `x` along `axis`.
class	Cumsum<T> Compute the cumulative sum of the tensor `x` along `axis`.
class	Digamma<T extends Number> Computes Psi, the derivative of Lgamma (the log of the absolute value of
class	Div<T> Returns x / y element-wise.
class	DivNoNan<T> Returns 0 if the denominator is zero.
class	Equal Returns the truth value of (x == y) element-wise.
class	Erf<T extends Number> Computes the Gauss error function of `x` element-wise.
class	Erfc<T extends Number> Computes the complementary error function of `x` element-wise.
class	Exp<T> Computes exponential of x element-wise.
class	Expm1<T> Computes `exp(x) - 1` element-wise.
class	Fact Output a fact about factorials.
class	Floor<T extends Number> Returns element-wise largest integer not greater than x.
class	FloorDiv<T> Returns x // y element-wise.
class	FloorMod<T extends Number> Returns element-wise remainder of division.
class	Greater Returns the truth value of (x > y) element-wise.
class	GreaterEqual Returns the truth value of (x >= y) element-wise.
class	Igamma<T extends Number> Compute the lower regularized incomplete Gamma function `P(a, x)`.
class	Igammac<T extends Number> Compute the upper regularized incomplete Gamma function `Q(a, x)`.
class	IgammaGradA<T extends Number> Computes the gradient of `igamma(a, x)` wrt `a`.
class	Imag<U extends Number> Returns the imaginary part of a complex number.
class	InvertPermutation<T extends Number> Computes the inverse permutation of a tensor.
class	IsFinite Returns which elements of x are finite.
class	IsInf Returns which elements of x are Inf.
class	IsNan Returns which elements of x are NaN.
class	Less Returns the truth value of (x < y) element-wise.
class	LessEqual Returns the truth value of (x <= y) element-wise.
class	Lgamma<T extends Number> Computes the log of the absolute value of `Gamma(x)` element-wise.
class	Log<T> Computes natural logarithm of x element-wise.
class	Log1p<T> Computes natural logarithm of (1 + x) element-wise.
class	LogicalAnd Returns the truth value of x AND y element-wise.
class	LogicalNot Returns the truth value of NOT x element-wise.
class	LogicalOr Returns the truth value of x OR y element-wise.
class	Maximum<T extends Number> Returns the max of x and y (i.e.
class	Mean<T> Computes the mean of elements across dimensions of a tensor.
class	Minimum<T extends Number> Returns the min of x and y (i.e.
class	Mod<T extends Number> Returns element-wise remainder of division.
class	Mul<T> Returns x * y element-wise.
class	Neg<T> Computes numerical negative value element-wise.
class	NotEqual Returns the truth value of (x != y) element-wise.
class	Polygamma<T extends Number> Compute the polygamma function \\(\psi^{(n)}(x)\\).
class	PopulationCount Computes element-wise population count (a.k.a.
class	Pow<T> Computes the power of one value to another.
class	QuantizedAdd<V> Returns x + y element-wise, working on quantized buffers.
class	QuantizedMul<V> Returns x * y element-wise, working on quantized buffers.
class	Real<U extends Number> Returns the real part of a complex number.
class	RealDiv<T> Returns x / y element-wise for real types.
class	Reciprocal<T> Computes the reciprocal of x element-wise.
class	ReciprocalGrad<T> Computes the gradient for the inverse of `x` wrt its input.
class	Rint<T extends Number> Returns element-wise integer closest to x.
class	Round<T> Rounds the values of a tensor to the nearest integer, element-wise.
class	Rsqrt<T> Computes reciprocal of square root of x element-wise.
class	RsqrtGrad<T> Computes the gradient for the rsqrt of `x` wrt its input.
class	SegmentMax<T extends Number> Computes the maximum along segments of a tensor.
class	SegmentMean<T> Computes the mean along segments of a tensor.
class	SegmentMin<T extends Number> Computes the minimum along segments of a tensor.
class	SegmentProd<T> Computes the product along segments of a tensor.
class	SegmentSum<T> Computes the sum along segments of a tensor.
class	Sigmoid<T> Computes sigmoid of `x` element-wise.
class	SigmoidGrad<T> Computes the gradient of the sigmoid of `x` wrt its input.
class	Sign<T> Returns an element-wise indication of the sign of a number.
class	Sin<T> Computes sine of x element-wise.
class	Sinh<T> Computes hyperbolic sine of x element-wise.
class	Softplus<T extends Number> Computes softplus: `log(exp(features) + 1)`.
class	SoftplusGrad<T extends Number> Computes softplus gradients for a softplus operation.
class	Sqrt<T> Computes square root of x element-wise.
class	SqrtGrad<T> Computes the gradient for the sqrt of `x` wrt its input.
class	Square<T> Computes square of x element-wise.
class	SquaredDifference<T> Returns (x - y)(x - y) element-wise.
class	Sub<T> Returns x - y element-wise.
class	Tan<T> Computes tan of x element-wise.
class	Tanh<T> Computes hyperbolic tangent of `x` element-wise.
class	TanhGrad<T> Computes the gradient for the tanh of `x` wrt its input.
class	TruncateDiv<T> Returns x / y element-wise for integer types.
class	TruncateMod<T extends Number> Returns element-wise remainder of division.
class	UnsortedSegmentMax<T extends Number> Computes the maximum along segments of a tensor.
class	UnsortedSegmentMin<T extends Number> Computes the minimum along segments of a tensor.
class	UnsortedSegmentProd<T> Computes the product along segments of a tensor.
class	UnsortedSegmentSum<T> Computes the sum along segments of a tensor.
class	Xdivy<T> Returns 0 if x == 0, and x / y otherwise, elementwise.
class	Xlogy<T> Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
class	Zeta<T extends Number> Compute the Hurwitz zeta function \\(\zeta(x, q)\\).

