All Classes and Interfaces
Class
Description
Raise a exception to abort the process when called.
Optional attributes for
AbortComputes the absolute value of a tensor.
Abstract base class for Activations
Base class for Constraints.
Helper base class for custom gradient adapters INTERNAL USE ONLY
Base class for Regularizers
Returns the element-wise sum of a list of tensors.
Applies a gradient to a given accumulator.
Returns the number of gradients aggregated in the given accumulators.
Updates the accumulator with a new value for global_step.
Extracts the average gradient in the given ConditionalAccumulator.
Metric that calculates how often predictions equals labels.
Computes acos of x element-wise.
Computes inverse hyperbolic cosine of x element-wise.
Interface for Activations
The Enumerations for creating Activations based an activation name, with either an empty
constructor or a constructor that takes a Map object that contains the Activation's state.
Optimizer that implements the Adadelta algorithm.
Optimizer that implements the Adagrad algorithm.
Optimizer that implements the Adagrad Dual-Averaging algorithm.
Optimizer that implements the Adam algorithm.
Optimizer that implements the Adamax algorithm.
Returns x + y element-wise.
Add an
N-minibatch SparseTensor to a SparseTensorsMap, return N handles.Optional attributes for
AddManySparseToTensorsMapAdd all input tensors element wise.
Add a
SparseTensor to a SparseTensorsMap return its handle.Optional attributes for
AddSparseToTensorsMapAdjust the contrast of one or more images.
Adjust the hue of one or more images.
Adjust the saturation of one or more images.
Computes the "logical and" of elements across dimensions of a tensor.
Optional attributes for
AllGenerates labels for candidate sampling with a learned unigram distribution.
Optional attributes for
AllCandidateSamplerProtobuf type
tensorflow.AllocationDescriptionProtobuf type
tensorflow.AllocationDescription
An allocation/de-allocation operation performed by the allocator.
An allocation/de-allocation operation performed by the allocator.
Protobuf type
tensorflow.AllocatorMemoryUsedProtobuf type
tensorflow.AllocatorMemoryUsedAn Op to exchange data across TPU replicas.
Returns the argument of a complex number.
Creates a uninitialized anonymous hash table.
A container for an iterator resource.
The AnonymousMemoryCache operation
A container for a multi device iterator resource.
Creates an empty anonymous mutable hash table that uses tensors as the backing store.
Optional attributes for
AnonymousMutableDenseHashTableCreates an empty anonymous mutable hash table.
Creates an empty anonymous mutable hash table of vector values.
Optional attributes for
AnonymousMutableHashTableOfTensorsThe AnonymousRandomSeedGenerator operation
The AnonymousSeedGenerator operation
Computes the "logical or" of elements across dimensions of a tensor.
Optional attributes for
Any
Used to specify and override the default API & behavior in the
generated code for client languages, from what you would get from
the OpDef alone.
Protobuf type
tensorflow.ApiDef.ArgProtobuf type
tensorflow.ApiDef.Arg
Description of the graph-construction-time configuration of this
Op.
Description of the graph-construction-time configuration of this
Op.
Used to specify and override the default API & behavior in the
generated code for client languages, from what you would get from
the OpDef alone.
If you specify any endpoint, this will replace all of the
inherited endpoints.
If you specify any endpoint, this will replace all of the
inherited endpoints.
Protobuf enum
tensorflow.ApiDef.VisibilityProtobuf type
tensorflow.ApiDefsProtobuf type
tensorflow.ApiDefsUpdate '*var' according to the adadelta scheme.
accum = rho() * accum + (1 - rho()) * grad.square();
update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
update_accum = rho() * update_accum + (1 - rho()) * update.square();
var -= update;
Optional attributes for
ApplyAdadeltaUpdate '*var' according to the adagrad scheme.
accum += grad * grad
var -= lr * grad * (1 / sqrt(accum))
Optional attributes for
ApplyAdagradUpdate '*var' according to the proximal adagrad scheme.
Optional attributes for
ApplyAdagradDaUpdate '*var' according to the adagrad scheme.
accum += grad * grad
var -= lr * grad * (1 / sqrt(accum))
Optional attributes for
ApplyAdagradV2Update '*var' according to the Adam algorithm.
Optional attributes for
ApplyAdamUpdate '*var' according to the AdaMax algorithm.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- max(beta2 * v_{t-1}, abs(g))
variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
Optional attributes for
ApplyAdaMaxUpdate '*var' according to the AddSign update.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
update <- (alpha + sign_decay * sign(g) *sign(m)) * g
variable <- variable - lr_t * update
Optional attributes for
ApplyAddSignUpdate '*var' according to the centered RMSProp algorithm.
Optional attributes for
ApplyCenteredRmsPropUpdate '*var' according to the Ftrl-proximal scheme.
grad_with_shrinkage = grad + 2 * l2_shrinkage * var
accum_new = accum + grad * grad
linear += grad_with_shrinkage -
(accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
accum = accum_new
Optional attributes for
ApplyFtrlUpdate '*var' by subtracting 'alpha' * 'delta' from it.
Optional attributes for
ApplyGradientDescentUpdate '*var' according to the momentum scheme.
Optional attributes for
ApplyMomentumUpdate '*var' according to the AddSign update.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
variable <- variable - lr_t * update
Optional attributes for
ApplyPowerSignUpdate '*var' and '*accum' according to FOBOS with Adagrad learning rate.
accum += grad * grad
prox_v = var - lr * grad * (1 / sqrt(accum))
var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0}
Optional attributes for
ApplyProximalAdagradUpdate '*var' as FOBOS algorithm with fixed learning rate.
prox_v = var - alpha * delta
var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0}
Optional attributes for
ApplyProximalGradientDescentUpdate '*var' according to the RMSProp algorithm.
Optional attributes for
ApplyRmsPropReturns the truth value of abs(x-y) < tolerance element-wise.
Optional attributes for
ApproximateEqualReturns min/max k values and their indices of the input operand in an approximate manner.
Optional attributes for
ApproxTopKReturns the index with the largest value across dimensions of a tensor.
Returns the index with the smallest value across dimensions of a tensor.
Computes the trignometric inverse sine of x element-wise.
Computes inverse hyperbolic sine of x element-wise.
The AssertCardinalityDataset operation
A transformation that asserts which transformations happen next.
The ExperimentalAssertNextDataset operation
A transformation that asserts which transformations happened previously.
Asserts that the given condition is true.
Optional attributes for
AssertThat
An asset file def for a single file or a set of sharded files with the same
name.
An asset file def for a single file or a set of sharded files with the same
name.
Update 'ref' by assigning 'value' to it.
Optional attributes for
AssignUpdate 'ref' by adding 'value' to it.
Optional attributes for
AssignAddAdds a value to the current value of a variable.
Update 'ref' by subtracting 'value' from it.
Optional attributes for
AssignSubSubtracts a value from the current value of a variable.
Concats input tensor across all dimensions.
Optional attributes for
AssignVariableConcatNDAssigns a new value to a variable.
Optional attributes for
AssignVariableOpConverts each entry in the given tensor to strings.
Optional attributes for
AsStringComputes the trignometric inverse tangent of x element-wise.
Computes arctangent of
y/x element-wise, respecting signs of the arguments.Computes inverse hyperbolic tangent of x element-wise.
Metadata of an op's attribute.
Protocol buffer representing the value for an attr used to configure an Op.
Protocol buffer representing the value for an attr used to configure an Op.
LINT.IfChange
LINT.IfChange
Metric that computes the approximate AUC (Area under the curve) via a Riemann sum.
Specifies the type of the curve to be computed,
AUCCurve.ROC for a Receiver Operator
Characteristic curve [default] or AUCCurve.PR for a Precision-Recall-curve.Specifies the Riemann summation method used.
An API for building
audio operations as OpsProduces a visualization of audio data over time.
Optional attributes for
AudioSpectrogramOutputs a
Summary protocol buffer with audio.Optional attributes for
AudioSummaryProtobuf type
tensorflow.AutoParallelOptionsProtobuf type
tensorflow.AutoParallelOptionsCreates a dataset that shards the input dataset.
Creates a dataset that shards the input dataset.
Optional attributes for
AutoShardDatasetOptional attributes for
AutoShardDataset
Matches DeviceAttributes
Matches DeviceAttributes
Performs average pooling on the input.
Optional attributes for
AvgPoolPerforms 3D average pooling on the input.
Optional attributes for
AvgPool3dComputes gradients of average pooling function.
Optional attributes for
AvgPool3dGradComputes gradients of the average pooling function.
Optional attributes for
AvgPoolGradAxes Operations
The BandedTriangularSolve operation
Optional attributes for
BandedTriangularSolveCopy a tensor setting everything outside a central band in each innermost matrix to zero.
Defines a barrier that persists across different graph executions.
Optional attributes for
BarrierCloses the given barrier.
Optional attributes for
BarrierCloseComputes the number of incomplete elements in the given barrier.
For each key, assigns the respective value to the specified component.
Computes the number of complete elements in the given barrier.
Takes the given number of completed elements from a barrier.
Optional attributes for
BarrierTakeManyAbstract base class for all Initializers
Base class for Metrics
Batches all input tensors nondeterministically.
Optional attributes for
BatchThe BatchCholesky operation
The BatchCholeskyGrad operation
Creates a dataset that batches
batch_size elements from input_dataset.Optional attributes for
BatchDatasetThe BatchFFT operation
The BatchFFT2D operation
The BatchFFT3D operation
Batches all the inputs tensors to the computation done by the function.
Optional attributes for
BatchFunctionThe BatchIFFT operation
The BatchIFFT2D operation
The BatchIFFT3D operation
Multiplies slices of two tensors in batches.
Optional attributes for
BatchMatMulThe BatchMatrixBandPart operation
The BatchMatrixDeterminant operation
The BatchMatrixDiag operation
The BatchMatrixDiagPart operation
The BatchMatrixInverse operation
DEPRECATED: This operation is deprecated and will be removed in a future version.
Optional attributes for
BatchMatrixInverseThe BatchMatrixSetDiag operation
The BatchMatrixSolve operation
Optional attributes for
BatchMatrixSolveThe BatchMatrixSolveLs operation
Optional attributes for
BatchMatrixSolveLsThe BatchMatrixTriangularSolve operation
Optional attributes for
BatchMatrixTriangularSolveBatch normalization.
Gradients for batch normalization.
The BatchSelfAdjointEigV2 operation
Optional attributes for
BatchSelfAdjointEigThe BatchSvd operation
Optional attributes for
BatchSvdBatchToSpace for 4-D tensors of type T.
BatchToSpace for N-D tensors of type T.
Protobuf type
tensorflow.BenchmarkEntriesProtobuf type
tensorflow.BenchmarkEntries
Each unit test or benchmark in a test or benchmark run provides
some set of information.
Each unit test or benchmark in a test or benchmark run provides
some set of information.
The BesselI0 operation
The BesselI0e operation
The BesselI1 operation
The BesselI1e operation
The BesselJ0 operation
The BesselJ1 operation
The BesselK0 operation
The BesselK0e operation
The BesselK1 operation
The BesselK1e operation
The BesselY0 operation
The BesselY1 operation
Compute the regularized incomplete beta integral \(I_x(a, b)\).
Protobuf type
tensorflow.BinSummaryProtobuf type
tensorflow.BinSummary
Some of the data from AllocatorStats
Some of the data from AllocatorStats
Protobuf type
tensorflow.MemChunkProtobuf type
tensorflow.MemChunkProtobuf type
tensorflow.MemoryDumpProtobuf type
tensorflow.MemoryDumpProtobuf type
tensorflow.SnapShotProtobuf type
tensorflow.SnapShotAdds
bias to value.Optional attributes for
BiasAddThe backward operation for "BiasAdd" on the "bias" tensor.
Optional attributes for
BiasAddGradMetric that calculates how often predictions matches binary labels.
Computes the cross-entropy loss between true labels and predicted labels.
A Metric that computes the binary cross-entropy loss between true labels and predicted labels.
Counts the number of occurrences of each value in an integer array.
Bitcasts a tensor from one type to another without copying data.
Elementwise computes the bitwise AND of
x and y.An API for building
bitwise operations as OpsElementwise computes the bitwise OR of
x and y.Elementwise computes the bitwise XOR of
x and y.Computes the LSTM cell forward propagation for all the time steps.
Optional attributes for
BlockLSTMComputes the LSTM cell backward propagation for the entire time sequence.
Optional attributes for
BooleanMaskOptional attributes for
BooleanMaskUpdateReturn the shape of s0 op s1 with broadcast.
Return the reduction indices for computing gradients of s0 op s1 with broadcast.
Broadcast an array for a compatible shape.
Bucketizes 'input' based on 'boundaries'.
Protobuf type
tensorflow.BuildConfigurationProtobuf type
tensorflow.BuildConfiguration
Describes the metadata related to a checkpointed tensor.
Describes the metadata related to a checkpointed tensor.
Special header that is associated with a bundle.
Special header that is associated with a bundle.
An enum indicating the endianness of the platform that produced this
bundle.
LINT.IfChange
Containers to hold repeated fundamental values.
LINT.IfChange
Containers to hold repeated fundamental values.
Records the bytes size of each element of
input_dataset in a StatsAggregator.Records the bytes size of each element of
input_dataset in a StatsAggregator.The CacheDatasetV2 operation
Optional attributes for
CacheDataset
Defines a subgraph in another `GraphDef` as a set of feed points and nodes
to be fetched or executed.
Defines a subgraph in another `GraphDef` as a set of feed points and nodes
to be fetched or executed.
An n-way switch statement which calls a single branch function.
Optional attributes for
CaseCast x of type SrcT to y of DstT.
Optional attributes for
CastA helper class for casting an Operand
Metric that calculates how often predictions matches one-hot labels.
Computes the crossentropy loss between the labels and predictions.
A Metric that computes the categorical cross-entropy loss between true labels and predicted
labels.
Computes the categorical hinge loss between labels and predictions.
A Metric that computes the categorical hinge loss metric between labels and predictions.
Returns element-wise smallest integer not less than x.
Checks a tensor for NaN, -Inf and +Inf values.
Checks whether a tensor is located in host memory pinned for GPU.
Computes the Cholesky decomposition of one or more square matrices.
Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
The ChooseFastestBranchDataset operation
The ChooseFastestDataset operation
The ExperimentalChooseFastestDataset operation
Clips tensor values to a specified min and max.
The CloseSummaryWriter operation
Defines a TensorFlow cluster as a set of jobs.
Defines a TensorFlow cluster as a set of jobs.
Defines the device filters for jobs in a cluster.
Defines the device filters for jobs in a cluster.
An API for building
cluster operations as Ops
The canonical error codes for TensorFlow APIs.
Code location information: A stack trace with host-name information.
Code location information: A stack trace with host-name information.
An op that merges the string-encoded memory config protos from all hosts.
CollectionDef should cover most collections.
AnyList is used for collecting Any protos.
AnyList is used for collecting Any protos.
CollectionDef should cover most collections.
BytesList is used for collecting strings and serialized protobufs.
BytesList is used for collecting strings and serialized protobufs.
FloatList is used for collecting float values.
FloatList is used for collecting float values.
Int64List is used for collecting int, int64 and long values.
Int64List is used for collecting int, int64 and long values.
NodeList is used for collecting nodes in graph.
NodeList is used for collecting nodes in graph.
Mutually exchanges multiple tensors of identical type and shape.
Optional attributes for
CollectiveAllToAllAssign group keys based on group assignment.
Receives a tensor value broadcast from another device.
Optional attributes for
CollectiveBcastRecvBroadcasts a tensor value to one or more other devices.
Optional attributes for
CollectiveBcastSendMutually accumulates multiple tensors of identical type and shape.
Optional attributes for
CollectiveGatherInitializes a group for collective operations.
Optional attributes for
CollectiveInitializeCommunicatorAn API for building
collective operations as OpsAn Op to permute tensors across replicated TPU instances.
Mutually reduces multiple tensors of identical type and shape.
Optional attributes for
CollectiveReduceMutually reduces multiple tensors of identical type and shape and scatters the result.
Optional attributes for
CollectiveReduceScatterGreedily selects a subset of bounding boxes in descending order of score,
This operation performs non_max_suppression on the inputs per batch, across
all classes.
Optional attributes for
CombinedNonMaxSuppressionProtobuf type
tensorflow.CommitIdProtobuf type
tensorflow.CommitIdReturns the result of a TPU compilation.
Compiles a computations for execution on one or more TPU devices.
Asserts that compilation succeeded.
Converts two real numbers to a complex number.
Computes the complex absolute value of a tensor.
Metadata for CompositeTensorVariant, used when serializing as Variant.
Metadata for CompositeTensorVariant, used when serializing as Variant.
Encodes an
ExtensionType value into a variant scalar Tensor.Decodes a
variant scalar Tensor into an ExtensionType value.Compresses a dataset element.
Computes the ids of the positions in sampled_candidates that match true_labels.
Optional attributes for
ComputeAccidentalHitsComputes the static batch size of a dataset sans partial batches.
