Uses of Package
org.tensorflow.op.core

Packages that use org.tensorflow.op.core

Package	Description
org.tensorflow.op
org.tensorflow.op.core

Classes in org.tensorflow.op.core used by org.tensorflow.op

Class and Description
Abort Raise a exception to abort the process when called.
Abort.Options Optional attributes for Abort
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.
AddManySparseToTensorsMap.Options Optional attributes for AddManySparseToTensorsMap
AddN Add all input tensors element wise.
AddSparseToTensorsMap Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
AddSparseToTensorsMap.Options Optional attributes for AddSparseToTensorsMap
AddV2 Returns x + y element-wise.
AdjustContrast 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.
All.Options Optional attributes for All
AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
AllCandidateSampler.Options Optional attributes for AllCandidateSampler
Angle Returns the argument of a complex number.
AnonymousIterator A container for an iterator resource.
Any Computes the "logical or" of elements across dimensions of a tensor.
Any.Options Optional attributes for Any
ApplyAdadelta Update '*var' according to the adadelta scheme.
ApplyAdadelta.Options Optional attributes for ApplyAdadelta
ApplyAdagrad Update '*var' according to the adagrad scheme.
ApplyAdagrad.Options Optional attributes for ApplyAdagrad
ApplyAdagradDA Update '*var' according to the proximal adagrad scheme.
ApplyAdagradDA.Options Optional attributes for ApplyAdagradDA
ApplyAdam Update '*var' according to the Adam algorithm.
ApplyAdam.Options Optional attributes for ApplyAdam
ApplyAddSign Update '*var' according to the AddSign update.
ApplyAddSign.Options Optional attributes for ApplyAddSign
ApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ApplyCenteredRMSProp.Options Optional attributes for ApplyCenteredRMSProp
ApplyFtrl Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrl.Options Optional attributes for ApplyFtrl
ApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrlV2.Options Optional attributes for ApplyFtrlV2
ApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.
ApplyGradientDescent.Options Optional attributes for ApplyGradientDescent
ApplyMomentum Update '*var' according to the momentum scheme.
ApplyMomentum.Options Optional attributes for ApplyMomentum
ApplyPowerSign Update '*var' according to the AddSign update.
ApplyPowerSign.Options Optional attributes for ApplyPowerSign
ApplyProximalAdagrad Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
ApplyProximalAdagrad.Options Optional attributes for ApplyProximalAdagrad
ApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyProximalGradientDescent.Options Optional attributes for ApplyProximalGradientDescent
ApplyRMSProp Update '*var' according to the RMSProp algorithm.
ApplyRMSProp.Options Optional attributes for ApplyRMSProp
ApproximateEqual Returns the truth value of abs(x-y) < tolerance element-wise.
ApproximateEqual.Options Optional attributes for ApproximateEqual
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.
Asin Computes asin of x element-wise.
Asinh Computes inverse hyperbolic sine of x element-wise.
Assign Update 'ref' by assigning 'value' to it.
Assign.Options Optional attributes for Assign
AssignAdd Update 'ref' by adding 'value' to it.
AssignAdd.Options Optional attributes for AssignAdd
AssignAddVariableOp Adds a value to the current value of a variable.
AssignSub Update 'ref' by subtracting 'value' from it.
AssignSub.Options Optional attributes for AssignSub
AssignSubVariableOp Subtracts a value from the current value of a variable.
AssignVariableOp Assigns a new value to a variable.
AsString Converts each entry in the given tensor to strings.
AsString.Options Optional attributes for AsString
Atan Computes atan 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.
AudioSpectrogram.Options Optional attributes for AudioSpectrogram
AudioSummary Outputs a `Summary` protocol buffer with audio.
AudioSummary.Options Optional attributes for AudioSummary
AvgPool Performs average pooling on the input.
AvgPool.Options Optional attributes for AvgPool
AvgPool3D Performs 3D average pooling on the input.
AvgPool3D.Options Optional attributes for AvgPool3D
AvgPool3DGrad Computes gradients of average pooling function.
AvgPool3DGrad.Options Optional attributes for AvgPool3DGrad
Barrier Defines a barrier that persists across different graph executions.
Barrier.Options Optional attributes for Barrier
BarrierClose Closes the given barrier.
BarrierClose.Options Optional attributes for BarrierClose
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.
BarrierTakeMany.Options Optional attributes for BarrierTakeMany
Batch Batches all input tensors nondeterministically.
Batch.Options Optional attributes for Batch
BatchCholesky
BatchCholeskyGrad
BatchFFT
BatchFFT2D
BatchFFT3D
BatchIFFT
BatchIFFT2D
BatchIFFT3D
BatchMatMul Multiplies slices of two tensors in batches.
BatchMatMul.Options Optional attributes for BatchMatMul
BatchMatrixBandPart
BatchMatrixDeterminant
BatchMatrixDiag
BatchMatrixDiagPart
BatchMatrixInverse
BatchMatrixInverse.Options Optional attributes for BatchMatrixInverse
BatchMatrixSetDiag
BatchMatrixSolve
BatchMatrixSolve.Options Optional attributes for BatchMatrixSolve
BatchMatrixSolveLs
BatchMatrixSolveLs.Options Optional attributes for BatchMatrixSolveLs
BatchMatrixTriangularSolve
BatchMatrixTriangularSolve.Options Optional attributes for BatchMatrixTriangularSolve
BatchNormWithGlobalNormalization Batch normalization.
BatchNormWithGlobalNormalizationGrad Gradients for batch normalization.
BatchSelfAdjointEig
BatchSelfAdjointEigV2
BatchSelfAdjointEigV2.Options Optional attributes for BatchSelfAdjointEigV2
BatchSvd
BatchSvd.Options Optional attributes for 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`.
BiasAdd.Options Optional attributes for BiasAdd
BiasAddGrad The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddGrad.Options Optional attributes for BiasAddGrad
BigQueryReader A Reader that outputs rows from a BigQuery table as tensorflow Examples.
BigQueryReader.Options Optional attributes for BigQueryReader
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`.
BroadcastDynamicShape Return the shape of s0 op s1 with broadcast.
BroadcastTo Broadcast an array for a compatible shape.
Bucketize Bucketizes 'input' based on 'boundaries'.
Cast Cast x of type SrcT to y of DstT.
Cast.Options Optional attributes for Cast
Ceil Returns element-wise smallest integer not less than x.
CheckNumerics Checks a tensor for NaN 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.
ClipByValue Clips tensor values to a specified min and max.
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.
ComputeAccidentalHits.Options Optional attributes for ComputeAccidentalHits
Concat Concatenates tensors along one dimension.
ConditionalAccumulator A conditional accumulator for aggregating gradients.
ConditionalAccumulator.Options Optional attributes for ConditionalAccumulator
Conj Returns the complex conjugate of a complex number.
ConjugateTranspose Shuffle dimensions of x according to a permutation and conjugate the result.
Constant An operator producing a constant value.
ConsumeMutexLock This op consumes a lock created by `MutexLock`.
ControlTrigger Does nothing.
Conv2D Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2D.Options Optional attributes for Conv2D
Conv2DBackpropFilter Computes the gradients of convolution with respect to the filter.
Conv2DBackpropFilter.Options Optional attributes for Conv2DBackpropFilter
Conv2DBackpropInput Computes the gradients of convolution with respect to the input.
Conv2DBackpropInput.Options Optional attributes for Conv2DBackpropInput
Conv3D Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3D.Options Optional attributes for Conv3D
Conv3DBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilter.Options Optional attributes for Conv3DBackpropFilter
Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilterV2.Options Optional attributes for Conv3DBackpropFilterV2
Conv3DBackpropInput Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInput.Options Optional attributes for Conv3DBackpropInput
Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInputV2.Options Optional attributes for Conv3DBackpropInputV2
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.
CropAndResize.Options Optional attributes for CropAndResize
CropAndResizeGradBoxes Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradBoxes.Options Optional attributes for CropAndResizeGradBoxes
CropAndResizeGradImage Computes the gradient of the crop_and_resize op wrt the input image tensor.
CropAndResizeGradImage.Options Optional attributes for CropAndResizeGradImage
Cross Compute the pairwise cross product.
CTCBeamSearchDecoder Performs beam search decoding on the logits given in input.
CTCBeamSearchDecoder.Options Optional attributes for CTCBeamSearchDecoder
CTCGreedyDecoder Performs greedy decoding on the logits given in inputs.
CTCGreedyDecoder.Options Optional attributes for CTCGreedyDecoder
CTCLoss Calculates the CTC Loss (log probability) for each batch entry.
CTCLoss.Options Optional attributes for CTCLoss
CudnnRNN A RNN backed by cuDNN.
CudnnRNN.Options Optional attributes for CudnnRNN
CudnnRNNBackprop Backprop step of CudnnRNN.
CudnnRNNBackprop.Options Optional attributes for CudnnRNNBackprop
CudnnRNNCanonicalToParams Converts CudnnRNN params from canonical form to usable form.
CudnnRNNCanonicalToParams.Options Optional attributes for CudnnRNNCanonicalToParams
CudnnRNNParamsSize Computes size of weights that can be used by a Cudnn RNN model.
CudnnRNNParamsSize.Options Optional attributes for CudnnRNNParamsSize
CudnnRNNParamsToCanonical Retrieves CudnnRNN params in canonical form.
CudnnRNNParamsToCanonical.Options Optional attributes for CudnnRNNParamsToCanonical
Cumprod Compute the cumulative product of the tensor `x` along `axis`.
Cumprod.Options Optional attributes for Cumprod
Cumsum Compute the cumulative sum of the tensor `x` along `axis`.
Cumsum.Options Optional attributes for Cumsum
DataFormatDimMap Returns the dimension index in the destination data format given the one in
DataFormatDimMap.Options Optional attributes for DataFormatDimMap
DataFormatVecPermute Returns the permuted vector/tensor in the destination data format given the
DataFormatVecPermute.Options Optional attributes for DataFormatVecPermute
DebugGradientIdentity Identity op for gradient debugging.
DebugGradientRefIdentity Identity op for gradient debugging.
DecodeAndCropJpeg Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeAndCropJpeg.Options Optional attributes for DecodeAndCropJpeg
DecodeBase64 Decode web-safe base64-encoded strings.
DecodeBmp Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeBmp.Options Optional attributes for DecodeBmp
DecodeCompressed Decompress strings.
DecodeCompressed.Options Optional attributes for DecodeCompressed
DecodeCSV Convert CSV records to tensors.
DecodeCSV.Options Optional attributes for DecodeCSV
DecodeGif Decode the first frame of a GIF-encoded image to a uint8 tensor.
DecodeJpeg Decode a JPEG-encoded image to a uint8 tensor.
DecodeJpeg.Options Optional attributes for DecodeJpeg
DecodeJSONExample Convert JSON-encoded Example records to binary protocol buffer strings.
DecodePng Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodePng.Options Optional attributes for DecodePng
DecodeProtoV2 The op extracts fields from a serialized protocol buffers message into tensors.
DecodeProtoV2.Options Optional attributes for DecodeProtoV2
DecodeRaw Reinterpret the bytes of a string as a vector of numbers.
DecodeRaw.Options Optional attributes for DecodeRaw
DecodeWav Decode a 16-bit PCM WAV file to a float tensor.
DecodeWav.Options Optional attributes for DecodeWav
DeepCopy Makes a copy of `x`.
DeleteSessionTensor Delete the tensor specified by its handle in the session.
DenseToDenseSetOperation Applies set operation along last dimension of 2 `Tensor` inputs.
DenseToDenseSetOperation.Options Optional attributes for DenseToDenseSetOperation
DenseToSparseSetOperation Applies set operation along last dimension of `Tensor` and `SparseTensor`.
DenseToSparseSetOperation.Options Optional attributes for DenseToSparseSetOperation
DepthToSpace DepthToSpace for tensors of type T.
DepthToSpace.Options Optional attributes for DepthToSpace
DepthwiseConv2dNative Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNative.Options Optional attributes for DepthwiseConv2dNative
DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropFilter.Options Optional attributes for DepthwiseConv2dNativeBackpropFilter
DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input.
DepthwiseConv2dNativeBackpropInput.Options Optional attributes for DepthwiseConv2dNativeBackpropInput
Dequantize Dequantize the 'input' tensor into a float Tensor.
Dequantize.Options Optional attributes for Dequantize
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.
DestroyResourceOp.Options Optional attributes for DestroyResourceOp
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.
Div Returns x / y element-wise.
DivNoNan Returns 0 if the denominator is zero.
DrawBoundingBoxes 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.
EditDistance Computes the (possibly normalized) Levenshtein Edit Distance.
EditDistance.Options Optional attributes for EditDistance
Elu Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
Empty Creates a tensor with the given shape.
Empty.Options Optional attributes for Empty
EmptyTensorList Creates and returns an empty tensor list.
EncodeBase64 Encode strings into web-safe base64 format.
EncodeBase64.Options Optional attributes for EncodeBase64
EncodeJpeg JPEG-encode an image.
EncodeJpeg.Options Optional attributes for EncodeJpeg
EncodePng PNG-encode an image.
EncodePng.Options Optional attributes for EncodePng
EncodeProto The op serializes protobuf messages provided in the input tensors.
EncodeProto.Options Optional attributes for EncodeProto
EncodeWav Encode audio data using the WAV file format.
EnsureShape Ensures that the tensor's shape matches the expected shape.
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.
Exp Computes exponential of x element-wise.
ExpandDims Inserts a dimension of 1 into a tensor's shape.
Expm1 Computes exponential of x - 1 element-wise.
ExtractGlimpse Extracts a glimpse from the input tensor.
ExtractGlimpse.Options Optional attributes for ExtractGlimpse
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.
Fact Output a fact about factorials.
FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgs.Options Optional attributes for FakeQuantWithMinMaxArgs
FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxArgsGradient.Options Optional attributes for FakeQuantWithMinMaxArgsGradient
FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
FakeQuantWithMinMaxVars.Options Optional attributes for FakeQuantWithMinMaxVars
FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsGradient.Options Optional attributes for FakeQuantWithMinMaxVarsGradient
FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
FakeQuantWithMinMaxVarsPerChannel.Options Optional attributes for FakeQuantWithMinMaxVarsPerChannel
FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
FakeQuantWithMinMaxVarsPerChannelGradient.Options Optional attributes for FakeQuantWithMinMaxVarsPerChannelGradient
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.
FIFOQueue.Options Optional attributes for FIFOQueue
Fill Creates a tensor filled with a scalar value.
FixedLengthRecordReader A Reader that outputs fixed-length records from a file.
FixedLengthRecordReader.Options Optional attributes for FixedLengthRecordReader
FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
FixedUnigramCandidateSampler.Options Optional attributes for FixedUnigramCandidateSampler
Floor Returns element-wise largest integer not greater than x.
FloorDiv Returns x // y element-wise.
FloorMod Returns element-wise remainder of division.
FractionalAvgPool Performs fractional average pooling on the input.
FractionalAvgPool.Options Optional attributes for FractionalAvgPool
FractionalMaxPool Performs fractional max pooling on the input.
FractionalMaxPool.Options Optional attributes for FractionalMaxPool
FusedBatchNorm Batch normalization.
FusedBatchNorm.Options Optional attributes for FusedBatchNorm
FusedBatchNormGrad Gradient for batch normalization.
FusedBatchNormGrad.Options Optional attributes for FusedBatchNormGrad
FusedBatchNormGradV2 Gradient for batch normalization.
FusedBatchNormGradV2.Options Optional attributes for FusedBatchNormGradV2
FusedBatchNormV2 Batch normalization.
FusedBatchNormV2.Options Optional attributes for FusedBatchNormV2
FusedPadConv2D Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2D Performs a resize and padding as a preprocess during a convolution.
FusedResizeAndPadConv2D.Options Optional attributes for FusedResizeAndPadConv2D
Gather Gather slices from `params` according to `indices`.
Gather.Options Optional attributes for Gather
GatherNd Gather slices from `params` into a Tensor with shape specified by `indices`.
GatherV2 Gather slices from `params` axis `axis` according to `indices`.
GcsConfigureBlockCache Re-configures the GCS block cache with the new configuration values.
GcsConfigureCredentials Configures the credentials used by the GCS client of the local TF runtime.
GenerateBigQueryReaderPartitions Generates serialized partition messages suitable for batch reads.
GenerateBigQueryReaderPartitions.Options Optional attributes for GenerateBigQueryReaderPartitions
GenerateVocabRemapping Given a path to new and old vocabulary files, returns a remapping Tensor of
GenerateVocabRemapping.Options Optional attributes for GenerateVocabRemapping
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.
Gradients Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
Gradients.Options Optional attributes for Gradients
Greater Returns the truth value of (x > y) element-wise.
GreaterEqual Returns the truth value of (x >= y) element-wise.
GuaranteeConst Gives a guarantee to the TF runtime that the input tensor is a constant.
HashTable Creates a non-initialized hash table.
HashTable.Options Optional attributes for HashTable
HistogramFixedWidth Return histogram of values.
HistogramSummary Outputs a `Summary` protocol buffer with a histogram.
HSVToRGB Convert one or more images from HSV to RGB.
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.
IdentityReader.Options Optional attributes for IdentityReader
IFFT Inverse fast Fourier transform.
IFFT2D Inverse 2D fast Fourier transform.
IFFT3D Inverse 3D fast Fourier transform.
Igamma Compute the lower regularized incomplete Gamma function `P(a, x)`.
Igammac Compute the upper regularized incomplete Gamma function `Q(a, x)`.
Imag Returns the imaginary part of a complex number.
ImageSummary Outputs a `Summary` protocol buffer with images.
ImageSummary.Options Optional attributes for ImageSummary
ImmutableConst Returns immutable tensor from memory region.
InitializeTable Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromTextFile Initializes a table from a text file.
InitializeTableFromTextFile.Options Optional attributes for InitializeTableFromTextFile
InplaceAdd Adds v into specified rows of x.
InplaceSub Subtracts `v` into specified rows of `x`.
InplaceUpdate Updates specified rows with values in `v`.
InTopK Says whether the targets are in the top `K` predictions.
InTopKV2 Says whether the targets are in the top `K` predictions.
Inv Computes the reciprocal of x element-wise.
Invert Flips all bits elementwise.
InvertPermutation Computes the inverse permutation of a tensor.
IRFFT Inverse real-valued fast Fourier transform.
IRFFT2D Inverse 2D real-valued fast Fourier transform.
IRFFT3D Inverse 3D real-valued fast Fourier transform.
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.
IteratorFromStringHandle.Options Optional attributes for IteratorFromStringHandle
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.
LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
LearnedUnigramCandidateSampler.Options Optional attributes for LearnedUnigramCandidateSampler
LeftShift Elementwise computes the bitwise left-shift of `x` and `y`.
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.
LMDBReader A Reader that outputs the records from a LMDB file.
LMDBReader.Options Optional attributes for LMDBReader
LoadAndRemapMatrix Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
LoadAndRemapMatrix.Options Optional attributes for LoadAndRemapMatrix
Log Computes natural logarithm of x element-wise.
Log1p Computes natural logarithm of (1 + x) element-wise.
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.
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.
LogUniformCandidateSampler.Options Optional attributes for LogUniformCandidateSampler
LookupTableExport Outputs all keys and values in the table.
LookupTableFind Looks up keys in a table, outputs the corresponding values.
LookupTableImport Replaces the contents of the table with the specified keys and values.
LookupTableInsert Updates the table to associates keys with values.
LookupTableSize Computes the number of elements in the given table.
LoopCond Forwards the input to the output.
LRN Local Response Normalization.
LRN.Options Optional attributes for LRN
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`.
MapClear Op removes all elements in the underlying container.
MapClear.Options Optional attributes for MapClear
MapIncompleteSize Op returns the number of incomplete elements in the underlying container.
MapIncompleteSize.Options Optional attributes for MapIncompleteSize
MapPeek Op peeks at the values at the specified key.
MapPeek.Options Optional attributes for MapPeek
MapSize Op returns the number of elements in the underlying container.
MapSize.Options Optional attributes for MapSize
MapStage Stage (key, values) in the underlying container which behaves like a hashtable.
MapStage.Options Optional attributes for MapStage
MapUnstage Op removes and returns the values associated with the key
MapUnstage.Options Optional attributes for MapUnstage
MapUnstageNoKey Op removes and returns a random (key, value)
MapUnstageNoKey.Options Optional attributes for MapUnstageNoKey
MatchingFiles Returns the set of files matching one or more glob patterns.
MatMul Multiply the matrix "a" by the matrix "b".
MatMul.Options Optional attributes for MatMul
MatrixBandPart Copy a tensor setting everything outside a central band in each innermost matrix
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.
MatrixInverse Computes the inverse of one or more square invertible matrices or their
MatrixInverse.Options Optional attributes for MatrixInverse
MatrixSetDiag Returns a batched matrix tensor with new batched diagonal values.
MatrixSolve Solves systems of linear equations.
MatrixSolve.Options Optional attributes for MatrixSolve
MatrixSolveLs Solves one or more linear least-squares problems.
MatrixSolveLs.Options Optional attributes for MatrixSolveLs
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
MatrixTriangularSolve.Options Optional attributes for MatrixTriangularSolve
Max Computes the maximum of elements across dimensions of a tensor.
Max.Options Optional attributes for Max
Maximum Returns the max of x and y (i.e.
MaxPool Performs max pooling on the input.
MaxPool.Options Optional attributes for MaxPool
MaxPool3D Performs 3D max pooling on the input.
MaxPool3D.Options Optional attributes for MaxPool3D
MaxPool3DGrad Computes gradients of max pooling function.
MaxPool3DGrad.Options Optional attributes for MaxPool3DGrad
MaxPool3DGradGrad Computes second-order gradients of the maxpooling function.
MaxPool3DGradGrad.Options Optional attributes for MaxPool3DGradGrad
MaxPoolGradGrad Computes second-order gradients of the maxpooling function.
MaxPoolGradGrad.Options Optional attributes for MaxPoolGradGrad
MaxPoolGradGradV2 Computes second-order gradients of the maxpooling function.
MaxPoolGradGradV2.Options Optional attributes for MaxPoolGradGradV2
MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.
MaxPoolGradV2 Computes gradients of the maxpooling function.
MaxPoolGradV2.Options Optional attributes for MaxPoolGradV2
MaxPoolV2 Performs max pooling on the input.
MaxPoolV2.Options Optional attributes for MaxPoolV2
MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.
Mean Computes the mean of elements across dimensions of a tensor.
Mean.Options Optional attributes for Mean
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.
MergeV2Checkpoints.Options Optional attributes for MergeV2Checkpoints
Mfcc Transforms a spectrogram into a form that's useful for speech recognition.
Mfcc.Options Optional attributes for Mfcc
Min Computes the minimum of elements across dimensions of a tensor.
Min.Options Optional attributes for Min
Minimum Returns the min of x and y (i.e.
MirrorPad Pads a tensor with mirrored values.
Mod Returns element-wise remainder of division.
Mul Returns x * y element-wise.
Multinomial Draws samples from a multinomial distribution.
Multinomial.Options Optional attributes for Multinomial
Multiply Returns x * y element-wise.
MutableDenseHashTable Creates an empty hash table that uses tensors as the backing store.
MutableDenseHashTable.Options Optional attributes for MutableDenseHashTable
MutableHashTable Creates an empty hash table.
MutableHashTable.Options Optional attributes for MutableHashTable
MutableHashTableOfTensors Creates an empty hash table.
MutableHashTableOfTensors.Options Optional attributes for MutableHashTableOfTensors
MutexLock Locks a mutex resource.
MutexV2 Creates a Mutex resource that can be locked by `MutexLock`.
MutexV2.Options Optional attributes for MutexV2
Neg Computes numerical negative value element-wise.
Negate Computes numerical negative value element-wise.
NegTrain Training via negative sampling.
NextIteration Makes its input available to the next iteration.
NonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppression.Options Optional attributes for NonMaxSuppression
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,
NonMaxSuppressionV4.Options Optional attributes for NonMaxSuppressionV4
NonMaxSuppressionWithOverlaps Greedily selects a subset of bounding boxes in descending order of score,
NoOp Does nothing.
NotEqual Returns the truth value of (x != y) element-wise.
NthElement Finds values of the `n`-th order statistic for the last dimension.
NthElement.Options Optional attributes for NthElement
OneHot Returns a one-hot tensor.
OneHot.Options Optional attributes for OneHot
OnesLike Returns a tensor of ones with the same shape and type as x.
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.
OrderedMapClear.Options Optional attributes for OrderedMapClear
OrderedMapIncompleteSize Op returns the number of incomplete elements in the underlying container.
OrderedMapIncompleteSize.Options Optional attributes for OrderedMapIncompleteSize
OrderedMapPeek Op peeks at the values at the specified key.
OrderedMapPeek.Options Optional attributes for OrderedMapPeek
OrderedMapSize Op returns the number of elements in the underlying container.
OrderedMapSize.Options Optional attributes for OrderedMapSize
OrderedMapStage Stage (key, values) in the underlying container which behaves like a ordered
OrderedMapStage.Options Optional attributes for OrderedMapStage
OrderedMapUnstage Op removes and returns the values associated with the key
OrderedMapUnstage.Options Optional attributes for OrderedMapUnstage
OrderedMapUnstageNoKey Op removes and returns the (key, value) element with the smallest
OrderedMapUnstageNoKey.Options Optional attributes for OrderedMapUnstageNoKey
Pad Pads a tensor with zeros.
PaddingFIFOQueue A queue that produces elements in first-in first-out order.
PaddingFIFOQueue.Options Optional attributes for PaddingFIFOQueue
PadV2 Pads a tensor.
ParallelConcat Concatenates a list of `N` tensors along the first dimension.
ParallelDynamicStitch Interleave the values from the `data` tensors into a single tensor.
ParameterizedTruncatedNormal Outputs random values from a normal distribution.
ParameterizedTruncatedNormal.Options Optional attributes for ParameterizedTruncatedNormal
ParseExample Transforms a vector of brain.Example protos (as strings) into typed tensors.
ParseSequenceExample Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
ParseSequenceExample.Options Optional attributes for ParseSequenceExample
ParseSingleExample Transforms a tf.Example proto (as a string) into typed tensors.
ParseSingleSequenceExample Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
ParseSingleSequenceExample.Options Optional attributes for ParseSingleSequenceExample
ParseTensor Transforms a serialized tensorflow.TensorProto proto into a Tensor.
Placeholder A placeholder op for a value that will be fed into the computation.
Placeholder.Options Optional attributes for Placeholder
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.
Pow Computes the power of one value to another.
PreventGradient An identity op that triggers an error if a gradient is requested.
PreventGradient.Options Optional attributes for PreventGradient
Print Prints a list of tensors.
Print.Options Optional attributes for Print
PrintV2 Prints a string scalar.
PrintV2.Options Optional attributes for PrintV2
PriorityQueue A queue that produces elements sorted by the first component value.
PriorityQueue.Options Optional attributes for PriorityQueue
Prod Computes the product of elements across dimensions of a tensor.
Prod.Options Optional attributes for Prod
Qr Computes the QR decompositions of one or more matrices.
Qr.Options Optional attributes for Qr
QuantizeAndDequantize Use QuantizeAndDequantizeV2 instead.