Uses of Op in org.tensorflow.op.nn

Classes in org.tensorflow.op.nn that implement Op

Modifier and Type	Class and Description
class	AvgPool<T extends Number> Performs average pooling on the input.
class	AvgPool3d<T extends Number> Performs 3D average pooling on the input.
class	AvgPool3dGrad<T extends Number> Computes gradients of average pooling function.
class	AvgPoolGrad<T extends Number> Computes gradients of the average pooling function.
class	BatchNormWithGlobalNormalization<T> Batch normalization.
class	BatchNormWithGlobalNormalizationGrad<T> Gradients for batch normalization.
class	BiasAdd<T> Adds `bias` to `value`.
class	BiasAddGrad<T> The backward operation for "BiasAdd" on the "bias" tensor.
class	ComputeAccidentalHits Computes the ids of the positions in sampled_candidates that match true_labels.
class	Conv2d<T extends Number> Computes a 2-D convolution given 4-D `input` and `filter` tensors.
class	Conv2dBackpropFilter<T extends Number> Computes the gradients of convolution with respect to the filter.
class	Conv2dBackpropInput<T extends Number> Computes the gradients of convolution with respect to the input.
class	Conv3d<T extends Number> Computes a 3-D convolution given 5-D `input` and `filter` tensors.
class	Conv3dBackpropFilter<T extends Number> Computes the gradients of 3-D convolution with respect to the filter.
class	Conv3dBackpropInput<U extends Number> Computes the gradients of 3-D convolution with respect to the input.
class	CtcBeamSearchDecoder<T extends Number> Performs beam search decoding on the logits given in input.
class	CtcGreedyDecoder<T extends Number> Performs greedy decoding on the logits given in inputs.
class	CtcLoss<T extends Number> Calculates the CTC Loss (log probability) for each batch entry.
class	CudnnRnn<T extends Number> A RNN backed by cuDNN.
class	CudnnRnnBackprop<T extends Number> Backprop step of CudnnRNN.
class	CudnnRnnCanonicalToParams<T extends Number> Converts CudnnRNN params from canonical form to usable form.
class	CudnnRnnParamsSize<U extends Number> Computes size of weights that can be used by a Cudnn RNN model.
class	CudnnRnnParamsToCanonical<T extends Number> Retrieves CudnnRNN params in canonical form.
class	DataFormatDimMap<T extends Number> Returns the dimension index in the destination data format given the one in
class	DataFormatVecPermute<T extends Number> Returns the permuted vector/tensor in the destination data format given the
class	DepthToSpace<T> DepthToSpace for tensors of type T.
class	DepthwiseConv2dNative<T extends Number> Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
class	DepthwiseConv2dNativeBackpropFilter<T extends Number> Computes the gradients of depthwise convolution with respect to the filter.
class	DepthwiseConv2dNativeBackpropInput<T extends Number> Computes the gradients of depthwise convolution with respect to the input.
class	Dilation2d<T extends Number> Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
class	Dilation2dBackpropFilter<T extends Number> Computes the gradient of morphological 2-D dilation with respect to the filter.
class	Dilation2dBackpropInput<T extends Number> Computes the gradient of morphological 2-D dilation with respect to the input.
class	Elu<T extends Number> Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
class	EluGrad<T extends Number> Computes gradients for the exponential linear (Elu) operation.
class	FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
class	FractionalAvgPool<T extends Number> Performs fractional average pooling on the input.
class	FractionalAvgPoolGrad<T extends Number> Computes gradient of the FractionalAvgPool function.
class	FractionalMaxPool<T extends Number> Performs fractional max pooling on the input.
class	FractionalMaxPoolGrad<T extends Number> Computes gradient of the FractionalMaxPool function.