An op computes the size of the deduplication data from embedding core and returns the updated config.
An op computes tuple mask of deduplication data from embedding core.
Concatenates tensors along one dimension.
Creates a dataset that concatenates
input_dataset with another_dataset.Optional attributes for
ConcatenateDatasetConcats input tensor across all dimensions.
Optional attributes for
ConcatNDComputes offsets of concat inputs within its output.
A graph that can be invoked as a single function, with an input and output signature.
Protocol buffer representing a CondContext object.
Protocol buffer representing a CondContext object.
A conditional accumulator for aggregating gradients.
Optional attributes for
ConditionalAccumulator
Session configuration parameters.
Session configuration parameters.
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
An enum that describes the state of the MLIR bridge rollout.
An op that sets up the centralized structures for a distributed TPU system.
Optional attributes for
ConfigureAndInitializeGlobalTPUSets up the centralized structures for a distributed TPU system.
Optional attributes for
ConfigureDistributedTPUSets up TPUEmbedding in a distributed TPU system.
An op that configures the TPUEmbedding software on a host.
An op that configures the TPUEmbedding software on a host.
Confusion Matrix Operations
Returns the complex conjugate of a complex number.
Shuffle dimensions of x according to a permutation and conjugate the result.
An op that sets up communication between TPUEmbedding host software instances
after ConfigureTPUEmbeddingHost has been called on each host.
Initializer that generates tensors with a constant value.
An operator producing a constant value.
This op consumes a lock created by
MutexLock.
Container for any kind of control flow context.
Container for any kind of control flow context.
Does nothing.
Computes a N-D convolution given (N+1+batch_dims)-D
input and (N+2)-D filter tensors.Optional attributes for
ConvComputes a 2-D convolution given 4-D
input and filter tensors.Optional attributes for
Conv2dComputes the gradients of convolution with respect to the filter.
Optional attributes for
Conv2dBackpropFilterComputes the gradients of convolution with respect to the filter.
Optional attributes for
Conv2dBackpropFilterV2Computes the gradients of convolution with respect to the input.
Optional attributes for
Conv2dBackpropInputComputes the gradients of convolution with respect to the input.
Optional attributes for
Conv2dBackpropInputV2Computes a 3-D convolution given 5-D
input and filter tensors.Optional attributes for
Conv3dComputes the gradients of 3-D convolution with respect to the filter.
Optional attributes for
Conv3dBackpropFilterComputes the gradients of 3-D convolution with respect to the input.
Optional attributes for
Conv3dBackpropInputThe ConvertToCooTensor operation
The ConvertToListOfSparseCoreCooTensors operation
The ConvertToSparseCoreCsrWrappedCooTensor operation
Represents a job type and the number of tasks under this job.
Represents a job type and the number of tasks under this job.
Coordination service configuration parameters.
Coordination service configuration parameters.
Copy a tensor from CPU-to-CPU or GPU-to-GPU.
Optional attributes for
CopyCopy a tensor to host.
Optional attributes for
CopyHostThe CopyToMesh operation
The CopyToMeshGrad operation
If included as a payload, this message contains the error source information
where the error was raised.
If included as a payload, this message contains the error source information
where the error was raised.
Protobuf enum
tensorflow.core.platform.ErrorSourceProto.ErrorSourceComputes cos of x element-wise.
Computes hyperbolic cosine of x element-wise.
Computes the cosine similarity between labels and predictions.
A metric that computes the cosine similarity metric between labels and predictions.
Protobuf type
tensorflow.CostGraphDef
Total cost of this graph, typically used for balancing decisions.
Total cost of this graph, typically used for balancing decisions.
Protobuf type
tensorflow.CostGraphDefProtobuf type
tensorflow.CostGraphDef.NodeProtobuf type
tensorflow.CostGraphDef.Node
Inputs of this node.
Inputs of this node.
Outputs of this node.
Outputs of this node.
Increments 'ref' until it reaches 'limit'.
Protobuf type
tensorflow.core.CppShapeInferenceInputsNeededProtobuf type
tensorflow.core.CppShapeInferenceInputsNeededProtobuf type
tensorflow.core.CppShapeInferenceResultProtobuf type
tensorflow.core.CppShapeInferenceResultProtobuf type
tensorflow.core.CppShapeInferenceResult.HandleDataProtobuf type
tensorflow.core.CppShapeInferenceResult.HandleDataProtobuf type
tensorflow.core.CppShapeInferenceResult.HandleShapeAndTypeProtobuf type
tensorflow.core.CppShapeInferenceResult.HandleShapeAndTypeProtobuf type
tensorflow.CPUInfoProtobuf type
tensorflow.CPUInfoThe CreateSummaryDbWriter operation
The CreateSummaryFileWriter operation
Extracts crops from the input image tensor and resizes them.
Optional attributes for
CropAndResizeComputes the gradient of the crop_and_resize op wrt the input boxes tensor.
Optional attributes for
CropAndResizeGradBoxesComputes the gradient of the crop_and_resize op wrt the input image tensor.
Optional attributes for
CropAndResizeGradImageCompute the pairwise cross product.
An Op to sum inputs across replicated TPU instances.
Reads out the CSR components at batch
index.Convert a (possibly batched) CSRSparseMatrix to dense.
Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.
The CSVDatasetV2 operation
The ExperimentalCSVDataset operation
Performs beam search decoding on the logits given in input.
Optional attributes for
CtcBeamSearchDecoderPerforms greedy decoding on the logits given in inputs.
Optional attributes for
CtcGreedyDecoderCalculates the CTC Loss (log probability) for each batch entry.
Optional attributes for
CtcLossCalculates the CTC Loss (log probability) for each batch entry.
Optional attributes for
CTCLossV2A RNN backed by cuDNN.
Optional attributes for
CudnnRNNBackprop step of CudnnRNNV3.
Optional attributes for
CudnnRNNBackpropConverts CudnnRNN params from canonical form to usable form.
Optional attributes for
CudnnRNNCanonicalToParamsComputes size of weights that can be used by a Cudnn RNN model.
Optional attributes for
CudnnRnnParamsSizeRetrieves CudnnRNN params in canonical form.
Optional attributes for
CudnnRNNParamsToCanonicalCompute the cumulative product of the tensor
x along axis.Optional attributes for
CumprodCompute the cumulative sum of the tensor
x along axis.Optional attributes for
CumsumCompute the cumulative product of the tensor
x along axis.Optional attributes for
CumulativeLogsumexpA custom gradient for ops of type
T.Protobuf enum
tensorflow.DataClassAn API for building
data.experimental operations as OpsReturns the dimension index in the destination data format given the one in
the source data format.
Optional attributes for
DataFormatDimMapPermute input tensor from
src_format to dst_format.Optional attributes for
DataFormatVecPermuteAn API for building
data operations as OpsProtobuf type
tensorflow.data.CrossTrainerCacheOptionsProtobuf type
tensorflow.data.CrossTrainerCacheOptions
Data service config available to the client through GetDataServiceConfig RPC.
Data service config available to the client through GetDataServiceConfig RPC.
Metadata related to tf.data service datasets.
Metadata related to tf.data service datasets.
Protobuf enum
tensorflow.data.DataServiceMetadata.Compression
tf.data service deployment mode.
Next tag: 2
Next tag: 2
Specifies how data is sharded among tf.data service workers.
Creates a dataset that reads data from the tf.data service.
Optional attributes for
DataServiceDatasetRepresents a potentially large list of independent elements (samples), and allows iteration and
transformations to be performed across these elements.
Metadata describing a compressed component of a dataset element.
Metadata describing a compressed component of a dataset element.
Protobuf type
tensorflow.data.CompressedElementProtobuf type
tensorflow.data.CompressedElement
An uncompressed dataset element.
An uncompressed dataset element.
Returns the cardinality of
input_dataset.Returns the cardinality of
input_dataset.Optional attributes for
DatasetCardinalityReturns the fingerprint of
input_dataset.Creates a dataset from the given
graph_def.Represents the state of an iteration through a tf.data Datset.
next: 2
next: 2
An optional represents the result of a dataset getNext operation that may fail, when the end of
the dataset has been reached.
Represents the type of auto-sharding we enable.
next: 6
next: 6
next: 2
next: 2
Protobuf enum
tensorflow.data.CardinalityOptions.ComputeLevel
next: 3
next: 3
Represents how to handle external state during serialization.
next: 22
next: 22
Message stored with Dataset objects to control how datasets are processed and
optimized.
next: 13
Message stored with Dataset objects to control how datasets are processed and
optimized.
next: 13
next: 2
next: 2
next: 3
next: 3
Returns a serialized GraphDef representing
input_dataset.Optional attributes for
DatasetToGraphOutputs the single element from the given dataset.
Optional attributes for
DatasetToSingleElementWrites the given dataset to the given file using the TFRecord format.
Writes the given dataset to the given file using the TFRecord format.
(== suppress_warning documentation-presence ==)
LINT.IfChange
The Dawsn operation
An Event related to the debugging of a TensorFlow program.
An Event related to the debugging of a TensorFlow program.
A device on which ops and/or tensors are instrumented by the debugger.
A device on which ops and/or tensors are instrumented by the debugger.
A debugger-instrumented graph.
A debugger-instrumented graph.
Protobuf type
tensorflow.DebuggedSourceFileProtobuf type
tensorflow.DebuggedSourceFileProtobuf type
tensorflow.DebuggedSourceFilesProtobuf type
tensorflow.DebuggedSourceFilesAn API for building
debugging operations as OpsIdentity op for gradient debugging.
Identity op for gradient debugging.
Provides an identity mapping of the non-Ref type input tensor for debugging.
Optional attributes for
DebugIdentity
Metadata about the debugger and the debugged TensorFlow program.
Metadata about the debugger and the debugged TensorFlow program.
Debug NaN Value Counter Op.
Optional attributes for
DebugNanCountDebug Numeric Summary V2 Op.
Optional attributes for
DebugNumericsSummary
Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).
Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).
Option for watching a node in TensorFlow Debugger (tfdbg).
Option for watching a node in TensorFlow Debugger (tfdbg).
Decode and Crop a JPEG-encoded image to a uint8 tensor.
Optional attributes for
DecodeAndCropJpegDecode web-safe base64-encoded strings.
Decode the first frame of a BMP-encoded image to a uint8 tensor.
Optional attributes for
DecodeBmpDecompress strings.
Optional attributes for
DecodeCompressedConvert CSV records to tensors.
Optional attributes for
DecodeCsvDecode the frame(s) of a GIF-encoded image to a uint8 tensor.
Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.
Optional attributes for
DecodeImageDecode a JPEG-encoded image to a uint8 tensor.
Optional attributes for
DecodeJpegConvert JSON-encoded Example records to binary protocol buffer strings.
Reinterpret the bytes of a string as a vector of numbers.
Optional attributes for
DecodePaddedRawDecode a PNG-encoded image to a uint8 or uint16 tensor.
Optional attributes for
DecodePngThe op extracts fields from a serialized protocol buffers message into tensors.
Optional attributes for
DecodeProtoReinterpret the bytes of a string as a vector of numbers.
Optional attributes for
DecodeRawDecode a 16-bit PCM WAV file to a float tensor.
Optional attributes for
DecodeWavMakes a copy of
x.A container for an iterator resource.
The DeleteMemoryCache operation
A container for an iterator resource.
The DeleteRandomSeedGenerator operation
The DeleteSeedGenerator operation
Delete the tensor specified by its handle in the session.
Counts the number of occurrences of each value in an integer array.
Optional attributes for
DenseBincountPerforms sparse-output bin counting for a tf.tensor input.
Optional attributes for
DenseCountSparseOutputConverts a dense tensor to a (possibly batched) CSRSparseMatrix.
Applies set operation along last dimension of 2
Tensor inputs.Optional attributes for
DenseToDenseSetOperationCreates a dataset that batches input elements into a SparseTensor.
Creates a dataset that batches input elements into a SparseTensor.
Applies set operation along last dimension of
Tensor and SparseTensor.Optional attributes for
DenseToSparseSetOperationDepthToSpace for tensors of type T.
Optional attributes for
DepthToSpaceComputes a 2-D depthwise convolution given 4-D
input and filter tensors.Optional attributes for
DepthwiseConv2dNativeComputes the gradients of depthwise convolution with respect to the filter.
Optional attributes for
DepthwiseConv2dNativeBackpropFilterComputes the gradients of depthwise convolution with respect to the input.
Optional attributes for
DepthwiseConv2dNativeBackpropInputDequantize the 'input' tensor into a float or bfloat16 Tensor.
Optional attributes for
DequantizeConverts the given variant tensor to an iterator and stores it in the given resource.
Deserialize and concatenate
SparseTensors from a serialized minibatch.Deserialize
SparseTensor objects.Deletes the resource specified by the handle.
Optional attributes for
DestroyResourceOpDestroys the temporary variable and returns its final value.
Computes the determinant of one or more square matrices.
Protobuf type
tensorflow.DeviceAttributesProtobuf type
tensorflow.DeviceAttributesReturn the index of device the op runs.
Protobuf type
tensorflow.DeviceLocalityProtobuf type
tensorflow.DeviceLocalityProtobuf type
tensorflow.DevicePropertiesProtobuf type
tensorflow.DevicePropertiesProtobuf type
tensorflow.NamedDeviceProtobuf type
tensorflow.NamedDeviceRepresents a (possibly partial) specification for a TensorFlow device.
A Builder class for building
DeviceSpec class.Protobuf type
tensorflow.DeviceStepStatsProtobuf type
tensorflow.DeviceStepStatsComputes Psi, the derivative of Lgamma (the log of the absolute value of
Gamma(x)), element-wise.Computes the grayscale dilation of 4-D
input and 3-D filter tensors.Computes the gradient of morphological 2-D dilation with respect to the filter.
Computes the gradient of morphological 2-D dilation with respect to the input.
A substitute for
InterleaveDataset on a fixed list of N datasets.A substitute for
InterleaveDataset on a fixed list of N datasets.Optional attributes for
DirectedInterleaveDatasetTurns off the copy-on-read mode.
Used to serialize and transmit tensorflow::Status payloads through
grpc::Status `error_details` since grpc::Status lacks payload API.
Used to serialize and transmit tensorflow::Status payloads through
grpc::Status `error_details` since grpc::Status lacks payload API.
If included as a payload, this message flags the Status to have lost payloads
during the GRPC transmission.
If included as a payload, this message flags the Status to have lost payloads
during the GRPC transmission.
If included as a payload, this message flags the Status to be a possible
outcome of a worker restart.
If included as a payload, this message flags the Status to be a possible
outcome of a worker restart.
The DistributedSave operation
Optional attributes for
DistributedSaveAn API for building
distribute operations as OpsReturns x / y element-wise.
Returns 0 if the denominator is zero.
Draw bounding boxes on a batch of images.
The DTensorRestoreV2 operation
An API for building
dtypes operations as OpsThe DummyIterationCounter operation
The DummyMemoryCache operation
The DummySeedGenerator operation
Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
embedding_indices[i] and aggregation_weights[i] correspond
to the ith feature.
Optional attributes for
DynamicEnqueueTPUEmbeddingArbitraryTensorBatchThe DynamicEnqueueTPUEmbeddingRaggedTensorBatch operation
Optional attributes for
DynamicEnqueueTPUEmbeddingRaggedTensorBatchPartitions
data into num_partitions tensors using indices from partitions.Interleave the values from the
data tensors into a single tensor.An environment for executing TensorFlow operations eagerly.
Controls how to act when we try to run an operation on a given device but some input tensors
are not on that device.
Computes the (possibly normalized) Levenshtein Edit Distance.
Optional attributes for
EditDistanceComputes the eigen decomposition of one or more square matrices.
Optional attributes for
EigTensor contraction according to Einstein summation convention.
Computes the exponential linear function.
Exponential linear unit.
Computes gradients for the exponential linear (Elu) operation.
An op enabling differentiation of TPU Embeddings.
Creates a tensor with the given shape.
Optional attributes for
EmptyCreates and returns an empty tensor list.
Creates and returns an empty tensor map.
handle: an empty tensor map
Encode strings into web-safe base64 format.
Optional attributes for
EncodeBase64JPEG-encode an image.
Optional attributes for
EncodeJpegJPEG encode input image with provided compression quality.
PNG-encode an image.
Optional attributes for
EncodePngThe op serializes protobuf messages provided in the input tensors.
Optional attributes for
EncodeProtoEncode audio data using the WAV file format.
Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
embedding_indices[i] and aggregation_weights[i] correspond
to the ith feature.
Optional attributes for
EnqueueTPUEmbeddingArbitraryTensorBatchAn op that enqueues a list of input batch tensors to TPUEmbedding.
Optional attributes for
EnqueueTPUEmbeddingBatchAn op that enqueues a list of input batch tensors to TPUEmbedding.
Optional attributes for
EnqueueTPUEmbeddingIntegerBatchEases the porting of code that uses tf.nn.embedding_lookup().
sample_splits[i], embedding_indices[i] and aggregation_weights[i] correspond
to the ith feature. table_ids[i] indicates which embedding table to look up ith
feature.
