tensorflow::ops

Summary

Typedefs
`Mul`	typedef `Multiply`
`Neg`	typedef `Negate`
`ReduceAll`	typedef `All`
`ReduceAny`	typedef `Any`
`ReduceMax`	typedef `Max`
`ReduceMean`	typedef `Mean`
`ReduceMin`	typedef `Min`
`ReduceProd`	typedef `Prod`
`ReduceSum`	typedef `Sum`
`Sub`	typedef `Subtract`

Functions
`AsNodeOut(const Scope & scope, const Input & inp)`	`NodeBuilder::NodeOut`
`AsNodeOutList(const Scope & scope, const InputList & inp)`	`std::vector< NodeBuilder::NodeOut >`
`AudioSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate)`
`AudioSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, ::tensorflow::Input sample_rate, const AudioSummary::Attrs & attrs)`
`BadColor(const TensorProto & x)`	`TF_MUST_USE_RESULT Attrs` Color to use for pixels with non-finite values.
`Const(const Scope & scope, const Input::Initializer & val)`	`Output`
`Const(const Scope & scope, const T & v, const TensorShape shape)`	`Output`
`Const(const Scope & scope, const std::initializer_list< T > & v, const TensorShape shape)`	`Output`
`ConstFromProto(const Scope & scope, const TensorProto & proto)`	`Output`
`ImageSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor)`
`ImageSummary(const ::tensorflow::Scope & scope, ::tensorflow::Input tag, ::tensorflow::Input tensor, const ImageSummary::Attrs & attrs)`
`MaxImages(int64 x)`	`Attrs`
`MaxOutputs(int64 x)`	`Attrs`
`node() const`	`::tensorflow::Node *`
`range(It represents the value of a pixel in the output image).Non-finite values in the input tensor are replaced by this tensor in the output image.The default value is the color red.*Arguments`	`image *If max_images is greater the summary value tags are generated sequentially as tag tag etc *The bad_color argument is the color to use in the generated images for non finite input values It is a uint8 D tensor of length channels *Each element must be in the` Max number of batch elements to generate images for.

Classes
tensorflow::ops::Abort	Raise a exception to abort the process when called.
tensorflow::ops::Abs	Computes the absolute value of a tensor.
tensorflow::ops::AccumulateNV2	Returns the element-wise sum of a list of tensors.
tensorflow::ops::AccumulatorApplyGradient	Applies a gradient to a given accumulator.
tensorflow::ops::AccumulatorNumAccumulated	Returns the number of gradients aggregated in the given accumulators.
tensorflow::ops::AccumulatorSetGlobalStep	Updates the accumulator with a new value for global_step.
tensorflow::ops::AccumulatorTakeGradient	Extracts the average gradient in the given ConditionalAccumulator.
tensorflow::ops::Acos	Computes acos of x element-wise.
tensorflow::ops::Acosh	Computes inverse hyperbolic cosine of x element-wise.
tensorflow::ops::Add	Returns x + y element-wise.
tensorflow::ops::AddManySparseToTensorsMap	Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
tensorflow::ops::AddN	Add all input tensors element wise.
tensorflow::ops::AddSparseToTensorsMap	Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
tensorflow::ops::AddV2	Returns x + y element-wise.
tensorflow::ops::AdjustContrast	Adjust the contrast of one or more images.
tensorflow::ops::AdjustHue	Adjust the hue of one or more images.
tensorflow::ops::AdjustSaturation	Adjust the saturation of one or more images.
tensorflow::ops::All	Computes the "logical and" of elements across dimensions of a tensor.
tensorflow::ops::AllCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
tensorflow::ops::Angle	Returns the argument of a complex number.
tensorflow::ops::Any	Computes the "logical or" of elements across dimensions of a tensor.
tensorflow::ops::ApplyAdadelta	Update '*var' according to the adadelta scheme.
tensorflow::ops::ApplyAdagrad	Update '*var' according to the adagrad scheme.
tensorflow::ops::ApplyAdagradDA	Update '*var' according to the proximal adagrad scheme.
tensorflow::ops::ApplyAdam	Update '*var' according to the Adam algorithm.
tensorflow::ops::ApplyAddSign	Update '*var' according to the AddSign update.
tensorflow::ops::ApplyCenteredRMSProp	Update '*var' according to the centered RMSProp algorithm.
tensorflow::ops::ApplyFtrl	Update '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ApplyFtrlV2	Update '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ApplyGradientDescent	Update 'var' by subtracting 'alpha' 'delta' from it.
tensorflow::ops::ApplyMomentum	Update '*var' according to the momentum scheme.
tensorflow::ops::ApplyPowerSign	Update '*var' according to the AddSign update.