class	FusedBatchNorm<T extends Number,U extends Number> Batch normalization.
class	FusedBatchNormGrad<T extends Number,U extends Number> Gradient for batch normalization.
class	FusedPadConv2d<T extends Number> Performs a padding as a preprocess during a convolution.
class	FusedResizeAndPadConv2d<T extends Number> Performs a resize and padding as a preprocess during a convolution.
class	InTopK Says whether the targets are in the top `K` predictions.
class	InvGrad<T> Computes the gradient for the inverse of `x` wrt its input.
class	L2Loss<T extends Number> L2 Loss.
class	LeakyRelu<T extends Number> Computes rectified linear: `max(features, features * alpha)`.
class	LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
class	LocalResponseNormalization<T extends Number> Local Response Normalization.
class	LocalResponseNormalizationGrad<T extends Number> Gradients for Local Response Normalization.
class	LogSoftmax<T extends Number> Computes log softmax activations.
class	MaxPool<T> Performs max pooling on the input.
class	MaxPool3d<T extends Number> Performs 3D max pooling on the input.
class	MaxPool3dGrad<U extends Number> Computes gradients of max pooling function.
class	MaxPool3dGradGrad<T extends Number> Computes second-order gradients of the maxpooling function.
class	MaxPoolGrad<T extends Number> Computes gradients of the maxpooling function.
class	MaxPoolGradGrad<T extends Number> Computes second-order gradients of the maxpooling function.
class	MaxPoolGradGradWithArgmax<T extends Number> Computes second-order gradients of the maxpooling function.
class	MaxPoolGradWithArgmax<T extends Number> Computes gradients of the maxpooling function.
class	MaxPoolWithArgmax<T extends Number,U extends Number> Performs max pooling on the input and outputs both max values and indices.
class	NthElement<T extends Number> Finds values of the `n`-th order statistic for the last dimension.
class	QuantizedAvgPool<T> Produces the average pool of the input tensor for quantized types.
class	QuantizedBatchNormWithGlobalNormalization<U> Quantized Batch normalization.
class	QuantizedBiasAdd<V> Adds Tensor 'bias' to Tensor 'input' for Quantized types.
class	QuantizedConv2d<V> Computes a 2D convolution given quantized 4D input and filter tensors.
class	QuantizedInstanceNorm<T> Quantized Instance normalization.
class	QuantizedMaxPool<T> Produces the max pool of the input tensor for quantized types.
class	QuantizedRelu<U> Computes Quantized Rectified Linear: `max(features, 0)`
class	QuantizedRelu6<U> Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
class	QuantizedReluX<U> Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
class	Relu<T> Computes rectified linear: `max(features, 0)`.
class	Relu6<T extends Number> Computes rectified linear 6: `min(max(features, 0), 6)`.
class	Relu6Grad<T extends Number> Computes rectified linear 6 gradients for a Relu6 operation.
class	ReluGrad<T extends Number> Computes rectified linear gradients for a Relu operation.
class	Selu<T extends Number> Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
class	SeluGrad<T extends Number> Computes gradients for the scaled exponential linear (Selu) operation.
class	Softmax<T extends Number> Computes softmax activations.
class	SoftmaxCrossEntropyWithLogits<T extends Number> Computes softmax cross entropy cost and gradients to backpropagate.
class	Softsign<T extends Number> Computes softsign: `features / (abs(features) + 1)`.
class	SoftsignGrad<T extends Number> Computes softsign gradients for a softsign operation.
class	SpaceToBatch<T> SpaceToBatch for 4-D tensors of type T.
class	SpaceToDepth<T> SpaceToDepth for tensors of type T.
class	SparseSoftmaxCrossEntropyWithLogits<T extends Number> Computes softmax cross entropy cost and gradients to backpropagate.
class	TopK<T extends Number> Finds values and indices of the `k` largest elements for the last dimension.