Optional attributes for
EnqueueTPUEmbeddingRaggedTensorBatchAn op that enqueues TPUEmbedding input indices from a SparseTensor.
Optional attributes for
EnqueueTPUEmbeddingSparseBatchEases the porting of code that uses tf.nn.embedding_lookup_sparse().
sample_indices[i], embedding_indices[i] and aggregation_weights[i] correspond
to the ith feature. table_ids[i] indicates which embedding table to look up ith
feature.
Optional attributes for
EnqueueTPUEmbeddingSparseTensorBatchEnsures that the tensor's shape matches the expected shape.
Creates or finds a child frame, and makes
data available to the child frame.Optional attributes for
EnterProtobuf type
tensorflow.EntryValueProtobuf type
tensorflow.EntryValueReturns the truth value of (x == y) element-wise.
Optional attributes for
EqualComputes the Gauss error function of
x element-wise.Computes the complementary error function of
x element-wise.The Erfinv operation
Computes the euclidean norm of elements across dimensions of a tensor.
Optional attributes for
EuclideanNorm
Protocol buffer representing an event that happened during
the execution of a Brain model.
Protocol buffer representing an event that happened during
the execution of a Brain model.
Protobuf type
tensorflow.ExampleProtobuf type
tensorflow.ExampleProtobuf type
tensorflow.ExampleParserConfigurationProtobuf type
tensorflow.ExampleParserConfigurationOp that loads and executes a TPU program on a TPU device.
Op that executes a program with optional in-place variable updates.
An op that executes the TPUEmbedding partitioner on the central configuration
device and computes the HBM size (in bytes) required for TPUEmbedding operation.
Data relating to the eager execution of an op or a Graph.
Data relating to the eager execution of an op or a Graph.
Defines an environment for creating and executing TensorFlow
Operations.Exits the current frame to its parent frame.
Computes exponential of x element-wise.
Inserts a dimension of 1 into a tensor's shape.
The Expint operation
Computes
exp(x) - 1 element-wise.
i.e.Exponential activation function.
Extracts a glimpse from the input tensor.
Optional attributes for
ExtractGlimpseExtract
patches from images and put them in the "depth" output dimension.Extract the shape information of a JPEG-encoded image.
Extract
patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.Output a fact about factorials.
This op is used as a placeholder in If branch functions.
Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same shape and type.
Optional attributes for
FakeQuantWithMinMaxArgsCompute gradients for a FakeQuantWithMinMaxArgs operation.
Optional attributes for
FakeQuantWithMinMaxArgsGradientFake-quantize the 'inputs' tensor of type float via global float scalars
Fake-quantize the
inputs tensor of type float via global float scalars
min and max to outputs tensor of same shape as inputs.Optional attributes for
FakeQuantWithMinMaxVarsCompute gradients for a FakeQuantWithMinMaxVars operation.
Optional attributes for
FakeQuantWithMinMaxVarsGradientFake-quantize the 'inputs' tensor of type float via per-channel floats
Fake-quantize the
inputs tensor of type float per-channel and one of the
shapes: [d], [b, d] [b, h, w, d] via per-channel floats min and max
of shape [d] to outputs tensor of same shape as inputs.Optional attributes for
FakeQuantWithMinMaxVarsPerChannelCompute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
Optional attributes for
FakeQuantWithMinMaxVarsPerChannelGradientDeprecated.
Metric that calculates the number of false negatives.
Metric that calculates the number of false positives.
Containers for non-sequential data.
Containers for non-sequential data.
Protobuf type
tensorflow.FeatureConfigurationProtobuf type
tensorflow.FeatureConfiguration
Containers for sequential data.
Containers for sequential data.
Protobuf type
tensorflow.FeatureListsProtobuf type
tensorflow.FeatureListsProtobuf type
tensorflow.FeaturesProtobuf type
tensorflow.FeaturesFast Fourier transform.
2D fast Fourier transform.
3D fast Fourier transform.
ND fast Fourier transform.
A queue that produces elements in first-in first-out order.
Optional attributes for
FifoQueueSet configuration of the file system.
Creates a tensor filled with a scalar value.
Creates a dataset containing elements of first component of
input_dataset having true in the last component.Creates a dataset containing elements of
input_dataset matching predicate.Optional attributes for
FilterDatasetCreates a dataset by applying
tf.data.Options to input_dataset.Optional attributes for
FinalizeDatasetAn op that finalizes the TPUEmbedding configuration.
Generates fingerprint values.
Protocol buffer representing a SavedModel Fingerprint.
Protocol buffer representing a SavedModel Fingerprint.
Protobuf type
tensorflow.FixedLenFeatureProtoProtobuf type
tensorflow.FixedLenFeatureProtoThe FixedLengthRecordDatasetV2 operation
Optional attributes for
FixedLengthRecordDatasetA Reader that outputs fixed-length records from a file.
Optional attributes for
FixedLengthRecordReaderGenerates labels for candidate sampling with a learned unigram distribution.
Optional attributes for
FixedUnigramCandidateSamplerCreates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
FlatMapDatasetProtobuf type
tensorflow.FloatListProtobuf type
tensorflow.FloatListReturns element-wise largest integer not greater than x.
Returns x // y element-wise.
Returns element-wise remainder of division.
The FlushSummaryWriter operation
Applies a for loop.
Performs fractional average pooling on the input.
Optional attributes for
FractionalAvgPoolComputes gradient of the FractionalAvgPool function.
Optional attributes for
FractionalAvgPoolGradPerforms fractional max pooling on the input.
Optional attributes for
FractionalMaxPoolComputes gradient of the FractionalMaxPool function.
Optional attributes for
FractionalMaxPoolGradAn API for building framework operations as
OpsThe FresnelCos operation
The FresnelSin operation
Optimizer that implements the FTRL algorithm.
Highly experimental and very likely to change.
Highly experimental and very likely to change.
LINT.IfChange
Experimental.
Ops for calling
ConcreteFunction.
A function can be instantiated when the runtime can bind every attr
with a value.
Attributes for function arguments.
Attributes for function arguments.
A function can be instantiated when the runtime can bind every attr
with a value.
A library is a set of named functions.
A library is a set of named functions.
Batch normalization.
Optional attributes for
FusedBatchNormGradient for batch normalization.
Optional attributes for
FusedBatchNormGradPerforms a padding as a preprocess during a convolution.
Performs a resize and padding as a preprocess during a convolution.
Optional attributes for
FusedResizeAndPadConv2dGather slices from
params axis axis according to indices.Optional attributes for
GatherGather slices from
params into a Tensor with shape specified by indices.Optional attributes for
GatherNdApplies the Gaussian error linear unit (GELU) activation function.
The Gaussian Error Linear Unit (GELU) activation function.
This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497
Optional attributes for
GenerateBoundingBoxProposalsGiven a path to new and old vocabulary files, returns a remapping Tensor of
length
num_new_vocab, where remapping[i] contains the row number in the old
vocabulary that corresponds to row i in the new vocabulary (starting at line
new_vocab_offset and up to num_new_vocab entities), or -1 if entry i
in the new vocabulary is not in the old vocabulary.Optional attributes for
GenerateVocabRemappingCreates a dataset that invokes a function to generate elements.
Optional attributes for
GeneratorDatasetGets the element at the specified index in a dataset.
The GetMinibatchesInCsrWithPhysicalReplica operation
The GetMinibatchSplitsWithPhysicalReplica operation
Returns the
tf.data.Options attached to input_dataset.Store the input tensor in the state of the current session.
Get the value of the tensor specified by its handle.
The GetStatsFromListOfSparseCoreCooTensors operation
An op returns the TPU task ID from TPU topology.
The GlobalIterId operation
The GlobalShuffleDataset operation
Optional attributes for
GlobalShuffleDatasetThe Glorot initializer, also called Xavier initializer.
Protobuf type
tensorflow.GPUInfoProtobuf type
tensorflow.GPUInfoProtobuf type
tensorflow.GPUOptionsProtobuf type
tensorflow.GPUOptionsProtobuf type
tensorflow.GPUOptions.ExperimentalProtobuf type
tensorflow.GPUOptions.Experimental
Whether to merge data transfer streams into the compute stream in the
same stream group.
Whether to merge data transfer streams into the compute stream in the
same stream group.
Configuration for breaking down a visible GPU into multiple "virtual"
devices.
Configuration for breaking down a visible GPU into multiple "virtual"
devices.
GradientDef defines the gradient function of a function defined in
a function library.
GradientDef defines the gradient function of a function defined in
a function library.
Basic Stochastic gradient descent optimizer.
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...Optional attributes for
GradientsA data flow graph representing a TensorFlow computation.
Used to instantiate an abstract class which overrides the buildSubgraph method to build a
conditional or body subgraph for a while loop.
Protobuf type
tensorflow.GraphDebugInfoProtobuf type
tensorflow.GraphDebugInfo
This represents a file/line location in the source code.
This represents a file/line location in the source code.
This represents a stack trace which is a ordered list of `FileLineCol`.
This represents a stack trace which is a ordered list of `FileLineCol`.
Represents the graph of operations
Represents the graph of operations
Data relating to an execution of a Graph (e.g., an eager execution of a
FuncGraph).
Data relating to an execution of a Graph (e.g., an eager execution of a
FuncGraph).
The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2).
The creation of an op in a TensorFlow Graph (e.g., FuncGraph in TF2).
Protobuf type
tensorflow.GraphOptionsProtobuf type
tensorflow.GraphOptionsProtobuf type
tensorflow.GraphTransferConstNodeInfoProtobuf type
tensorflow.GraphTransferConstNodeInfoProtobuf type
tensorflow.GraphTransferGraphInputNodeInfoProtobuf type
tensorflow.GraphTransferGraphInputNodeInfoProtobuf type
tensorflow.GraphTransferGraphOutputNodeInfoProtobuf type
tensorflow.GraphTransferGraphOutputNodeInfo
Protocol buffer representing a handle to a tensorflow resource.
Protocol buffer representing a handle to a tensorflow resource.
Protobuf enum
tensorflow.GraphTransferInfo.DestinationProtobuf type
tensorflow.GraphTransferNodeInfoProtobuf type
tensorflow.GraphTransferNodeInfoProtobuf type
tensorflow.GraphTransferNodeInputProtobuf type
tensorflow.GraphTransferNodeInputProtobuf type
tensorflow.GraphTransferNodeInputInfoProtobuf type
tensorflow.GraphTransferNodeInputInfoProtobuf type
tensorflow.GraphTransferNodeOutputInfoProtobuf type
tensorflow.GraphTransferNodeOutputInfoReturns the truth value of (x > y) element-wise.
Returns the truth value of (x >= y) element-wise.
Creates a dataset that computes a group-by on
input_dataset.Creates a dataset that computes a group-by on
input_dataset.Creates a dataset that computes a windowed group-by on
input_dataset.
// TODO(mrry): Support non-int64 keys.Creates a dataset that computes a windowed group-by on
input_dataset.
// TODO(mrry): Support non-int64 keys.Optional attributes for
GroupByWindowDatasetComputes the GRU cell forward propagation for 1 time step.
Computes the GRU cell back-propagation for 1 time step.
Gives a guarantee to the TF runtime that the input tensor is a constant.
Hard sigmoid activation.
Creates a non-initialized hash table.
Optional attributes for
HashTableHe initializer.
Container class for core methods which add or perform several operations and return one of them.
Computes the hinge loss between labels and predictions.
A metric that computes the hinge loss metric between labels and predictions.
Return histogram of values.
Serialization format for histogram module in
tsl/lib/histogram/histogram.h
Serialization format for histogram module in
tsl/lib/histogram/histogram.h
Outputs a
Summary protocol buffer with a histogram.Returns a constant tensor on the host.
Convert one or more images from HSV to RGB.
Computes the Huber loss between labels and predictions.
Initializer that generates the identity matrix.
Return a tensor with the same shape and contents as the input tensor or value.
Returns a list of tensors with the same shapes and contents as the input
tensors.
A Reader that outputs the queued work as both the key and value.
Optional attributes for
IdentityReaderoutput = cond ?
Optional attributes for
IfInverse fast Fourier transform.
Inverse 2D fast Fourier transform.
Inverse 3D fast Fourier transform.
ND inverse fast Fourier transform.
Compute the lower regularized incomplete Gamma function
P(a, x).Compute the upper regularized incomplete Gamma function
Q(a, x).Computes the gradient of
igamma(a, x) wrt a.Creates a dataset that contains the elements of
input_dataset ignoring errors.Creates a dataset that contains the elements of
input_dataset ignoring errors.Optional attributes for
IgnoreErrorsDatasetOptional attributes for
IgnoreErrorsDatasetReturns the imaginary part of a complex number.
An API for building
image operations as OpsApplies the given transform to each of the images.
Optional attributes for
ImageProjectiveTransformV2Applies the given transform to each of the images.
Optional attributes for
ImageProjectiveTransformV3Outputs a
Summary protocol buffer with images.Optional attributes for
ImageSummaryReturns immutable tensor from memory region.
The ImportEvent operation
The IndexFlatMapDataset operation
Optional attributes for
IndexFlatMapDatasetA placeholder op for a value that will be fed into the computation.
Fetches multiple values from infeed as an XLA tuple.
An op which feeds a single Tensor value into the computation.
Optional attributes for
InfeedEnqueueAn op which enqueues prelinearized buffer into TPU infeed.
Optional attributes for
InfeedEnqueuePrelinearizedBufferFeeds multiple Tensor values into the computation as an XLA tuple.
Optional attributes for
InfeedEnqueueTupleAn interface for Initializers
Table initializer that takes two tensors for keys and values respectively.
The InitializeTableFromDataset operation
Initializes a table from a text file.
Optional attributes for
InitializeTableFromTextFileAdds v into specified rows of x.
Subtracts `v` into specified rows of `x`.
Updates specified rows 'i' with values 'v'.
Protobuf type
tensorflow.Int64ListProtobuf type
tensorflow.Int64ListProtobuf type
tensorflow.InterconnectLinkProtobuf type
tensorflow.InterconnectLinkCreates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
InterleaveDatasetSays whether the targets are in the top
K predictions.Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes).
Optional attributes for
InvInvert (flip) each bit of supported types; for example, type
uint8 value 01010101 becomes 10101010.Computes the inverse permutation of a tensor.
Computes the gradient for the inverse of
x wrt its input.An API for building
io operations as OpsInverse real-valued fast Fourier transform.
Inverse 2D real-valued fast Fourier transform.
Inverse 3D real-valued fast Fourier transform.
ND inverse real fast Fourier transform.
Returns which elements of x are finite.
Returns which elements of x are Inf.
Returns which elements of x are NaN.
Solves a batch of isotonic regression problems.
Whether TPU Embedding is initialized in a distributed TPU system.
Optional attributes for
IsTPUEmbeddingInitializedChecks whether a tensor has been initialized.
The IteratorV2 operation
The IteratorFromStringHandleV2 operation
Optional attributes for
IteratorFromStringHandleReturns the name of the device on which
resource has been placed.Returns the name of the device on which
resource has been placed.Returns the serialized model proto of an iterator resource.
Gets the next output from the given iterator .
Gets the next output from the given iterator as an Optional variant.
Gets the next output from the given iterator.
Converts the given
resource_handle representing an iterator to a string.
Defines a single job in a TensorFlow cluster.
Defines a single job in a TensorFlow cluster.
Defines the device filters for tasks in a job.
Defines the device filters for tasks in a job.
Joins the strings in the given list of string tensors into one tensor;
with the given separator (default is an empty separator).
Optional attributes for
JoinProtobuf type
tensorflow.KernelDefProtobuf type
tensorflow.KernelDef.AttrConstraintProtobuf type
tensorflow.KernelDef.AttrConstraintProtobuf type
tensorflow.KernelDef
A collection of KernelDefs
A collection of KernelDefs
Computes Kullback-Leibler divergence loss between labels and predictions.
A metric that computes the Kullback-Leibler divergence loss metric between labels and
predictions.
Returns the index of a data point that should be added to the seed set.
Selects num_to_sample rows of input using the KMeans++ criterion.
Computes the Kth order statistic of a data set.
A regularizer that applies an L1 or Lasso(least absolute shrinkage and selection operator)
Regression, regularization penalty.
A regularizer that applies both L1 and L2 regularization penalties.
A regularizer that applies a L2 (Ridge Regression) regularization penalty.
L2 Loss.
L2 Normalization Operations
Records the latency of producing
input_dataset elements in a StatsAggregator.Records the latency of producing
input_dataset elements in a StatsAggregator.Computes rectified linear:
max(features, features * alpha).Optional attributes for
LeakyReluComputes rectified linear gradients for a LeakyRelu operation.
Optional attributes for
LeakyReluGradGenerates labels for candidate sampling with a learned unigram distribution.
Optional attributes for
LearnedUnigramCandidateSamplerLeCun normal initializer.
Elementwise computes the bitwise left-shift of
x and y.Creates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
LegacyParallelInterleaveDatasetReturns the truth value of (x < y) element-wise.
Returns the truth value of (x <= y) element-wise.
