Module: tf.compat.v1.raw_ops
Public API for tf.raw_ops namespace.
Functions
Abort(...): Raise a exception to abort the process when called.
Abs(...): Computes the absolute value of a tensor.
AccumulateNV2(...): Returns the element-wise sum of a list of tensors.
AccumulatorApplyGradient(...): Applies a gradient to a given accumulator.
AccumulatorNumAccumulated(...): Returns the number of gradients aggregated in the given accumulators.
AccumulatorSetGlobalStep(...): Updates the accumulator with a new value for global_step.
AccumulatorTakeGradient(...): Extracts the average gradient in the given ConditionalAccumulator.
Acos(...): Computes acos of x element-wise.
Acosh(...): Computes inverse hyperbolic cosine of x element-wise.
Add(...): Returns x + y element-wise.
AddManySparseToTensorsMap(...): Add an N-minibatch SparseTensor to a SparseTensorsMap, return N handles.
AddN(...): Add all input tensors element wise.
AddSparseToTensorsMap(...): Add a SparseTensor to a SparseTensorsMap return its handle.
AddV2(...): Returns x + y element-wise.
AdjustContrast(...): Deprecated. Disallowed in GraphDef version >= 2.
AdjustContrastv2(...): Adjust the contrast of one or more images.
AdjustHue(...): Adjust the hue of one or more images.
AdjustSaturation(...): Adjust the saturation of one or more images.
All(...): Computes the "logical and" of elements across dimensions of a tensor.
AllCandidateSampler(...): Generates labels for candidate sampling with a learned unigram distribution.
AllToAll(...): An Op to exchange data across TPU replicas.
Angle(...): Returns the argument of a complex number.
AnonymousIterator(...): A container for an iterator resource.
AnonymousIteratorV2(...): A container for an iterator resource.
AnonymousMultiDeviceIterator(...): A container for a multi device iterator resource.
AnonymousRandomSeedGenerator(...)
Any(...): Computes the "logical or" of elements across dimensions of a tensor.
ApplyAdaMax(...): Update '*var' according to the AdaMax algorithm.
ApplyAdadelta(...): Update '*var' according to the adadelta scheme.
ApplyAdagrad(...): Update '*var' according to the adagrad scheme.
ApplyAdagradDA(...): Update '*var' according to the proximal adagrad scheme.
ApplyAdagradV2(...): Update '*var' according to the adagrad scheme.
ApplyAdam(...): Update '*var' according to the Adam algorithm.
ApplyAddSign(...): Update '*var' according to the AddSign update.
ApplyCenteredRMSProp(...): Update '*var' according to the centered RMSProp algorithm.
ApplyFtrl(...): Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrlV2(...): Update '*var' according to the Ftrl-proximal scheme.
ApplyGradientDescent(...): Update '*var' by subtracting 'alpha' * 'delta' from it.
ApplyMomentum(...): Update '*var' according to the momentum scheme.
ApplyPowerSign(...): Update '*var' according to the AddSign update.
ApplyProximalAdagrad(...): Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ApplyProximalGradientDescent(...): Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyRMSProp(...): Update '*var' according to the RMSProp algorithm.
ApproximateEqual(...): Returns the truth value of abs(x-y) < tolerance element-wise.
ArgMax(...): Returns the index with the largest value across dimensions of a tensor.
ArgMin(...): Returns the index with the smallest value across dimensions of a tensor.
AsString(...): Converts each entry in the given tensor to strings.
Asin(...): Computes the trignometric inverse sine of x element-wise.
Asinh(...): Computes inverse hyperbolic sine of x element-wise.
Assert(...): Asserts that the given condition is true.
AssertNextDataset(...): A transformation that asserts which transformations happen next.
Assign(...): Update 'ref' by assigning 'value' to it.
AssignAdd(...): Update 'ref' by adding 'value' to it.
AssignAddVariableOp(...): Adds a value to the current value of a variable.
AssignSub(...): Update 'ref' by subtracting 'value' from it.
AssignSubVariableOp(...): Subtracts a value from the current value of a variable.
AssignVariableOp(...): Assigns a new value to a variable.
Atan(...): Computes the trignometric inverse tangent of x element-wise.
Atan2(...): Computes arctangent of y/x element-wise, respecting signs of the arguments.
Atanh(...): Computes inverse hyperbolic tangent of x element-wise.
AudioSpectrogram(...): Produces a visualization of audio data over time.
AudioSummary(...): Outputs a Summary protocol buffer with audio.
AudioSummaryV2(...): Outputs a Summary protocol buffer with audio.
AutoShardDataset(...): Creates a dataset that shards the input dataset.
AvgPool(...): Performs average pooling on the input.
AvgPool3D(...): Performs 3D average pooling on the input.
AvgPool3DGrad(...): Computes gradients of average pooling function.
AvgPoolGrad(...): Computes gradients of the average pooling function.
Barrier(...): Defines a barrier that persists across different graph executions.
BarrierClose(...): Closes the given barrier.
BarrierIncompleteSize(...): Computes the number of incomplete elements in the given barrier.
BarrierInsertMany(...): For each key, assigns the respective value to the specified component.
BarrierReadySize(...): Computes the number of complete elements in the given barrier.
BarrierTakeMany(...): Takes the given number of completed elements from a barrier.
Batch(...): Batches all input tensors nondeterministically.
BatchDataset(...): Creates a dataset that batches batch_size elements from input_dataset.
BatchDatasetV2(...): Creates a dataset that batches batch_size elements from input_dataset.
BatchFunction(...): Batches all the inputs tensors to the computation done by the function.
BatchMatMul(...): Multiplies slices of two tensors in batches.
BatchMatMulV2(...): Multiplies slices of two tensors in batches.
BatchMatrixTriangularSolve(...)
BatchNormWithGlobalNormalization(...): Batch normalization.
BatchNormWithGlobalNormalizationGrad(...): Gradients for batch normalization.
BatchToSpace(...): BatchToSpace for 4-D tensors of type T.
BatchToSpaceND(...): BatchToSpace for N-D tensors of type T.
Betainc(...): Compute the regularized incomplete beta integral \(I_x(a, b)\).
BiasAdd(...): Adds bias to value.
BiasAddGrad(...): The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddV1(...): Adds bias to value.
Bincount(...): Counts the number of occurrences of each value in an integer array.
Bitcast(...): Bitcasts a tensor from one type to another without copying data.
BitwiseAnd(...): Elementwise computes the bitwise AND of x and y.
BitwiseOr(...): Elementwise computes the bitwise OR of x and y.
BitwiseXor(...): Elementwise computes the bitwise XOR of x and y.
BlockLSTM(...): Computes the LSTM cell forward propagation for all the time steps.
BlockLSTMGrad(...): Computes the LSTM cell backward propagation for the entire time sequence.
BlockLSTMGradV2(...): Computes the LSTM cell backward propagation for the entire time sequence.
BlockLSTMV2(...): Computes the LSTM cell forward propagation for all the time steps.
BoostedTreesAggregateStats(...): Aggregates the summary of accumulated stats for the batch.
BoostedTreesBucketize(...): Bucketize each feature based on bucket boundaries.
BoostedTreesCalculateBestFeatureSplit(...): Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCalculateBestFeatureSplitV2(...): Calculates gains for each feature and returns the best possible split information for each node. However, if no split is found, then no split information is returned for that node.
BoostedTreesCalculateBestGainsPerFeature(...): Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCenterBias(...): Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior. Returns a boolean indicating whether to continue centering.
BoostedTreesCreateEnsemble(...): Creates a tree ensemble model and returns a handle to it.
BoostedTreesCreateQuantileStreamResource(...): Create the Resource for Quantile Streams.
BoostedTreesDeserializeEnsemble(...): Deserializes a serialized tree ensemble config and replaces current tree
BoostedTreesEnsembleResourceHandleOp(...): Creates a handle to a BoostedTreesEnsembleResource
BoostedTreesExampleDebugOutputs(...): Debugging/model interpretability outputs for each example.
BoostedTreesFlushQuantileSummaries(...): Flush the quantile summaries from each quantile stream resource.
BoostedTreesGetEnsembleStates(...): Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
BoostedTreesMakeQuantileSummaries(...): Makes the summary of quantiles for the batch.
BoostedTreesMakeStatsSummary(...): Makes the summary of accumulated stats for the batch.
BoostedTreesPredict(...): Runs multiple additive regression ensemble predictors on input instances and
BoostedTreesQuantileStreamResourceAddSummaries(...): Add the quantile summaries to each quantile stream resource.
BoostedTreesQuantileStreamResourceDeserialize(...): Deserialize bucket boundaries and ready flag into current QuantileAccumulator.
BoostedTreesQuantileStreamResourceFlush(...): Flush the summaries for a quantile stream resource.
BoostedTreesQuantileStreamResourceGetBucketBoundaries(...): Generate the bucket boundaries for each feature based on accumulated summaries.
BoostedTreesQuantileStreamResourceHandleOp(...): Creates a handle to a BoostedTreesQuantileStreamResource.
BoostedTreesSerializeEnsemble(...): Serializes the tree ensemble to a proto.
BoostedTreesSparseAggregateStats(...): Aggregates the summary of accumulated stats for the batch.
BoostedTreesSparseCalculateBestFeatureSplit(...): Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesTrainingPredict(...): Runs multiple additive regression ensemble predictors on input instances and
BoostedTreesUpdateEnsemble(...): Updates the tree ensemble by either adding a layer to the last tree being grown
BoostedTreesUpdateEnsembleV2(...): Updates the tree ensemble by adding a layer to the last tree being grown
BroadcastArgs(...): Return the shape of s0 op s1 with broadcast.
BroadcastGradientArgs(...): Return the reduction indices for computing gradients of s0 op s1 with broadcast.
BroadcastTo(...): Broadcast an array for a compatible shape.
Bucketize(...): Bucketizes 'input' based on 'boundaries'.
BytesProducedStatsDataset(...): Records the bytes size of each element of input_dataset in a StatsAggregator.
CSRSparseMatrixComponents(...): Reads out the CSR components at batch index.
CSRSparseMatrixToDense(...): Convert a (possibly batched) CSRSparseMatrix to dense.
CSRSparseMatrixToSparseTensor(...): Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.
CTCBeamSearchDecoder(...): Performs beam search decoding on the logits given in input.
CTCGreedyDecoder(...): Performs greedy decoding on the logits given in inputs.
CTCLoss(...): Calculates the CTC Loss (log probability) for each batch entry. Also calculates
CTCLossV2(...): Calculates the CTC Loss (log probability) for each batch entry. Also calculates
CacheDataset(...): Creates a dataset that caches elements from input_dataset.
Case(...): An n-way switch statement which calls a single branch function.
Cast(...): Cast x of type SrcT to y of DstT.
Ceil(...): Returns element-wise smallest integer not less than x.
CheckNumerics(...): Checks a tensor for NaN and Inf values.
CheckNumericsV2(...): Checks a tensor for NaN, -Inf and +Inf values.
Cholesky(...): Computes the Cholesky decomposition of one or more square matrices.
CholeskyGrad(...): Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
ChooseFastestBranchDataset(...)
ClipByValue(...): Clips tensor values to a specified min and max.
CollectiveBcastRecv(...): Receives a tensor value broadcast from another device.
CollectiveBcastSend(...): Broadcasts a tensor value to one or more other devices.
CollectiveGather(...): Mutually accumulates multiple tensors of identical type and shape.