QuantizeAndDequantize.Options Optional attributes for QuantizeAndDequantize
QuantizeAndDequantizeV2 Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV2.Options Optional attributes for QuantizeAndDequantizeV2
QuantizeAndDequantizeV3 Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3.Options Optional attributes for QuantizeAndDequantizeV3
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.
QuantizedConv2D.Options Optional attributes for QuantizedConv2D
QuantizedInstanceNorm Quantized Instance normalization.
QuantizedInstanceNorm.Options Optional attributes for QuantizedInstanceNorm
QuantizedMatMul Perform a quantized matrix multiplication of `a` by the matrix `b`.
QuantizedMatMul.Options Optional attributes for QuantizedMatMul
QuantizedMaxPool Produces the max pool of the input tensor for quantized types.
QuantizedMul Returns x * y element-wise, working on quantized buffers.
QuantizeDownAndShrinkRange Convert the quantized 'input' tensor into a lower-precision 'output', using the
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.
QuantizedResizeBilinear.Options Optional attributes for QuantizedResizeBilinear
QuantizeV2 Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizeV2.Options Optional attributes for QuantizeV2
QueueClose Closes the given queue.
QueueClose.Options Optional attributes for QueueClose
QueueDequeue Dequeues a tuple of one or more tensors from the given queue.
QueueDequeue.Options Optional attributes for QueueDequeue
QueueDequeueMany Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueMany.Options Optional attributes for QueueDequeueMany
QueueDequeueUpTo Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueUpTo.Options Optional attributes for QueueDequeueUpTo
QueueEnqueue Enqueues a tuple of one or more tensors in the given queue.
QueueEnqueue.Options Optional attributes for QueueEnqueue
QueueEnqueueMany Enqueues zero or more tuples of one or more tensors in the given queue.
QueueEnqueueMany.Options Optional attributes for QueueEnqueueMany
QueueIsClosed Returns true if queue is closed.
QueueIsClosedV2 Returns true if queue is closed.
QueueSize Computes the number of elements in the given queue.
RandomCrop Randomly crop `image`.
RandomCrop.Options Optional attributes for RandomCrop
RandomGamma Outputs random values from the Gamma distribution(s) described by alpha.
RandomGamma.Options Optional attributes for RandomGamma
RandomNormal Outputs random values from a normal distribution.
RandomNormal.Options Optional attributes for RandomNormal
RandomPoisson Use RandomPoissonV2 instead.
RandomPoisson.Options Optional attributes for RandomPoisson
RandomPoissonV2 Outputs random values from the Poisson distribution(s) described by rate.
RandomPoissonV2.Options Optional attributes for RandomPoissonV2
RandomShuffle Randomly shuffles a tensor along its first dimension.
RandomShuffle.Options Optional attributes for RandomShuffle
RandomShuffleQueue A queue that randomizes the order of elements.
RandomShuffleQueue.Options Optional attributes for RandomShuffleQueue
RandomUniform Outputs random values from a uniform distribution.
RandomUniform.Options Optional attributes for RandomUniform
RandomUniformInt Outputs random integers from a uniform distribution.
RandomUniformInt.Options Optional attributes for RandomUniformInt
Range Creates a sequence of numbers.
Rank Returns the rank of a tensor.
ReaderNumRecordsProduced Returns the number of records this Reader has produced.
ReaderNumWorkUnitsCompleted 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.
ReaderReset Restore a Reader to its initial clean state.
ReaderRestoreState Restore a reader to a previously saved state.
ReaderSerializeState Produce a string tensor that encodes the state of a Reader.
ReadFile Reads and outputs the entire contents of the input filename.
ReadVariableOp Reads the value of a variable.
Real Returns the real part of a complex number.
RealDiv Returns x / y element-wise for real types.
Reciprocal Computes the reciprocal of x element-wise.
RecordInput Emits randomized records.
RecordInput.Options Optional attributes for RecordInput
ReduceAll Computes the "logical and" of elements across dimensions of a tensor.
ReduceAll.Options Optional attributes for ReduceAll
ReduceAny Computes the "logical or" of elements across dimensions of a tensor.
ReduceAny.Options Optional attributes for ReduceAny
ReduceJoin Joins a string Tensor across the given dimensions.
ReduceJoin.Options Optional attributes for ReduceJoin
ReduceMax Computes the maximum of elements across dimensions of a tensor.
ReduceMax.Options Optional attributes for ReduceMax
ReduceMean Computes the mean of elements across dimensions of a tensor.
ReduceMean.Options Optional attributes for ReduceMean
ReduceMin Computes the minimum of elements across dimensions of a tensor.
ReduceMin.Options Optional attributes for ReduceMin
ReduceProd Computes the product of elements across dimensions of a tensor.
ReduceProd.Options Optional attributes for ReduceProd
ReduceSum Computes the sum of elements across dimensions of a tensor.
ReduceSum.Options Optional attributes for ReduceSum
RefNextIteration Makes its input available to the next iteration.
RefSelect Forwards the `index`th 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 the match of pattern in input with rewrite.
RegexReplace.Options Optional attributes for RegexReplace
Relu Computes rectified linear: `max(features, 0)`.
Relu6 Computes rectified linear 6: `min(max(features, 0), 6)`.
RemoteFusedGraphExecute Execute a sub graph on a remote processor.
RequantizationRange Given a quantized tensor described by (input, input_min, input_max), outputs a
Requantize Convert the quantized 'input' tensor into a lower-precision 'output', using the
Reshape Reshapes a tensor.
ResizeArea Resize `images` to `size` using area interpolation.
ResizeArea.Options Optional attributes for ResizeArea
ResizeBicubic Resize `images` to `size` using bicubic interpolation.
ResizeBicubic.Options Optional attributes for ResizeBicubic
ResizeBilinear Resize `images` to `size` using bilinear interpolation.
ResizeBilinear.Options Optional attributes for ResizeBilinear
ResizeNearestNeighbor Resize `images` to `size` using nearest neighbor interpolation.
ResizeNearestNeighbor.Options Optional attributes for ResizeNearestNeighbor
ResourceApplyAdadelta Update '*var' according to the adadelta scheme.
ResourceApplyAdadelta.Options Optional attributes for ResourceApplyAdadelta
ResourceApplyAdagrad Update '*var' according to the adagrad scheme.
ResourceApplyAdagrad.Options Optional attributes for ResourceApplyAdagrad
ResourceApplyAdagradDA Update '*var' according to the proximal adagrad scheme.
ResourceApplyAdagradDA.Options Optional attributes for ResourceApplyAdagradDA
ResourceApplyAdam Update '*var' according to the Adam algorithm.
ResourceApplyAdam.Options Optional attributes for ResourceApplyAdam
ResourceApplyAdamWithAmsgrad Update '*var' according to the Adam algorithm.
ResourceApplyAdamWithAmsgrad.Options Optional attributes for ResourceApplyAdamWithAmsgrad
ResourceApplyAddSign Update '*var' according to the AddSign update.
ResourceApplyAddSign.Options Optional attributes for ResourceApplyAddSign
ResourceApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ResourceApplyCenteredRMSProp.Options Optional attributes for ResourceApplyCenteredRMSProp
ResourceApplyFtrl Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrl.Options Optional attributes for ResourceApplyFtrl
ResourceApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrlV2.Options Optional attributes for ResourceApplyFtrlV2
ResourceApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.
ResourceApplyGradientDescent.Options Optional attributes for ResourceApplyGradientDescent
ResourceApplyKerasMomentum Update '*var' according to the momentum scheme.
ResourceApplyKerasMomentum.Options Optional attributes for ResourceApplyKerasMomentum
ResourceApplyMomentum Update '*var' according to the momentum scheme.
ResourceApplyMomentum.Options Optional attributes for ResourceApplyMomentum
ResourceApplyPowerSign Update '*var' according to the AddSign update.
ResourceApplyPowerSign.Options Optional attributes for ResourceApplyPowerSign
ResourceApplyProximalAdagrad Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
ResourceApplyProximalAdagrad.Options Optional attributes for ResourceApplyProximalAdagrad
ResourceApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.
ResourceApplyProximalGradientDescent.Options Optional attributes for ResourceApplyProximalGradientDescent
ResourceApplyRMSProp Update '*var' according to the RMSProp algorithm.
ResourceApplyRMSProp.Options Optional attributes for ResourceApplyRMSProp
ResourceCountUpTo Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGather.Options Optional attributes for ResourceGather
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 Adds sparse `updates` to individual values or slices within a given
ResourceScatterNdAdd.Options Optional attributes for ResourceScatterNdAdd
ResourceScatterNdUpdate Applies sparse `updates` to individual values or slices within a given
ResourceScatterNdUpdate.Options Optional attributes for ResourceScatterNdUpdate
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().
ResourceSparseApplyAdadelta.Options Optional attributes for ResourceSparseApplyAdadelta
ResourceSparseApplyAdagrad Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
ResourceSparseApplyAdagrad.Options Optional attributes for ResourceSparseApplyAdagrad
ResourceSparseApplyAdagradDA Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradDA.Options Optional attributes for ResourceSparseApplyAdagradDA
ResourceSparseApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyCenteredRMSProp.Options Optional attributes for ResourceSparseApplyCenteredRMSProp
ResourceSparseApplyFtrl Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrl.Options Optional attributes for ResourceSparseApplyFtrl
ResourceSparseApplyFtrlV2 Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrlV2.Options Optional attributes for ResourceSparseApplyFtrlV2
ResourceSparseApplyKerasMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
ResourceSparseApplyKerasMomentum.Options Optional attributes for ResourceSparseApplyKerasMomentum
ResourceSparseApplyMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
ResourceSparseApplyMomentum.Options Optional attributes for ResourceSparseApplyMomentum
ResourceSparseApplyProximalAdagrad Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
ResourceSparseApplyProximalAdagrad.Options Optional attributes for ResourceSparseApplyProximalAdagrad
ResourceSparseApplyProximalGradientDescent Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyProximalGradientDescent.Options Optional attributes for ResourceSparseApplyProximalGradientDescent
ResourceSparseApplyRMSProp Update '*var' according to the RMSProp algorithm.
ResourceSparseApplyRMSProp.Options Optional attributes for ResourceSparseApplyRMSProp
ResourceStridedSliceAssign Assign `value` to the sliced l-value reference of `ref`.
ResourceStridedSliceAssign.Options Optional attributes for ResourceStridedSliceAssign
Restore Restores a tensor from checkpoint files.
Restore.Options Optional attributes for Restore
RestoreSlice Restores a tensor from checkpoint files.
RestoreSlice.Options Optional attributes for RestoreSlice
RestoreV2 Restores tensors from a V2 checkpoint.
Reverse Reverses specific dimensions of a tensor.
ReverseSequence Reverses variable length slices.
ReverseSequence.Options Optional attributes for ReverseSequence
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.
RightShift Elementwise computes the bitwise right-shift of `x` and `y`.
Rint Returns element-wise integer closest to x.
Roll Rolls the elements of a tensor along an axis.
Round Rounds the values of a tensor to the nearest integer, element-wise.
Rpc Perform batches of RPC requests.
Rpc.Options Optional attributes for Rpc
Rsqrt Computes reciprocal of square root of x element-wise.
SampleDistortedBoundingBox Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBox.Options Optional attributes for SampleDistortedBoundingBox
SampleDistortedBoundingBoxV2 Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBoxV2.Options Optional attributes for SampleDistortedBoundingBoxV2
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.
ScatterAdd Adds sparse updates to a variable reference.
ScatterAdd.Options Optional attributes for ScatterAdd
ScatterDiv Divides a variable reference by sparse updates.
ScatterDiv.Options Optional attributes for ScatterDiv
ScatterMax Reduces sparse updates into a variable reference using the `max` operation.
ScatterMax.Options Optional attributes for ScatterMax
ScatterMin Reduces sparse updates into a variable reference using the `min` operation.
ScatterMin.Options Optional attributes for ScatterMin
ScatterMul Multiplies sparse updates into a variable reference.
ScatterMul.Options Optional attributes for ScatterMul
ScatterNd Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd Applies sparse addition between `updates` and individual values or slices
ScatterNdAdd.Options Optional attributes for ScatterNdAdd
ScatterNdNonAliasingAdd Applies sparse addition to `input` using individual values or slices
ScatterNdSub Applies sparse subtraction between `updates` and individual values or slices
ScatterNdSub.Options Optional attributes for ScatterNdSub
ScatterNdUpdate Applies sparse `updates` to individual values or slices within a given
ScatterNdUpdate.Options Optional attributes for ScatterNdUpdate
ScatterSub Subtracts sparse updates to a variable reference.
ScatterSub.Options Optional attributes for ScatterSub
ScatterUpdate Applies sparse updates to a variable reference.
ScatterUpdate.Options Optional attributes for ScatterUpdate
SdcaFprint Computes fingerprints of the input strings.
SdcaOptimizer Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaOptimizer.Options Optional attributes for SdcaOptimizer
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.
SelfAdjointEig Computes the eigen decomposition of one or more square self-adjoint matrices.
SelfAdjointEig.Options Optional attributes for SelfAdjointEig
Selu Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
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.
SetDiff1D Computes the difference between two lists of numbers or strings.
SetSize Number of unique elements along last dimension of input `set`.
SetSize.Options Optional attributes for SetSize
Shape Returns the shape of a tensor.
ShapeN Returns shape of tensors.
ShardedFilename Generate a sharded filename.
ShardedFilespec Generate a glob pattern matching all sharded file names.
Sigmoid Computes sigmoid of `x` element-wise.
Sign Returns an element-wise indication of the sign of a number.
Sin Computes sin of x element-wise.
Sinh Computes hyperbolic sine of x element-wise.
Size Returns the size of a tensor.
Skipgram Parses a text file and creates a batch of examples.
Skipgram.Options Optional attributes for Skipgram
Slice Return a slice from 'input'.