Computes the log of the absolute value of
Gamma(x) element-wise.An API for building
linalg operations as OpsAn API for building
linalg.sparse operations as OpsLinear activation function (pass-through).
Generates values in an interval.
Creates a dataset that emits each of
tensors once.Optional attributes for
ListDatasetThe ListSnapshotChunksDataset operation
The ExperimentalLMDBDataset operation
Creates a dataset that emits the key-value pairs in one or more LMDB files.
A Reader that outputs the records from a LMDB file.
Optional attributes for
LmdbReaderAn op that loads optimization parameters into embedding memory.
Loads a 2-D (matrix)
Tensor with name old_tensor_name from the checkpoint
at ckpt_path and potentially reorders its rows and columns using the
specified remappings.Optional attributes for
LoadAndRemapMatrixThe LoadDataset operation
Optional attributes for
LoadDatasetLoad Adadelta embedding parameters.
Optional attributes for
LoadTPUEmbeddingAdadeltaParametersLoad Adagrad Momentum embedding parameters.
Optional attributes for
LoadTPUEmbeddingAdagradMomentumParametersLoad Adagrad embedding parameters.
Optional attributes for
LoadTPUEmbeddingAdagradParametersLoad ADAM embedding parameters.
Optional attributes for
LoadTPUEmbeddingADAMParametersLoad centered RMSProp embedding parameters.
Optional attributes for
LoadTPUEmbeddingCenteredRMSPropParametersLoad frequency estimator embedding parameters.
Optional attributes for
LoadTPUEmbeddingFrequencyEstimatorParametersLoad FTRL embedding parameters.
Optional attributes for
LoadTPUEmbeddingFTRLParametersLoad MDL Adagrad Light embedding parameters.
Optional attributes for
LoadTPUEmbeddingMDLAdagradLightParametersLoad Momentum embedding parameters.
Optional attributes for
LoadTPUEmbeddingMomentumParametersLoad proximal Adagrad embedding parameters.
Optional attributes for
LoadTPUEmbeddingProximalAdagradParametersThe LoadTPUEmbeddingProximalYogiParameters operation
Optional attributes for
LoadTPUEmbeddingProximalYogiParametersLoad RMSProp embedding parameters.
Optional attributes for
LoadTPUEmbeddingRMSPropParametersLoad SGD embedding parameters.
Optional attributes for
LoadTPUEmbeddingStochasticGradientDescentParametersProtobuf type
tensorflow.LocalLinksProtobuf type
tensorflow.LocalLinksLocal Response Normalization.
Optional attributes for
LocalResponseNormalizationGradients for Local Response Normalization.
Optional attributes for
LocalResponseNormalizationGradComputes natural logarithm of x element-wise.
Computes natural logarithm of (1 + x) element-wise.
Computes Computes the logarithm of the hyperbolic cosine of the prediction error.
A metric that computes the logarithm of the hyperbolic cosine of the prediction error metric
between labels and predictions.
Returns the truth value of x AND y element-wise.
Returns the truth value of
NOT x element-wise.Returns the truth value of x OR y element-wise.
Computes the sign and the log of the absolute value of the determinant of
one or more square matrices.
Deprecated.
Protocol buffer used for logging messages to the events file.
Deprecated.
Deprecated.
Computes log softmax activations.
Generates labels for candidate sampling with a log-uniform distribution.
Optional attributes for
LogUniformCandidateSamplerOutputs all keys and values in the table.
Looks up keys in a table, outputs the corresponding values.
Replaces the contents of the table with the specified keys and values.
Updates the table to associates keys with values.
Removes keys and its associated values from a table.
Computes the number of elements in the given table.
Forwards the input to the output.
Interface for loss calc ulation
Built-in loss functions.
Converts all uppercase characters into their respective lowercase replacements.
Optional attributes for
LowerApplies lower_bound(sorted_search_values, values) along each row.
Computes the LSTM cell forward propagation for 1 time step.
Optional attributes for
LSTMBlockCellComputes the LSTM cell backward propagation for 1 timestep.
Computes the LU decomposition of one or more square matrices.
Protobuf type
tensorflow.MachineConfigurationProtobuf type
tensorflow.MachineConfigurationMakes a new iterator from the given
dataset and stores it in iterator.Make all elements in the non-Batch dimension unique, but "close" to
their initial value.
Creates a dataset that fuses mapping with batching.
Creates a dataset that fuses mapping with batching.
Optional attributes for
MapAndBatchDatasetOptional attributes for
MapAndBatchDatasetOp removes all elements in the underlying container.
Optional attributes for
MapClearCreates a dataset that applies
f to the outputs of input_dataset.Creates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
MapDatasetOptional attributes for
MapDatasetMaps a function on the list of tensors unpacked from arguments on dimension 0.
Optional attributes for
MapDefunOp returns the number of incomplete elements in the underlying container.
Optional attributes for
MapIncompleteSizeOp peeks at the values at the specified key.
Optional attributes for
MapPeekOp returns the number of elements in the underlying container.
Optional attributes for
MapSizeStage (key, values) in the underlying container which behaves like a hashtable.
Optional attributes for
MapStageOp removes and returns the values associated with the key
from the underlying container.
Optional attributes for
MapUnstageOp removes and returns a random (key, value)
from the underlying container.
Optional attributes for
MapUnstageNoKeyReturns the set of files matching one or more glob patterns.
The ExperimentalMatchingFilesDataset operation
The MatchingFilesDataset operation
An API for building
math operations as OpsAn API for building
math.special operations as OpsMultiplication matrix operations
Multiply the matrix "a" by the matrix "b".
Optional attributes for
MatMulReturns a batched diagonal tensor with given batched diagonal values.
Returns the batched diagonal part of a batched tensor.
Returns the batched diagonal part of a batched tensor.
Optional attributes for
MatrixDiagPartV3Returns a batched diagonal tensor with given batched diagonal values.
Optional attributes for
MatrixDiagV3Deprecated, use python implementation tf.linalg.matrix_exponential.
Computes the matrix logarithm of one or more square matrices:
\(log(exp(A)) = A\)
Returns a batched matrix tensor with new batched diagonal values.
Optional attributes for
MatrixSetDiagSolves one or more linear least-squares problems.
Optional attributes for
MatrixSolveLsComputes the maximum of elements across dimensions of a tensor.
Optional attributes for
MaxReturns the max of x and y (i.e. x > y ?
Creates a dataset that overrides the maximum intra-op parallelism.
Creates a dataset that overrides the maximum intra-op parallelism.
Constrains the weights incident to each hidden unit to have a norm less than or equal to a
desired value.
Performs max pooling on the input.
Optional attributes for
MaxPoolPerforms 3D max pooling on the input.
Optional attributes for
MaxPool3dComputes gradients of 3D max pooling function.
Optional attributes for
MaxPool3dGradComputes second-order gradients of the maxpooling function.
Optional attributes for
MaxPool3dGradGradComputes gradients of the maxpooling function.
Optional attributes for
MaxPoolGradComputes second-order gradients of the maxpooling function.
Optional attributes for
MaxPoolGradGradComputes second-order gradients of the maxpooling function.
Optional attributes for
MaxPoolGradGradWithArgmaxComputes gradients of the maxpooling function.
Optional attributes for
MaxPoolGradWithArgmaxPerforms max pooling on the input and outputs both max values and indices.
Optional attributes for
MaxPoolWithArgmaxA metric that that implements a weighted mean
MetricReduction.WEIGHTED_MEANComputes the mean of elements across dimensions of a tensor.
Optional attributes for
MeanComputes the mean of absolute difference between labels and predictions.
A metric that computes the mean of absolute difference between labels and predictions.
Computes the mean absolute percentage error between labels and predictions.
A metric that computes the mean of absolute difference between labels and predictions.
Computes the mean Intersection-Over-Union metric.
Computes the mean relative error by normalizing with the given values.
Computes the mean of squares of errors between labels and predictions.
A metric that computes the mean of absolute difference between labels and predictions.
Computes the mean squared logarithmic errors between labels and predictions.
A metric that computes the mean of absolute difference between labels and predictions.
Metric that computes the element-wise (weighted) mean of the given tensors.
A directory of regions in a memmapped file.
A directory of regions in a memmapped file.
A message that describes one region of memmapped file.
A message that describes one region of memmapped file.
Protobuf type
tensorflow.MemoryInfoProtobuf type
tensorflow.MemoryInfoProtobuf type
tensorflow.MemoryLogRawAllocationProtobuf type
tensorflow.MemoryLogRawAllocationProtobuf type
tensorflow.MemoryLogRawDeallocationProtobuf type
tensorflow.MemoryLogRawDeallocationProtobuf type
tensorflow.MemoryLogStepProtobuf type
tensorflow.MemoryLogStepProtobuf type
tensorflow.MemoryLogTensorAllocationProtobuf type
tensorflow.MemoryLogTensorAllocationProtobuf type
tensorflow.MemoryLogTensorDeallocationProtobuf type
tensorflow.MemoryLogTensorDeallocationProtobuf type
tensorflow.MemoryLogTensorOutputProtobuf type
tensorflow.MemoryLogTensorOutput
For memory tracking.
For memory tracking.
Forwards the value of an available tensor from
inputs to output.An op merges elements of integer and float tensors into deduplication data as
XLA tuple.
Optional attributes for
MergeDedupDataMerges summaries.
V2 format specific: merges the metadata files of sharded checkpoints.
Optional attributes for
MergeV2Checkpoints
Protocol buffer containing the following which are necessary to restart
training, run inference.
Protocol buffer containing the following which are necessary to restart
training, run inference.
Meta information regarding the graph to be exported.
Meta information regarding the graph to be exported.
Interface for metrics
Protobuf type
tensorflow.MetricEntryProtobuf type
tensorflow.MetricEntryDefines the different types of metric reductions
Static methods for computing metrics.
Transforms a spectrogram into a form that's useful for speech recognition.
Optional attributes for
MfccComputes the minimum of elements across dimensions of a tensor.
Optional attributes for
MinReturns the min of x and y (i.e. x < y ?
Constrains the weights to have the norm between a lower bound and an upper bound.
Pads a tensor with mirrored values.
Gradient op for
MirrorPad op.Wraps an arbitrary MLIR computation expressed as a module with a main() function.
Returns element-wise remainder of division.
Algorithm used for model autotuning optimization.
Protocol buffer representing the data used by the autotuning modeling
framework.
Protocol buffer representing the data used by the autotuning modeling
framework.
General representation of a node in the model.
General representation of a node in the model.
Represents a node parameter.
Represents a node parameter.
Contains parameters of the model autotuning optimization.
Contains parameters of the model autotuning optimization.
Class of a node in the performance model.
Identity transformation that models performance.
Optional attributes for
ModelDatasetStochastic gradient descent plus momentum, either nesterov or traditional.
Returns x * y element-wise.
Returns x * y element-wise.
Creates a MultiDeviceIterator resource.
Generates a MultiDeviceIterator resource from its provided string handle.
Optional attributes for
MultiDeviceIteratorFromStringHandleGets next element for the provided shard number.
Initializes the multi device iterator with the given dataset.
Produces a string handle for the given MultiDeviceIterator.
Draws samples from a multinomial distribution.
Optional attributes for
MultinomialCreates an empty hash table that uses tensors as the backing store.
Optional attributes for
MutableDenseHashTableCreates an empty hash table.
Optional attributes for
MutableHashTableCreates an empty hash table.
Optional attributes for
MutableHashTableOfTensorsCreates a Mutex resource that can be locked by
MutexLock.Optional attributes for
MutexLocks a mutex resource.
Nadam Optimizer that implements the NAdam algorithm.
A list of attr names and their values.
A list of attr names and their values.
A pair of tensor name and tensor values.
A pair of tensor name and tensor values.
A
Scope implementation backed by a native scope.Deprecated.
Outputs a tensor containing the reduction across all input tensors.
Deprecated.
use
NcclBroadcast insteadSends
input to all devices that are connected to the output.Deprecated.
use
NcclReduce insteadReduces
input from num_devices using reduction to a single device.The Ndtri operation
Selects the k nearest centers for each point.
Computes numerical negative value element-wise.
Training via negative sampling.
Returns the next representable value of
x1 in the direction of x2, element-wise.Makes its input available to the next iteration.
Creates Framework nerual network Operations
An API for building
nn operations as OpsProtobuf type
tensorflow.NodeDefProtobuf type
tensorflow.NodeDefProtobuf type
tensorflow.NodeDef.ExperimentalDebugInfoProtobuf type
tensorflow.NodeDef.ExperimentalDebugInfo
Time/size stats recorded for a single execution of a graph node.
Time/size stats recorded for a single execution of a graph node.
Output sizes recorded for a single execution of a graph node.
Output sizes recorded for a single execution of a graph node.
Non-deterministically generates some integers.
Greedily selects a subset of bounding boxes in descending order of score,
pruning away boxes that have high intersection-over-union (IOU) overlap
with previously selected boxes.
Optional attributes for
NonMaxSuppressionGreedily selects a subset of bounding boxes in descending order of score,
pruning away boxes that have high overlaps
with previously selected boxes.
Constrains the weights to be non-negative.
The ExperimentalNonSerializableDataset operation
The NonSerializableDataset operation
Does nothing.
Exception that indicates that static shapes are not able to broadcast among each other during
arithmetic operations.
Returns the truth value of (x !
Optional attributes for
NotEqualFinds values of the
n-th order statistic for the last dimension.Optional attributes for
NthElementReturns a one-hot tensor.
Optional attributes for
OneHotInitializer that generates tensors initialized to 1.
An operator creating a constant initialized with ones of the shape given by `dims`.
Makes a "one-shot" iterator that can be iterated only once.
Optional attributes for
OneShotIteratorReturns a tensor of ones with the same shape and type as x.
A logical unit of computation.
Defines an operation.
For describing inputs and outputs.
For describing inputs and outputs.
Description of the graph-construction-time configuration of this
Op.
Description of the graph-construction-time configuration of this
Op.
Defines an operation.
Information about version-dependent deprecation of an op
Information about version-dependent deprecation of an op
Interface implemented by operands of a TensorFlow operation.
Utilities for manipulating operand related types and lists.
Performs computation on Tensors.
Helper type for attribute getters, so we don't clutter the operation classes too much.
A builder for
Operations.Annotation used by classes to make TensorFlow operations conveniently accessible via
org.tensorflow.op.Ops or one of its groups.A compile-time Processor that aggregates classes annotated with
org.tensorflow.op.annotation.Operator and generates the Ops convenience API.An annotation to provide metadata about an op inputs accessor class.
A collection of OpDefs
A collection of OpDefs
An annotation to provide metadata about an op.
Protobuf type
tensorflow.LogNormalDistributionProtobuf type
tensorflow.LogNormalDistributionProtobuf type
tensorflow.NormalDistributionProtobuf type
tensorflow.NormalDistribution
Description of an operation as well as the parameters expected to impact its
performance.
Description of an operation as well as the parameters expected to impact its
performance.
Input data types, shapes and values if known.
Input data types, shapes and values if known.
Performance data for tensorflow operations
Performance data for tensorflow operations
Memory usage data for a tensorflow operation.
Memory usage data for a tensorflow operation.
A collection of OpPerformance data points.
A collection of OpPerformance data points.
Description of the session when an op is run.
Description of the session when an op is run.
An API for building operations as
OpsA Java implementation of
Scope.Creates a dataset by applying related optimizations to
input_dataset.Optional attributes for
OptimizeDataset
Optimized function graph after instantiation-related graph optimization
passes (up till before graph partitioning).
Optimized function graph after instantiation-related graph optimization
passes (up till before graph partitioning).
Enum for distinguishing the origin where the proto is created.
Base class for gradient optimizers.
A class that holds a paired gradient and variable.
Optional attributes for
Optimizer
Options passed to the graph optimizer
Options passed to the graph optimizer
Control the use of the compiler/jit.
Optimization level
Enumerator used to create a new Optimizer with default parameters.
Constructs an Optional variant from a tuple of tensors.
Returns the value stored in an Optional variant or raises an error if none exists.
Returns true if and only if the given Optional variant has a value.
Creates an Optional variant with no value.
Creates a dataset by attaching tf.data.Options to
input_dataset.Optional attributes for
OptionsDatasetOp removes all elements in the underlying container.
Optional attributes for
OrderedMapClearOp returns the number of incomplete elements in the underlying container.
Optional attributes for
OrderedMapIncompleteSizeOp peeks at the values at the specified key.
Optional attributes for
OrderedMapPeekOp returns the number of elements in the underlying container.
Optional attributes for
OrderedMapSizeStage (key, values) in the underlying container which behaves like a ordered
associative container.
Optional attributes for
OrderedMapStageOp removes and returns the values associated with the key
from the underlying container.
Optional attributes for
OrderedMapUnstageOp removes and returns the (key, value) element with the smallest
key from the underlying container.
Optional attributes for
OrderedMapUnstageNoKeyA TPU core selector Op.
Initializer that generates an orthogonal matrix.
Retrieves a single tensor from the computation outfeed.