CollectiveGatherV2(...): Mutually accumulates multiple tensors of identical type and shape.
CollectivePermute(...): An Op to permute tensors across replicated TPU instances.
CollectiveReduce(...): Mutually reduces multiple tensors of identical type and shape.
CollectiveReduceV2(...): Mutually reduces multiple tensors of identical type and shape.
CombinedNonMaxSuppression(...): Greedily selects a subset of bounding boxes in descending order of score,
CompareAndBitpack(...): Compare values of input to threshold and pack resulting bits into a uint8.
Complex(...): Converts two real numbers to a complex number.
ComplexAbs(...): Computes the complex absolute value of a tensor.
CompressElement(...): Compresses a dataset element.
ComputeAccidentalHits(...): Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeBatchSize(...): Computes the static batch size of a dataset sans partial batches.
Concat(...): Concatenates tensors along one dimension.
ConcatOffset(...): Computes offsets of concat inputs within its output.
ConcatV2(...): Concatenates tensors along one dimension.
ConcatenateDataset(...): Creates a dataset that concatenates input_dataset with another_dataset.
ConditionalAccumulator(...): A conditional accumulator for aggregating gradients.
ConfigureDistributedTPU(...): Sets up the centralized structures for a distributed TPU system.
ConfigureTPUEmbedding(...): Sets up TPUEmbedding in a distributed TPU system.
Conj(...): Returns the complex conjugate of a complex number.
ConjugateTranspose(...): Shuffle dimensions of x according to a permutation and conjugate the result.
Const(...): Returns a constant tensor.
ConsumeMutexLock(...): This op consumes a lock created by MutexLock.
ControlTrigger(...): Does nothing. Serves as a control trigger for scheduling.
Conv2D(...): Computes a 2-D convolution given 4-D input and filter tensors.
Conv2DBackpropFilter(...): Computes the gradients of convolution with respect to the filter.
Conv2DBackpropInput(...): Computes the gradients of convolution with respect to the input.
Conv3D(...): Computes a 3-D convolution given 5-D input and filter tensors.
Conv3DBackpropFilter(...): Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilterV2(...): Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropInput(...): Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInputV2(...): Computes the gradients of 3-D convolution with respect to the input.
Copy(...): Copy a tensor from CPU-to-CPU or GPU-to-GPU.
CopyHost(...): Copy a tensor to host.
Cos(...): Computes cos of x element-wise.
Cosh(...): Computes hyperbolic cosine of x element-wise.
CountUpTo(...): Increments 'ref' until it reaches 'limit'.
CropAndResize(...): Extracts crops from the input image tensor and resizes them.
CropAndResizeGradBoxes(...): Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradImage(...): Computes the gradient of the crop_and_resize op wrt the input image tensor.
Cross(...): Compute the pairwise cross product.
CrossReplicaSum(...): An Op to sum inputs across replicated TPU instances.
CudnnRNN(...): A RNN backed by cuDNN.
CudnnRNNBackprop(...): Backprop step of CudnnRNN.
CudnnRNNBackpropV2(...): Backprop step of CudnnRNN.
CudnnRNNBackpropV3(...): Backprop step of CudnnRNNV3.
CudnnRNNCanonicalToParams(...): Converts CudnnRNN params from canonical form to usable form.
CudnnRNNCanonicalToParamsV2(...): Converts CudnnRNN params from canonical form to usable form. It supports the projection in LSTM.
CudnnRNNParamsSize(...): Computes size of weights that can be used by a Cudnn RNN model.
CudnnRNNParamsToCanonical(...): Retrieves CudnnRNN params in canonical form.
CudnnRNNParamsToCanonicalV2(...): Retrieves CudnnRNN params in canonical form. It supports the projection in LSTM.
CudnnRNNV2(...): A RNN backed by cuDNN.
CudnnRNNV3(...): A RNN backed by cuDNN.
Cumprod(...): Compute the cumulative product of the tensor x along axis.
Cumsum(...): Compute the cumulative sum of the tensor x along axis.
CumulativeLogsumexp(...): Compute the cumulative product of the tensor x along axis.
DataFormatDimMap(...): Returns the dimension index in the destination data format given the one in
DataFormatVecPermute(...): Permute input tensor from src_format to dst_format.
DataServiceDataset(...): Creates a dataset that reads data from the tf.data service.
DatasetCardinality(...): Returns the cardinality of input_dataset.
DatasetFromGraph(...): Creates a dataset from the given graph_def.
DatasetToGraph(...): Returns a serialized GraphDef representing input_dataset.
DatasetToGraphV2(...): Returns a serialized GraphDef representing input_dataset.
DatasetToSingleElement(...): Outputs the single element from the given dataset.
DatasetToTFRecord(...): Writes the given dataset to the given file using the TFRecord format.
DebugGradientIdentity(...): Identity op for gradient debugging.
DebugGradientRefIdentity(...): Identity op for gradient debugging.
DebugIdentity(...): Provides an identity mapping of the non-Ref type input tensor for debugging.
DebugIdentityV2(...): Debug Identity V2 Op.
DebugNanCount(...): Debug NaN Value Counter Op.
DebugNumericSummary(...): Debug Numeric Summary Op.
DebugNumericSummaryV2(...): Debug Numeric Summary V2 Op.
DecodeAndCropJpeg(...): Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeBase64(...): Decode web-safe base64-encoded strings.
DecodeBmp(...): Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeCSV(...): Convert CSV records to tensors. Each column maps to one tensor.
DecodeCompressed(...): Decompress strings.
DecodeGif(...): Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
DecodeImage(...): Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.
DecodeJSONExample(...): Convert JSON-encoded Example records to binary protocol buffer strings.
DecodeJpeg(...): Decode a JPEG-encoded image to a uint8 tensor.
DecodePaddedRaw(...): Reinterpret the bytes of a string as a vector of numbers.
DecodePng(...): Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodeProtoV2(...): The op extracts fields from a serialized protocol buffers message into tensors.
DecodeRaw(...): Reinterpret the bytes of a string as a vector of numbers.
DecodeWav(...): Decode a 16-bit PCM WAV file to a float tensor.
DeepCopy(...): Makes a copy of x.
DeleteIterator(...): A container for an iterator resource.
DeleteMultiDeviceIterator(...): A container for an iterator resource.
DeleteRandomSeedGenerator(...)
DeleteSessionTensor(...): Delete the tensor specified by its handle in the session.
DenseBincount(...): Counts the number of occurrences of each value in an integer array.
DenseCountSparseOutput(...): Performs sparse-output bin counting for a tf.tensor input.
DenseToCSRSparseMatrix(...): Converts a dense tensor to a (possibly batched) CSRSparseMatrix.
DenseToDenseSetOperation(...): Applies set operation along last dimension of 2 Tensor inputs.
DenseToSparseBatchDataset(...): Creates a dataset that batches input elements into a SparseTensor.
DenseToSparseSetOperation(...): Applies set operation along last dimension of Tensor and SparseTensor.
DepthToSpace(...): DepthToSpace for tensors of type T.
DepthwiseConv2dNative(...): Computes a 2-D depthwise convolution given 4-D input and filter tensors.
DepthwiseConv2dNativeBackpropFilter(...): Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropInput(...): Computes the gradients of depthwise convolution with respect to the input.
Dequantize(...): Dequantize the 'input' tensor into a float or bfloat16 Tensor.
DeserializeIterator(...): Converts the given variant tensor to an iterator and stores it in the given resource.
DeserializeManySparse(...): Deserialize and concatenate SparseTensors from a serialized minibatch.
DeserializeSparse(...): Deserialize SparseTensor objects.
DestroyResourceOp(...): Deletes the resource specified by the handle.
DestroyTemporaryVariable(...): Destroys the temporary variable and returns its final value.
DeviceIndex(...): Return the index of device the op runs.
Diag(...): Returns a diagonal tensor with a given diagonal values.
DiagPart(...): Returns the diagonal part of the tensor.
Digamma(...): Computes Psi, the derivative of Lgamma (the log of the absolute value of
Dilation2D(...): Computes the grayscale dilation of 4-D input and 3-D filter tensors.
Dilation2DBackpropFilter(...): Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2DBackpropInput(...): Computes the gradient of morphological 2-D dilation with respect to the input.
DirectedInterleaveDataset(...): A substitute for InterleaveDataset on a fixed list of N datasets.
Div(...): Returns x / y element-wise.
DivNoNan(...): Returns 0 if the denominator is zero.
DrawBoundingBoxes(...): Draw bounding boxes on a batch of images.
DrawBoundingBoxesV2(...): Draw bounding boxes on a batch of images.
DynamicPartition(...): Partitions data into num_partitions tensors using indices from partitions.
DynamicStitch(...): Interleave the values from the data tensors into a single tensor.
EagerPyFunc(...): Eagerly executes a python function to compute func(input)->output. The
EditDistance(...): Computes the (possibly normalized) Levenshtein Edit Distance.
Eig(...): Computes the eigen decomposition of one or more square matrices.
Einsum(...): Tensor contraction according to Einstein summation convention.
Elu(...): Computes exponential linear: exp(features) - 1 if < 0, features otherwise.
EluGrad(...): Computes gradients for the exponential linear (Elu) operation.
Empty(...): Creates a tensor with the given shape.
EmptyTensorList(...): Creates and returns an empty tensor list.
EncodeBase64(...): Encode strings into web-safe base64 format.
EncodeJpeg(...): JPEG-encode an image.
EncodeJpegVariableQuality(...): JPEG encode input image with provided compression quality.
EncodePng(...): PNG-encode an image.
EncodeProto(...): The op serializes protobuf messages provided in the input tensors.
EncodeWav(...): Encode audio data using the WAV file format.
EnqueueTPUEmbeddingIntegerBatch(...): An op that enqueues a list of input batch tensors to TPUEmbedding.
EnqueueTPUEmbeddingRaggedTensorBatch(...): Eases the porting of code that uses tf.nn.embedding_lookup().
EnqueueTPUEmbeddingSparseBatch(...): An op that enqueues TPUEmbedding input indices from a SparseTensor.
EnqueueTPUEmbeddingSparseTensorBatch(...): Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
EnsureShape(...): Ensures that the tensor's shape matches the expected shape.
Enter(...): Creates or finds a child frame, and makes data available to the child frame.
Equal(...): Returns the truth value of (x == y) element-wise.
Erf(...): Computes the Gauss error function of x element-wise.
Erfc(...): Computes the complementary error function of x element-wise.
EuclideanNorm(...): Computes the euclidean norm of elements across dimensions of a tensor.
Exit(...): Exits the current frame to its parent frame.
Exp(...): Computes exponential of x element-wise. \(y = e^x\).
ExpandDims(...): Inserts a dimension of 1 into a tensor's shape.
ExperimentalAssertNextDataset(...)
ExperimentalAutoShardDataset(...): Creates a dataset that shards the input dataset.
ExperimentalBytesProducedStatsDataset(...): Records the bytes size of each element of input_dataset in a StatsAggregator.
ExperimentalChooseFastestDataset(...)
ExperimentalDatasetCardinality(...): Returns the cardinality of input_dataset.
ExperimentalDatasetToTFRecord(...): Writes the given dataset to the given file using the TFRecord format.
ExperimentalDenseToSparseBatchDataset(...): Creates a dataset that batches input elements into a SparseTensor.
