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.

DecodeCompressed(...): Decompress strings.

DecodeGif(...): Decode the frame(s) of a GIF-encoded image to a uint8 tensor.

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.

DeleteMemoryCache(...)

DeleteMultiDeviceIterator(...): A container for an iterator resource.

DeleteRandomSeedGenerator(...)

DeleteSessionTensor(...): Delete the tensor specified by its handle in the session.

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.

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.

DummyMemoryCache(...)

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.

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.

Erfinv(...)

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.

ExperimentalCSVDataset(...)

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.

ExperimentalLMDBDataset(...)

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(...)

ExperimentalSleepDataset(...)

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.

Expint(...)

Expm1(...): Computes exp(x) - 1 element-wise.

ExtractGlimpse(...): 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 min

FakeQuantWithMinMaxVarsGradient(...): Compute gradients for a FakeQuantWithMinMaxVars operation.

FakeQuantWithMinMaxVarsPerChannel(...): Fake-quantize the 'inputs' tensor of type float and one of the shapes: [d],

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

FlushSummaryWriter(...)

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.

FresnelCos(...)

FresnelSin(...)

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.

ImageSummary(...): Outputs a Summary protocol buffer with images.

ImmutableConst(...): Returns immutable tensor from memory region.

ImportEvent(...)

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.

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 with values in 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.

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.

IteratorV2(...)

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.

LoadTPUEmbeddingRMSPropParameters(...): Load RMSProp embedding parameters.

LoadTPUEmbeddingRMSPropParametersGradAccumDebug(...): Load RMSProp embedding parameters with debug support.

LoadTPUEmbeddingStochasticGradientDescentParameters(...): 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.