Optional attributes for
OutfeedDequeueRetrieve multiple values from the computation outfeed.
Optional attributes for
OutfeedDequeueTupleRetrieve multiple values from the computation outfeed.
Retrieves a single tensor from the computation outfeed.
Enqueue a Tensor on the computation outfeed.
Enqueue multiple Tensor values on the computation outfeed.
A symbolic handle to a tensor produced by an
Operation.Pads a tensor.
Creates a dataset that batches and pads
batch_size elements from the input.Optional attributes for
PaddedBatchDatasetA queue that produces elements in first-in first-out order.
Optional attributes for
PaddingFifoQueueThe ParallelBatchDataset operation
Optional attributes for
ParallelBatchDatasetConcatenates a list of
N tensors along the first dimension.Interleave the values from the
data tensors into a single tensor.Creates a dataset containing elements of
input_dataset matching predicate.Optional attributes for
ParallelFilterDatasetCreates a dataset that applies
f to the outputs of input_dataset.Creates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
ParallelInterleaveDatasetCreates a dataset that applies
f to the outputs of input_dataset.Optional attributes for
ParallelMapDatasetOutputs random values from a normal distribution.
Optional attributes for
ParameterizedTruncatedNormalTransforms a vector of tf.Example protos (as strings) into typed tensors.
Transforms
input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.Transforms
input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.Optional attributes for
ParseExampleDatasetOptional attributes for
ParseExampleDatasetTransforms a vector of tf.io.SequenceExample protos (as strings) into
typed tensors.
Optional attributes for
ParseSequenceExampleTransforms a tf.Example proto (as a string) into typed tensors.
Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
Optional attributes for
ParseSingleSequenceExampleTransforms a serialized tensorflow.TensorProto proto into a Tensor.
returns
f(inputs), where f's body is placed and partitioned.Calls a function placed on a specified TPU device.
Optional attributes for
PartitionedCallOptional attributes for
PartitionedCallAn op that groups a list of partitioned inputs together.
Optional attributes for
PartitionedInputAn op that demultiplexes a tensor to be sharded by XLA to a list of partitioned
outputs outside the XLA computation.
A placeholder op for a value that will be fed into the computation.
Optional attributes for
PlaceholderA placeholder op that passes through
input when its output is not fed.Protobuf type
tensorflow.PlatformInfoProtobuf type
tensorflow.PlatformInfoComputes the Poisson loss between labels and predictions.
A metric that computes the poisson loss metric between labels and predictions.
Compute the polygamma function \(\psi^{(n)}(x)\).
Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
Computes the power of one value to another.
Computes the precision of the predictions with respect to the labels.
Computes best precision where recall is >= specified value.
Creates a dataset that asynchronously prefetches elements from
input_dataset.Optional attributes for
PrefetchDatasetAn op which linearizes one Tensor value to an opaque variant tensor.
Optional attributes for
PrelinearizeAn op which linearizes multiple Tensor values to an opaque variant tensor.
Optional attributes for
PrelinearizeTupleAn identity op that triggers an error if a gradient is requested.
Optional attributes for
PreventGradientPrints a string scalar.
Optional attributes for
PrintA queue that produces elements sorted by the first component value.
Optional attributes for
PriorityQueueCreates a dataset that uses a custom thread pool to compute
input_dataset.Creates a dataset that uses a custom thread pool to compute
input_dataset.Computes the product of elements across dimensions of a tensor.
Optional attributes for
Prod
Next ID: 11
Next ID: 11
Protobuf enum
tensorflow.ProfileOptions.DeviceType
Options for remote profiler session manager.
Options for remote profiler session manager.
Computes the QR decompositions of one or more matrices.
Optional attributes for
QrAn API for building
quantization operations as OpsQuantize the 'input' tensor of type float to 'output' tensor of type 'T'.
Optional attributes for
QuantizeQuantizes then dequantizes a tensor.
Optional attributes for
QuantizeAndDequantizeQuantizes then dequantizes a tensor.
Optional attributes for
QuantizeAndDequantizeV3Quantizes then dequantizes a tensor.
Optional attributes for
QuantizeAndDequantizeV4Returns the gradient of
QuantizeAndDequantizeV4.Optional attributes for
QuantizeAndDequantizeV4GradReturns x + y element-wise, working on quantized buffers.
Produces the average pool of the input tensor for quantized types.
Quantized Batch normalization.
Adds Tensor 'bias' to Tensor 'input' for Quantized types.
Concatenates quantized tensors along one dimension.
Computes a 2D convolution given quantized 4D input and filter tensors.
Optional attributes for
QuantizedConv2dThe QuantizedConv2DAndRelu operation
Optional attributes for
QuantizedConv2DAndReluThe QuantizedConv2DAndReluAndRequantize operation
Optional attributes for
QuantizedConv2DAndReluAndRequantizeThe QuantizedConv2DAndRequantize operation
Optional attributes for
QuantizedConv2DAndRequantizeComputes QuantizedConv2D per channel.
Optional attributes for
QuantizedConv2DPerChannelThe QuantizedConv2DWithBias operation
Optional attributes for
QuantizedConv2DWithBiasThe QuantizedConv2DWithBiasAndRelu operation
Optional attributes for
QuantizedConv2DWithBiasAndReluThe QuantizedConv2DWithBiasAndReluAndRequantize operation
Optional attributes for
QuantizedConv2DWithBiasAndReluAndRequantizeThe QuantizedConv2DWithBiasAndRequantize operation
Optional attributes for
QuantizedConv2DWithBiasAndRequantizeThe QuantizedConv2DWithBiasSignedSumAndReluAndRequantize operation
Optional attributes for
QuantizedConv2DWithBiasSignedSumAndReluAndRequantizeThe QuantizedConv2DWithBiasSumAndRelu operation
Optional attributes for
QuantizedConv2DWithBiasSumAndReluThe QuantizedConv2DWithBiasSumAndReluAndRequantize operation
Optional attributes for
QuantizedConv2DWithBiasSumAndReluAndRequantizeComputes quantized depthwise Conv2D.
Optional attributes for
QuantizedDepthwiseConv2DComputes quantized depthwise Conv2D with Bias.
Optional attributes for
QuantizedDepthwiseConv2DWithBiasComputes quantized depthwise Conv2D with Bias and Relu.
Optional attributes for
QuantizedDepthwiseConv2DWithBiasAndReluComputes quantized depthwise Conv2D with Bias, Relu and Requantize.
Optional attributes for
QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeQuantized Instance normalization.
Optional attributes for
QuantizedInstanceNormPerform a quantized matrix multiplication of
a by the matrix b.Optional attributes for
QuantizedMatMulPerforms a quantized matrix multiplication of
a by the matrix b with bias
add.Optional attributes for
QuantizedMatMulWithBiasThe QuantizedMatMulWithBiasAndDequantize operation
Optional attributes for
QuantizedMatMulWithBiasAndDequantizePerform a quantized matrix multiplication of
a by the matrix b with bias
add and relu fusion.Optional attributes for
QuantizedMatMulWithBiasAndReluPerform a quantized matrix multiplication of
a by the matrix b with bias
add and relu and requantize fusion.Optional attributes for
QuantizedMatMulWithBiasAndReluAndRequantizeThe QuantizedMatMulWithBiasAndRequantize operation
Optional attributes for
QuantizedMatMulWithBiasAndRequantizeProduces the max pool of the input tensor for quantized types.
Returns x * y element-wise, working on quantized buffers.
Convert the quantized 'input' tensor into a lower-precision 'output', using the
actual distribution of the values to maximize the usage of the lower bit depth
and adjusting the output min and max ranges accordingly.
Computes Quantized Rectified Linear:
max(features, 0)Computes Quantized Rectified Linear 6:
min(max(features, 0), 6)Computes Quantized Rectified Linear X:
min(max(features, 0), max_value)Reshapes a quantized tensor as per the Reshape op.
Resize quantized
images to size using quantized bilinear interpolation.Optional attributes for
QuantizedResizeBilinearCloses the given queue.
Optional attributes for
QueueCloseDequeues a tuple of one or more tensors from the given queue.
Optional attributes for
QueueDequeueDequeues
n tuples of one or more tensors from the given queue.Optional attributes for
QueueDequeueManyDequeues
n tuples of one or more tensors from the given queue.Optional attributes for
QueueDequeueUpToEnqueues a tuple of one or more tensors in the given queue.
Optional attributes for
QueueEnqueueEnqueues zero or more tuples of one or more tensors in the given queue.
Optional attributes for
QueueEnqueueManyReturns true if queue is closed.
Protocol buffer representing a QueueRunner.
Protocol buffer representing a QueueRunner.
Computes the number of elements in the given queue.
Counts the number of occurrences of each value in an integer array.
Optional attributes for
RaggedBincountPerforms sparse-output bin counting for a ragged tensor input.
Optional attributes for
RaggedCountSparseOutputGenerates a feature cross from a list of tensors, and returns it as a
RaggedTensor.
The RaggedFillEmptyRows operation
The RaggedFillEmptyRowsGrad operation
Gather ragged slices from
params axis 0 according to indices.An API for building
ragged operations as OpsReturns a
RaggedTensor containing the specified sequences of numbers.Decodes a
variant Tensor into a RaggedTensor.Converts a
RaggedTensor into a SparseTensor with the same values.
input=ragged.from_nested_row_splits(rt_dense_values, rt_nested_splits)
output=SparseTensor(indices=sparse_indices, values=sparse_values,
dense_shape=sparse_dense_shape)Create a dense tensor from a ragged tensor, possibly altering its shape.
Encodes a
RaggedTensor into a variant Tensor.Helper used to compute the gradient for
RaggedTensorToVariant.Randomly crop
image.Optional attributes for
RandomCropCreates a Dataset that returns pseudorandom numbers.
Creates a Dataset that returns pseudorandom numbers.
Optional attributes for
RandomDatasetAn API for building
random.experimental operations as OpsOutputs random values from the Gamma distribution(s) described by alpha.
Optional attributes for
RandomGammaComputes the derivative of a Gamma random sample w.r.t.
Outputs the position of
value in a permutation of [0, ..., max_index].Optional attributes for
RandomIndexShuffleInitializer that generates tensors with a normal distribution.
An API for building
random operations as OpsOutputs random values from the Poisson distribution(s) described by rate.
Optional attributes for
RandomPoissonRandomly shuffles a tensor along its first dimension.
Optional attributes for
RandomShuffleA queue that randomizes the order of elements.
Optional attributes for
RandomShuffleQueueOutputs random values from a normal distribution.
Optional attributes for
RandomStandardNormalInitializer that generates tensors with a uniform distribution.
Outputs random values from a uniform distribution.
Optional attributes for
RandomUniformOutputs random integers from a uniform distribution.
Optional attributes for
RandomUniformIntCreates a sequence of numbers.
Creates a dataset with a range of values.
Optional attributes for
RangeDatasetReturns the rank of a tensor.
A custom gradient for an op of unspecified type.
A base class for operation input accessors.
A tensor which memory has not been mapped to a data space directly accessible from the JVM.
For serializing and restoring the state of ReaderBase, see
reader_base.h for details.
For serializing and restoring the state of ReaderBase, see
reader_base.h for details.
Returns the number of records this Reader has produced.
Returns the number of work units this Reader has finished processing.
Returns the next record (key, value pair) produced by a Reader.
Returns up to
num_records (key, value) pairs produced by a Reader.Restore a Reader to its initial clean state.
Restore a reader to a previously saved state.
Produce a string tensor that encodes the state of a Reader.
Reads and outputs the entire contents of the input filename.
Reads the value of a variable.
Splits resource variable input tensor across all dimensions.
Optional attributes for
ReadVariableSplitNDReturns the real part of a complex number.
Returns x / y element-wise for real types.
Creates a dataset that changes the batch size.
Optional attributes for
RebatchDatasetCreates a dataset that changes the batch size.
Computes the recall of the predictions with respect to the labels.
Computes best recall where precision is >= specified value.
Computes the reciprocal of x element-wise.
Computes the gradient for the inverse of
x wrt its input.Emits randomized records.
Optional attributes for
RecordInputReceives the named tensor from send_device on recv_device.
Optional attributes for
RecvAn op that receives embedding activations on the TPU.
Computes the "logical and" of elements across dimensions of a tensor.
Optional attributes for
ReduceAllComputes the "logical or" of elements across dimensions of a tensor.
Optional attributes for
ReduceAnyReduces the input dataset to a singleton using a reduce function.
Optional attributes for
ReduceDatasetJoins a string Tensor across the given dimensions.
Optional attributes for
ReduceJoinReduce Log Sum Exp Operations
Computes the maximum of elements across dimensions of a tensor.
Optional attributes for
ReduceMaxComputes the minimum of elements across dimensions of a tensor.
Optional attributes for
ReduceMinComputes the product of elements across dimensions of a tensor.
Optional attributes for
ReduceProdComputes the sum of elements across dimensions of a tensor.
Optional attributes for
ReduceSumType of AbstractLoss Reduction
Creates or finds a child frame, and makes
data available to the child frame.Optional attributes for
RefEnterExits the current frame to its parent frame.
Return the same ref tensor as the input ref tensor.
Forwards the value of an available tensor from
inputs to output.Makes its input available to the next iteration.
Forwards the
indexth element of inputs to output.Forwards the ref tensor
data to the output port determined by pred.Check if the input matches the regex pattern.
Replaces matches of the
pattern regular expression in input with the
replacement string provided in rewrite.Optional attributes for
RegexReplaceRegisters a dataset with the tf.data service.
Optional attributes for
RegisterDataset
RegisteredGradient stores a gradient function that is registered in the
gradients library and used in the ops of a function in the function library.
RegisteredGradient stores a gradient function that is registered in the
gradients library and used in the ops of a function in the function library.
The Relayout operation
The RelayoutLike operation
Computes rectified linear:
max(features, 0).Rectified Linear Unit(ReLU) activation.
Computes rectified linear 6:
min(max(features, 0), 6).Computes rectified linear 6 gradients for a Relu6 operation.
Computes rectified linear gradients for a Relu operation.
Runs function
f on a remote device indicated by target.Protobuf type
tensorflow.eager.RemoteTensorHandleProtobuf type
tensorflow.eager.RemoteTensorHandleCreates a dataset that emits the outputs of
input_dataset count times.Optional attributes for
RepeatDatasetConnects N inputs to an N-way replicated TPU computation.
Optional attributes for
ReplicatedInputConnects N outputs from an N-way replicated TPU computation.
Metadata indicating how the TPU computation should be replicated.
Optional attributes for
ReplicateMetadataComputes a range that covers the actual values present in a quantized tensor.
Computes requantization range per channel.
Converts the quantized
input tensor into a lower-precision output.Requantizes input with min and max values known per channel.
Protobuf type
tensorflow.RequestedExitCodeProtobuf type
tensorflow.RequestedExitCodeReshapes a tensor.
Resize
images to size using area interpolation.Optional attributes for
ResizeAreaResize
images to size using bicubic interpolation.Optional attributes for
ResizeBicubicComputes the gradient of bicubic interpolation.
Optional attributes for
ResizeBicubicGradResize
images to size using bilinear interpolation.Optional attributes for
ResizeBilinearComputes the gradient of bilinear interpolation.
Optional attributes for
ResizeBilinearGradResize
images to size using nearest neighbor interpolation.Optional attributes for
ResizeNearestNeighborComputes the gradient of nearest neighbor interpolation.
Optional attributes for
ResizeNearestNeighborGradApplies a gradient to a given accumulator.
Returns the number of gradients aggregated in the given accumulators.
Updates the accumulator with a new value for global_step.
Extracts the average gradient in the given ConditionalAccumulator.
Update '*var' according to the adadelta scheme.
accum = rho() * accum + (1 - rho()) * grad.square();
update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad;
update_accum = rho() * update_accum + (1 - rho()) * update.square();
var -= update;
Optional attributes for
ResourceApplyAdadeltaUpdate '*var' according to the adagrad scheme.
accum += grad * grad
var -= lr * grad * (1 / (sqrt(accum) + epsilon))
Optional attributes for
ResourceApplyAdagradUpdate '*var' according to the proximal adagrad scheme.
Optional attributes for
ResourceApplyAdagradDaUpdate '*var' according to the Adam algorithm.
Optional attributes for
ResourceApplyAdamUpdate '*var' according to the AdaMax algorithm.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
v_t <- max(beta2 * v_{t-1}, abs(g))
variable <- variable - learning_rate / (1 - beta1^t) * m_t / (v_t + epsilon)
Optional attributes for
ResourceApplyAdaMaxUpdate '*var' according to the Adam algorithm.
Optional attributes for
ResourceApplyAdamWithAmsgradUpdate '*var' according to the AddSign update.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
update <- (alpha + sign_decay * sign(g) *sign(m)) * g
variable <- variable - lr_t * update
Optional attributes for
ResourceApplyAddSignUpdate '*var' according to the centered RMSProp algorithm.