Uses of Op in org.tensorflow.op.quantization

Classes in org.tensorflow.op.quantization that implement Op

Modifier and Type	Class and Description
class	Dequantize Dequantize the 'input' tensor into a float Tensor.
class	FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
class	FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation.
class	FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
class	FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation.
class	FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
class	FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
class	Quantize<T> Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
class	QuantizeAndDequantize<T extends Number> Quantizes then dequantizes a tensor.
class	QuantizeDownAndShrinkRange<U> Convert the quantized 'input' tensor into a lower-precision 'output', using the
class	RequantizationRange Computes a range that covers the actual values present in a quantized tensor.
class	Requantize<U> Converts the quantized `input` tensor into a lower-precision `output`.

Uses of Op in org.tensorflow.op.random

Classes in org.tensorflow.op.random that implement Op

Modifier and Type	Class and Description
class	AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
class	LogUniformCandidateSampler Generates labels for candidate sampling with a log-uniform distribution.
class	Multinomial<U extends Number> Draws samples from a multinomial distribution.
class	ParameterizedTruncatedNormal<U extends Number> Outputs random values from a normal distribution.
class	RandomGamma<U extends Number> Outputs random values from the Gamma distribution(s) described by alpha.
class	RandomGammaGrad<T extends Number> Computes the derivative of a Gamma random sample w.r.t.
class	RandomPoisson<V extends Number> Outputs random values from the Poisson distribution(s) described by rate.
class	RandomShuffle<T> Randomly shuffles a tensor along its first dimension.
class	RandomStandardNormal<U extends Number> Outputs random values from a normal distribution.
class	RandomUniform<U extends Number> Outputs random values from a uniform distribution.
class	RandomUniformInt<U extends Number> Outputs random integers from a uniform distribution.
class	RecordInput Emits randomized records.
class	StatelessMultinomial<V extends Number> Draws samples from a multinomial distribution.
class	StatelessRandomNormal<V extends Number> Outputs deterministic pseudorandom values from a normal distribution.
class	StatelessRandomUniform<V extends Number> Outputs deterministic pseudorandom random values from a uniform distribution.
class	StatelessRandomUniformInt<V extends Number> Outputs deterministic pseudorandom random integers from a uniform distribution.
class	StatelessTruncatedNormal<V extends Number> Outputs deterministic pseudorandom values from a truncated normal distribution.
class	TruncatedNormal<U extends Number> Outputs random values from a truncated normal distribution.
class	UniformCandidateSampler Generates labels for candidate sampling with a uniform distribution.