ExperimentalDirectedInterleaveDataset(...): A substitute for InterleaveDataset on a fixed list of N datasets.
ExperimentalGroupByReducerDataset(...): Creates a dataset that computes a group-by on input_dataset.
ExperimentalGroupByWindowDataset(...): Creates a dataset that computes a windowed group-by on input_dataset.
ExperimentalIgnoreErrorsDataset(...): Creates a dataset that contains the elements of input_dataset ignoring errors.
ExperimentalIteratorGetDevice(...): Returns the name of the device on which resource has been placed.
ExperimentalLatencyStatsDataset(...): Records the latency of producing input_dataset elements in a StatsAggregator.
ExperimentalMapAndBatchDataset(...): Creates a dataset that fuses mapping with batching.
ExperimentalMapDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
ExperimentalMatchingFilesDataset(...)
ExperimentalMaxIntraOpParallelismDataset(...): Creates a dataset that overrides the maximum intra-op parallelism.
ExperimentalNonSerializableDataset(...)
ExperimentalParallelInterleaveDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
ExperimentalParseExampleDataset(...): Transforms input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.
ExperimentalPrivateThreadPoolDataset(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
ExperimentalRandomDataset(...): Creates a Dataset that returns pseudorandom numbers.
ExperimentalRebatchDataset(...): Creates a dataset that changes the batch size.
ExperimentalScanDataset(...): Creates a dataset successively reduces f over the elements of input_dataset.
ExperimentalSetStatsAggregatorDataset(...)
ExperimentalSlidingWindowDataset(...): Creates a dataset that passes a sliding window over input_dataset.
ExperimentalSqlDataset(...): Creates a dataset that executes a SQL query and emits rows of the result set.
ExperimentalStatsAggregatorHandle(...): Creates a statistics manager resource.
ExperimentalStatsAggregatorSummary(...): Produces a summary of any statistics recorded by the given statistics manager.
ExperimentalTakeWhileDataset(...): Creates a dataset that stops iteration when predicate` is false.
ExperimentalThreadPoolDataset(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
ExperimentalThreadPoolHandle(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
ExperimentalUnbatchDataset(...): A dataset that splits the elements of its input into multiple elements.
ExperimentalUniqueDataset(...): Creates a dataset that contains the unique elements of input_dataset.
Expm1(...): Computes exp(x) - 1 element-wise.
ExtractGlimpse(...): Extracts a glimpse from the input tensor.
ExtractGlimpseV2(...): Extracts a glimpse from the input tensor.
ExtractImagePatches(...): Extract patches from images and put them in the "depth" output dimension.
ExtractJpegShape(...): Extract the shape information of a JPEG-encoded image.
ExtractVolumePatches(...): Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.
FFT(...): Fast Fourier transform.
FFT2D(...): 2D fast Fourier transform.
FFT3D(...): 3D fast Fourier transform.
FIFOQueue(...): A queue that produces elements in first-in first-out order.
FIFOQueueV2(...): A queue that produces elements in first-in first-out order.
Fact(...): Output a fact about factorials.
FakeParam(...): This op is used as a placeholder in If branch functions. It doesn't provide a
FakeQuantWithMinMaxArgs(...): Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgsGradient(...): Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxVars(...): Fake-quantize the 'inputs' tensor of type float via global float scalars
FakeQuantWithMinMaxVarsGradient(...): Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsPerChannel(...): Fake-quantize the 'inputs' tensor of type float via per-channel floats
FakeQuantWithMinMaxVarsPerChannelGradient(...): Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
FakeQueue(...): Deprecated. Do not use.
Fill(...): Creates a tensor filled with a scalar value.
FilterByLastComponentDataset(...): Creates a dataset containing elements of first component of input_dataset having true in the last component.
FilterDataset(...): Creates a dataset containing elements of input_dataset matching predicate.
Fingerprint(...): Generates fingerprint values.
FixedLengthRecordDataset(...): Creates a dataset that emits the records from one or more binary files.
FixedLengthRecordDatasetV2(...)
FixedLengthRecordReader(...): A Reader that outputs fixed-length records from a file.
FixedLengthRecordReaderV2(...): A Reader that outputs fixed-length records from a file.
FixedUnigramCandidateSampler(...): Generates labels for candidate sampling with a learned unigram distribution.
FlatMapDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
Floor(...): Returns element-wise largest integer not greater than x.
FloorDiv(...): Returns x // y element-wise.
FloorMod(...): Returns element-wise remainder of division. When x < 0 xor y < 0 is
For(...): ```python
FractionalAvgPool(...): Performs fractional average pooling on the input.
FractionalAvgPoolGrad(...): Computes gradient of the FractionalAvgPool function.
FractionalMaxPool(...): Performs fractional max pooling on the input.
FractionalMaxPoolGrad(...): Computes gradient of the FractionalMaxPool function.
FusedBatchNorm(...): Batch normalization.
FusedBatchNormGrad(...): Gradient for batch normalization.
FusedBatchNormGradV2(...): Gradient for batch normalization.
FusedBatchNormGradV3(...): Gradient for batch normalization.
FusedBatchNormV2(...): Batch normalization.
FusedBatchNormV3(...): Batch normalization.
FusedPadConv2D(...): Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2D(...): Performs a resize and padding as a preprocess during a convolution.
GRUBlockCell(...): Computes the GRU cell forward propagation for 1 time step.
GRUBlockCellGrad(...): Computes the GRU cell back-propagation for 1 time step.
Gather(...): Gather slices from params according to indices.
GatherNd(...): Gather slices from params into a Tensor with shape specified by indices.
GatherV2(...): Gather slices from params axis axis according to indices.
GenerateBoundingBoxProposals(...): This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497
GenerateVocabRemapping(...): Given a path to new and old vocabulary files, returns a remapping Tensor of
GeneratorDataset(...): Creates a dataset that invokes a function to generate elements.
GetSessionHandle(...): Store the input tensor in the state of the current session.
GetSessionHandleV2(...): Store the input tensor in the state of the current session.
GetSessionTensor(...): Get the value of the tensor specified by its handle.
Greater(...): Returns the truth value of (x > y) element-wise.
GreaterEqual(...): Returns the truth value of (x >= y) element-wise.
GroupByReducerDataset(...): Creates a dataset that computes a group-by on input_dataset.
GroupByWindowDataset(...): Creates a dataset that computes a windowed group-by on input_dataset.
GuaranteeConst(...): Gives a guarantee to the TF runtime that the input tensor is a constant.
HSVToRGB(...): Convert one or more images from HSV to RGB.
HashTable(...): Creates a non-initialized hash table.
HashTableV2(...): Creates a non-initialized hash table.
HistogramFixedWidth(...): Return histogram of values.
HistogramSummary(...): Outputs a Summary protocol buffer with a histogram.
IFFT(...): Inverse fast Fourier transform.
IFFT2D(...): Inverse 2D fast Fourier transform.
IFFT3D(...): Inverse 3D fast Fourier transform.
IRFFT(...): Inverse real-valued fast Fourier transform.
IRFFT2D(...): Inverse 2D real-valued fast Fourier transform.
IRFFT3D(...): Inverse 3D real-valued fast Fourier transform.
Identity(...): Return a tensor with the same shape and contents as the input tensor or value.
IdentityN(...): Returns a list of tensors with the same shapes and contents as the input
IdentityReader(...): A Reader that outputs the queued work as both the key and value.
IdentityReaderV2(...): A Reader that outputs the queued work as both the key and value.
If(...): output = cond ? then_branch(input) : else_branch(input)
Igamma(...): Compute the lower regularized incomplete Gamma function P(a, x).
IgammaGradA(...): Computes the gradient of igamma(a, x) wrt a.
Igammac(...): Compute the upper regularized incomplete Gamma function Q(a, x).
IgnoreErrorsDataset(...): Creates a dataset that contains the elements of input_dataset ignoring errors.
Imag(...): Returns the imaginary part of a complex number.
ImageProjectiveTransformV2(...): Applies the given transform to each of the images.
ImageProjectiveTransformV3(...): Applies the given transform to each of the images.
ImageSummary(...): Outputs a Summary protocol buffer with images.
ImmutableConst(...): Returns immutable tensor from memory region.
InTopK(...): Says whether the targets are in the top K predictions.
InTopKV2(...): Says whether the targets are in the top K predictions.
InfeedDequeue(...): A placeholder op for a value that will be fed into the computation.
InfeedDequeueTuple(...): Fetches multiple values from infeed as an XLA tuple.
InfeedEnqueue(...): An op which feeds a single Tensor value into the computation.
InfeedEnqueuePrelinearizedBuffer(...): An op which enqueues prelinearized buffer into TPU infeed.
InfeedEnqueueTuple(...): Feeds multiple Tensor values into the computation as an XLA tuple.
InitializeTable(...): Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromDataset(...)
InitializeTableFromTextFile(...): Initializes a table from a text file.
InitializeTableFromTextFileV2(...): Initializes a table from a text file.
InitializeTableV2(...): Table initializer that takes two tensors for keys and values respectively.
InplaceAdd(...): Adds v into specified rows of x.
InplaceSub(...): Subtracts v into specified rows of x.
InplaceUpdate(...): Updates specified rows 'i' with values 'v'.
InterleaveDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
Inv(...): Computes the reciprocal of x element-wise.
InvGrad(...): Computes the gradient for the inverse of x wrt its input.
Invert(...): Invert (flip) each bit of supported types; for example, type uint8 value 01010101 becomes 10101010.
InvertPermutation(...): Computes the inverse permutation of a tensor.
IsBoostedTreesEnsembleInitialized(...): Checks whether a tree ensemble has been initialized.
IsBoostedTreesQuantileStreamResourceInitialized(...): Checks whether a quantile stream has been initialized.
IsFinite(...): Returns which elements of x are finite.
IsInf(...): Returns which elements of x are Inf.
IsNan(...): Returns which elements of x are NaN.
IsVariableInitialized(...): Checks whether a tensor has been initialized.
IsotonicRegression(...): Solves a batch of isotonic regression problems.
Iterator(...): A container for an iterator resource.
IteratorFromStringHandle(...): Converts the given string representing a handle to an iterator to a resource.
IteratorFromStringHandleV2(...)
IteratorGetDevice(...): Returns the name of the device on which resource has been placed.
IteratorGetNext(...): Gets the next output from the given iterator .
IteratorGetNextAsOptional(...): Gets the next output from the given iterator as an Optional variant.
IteratorGetNextSync(...): Gets the next output from the given iterator.
IteratorToStringHandle(...): Converts the given resource_handle representing an iterator to a string.
L2Loss(...): L2 Loss.
LMDBDataset(...): Creates a dataset that emits the key-value pairs in one or more LMDB files.
LMDBReader(...): A Reader that outputs the records from a LMDB file.
LRN(...): Local Response Normalization.
LRNGrad(...): Gradients for Local Response Normalization.
LSTMBlockCell(...): Computes the LSTM cell forward propagation for 1 time step.
LSTMBlockCellGrad(...): Computes the LSTM cell backward propagation for 1 timestep.
LatencyStatsDataset(...): Records the latency of producing input_dataset elements in a StatsAggregator.
LeakyRelu(...): Computes rectified linear: max(features, features * alpha).
LeakyReluGrad(...): Computes rectified linear gradients for a LeakyRelu operation.
LearnedUnigramCandidateSampler(...): Generates labels for candidate sampling with a learned unigram distribution.
