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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.

AnonymousMemoryCache(...)

AnonymousMultiDeviceIterator(...): A container for a multi device iterator resource.

AnonymousRandomSeedGenerator(...)

AnonymousSeedGenerator(...)

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.

AssertCardinalityDataset(...)

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.

BandedTriangularSolve(...)

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.

BatchCholesky(...)

BatchCholeskyGrad(...)

BatchDataset(...): Creates a dataset that batches batch_size elements from input_dataset.

BatchDatasetV2(...): Creates a dataset that batches batch_size elements from input_dataset.

BatchFFT(...)

BatchFFT2D(...)

BatchFFT3D(...)

BatchFunction(...): Batches all the inputs tensors to the computation done by the function.

BatchIFFT(...)

BatchIFFT2D(...)

BatchIFFT3D(...)

BatchMatMul(...): Multiplies slices of two tensors in batches.

BatchMatMulV2(...): Multiplies slices of two tensors in batches.

BatchMatrixBandPart(...)

BatchMatrixDeterminant(...)

BatchMatrixDiag(...)

BatchMatrixDiagPart(...)

BatchMatrixInverse(...)

BatchMatrixSetDiag(...)

BatchMatrixSolve(...)

BatchMatrixSolveLs(...)

BatchMatrixTriangularSolve(...)

BatchNormWithGlobalNormalization(...): Batch normalization.

BatchNormWithGlobalNormalizationGrad(...): Gradients for batch normalization.

BatchSelfAdjointEig(...)

BatchSelfAdjointEigV2(...)

BatchSvd(...)

BatchToSpace(...): BatchToSpace for 4-D tensors of type T.

BatchToSpaceND(...): BatchToSpace for N-D tensors of type T.

BesselI0(...)

BesselI0e(...)

BesselI1(...)

BesselI1e(...)

BesselJ0(...)

BesselJ1(...)

BesselK0(...)

BesselK0e(...)

BesselK1(...)

BesselK1e(...)

BesselY0(...)

BesselY1(...)

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.

CSVDataset(...)

CSVDatasetV2(...)

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.

CacheDatasetV2(...)