Uses of Op in org.tensorflow.op.signal

Classes in org.tensorflow.op.signal that implement Op

Modifier and Type	Class and Description
class	BatchFft
class	BatchFft2d
class	BatchFft3d
class	BatchIfft
class	BatchIfft2d
class	BatchIfft3d
class	Fft<T> Fast Fourier transform.
class	Fft2d<T> 2D fast Fourier transform.
class	Fft3d<T> 3D fast Fourier transform.
class	Ifft<T> Inverse fast Fourier transform.
class	Ifft2d<T> Inverse 2D fast Fourier transform.
class	Ifft3d<T> Inverse 3D fast Fourier transform.
class	Irfft Inverse real-valued fast Fourier transform.
class	Irfft2d Inverse 2D real-valued fast Fourier transform.
class	Irfft3d Inverse 3D real-valued fast Fourier transform.
class	Rfft Real-valued fast Fourier transform.
class	Rfft2d 2D real-valued fast Fourier transform.
class	Rfft3d 3D real-valued fast Fourier transform.

Uses of Op in org.tensorflow.op.sparse

Classes in org.tensorflow.op.sparse that implement Op

Modifier and Type	Class and Description
class	AddManySparseToTensorsMap Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
class	AddSparseToTensorsMap Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
class	DenseToDenseSetOperation<T> Applies set operation along last dimension of 2 `Tensor` inputs.
class	DenseToSparseSetOperation<T> Applies set operation along last dimension of `Tensor` and `SparseTensor`.
class	DeserializeSparse<U> Deserialize `SparseTensor` objects.
class	SparseAccumulatorApplyGradient Applies a sparse gradient to a given accumulator.
class	SparseAccumulatorTakeGradient<T> Extracts the average sparse gradient in a SparseConditionalAccumulator.
class	SparseAdd<T> Adds two `SparseTensor` objects to produce another `SparseTensor`.
class	SparseAddGrad<T> The gradient operator for the SparseAdd op.
class	SparseConcat<T> Concatenates a list of `SparseTensor` along the specified dimension.
class	SparseConditionalAccumulator A conditional accumulator for aggregating sparse gradients.
class	SparseCross<T> Generates sparse cross from a list of sparse and dense tensors.
class	SparseDenseCwiseAdd<T> Adds up a SparseTensor and a dense Tensor, using these special rules:
class	SparseDenseCwiseDiv<T> Component-wise divides a SparseTensor by a dense Tensor.
class	SparseDenseCwiseMul<T> Component-wise multiplies a SparseTensor by a dense Tensor.
class	SparseFillEmptyRows<T> Fills empty rows in the input 2-D `SparseTensor` with a default value.
class	SparseFillEmptyRowsGrad<T> The gradient of SparseFillEmptyRows.
class	SparseMatMul Multiply matrix "a" by matrix "b".
class	SparseReduceMax<T extends Number> Computes the max of elements across dimensions of a SparseTensor.
class	SparseReduceMaxSparse<T extends Number> Computes the max of elements across dimensions of a SparseTensor.
class	SparseReduceSum<T> Computes the sum of elements across dimensions of a SparseTensor.
class	SparseReduceSumSparse<T> Computes the sum of elements across dimensions of a SparseTensor.
class	SparseReorder<T> Reorders a SparseTensor into the canonical, row-major ordering.
class	SparseReshape Reshapes a SparseTensor to represent values in a new dense shape.
class	SparseSegmentMean<T extends Number> Computes the mean along sparse segments of a tensor.
class	SparseSegmentMeanGrad<T extends Number> Computes gradients for SparseSegmentMean.
class	SparseSegmentMeanWithNumSegments<T extends Number> Computes the mean along sparse segments of a tensor.
class	SparseSegmentSqrtN<T extends Number> Computes the sum along sparse segments of a tensor divided by the sqrt of N.
class	SparseSegmentSqrtNGrad<T extends Number> Computes gradients for SparseSegmentSqrtN.
class	SparseSegmentSqrtNWithNumSegments<T extends Number> Computes the sum along sparse segments of a tensor divided by the sqrt of N.
class	SparseSegmentSum<T extends Number> Computes the sum along sparse segments of a tensor.
class	SparseSegmentSumWithNumSegments<T extends Number> Computes the sum along sparse segments of a tensor.
class	SparseSlice<T> Slice a `SparseTensor` based on the `start` and `size`.
class	SparseSliceGrad<T> The gradient operator for the SparseSlice op.
class	SparseSoftmax<T extends Number> Applies softmax to a batched N-D `SparseTensor`.
class	SparseSparseMaximum<T extends Number> Returns the element-wise max of two SparseTensors.
class	SparseSparseMinimum<T> Returns the element-wise min of two SparseTensors.
class	SparseSplit<T> Split a `SparseTensor` into `num_split` tensors along one dimension.
class	SparseTensorDenseAdd<U> Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
class	SparseTensorDenseMatMul<U> Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
class	SparseToDense<U> Converts a sparse representation into a dense tensor.
class	SparseToSparseSetOperation<T> Applies set operation along last dimension of 2 `SparseTensor` inputs.
class	TakeManySparseFromTensorsMap<T> Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.