LeftShift(...): Elementwise computes the bitwise left-shift of x and y.
LegacyParallelInterleaveDatasetV2(...): Creates a dataset that applies f to the outputs of input_dataset.
Less(...): Returns the truth value of (x < y) element-wise.
LessEqual(...): Returns the truth value of (x <= y) element-wise.
Lgamma(...): Computes the log of the absolute value of Gamma(x) element-wise.
LinSpace(...): Generates values in an interval.
ListDiff(...): Computes the difference between two lists of numbers or strings.
LoadAndRemapMatrix(...): Loads a 2-D (matrix) Tensor with name old_tensor_name from the checkpoint
LoadTPUEmbeddingADAMParameters(...): Load ADAM embedding parameters.
LoadTPUEmbeddingADAMParametersGradAccumDebug(...): Load ADAM embedding parameters with debug support.
LoadTPUEmbeddingAdadeltaParameters(...): Load Adadelta embedding parameters.
LoadTPUEmbeddingAdadeltaParametersGradAccumDebug(...): Load Adadelta parameters with debug support.
LoadTPUEmbeddingAdagradParameters(...): Load Adagrad embedding parameters.
LoadTPUEmbeddingAdagradParametersGradAccumDebug(...): Load Adagrad embedding parameters with debug support.
LoadTPUEmbeddingCenteredRMSPropParameters(...): Load centered RMSProp embedding parameters.
LoadTPUEmbeddingFTRLParameters(...): Load FTRL embedding parameters.
LoadTPUEmbeddingFTRLParametersGradAccumDebug(...): Load FTRL embedding parameters with debug support.
LoadTPUEmbeddingMDLAdagradLightParameters(...): Load MDL Adagrad Light embedding parameters.
LoadTPUEmbeddingMomentumParameters(...): Load Momentum embedding parameters.
LoadTPUEmbeddingMomentumParametersGradAccumDebug(...): Load Momentum embedding parameters with debug support.
LoadTPUEmbeddingProximalAdagradParameters(...): Load proximal Adagrad embedding parameters.
LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug(...): Load proximal Adagrad embedding parameters with debug support.
LoadTPUEmbeddingProximalYogiParameters(...)
LoadTPUEmbeddingProximalYogiParametersGradAccumDebug(...)
LoadTPUEmbeddingRMSPropParameters(...): Load RMSProp embedding parameters.
LoadTPUEmbeddingRMSPropParametersGradAccumDebug(...): Load RMSProp embedding parameters with debug support.
LoadTPUEmbeddingStochasticGradientDescentParameters(...): Load SGD embedding parameters.
LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(...): Load SGD embedding parameters.
Log(...): Computes natural logarithm of x element-wise.
Log1p(...): Computes natural logarithm of (1 + x) element-wise.
LogMatrixDeterminant(...): Computes the sign and the log of the absolute value of the determinant of
LogSoftmax(...): Computes log softmax activations.
LogUniformCandidateSampler(...): Generates labels for candidate sampling with a log-uniform distribution.
LogicalAnd(...): Returns the truth value of x AND y element-wise.
LogicalNot(...): Returns the truth value of NOT x element-wise.
LogicalOr(...): Returns the truth value of x OR y element-wise.
LookupTableExport(...): Outputs all keys and values in the table.
LookupTableExportV2(...): Outputs all keys and values in the table.
LookupTableFind(...): Looks up keys in a table, outputs the corresponding values.
LookupTableFindV2(...): Looks up keys in a table, outputs the corresponding values.
LookupTableImport(...): Replaces the contents of the table with the specified keys and values.
LookupTableImportV2(...): Replaces the contents of the table with the specified keys and values.
LookupTableInsert(...): Updates the table to associates keys with values.
LookupTableInsertV2(...): Updates the table to associates keys with values.
LookupTableRemoveV2(...): Removes keys and its associated values from a table.
LookupTableSize(...): Computes the number of elements in the given table.
LookupTableSizeV2(...): Computes the number of elements in the given table.
LoopCond(...): Forwards the input to the output.
LowerBound(...): Applies lower_bound(sorted_search_values, values) along each row.
Lu(...): Computes the LU decomposition of one or more square matrices.
MakeIterator(...): Makes a new iterator from the given dataset and stores it in iterator.
MapAndBatchDataset(...): Creates a dataset that fuses mapping with batching.
MapClear(...): Op removes all elements in the underlying container.
MapDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
MapDefun(...): Maps a function on the list of tensors unpacked from arguments on dimension 0.
MapIncompleteSize(...): Op returns the number of incomplete elements in the underlying container.
MapPeek(...): Op peeks at the values at the specified key. If the
MapSize(...): Op returns the number of elements in the underlying container.
MapStage(...): Stage (key, values) in the underlying container which behaves like a hashtable.
MapUnstage(...): Op removes and returns the values associated with the key
MapUnstageNoKey(...): Op removes and returns a random (key, value)
MatMul(...): Multiply the matrix "a" by the matrix "b".
MatchingFiles(...): Returns the set of files matching one or more glob patterns.
MatrixBandPart(...): Copy a tensor setting everything outside a central band in each innermost matrix to zero.
MatrixDeterminant(...): Computes the determinant of one or more square matrices.
MatrixDiag(...): Returns a batched diagonal tensor with a given batched diagonal values.
MatrixDiagPart(...): Returns the batched diagonal part of a batched tensor.
MatrixDiagPartV2(...): Returns the batched diagonal part of a batched tensor.
MatrixDiagPartV3(...): Returns the batched diagonal part of a batched tensor.
MatrixDiagV2(...): Returns a batched diagonal tensor with given batched diagonal values.
MatrixDiagV3(...): Returns a batched diagonal tensor with given batched diagonal values.
MatrixExponential(...): Deprecated, use python implementation tf.linalg.matrix_exponential.
MatrixInverse(...): Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes).
MatrixLogarithm(...): Computes the matrix logarithm of one or more square matrices:
MatrixSetDiag(...): Returns a batched matrix tensor with new batched diagonal values.
MatrixSetDiagV2(...): Returns a batched matrix tensor with new batched diagonal values.
MatrixSetDiagV3(...): Returns a batched matrix tensor with new batched diagonal values.
MatrixSolve(...): Solves systems of linear equations.
MatrixSolveLs(...): Solves one or more linear least-squares problems.
MatrixSquareRoot(...): Computes the matrix square root of one or more square matrices:
MatrixTriangularSolve(...): Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.
Max(...): Computes the maximum of elements across dimensions of a tensor.
MaxIntraOpParallelismDataset(...): Creates a dataset that overrides the maximum intra-op parallelism.
MaxPool(...): Performs max pooling on the input.
MaxPool3D(...): Performs 3D max pooling on the input.
MaxPool3DGrad(...): Computes gradients of 3D max pooling function.
MaxPool3DGradGrad(...): Computes second-order gradients of the maxpooling function.
MaxPoolGrad(...): Computes gradients of the maxpooling function.
MaxPoolGradGrad(...): Computes second-order gradients of the maxpooling function.
MaxPoolGradGradV2(...): Computes second-order gradients of the maxpooling function.
MaxPoolGradGradWithArgmax(...): Computes second-order gradients of the maxpooling function.
MaxPoolGradV2(...): Computes gradients of the maxpooling function.
MaxPoolGradWithArgmax(...): Computes gradients of the maxpooling function.
MaxPoolV2(...): Performs max pooling on the input.
MaxPoolWithArgmax(...): Performs max pooling on the input and outputs both max values and indices.
Maximum(...): Returns the max of x and y (i.e. x > y ? x : y) element-wise.
Mean(...): Computes the mean of elements across dimensions of a tensor.
Merge(...): Forwards the value of an available tensor from inputs to output.
MergeSummary(...): Merges summaries.
MergeV2Checkpoints(...): V2 format specific: merges the metadata files of sharded checkpoints. The
Mfcc(...): Transforms a spectrogram into a form that's useful for speech recognition.
Min(...): Computes the minimum of elements across dimensions of a tensor.
Minimum(...): Returns the min of x and y (i.e. x < y ? x : y) element-wise.
MirrorPad(...): Pads a tensor with mirrored values.
MirrorPadGrad(...): Gradient op for MirrorPad op. This op folds a mirror-padded tensor.
Mod(...): Returns element-wise remainder of division. This emulates C semantics in that
ModelDataset(...): Identity transformation that models performance.
Mul(...): Returns x * y element-wise.
MulNoNan(...): Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN.
MultiDeviceIterator(...): Creates a MultiDeviceIterator resource.
MultiDeviceIteratorFromStringHandle(...): Generates a MultiDeviceIterator resource from its provided string handle.
MultiDeviceIteratorGetNextFromShard(...): Gets next element for the provided shard number.
MultiDeviceIteratorInit(...): Initializes the multi device iterator with the given dataset.
MultiDeviceIteratorToStringHandle(...): Produces a string handle for the given MultiDeviceIterator.
Multinomial(...): Draws samples from a multinomial distribution.
MutableDenseHashTable(...): Creates an empty hash table that uses tensors as the backing store.
MutableDenseHashTableV2(...): Creates an empty hash table that uses tensors as the backing store.
MutableHashTable(...): Creates an empty hash table.
MutableHashTableOfTensors(...): Creates an empty hash table.
MutableHashTableOfTensorsV2(...): Creates an empty hash table.
MutableHashTableV2(...): Creates an empty hash table.
MutexLock(...): Locks a mutex resource. The output is the lock. So long as the lock tensor
MutexV2(...): Creates a Mutex resource that can be locked by MutexLock.
NcclAllReduce(...): Outputs a tensor containing the reduction across all input tensors.
NcclBroadcast(...): Sends input to all devices that are connected to the output.
NcclReduce(...): Reduces input from num_devices using reduction to a single device.
Neg(...): Computes numerical negative value element-wise.
NextAfter(...): Returns the next representable value of x1 in the direction of x2, element-wise.
NextIteration(...): Makes its input available to the next iteration.
NoOp(...): Does nothing. Only useful as a placeholder for control edges.
NonDeterministicInts(...): Non-deterministically generates some integers.
NonMaxSuppression(...): Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV2(...): Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV3(...): Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV4(...): Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionV5(...): Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppressionWithOverlaps(...): Greedily selects a subset of bounding boxes in descending order of score,
NotEqual(...): Returns the truth value of (x != y) element-wise.
NthElement(...): Finds values of the n-th order statistic for the last dimension.
OneHot(...): Returns a one-hot tensor.
OneShotIterator(...): Makes a "one-shot" iterator that can be iterated only once.
OnesLike(...): Returns a tensor of ones with the same shape and type as x.
OptimizeDataset(...): Creates a dataset by applying optimizations to input_dataset.
OptimizeDatasetV2(...): Creates a dataset by applying related optimizations to input_dataset.
OptionalFromValue(...): Constructs an Optional variant from a tuple of tensors.
OptionalGetValue(...): Returns the value stored in an Optional variant or raises an error if none exists.
OptionalHasValue(...): Returns true if and only if the given Optional variant has a value.
OptionalNone(...): Creates an Optional variant with no value.
OrderedMapClear(...): Op removes all elements in the underlying container.
OrderedMapIncompleteSize(...): Op returns the number of incomplete elements in the underlying container.
OrderedMapPeek(...): Op peeks at the values at the specified key. If the
OrderedMapSize(...): Op returns the number of elements in the underlying container.