Snapshot Returns a copy of the input tensor.
Softmax Computes softmax activations.
SoftmaxCrossEntropyWithLogits Computes softmax cross entropy cost and gradients to backpropagate.
Softplus Computes softplus: `log(exp(features) + 1)`.
Softsign Computes softsign: `features / (abs(features) + 1)`.
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.
SpaceToDepth.Options Optional attributes for SpaceToDepth
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().
SparseApplyAdadelta.Options Optional attributes for SparseApplyAdadelta
SparseApplyAdagrad Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
SparseApplyAdagrad.Options Optional attributes for SparseApplyAdagrad
SparseApplyAdagradDA Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
SparseApplyAdagradDA.Options Optional attributes for SparseApplyAdagradDA
SparseApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
SparseApplyCenteredRMSProp.Options Optional attributes for SparseApplyCenteredRMSProp
SparseApplyFtrl Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrl.Options Optional attributes for SparseApplyFtrl
SparseApplyFtrlV2 Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrlV2.Options Optional attributes for SparseApplyFtrlV2
SparseApplyMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
SparseApplyMomentum.Options Optional attributes for SparseApplyMomentum
SparseApplyProximalAdagrad Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
SparseApplyProximalAdagrad.Options Optional attributes for SparseApplyProximalAdagrad
SparseApplyProximalGradientDescent Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyProximalGradientDescent.Options Optional attributes for SparseApplyProximalGradientDescent
SparseApplyRMSProp Update '*var' according to the RMSProp algorithm.
SparseApplyRMSProp.Options Optional attributes for SparseApplyRMSProp
SparseConcat Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator A conditional accumulator for aggregating sparse gradients.
SparseConditionalAccumulator.Options Optional attributes for SparseConditionalAccumulator
SparseCross 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".
SparseMatMul.Options Optional attributes for SparseMatMul
SparseReduceMax Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMax.Options Optional attributes for SparseReduceMax
SparseReduceMaxSparse Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse.Options Optional attributes for SparseReduceMaxSparse
SparseReduceSum Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSum.Options Optional attributes for SparseReduceSum
SparseReduceSumSparse Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse.Options Optional attributes for SparseReduceSumSparse
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".
SparseTensorDenseMatMul.Options Optional attributes for SparseTensorDenseMatMul
SparseToDense Converts a sparse representation into a dense tensor.
SparseToDense.Options Optional attributes for SparseToDense
SparseToSparseSetOperation Applies set operation along last dimension of 2 `SparseTensor` inputs.
SparseToSparseSetOperation.Options Optional attributes for SparseToSparseSetOperation
Split Splits a tensor into `num_split` tensors along one dimension.
SplitV Splits a tensor into `num_split` tensors along one dimension.
Sqrt Computes square root of x element-wise.
Square Computes square of x element-wise.
SquaredDifference Returns (x - y)(x - y) element-wise.
Squeeze Removes dimensions of size 1 from the shape of a tensor.
Squeeze.Options Optional attributes for Squeeze
Stack Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Stack.Options Optional attributes for Stack
Stage Stage values similar to a lightweight Enqueue.
Stage.Options Optional attributes for Stage
StageClear Op removes all elements in the underlying container.
StageClear.Options Optional attributes for StageClear
StagePeek Op peeks at the values at the specified index.
StagePeek.Options Optional attributes for StagePeek
StageSize Op returns the number of elements in the underlying container.
StageSize.Options Optional attributes for StageSize
StatelessMultinomial Draws samples from a multinomial distribution.
StatelessRandomNormal Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomUniform Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessTruncatedNormal Outputs deterministic pseudorandom values from a truncated normal distribution.
StopGradient Stops gradient computation.
StridedSlice Return a strided slice from `input`.
StridedSlice.Options Optional attributes for StridedSlice
StridedSliceAssign Assign `value` to the sliced l-value reference of `ref`.
StridedSliceAssign.Options Optional attributes for StridedSliceAssign
StridedSliceGrad Returns the gradient of `StridedSlice`.
StridedSliceGrad.Options Optional attributes for StridedSliceGrad
StringFormat Formats a string template using a list of tensors.
StringFormat.Options Optional attributes for StringFormat
StringJoin Joins the strings in the given list of string tensors into one tensor;
StringJoin.Options Optional attributes for StringJoin
StringLength String lengths of `input`.
StringLength.Options Optional attributes for StringLength
StringSplit Split elements of `input` based on `delimiter` into a `SparseTensor`.
StringSplit.Options Optional attributes for StringSplit
StringSplitV2 Split elements of `source` based on `sep` into a `SparseTensor`.
StringSplitV2.Options Optional attributes for StringSplitV2
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.
Sub Returns x - y element-wise.
Substr Return substrings from `Tensor` of strings.
Substr.Options Optional attributes for Substr
Subtract Returns x - y element-wise.
Sum Computes the sum of elements across dimensions of a tensor.
Sum.Options Optional attributes for Sum
Svd Computes the singular value decompositions of one or more matrices.
Svd.Options Optional attributes for Svd
TakeManySparseFromTensorsMap Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
TakeManySparseFromTensorsMap.Options Optional attributes for TakeManySparseFromTensorsMap
Tan Computes tan of x element-wise.
Tanh Computes hyperbolic tangent of `x` element-wise.
TemporaryVariable Returns a tensor that may be mutated, but only persists within a single step.
TemporaryVariable.Options Optional attributes for TemporaryVariable
TensorArray An array of Tensors of given size.
TensorArray.Options Optional attributes for TensorArray
TensorArrayClose Delete the TensorArray from its resource container.
TensorArrayConcat Concat the elements from the TensorArray into value `value`.
TensorArrayConcat.Options Optional attributes for TensorArrayConcat
TensorArrayGather Gather specific elements from the TensorArray into output `value`.
TensorArrayGather.Options Optional attributes for TensorArrayGather
TensorArrayGrad 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.
TensorArrayPack
TensorArrayPack.Options Optional attributes for TensorArrayPack
TensorArrayRead Read an element from the TensorArray into output `value`.
TensorArrayScatter Scatter the data from the input value into specific TensorArray elements.
TensorArraySize Get the current size of the TensorArray.
TensorArraySplit Split the data from the input value into TensorArray elements.
TensorArrayUnpack
TensorArrayWrite Push an element onto the tensor_array.
TensorListConcat Concats all tensors in the list along the 0th dimension.
TensorListConcatLists
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
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 list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`.
TensorListPushBackBatch
TensorListReserve List of the given size with empty elements.
TensorListScatter Creates a TensorList by indexing into a Tensor.
TensorListSetItem
TensorListSplit Splits a tensor into a list.
TensorListStack Stacks all tensors in the list.
TensorListStack.Options Optional attributes for TensorListStack
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`.
TensorSummary Outputs a `Summary` protocol buffer with a tensor.
TensorSummary.Options Optional attributes for TensorSummary
TensorSummaryV2 Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
TextLineReader A Reader that outputs the lines of a file delimited by '\n'.
TextLineReader.Options Optional attributes for TextLineReader
TFRecordReader A Reader that outputs the records from a TensorFlow Records file.
TFRecordReader.Options Optional attributes for TFRecordReader
Tile Constructs a tensor by tiling a given tensor.
TileGrad Returns the gradient of `Tile`.
Timestamp Provides the time since epoch in seconds.
TopK Finds values and indices of the `k` largest elements for the last dimension.
TopK.Options Optional attributes for TopK
Transpose Shuffle dimensions of x according to a permutation.
TruncateDiv Returns x / y element-wise for integer types.
TruncatedNormal Outputs random values from a truncated normal distribution.
TruncatedNormal.Options Optional attributes for TruncatedNormal
TruncateMod Returns element-wise remainder of division.
TryRpc Perform batches of RPC requests.
TryRpc.Options Optional attributes for TryRpc
Unbatch Reverses the operation of Batch for a single output Tensor.
Unbatch.Options Optional attributes for Unbatch
UnbatchGrad Gradient of Unbatch.
UnbatchGrad.Options Optional attributes for UnbatchGrad
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.
UnicodeTranscode.Options Optional attributes for UnicodeTranscode
UniformCandidateSampler Generates labels for candidate sampling with a uniform distribution.
UniformCandidateSampler.Options Optional attributes for UniformCandidateSampler
Unique Finds unique elements in a 1-D tensor.
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.
UnravelIndex Converts a flat index or array of flat indices into a tuple of
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.
Unstack Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
Unstack.Options Optional attributes for Unstack
Unstage Op is similar to a lightweight Dequeue.
Unstage.Options Optional attributes for Unstage
VarHandleOp Creates a handle to a Variable resource.
VarHandleOp.Options Optional attributes for VarHandleOp
Variable Holds state in the form of a tensor that persists across steps.
Variable.Options Optional attributes for Variable
VariableShape Returns the shape of the variable pointed to by `resource`.
VarIsInitializedOp Checks whether a resource handle-based variable has been initialized.
Where Returns locations of nonzero / true values in a tensor.
Where3 Selects elements from `x` or `y`, depending on `condition`.
WholeFileReader A Reader that outputs the entire contents of a file as a value.
WholeFileReader.Options Optional attributes for WholeFileReader
WriteFile Writes contents to the file at input filename.
Xdivy Returns 0 if x == 0, and x / y otherwise, elementwise.
Xlogy Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
Zeros An operator creating a constant initialized with zeros of the shape given by `dims`.
ZerosLike Returns a tensor of zeros with the same shape and type as x.
Zeta Compute the Hurwitz zeta function \\(\zeta(x, q)\\).

Classes in org.tensorflow.op.core used by org.tensorflow.op.core

Class and Description
Abort Raise a exception to abort the process when called.
Abort.Options Optional attributes for Abort
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.
AddManySparseToTensorsMap.Options Optional attributes for AddManySparseToTensorsMap
AddN Add all input tensors element wise.
AddSparseToTensorsMap Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
AddSparseToTensorsMap.Options Optional attributes for AddSparseToTensorsMap
AddV2 Returns x + y element-wise.
AdjustContrast 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.
All.Options Optional attributes for All
AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
AllCandidateSampler.Options Optional attributes for AllCandidateSampler
Angle Returns the argument of a complex number.
AnonymousIterator A container for an iterator resource.
Any Computes the "logical or" of elements across dimensions of a tensor.
Any.Options Optional attributes for Any
ApplyAdadelta Update '*var' according to the adadelta scheme.
ApplyAdadelta.Options Optional attributes for ApplyAdadelta
ApplyAdagrad Update '*var' according to the adagrad scheme.
ApplyAdagrad.Options Optional attributes for ApplyAdagrad
ApplyAdagradDA Update '*var' according to the proximal adagrad scheme.
ApplyAdagradDA.Options Optional attributes for ApplyAdagradDA
ApplyAdam Update '*var' according to the Adam algorithm.
ApplyAdam.Options Optional attributes for ApplyAdam
ApplyAdaMax Update '*var' according to the AdaMax algorithm.
ApplyAdaMax.Options Optional attributes for ApplyAdaMax
ApplyAddSign Update '*var' according to the AddSign update.
ApplyAddSign.Options Optional attributes for ApplyAddSign
ApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ApplyCenteredRMSProp.Options Optional attributes for ApplyCenteredRMSProp
ApplyFtrl Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrl.Options Optional attributes for ApplyFtrl
ApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.
ApplyFtrlV2.Options Optional attributes for ApplyFtrlV2
ApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.
ApplyGradientDescent.Options Optional attributes for ApplyGradientDescent
ApplyMomentum Update '*var' according to the momentum scheme.
ApplyMomentum.Options Optional attributes for ApplyMomentum
ApplyPowerSign Update '*var' according to the AddSign update.
ApplyPowerSign.Options Optional attributes for ApplyPowerSign
ApplyProximalAdagrad Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
ApplyProximalAdagrad.Options Optional attributes for ApplyProximalAdagrad
ApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyProximalGradientDescent.Options Optional attributes for ApplyProximalGradientDescent
ApplyRMSProp Update '*var' according to the RMSProp algorithm.
ApplyRMSProp.Options Optional attributes for ApplyRMSProp
ApproximateEqual Returns the truth value of abs(x-y) < tolerance element-wise.
ApproximateEqual.Options Optional attributes for ApproximateEqual
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.
Asin Computes asin of x element-wise.
Asinh Computes inverse hyperbolic sine of x element-wise.
Assert Asserts that the given condition is true.
Assert.Options Optional attributes for Assert
Assign Update 'ref' by assigning 'value' to it.
Assign.Options Optional attributes for Assign
AssignAdd Update 'ref' by adding 'value' to it.
AssignAdd.Options Optional attributes for AssignAdd
AssignAddVariableOp Adds a value to the current value of a variable.
AssignSub Update 'ref' by subtracting 'value' from it.
AssignSub.Options Optional attributes for AssignSub
AssignSubVariableOp Subtracts a value from the current value of a variable.
AssignVariableOp Assigns a new value to a variable.
AsString Converts each entry in the given tensor to strings.
AsString.Options Optional attributes for AsString
Atan Computes atan 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.
AudioSpectrogram.Options Optional attributes for AudioSpectrogram
AudioSummary Outputs a `Summary` protocol buffer with audio.
AudioSummary.Options Optional attributes for AudioSummary
AvgPool Performs average pooling on the input.
AvgPool.Options Optional attributes for AvgPool
AvgPool3D Performs 3D average pooling on the input.
AvgPool3D.Options Optional attributes for AvgPool3D
AvgPool3DGrad Computes gradients of average pooling function.
AvgPool3DGrad.Options Optional attributes for AvgPool3DGrad
AvgPoolGrad Computes gradients of the average pooling function.
AvgPoolGrad.Options Optional attributes for AvgPoolGrad
Barrier Defines a barrier that persists across different graph executions.