Optional attributes for
ResourceApplyCenteredRmsPropUpdate '*var' according to the Ftrl-proximal scheme.
accum_new = accum + grad * grad
grad_with_shrinkage = grad + 2 * l2_shrinkage * var
linear += grad_with_shrinkage +
(accum_new^(-lr_power) - accum^(-lr_power)) / lr * var
quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2
var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0
accum = accum_new
Optional attributes for
ResourceApplyFtrlUpdate '*var' by subtracting 'alpha' * 'delta' from it.
Optional attributes for
ResourceApplyGradientDescentUpdate '*var' according to the momentum scheme.
Optional attributes for
ResourceApplyKerasMomentumUpdate '*var' according to the momentum scheme.
Optional attributes for
ResourceApplyMomentumUpdate '*var' according to the AddSign update.
m_t <- beta1 * m_{t-1} + (1 - beta1) * g
update <- exp(logbase * sign_decay * sign(g) * sign(m_t)) * g
variable <- variable - lr_t * update
Optional attributes for
ResourceApplyPowerSignUpdate '*var' and '*accum' according to FOBOS with Adagrad learning rate.
accum += grad * grad
prox_v = var - lr * grad * (1 / sqrt(accum))
var = sign(prox_v)/(1+lrl2) * max{|prox_v|-lrl1,0}
Optional attributes for
ResourceApplyProximalAdagradUpdate '*var' as FOBOS algorithm with fixed learning rate.
prox_v = var - alpha * delta
var = sign(prox_v)/(1+alphal2) * max{|prox_v|-alphal1,0}
Optional attributes for
ResourceApplyProximalGradientDescentUpdate '*var' according to the RMSProp algorithm.
Optional attributes for
ResourceApplyRmsPropA conditional accumulator for aggregating gradients.
Optional attributes for
ResourceConditionalAccumulatorIncrements variable pointed to by 'resource' until it reaches 'limit'.
Protobuf type
tensorflow.eager.ResourceDtypeAndShapeProtobuf type
tensorflow.eager.ResourceDtypeAndShapeGather slices from the variable pointed to by
resource according to indices.Optional attributes for
ResourceGatherThe ResourceGatherNd operation
Protocol buffer representing a handle to a tensorflow resource.
Protocol buffer representing a handle to a tensorflow resource.
Protocol buffer representing a pair of (data type, tensor shape).
Protocol buffer representing a pair of (data type, tensor shape).
Adds sparse updates to the variable referenced by
resource.Divides sparse updates into the variable referenced by
resource.Reduces sparse updates into the variable referenced by
resource using the max operation.Reduces sparse updates into the variable referenced by
resource using the min operation.Multiplies sparse updates into the variable referenced by
resource.Applies sparse addition to individual values or slices in a Variable.
Optional attributes for
ResourceScatterNdAddThe ResourceScatterNdMax operation
Optional attributes for
ResourceScatterNdMaxThe ResourceScatterNdMin operation
Optional attributes for
ResourceScatterNdMinApplies sparse subtraction to individual values or slices in a Variable.
Optional attributes for
ResourceScatterNdSubApplies sparse
updates to individual values or slices within a given
variable according to indices.Optional attributes for
ResourceScatterNdUpdateSubtracts sparse updates from the variable referenced by
resource.Assigns sparse updates to the variable referenced by
resource.var: Should be from a Variable().
Optional attributes for
ResourceSparseApplyAdadeltaUpdate relevant entries in '*var' and '*accum' according to the adagrad scheme.
Optional attributes for
ResourceSparseApplyAdagradUpdate entries in '*var' and '*accum' according to the proximal adagrad scheme.
Optional attributes for
ResourceSparseApplyAdagradDaUpdate relevant entries in '*var' and '*accum' according to the adagrad scheme.
Optional attributes for
ResourceSparseApplyAdagradV2Update '*var' according to the centered RMSProp algorithm.
Optional attributes for
ResourceSparseApplyCenteredRmsPropUpdate relevant entries in '*var' according to the Ftrl-proximal scheme.
Optional attributes for
ResourceSparseApplyFtrlUpdate relevant entries in '*var' and '*accum' according to the momentum scheme.
Optional attributes for
ResourceSparseApplyKerasMomentumUpdate relevant entries in '*var' and '*accum' according to the momentum scheme.
Optional attributes for
ResourceSparseApplyMomentumSparse update entries in '*var' and '*accum' according to FOBOS algorithm.
Optional attributes for
ResourceSparseApplyProximalAdagradSparse update '*var' as FOBOS algorithm with fixed learning rate.
Optional attributes for
ResourceSparseApplyProximalGradientDescentUpdate '*var' according to the RMSProp algorithm.
Optional attributes for
ResourceSparseApplyRmsPropAssign
value to the sliced l-value reference of ref.Optional attributes for
ResourceStridedSliceAssignRestores tensors from a V2 checkpoint.
Restores a tensor from checkpoint files.
Optional attributes for
RestoreSliceAn op that retrieves optimization parameters from embedding to host memory.
Retrieve Adadelta embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingAdadeltaParametersRetrieve Adagrad Momentum embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingAdagradMomentumParametersRetrieve Adagrad embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingAdagradParametersRetrieve ADAM embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingADAMParametersRetrieve centered RMSProp embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingCenteredRMSPropParametersRetrieve frequency estimator embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingFrequencyEstimatorParametersRetrieve FTRL embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingFTRLParametersRetrieve MDL Adagrad Light embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingMDLAdagradLightParametersRetrieve Momentum embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingMomentumParametersRetrieve proximal Adagrad embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingProximalAdagradParametersThe RetrieveTPUEmbeddingProximalYogiParameters operation
Optional attributes for
RetrieveTPUEmbeddingProximalYogiParametersRetrieve RMSProp embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingRMSPropParametersRetrieve SGD embedding parameters.
Optional attributes for
RetrieveTPUEmbeddingStochasticGradientDescentParametersReverses specific dimensions of a tensor.
Reverses variable length slices.
Optional attributes for
ReverseSequenceThe RewriteDataset operation
Graph rewriting is experimental and subject to change, not covered by any
API stability guarantees.
Graph rewriting is experimental and subject to change, not covered by any
API stability guarantees.
Enum for layout conversion between NCHW and NHWC on CPU.
Message to describe custom graph optimizer and its parameters
Message to describe custom graph optimizer and its parameters
Protobuf enum
tensorflow.RewriterConfig.MemOptType
Enum controlling the number of times to run optimizers.
Protobuf enum
tensorflow.RewriterConfig.ToggleReal-valued fast Fourier transform.
2D real-valued fast Fourier transform.
3D real-valued fast Fourier transform.
ND fast real Fourier transform.
Converts one or more images from RGB to HSV.
Elementwise computes the bitwise right-shift of
x and y.Returns element-wise integer closest to x.
Optimizer that implements the RMSProp algorithm.
Advance the counter of a counter-based RNG.
Advance the counter of a counter-based RNG.
Rolls the elements of a tensor along an axis.
Computes root mean squared error metric between
labels and predictions .Rounds the values of a tensor to the nearest integer, element-wise.
RPC options for distributed runtime.
RPC options for distributed runtime.
Computes reciprocal of square root of x element-wise.
Computes the gradient for the rsqrt of
x wrt its input.
Run-specific items such as arguments to the test / benchmark.
Run-specific items such as arguments to the test / benchmark.
Metadata output (i.e., non-Tensor) for a single Run() call.
Metadata output (i.e., non-Tensor) for a single Run() call.
Protobuf type
tensorflow.RunMetadata.FunctionGraphsProtobuf type
tensorflow.RunMetadata.FunctionGraphs
Options for a single Run() call.
Options for a single Run() call.
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
Everything inside Experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
Options for run handler thread pool.
Options for run handler thread pool.
TODO(pbar) Turn this into a TraceOptions proto which allows
tracing to be controlled in a more orthogonal manner?
Generate a single randomly distorted bounding box for an image.
Optional attributes for
SampleDistortedBoundingBoxCreates a dataset that takes a Bernoulli sample of the contents of another dataset.
Saves tensors in V2 checkpoint format.
The SaveDatasetV2 operation
Optional attributes for
SaveDataset
SavedModel is the high level serialization format for TensorFlow Models.
SavedModel is the high level serialization format for TensorFlow Models.
SavedModelBundle represents a model loaded from storage.
Options for exporting a SavedModel.
Options for loading a SavedModel.
Protobuf type
tensorflow.CapturedTensorProtobuf type
tensorflow.CapturedTensor
Represents `FunctionSpec` used in `Function`.
Represents `FunctionSpec` used in `Function`.
Whether the function should be compiled by XLA.
Protobuf type
tensorflow.SaveableObjectProtobuf type
tensorflow.SaveableObject
A SavedAsset points to an asset in the MetaGraph.
A SavedAsset points to an asset in the MetaGraph.
Protobuf type
tensorflow.SavedBareConcreteFunctionProtobuf type
tensorflow.SavedBareConcreteFunction
Stores low-level information about a concrete function.
Stores low-level information about a concrete function.
Protobuf type
tensorflow.SavedConstantProtobuf type
tensorflow.SavedConstant
A function with multiple signatures, possibly with non-Tensor arguments.
A function with multiple signatures, possibly with non-Tensor arguments.
Protobuf type
tensorflow.SavedObjectProtobuf type
tensorflow.SavedObjectProtobuf type
tensorflow.SavedObjectGraphProtobuf type
tensorflow.SavedObjectGraph
A SavedResource represents a TF object that holds state during its lifetime.
A SavedResource represents a TF object that holds state during its lifetime.
A SavedUserObject is an object (in the object-oriented language of the
TensorFlow program) of some user- or framework-defined class other than
those handled specifically by the other kinds of SavedObjects.
A SavedUserObject is an object (in the object-oriented language of the
TensorFlow program) of some user- or framework-defined class other than
those handled specifically by the other kinds of SavedObjects.
Represents a Variable that is initialized by loading the contents from the
checkpoint.
Represents a Variable that is initialized by loading the contents from the
checkpoint.
Saved tensor slice: it stores the name of the tensors, the slice, and the
raw data.
Saved tensor slice: it stores the name of the tensors, the slice, and the
raw data.
Metadata describing the set of slices of the same tensor saved in a
checkpoint file.
Metadata describing the set of slices of the same tensor saved in a
checkpoint file.
Metadata describing the set of tensor slices saved in a checkpoint file.
Metadata describing the set of tensor slices saved in a checkpoint file.
Each record in a v3 checkpoint file is a serialized SavedTensorSlices
message.
Each record in a v3 checkpoint file is a serialized SavedTensorSlices
message.
Protocol buffer representing the configuration of a Saver.
Protocol buffer representing the configuration of a Saver.
A version number that identifies a different on-disk checkpoint format.
Protobuf type
tensorflow.SaveSliceInfoDefProtobuf type
tensorflow.SaveSliceInfoDefSaves input tensors slices to disk.
Outputs a
Summary protocol buffer with scalar values.The ScaleAndTranslate operation
Optional attributes for
ScaleAndTranslateThe ScaleAndTranslateGrad operation
Optional attributes for
ScaleAndTranslateGradCreates a dataset successively reduces
f over the elements of input_dataset.Creates a dataset successively reduces
f over the elements of input_dataset.Optional attributes for
ScanDatasetOptional attributes for
ScanDatasetAdds sparse updates to a variable reference.
Optional attributes for
ScatterAddDivides a variable reference by sparse updates.
Optional attributes for
ScatterDivReduces sparse updates into a variable reference using the
max operation.Optional attributes for
ScatterMaxReduces sparse updates into a variable reference using the
min operation.Optional attributes for
ScatterMinMultiplies sparse updates into a variable reference.
Optional attributes for
ScatterMulScatters
updates into a tensor of shape shape according to indices.Optional attributes for
ScatterNdApplies sparse addition to individual values or slices in a Variable.
Optional attributes for
ScatterNdAddComputes element-wise maximum.
Optional attributes for
ScatterNdMaxComputes element-wise minimum.
Optional attributes for
ScatterNdMinApplies sparse addition to
input using individual values or slices
from updates according to indices indices.Optional attributes for
ScatterNdNonAliasingAddApplies sparse subtraction to individual values or slices in a Variable.
within a given variable according to
indices.Optional attributes for
ScatterNdSubApplies sparse
updates to individual values or slices within a given
variable according to indices.Optional attributes for
ScatterNdUpdateSubtracts sparse updates to a variable reference.
Optional attributes for
ScatterSubApplies sparse updates to a variable reference.
Optional attributes for
ScatterUpdateManages groups of related properties when creating Tensorflow Operations, such as a common name
prefix.
Protobuf type
tensorflow.ScopedAllocatorOptionsProtobuf type
tensorflow.ScopedAllocatorOptionsComputes fingerprints of the input strings.
Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
linear models with L1 + L2 regularization.
Optional attributes for
SdcaOptimizerApplies L1 regularization shrink step on the parameters.
Computes the maximum along segments of a tensor.
Computes the mean along segments of a tensor.
Computes the minimum along segments of a tensor.
Computes the product along segments of a tensor.
Computes the sum along segments of a tensor.
The SelectV2 operation
Computes the eigen decomposition of one or more square self-adjoint matrices.
Optional attributes for
SelfAdjointEigComputes scaled exponential linear:
scale * alpha * (exp(features) - 1)
if < 0, scale * features otherwise.Scaled Exponential Linear Unit (SELU).
Computes gradients for the scaled exponential linear (Selu) operation.
Sends the named tensor from send_device to recv_device.
Optional attributes for
SendPerforms gradient updates of embedding tables.
Optional attributes for
SendTPUEmbeddingGradientsComputes best sensitivity where sensitivity is >= specified value.
Protobuf type
tensorflow.SequenceExampleProtobuf type
tensorflow.SequenceExample
Represents a serialized tf.dtypes.Dtype
Represents a serialized tf.dtypes.Dtype
Converts the given
resource_handle representing an iterator to a variant tensor.Optional attributes for
SerializeIteratorSerialize an
N-minibatch SparseTensor into an [N, 3] Tensor object.Serialize a
SparseTensor into a [3] Tensor object.Transforms a Tensor into a serialized TensorProto proto.
An in-process TensorFlow server, for use in distributed training.
Defines the configuration of a single TensorFlow server.
Defines the configuration of a single TensorFlow server.
Configuration for a tf.data service DispatchServer.
Configuration for a tf.data service DispatchServer.
Configuration for a tf.data service WorkerServer.
Configuration for a tf.data service WorkerServer.
Driver for
Graph execution.A callable function backed by a session.
Protocol buffer used for logging session state.
Protocol buffer used for logging session state.
Protobuf enum
tensorflow.SessionLog.SessionStatus
Metadata about the session.
Metadata about the session.
Computes the difference between two lists of numbers or strings.
Creates Framework set Operations
Enumeration containing the string operation values to be passed to the TensorFlow Sparse Ops
function
SparseOps.denseToDenseSetOperation(Operand, Operand, String, DenseToDenseSetOperation.Options...)Number of unique elements along last dimension of input
set.Optional attributes for
SetSizeThe ExperimentalSetStatsAggregatorDataset operation
The SetStatsAggregatorDataset operation
Returns the shape of a tensor.
Returns shape of tensors.
An API for building
shape operations as OpsAn operator providing methods on org.tensorflow.op.core.Shape tensors and 1d operands that
represent the dimensions of a shape.
Various methods for processing with Shapes and Operands
Creates a
Dataset that includes only 1/num_shards of this dataset.Optional attributes for
ShardDatasetGenerate a sharded filename.
Generate a glob pattern matching all sharded file names.
The ShuffleAndRepeatDatasetV2 operation
Optional attributes for
ShuffleAndRepeatDatasetThe ShuffleDatasetV3 operation
Optional attributes for
ShuffleDatasetShuts down a running distributed TPU system.
An op that shuts down the TPU system.
Sigmoid activation.
Computes sigmoid of
x element-wise.Computes the gradient of the sigmoid of
x wrt its input.Returns an element-wise indication of the sign of a number.
An API for building
signal operations as OpsDescribe the inputs and outputs of an executable entity, such as a
ConcreteFunction,
among other useful metadata.Builds a new function signature.
SignatureDef defines the signature of a computation supported by a TensorFlow
graph.
SignatureDef defines the signature of a computation supported by a TensorFlow
graph.
Computes sine of x element-wise.
Computes hyperbolic sine of x element-wise.
Returns the size of a tensor.
Creates a dataset that skips
count elements from the input_dataset.Optional attributes for
SkipDatasetParses a text file and creates a batch of examples.
Optional attributes for
SkipgramThe ExperimentalSleepDataset operation
The SleepDataset operation
Return a slice from 'input'.
Creates a dataset that passes a sliding window over
input_dataset.Creates a dataset that passes a sliding window over
input_dataset.Optional attributes for
SlidingWindowDatasetReturns a copy of the input tensor.
Metadata for a `tf.data.Dataset` distributed snapshot.
Metadata for a `tf.data.Dataset` distributed snapshot.
This stores the metadata information present in each snapshot record.
This stores the metadata information present in each snapshot record.
Each SnapshotRecord represents one batch of pre-processed input data.