OrderedMapStage(...): Stage (key, values) in the underlying container which behaves like a ordered
OrderedMapUnstage(...): Op removes and returns the values associated with the key
OrderedMapUnstageNoKey(...): Op removes and returns the (key, value) element with the smallest
OutfeedDequeue(...): Retrieves a single tensor from the computation outfeed.
OutfeedDequeueTuple(...): Retrieve multiple values from the computation outfeed.
OutfeedDequeueTupleV2(...): Retrieve multiple values from the computation outfeed. Device ordinal is a
OutfeedDequeueV2(...): Retrieves a single tensor from the computation outfeed. Device ordinal is a
OutfeedEnqueue(...): Enqueue a Tensor on the computation outfeed.
OutfeedEnqueueTuple(...): Enqueue multiple Tensor values on the computation outfeed.
Pack(...): Packs a list of N rank-R tensors into one rank-(R+1) tensor.
Pad(...): Pads a tensor with zeros.
PadV2(...): Pads a tensor.
PaddedBatchDataset(...): Creates a dataset that batches and pads batch_size elements from the input.
PaddedBatchDatasetV2(...): Creates a dataset that batches and pads batch_size elements from the input.
PaddingFIFOQueue(...): A queue that produces elements in first-in first-out order.
PaddingFIFOQueueV2(...): A queue that produces elements in first-in first-out order.
ParallelConcat(...): Concatenates a list of N tensors along the first dimension.
ParallelDynamicStitch(...): Interleave the values from the data tensors into a single tensor.
ParallelInterleaveDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
ParallelInterleaveDatasetV2(...): Creates a dataset that applies f to the outputs of input_dataset.
ParallelInterleaveDatasetV3(...): Creates a dataset that applies f to the outputs of input_dataset.
ParallelInterleaveDatasetV4(...): Creates a dataset that applies f to the outputs of input_dataset.
ParallelMapDataset(...): Creates a dataset that applies f to the outputs of input_dataset.
ParallelMapDatasetV2(...): Creates a dataset that applies f to the outputs of input_dataset.
ParameterizedTruncatedNormal(...): Outputs random values from a normal distribution. The parameters may each be a
ParseExample(...): Transforms a vector of brain.Example protos (as strings) into typed tensors.
ParseExampleDataset(...): Transforms input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.
ParseExampleDatasetV2(...): Transforms input_dataset containing Example protos as vectors of DT_STRING into a dataset of Tensor or SparseTensor objects representing the parsed features.
ParseExampleV2(...): Transforms a vector of tf.Example protos (as strings) into typed tensors.
ParseSequenceExample(...): Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
ParseSequenceExampleV2(...): Transforms a vector of tf.io.SequenceExample protos (as strings) into
ParseSingleExample(...): Transforms a tf.Example proto (as a string) into typed tensors.
ParseSingleSequenceExample(...): Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
ParseTensor(...): Transforms a serialized tensorflow.TensorProto proto into a Tensor.
PartitionedCall(...): returns f(inputs), where f's body is placed and partitioned.
Placeholder(...): A placeholder op for a value that will be fed into the computation.
PlaceholderV2(...): A placeholder op for a value that will be fed into the computation.
PlaceholderWithDefault(...): A placeholder op that passes through input when its output is not fed.
Polygamma(...): Compute the polygamma function \(\psi^{(n)}(x)\).
PopulationCount(...): Computes element-wise population count (a.k.a. popcount, bitsum, bitcount).
Pow(...): Computes the power of one value to another.
PrefetchDataset(...): Creates a dataset that asynchronously prefetches elements from input_dataset.
Prelinearize(...): An op which linearizes one Tensor value to an opaque variant tensor.
PrelinearizeTuple(...): An op which linearizes multiple Tensor values to an opaque variant tensor.
PreventGradient(...): An identity op that triggers an error if a gradient is requested.
Print(...): Prints a list of tensors.
PrintV2(...): Prints a string scalar.
PriorityQueue(...): A queue that produces elements sorted by the first component value.
PriorityQueueV2(...): A queue that produces elements sorted by the first component value.
PrivateThreadPoolDataset(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
Prod(...): Computes the product of elements across dimensions of a tensor.
PyFunc(...): Invokes a python function to compute func(input)->output.
PyFuncStateless(...): A stateless version of PyFunc.
Qr(...): Computes the QR decompositions of one or more matrices.
QuantizeAndDequantize(...): Use QuantizeAndDequantizeV2 instead.
QuantizeAndDequantizeV2(...): Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3(...): Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV4(...): Returns the gradient of QuantizeAndDequantizeV4.
QuantizeAndDequantizeV4Grad(...): Returns the gradient of QuantizeAndDequantizeV4.
QuantizeDownAndShrinkRange(...): Convert the quantized 'input' tensor into a lower-precision 'output', using the
QuantizeV2(...): Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizedAdd(...): Returns x + y element-wise, working on quantized buffers.
QuantizedAvgPool(...): Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization(...): Quantized Batch normalization.
QuantizedBiasAdd(...): Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConcat(...): Concatenates quantized tensors along one dimension.
QuantizedConv2D(...): Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedConv2DAndReluAndRequantize(...)
QuantizedConv2DAndRequantize(...)
QuantizedConv2DPerChannel(...): Computes QuantizedConv2D per channel.
QuantizedConv2DWithBiasAndRelu(...)
QuantizedConv2DWithBiasAndReluAndRequantize(...)
QuantizedConv2DWithBiasAndRequantize(...)
QuantizedConv2DWithBiasSignedSumAndReluAndRequantize(...)
QuantizedConv2DWithBiasSumAndRelu(...)
QuantizedConv2DWithBiasSumAndReluAndRequantize(...)
QuantizedDepthwiseConv2D(...): Computes quantized depthwise Conv2D.
QuantizedDepthwiseConv2DWithBias(...): Computes quantized depthwise Conv2D with Bias.
QuantizedDepthwiseConv2DWithBiasAndRelu(...): Computes quantized depthwise Conv2D with Bias and Relu.
QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize(...): Computes quantized depthwise Conv2D with Bias, Relu and Requantize.
QuantizedInstanceNorm(...): Quantized Instance normalization.
QuantizedMatMul(...): Perform a quantized matrix multiplication of a by the matrix b.
QuantizedMatMulWithBias(...): Performs a quantized matrix multiplication of a by the matrix b with bias
QuantizedMatMulWithBiasAndDequantize(...)
QuantizedMatMulWithBiasAndRelu(...): Perform a quantized matrix multiplication of a by the matrix b with bias
QuantizedMatMulWithBiasAndReluAndRequantize(...): Perform a quantized matrix multiplication of a by the matrix b with bias
QuantizedMatMulWithBiasAndRequantize(...)
QuantizedMaxPool(...): Produces the max pool of the input tensor for quantized types.
QuantizedMul(...): Returns x * y element-wise, working on quantized buffers.
QuantizedRelu(...): Computes Quantized Rectified Linear: max(features, 0)
QuantizedRelu6(...): Computes Quantized Rectified Linear 6: min(max(features, 0), 6)
QuantizedReluX(...): Computes Quantized Rectified Linear X: min(max(features, 0), max_value)
QuantizedReshape(...): Reshapes a quantized tensor as per the Reshape op.
QuantizedResizeBilinear(...): Resize quantized images to size using quantized bilinear interpolation.
QueueClose(...): Closes the given queue.
QueueCloseV2(...): Closes the given queue.
QueueDequeue(...): Dequeues a tuple of one or more tensors from the given queue.
QueueDequeueMany(...): Dequeues n tuples of one or more tensors from the given queue.
QueueDequeueManyV2(...): Dequeues n tuples of one or more tensors from the given queue.
QueueDequeueUpTo(...): Dequeues n tuples of one or more tensors from the given queue.
QueueDequeueUpToV2(...): Dequeues n tuples of one or more tensors from the given queue.
QueueDequeueV2(...): Dequeues a tuple of one or more tensors from the given queue.
QueueEnqueue(...): Enqueues a tuple of one or more tensors in the given queue.
QueueEnqueueMany(...): Enqueues zero or more tuples of one or more tensors in the given queue.
QueueEnqueueManyV2(...): Enqueues zero or more tuples of one or more tensors in the given queue.
QueueEnqueueV2(...): Enqueues a tuple of one or more tensors in the given queue.
QueueIsClosed(...): Returns true if queue is closed.
QueueIsClosedV2(...): Returns true if queue is closed.
QueueSize(...): Computes the number of elements in the given queue.
QueueSizeV2(...): Computes the number of elements in the given queue.
RFFT(...): Real-valued fast Fourier transform.
RFFT2D(...): 2D real-valued fast Fourier transform.
RFFT3D(...): 3D real-valued fast Fourier transform.
RGBToHSV(...): Converts one or more images from RGB to HSV.
RaggedBincount(...): Counts the number of occurrences of each value in an integer array.
RaggedCountSparseOutput(...): Performs sparse-output bin counting for a ragged tensor input.
RaggedCross(...): Generates a feature cross from a list of tensors, and returns it as a
RaggedGather(...): Gather ragged slices from params axis 0 according to indices.
RaggedRange(...): Returns a RaggedTensor containing the specified sequences of numbers.
RaggedTensorFromVariant(...): Decodes a variant Tensor into a RaggedTensor.
RaggedTensorToSparse(...): Converts a RaggedTensor into a SparseTensor with the same values.
RaggedTensorToTensor(...): Create a dense tensor from a ragged tensor, possibly altering its shape.
RaggedTensorToVariant(...): Encodes a RaggedTensor into a variant Tensor.
RaggedTensorToVariantGradient(...): Helper used to compute the gradient for RaggedTensorToVariant.
RandomCrop(...): Randomly crop image.
RandomDataset(...): Creates a Dataset that returns pseudorandom numbers.
RandomGamma(...): Outputs random values from the Gamma distribution(s) described by alpha.
RandomGammaGrad(...): Computes the derivative of a Gamma random sample w.r.t. alpha.
RandomPoisson(...): Use RandomPoissonV2 instead.
RandomPoissonV2(...): Outputs random values from the Poisson distribution(s) described by rate.
RandomShuffle(...): Randomly shuffles a tensor along its first dimension.
RandomShuffleQueue(...): A queue that randomizes the order of elements.
RandomShuffleQueueV2(...): A queue that randomizes the order of elements.
RandomStandardNormal(...): Outputs random values from a normal distribution.
RandomUniform(...): Outputs random values from a uniform distribution.
RandomUniformInt(...): Outputs random integers from a uniform distribution.
Range(...): Creates a sequence of numbers.
RangeDataset(...): Creates a dataset with a range of values. Corresponds to python's xrange.
Rank(...): Returns the rank of a tensor.
ReadFile(...): Reads and outputs the entire contents of the input filename.
ReadVariableOp(...): Reads the value of a variable.
ReaderNumRecordsProduced(...): Returns the number of records this Reader has produced.
ReaderNumRecordsProducedV2(...): Returns the number of records this Reader has produced.
ReaderNumWorkUnitsCompleted(...): Returns the number of work units this Reader has finished processing.
ReaderNumWorkUnitsCompletedV2(...): Returns the number of work units this Reader has finished processing.
ReaderRead(...): Returns the next record (key, value pair) produced by a Reader.
ReaderReadUpTo(...): Returns up to num_records (key, value) pairs produced by a Reader.
ReaderReadUpToV2(...): Returns up to num_records (key, value) pairs produced by a Reader.