tensorflow::ops::ApplyProximalAdagrad	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
tensorflow::ops::ApplyProximalGradientDescent	Update '*var' as FOBOS algorithm with fixed learning rate.
tensorflow::ops::ApplyRMSProp	Update '*var' according to the RMSProp algorithm.
tensorflow::ops::ApproximateEqual	Returns the truth value of abs(x-y) < tolerance element-wise.
tensorflow::ops::ArgMax	Returns the index with the largest value across dimensions of a tensor.
tensorflow::ops::ArgMin	Returns the index with the smallest value across dimensions of a tensor.
tensorflow::ops::AsString	Converts each entry in the given tensor to strings.
tensorflow::ops::Asin	Computes the trignometric inverse sine of x element-wise.
tensorflow::ops::Asinh	Computes inverse hyperbolic sine of x element-wise.
tensorflow::ops::Assert	Asserts that the given condition is true.
tensorflow::ops::Assign	Update 'ref' by assigning 'value' to it.
tensorflow::ops::AssignAdd	Update 'ref' by adding 'value' to it.
tensorflow::ops::AssignSub	Update 'ref' by subtracting 'value' from it.
tensorflow::ops::Atan	Computes the trignometric inverse tangent of x element-wise.
tensorflow::ops::Atan2	Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
tensorflow::ops::Atanh	Computes inverse hyperbolic tangent of x element-wise.
tensorflow::ops::AvgPool	Performs average pooling on the input.
tensorflow::ops::AvgPool3D	Performs 3D average pooling on the input.
tensorflow::ops::AvgPool3DGrad	Computes gradients of average pooling function.
tensorflow::ops::Barrier	Defines a barrier that persists across different graph executions.
tensorflow::ops::BarrierClose	Closes the given barrier.
tensorflow::ops::BarrierIncompleteSize	Computes the number of incomplete elements in the given barrier.
tensorflow::ops::BarrierInsertMany	For each key, assigns the respective value to the specified component.
tensorflow::ops::BarrierReadySize	Computes the number of complete elements in the given barrier.
tensorflow::ops::BarrierTakeMany	Takes the given number of completed elements from a barrier.
tensorflow::ops::BatchMatMul	Multiplies slices of two tensors in batches.
tensorflow::ops::BatchMatMulV2	Multiplies slices of two tensors in batches.
tensorflow::ops::BatchToSpace	BatchToSpace for 4-D tensors of type T.
tensorflow::ops::BatchToSpaceND	BatchToSpace for N-D tensors of type T.
tensorflow::ops::Betainc	Compute the regularized incomplete beta integral \(I_x(a, b)\).
tensorflow::ops::BiasAdd	Adds `bias` to `value`.
tensorflow::ops::BiasAddGrad	The backward operation for "BiasAdd" on the "bias" tensor.
tensorflow::ops::Bincount	Counts the number of occurrences of each value in an integer array.
tensorflow::ops::Bitcast	Bitcasts a tensor from one type to another without copying data.
tensorflow::ops::BroadcastDynamicShape	Return the shape of s0 op s1 with broadcast.
tensorflow::ops::BroadcastTo	Broadcast an array for a compatible shape.
tensorflow::ops::Bucketize	Bucketizes 'input' based on 'boundaries'.
tensorflow::ops::Cast	Cast x of type SrcT to y of DstT.
tensorflow::ops::Ceil	Returns element-wise smallest integer not less than x.
tensorflow::ops::CheckNumerics	Checks a tensor for NaN and Inf values.
tensorflow::ops::ClipByValue	Clips tensor values to a specified min and max.
tensorflow::ops::CombinedNonMaxSuppression	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::CompareAndBitpack	Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
tensorflow::ops::Complex	Converts two real numbers to a complex number.
tensorflow::ops::ComplexAbs	Computes the complex absolute value of a tensor.
tensorflow::ops::ComputeAccidentalHits	Computes the ids of the positions in sampled_candidates that match true_labels.
tensorflow::ops::Concat	Concatenates tensors along one dimension.
tensorflow::ops::ConditionalAccumulator	A conditional accumulator for aggregating gradients.
tensorflow::ops::Conj	Returns the complex conjugate of a complex number.
tensorflow::ops::ConjugateTranspose	Shuffle dimensions of x according to a permutation and conjugate the result.
tensorflow::ops::ControlTrigger	Does nothing.
tensorflow::ops::Conv2D	Computes a 2-D convolution given 4-D `input` and `filter` tensors.
tensorflow::ops::Conv2DBackpropFilter	Computes the gradients of convolution with respect to the filter.
tensorflow::ops::Conv2DBackpropInput	Computes the gradients of convolution with respect to the input.
tensorflow::ops::Conv3D	Computes a 3-D convolution given 5-D `input` and `filter` tensors.
tensorflow::ops::Conv3DBackpropFilterV2	Computes the gradients of 3-D convolution with respect to the filter.