Each SnapshotRecord represents one batch of pre-processed input data.
Metadata for all the tensors in a Snapshot Record.
Metadata for all the tensors in a Snapshot Record.
Metadata for a single tensor in the Snapshot Record.
Metadata for a single tensor in the Snapshot Record.
The SnapshotChunkDataset operation
Optional attributes for
SnapshotChunkDatasetCreates a dataset that will write to / read from a snapshot.
Optional attributes for
SnapshotDatasetThe SnapshotDatasetReader operation
Optional attributes for
SnapshotDatasetReaderThe SnapshotNestedDatasetReader operation
Generates points from the Sobol sequence.
Softmax converts a real vector to a vector of categorical probabilities.
Computes softmax activations.
Computes softmax cross entropy cost and gradients to backpropagate.
Softplus activation function,
softplus(x) = log(exp(x) + 1).The Softplus operation
Computes softplus gradients for a softplus operation.
Softsign activation function,
softsign(x) = x / (abs(x) + 1).Computes softsign:
features / (abs(features) + 1).Computes softsign gradients for a softsign operation.
Solves systems of linear equations.
Optional attributes for
SolveThe SortListOfSparseCoreCooTensors operation
Content of a source file involved in the execution of the debugged TensorFlow
program.
Content of a source file involved in the execution of the debugged TensorFlow
program.
Holds the information of the source that writes the events.
Holds the information of the source that writes the events.
SpaceToBatch for 4-D tensors of type T.
SpaceToBatch for N-D tensors of type T.
SpaceToDepth for tensors of type T.
Optional attributes for
SpaceToDepthApplies a sparse gradient to a given accumulator.
Extracts the average sparse gradient in a SparseConditionalAccumulator.
Adds two
SparseTensor objects to produce another SparseTensor.The gradient operator for the SparseAdd op.
var: Should be from a Variable().
Optional attributes for
SparseApplyAdadeltaUpdate relevant entries in '*var' and '*accum' according to the adagrad scheme.
Optional attributes for
SparseApplyAdagradUpdate entries in '*var' and '*accum' according to the proximal adagrad scheme.
Optional attributes for
SparseApplyAdagradDaUpdate '*var' according to the centered RMSProp algorithm.
Optional attributes for
SparseApplyCenteredRmsPropUpdate relevant entries in '*var' according to the Ftrl-proximal scheme.
Optional attributes for
SparseApplyFtrlUpdate relevant entries in '*var' and '*accum' according to the momentum scheme.
Optional attributes for
SparseApplyMomentumSparse update entries in '*var' and '*accum' according to FOBOS algorithm.
Optional attributes for
SparseApplyProximalAdagradSparse update '*var' as FOBOS algorithm with fixed learning rate.
Optional attributes for
SparseApplyProximalGradientDescentUpdate '*var' according to the RMSProp algorithm.
Optional attributes for
SparseApplyRmsPropCounts the number of occurrences of each value in an integer array.
Optional attributes for
SparseBincountCalculates how often predictions matches integer labels.
Computes the crossentropy loss between labels and predictions.
A metric that computes the sparse categorical cross-entropy loss between true labels and
predicted labels.
Concatenates a list of
SparseTensor along the specified dimension.A conditional accumulator for aggregating sparse gradients.
Optional attributes for
SparseConditionalAccumulatorPerforms sparse-output bin counting for a sparse tensor input.
Optional attributes for
SparseCountSparseOutputGenerates sparse cross from a list of sparse and dense tensors.
Generates sparse cross from a list of sparse and dense tensors.
Adds up a SparseTensor and a dense Tensor, using these special rules:
(1) Broadcasts the dense side to have the same shape as the sparse side, if
eligible;
(2) Then, only the dense values pointed to by the indices of the SparseTensor
participate in the cwise addition.
Component-wise divides a SparseTensor by a dense Tensor.
Component-wise multiplies a SparseTensor by a dense Tensor.
Fills empty rows in the input 2-D
SparseTensor with a default value.The gradient of SparseFillEmptyRows.
Multiply matrix "a" by matrix "b".
Optional attributes for
SparseMatMulSparse addition of two CSR matrices, C = alpha * A + beta * B.
Matrix-multiplies a sparse matrix with a dense matrix.
Optional attributes for
SparseMatrixMatMulElement-wise multiplication of a sparse matrix with a dense tensor.
Returns the number of nonzeroes of
sparse_matrix.Computes the Approximate Minimum Degree (AMD) ordering of
input.Calculates the softmax of a CSRSparseMatrix.
Calculates the gradient of the SparseMatrixSoftmax op.
Computes the sparse Cholesky decomposition of
input.Sparse-matrix-multiplies two CSR matrices
a and b.Optional attributes for
SparseMatrixSparseMatMulTransposes the inner (matrix) dimensions of a CSRSparseMatrix.
Optional attributes for
SparseMatrixTransposeCreates an all-zeros CSRSparseMatrix with shape
dense_shape.An API for building
sparse operations as OpsComputes the max of elements across dimensions of a SparseTensor.
Optional attributes for
SparseReduceMaxComputes the max of elements across dimensions of a SparseTensor.
Optional attributes for
SparseReduceMaxSparseComputes the sum of elements across dimensions of a SparseTensor.
Optional attributes for
SparseReduceSumComputes the sum of elements across dimensions of a SparseTensor.
Optional attributes for
SparseReduceSumSparseReorders a SparseTensor into the canonical, row-major ordering.
Reshapes a SparseTensor to represent values in a new dense shape.
Computes the mean along sparse segments of a tensor.
Optional attributes for
SparseSegmentMeanComputes gradients for SparseSegmentMean.
Computes the mean along sparse segments of a tensor.
Optional attributes for
SparseSegmentMeanWithNumSegmentsComputes the sum along sparse segments of a tensor divided by the sqrt of N.
Optional attributes for
SparseSegmentSqrtNComputes gradients for SparseSegmentSqrtN.
Computes the sum along sparse segments of a tensor divided by the sqrt of N.
Optional attributes for
SparseSegmentSqrtNWithNumSegmentsComputes the sum along sparse segments of a tensor.
Optional attributes for
SparseSegmentSumComputes gradients for SparseSegmentSum.
Computes the sum along sparse segments of a tensor.
Optional attributes for
SparseSegmentSumWithNumSegmentsSlice a
SparseTensor based on the start and size.The gradient operator for the SparseSlice op.
Applies softmax to a batched N-D
SparseTensor.Computes softmax cross entropy cost and gradients to backpropagate.
Returns the element-wise max of two SparseTensors.
Returns the element-wise min of two SparseTensors.
Split a
SparseTensor into num_split tensors along one dimension.This is a helper class that represents a sparse tensor who's attributes may be passed to
SparseOps methods.A virtual type of
Tensor composed of three dense tensors (indices, values and dimensions)
used to represent the sparse data into a multi-dimensional dense space.Adds up a
SparseTensor and a dense Tensor, producing a dense Tensor.Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
Optional attributes for
SparseTensorDenseMatMulCreates a dataset that splits a SparseTensor into elements row-wise.
Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.
Converts a sparse representation into a dense tensor.
Optional attributes for
SparseToDenseComputes how often integer targets are in the top `K` predictions.
Applies set operation along last dimension of 2
SparseTensor inputs.Optional attributes for
SparseToSparseSetOperationComputes best specificity where sensitivity is >= specified value.
The Spence operation
Splits a tensor into
num_split tensors along one dimension.An op splits input deduplication data XLA tuple into integer and floating point
tensors.
Optional attributes for
SplitDedupDataSplits input tensor across all dimensions.
Optional attributes for
SplitNDSplits a tensor into
num_split tensors along one dimension.Creates a dataset that executes a SQL query and emits rows of the result set.
Creates a dataset that executes a SQL query and emits rows of the result set.
Computes square root of x element-wise.
Computes the gradient for the sqrt of
x wrt its input.Computes the matrix square root of one or more square matrices:
matmul(sqrtm(A), sqrtm(A)) = A
Computes square of x element-wise.
Returns conj(x - y)(x - y) element-wise.
Computes the squared hinge loss between labels and predictions.
A metric that computes the squared hinge loss metric between labels and predictions.
Removes dimensions of size 1 from the shape of a tensor.
Optional attributes for
SqueezePacks a list of
N rank-R tensors into one rank-(R+1) tensor.Optional attributes for
StackDelete the stack from its resource container.
A stack that produces elements in first-in last-out order.
Optional attributes for
StackCreate
A stack frame with ID.
A stack frame with ID.
Pop the element at the top of the stack.
Push an element onto the stack.
Optional attributes for
StackPushStage values similar to a lightweight Enqueue.
Optional attributes for
StageOp removes all elements in the underlying container.
Optional attributes for
StageClearOp peeks at the values at the specified index.
Optional attributes for
StagePeekOp returns the number of elements in the underlying container.
Optional attributes for
StageSizeAn n-way switch statement which calls a single branch function.
output = cond ?
returns
f(inputs), where f's body is placed and partitioned.Optional attributes for
StatefulPartitionedCallThe StatefulRandomBinomial operation
Outputs random values from a normal distribution.
Outputs random values from a truncated normal distribution.
Outputs random values from a uniform distribution.
Outputs random integers from a uniform distribution.
Outputs random integers from a uniform distribution.
output = input; While (Cond(output)) { output = Body(output) }
An n-way switch statement which calls a single branch function.
output = cond ?
Draws samples from a multinomial distribution.
The StatelessParameterizedTruncatedNormal operation
Outputs deterministic pseudorandom random numbers from a binomial distribution.
Outputs deterministic pseudorandom random numbers from a gamma distribution.
Picks the best counter-based RNG algorithm based on device.
Scrambles seed into key and counter, using the best algorithm based on device.
Picks the best algorithm based on device, and scrambles seed into key and counter.
Outputs deterministic pseudorandom values from a normal distribution.
Outputs deterministic pseudorandom values from a normal distribution.
Outputs deterministic pseudorandom random numbers from a Poisson distribution.
Outputs deterministic pseudorandom random values from a uniform distribution.
Outputs deterministic pseudorandom random integers from a uniform distribution.
Outputs deterministic pseudorandom random integers from a uniform distribution.
Outputs deterministic pseudorandom random integers from a uniform distribution.
Outputs deterministic pseudorandom random integers from a uniform distribution.
Outputs deterministic pseudorandom random values from a uniform distribution.
Generate a randomly distorted bounding box for an image deterministically.
Optional attributes for
StatelessSampleDistortedBoundingBoxRandomly and deterministically shuffles a tensor along its first dimension.
Outputs deterministic pseudorandom values from a truncated normal distribution.
Outputs deterministic pseudorandom values from a truncated normal distribution.
output = input; While (Cond(output)) { output = Body(output) }
Check if the input matches the regex pattern.
Replaces the match of pattern in input with rewrite.
Optional attributes for
StaticRegexReplaceCreates a statistics manager resource.
The StatsAggregatorHandleV2 operation
Optional attributes for
StatsAggregatorHandleOptional attributes for
StatsAggregatorHandleSet a summary_writer_interface to record statistics using given stats_aggregator.
Produces a summary of any statistics recorded by the given statistics manager.
Produces a summary of any statistics recorded by the given statistics manager.
Wire-format for Status.
Wire-format for Status.
Protobuf type
tensorflow.StepStatsProtobuf type
tensorflow.StepStatsStochastically cast a given tensor from floats to ints.
Stops gradient computation.
The StoreMinibatchStatisticsInFdo operation
Return a strided slice from
input.Optional attributes for
StridedSliceAssign
value to the sliced l-value reference of ref.Optional attributes for
StridedSliceAssignReturns the gradient of
StridedSlice.Optional attributes for
StridedSliceGradHelper endpoint methods for Python like indexing.
Formats a string template using a list of tensors.
Optional attributes for
StringFormatString lengths of
input.Optional attributes for
StringLengthCreates ngrams from ragged string data.
An API for building
strings operations as OpsSplit elements of
source based on sep into a SparseTensor.Optional attributes for
StringSplitStrip leading and trailing whitespaces from the Tensor.
A protobuf to represent tf.BoundedTensorSpec.
A protobuf to represent tf.BoundedTensorSpec.
Represents a Python dict keyed by `str`.
Represents a Python dict keyed by `str`.
Represents a Python list.
Represents a Python list.
Represents Python's namedtuple.
Represents Python's namedtuple.
Represents None.
Represents None.
Represents a (key, value) pair.
Represents a (key, value) pair.
`StructuredValue` represents a dynamically typed value representing various
data structures that are inspired by Python data structures typically used in
TensorFlow functions as inputs and outputs.
`StructuredValue` represents a dynamically typed value representing various
data structures that are inspired by Python data structures typically used in
TensorFlow functions as inputs and outputs.
A protobuf to represent tf.TensorSpec.
A protobuf to represent tf.TensorSpec.
Represents a Python tuple.
Represents a Python tuple.
Represents a tf.TypeSpec
Represents a tf.TypeSpec
Protobuf enum
tensorflow.TypeSpecProto.TypeSpecClassReturns x - y element-wise.
Return substrings from
Tensor of strings.Optional attributes for
SubstrComputes the (weighted) sum of the given values.
Computes the sum of elements across dimensions of a tensor.
Optional attributes for
Sum
A Summary is a set of named values to be displayed by the
visualizer.
Protobuf type
tensorflow.Summary.AudioProtobuf type
tensorflow.Summary.Audio
A Summary is a set of named values to be displayed by the
visualizer.
Protobuf type
tensorflow.Summary.ImageProtobuf type
tensorflow.Summary.ImageProtobuf type
tensorflow.Summary.ValueProtobuf type
tensorflow.Summary.Value
Metadata associated with a series of Summary data
Metadata associated with a series of Summary data
A SummaryMetadata encapsulates information on which plugins are able to make
use of a certain summary value.
A SummaryMetadata encapsulates information on which plugins are able to make
use of a certain summary value.
Protobuf type
tensorflow.SummaryMetadata.PluginDataProtobuf type
tensorflow.SummaryMetadata.PluginDataAn API for building
summary operations as OpsThe SummaryWriter operation
Optional attributes for
SummaryWriterComputes the singular value decompositions of one or more matrices.
Optional attributes for
SvdSwish activation function.
Forwards
data to the output port determined by pred.Computes the gradient function for function f via backpropagation.
Synchronizes the device this op is run on.
For logging the metadata output for a single session.run() call.
For logging the metadata output for a single session.run() call.
Creates a dataset that contains
count elements from the input_dataset.Optional attributes for
TakeDatasetRead
SparseTensors from a SparseTensorsMap and concatenate them.Optional attributes for
TakeManySparseFromTensorsMapCreates a dataset that stops iteration when predicate` is false.
Creates a dataset that stops iteration when predicate` is false.
Optional attributes for
TakeWhileDatasetComputes tan of x element-wise.
Hyperbolic tangent activation function.
Computes hyperbolic tangent of
x element-wise.Computes the gradient for the tanh of
x wrt its input.
Defines the device filters for a remote task.
Defines the device filters for a remote task.
Brain 16-bit float tensor type.
Boolean tensor type.
Returns a tensor that may be mutated, but only persists within a single step.
Optional attributes for
TemporaryVariableA statically typed multi-dimensional array.
An array of Tensors of given size.
Optional attributes for
TensorArrayDelete the TensorArray from its resource container.
Concat the elements from the TensorArray into value
value.Optional attributes for
TensorArrayConcatGather specific elements from the TensorArray into output
value.Optional attributes for
TensorArrayGatherCreates a TensorArray for storing the gradients of values in the given handle.
Creates a TensorArray for storing multiple gradients of values in the given handle.
The TensorArrayPack operation
Optional attributes for
TensorArrayPackRead an element from the TensorArray into output
value.Scatter the data from the input value into specific TensorArray elements.
Get the current size of the TensorArray.
Split the data from the input value into TensorArray elements.
The TensorArrayUnpack operation
Push an element onto the tensor_array.
Defines a connection between two tensors in a `GraphDef`.
Defines a connection between two tensors in a `GraphDef`.
Creates a dataset that emits
components as a tuple of tensors once.Optional attributes for
TensorDataset
Available modes for extracting debugging information from a Tensor.
Protobuf type
tensorflow.TensorDescriptionProtobuf type
tensorflow.TensorDescriptionReturns a diagonal tensor with a given diagonal values.
Returns the diagonal part of the tensor.
tensor contraction Operations
Static utility methods describing the TensorFlow runtime.
Unchecked exception thrown by TensorFlow core classes
A function that can be called with tensors.
Information about a Tensor necessary for feeding or retrieval.
Information about a Tensor necessary for feeding or retrieval.
Generic encoding for composite tensors.
Generic encoding for composite tensors.
For sparse tensors, The COO encoding stores a triple of values, indices,
and shape.
For sparse tensors, The COO encoding stores a triple of values, indices,
and shape.
Concats all tensors in the list along the 0th dimension.
The TensorListConcatLists operation
The shape of the elements of the given list, as a tensor.
input_handle: the list
element_shape: the shape of elements of the list
Creates a TensorList which, when stacked, has the value of
tensor.Creates a Tensor by indexing into the TensorList.