Barrier.Options Optional attributes for Barrier
BarrierClose Closes the given barrier.
BarrierClose.Options Optional attributes for BarrierClose
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.
BarrierTakeMany.Options Optional attributes for BarrierTakeMany
Batch Batches all input tensors nondeterministically.
Batch.Options Optional attributes for Batch
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
BatchIFFT
BatchIFFT2D
BatchIFFT3D
BatchMatMul Multiplies slices of two tensors in batches.
BatchMatMul.Options Optional attributes for BatchMatMul
BatchMatrixBandPart
BatchMatrixDeterminant
BatchMatrixDiag
BatchMatrixDiagPart
BatchMatrixInverse
BatchMatrixInverse.Options Optional attributes for BatchMatrixInverse
BatchMatrixSetDiag
BatchMatrixSolve
BatchMatrixSolve.Options Optional attributes for BatchMatrixSolve
BatchMatrixSolveLs
BatchMatrixSolveLs.Options Optional attributes for BatchMatrixSolveLs
BatchMatrixTriangularSolve
BatchMatrixTriangularSolve.Options Optional attributes for BatchMatrixTriangularSolve
BatchNormWithGlobalNormalization Batch normalization.
BatchNormWithGlobalNormalizationGrad Gradients for batch normalization.
BatchSelfAdjointEig
BatchSelfAdjointEigV2
BatchSelfAdjointEigV2.Options Optional attributes for BatchSelfAdjointEigV2
BatchSvd
BatchSvd.Options Optional attributes for 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`.
BiasAdd.Options Optional attributes for BiasAdd
BiasAddGrad The backward operation for "BiasAdd" on the "bias" tensor.
BiasAddGrad.Options Optional attributes for BiasAddGrad
BigQueryReader A Reader that outputs rows from a BigQuery table as tensorflow Examples.
BigQueryReader.Options Optional attributes for BigQueryReader
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`.
BoostedTreesBucketize Bucketize each feature based on bucket boundaries.
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.
BoostedTreesCreateEnsemble Creates a tree ensemble model and returns a handle to it.
BoostedTreesCreateQuantileStreamResource Create the Resource for Quantile Streams.
BoostedTreesCreateQuantileStreamResource.Options Optional attributes for BoostedTreesCreateQuantileStreamResource
BoostedTreesDeserializeEnsemble Deserializes a serialized tree ensemble config and replaces current tree
BoostedTreesEnsembleResourceHandleOp Creates a handle to a BoostedTreesEnsembleResource
BoostedTreesEnsembleResourceHandleOp.Options Optional attributes for BoostedTreesEnsembleResourceHandleOp
BoostedTreesExampleDebugOutputs Debugging/model interpretability outputs for each example.
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.
BoostedTreesQuantileStreamResourceFlush.Options Optional attributes for BoostedTreesQuantileStreamResourceFlush
BoostedTreesQuantileStreamResourceGetBucketBoundaries Generate the bucket boundaries for each feature based on accumulated summaries.
BoostedTreesQuantileStreamResourceHandleOp Creates a handle to a BoostedTreesQuantileStreamResource.
BoostedTreesQuantileStreamResourceHandleOp.Options Optional attributes for BoostedTreesQuantileStreamResourceHandleOp
BoostedTreesSerializeEnsemble Serializes the tree ensemble to a proto.
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
BroadcastDynamicShape 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'.
CacheDataset Creates a dataset that caches elements from `input_dataset`.
Cast Cast x of type SrcT to y of DstT.
Cast.Options Optional attributes for Cast
Ceil Returns element-wise smallest integer not less than x.
CheckNumerics Checks a tensor for NaN 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.
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.
CollectiveReduce Mutually reduces multiple tensors of identical type and shape.
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.
ComputeAccidentalHits.Options Optional attributes for ComputeAccidentalHits
Concat Concatenates tensors along one dimension.
ConcatenateDataset Creates a dataset that concatenates `input_dataset` with `another_dataset`.
ConditionalAccumulator A conditional accumulator for aggregating gradients.
ConditionalAccumulator.Options Optional attributes for ConditionalAccumulator
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.
Constant An operator producing a constant value.
ConsumeMutexLock This op consumes a lock created by `MutexLock`.
ControlTrigger Does nothing.
Conv2D Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2D.Options Optional attributes for Conv2D
Conv2DBackpropFilter Computes the gradients of convolution with respect to the filter.
Conv2DBackpropFilter.Options Optional attributes for Conv2DBackpropFilter
Conv2DBackpropInput Computes the gradients of convolution with respect to the input.
Conv2DBackpropInput.Options Optional attributes for Conv2DBackpropInput
Conv3D Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3D.Options Optional attributes for Conv3D
Conv3DBackpropFilter Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilter.Options Optional attributes for Conv3DBackpropFilter
Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter.
Conv3DBackpropFilterV2.Options Optional attributes for Conv3DBackpropFilterV2
Conv3DBackpropInput Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInput.Options Optional attributes for Conv3DBackpropInput
Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input.
Conv3DBackpropInputV2.Options Optional attributes for Conv3DBackpropInputV2
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.
CropAndResize.Options Optional attributes for CropAndResize
CropAndResizeGradBoxes Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradBoxes.Options Optional attributes for CropAndResizeGradBoxes
CropAndResizeGradImage Computes the gradient of the crop_and_resize op wrt the input image tensor.
CropAndResizeGradImage.Options Optional attributes for CropAndResizeGradImage
Cross Compute the pairwise cross product.
CTCBeamSearchDecoder Performs beam search decoding on the logits given in input.
CTCBeamSearchDecoder.Options Optional attributes for CTCBeamSearchDecoder
CTCGreedyDecoder Performs greedy decoding on the logits given in inputs.
CTCGreedyDecoder.Options Optional attributes for CTCGreedyDecoder
CTCLoss Calculates the CTC Loss (log probability) for each batch entry.
CTCLoss.Options Optional attributes for CTCLoss
CudnnRNN A RNN backed by cuDNN.
CudnnRNN.Options Optional attributes for CudnnRNN
CudnnRNNBackprop Backprop step of CudnnRNN.
CudnnRNNBackprop.Options Optional attributes for CudnnRNNBackprop
CudnnRNNBackpropV2 Backprop step of CudnnRNN.
CudnnRNNBackpropV2.Options Optional attributes for CudnnRNNBackpropV2
CudnnRNNCanonicalToParams Converts CudnnRNN params from canonical form to usable form.
CudnnRNNCanonicalToParams.Options Optional attributes for CudnnRNNCanonicalToParams
CudnnRNNParamsSize Computes size of weights that can be used by a Cudnn RNN model.
CudnnRNNParamsSize.Options Optional attributes for CudnnRNNParamsSize
CudnnRNNParamsToCanonical Retrieves CudnnRNN params in canonical form.
CudnnRNNParamsToCanonical.Options Optional attributes for CudnnRNNParamsToCanonical
CudnnRNNV2 A RNN backed by cuDNN.
CudnnRNNV2.Options Optional attributes for CudnnRNNV2
Cumprod Compute the cumulative product of the tensor `x` along `axis`.
Cumprod.Options Optional attributes for Cumprod
Cumsum Compute the cumulative sum of the tensor `x` along `axis`.
Cumsum.Options Optional attributes for Cumsum
DataFormatDimMap Returns the dimension index in the destination data format given the one in
DataFormatDimMap.Options Optional attributes for DataFormatDimMap
DataFormatVecPermute Returns the permuted vector/tensor in the destination data format given the
DataFormatVecPermute.Options Optional attributes for DataFormatVecPermute
DatasetToGraph Returns a serialized GraphDef representing `input_dataset`.
DatasetToSingleElement Outputs the single element from the given dataset.
DebugGradientIdentity Identity op for gradient debugging.
DebugGradientRefIdentity Identity op for gradient debugging.
DecodeAndCropJpeg Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeAndCropJpeg.Options Optional attributes for DecodeAndCropJpeg
DecodeBase64 Decode web-safe base64-encoded strings.
DecodeBmp Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeBmp.Options Optional attributes for DecodeBmp
DecodeCompressed Decompress strings.
DecodeCompressed.Options Optional attributes for DecodeCompressed
DecodeCSV Convert CSV records to tensors.
DecodeCSV.Options Optional attributes for DecodeCSV
DecodeGif Decode the first frame of a GIF-encoded image to a uint8 tensor.
DecodeJpeg Decode a JPEG-encoded image to a uint8 tensor.
DecodeJpeg.Options Optional attributes for DecodeJpeg
DecodeJSONExample Convert JSON-encoded Example records to binary protocol buffer strings.
DecodePng Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodePng.Options Optional attributes for DecodePng
DecodeProtoV2 The op extracts fields from a serialized protocol buffers message into tensors.
DecodeProtoV2.Options Optional attributes for DecodeProtoV2
DecodeRaw Reinterpret the bytes of a string as a vector of numbers.
DecodeRaw.Options Optional attributes for DecodeRaw
DecodeWav Decode a 16-bit PCM WAV file to a float tensor.
DecodeWav.Options Optional attributes for DecodeWav
DeepCopy Makes a copy of `x`.
DeleteSessionTensor Delete the tensor specified by its handle in the session.
DenseToDenseSetOperation Applies set operation along last dimension of 2 `Tensor` inputs.
DenseToDenseSetOperation.Options Optional attributes for DenseToDenseSetOperation
DenseToSparseSetOperation Applies set operation along last dimension of `Tensor` and `SparseTensor`.
DenseToSparseSetOperation.Options Optional attributes for DenseToSparseSetOperation
DepthToSpace DepthToSpace for tensors of type T.
DepthToSpace.Options Optional attributes for DepthToSpace
DepthwiseConv2dNative Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNative.Options Optional attributes for DepthwiseConv2dNative
DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropFilter.Options Optional attributes for DepthwiseConv2dNativeBackpropFilter
DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input.
DepthwiseConv2dNativeBackpropInput.Options Optional attributes for DepthwiseConv2dNativeBackpropInput
Dequantize Dequantize the 'input' tensor into a float Tensor.
Dequantize.Options Optional attributes for Dequantize
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.
DestroyResourceOp.Options Optional attributes for DestroyResourceOp
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.
Div Returns x / y element-wise.
DivNoNan Returns 0 if the denominator is zero.
DrawBoundingBoxes 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.
EditDistance Computes the (possibly normalized) Levenshtein Edit Distance.
EditDistance.Options Optional attributes for EditDistance
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.
Empty.Options Optional attributes for Empty
EmptyTensorList Creates and returns an empty tensor list.
EncodeBase64 Encode strings into web-safe base64 format.
EncodeBase64.Options Optional attributes for EncodeBase64
EncodeJpeg JPEG-encode an image.
EncodeJpeg.Options Optional attributes for EncodeJpeg
EncodePng PNG-encode an image.
EncodePng.Options Optional attributes for EncodePng
EncodeProto The op serializes protobuf messages provided in the input tensors.
EncodeProto.Options Optional attributes for EncodeProto
EncodeWav Encode audio data using the WAV file format.
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.
Enter.Options Optional attributes for Enter
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.
Exit Exits the current frame to its parent frame.
Exp Computes exponential of x element-wise.
ExpandDims Inserts a dimension of 1 into a tensor's shape.
ExperimentalAssertNextDataset
ExperimentalBytesProducedStatsDataset Records the bytes size of each element of `input_dataset` in a StatsAggregator.
ExperimentalCSVDataset
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.
ExperimentalIdentityIndexedDataset
ExperimentalIgnoreErrorsDataset Creates a dataset that contains the elements of `input_dataset` ignoring errors.
ExperimentalIndexedDatasetGet
ExperimentalIndexedDatasetMaterialize
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.
ExperimentalLMDBDataset
ExperimentalMatchingFilesDataset
ExperimentalMaterializedIndexDatasetHandle
ExperimentalMaxIntraOpParallelismDataset Creates a dataset that overrides the maximum intra-op parallelism.
ExperimentalNonSerializableDataset
ExperimentalParseExampleDataset Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
ExperimentalParseExampleDataset.Options Optional attributes for ExperimentalParseExampleDataset
ExperimentalPrivateThreadPoolDataset Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ExperimentalRandomDataset Creates a Dataset that returns pseudorandom numbers.
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.
ExperimentalStatsAggregatorHandle.Options Optional attributes for ExperimentalStatsAggregatorHandle
ExperimentalStatsAggregatorSummary Produces a summary of any statistics recorded by the given statistics manager.
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`.
ExperimentalThreadPoolHandle.Options Optional attributes for ExperimentalThreadPoolHandle
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 exponential of x - 1 element-wise.
ExtractGlimpse Extracts a glimpse from the input tensor.
ExtractGlimpse.Options Optional attributes for ExtractGlimpse
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.
Fact Output a fact about factorials.
FakeQuantWithMinMaxArgs Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgs.Options Optional attributes for FakeQuantWithMinMaxArgs
FakeQuantWithMinMaxArgsGradient Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxArgsGradient.Options Optional attributes for FakeQuantWithMinMaxArgsGradient
FakeQuantWithMinMaxVars Fake-quantize the 'inputs' tensor of type float via global float scalars `min`
FakeQuantWithMinMaxVars.Options Optional attributes for FakeQuantWithMinMaxVars
FakeQuantWithMinMaxVarsGradient Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsGradient.Options Optional attributes for FakeQuantWithMinMaxVarsGradient
FakeQuantWithMinMaxVarsPerChannel Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`,
FakeQuantWithMinMaxVarsPerChannel.Options Optional attributes for FakeQuantWithMinMaxVarsPerChannel
FakeQuantWithMinMaxVarsPerChannelGradient Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
FakeQuantWithMinMaxVarsPerChannelGradient.Options Optional attributes for FakeQuantWithMinMaxVarsPerChannelGradient
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.
FIFOQueue.Options Optional attributes for FIFOQueue
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.
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.