tensorflow::ops::Conv3DBackpropInputV2	Computes the gradients of 3-D convolution with respect to the input.
tensorflow::ops::Cos	Computes cos of x element-wise.
tensorflow::ops::Cosh	Computes hyperbolic cosine of x element-wise.
tensorflow::ops::CountUpTo	Increments 'ref' until it reaches 'limit'.
tensorflow::ops::CropAndResize	Extracts crops from the input image tensor and resizes them.
tensorflow::ops::CropAndResizeGradBoxes	Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
tensorflow::ops::CropAndResizeGradImage	Computes the gradient of the crop_and_resize op wrt the input image tensor.
tensorflow::ops::Cross	Compute the pairwise cross product.
tensorflow::ops::Cumprod	Compute the cumulative product of the tensor `x` along `axis`.
tensorflow::ops::Cumsum	Compute the cumulative sum of the tensor `x` along `axis`.
tensorflow::ops::DataFormatDimMap	Returns the dimension index in the destination data format given the one in.
tensorflow::ops::DataFormatVecPermute	Returns the permuted vector/tensor in the destination data format given the.
tensorflow::ops::DebugGradientIdentity	Identity op for gradient debugging.
tensorflow::ops::DebugGradientRefIdentity	Identity op for gradient debugging.
tensorflow::ops::DecodeAndCropJpeg	Decode and Crop a JPEG-encoded image to a uint8 tensor.
tensorflow::ops::DecodeBase64	Decode web-safe base64-encoded strings.
tensorflow::ops::DecodeBmp	Decode the first frame of a BMP-encoded image to a uint8 tensor.
tensorflow::ops::DecodeCSV	Convert CSV records to tensors.
tensorflow::ops::DecodeCompressed	Decompress strings.
tensorflow::ops::DecodeGif	Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
tensorflow::ops::DecodeJSONExample	Convert JSON-encoded Example records to binary protocol buffer strings.
tensorflow::ops::DecodeJpeg	Decode a JPEG-encoded image to a uint8 tensor.
tensorflow::ops::DecodePaddedRaw	Reinterpret the bytes of a string as a vector of numbers.
tensorflow::ops::DecodePng	Decode a PNG-encoded image to a uint8 or uint16 tensor.
tensorflow::ops::DecodeRaw	Reinterpret the bytes of a string as a vector of numbers.
tensorflow::ops::DeepCopy	Makes a copy of `x`.
tensorflow::ops::DeleteSessionTensor	Delete the tensor specified by its handle in the session.
tensorflow::ops::DenseBincount	Counts the number of occurrences of each value in an integer array.
tensorflow::ops::DepthToSpace	DepthToSpace for tensors of type T.
tensorflow::ops::DepthwiseConv2dNative	Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
tensorflow::ops::DepthwiseConv2dNativeBackpropFilter	Computes the gradients of depthwise convolution with respect to the filter.
tensorflow::ops::DepthwiseConv2dNativeBackpropInput	Computes the gradients of depthwise convolution with respect to the input.
tensorflow::ops::Dequantize	Dequantize the 'input' tensor into a float or bfloat16 Tensor.
tensorflow::ops::DeserializeManySparse	Deserialize and concatenate `SparseTensors` from a serialized minibatch.
tensorflow::ops::DeserializeSparse	Deserialize `SparseTensor` objects.
tensorflow::ops::DestroyTemporaryVariable	Destroys the temporary variable and returns its final value.
tensorflow::ops::Diag	Returns a diagonal tensor with a given diagonal values.
tensorflow::ops::DiagPart	Returns the diagonal part of the tensor.
tensorflow::ops::Digamma	Computes Psi, the derivative of Lgamma (the log of the absolute value of.
tensorflow::ops::Dilation2D	Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
tensorflow::ops::Dilation2DBackpropFilter	Computes the gradient of morphological 2-D dilation with respect to the filter.
tensorflow::ops::Dilation2DBackpropInput	Computes the gradient of morphological 2-D dilation with respect to the input.
tensorflow::ops::Div	Returns x / y element-wise.
tensorflow::ops::DivNoNan	Returns 0 if the denominator is zero.
tensorflow::ops::DrawBoundingBoxes	Draw bounding boxes on a batch of images.
tensorflow::ops::DrawBoundingBoxesV2	Draw bounding boxes on a batch of images.
tensorflow::ops::DynamicPartition	Partitions `data` into `num_partitions` tensors using indices from `partitions`.
tensorflow::ops::DynamicStitch	Interleave the values from the `data` tensors into a single tensor.
tensorflow::ops::EditDistance	Computes the (possibly normalized) Levenshtein Edit Distance.
tensorflow::ops::Elu	Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
tensorflow::ops::Empty	Creates a tensor with the given shape.