ReaderReadV2(...): Returns the next record (key, value pair) produced by a Reader.
ReaderReset(...): Restore a Reader to its initial clean state.
ReaderResetV2(...): Restore a Reader to its initial clean state.
ReaderRestoreState(...): Restore a reader to a previously saved state.
ReaderRestoreStateV2(...): Restore a reader to a previously saved state.
ReaderSerializeState(...): Produce a string tensor that encodes the state of a Reader.
ReaderSerializeStateV2(...): Produce a string tensor that encodes the state of a Reader.
Real(...): Returns the real part of a complex number.
RealDiv(...): Returns x / y element-wise for real types.
RebatchDataset(...): Creates a dataset that changes the batch size.
RebatchDatasetV2(...): Creates a dataset that changes the batch size.
Reciprocal(...): Computes the reciprocal of x element-wise.
ReciprocalGrad(...): Computes the gradient for the inverse of x wrt its input.
RecordInput(...): Emits randomized records.
Recv(...): Receives the named tensor from send_device on recv_device.
RecvTPUEmbeddingActivations(...): An op that receives embedding activations on the TPU.
ReduceDataset(...): Reduces the input dataset to a singleton using a reduce function.
ReduceJoin(...): Joins a string Tensor across the given dimensions.
RefEnter(...): Creates or finds a child frame, and makes data available to the child frame.
RefExit(...): Exits the current frame to its parent frame.
RefIdentity(...): Return the same ref tensor as the input ref tensor.
RefMerge(...): Forwards the value of an available tensor from inputs to output.
RefNextIteration(...): Makes its input available to the next iteration.
RefSelect(...): Forwards the indexth element of inputs to output.
RefSwitch(...): Forwards the ref tensor data to the output port determined by pred.
RegexFullMatch(...): Check if the input matches the regex pattern.
RegexReplace(...): Replaces matches of the pattern regular expression in input with the
RegisterDataset(...): Registers a dataset with the tf.data service.
Relu(...): Computes rectified linear: max(features, 0).
Relu6(...): Computes rectified linear 6: min(max(features, 0), 6).
Relu6Grad(...): Computes rectified linear 6 gradients for a Relu6 operation.
ReluGrad(...): Computes rectified linear gradients for a Relu operation.
RemoteCall(...): Runs function f on a remote device indicated by target.
RepeatDataset(...): Creates a dataset that emits the outputs of input_dataset count times.
RequantizationRange(...): Computes a range that covers the actual values present in a quantized tensor.
RequantizationRangePerChannel(...): Computes requantization range per channel.
Requantize(...): Converts the quantized input tensor into a lower-precision output.
RequantizePerChannel(...): Requantizes input with min and max values known per channel.
Reshape(...): Reshapes a tensor.
ResizeArea(...): Resize images to size using area interpolation.
ResizeBicubic(...): Resize images to size using bicubic interpolation.
ResizeBicubicGrad(...): Computes the gradient of bicubic interpolation.
ResizeBilinear(...): Resize images to size using bilinear interpolation.
ResizeBilinearGrad(...): Computes the gradient of bilinear interpolation.
ResizeNearestNeighbor(...): Resize images to size using nearest neighbor interpolation.
ResizeNearestNeighborGrad(...): Computes the gradient of nearest neighbor interpolation.
ResourceAccumulatorApplyGradient(...): Applies a gradient to a given accumulator.
ResourceAccumulatorNumAccumulated(...): Returns the number of gradients aggregated in the given accumulators.
ResourceAccumulatorSetGlobalStep(...): Updates the accumulator with a new value for global_step.
ResourceAccumulatorTakeGradient(...): Extracts the average gradient in the given ConditionalAccumulator.
ResourceApplyAdaMax(...): Update '*var' according to the AdaMax algorithm.
ResourceApplyAdadelta(...): Update '*var' according to the adadelta scheme.
ResourceApplyAdagrad(...): Update '*var' according to the adagrad scheme.
ResourceApplyAdagradDA(...): Update '*var' according to the proximal adagrad scheme.
ResourceApplyAdagradV2(...): Update '*var' according to the adagrad scheme.
ResourceApplyAdam(...): Update '*var' according to the Adam algorithm.
ResourceApplyAdamWithAmsgrad(...): Update '*var' according to the Adam algorithm.
ResourceApplyAddSign(...): Update '*var' according to the AddSign update.
ResourceApplyCenteredRMSProp(...): Update '*var' according to the centered RMSProp algorithm.
ResourceApplyFtrl(...): Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrlV2(...): Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyGradientDescent(...): Update '*var' by subtracting 'alpha' * 'delta' from it.
ResourceApplyKerasMomentum(...): Update '*var' according to the momentum scheme.
ResourceApplyMomentum(...): Update '*var' according to the momentum scheme.
ResourceApplyPowerSign(...): Update '*var' according to the AddSign update.
ResourceApplyProximalAdagrad(...): Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ResourceApplyProximalGradientDescent(...): Update '*var' as FOBOS algorithm with fixed learning rate.
ResourceApplyRMSProp(...): Update '*var' according to the RMSProp algorithm.
ResourceConditionalAccumulator(...): A conditional accumulator for aggregating gradients.
ResourceCountUpTo(...): Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather(...): Gather slices from the variable pointed to by resource according to indices.
ResourceScatterAdd(...): Adds sparse updates to the variable referenced by resource.
ResourceScatterDiv(...): Divides sparse updates into the variable referenced by resource.
ResourceScatterMax(...): Reduces sparse updates into the variable referenced by resource using the max operation.
ResourceScatterMin(...): Reduces sparse updates into the variable referenced by resource using the min operation.
ResourceScatterMul(...): Multiplies sparse updates into the variable referenced by resource.
ResourceScatterNdAdd(...): Applies sparse addition to individual values or slices in a Variable.
ResourceScatterNdSub(...): Applies sparse subtraction to individual values or slices in a Variable.
ResourceScatterNdUpdate(...): Applies sparse updates to individual values or slices within a given
ResourceScatterSub(...): Subtracts sparse updates from the variable referenced by resource.
ResourceScatterUpdate(...): Assigns sparse updates to the variable referenced by resource.
ResourceSparseApplyAdadelta(...): var: Should be from a Variable().
ResourceSparseApplyAdagrad(...): Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyAdagradDA(...): Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradV2(...): Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyCenteredRMSProp(...): Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyFtrl(...): Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrlV2(...): Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyKerasMomentum(...): Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyMomentum(...): Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyProximalAdagrad(...): Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
ResourceSparseApplyProximalGradientDescent(...): Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyRMSProp(...): Update '*var' according to the RMSProp algorithm.
ResourceStridedSliceAssign(...): Assign value to the sliced l-value reference of ref.
Restore(...): Restores a tensor from checkpoint files.
RestoreSlice(...): Restores a tensor from checkpoint files.
RestoreV2(...): Restores tensors from a V2 checkpoint.
RetrieveTPUEmbeddingADAMParameters(...): Retrieve ADAM embedding parameters.
RetrieveTPUEmbeddingADAMParametersGradAccumDebug(...): Retrieve ADAM embedding parameters with debug support.
RetrieveTPUEmbeddingAdadeltaParameters(...): Retrieve Adadelta embedding parameters.
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug(...): Retrieve Adadelta embedding parameters with debug support.
RetrieveTPUEmbeddingAdagradParameters(...): Retrieve Adagrad embedding parameters.
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug(...): Retrieve Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingCenteredRMSPropParameters(...): Retrieve centered RMSProp embedding parameters.
RetrieveTPUEmbeddingFTRLParameters(...): Retrieve FTRL embedding parameters.
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug(...): Retrieve FTRL embedding parameters with debug support.
RetrieveTPUEmbeddingMDLAdagradLightParameters(...): Retrieve MDL Adagrad Light embedding parameters.
RetrieveTPUEmbeddingMomentumParameters(...): Retrieve Momentum embedding parameters.
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug(...): Retrieve Momentum embedding parameters with debug support.
RetrieveTPUEmbeddingProximalAdagradParameters(...): Retrieve proximal Adagrad embedding parameters.
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug(...): Retrieve proximal Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingProximalYogiParameters(...)
RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug(...)
RetrieveTPUEmbeddingRMSPropParameters(...): Retrieve RMSProp embedding parameters.
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug(...): Retrieve RMSProp embedding parameters with debug support.
RetrieveTPUEmbeddingStochasticGradientDescentParameters(...): Retrieve SGD embedding parameters.
RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug(...): Retrieve SGD embedding parameters with debug support.
Reverse(...): Reverses specific dimensions of a tensor.
ReverseSequence(...): Reverses variable length slices.
ReverseV2(...): Reverses specific dimensions of a tensor.
RightShift(...): Elementwise computes the bitwise right-shift of x and y.
Rint(...): Returns element-wise integer closest to x.
RngReadAndSkip(...): Advance the counter of a counter-based RNG.
RngSkip(...): Advance the counter of a counter-based RNG.
Roll(...): Rolls the elements of a tensor along an axis.
Round(...): Rounds the values of a tensor to the nearest integer, element-wise.
Rsqrt(...): Computes reciprocal of square root of x element-wise.
RsqrtGrad(...): Computes the gradient for the rsqrt of x wrt its input.
SampleDistortedBoundingBox(...): Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBoxV2(...): Generate a single randomly distorted bounding box for an image.
SamplingDataset(...): Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
Save(...): Saves the input tensors to disk.
SaveSlices(...): Saves input tensors slices to disk.
SaveV2(...): Saves tensors in V2 checkpoint format.
ScalarSummary(...): Outputs a Summary protocol buffer with scalar values.
ScanDataset(...): Creates a dataset successively reduces f over the elements of input_dataset.
ScatterAdd(...): Adds sparse updates to a variable reference.
ScatterDiv(...): Divides a variable reference by sparse updates.
ScatterMax(...): Reduces sparse updates into a variable reference using the max operation.
ScatterMin(...): Reduces sparse updates into a variable reference using the min operation.
ScatterMul(...): Multiplies sparse updates into a variable reference.
ScatterNd(...): Scatter updates into a new tensor according to indices.
ScatterNdAdd(...): Applies sparse addition to individual values or slices in a Variable.
ScatterNdMax(...): Computes element-wise maximum.
ScatterNdMin(...): Computes element-wise minimum.
ScatterNdNonAliasingAdd(...): Applies sparse addition to input using individual values or slices
ScatterNdSub(...): Applies sparse subtraction to individual values or slices in a Variable.
ScatterNdUpdate(...): Applies sparse updates to individual values or slices within a given
ScatterSub(...): Subtracts sparse updates to a variable reference.
ScatterUpdate(...): Applies sparse updates to a variable reference.
SdcaFprint(...): Computes fingerprints of the input strings.
SdcaOptimizer(...): Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaOptimizerV2(...): Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaShrinkL1(...): Applies L1 regularization shrink step on the parameters.
SegmentMax(...): Computes the maximum along segments of a tensor.
SegmentMean(...): Computes the mean along segments of a tensor.
SegmentMin(...): Computes the minimum along segments of a tensor.
SegmentProd(...): Computes the product along segments of a tensor.
SegmentSum(...): Computes the sum along segments of a tensor.
Select(...): Selects elements from x or y, depending on condition.
SelfAdjointEig(...): Computes the Eigen Decomposition of a batch of square self-adjoint matrices.