Uses of Op in org.tensorflow.op.strings

Classes in org.tensorflow.op.strings that implement Op

Modifier and Type	Class and Description
class	Join Joins the strings in the given list of string tensors into one tensor;
class	ReduceJoin Joins a string Tensor across the given dimensions.
class	RegexFullMatch Check if the input matches the regex pattern.
class	RegexReplace Replaces matches of the `pattern` regular expression in `input` with the replacement string provided in `rewrite`.
class	StaticRegexFullMatch Check if the input matches the regex pattern.
class	StaticRegexReplace Replaces the match of pattern in input with rewrite.
class	StringFormat Formats a string template using a list of tensors.
class	StringLength String lengths of `input`.
class	StringSplit Split elements of `source` based on `sep` into a `SparseTensor`.
class	Strip Strip leading and trailing whitespaces from the Tensor.
class	Substr Return substrings from `Tensor` of strings.
class	ToHashBucket Converts each string in the input Tensor to its hash mod by a number of buckets.
class	ToHashBucketFast Converts each string in the input Tensor to its hash mod by a number of buckets.
class	ToHashBucketStrong Converts each string in the input Tensor to its hash mod by a number of buckets.
class	ToNumber<T extends Number> Converts each string in the input Tensor to the specified numeric type.
class	UnicodeDecodeWithOffsets<T extends Number> Decodes each string in `input` into a sequence of Unicode code points.
class	UnicodeScript Determine the script codes of a given tensor of Unicode integer code points.
class	UnicodeTranscode Transcode the input text from a source encoding to a destination encoding.

Uses of Op in org.tensorflow.op.summary

Classes in org.tensorflow.op.summary that implement Op

Modifier and Type	Class and Description
class	AudioSummary Outputs a `Summary` protocol buffer with audio.
class	CloseSummaryWriter
class	CreateSummaryDbWriter
class	CreateSummaryFileWriter
class	FlushSummaryWriter
class	HistogramSummary Outputs a `Summary` protocol buffer with a histogram.
class	ImageSummary Outputs a `Summary` protocol buffer with images.
class	ImportEvent
class	MergeSummary Merges summaries.
class	ScalarSummary Outputs a `Summary` protocol buffer with scalar values.
class	StatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.
class	SummaryWriter
class	TensorSummary Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
class	WriteAudioSummary
class	WriteGraphSummary
class	WriteHistogramSummary
class	WriteImageSummary
class	WriteScalarSummary
class	WriteSummary