Returns the item in the list with the given index.
input_handle: the list
index: the position in the list from which an element will be retrieved
item: the element at that position
Returns the number of tensors in the input tensor list.
input_handle: the input list
length: the number of tensors in the list
Returns the last element of the input list as well as a list with all but that element.
Returns a list which has the passed-in
Tensor as last element and the other elements of the given list in input_handle.
tensor: The tensor to put on the list.
input_handle: The old list.
output_handle: A list with the elements of the old list followed by tensor.
element_dtype: the type of elements in the list.
element_shape: a shape compatible with that of elements in the list.The TensorListPushBackBatch operation
List of the given size with empty elements.
element_shape: the shape of the future elements of the list
num_elements: the number of elements to reserve
handle: the output list
element_dtype: the desired type of elements in the list.
Resizes the list.
input_handle: the input list
size: size of the output list
Creates a TensorList by indexing into a Tensor.
Scatters tensor at indices in an input list.
Sets the index-th position of the list to contain the given tensor.
input_handle: the list
index: the position in the list to which the tensor will be assigned
item: the element to be assigned to that position
output_handle: the new list, with the element in the proper position
Optional attributes for
TensorListSetItemSplits a tensor into a list.
list[i] corresponds to lengths[i] tensors from the input tensor.
Stacks all tensors in the list.
Optional attributes for
TensorListStackReturns a tensor map with item from given key erased.
input_handle: the original map
output_handle: the map with value from given key removed
key: the key of the value to be erased
Returns whether the given key exists in the map.
input_handle: the input map
key: the key to check
has_key: whether the key is already in the map or not
Returns a map that is the 'input_handle' with the given key-value pair inserted.
input_handle: the original map
output_handle: the map with key and value inserted
key: the key to be inserted
value: the value to be inserted
Returns the value from a given key in a tensor map.
input_handle: the input map
key: the key to be looked up
value: the value found from the given key
Maps the native memory of a
RawTensor to a n-dimensional typed data space accessible from
the JVM.Returns the number of tensors in the input tensor map.
input_handle: the input map
size: the number of tensors in the map
Returns a Tensor stack of all keys in a tensor map.
input_handle: the input map
keys: the returned Tensor of all keys in the map
Protocol buffer representing a tensor.
Protocol buffer representing a tensor.
Adds sparse
updates to an existing tensor according to indices.Optional attributes for
TensorScatterNdAddApply a sparse update to a tensor taking the element-wise maximum.
Optional attributes for
TensorScatterNdMaxThe TensorScatterMin operation
Optional attributes for
TensorScatterNdMinSubtracts sparse
updates from an existing tensor according to indices.Optional attributes for
TensorScatterNdSubScatter
updates into an existing tensor according to indices.Optional attributes for
TensorScatterNdUpdate
Dimensions of a tensor.
Dimensions of a tensor.
One dimension of the tensor.
One dimension of the tensor.
Creates a dataset that emits each dim-0 slice of
components once.Optional attributes for
TensorSliceDataset
Can only be interpreted if you know the corresponding TensorShape.
Can only be interpreted if you know the corresponding TensorShape.
Extent of the slice in one dimension.
Extent of the slice in one dimension.
Assign
value to the sliced l-value reference of input.Optional attributes for
TensorStridedSliceUpdateOutputs a
Summary protocol buffer with a tensor and per-plugin data.Annotation for all tensor types.
The output of one benchmark / test run.
The type of benchmark.
The output of one benchmark / test run.
Creates a dataset that emits the lines of one or more text files.
Optional attributes for
TextLineDatasetA Reader that outputs the lines of a file delimited by '\n'.
Optional attributes for
TextLineReaderFunction to be implemented on the JVM side to be called back by the native library when it is time to attach gradient operations for the given op, graph and scope.
Unique identifier of a TensorFlow graph instance
Helper class for loading the TensorFlow Java native library.
IEEE-754 half-precision 16-bit float tensor type.
IEEE-754 single-precision 32-bit float tensor type.
IEEE-754 double-precision 64-bit float tensor type.
Common interface for all floating point tensors.
Creates a dataset that emits the records from one or more TFRecord files.
Optional attributes for
TfRecordDatasetA Reader that outputs the records from a TensorFlow Records file.
Optional attributes for
TfRecordReaderCreates a dataset that uses a custom thread pool to compute
input_dataset.Creates a dataset that uses a custom thread pool to compute
input_dataset.Creates a dataset that uses a custom thread pool to compute
input_dataset.Creates a dataset that uses a custom thread pool to compute
input_dataset.Optional attributes for
ThreadPoolHandleOptional attributes for
ThreadPoolHandleProtobuf type
tensorflow.ThreadPoolOptionProtoProtobuf type
tensorflow.ThreadPoolOptionProtoGenerates labels for candidate sampling with a learned unigram distribution.
Optional attributes for
ThreadUnsafeUnigramCandidateSamplerConstructs a tensor by tiling a given tensor.
Returns the gradient of
Tile.Provides the time since epoch in seconds.
32-bit signed integer tensor type.
64-bit signed integer tensor type.
Common interface for all integral numeric tensors.
Common interface for all numeric tensors.
Converts a tensor to a scalar predicate.
Converts each string in the input Tensor to its hash mod by a number of buckets.
Converts each string in the input Tensor to its hash mod by a number of buckets.
Converts each string in the input Tensor to its hash mod by a number of buckets.
Converts each string in the input Tensor to the specified numeric type.
Finds values and indices of the
k largest elements for the last dimension.Optional attributes for
TopKComputes the poisson loss metric between labels and predictions.
Returns the TopK unique values in the array in sorted order.
Returns the TopK values in the array in sorted order.
The TPUAnnotateTensorsWithDynamicShape operation
Deprecated.
use
CompilationResult insteadOp that copies host tensor to device with dynamic shape support.
Deprecated.
use
EmbeddingActivations insteadConverts XRT's uid handles to TensorFlow-friendly input format.
An API for building
tpu operations as OpsDeprecated.
use
ReplicatedInput insteadOptional attributes for
TPUReplicatedInputDeprecated.
use
ReplicatedOutput insteadDeprecated.
use
ReplicateMetadata insteadOptional attributes for
TPUReplicateMetadataOp that reshards on-device TPU variables to specified state.
Round-robin load balancing on TPU cores.
Protobuf type
tensorflow.RegisteredSaverProtobuf type
tensorflow.RegisteredSaverProtobuf type
tensorflow.TrackableObjectGraphProtobuf type
tensorflow.TrackableObjectGraphProtobuf type
tensorflow.TrackableObjectGraph.TrackableObjectProtobuf type
tensorflow.TrackableObjectGraph.TrackableObjectProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.ObjectReferenceProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.ObjectReferenceProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensorProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensorProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReferenceProtobuf type
tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReferenceAn API for building
train operations as Ops
Extra data needed on a non-RDMA RecvBufResponse.
Extra data needed on a non-RDMA RecvBufResponse.
Shuffle dimensions of x according to a permutation.
Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.
Optional attributes for
TriangularSolveCalculate product with tridiagonal matrix.
Solves tridiagonal systems of equations.
Optional attributes for
TridiagonalSolveMetric that calculates the number of true negatives.
Metric that calculates the number of true positives.
Returns x / y element-wise, rounded towards zero.
Initializer that generates a truncated normal distribution.
Outputs random values from a truncated normal distribution.
Optional attributes for
TruncatedNormalReturns element-wise remainder of division.
String type.
Common interface for all typed tensors.
16-bit unsigned integer tensor type.
8-bit unsigned integer tensor type.
Reverses the operation of Batch for a single output Tensor.
Optional attributes for
UnbatchA dataset that splits the elements of its input into multiple elements.
A dataset that splits the elements of its input into multiple elements.
Optional attributes for
UnbatchDatasetGradient of Unbatch.
Optional attributes for
UnbatchGradUncompresses a compressed dataset element.
Decodes each string in
input into a sequence of Unicode code points.Optional attributes for
UnicodeDecodeDecodes each string in
input into a sequence of Unicode code points.Optional attributes for
UnicodeDecodeWithOffsetsEncode a tensor of ints into unicode strings.
Optional attributes for
UnicodeEncodeDetermine the script codes of a given tensor of Unicode integer code points.
Transcode the input text from a source encoding to a destination encoding.
Optional attributes for
UnicodeTranscodeGenerates labels for candidate sampling with a uniform distribution.
Optional attributes for
UniformCandidateSamplerPerform dequantization on the quantized Tensor
input.Optional attributes for
UniformDequantizePerform quantization on Tensor
input.Optional attributes for
UniformQuantizePerform quantized add of quantized Tensor
lhs and quantized Tensor rhs to make quantized output.Optional attributes for
UniformQuantizedAddPerform clip by value on the quantized Tensor
operand.Optional attributes for
UniformQuantizedClipByValuePerform quantized convolution of quantized Tensor
lhs and quantized Tensor rhs. to make quantized output.Optional attributes for
UniformQuantizedConvolutionPerform hybrid quantized convolution of float Tensor
lhs and quantized Tensor rhs.Optional attributes for
UniformQuantizedConvolutionHybridPerform quantized dot of quantized Tensor
lhs and quantized Tensor rhs to make quantized output.Optional attributes for
UniformQuantizedDotPerform hybrid quantized dot of float Tensor
lhs and quantized Tensor rhs.Optional attributes for
UniformQuantizedDotHybrid
Describes the dimension numbers for Convolution op.
Describes the dimension numbers for Convolution op.
Given quantized tensor
input, requantize it with new quantization parameters.Optional attributes for
UniformRequantizeFinds unique elements along an axis of a tensor.
Creates a dataset that contains the unique elements of
input_dataset.Creates a dataset that contains the unique elements of
input_dataset.Optional attributes for
UniqueDatasetFinds unique elements along an axis of a tensor.
Constrains the weights to have unit norm.
Converts an array of flat indices into a tuple of coordinate arrays.
The UnsortedSegmentJoin operation
Optional attributes for
UnsortedSegmentJoinComputes the maximum along segments of a tensor.
Computes the minimum along segments of a tensor.
Computes the product along segments of a tensor.
Computes the sum along segments of a tensor.
Unpacks a given dimension of a rank-
R tensor into num rank-(R-1) tensors.Optional attributes for
UnstackOp is similar to a lightweight Dequeue.
Optional attributes for
UnstageThe UnwrapDatasetVariant operation
An op to update the task ID and global core array.
Converts all lowercase characters into their respective uppercase replacements.
Optional attributes for
UpperApplies upper_bound(sorted_search_values, values) along each row.
Protocol buffer representing the values in ControlFlowContext.
Protocol buffer representing the values in ControlFlowContext.
Creates a handle to a Variable resource.
Optional attributes for
VarHandleOpHolds state in the form of a tensor that persists across steps.
Optional attributes for
Variable
Indicates how a distributed variable will be aggregated.
Protocol buffer representing a Variable.
Protocol buffer representing a Variable.
Returns the shape of the variable pointed to by
resource.
Indicates when a distributed variable will be synced.
Initializer capable of adapting its scale to the shape of weights tensors.
The random distribution to use when initializing the values.
The mode to use for calculating the fan values.
Protocol buffer representing the serialization format of DT_VARIANT tensors.
Protocol buffer representing the serialization format of DT_VARIANT tensors.
Checks whether a resource handle-based variable has been initialized.
Protobuf type
tensorflow.VarLenFeatureProtoProtobuf type
tensorflow.VarLenFeatureProto
The config for graph verifiers.
The config for graph verifiers.
Protobuf enum
tensorflow.VerifierConfig.Toggle
Version information for a piece of serialized data
There are different types of versions for each type of data
(GraphDef, etc.), but they all have the same common shape
described here.
Version information for a piece of serialized data
There are different types of versions for each type of data
(GraphDef, etc.), but they all have the same common shape
described here.
Protobuf type
tensorflow.WatchdogConfigProtobuf type
tensorflow.WatchdogConfigA minimalist pointer scope only keeping weak references to its elements.
The WeightedFlatMapDataset operation
Optional attributes for
WeightedFlatMapDatasetReturns locations of nonzero / true values in a tensor.
output = input; While (Cond(output)) { output = Body(output) }
Optional attributes for
While
Protocol buffer representing a WhileContext object.
Protocol buffer representing a WhileContext object.
A Reader that outputs the entire contents of a file as a value.
Optional attributes for
WholeFileReaderCombines (nests of) input elements into a dataset of (nests of) windows.
Optional attributes for
WindowDatasetThe WindowOp operation
Current health status of a worker.
Worker heartbeat op.
Protobuf type
tensorflow.WorkerHeartbeatRequestProtobuf type
tensorflow.WorkerHeartbeatRequestProtobuf type
tensorflow.WorkerHeartbeatResponseProtobuf type
tensorflow.WorkerHeartbeatResponse
Indicates the behavior of the worker when an internal error or shutdown
signal is received.
The WrapDatasetVariant operation
Writes an audio summary.
Optional attributes for
WriteAudioSummaryWrites
contents to the file at input filename.Writes a graph summary.
Writes a histogram summary.
Writes an image summary.
Optional attributes for
WriteImageSummaryWrites a serialized proto summary.
Writes a scalar summary.
Writes a tensor summary.
Returns 0 if x == 0, and x / y otherwise, elementwise.
A pseudo-op to represent host-side computation in an XLA program.
Optional attributes for
XlaHostComputeAn API for building
xla operations as OpsAn op to receive a tensor from the host.
output: the tensor that will be received from the host.
An op that receives embedding activations on the TPU.
Receives deduplication data (indices and weights) from the embedding core.
An op to send a tensor to the host.
input: the tensor that will be sent to the host.
An op that performs gradient updates of embedding tables.
Optional attributes for
XlaSendTPUEmbeddingGradientsThe XlaSparseCoreAdagrad operation
The XlaSparseCoreAdagradMomentum operation
The XlaSparseCoreAdam operation
The XlaSparseCoreFtrl operation
The XlaSparseCoreSgd operation
The XlaSparseDenseMatmul operation
The XlaSparseDenseMatmulGradWithAdagradAndCsrInput operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdagradAndCsrInputThe XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSize operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdagradAndStaticBufferSizeThe XlaSparseDenseMatmulGradWithAdagradMomentumAndCsrInput operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdagradMomentumAndCsrInputThe XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSize operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdagradMomentumAndStaticBufferSizeThe XlaSparseDenseMatmulGradWithAdamAndCsrInput operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdamAndCsrInputThe XlaSparseDenseMatmulGradWithAdamAndStaticBufferSize operation
Optional attributes for
XlaSparseDenseMatmulGradWithAdamAndStaticBufferSizeThe XlaSparseDenseMatmulGradWithCsrInput operation
The XlaSparseDenseMatmulGradWithFtrlAndCsrInput operation
Optional attributes for
XlaSparseDenseMatmulGradWithFtrlAndCsrInputThe XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSize operation
Optional attributes for
XlaSparseDenseMatmulGradWithFtrlAndStaticBufferSizeThe XlaSparseDenseMatmulGradWithSgdAndCsrInput operation
Optional attributes for
XlaSparseDenseMatmulGradWithSgdAndCsrInputThe XlaSparseDenseMatmulGradWithSgdAndStaticBufferSize operation
Optional attributes for
XlaSparseDenseMatmulGradWithSgdAndStaticBufferSizeThe XlaSparseDenseMatmulWithCsrInput operation
The XlaSparseDenseMatmulWithStaticBufferSize operation
Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
An XEvent is a trace event, optionally annotated with XStats.
An XEvent is a trace event, optionally annotated with XStats.
Metadata for an XEvent, corresponds to an event type and is shared by
all XEvents with the same metadata_id.
Metadata for an XEvent, corresponds to an event type and is shared by
all XEvents with the same metadata_id.
An XLine is a timeline of trace events (XEvents).
An XLine is a timeline of trace events (XEvents).
An XPlane is a container of parallel timelines (XLines), generated by a
profiling source or by post-processing one or more XPlanes.
An XPlane is a container of parallel timelines (XLines), generated by a
profiling source or by post-processing one or more XPlanes.
A container of parallel XPlanes, generated by one or more profiling sources.
A container of parallel XPlanes, generated by one or more profiling sources.
An XStat is a named value associated with an XEvent, e.g., a performance
counter value, a metric computed by a formula applied over nested XEvents
and XStats.
An XStat is a named value associated with an XEvent, e.g., a performance
counter value, a metric computed by a formula applied over nested XEvents
and XStats.
Metadata for an XStat, corresponds to a stat type and is shared by all
XStats with the same metadata_id.
Metadata for an XStat, corresponds to a stat type and is shared by all
XStats with the same metadata_id.
Creates an Initializer that sets all values to zero.
An operator creating a constant initialized with zeros of the shape given by `dims`.
Returns a tensor of zeros with the same shape and type as x.
Compute the Hurwitz zeta function \(\zeta(x, q)\).
Creates a dataset that zips together
input_datasets.Optional attributes for
ZipDataset
NcclAllReduceinstead