FixedLengthRecordReader.Options Optional attributes for FixedLengthRecordReader
FixedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
FixedUnigramCandidateSampler.Options Optional attributes for FixedUnigramCandidateSampler
Floor Returns element-wise largest integer not greater than x.
FloorDiv Returns x // y element-wise.
FloorMod Returns element-wise remainder of division.
FlushSummaryWriter
FractionalAvgPool Performs fractional average pooling on the input.
FractionalAvgPool.Options Optional attributes for FractionalAvgPool
FractionalAvgPoolGrad Computes gradient of the FractionalAvgPool function.
FractionalAvgPoolGrad.Options Optional attributes for FractionalAvgPoolGrad
FractionalMaxPool Performs fractional max pooling on the input.
FractionalMaxPool.Options Optional attributes for FractionalMaxPool
FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function.
FractionalMaxPoolGrad.Options Optional attributes for FractionalMaxPoolGrad
FusedBatchNorm Batch normalization.
FusedBatchNorm.Options Optional attributes for FusedBatchNorm
FusedBatchNormGrad Gradient for batch normalization.
FusedBatchNormGrad.Options Optional attributes for FusedBatchNormGrad
FusedBatchNormGradV2 Gradient for batch normalization.
FusedBatchNormGradV2.Options Optional attributes for FusedBatchNormGradV2
FusedBatchNormV2 Batch normalization.
FusedBatchNormV2.Options Optional attributes for FusedBatchNormV2
FusedPadConv2D Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2D Performs a resize and padding as a preprocess during a convolution.
FusedResizeAndPadConv2D.Options Optional attributes for FusedResizeAndPadConv2D
Gather Gather slices from `params` according to `indices`.
Gather.Options Optional attributes for Gather
GatherNd Gather slices from `params` into a Tensor with shape specified by `indices`.
GatherV2 Gather slices from `params` axis `axis` according to `indices`.
GcsConfigureBlockCache Re-configures the GCS block cache with the new configuration values.
GcsConfigureCredentials Configures the credentials used by the GCS client of the local TF runtime.
GenerateBigQueryReaderPartitions Generates serialized partition messages suitable for batch reads.
GenerateBigQueryReaderPartitions.Options Optional attributes for GenerateBigQueryReaderPartitions
GenerateVocabRemapping Given a path to new and old vocabulary files, returns a remapping Tensor of
GenerateVocabRemapping.Options Optional attributes for GenerateVocabRemapping
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.
Gradients Adds operations to compute the partial derivatives of sum of ys w.r.t xs, i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2...
Gradients.Options Optional attributes for Gradients
Greater Returns the truth value of (x > y) element-wise.
GreaterEqual Returns the truth value of (x >= y) element-wise.
GuaranteeConst Gives a guarantee to the TF runtime that the input tensor is a constant.
HashTable Creates a non-initialized hash table.
HashTable.Options Optional attributes for HashTable
HistogramFixedWidth Return histogram of values.
HistogramSummary Outputs a `Summary` protocol buffer with a histogram.
HSVToRGB Convert one or more images from HSV to RGB.
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.
IdentityReader.Options Optional attributes for IdentityReader
IFFT Inverse fast Fourier transform.
IFFT2D Inverse 2D fast Fourier transform.
IFFT3D Inverse 3D fast Fourier transform.
Igamma Compute the lower regularized incomplete Gamma function `P(a, x)`.
Igammac Compute the upper regularized incomplete Gamma function `Q(a, x)`.
IgammaGradA Computes the gradient of `igamma(a, x)` wrt `a`.
Imag Returns the imaginary part of a complex number.
ImageSummary Outputs a `Summary` protocol buffer with images.
ImageSummary.Options Optional attributes for ImageSummary
ImmutableConst Returns immutable tensor from memory region.
ImportEvent
InitializeTable Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromTextFile Initializes a table from a text file.
InitializeTableFromTextFile.Options Optional attributes for InitializeTableFromTextFile
InplaceAdd Adds v into specified rows of x.
InplaceSub Subtracts `v` into specified rows of `x`.
InplaceUpdate Updates specified rows with values in `v`.
InTopK Says whether the targets are in the top `K` predictions.
InTopKV2 Says whether the targets are in the top `K` predictions.
Inv Computes the reciprocal of x element-wise.
Invert Flips all bits elementwise.
InvertPermutation Computes the inverse permutation of a tensor.
InvGrad Computes the gradient for the inverse of `x` wrt its input.
IRFFT Inverse real-valued fast Fourier transform.
IRFFT2D Inverse 2D real-valued fast Fourier transform.
IRFFT3D Inverse 3D real-valued fast Fourier transform.
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.
IteratorFromStringHandle.Options Optional attributes for IteratorFromStringHandle
IteratorFromStringHandleV2
IteratorFromStringHandleV2.Options Optional attributes for IteratorFromStringHandleV2
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.
LeakyRelu Computes rectified linear: `max(features, features * alpha)`.
LeakyRelu.Options Optional attributes for LeakyRelu
LeakyReluGrad Computes rectified linear gradients for a LeakyRelu operation.
LeakyReluGrad.Options Optional attributes for LeakyReluGrad
LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution.
LearnedUnigramCandidateSampler.Options Optional attributes for LearnedUnigramCandidateSampler
LeftShift Elementwise computes the bitwise left-shift of `x` and `y`.
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.
LMDBReader A Reader that outputs the records from a LMDB file.
LMDBReader.Options Optional attributes for LMDBReader
LoadAndRemapMatrix Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint
LoadAndRemapMatrix.Options Optional attributes for LoadAndRemapMatrix
Log Computes natural logarithm of x element-wise.
Log1p Computes natural logarithm of (1 + x) element-wise.
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.
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.
LogUniformCandidateSampler.Options Optional attributes for LogUniformCandidateSampler
LookupTableExport Outputs all keys and values in the table.
LookupTableFind Looks up keys in a table, outputs the corresponding values.
LookupTableImport Replaces the contents of the table with the specified keys and values.
LookupTableInsert Updates the table to associates keys with values.
LookupTableRemove Removes keys and its associated values from a table.
LookupTableSize 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.
LRN Local Response Normalization.
LRN.Options Optional attributes for LRN
LRNGrad Gradients for Local Response Normalization.
LRNGrad.Options Optional attributes for LRNGrad
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`.
MapClear Op removes all elements in the underlying container.
MapClear.Options Optional attributes for MapClear
MapIncompleteSize Op returns the number of incomplete elements in the underlying container.
MapIncompleteSize.Options Optional attributes for MapIncompleteSize
MapPeek Op peeks at the values at the specified key.
MapPeek.Options Optional attributes for MapPeek
MapSize Op returns the number of elements in the underlying container.
MapSize.Options Optional attributes for MapSize
MapStage Stage (key, values) in the underlying container which behaves like a hashtable.
MapStage.Options Optional attributes for MapStage
MapUnstage Op removes and returns the values associated with the key
MapUnstage.Options Optional attributes for MapUnstage
MapUnstageNoKey Op removes and returns a random (key, value)
MapUnstageNoKey.Options Optional attributes for MapUnstageNoKey
MatchingFiles Returns the set of files matching one or more glob patterns.
MatMul Multiply the matrix "a" by the matrix "b".
MatMul.Options Optional attributes for MatMul
MatrixBandPart Copy a tensor setting everything outside a central band in each innermost matrix
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.
MatrixInverse Computes the inverse of one or more square invertible matrices or their
MatrixInverse.Options Optional attributes for MatrixInverse
MatrixLogarithm Computes the matrix logarithm of one or more square matrices:
MatrixSetDiag Returns a batched matrix tensor with new batched diagonal values.
MatrixSolve Solves systems of linear equations.
MatrixSolve.Options Optional attributes for MatrixSolve
MatrixSolveLs Solves one or more linear least-squares problems.
MatrixSolveLs.Options Optional attributes for MatrixSolveLs
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
MatrixTriangularSolve.Options Optional attributes for MatrixTriangularSolve
Max Computes the maximum of elements across dimensions of a tensor.
Max.Options Optional attributes for Max
Maximum Returns the max of x and y (i.e.
MaxPool Performs max pooling on the input.
MaxPool.Options Optional attributes for MaxPool
MaxPool3D Performs 3D max pooling on the input.
MaxPool3D.Options Optional attributes for MaxPool3D
MaxPool3DGrad Computes gradients of max pooling function.
MaxPool3DGrad.Options Optional attributes for MaxPool3DGrad
MaxPool3DGradGrad Computes second-order gradients of the maxpooling function.
MaxPool3DGradGrad.Options Optional attributes for MaxPool3DGradGrad
MaxPoolGrad Computes gradients of the maxpooling function.
MaxPoolGrad.Options Optional attributes for MaxPoolGrad
MaxPoolGradGrad Computes second-order gradients of the maxpooling function.
MaxPoolGradGrad.Options Optional attributes for MaxPoolGradGrad
MaxPoolGradGradV2 Computes second-order gradients of the maxpooling function.
MaxPoolGradGradV2.Options Optional attributes for MaxPoolGradGradV2
MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function.
MaxPoolGradV2 Computes gradients of the maxpooling function.
MaxPoolGradV2.Options Optional attributes for MaxPoolGradV2
MaxPoolGradWithArgmax Computes gradients of the maxpooling function.
MaxPoolV2 Performs max pooling on the input.
MaxPoolV2.Options Optional attributes for MaxPoolV2
MaxPoolWithArgmax Performs max pooling on the input and outputs both max values and indices.
Mean Computes the mean of elements across dimensions of a tensor.
Mean.Options Optional attributes for Mean
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.
MergeV2Checkpoints.Options Optional attributes for MergeV2Checkpoints
Mfcc Transforms a spectrogram into a form that's useful for speech recognition.
Mfcc.Options Optional attributes for Mfcc
Min Computes the minimum of elements across dimensions of a tensor.
Min.Options Optional attributes for Min
Minimum Returns the min of x and y (i.e.
MirrorPad Pads a tensor with mirrored values.
MirrorPadGrad Gradient op for `MirrorPad` op.
Mod Returns element-wise remainder of division.
ModelDataset Identity transformation that models performance.
Mul Returns x * y element-wise.
MultiDeviceIterator Creates a MultiDeviceIterator resource.
MultiDeviceIteratorFromStringHandle Generates a MultiDeviceIterator resource from its provided string handle.
MultiDeviceIteratorFromStringHandle.Options Optional attributes for MultiDeviceIteratorFromStringHandle
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.
Multinomial.Options Optional attributes for Multinomial
Multiply Returns x * y element-wise.
MutableDenseHashTable Creates an empty hash table that uses tensors as the backing store.
MutableDenseHashTable.Options Optional attributes for MutableDenseHashTable
MutableHashTable Creates an empty hash table.
MutableHashTable.Options Optional attributes for MutableHashTable
MutableHashTableOfTensors Creates an empty hash table.
MutableHashTableOfTensors.Options Optional attributes for MutableHashTableOfTensors
MutexLock Locks a mutex resource.
MutexV2 Creates a Mutex resource that can be locked by `MutexLock`.
MutexV2.Options Optional attributes for MutexV2
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.
Negate Computes numerical negative value element-wise.
NegTrain Training via negative sampling.
NextIteration Makes its input available to the next iteration.
NonMaxSuppression Greedily selects a subset of bounding boxes in descending order of score,
NonMaxSuppression.Options Optional attributes for NonMaxSuppression
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,
NonMaxSuppressionV4.Options Optional attributes for NonMaxSuppressionV4
NonMaxSuppressionWithOverlaps Greedily selects a subset of bounding boxes in descending order of score,
NoOp Does nothing.
NotEqual Returns the truth value of (x != y) element-wise.
NthElement Finds values of the `n`-th order statistic for the last dimension.
NthElement.Options Optional attributes for NthElement
OneHot Returns a one-hot tensor.
OneHot.Options Optional attributes for OneHot
OnesLike Returns a tensor of ones with the same shape and type as x.
OptimizeDataset Creates a dataset by applying 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.
OrderedMapClear.Options Optional attributes for OrderedMapClear
OrderedMapIncompleteSize Op returns the number of incomplete elements in the underlying container.
OrderedMapIncompleteSize.Options Optional attributes for OrderedMapIncompleteSize
OrderedMapPeek Op peeks at the values at the specified key.
OrderedMapPeek.Options Optional attributes for OrderedMapPeek
OrderedMapSize Op returns the number of elements in the underlying container.
OrderedMapSize.Options Optional attributes for OrderedMapSize
OrderedMapStage Stage (key, values) in the underlying container which behaves like a ordered
OrderedMapStage.Options Optional attributes for OrderedMapStage
OrderedMapUnstage Op removes and returns the values associated with the key
OrderedMapUnstage.Options Optional attributes for OrderedMapUnstage
OrderedMapUnstageNoKey Op removes and returns the (key, value) element with the smallest
OrderedMapUnstageNoKey.Options Optional attributes for OrderedMapUnstageNoKey
Pad Pads a tensor with zeros.
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.
PaddingFIFOQueue.Options Optional attributes for PaddingFIFOQueue
PadV2 Pads a tensor.
ParallelConcat Concatenates a list of `N` tensors along the first dimension.
ParallelDynamicStitch Interleave the values from the `data` tensors into a single tensor.
ParameterizedTruncatedNormal Outputs random values from a normal distribution.
ParameterizedTruncatedNormal.Options Optional attributes for ParameterizedTruncatedNormal
ParseExample Transforms a vector of brain.Example protos (as strings) into typed tensors.
ParseSequenceExample Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
ParseSequenceExample.Options Optional attributes for ParseSequenceExample
ParseSingleExample Transforms a tf.Example proto (as a string) into typed tensors.