SelfAdjointEigV2(...): Computes the eigen decomposition of one or more square self-adjoint matrices.
Selu(...): Computes scaled exponential linear: scale * alpha * (exp(features) - 1)
SeluGrad(...): Computes gradients for the scaled exponential linear (Selu) operation.
Send(...): Sends the named tensor from send_device to recv_device.
SendTPUEmbeddingGradients(...): Performs gradient updates of embedding tables.
SerializeIterator(...): Converts the given resource_handle representing an iterator to a variant tensor.
SerializeManySparse(...): Serialize an N-minibatch SparseTensor into an [N, 3] Tensor object.
SerializeSparse(...): Serialize a SparseTensor into a [3] Tensor object.
SerializeTensor(...): Transforms a Tensor into a serialized TensorProto proto.
SetSize(...): Number of unique elements along last dimension of input set.
SetStatsAggregatorDataset(...)
Shape(...): Returns the shape of a tensor.
ShapeN(...): Returns shape of tensors.
ShardDataset(...): Creates a Dataset that includes only 1/num_shards of this dataset.
ShardedFilename(...): Generate a sharded filename. The filename is printf formatted as
ShardedFilespec(...): Generate a glob pattern matching all sharded file names.
ShuffleAndRepeatDataset(...): Creates a dataset that shuffles and repeats elements from input_dataset
ShuffleAndRepeatDatasetV2(...)
ShuffleDataset(...): Creates a dataset that shuffles elements from input_dataset pseudorandomly.
ShutdownDistributedTPU(...): Shuts down a running distributed TPU system.
Sigmoid(...): Computes sigmoid of x element-wise.
SigmoidGrad(...): Computes the gradient of the sigmoid of x wrt its input.
Sign(...): Returns an element-wise indication of the sign of a number.
Sin(...): Computes sine of x element-wise.
Sinh(...): Computes hyperbolic sine of x element-wise.
Size(...): Returns the size of a tensor.
SkipDataset(...): Creates a dataset that skips count elements from the input_dataset.
Slice(...): Return a slice from 'input'.
SlidingWindowDataset(...): Creates a dataset that passes a sliding window over input_dataset.
Snapshot(...): Returns a copy of the input tensor.
SnapshotDataset(...): Creates a dataset that will write to / read from a snapshot.
SnapshotDatasetV2(...): Creates a dataset that will write to / read from a snapshot.
SobolSample(...): Generates points from the Sobol sequence.
Softmax(...): Computes softmax activations.
SoftmaxCrossEntropyWithLogits(...): Computes softmax cross entropy cost and gradients to backpropagate.
Softplus(...): Computes softplus: log(exp(features) + 1).
SoftplusGrad(...): Computes softplus gradients for a softplus operation.
Softsign(...): Computes softsign: features / (abs(features) + 1).
SoftsignGrad(...): Computes softsign gradients for a softsign operation.
SpaceToBatch(...): SpaceToBatch for 4-D tensors of type T.
SpaceToBatchND(...): SpaceToBatch for N-D tensors of type T.
SpaceToDepth(...): SpaceToDepth for tensors of type T.
SparseAccumulatorApplyGradient(...): Applies a sparse gradient to a given accumulator.
SparseAccumulatorTakeGradient(...): Extracts the average sparse gradient in a SparseConditionalAccumulator.
SparseAdd(...): Adds two SparseTensor objects to produce another SparseTensor.
SparseAddGrad(...): The gradient operator for the SparseAdd op.
SparseApplyAdadelta(...): var: Should be from a Variable().
SparseApplyAdagrad(...): Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
SparseApplyAdagradDA(...): Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
SparseApplyAdagradV2(...): Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
SparseApplyCenteredRMSProp(...): Update '*var' according to the centered RMSProp algorithm.
SparseApplyFtrl(...): Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrlV2(...): Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyMomentum(...): Update relevant entries in 'var' and 'accum' according to the momentum scheme.
SparseApplyProximalAdagrad(...): Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
SparseApplyProximalGradientDescent(...): Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyRMSProp(...): Update '*var' according to the RMSProp algorithm.
SparseBincount(...): Counts the number of occurrences of each value in an integer array.
SparseConcat(...): Concatenates a list of SparseTensor along the specified dimension.
SparseConditionalAccumulator(...): A conditional accumulator for aggregating sparse gradients.
SparseCountSparseOutput(...): Performs sparse-output bin counting for a sparse tensor input.
SparseCross(...): Generates sparse cross from a list of sparse and dense tensors.
SparseCrossHashed(...): Generates sparse cross from a list of sparse and dense tensors.
SparseCrossV2(...): Generates sparse cross from a list of sparse and dense tensors.
SparseDenseCwiseAdd(...): Adds up a SparseTensor and a dense Tensor, using these special rules:
SparseDenseCwiseDiv(...): Component-wise divides a SparseTensor by a dense Tensor.
SparseDenseCwiseMul(...): Component-wise multiplies a SparseTensor by a dense Tensor.
SparseFillEmptyRows(...): Fills empty rows in the input 2-D SparseTensor with a default value.
SparseFillEmptyRowsGrad(...): The gradient of SparseFillEmptyRows.
SparseMatMul(...): Multiply matrix "a" by matrix "b".
SparseMatrixAdd(...): Sparse addition of two CSR matrices, C = alpha * A + beta * B.
SparseMatrixMatMul(...): Matrix-multiplies a sparse matrix with a dense matrix.
SparseMatrixMul(...): Element-wise multiplication of a sparse matrix with a dense tensor.
SparseMatrixNNZ(...): Returns the number of nonzeroes of sparse_matrix.
SparseMatrixOrderingAMD(...): Computes the Approximate Minimum Degree (AMD) ordering of input.
SparseMatrixSoftmax(...): Calculates the softmax of a CSRSparseMatrix.
SparseMatrixSoftmaxGrad(...): Calculates the gradient of the SparseMatrixSoftmax op.
SparseMatrixSparseCholesky(...): Computes the sparse Cholesky decomposition of input.
SparseMatrixSparseMatMul(...): Sparse-matrix-multiplies two CSR matrices a and b.
SparseMatrixTranspose(...): Transposes the inner (matrix) dimensions of a CSRSparseMatrix.
SparseMatrixZeros(...): Creates an all-zeros CSRSparseMatrix with shape dense_shape.
SparseReduceMax(...): Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse(...): Computes the max of elements across dimensions of a SparseTensor.
SparseReduceSum(...): Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse(...): Computes the sum of elements across dimensions of a SparseTensor.
SparseReorder(...): Reorders a SparseTensor into the canonical, row-major ordering.
SparseReshape(...): Reshapes a SparseTensor to represent values in a new dense shape.
SparseSegmentMean(...): Computes the mean along sparse segments of a tensor.
SparseSegmentMeanGrad(...): Computes gradients for SparseSegmentMean.
SparseSegmentMeanWithNumSegments(...): Computes the mean along sparse segments of a tensor.
SparseSegmentSqrtN(...): Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSqrtNGrad(...): Computes gradients for SparseSegmentSqrtN.
SparseSegmentSqrtNWithNumSegments(...): Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSum(...): Computes the sum along sparse segments of a tensor.
SparseSegmentSumWithNumSegments(...): Computes the sum along sparse segments of a tensor.
SparseSlice(...): Slice a SparseTensor based on the start and size.
SparseSliceGrad(...): The gradient operator for the SparseSlice op.
SparseSoftmax(...): Applies softmax to a batched N-D SparseTensor.
SparseSoftmaxCrossEntropyWithLogits(...): Computes softmax cross entropy cost and gradients to backpropagate.
SparseSparseMaximum(...): Returns the element-wise max of two SparseTensors.
SparseSparseMinimum(...): Returns the element-wise min of two SparseTensors.
SparseSplit(...): Split a SparseTensor into num_split tensors along one dimension.
SparseTensorDenseAdd(...): Adds up a SparseTensor and a dense Tensor, producing a dense Tensor.
SparseTensorDenseMatMul(...): Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
SparseTensorSliceDataset(...): Creates a dataset that splits a SparseTensor into elements row-wise.
SparseTensorToCSRSparseMatrix(...): Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.
SparseToDense(...): Converts a sparse representation into a dense tensor.
SparseToSparseSetOperation(...): Applies set operation along last dimension of 2 SparseTensor inputs.
Split(...): Splits a tensor into num_split tensors along one dimension.
SplitV(...): Splits a tensor into num_split tensors along one dimension.
SqlDataset(...): Creates a dataset that executes a SQL query and emits rows of the result set.
Sqrt(...): Computes square root of x element-wise.
SqrtGrad(...): Computes the gradient for the sqrt of x wrt its input.
Square(...): Computes square of x element-wise.
SquaredDifference(...): Returns conj(x - y)(x - y) element-wise.
Squeeze(...): Removes dimensions of size 1 from the shape of a tensor.
Stack(...): Deprecated, use StackV2.
StackClose(...): Deprecated, use StackCloseV2.
StackCloseV2(...): Delete the stack from its resource container.
StackPop(...): Deprecated, use StackPopV2.
StackPopV2(...): Pop the element at the top of the stack.
StackPush(...): Deprecated, use StackPushV2.
StackPushV2(...): Push an element onto the stack.
StackV2(...): A stack that produces elements in first-in last-out order.
Stage(...): Stage values similar to a lightweight Enqueue.
StageClear(...): Op removes all elements in the underlying container.
StagePeek(...): Op peeks at the values at the specified index. If the
StageSize(...): Op returns the number of elements in the underlying container.
StatefulPartitionedCall(...): returns f(inputs), where f's body is placed and partitioned.
StatefulStandardNormal(...): Outputs random values from a normal distribution. This op is deprecated in favor of op 'StatefulStandardNormalV2'
StatefulStandardNormalV2(...): Outputs random values from a normal distribution.
StatefulTruncatedNormal(...): Outputs random values from a truncated normal distribution.
StatefulUniform(...): Outputs random values from a uniform distribution.
StatefulUniformFullInt(...): Outputs random integers from a uniform distribution.
StatefulUniformInt(...): Outputs random integers from a uniform distribution.
StatelessCase(...): An n-way switch statement which calls a single branch function.
StatelessIf(...): output = cond ? then_branch(input) : else_branch(input)
StatelessMultinomial(...): Draws samples from a multinomial distribution.
StatelessParameterizedTruncatedNormal(...)
StatelessRandomBinomial(...): Outputs deterministic pseudorandom random numbers from a binomial distribution.
StatelessRandomGammaV2(...): Outputs deterministic pseudorandom random numbers from a gamma distribution.
StatelessRandomGetKeyCounterAlg(...): Picks the best algorithm based on device, and scrambles seed into key and counter.
StatelessRandomNormal(...): Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomNormalV2(...): Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomPoisson(...): Outputs deterministic pseudorandom random numbers from a Poisson distribution.
StatelessRandomUniform(...): Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessRandomUniformFullInt(...): Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformFullIntV2(...): Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformInt(...): Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformIntV2(...): Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformV2(...): Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessSampleDistortedBoundingBox(...): Generate a randomly distorted bounding box for an image deterministically.
StatelessTruncatedNormal(...): Outputs deterministic pseudorandom values from a truncated normal distribution.
StatelessTruncatedNormalV2(...): Outputs deterministic pseudorandom values from a truncated normal distribution.
StatelessWhile(...): output = input; While (Cond(output)) { output = Body(output) }
StaticRegexFullMatch(...): Check if the input matches the regex pattern.
