Module: tf.compat.v1.raw_ops

TensorFlow 1 version

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(...)

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.

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.

BesselI0e(...): Computes the Bessel i0e function of x element-wise.

BesselI1e(...): Computes the Bessel i1e function of x element-wise.

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(...)

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(...)

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(...)

ChooseFastestDataset(...)

ClipByValue(...): Clips tensor values to a specified min and max.

CloseSummaryWriter(...)

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.

CollectivePermute(...): An Op to permute tensors across replicated TPU instances.

CollectiveReduce(...): 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.

ComputeAccidentalHits(...): Computes the ids of the positions in sampled_candidates that match true_labels.

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'.

CreateSummaryDbWriter(...)

CreateSummaryFileWriter(...)

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(...): Returns the permuted vector/tensor in the destination data format given the

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.

Dawsn(...)

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.