ParseSingleSequenceExample Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
ParseSingleSequenceExample.Options Optional attributes for ParseSingleSequenceExample
ParseTensor Transforms a serialized tensorflow.TensorProto proto into a Tensor.
Placeholder A placeholder op for a value that will be fed into the computation.
Placeholder.Options Optional attributes for Placeholder
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.
Pow Computes the power of one value to another.
PrefetchDataset Creates a dataset that asynchronously prefetches elements from `input_dataset`.
PreventGradient An identity op that triggers an error if a gradient is requested.
PreventGradient.Options Optional attributes for PreventGradient
Print Prints a list of tensors.
Print.Options Optional attributes for Print
PrintV2 Prints a string scalar.
PrintV2.Options Optional attributes for PrintV2
PriorityQueue A queue that produces elements sorted by the first component value.
PriorityQueue.Options Optional attributes for PriorityQueue
Prod Computes the product of elements across dimensions of a tensor.
Prod.Options Optional attributes for Prod
Qr Computes the QR decompositions of one or more matrices.
Qr.Options Optional attributes for Qr
QuantizeAndDequantize Use QuantizeAndDequantizeV2 instead.
QuantizeAndDequantize.Options Optional attributes for QuantizeAndDequantize
QuantizeAndDequantizeV2 Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV2.Options Optional attributes for QuantizeAndDequantizeV2
QuantizeAndDequantizeV3 Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3.Options Optional attributes for QuantizeAndDequantizeV3
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.
QuantizedConv2D.Options Optional attributes for QuantizedConv2D
QuantizedInstanceNorm Quantized Instance normalization.
QuantizedInstanceNorm.Options Optional attributes for QuantizedInstanceNorm
QuantizedMatMul Perform a quantized matrix multiplication of `a` by the matrix `b`.
QuantizedMatMul.Options Optional attributes for QuantizedMatMul
QuantizedMaxPool Produces the max pool of the input tensor for quantized types.
QuantizedMul Returns x * y element-wise, working on quantized buffers.
QuantizeDownAndShrinkRange Convert the quantized 'input' tensor into a lower-precision 'output', using the
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.
QuantizedResizeBilinear.Options Optional attributes for QuantizedResizeBilinear
QuantizeV2 Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizeV2.Options Optional attributes for QuantizeV2
QueueClose Closes the given queue.
QueueClose.Options Optional attributes for QueueClose
QueueDequeue Dequeues a tuple of one or more tensors from the given queue.
QueueDequeue.Options Optional attributes for QueueDequeue
QueueDequeueMany Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueMany.Options Optional attributes for QueueDequeueMany
QueueDequeueUpTo Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueUpTo.Options Optional attributes for QueueDequeueUpTo
QueueEnqueue Enqueues a tuple of one or more tensors in the given queue.
QueueEnqueue.Options Optional attributes for QueueEnqueue
QueueEnqueueMany Enqueues zero or more tuples of one or more tensors in the given queue.
QueueEnqueueMany.Options Optional attributes for QueueEnqueueMany
QueueIsClosed Returns true if queue is closed.
QueueIsClosedV2 Returns true if queue is closed.
QueueSize Computes the number of elements in the given queue.
RaggedGather Gather ragged slices from `params` axis `0` according to `indices`.
RaggedRange Returns a `RaggedTensor` containing the specified sequences of numbers.
RaggedTensorToSparse Converts a `RaggedTensor` into a `SparseTensor` with the same values.
RandomCrop Randomly crop `image`.
RandomCrop.Options Optional attributes for RandomCrop
RandomGamma Outputs random values from the Gamma distribution(s) described by alpha.
RandomGamma.Options Optional attributes for RandomGamma
RandomGammaGrad Computes the derivative of a Gamma random sample w.r.t.
RandomNormal Outputs random values from a normal distribution.
RandomNormal.Options Optional attributes for RandomNormal
RandomPoisson Use RandomPoissonV2 instead.
RandomPoisson.Options Optional attributes for RandomPoisson
RandomPoissonV2 Outputs random values from the Poisson distribution(s) described by rate.
RandomPoissonV2.Options Optional attributes for RandomPoissonV2
RandomShuffle Randomly shuffles a tensor along its first dimension.
RandomShuffle.Options Optional attributes for RandomShuffle
RandomShuffleQueue A queue that randomizes the order of elements.
RandomShuffleQueue.Options Optional attributes for RandomShuffleQueue
RandomUniform Outputs random values from a uniform distribution.
RandomUniform.Options Optional attributes for RandomUniform
RandomUniformInt Outputs random integers from a uniform distribution.
RandomUniformInt.Options Optional attributes for RandomUniformInt
Range Creates a sequence of numbers.
RangeDataset Creates a dataset with a range of values.
Rank Returns the rank of a tensor.
ReaderNumRecordsProduced Returns the number of records this Reader has produced.
ReaderNumWorkUnitsCompleted 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.
ReaderReset Restore a Reader to its initial clean state.
ReaderRestoreState Restore a reader to a previously saved state.
ReaderSerializeState Produce a string tensor that encodes the state of a Reader.
ReadFile Reads and outputs the entire contents of the input filename.
ReadVariableOp Reads the value of a variable.
Real Returns the real part of a complex number.
RealDiv Returns x / y element-wise for real types.
Reciprocal Computes the reciprocal of x element-wise.
ReciprocalGrad Computes the gradient for the inverse of `x` wrt its input.
RecordInput Emits randomized records.
RecordInput.Options Optional attributes for RecordInput
ReduceAll Computes the "logical and" of elements across dimensions of a tensor.
ReduceAll.Options Optional attributes for ReduceAll
ReduceAny Computes the "logical or" of elements across dimensions of a tensor.
ReduceAny.Options Optional attributes for ReduceAny
ReduceJoin Joins a string Tensor across the given dimensions.
ReduceJoin.Options Optional attributes for ReduceJoin
ReduceMax Computes the maximum of elements across dimensions of a tensor.
ReduceMax.Options Optional attributes for ReduceMax
ReduceMean Computes the mean of elements across dimensions of a tensor.
ReduceMean.Options Optional attributes for ReduceMean
ReduceMin Computes the minimum of elements across dimensions of a tensor.
ReduceMin.Options Optional attributes for ReduceMin
ReduceProd Computes the product of elements across dimensions of a tensor.
ReduceProd.Options Optional attributes for ReduceProd
ReduceSum Computes the sum of elements across dimensions of a tensor.
ReduceSum.Options Optional attributes for ReduceSum
RefEnter Creates or finds a child frame, and makes `data` available to the child frame.
RefEnter.Options Optional attributes for RefEnter
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 `index`th 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 the match of pattern in input with rewrite.
RegexReplace.Options Optional attributes for RegexReplace
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.
RemoteFusedGraphExecute Execute a sub graph on a remote processor.
RepeatDataset Creates a dataset that emits the outputs of `input_dataset` `count` times.
RequantizationRange Given a quantized tensor described by (input, input_min, input_max), outputs a
Requantize Convert the quantized 'input' tensor into a lower-precision 'output', using the
Reshape Reshapes a tensor.
ResizeArea Resize `images` to `size` using area interpolation.
ResizeArea.Options Optional attributes for ResizeArea
ResizeBicubic Resize `images` to `size` using bicubic interpolation.
ResizeBicubic.Options Optional attributes for ResizeBicubic
ResizeBicubicGrad Computes the gradient of bicubic interpolation.
ResizeBicubicGrad.Options Optional attributes for ResizeBicubicGrad
ResizeBilinear Resize `images` to `size` using bilinear interpolation.
ResizeBilinear.Options Optional attributes for ResizeBilinear
ResizeBilinearGrad Computes the gradient of bilinear interpolation.
ResizeBilinearGrad.Options Optional attributes for ResizeBilinearGrad
ResizeNearestNeighbor Resize `images` to `size` using nearest neighbor interpolation.
ResizeNearestNeighbor.Options Optional attributes for ResizeNearestNeighbor
ResizeNearestNeighborGrad Computes the gradient of nearest neighbor interpolation.
ResizeNearestNeighborGrad.Options Optional attributes for ResizeNearestNeighborGrad
ResourceApplyAdadelta Update '*var' according to the adadelta scheme.
ResourceApplyAdadelta.Options Optional attributes for ResourceApplyAdadelta
ResourceApplyAdagrad Update '*var' according to the adagrad scheme.
ResourceApplyAdagrad.Options Optional attributes for ResourceApplyAdagrad
ResourceApplyAdagradDA Update '*var' according to the proximal adagrad scheme.
ResourceApplyAdagradDA.Options Optional attributes for ResourceApplyAdagradDA
ResourceApplyAdam Update '*var' according to the Adam algorithm.
ResourceApplyAdam.Options Optional attributes for ResourceApplyAdam
ResourceApplyAdaMax Update '*var' according to the AdaMax algorithm.
ResourceApplyAdaMax.Options Optional attributes for ResourceApplyAdaMax
ResourceApplyAdamWithAmsgrad Update '*var' according to the Adam algorithm.
ResourceApplyAdamWithAmsgrad.Options Optional attributes for ResourceApplyAdamWithAmsgrad
ResourceApplyAddSign Update '*var' according to the AddSign update.
ResourceApplyAddSign.Options Optional attributes for ResourceApplyAddSign
ResourceApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ResourceApplyCenteredRMSProp.Options Optional attributes for ResourceApplyCenteredRMSProp
ResourceApplyFtrl Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrl.Options Optional attributes for ResourceApplyFtrl
ResourceApplyFtrlV2 Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyFtrlV2.Options Optional attributes for ResourceApplyFtrlV2
ResourceApplyGradientDescent Update '*var' by subtracting 'alpha' * 'delta' from it.
ResourceApplyGradientDescent.Options Optional attributes for ResourceApplyGradientDescent
ResourceApplyKerasMomentum Update '*var' according to the momentum scheme.
ResourceApplyKerasMomentum.Options Optional attributes for ResourceApplyKerasMomentum
ResourceApplyMomentum Update '*var' according to the momentum scheme.
ResourceApplyMomentum.Options Optional attributes for ResourceApplyMomentum
ResourceApplyPowerSign Update '*var' according to the AddSign update.
ResourceApplyPowerSign.Options Optional attributes for ResourceApplyPowerSign
ResourceApplyProximalAdagrad Update '*var' and '*accum' according to FOBOS with Adagrad learning rate.
ResourceApplyProximalAdagrad.Options Optional attributes for ResourceApplyProximalAdagrad
ResourceApplyProximalGradientDescent Update '*var' as FOBOS algorithm with fixed learning rate.
ResourceApplyProximalGradientDescent.Options Optional attributes for ResourceApplyProximalGradientDescent
ResourceApplyRMSProp Update '*var' according to the RMSProp algorithm.
ResourceApplyRMSProp.Options Optional attributes for ResourceApplyRMSProp
ResourceCountUpTo Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGather.Options Optional attributes for ResourceGather
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 Adds sparse `updates` to individual values or slices within a given
ResourceScatterNdAdd.Options Optional attributes for ResourceScatterNdAdd
ResourceScatterNdUpdate Applies sparse `updates` to individual values or slices within a given
ResourceScatterNdUpdate.Options Optional attributes for ResourceScatterNdUpdate
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().
ResourceSparseApplyAdadelta.Options Optional attributes for ResourceSparseApplyAdadelta
ResourceSparseApplyAdagrad Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
ResourceSparseApplyAdagrad.Options Optional attributes for ResourceSparseApplyAdagrad
ResourceSparseApplyAdagradDA Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradDA.Options Optional attributes for ResourceSparseApplyAdagradDA
ResourceSparseApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyCenteredRMSProp.Options Optional attributes for ResourceSparseApplyCenteredRMSProp
ResourceSparseApplyFtrl Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrl.Options Optional attributes for ResourceSparseApplyFtrl
ResourceSparseApplyFtrlV2 Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyFtrlV2.Options Optional attributes for ResourceSparseApplyFtrlV2
ResourceSparseApplyKerasMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
ResourceSparseApplyKerasMomentum.Options Optional attributes for ResourceSparseApplyKerasMomentum
ResourceSparseApplyMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
ResourceSparseApplyMomentum.Options Optional attributes for ResourceSparseApplyMomentum
ResourceSparseApplyProximalAdagrad Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
ResourceSparseApplyProximalAdagrad.Options Optional attributes for ResourceSparseApplyProximalAdagrad
ResourceSparseApplyProximalGradientDescent Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyProximalGradientDescent.Options Optional attributes for ResourceSparseApplyProximalGradientDescent
ResourceSparseApplyRMSProp Update '*var' according to the RMSProp algorithm.
ResourceSparseApplyRMSProp.Options Optional attributes for ResourceSparseApplyRMSProp
ResourceStridedSliceAssign Assign `value` to the sliced l-value reference of `ref`.
ResourceStridedSliceAssign.Options Optional attributes for ResourceStridedSliceAssign
Restore Restores a tensor from checkpoint files.
Restore.Options Optional attributes for Restore
RestoreSlice Restores a tensor from checkpoint files.
RestoreSlice.Options Optional attributes for RestoreSlice
RestoreV2 Restores tensors from a V2 checkpoint.
Reverse Reverses specific dimensions of a tensor.
ReverseSequence Reverses variable length slices.
ReverseSequence.Options Optional attributes for ReverseSequence
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.
RightShift Elementwise computes the bitwise right-shift of `x` and `y`.
Rint Returns element-wise integer closest to x.
Roll Rolls the elements of a tensor along an axis.
Round Rounds the values of a tensor to the nearest integer, element-wise.
Rpc Perform batches of RPC requests.
Rpc.Options Optional attributes for Rpc
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.
SampleDistortedBoundingBox.Options Optional attributes for SampleDistortedBoundingBox
SampleDistortedBoundingBoxV2 Generate a single randomly distorted bounding box for an image.