StaticRegexReplace(...): Replaces the match of pattern in input with rewrite.
StatsAggregatorHandle(...): Creates a statistics manager resource.
StatsAggregatorSetSummaryWriter(...): Set a summary_writer_interface to record statistics using given stats_aggregator.
StatsAggregatorSummary(...): Produces a summary of any statistics recorded by the given statistics manager.
StopGradient(...): Stops gradient computation.
StridedSlice(...): Return a strided slice from input.
StridedSliceAssign(...): Assign value to the sliced l-value reference of ref.
StridedSliceGrad(...): Returns the gradient of StridedSlice.
StringFormat(...): Formats a string template using a list of tensors.
StringJoin(...): Joins the strings in the given list of string tensors into one tensor;
StringLength(...): String lengths of input.
StringLower(...): Converts all uppercase characters into their respective lowercase replacements.
StringNGrams(...): Creates ngrams from ragged string data.
StringSplit(...): Split elements of input based on delimiter into a SparseTensor.
StringSplitV2(...): Split elements of source based on sep into a SparseTensor.
StringStrip(...): Strip leading and trailing whitespaces from the Tensor.
StringToHashBucket(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToHashBucketFast(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToHashBucketStrong(...): Converts each string in the input Tensor to its hash mod by a number of buckets.
StringToNumber(...): Converts each string in the input Tensor to the specified numeric type.
StringUpper(...): Converts all lowercase characters into their respective uppercase replacements.
Sub(...): Returns x - y element-wise.
Substr(...): Return substrings from Tensor of strings.
Sum(...): Computes the sum of elements across dimensions of a tensor.
Svd(...): Computes the singular value decompositions of one or more matrices.
Switch(...): Forwards data to the output port determined by pred.
SymbolicGradient(...): Computes the gradient function for function f via backpropagation.
TFRecordDataset(...): Creates a dataset that emits the records from one or more TFRecord files.
TFRecordReader(...): A Reader that outputs the records from a TensorFlow Records file.
TFRecordReaderV2(...): A Reader that outputs the records from a TensorFlow Records file.
TPUCompilationResult(...): Returns the result of a TPU compilation.
TPUEmbeddingActivations(...): An op enabling differentiation of TPU Embeddings.
TPUOrdinalSelector(...): A TPU core selector Op.
TPUPartitionedCall(...): Calls a function placed on a specified TPU device.
TPUReplicateMetadata(...): Metadata indicating how the TPU computation should be replicated.
TPUReplicatedInput(...): Connects N inputs to an N-way replicated TPU computation.
TPUReplicatedOutput(...): Connects N outputs from an N-way replicated TPU computation.
TakeDataset(...): Creates a dataset that contains count elements from the input_dataset.
TakeManySparseFromTensorsMap(...): Read SparseTensors from a SparseTensorsMap and concatenate them.
TakeWhileDataset(...): Creates a dataset that stops iteration when predicate` is false.
Tan(...): Computes tan of x element-wise.
Tanh(...): Computes hyperbolic tangent of x element-wise.
TanhGrad(...): Computes the gradient for the tanh of x wrt its input.
TemporaryVariable(...): Returns a tensor that may be mutated, but only persists within a single step.
TensorArrayCloseV2(...): Deprecated. Use TensorArrayCloseV3
TensorArrayCloseV3(...): Delete the TensorArray from its resource container.
TensorArrayConcatV2(...): Deprecated. Use TensorArrayConcatV3
TensorArrayConcatV3(...): Concat the elements from the TensorArray into value value.
TensorArrayGatherV2(...): Deprecated. Use TensorArrayGatherV3
TensorArrayGatherV3(...): Gather specific elements from the TensorArray into output value.
TensorArrayGradV2(...): Deprecated. Use TensorArrayGradV3
TensorArrayGradV3(...): Creates a TensorArray for storing the gradients of values in the given handle.
TensorArrayGradWithShape(...): Creates a TensorArray for storing multiple gradients of values in the given handle.
TensorArrayReadV2(...): Deprecated. Use TensorArrayReadV3
TensorArrayReadV3(...): Read an element from the TensorArray into output value.
TensorArrayScatterV2(...): Deprecated. Use TensorArrayScatterV3
TensorArrayScatterV3(...): Scatter the data from the input value into specific TensorArray elements.
TensorArraySizeV2(...): Deprecated. Use TensorArraySizeV3
TensorArraySizeV3(...): Get the current size of the TensorArray.
TensorArraySplitV2(...): Deprecated. Use TensorArraySplitV3
TensorArraySplitV3(...): Split the data from the input value into TensorArray elements.
TensorArrayV2(...): Deprecated. Use TensorArrayV3
TensorArrayV3(...): An array of Tensors of given size.
TensorArrayWriteV2(...): Deprecated. Use TensorArrayGradV3
TensorArrayWriteV3(...): Push an element onto the tensor_array.
TensorDataset(...): Creates a dataset that emits components as a tuple of tensors once.
TensorListConcat(...): Concats all tensors in the list along the 0th dimension.
TensorListConcatV2(...): Concats all tensors in the list along the 0th dimension.
TensorListElementShape(...): The shape of the elements of the given list, as a tensor.
TensorListFromTensor(...): Creates a TensorList which, when stacked, has the value of tensor.
TensorListGather(...): Creates a Tensor by indexing into the TensorList.
TensorListGetItem(...): Returns the item in the list with the given index.
TensorListLength(...): Returns the number of tensors in the input tensor list.
TensorListPopBack(...): Returns the last element of the input list as well as a list with all but that element.
TensorListPushBack(...): Returns a list which has the passed-in Tensor as last element and the other elements of the given list in input_handle.
TensorListReserve(...): List of the given size with empty elements.
TensorListResize(...): Resizes the list.
TensorListScatter(...): Creates a TensorList by indexing into a Tensor.
TensorListScatterIntoExistingList(...): Scatters tensor at indices in an input list.
TensorListScatterV2(...): Creates a TensorList by indexing into a Tensor.
TensorListSetItem(...): Sets the index-th position of the list to contain the given tensor.
TensorListSplit(...): Splits a tensor into a list.
TensorListStack(...): Stacks all tensors in the list.
TensorScatterAdd(...): Adds sparse updates to an existing tensor according to indices.
TensorScatterSub(...): Subtracts sparse updates from an existing tensor according to indices.
TensorScatterUpdate(...): Scatter updates into an existing tensor according to indices.
TensorSliceDataset(...): Creates a dataset that emits each dim-0 slice of components once.
TensorStridedSliceUpdate(...): Assign value to the sliced l-value reference of input.
TensorSummary(...): Outputs a Summary protocol buffer with a tensor.
TensorSummaryV2(...): Outputs a Summary protocol buffer with a tensor and per-plugin data.
TextLineDataset(...): Creates a dataset that emits the lines of one or more text files.
TextLineReader(...): A Reader that outputs the lines of a file delimited by '\n'.
TextLineReaderV2(...): A Reader that outputs the lines of a file delimited by '\n'.
ThreadPoolDataset(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
ThreadPoolHandle(...): Creates a dataset that uses a custom thread pool to compute input_dataset.
ThreadUnsafeUnigramCandidateSampler(...): Generates labels for candidate sampling with a learned unigram distribution.
Tile(...): Constructs a tensor by tiling a given tensor.
TileGrad(...): Returns the gradient of Tile.
Timestamp(...): Provides the time since epoch in seconds.
ToBool(...): Converts a tensor to a scalar predicate.
TopK(...): Finds values and indices of the k largest elements for the last dimension.
TopKV2(...): Finds values and indices of the k largest elements for the last dimension.
Transpose(...): Shuffle dimensions of x according to a permutation.
TridiagonalMatMul(...): Calculate product with tridiagonal matrix.
TridiagonalSolve(...): Solves tridiagonal systems of equations.
TruncateDiv(...): Returns x / y element-wise for integer types.
TruncateMod(...): Returns element-wise remainder of division. This emulates C semantics in that
TruncatedNormal(...): Outputs random values from a truncated normal distribution.
Unbatch(...): Reverses the operation of Batch for a single output Tensor.
UnbatchDataset(...): A dataset that splits the elements of its input into multiple elements.
UnbatchGrad(...): Gradient of Unbatch.
UncompressElement(...): Uncompresses a compressed dataset element.
UnicodeDecode(...): Decodes each string in input into a sequence of Unicode code points.
UnicodeDecodeWithOffsets(...): Decodes each string in input into a sequence of Unicode code points.
UnicodeEncode(...): Encode a tensor of ints into unicode strings.
UnicodeScript(...): Determine the script codes of a given tensor of Unicode integer code points.
UnicodeTranscode(...): Transcode the input text from a source encoding to a destination encoding.
UniformCandidateSampler(...): Generates labels for candidate sampling with a uniform distribution.
Unique(...): Finds unique elements in a 1-D tensor.
UniqueDataset(...): Creates a dataset that contains the unique elements of input_dataset.
UniqueV2(...): Finds unique elements along an axis of a tensor.
UniqueWithCounts(...): Finds unique elements in a 1-D tensor.
UniqueWithCountsV2(...): Finds unique elements along an axis of a tensor.
Unpack(...): Unpacks a given dimension of a rank-R tensor into num rank-(R-1) tensors.
UnravelIndex(...): Converts an array of flat indices into a tuple of coordinate arrays.
UnsortedSegmentJoin(...): Joins the elements of inputs based on segment_ids.
UnsortedSegmentMax(...): Computes the maximum along segments of a tensor.
UnsortedSegmentMin(...): Computes the minimum along segments of a tensor.
UnsortedSegmentProd(...): Computes the product along segments of a tensor.
UnsortedSegmentSum(...): Computes the sum along segments of a tensor.
Unstage(...): Op is similar to a lightweight Dequeue.
UpperBound(...): Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp(...): Creates a handle to a Variable resource.
VarIsInitializedOp(...): Checks whether a resource handle-based variable has been initialized.
Variable(...): Use VariableV2 instead.
VariableShape(...): Returns the shape of the variable pointed to by resource.
VariableV2(...): Holds state in the form of a tensor that persists across steps.
Where(...): Returns locations of nonzero / true values in a tensor.
While(...): output = input; While (Cond(output)) { output = Body(output) }
WholeFileReader(...): A Reader that outputs the entire contents of a file as a value.
WholeFileReaderV2(...): A Reader that outputs the entire contents of a file as a value.
WindowDataset(...): Combines (nests of) input elements into a dataset of (nests of) windows.
WorkerHeartbeat(...): Worker heartbeat op.
WriteAudioSummary(...): Writes an audio summary.
WriteFile(...): Writes contents to the file at input filename. Creates file and recursively
WriteGraphSummary(...): Writes a graph summary.
WriteHistogramSummary(...): Writes a histogram summary.
WriteImageSummary(...): Writes an image summary.
WriteRawProtoSummary(...): Writes a serialized proto summary.
WriteScalarSummary(...): Writes a scalar summary.
WriteSummary(...): Writes a tensor summary.
Xdivy(...): Returns 0 if x == 0, and x / y otherwise, elementwise.
Xlog1py(...): Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
Xlogy(...): Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
ZerosLike(...): Returns a tensor of zeros with the same shape and type as x.
Zeta(...): Compute the Hurwitz zeta function \(\zeta(x, q)\).
ZipDataset(...): Creates a dataset that zips together input_datasets.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/compat/v1/raw_ops