Uses of Op in org.tensorflow.op.train

Classes in org.tensorflow.op.train that implement Op

Modifier and Type	Class and Description
class	AccumulatorApplyGradient Applies a gradient to a given accumulator.
class	AccumulatorNumAccumulated Returns the number of gradients aggregated in the given accumulators.
class	AccumulatorSetGlobalStep Updates the accumulator with a new value for global_step.
class	AccumulatorTakeGradient<T> Extracts the average gradient in the given ConditionalAccumulator.
class	ApplyAdadelta<T> Update '*var' according to the adadelta scheme.
class	ApplyAdagrad<T> Update '*var' according to the adagrad scheme.
class	ApplyAdagradDa<T> Update '*var' according to the proximal adagrad scheme.
class	ApplyAdam<T> Update '*var' according to the Adam algorithm.
class	ApplyAdaMax<T> Update '*var' according to the AdaMax algorithm.
class	ApplyAddSign<T> Update '*var' according to the AddSign update.
class	ApplyCenteredRmsProp<T> Update '*var' according to the centered RMSProp algorithm.
class	ApplyFtrl<T> Update '*var' according to the Ftrl-proximal scheme.
class	ApplyGradientDescent<T> Update '*var' by subtracting 'alpha' * 'delta' from it.
class	ApplyMomentum<T> Update '*var' according to the momentum scheme.
class	ApplyPowerSign<T> Update '*var' according to the AddSign update.
class	ApplyProximalAdagrad<T> Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
class	ApplyProximalGradientDescent<T> Update '*var' as FOBOS algorithm with fixed learning rate.
class	ApplyRmsProp<T> Update '*var' according to the RMSProp algorithm.
class	ConditionalAccumulator A conditional accumulator for aggregating gradients.
class	GenerateVocabRemapping Given a path to new and old vocabulary files, returns a remapping Tensor of
class	MergeV2Checkpoints V2 format specific: merges the metadata files of sharded checkpoints.
class	NegTrain Training via negative sampling.
class	PreventGradient<T> An identity op that triggers an error if a gradient is requested.
class	ResourceApplyAdadelta Update '*var' according to the adadelta scheme.
class	ResourceApplyAdagrad Update '*var' according to the adagrad scheme.
class	ResourceApplyAdagradDa Update '*var' according to the proximal adagrad scheme.
class	ResourceApplyAdam Update '*var' according to the Adam algorithm.
class	ResourceApplyAdaMax Update '*var' according to the AdaMax algorithm.
class	ResourceApplyAddSign Update '*var' according to the AddSign update.
class	ResourceApplyCenteredRmsProp Update '*var' according to the centered RMSProp algorithm.
class	ResourceApplyFtrl Update '*var' according to the Ftrl-proximal scheme.
class	ResourceApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.
class	ResourceApplyMomentum Update '*var' according to the momentum scheme.
class	ResourceApplyPowerSign Update '*var' according to the AddSign update.
class	ResourceApplyProximalAdagrad Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
class	ResourceApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.
class	ResourceApplyRmsProp Update '*var' according to the RMSProp algorithm.
class	ResourceSparseApplyAdadelta var: Should be from a Variable().
class	ResourceSparseApplyAdagrad Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
class	ResourceSparseApplyAdagradDa Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
class	ResourceSparseApplyCenteredRmsProp Update '*var' according to the centered RMSProp algorithm.
class	ResourceSparseApplyFtrl Update relevant entries in '*var' according to the Ftrl-proximal scheme.
class	ResourceSparseApplyMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
class	ResourceSparseApplyProximalAdagrad Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
class	ResourceSparseApplyProximalGradientDescent Sparse update '*var' as FOBOS algorithm with fixed learning rate.
class	ResourceSparseApplyRmsProp Update '*var' according to the RMSProp algorithm.
class	Restore Restores tensors from a V2 checkpoint.
class	RestoreSlice<T> Restores a tensor from checkpoint files.
class	Save Saves tensors in V2 checkpoint format.
class	SaveSlices Saves input tensors slices to disk.
class	SdcaFprint Computes fingerprints of the input strings.
class	SdcaOptimizer Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
class	SdcaShrinkL1 Applies L1 regularization shrink step on the parameters.
class	SparseApplyAdadelta<T> var: Should be from a Variable().
class	SparseApplyAdagrad<T> Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
class	SparseApplyAdagradDa<T> Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
class	SparseApplyCenteredRmsProp<T> Update '*var' according to the centered RMSProp algorithm.
class	SparseApplyFtrl<T> Update relevant entries in '*var' according to the Ftrl-proximal scheme.
class	SparseApplyMomentum<T> Update relevant entries in '*var' and '*accum' according to the momentum scheme.
class	SparseApplyProximalAdagrad<T> Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
class	SparseApplyProximalGradientDescent<T> Sparse update '*var' as FOBOS algorithm with fixed learning rate.
class	SparseApplyRmsProp<T> Update '*var' according to the RMSProp algorithm.
class	TileGrad<T> Returns the gradient of `Tile`.