SampleDistortedBoundingBoxV2.Options Optional attributes for SampleDistortedBoundingBoxV2
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.
ScatterAdd Adds sparse updates to a variable reference.
ScatterAdd.Options Optional attributes for ScatterAdd
ScatterDiv Divides a variable reference by sparse updates.
ScatterDiv.Options Optional attributes for ScatterDiv
ScatterMax Reduces sparse updates into a variable reference using the `max` operation.
ScatterMax.Options Optional attributes for ScatterMax
ScatterMin Reduces sparse updates into a variable reference using the `min` operation.
ScatterMin.Options Optional attributes for ScatterMin
ScatterMul Multiplies sparse updates into a variable reference.
ScatterMul.Options Optional attributes for ScatterMul
ScatterNd Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd Applies sparse addition between `updates` and individual values or slices
ScatterNdAdd.Options Optional attributes for ScatterNdAdd
ScatterNdNonAliasingAdd Applies sparse addition to `input` using individual values or slices
ScatterNdSub Applies sparse subtraction between `updates` and individual values or slices
ScatterNdSub.Options Optional attributes for ScatterNdSub
ScatterNdUpdate Applies sparse `updates` to individual values or slices within a given
ScatterNdUpdate.Options Optional attributes for ScatterNdUpdate
ScatterSub Subtracts sparse updates to a variable reference.
ScatterSub.Options Optional attributes for ScatterSub
ScatterUpdate Applies sparse updates to a variable reference.
ScatterUpdate.Options Optional attributes for ScatterUpdate
SdcaFprint Computes fingerprints of the input strings.
SdcaOptimizer Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaOptimizer.Options Optional attributes for SdcaOptimizer
SdcaOptimizerV2 Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for
SdcaOptimizerV2.Options Optional attributes for SdcaOptimizerV2
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.
SelfAdjointEig Computes the eigen decomposition of one or more square self-adjoint matrices.
SelfAdjointEig.Options Optional attributes for SelfAdjointEig
Selu Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
SeluGrad Computes gradients for the scaled exponential linear (Selu) operation.
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.
SetDiff1D Computes the difference between two lists of numbers or strings.
SetSize Number of unique elements along last dimension of input `set`.
SetSize.Options Optional attributes for SetSize
Shape Returns the shape of a tensor.
ShapeN Returns shape of tensors.
ShardedFilename Generate a sharded filename.
ShardedFilespec Generate a glob pattern matching all sharded file names.
ShuffleAndRepeatDataset Creates a dataset that shuffles and repeats elements from `input_dataset`
ShuffleDataset Creates a dataset that shuffles elements from `input_dataset` pseudorandomly.
ShuffleDataset.Options Optional attributes for ShuffleDataset
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 sin 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`.
Skipgram Parses a text file and creates a batch of examples.
Skipgram.Options Optional attributes for Skipgram
Slice Return a slice from 'input'.
Snapshot Returns a copy of the input tensor.
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.
SpaceToDepth.Options Optional attributes for SpaceToDepth
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().
SparseApplyAdadelta.Options Optional attributes for SparseApplyAdadelta
SparseApplyAdagrad Update relevant entries in '*var' and '*accum' according to the adagrad scheme.
SparseApplyAdagrad.Options Optional attributes for SparseApplyAdagrad
SparseApplyAdagradDA Update entries in '*var' and '*accum' according to the proximal adagrad scheme.
SparseApplyAdagradDA.Options Optional attributes for SparseApplyAdagradDA
SparseApplyCenteredRMSProp Update '*var' according to the centered RMSProp algorithm.
SparseApplyCenteredRMSProp.Options Optional attributes for SparseApplyCenteredRMSProp
SparseApplyFtrl Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrl.Options Optional attributes for SparseApplyFtrl
SparseApplyFtrlV2 Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyFtrlV2.Options Optional attributes for SparseApplyFtrlV2
SparseApplyMomentum Update relevant entries in '*var' and '*accum' according to the momentum scheme.
SparseApplyMomentum.Options Optional attributes for SparseApplyMomentum
SparseApplyProximalAdagrad Sparse update entries in '*var' and '*accum' according to FOBOS algorithm.
SparseApplyProximalAdagrad.Options Optional attributes for SparseApplyProximalAdagrad
SparseApplyProximalGradientDescent Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyProximalGradientDescent.Options Optional attributes for SparseApplyProximalGradientDescent
SparseApplyRMSProp Update '*var' according to the RMSProp algorithm.
SparseApplyRMSProp.Options Optional attributes for SparseApplyRMSProp
SparseConcat Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator A conditional accumulator for aggregating sparse gradients.
SparseConditionalAccumulator.Options Optional attributes for SparseConditionalAccumulator
SparseCross 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".
SparseMatMul.Options Optional attributes for SparseMatMul
SparseReduceMax Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMax.Options Optional attributes for SparseReduceMax
SparseReduceMaxSparse Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse.Options Optional attributes for SparseReduceMaxSparse
SparseReduceSum Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSum.Options Optional attributes for SparseReduceSum
SparseReduceSumSparse Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse.Options Optional attributes for SparseReduceSumSparse
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".
SparseTensorDenseMatMul.Options Optional attributes for SparseTensorDenseMatMul
SparseTensorSliceDataset Creates a dataset that splits a SparseTensor into elements row-wise.
SparseToDense Converts a sparse representation into a dense tensor.
SparseToDense.Options Optional attributes for SparseToDense
SparseToSparseSetOperation Applies set operation along last dimension of 2 `SparseTensor` inputs.
SparseToSparseSetOperation.Options Optional attributes for SparseToSparseSetOperation
Split Splits a tensor into `num_split` tensors along one dimension.
SplitV Splits a tensor into `num_split` tensors along one dimension.
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 (x - y)(x - y) element-wise.
Squeeze Removes dimensions of size 1 from the shape of a tensor.
Squeeze.Options Optional attributes for Squeeze
Stack Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Stack.Options Optional attributes for Stack
Stage Stage values similar to a lightweight Enqueue.
Stage.Options Optional attributes for Stage
StageClear Op removes all elements in the underlying container.
StageClear.Options Optional attributes for StageClear
StagePeek Op peeks at the values at the specified index.
StagePeek.Options Optional attributes for StagePeek
StageSize Op returns the number of elements in the underlying container.
StageSize.Options Optional attributes for StageSize
StatelessMultinomial Draws samples from a multinomial distribution.
StatelessRandomNormal Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomUniform Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessRandomUniformInt Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessTruncatedNormal Outputs deterministic pseudorandom values from a truncated normal distribution.
StaticRegexFullMatch Check if the input matches the regex pattern.
StaticRegexReplace Replaces the match of pattern in input with rewrite.
StaticRegexReplace.Options Optional attributes for StaticRegexReplace
StopGradient Stops gradient computation.
StridedSlice Return a strided slice from `input`.
StridedSlice.Options Optional attributes for StridedSlice
StridedSliceAssign Assign `value` to the sliced l-value reference of `ref`.
StridedSliceAssign.Options Optional attributes for StridedSliceAssign
StridedSliceGrad Returns the gradient of `StridedSlice`.
StridedSliceGrad.Options Optional attributes for StridedSliceGrad
StringFormat Formats a string template using a list of tensors.
StringFormat.Options Optional attributes for StringFormat
StringJoin Joins the strings in the given list of string tensors into one tensor;
StringJoin.Options Optional attributes for StringJoin
StringLength String lengths of `input`.
StringLength.Options Optional attributes for StringLength
StringSplit Split elements of `input` based on `delimiter` into a `SparseTensor`.
StringSplit.Options Optional attributes for StringSplit
StringSplitV2 Split elements of `source` based on `sep` into a `SparseTensor`.
StringSplitV2.Options Optional attributes for StringSplitV2
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.
Sub Returns x - y element-wise.
Substr Return substrings from `Tensor` of strings.
Substr.Options Optional attributes for Substr
Subtract Returns x - y element-wise.
Sum Computes the sum of elements across dimensions of a tensor.
Sum.Options Optional attributes for Sum
SummaryWriter
SummaryWriter.Options Optional attributes for SummaryWriter
Svd Computes the singular value decompositions of one or more matrices.
Svd.Options Optional attributes for Svd
Switch Forwards `data` to the output port determined by `pred`.
TakeDataset Creates a dataset that contains `count` elements from the `input_dataset`.
TakeManySparseFromTensorsMap Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
TakeManySparseFromTensorsMap.Options Optional attributes for TakeManySparseFromTensorsMap
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.
TemporaryVariable.Options Optional attributes for TemporaryVariable
TensorArray An array of Tensors of given size.
TensorArray.Options Optional attributes for TensorArray
TensorArrayClose Delete the TensorArray from its resource container.
TensorArrayConcat Concat the elements from the TensorArray into value `value`.
TensorArrayConcat.Options Optional attributes for TensorArrayConcat
TensorArrayGather Gather specific elements from the TensorArray into output `value`.
TensorArrayGather.Options Optional attributes for TensorArrayGather
TensorArrayGrad 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.
TensorArrayPack
TensorArrayPack.Options Optional attributes for TensorArrayPack
TensorArrayRead Read an element from the TensorArray into output `value`.
TensorArrayScatter Scatter the data from the input value into specific TensorArray elements.
TensorArraySize Get the current size of the TensorArray.
TensorArraySplit Split the data from the input value into TensorArray elements.
TensorArrayUnpack
TensorArrayWrite Push an element onto the tensor_array.
TensorDataset Creates a dataset that emits `components` as a tuple of tensors once.
TensorForestCreateTreeVariable Creates a tree resource and returns a handle to it.
TensorForestTreeDeserialize Deserializes a proto into the tree handle
TensorForestTreeIsInitializedOp Checks whether a tree has been initialized.
TensorForestTreePredict Output the logits for the given input data
TensorForestTreeResourceHandleOp Creates a handle to a TensorForestTreeResource
TensorForestTreeResourceHandleOp.Options Optional attributes for TensorForestTreeResourceHandleOp
TensorForestTreeSerialize Serializes the tree handle to a proto
TensorForestTreeSize Get the number of nodes in a tree
TensorListConcat Concats all tensors in the list along the 0th dimension.
TensorListConcatLists
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
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 list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`.
TensorListPushBackBatch
TensorListReserve List of the given size with empty elements.
TensorListScatter Creates a TensorList by indexing into a Tensor.
TensorListSetItem
TensorListSplit Splits a tensor into a list.
TensorListStack Stacks all tensors in the list.
TensorListStack.Options Optional attributes for TensorListStack
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.
TensorSummary Outputs a `Summary` protocol buffer with a tensor.
TensorSummary.Options Optional attributes for TensorSummary
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'.
TextLineReader.Options Optional attributes for TextLineReader
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.
TFRecordReader.Options Optional attributes for TFRecordReader
Tile Constructs a tensor by tiling a given tensor.
TileGrad Returns the gradient of `Tile`.
Timestamp Provides the time since epoch in seconds.
TopK Finds values and indices of the `k` largest elements for the last dimension.
TopK.Options Optional attributes for TopK
Transpose Shuffle dimensions of x according to a permutation.
TruncateDiv Returns x / y element-wise for integer types.
TruncatedNormal Outputs random values from a truncated normal distribution.
TruncatedNormal.Options Optional attributes for TruncatedNormal
TruncateMod Returns element-wise remainder of division.
TryRpc Perform batches of RPC requests.
TryRpc.Options Optional attributes for TryRpc
Unbatch Reverses the operation of Batch for a single output Tensor.
Unbatch.Options Optional attributes for Unbatch
UnbatchGrad Gradient of Unbatch.
UnbatchGrad.Options Optional attributes for UnbatchGrad
UnicodeDecode Decodes each string in `input` into a sequence of Unicode code points.
UnicodeDecode.Options Optional attributes for UnicodeDecode
UnicodeDecodeWithOffsets Decodes each string in `input` into a sequence of Unicode code points.
UnicodeDecodeWithOffsets.Options Optional attributes for UnicodeDecodeWithOffsets
UnicodeEncode Encode a tensor of ints into unicode strings.
UnicodeEncode.Options Optional attributes for UnicodeEncode
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.
UnicodeTranscode.Options Optional attributes for UnicodeTranscode
UniformCandidateSampler Generates labels for candidate sampling with a uniform distribution.
UniformCandidateSampler.Options Optional attributes for UniformCandidateSampler
Unique Finds unique elements in a 1-D tensor.
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.
UnravelIndex Converts a flat index or array of flat indices into a tuple of
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.
Unstack Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
Unstack.Options Optional attributes for Unstack
Unstage Op is similar to a lightweight Dequeue.
Unstage.Options Optional attributes for Unstage
UnwrapDatasetVariant
UpperBound Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp Creates a handle to a Variable resource.
VarHandleOp.Options Optional attributes for VarHandleOp
Variable Holds state in the form of a tensor that persists across steps.
Variable.Options Optional attributes for Variable
VariableShape Returns the shape of the variable pointed to by `resource`.
VarIsInitializedOp Checks whether a resource handle-based variable has been initialized.
Where Returns locations of nonzero / true values in a tensor.
Where3 Selects elements from `x` or `y`, depending on `condition`.
WholeFileReader A Reader that outputs the entire contents of a file as a value.
WholeFileReader.Options Optional attributes for WholeFileReader
WindowDataset A dataset that creates window datasets from the input dataset.
WrapDatasetVariant
WriteAudioSummary
WriteAudioSummary.Options Optional attributes for WriteAudioSummary
WriteFile Writes contents to the file at input filename.
WriteGraphSummary
WriteHistogramSummary
WriteImageSummary
WriteImageSummary.Options Optional attributes for WriteImageSummary
WriteScalarSummary
WriteSummary
Xdivy Returns 0 if x == 0, and x / y otherwise, elementwise.
Xlogy Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
Zeros An operator creating a constant initialized with zeros of the shape given by `dims`.
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`.