tensorflow::ops::EncodeBase64	Encode strings into web-safe base64 format.
tensorflow::ops::EncodeJpeg	JPEG-encode an image.
tensorflow::ops::EncodeJpegVariableQuality	JPEG encode input image with provided compression quality.
tensorflow::ops::EncodePng	PNG-encode an image.
tensorflow::ops::EnsureShape	Ensures that the tensor's shape matches the expected shape.
tensorflow::ops::Equal	Returns the truth value of (x == y) element-wise.
tensorflow::ops::Erf	Computes the Gauss error function of `x` element-wise.
tensorflow::ops::Erfc	Computes the complementary error function of `x` element-wise.
tensorflow::ops::Erfinv	TODO: add doc.
tensorflow::ops::EuclideanNorm	Computes the euclidean norm of elements across dimensions of a tensor.
tensorflow::ops::Exp	Computes exponential of x element-wise.
tensorflow::ops::ExpandDims	Inserts a dimension of 1 into a tensor's shape.
tensorflow::ops::Expm1	Computes `exp(x) - 1` element-wise.
tensorflow::ops::ExtractGlimpse	Extracts a glimpse from the input tensor.
tensorflow::ops::ExtractImagePatches	Extract `patches` from `images` and put them in the "depth" output dimension.
tensorflow::ops::ExtractJpegShape	Extract the shape information of a JPEG-encoded image.
tensorflow::ops::ExtractVolumePatches	Extract `patches` from `input` and put them in the "depth" output dimension.
tensorflow::ops::FIFOQueue	A queue that produces elements in first-in first-out order.
tensorflow::ops::Fact	Output a fact about factorials.
tensorflow::ops::FakeQuantWithMinMaxArgs	Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
tensorflow::ops::FakeQuantWithMinMaxArgsGradient	Compute gradients for a FakeQuantWithMinMaxArgs operation.
tensorflow::ops::FakeQuantWithMinMaxVars	Fake-quantize the 'inputs' tensor of type float via global float scalars.
tensorflow::ops::FakeQuantWithMinMaxVarsGradient	Compute gradients for a FakeQuantWithMinMaxVars operation.
tensorflow::ops::FakeQuantWithMinMaxVarsPerChannel	Fake-quantize the 'inputs' tensor of type float via per-channel floats.
tensorflow::ops::FakeQuantWithMinMaxVarsPerChannelGradient	Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
tensorflow::ops::Fill	Creates a tensor filled with a scalar value.
tensorflow::ops::Fingerprint	Generates fingerprint values.
tensorflow::ops::FixedLengthRecordReader	A Reader that outputs fixed-length records from a file.
tensorflow::ops::FixedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
tensorflow::ops::Floor	Returns element-wise largest integer not greater than x.
tensorflow::ops::FloorDiv	Returns x // y element-wise.
tensorflow::ops::FloorMod	Returns element-wise remainder of division.
tensorflow::ops::FractionalAvgPool	Performs fractional average pooling on the input.
tensorflow::ops::FractionalMaxPool	Performs fractional max pooling on the input.
tensorflow::ops::FusedBatchNorm	Batch normalization.
tensorflow::ops::FusedBatchNormGrad	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormGradV2	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormGradV3	Gradient for batch normalization.
tensorflow::ops::FusedBatchNormV2	Batch normalization.
tensorflow::ops::FusedBatchNormV3	Batch normalization.
tensorflow::ops::FusedPadConv2D	Performs a padding as a preprocess during a convolution.
tensorflow::ops::FusedResizeAndPadConv2D	Performs a resize and padding as a preprocess during a convolution.
tensorflow::ops::Gather	Gather slices from `params` according to `indices`.
tensorflow::ops::GatherNd	Gather slices from `params` into a Tensor with shape specified by `indices`.
tensorflow::ops::GatherV2	Gather slices from `params` axis `axis` according to `indices`.
tensorflow::ops::GetSessionHandle	Store the input tensor in the state of the current session.
tensorflow::ops::GetSessionHandleV2	Store the input tensor in the state of the current session.
tensorflow::ops::GetSessionTensor	Get the value of the tensor specified by its handle.
tensorflow::ops::Greater	Returns the truth value of (x > y) element-wise.
tensorflow::ops::GreaterEqual	Returns the truth value of (x >= y) element-wise.
tensorflow::ops::GuaranteeConst	Gives a guarantee to the TF runtime that the input tensor is a constant.
tensorflow::ops::HSVToRGB	Convert one or more images from HSV to RGB.
tensorflow::ops::HistogramFixedWidth	Return histogram of values.
tensorflow::ops::HistogramSummary	Outputs a `Summary` protocol buffer with a histogram.
tensorflow::ops::Identity	Return a tensor with the same shape and contents as the input tensor or value.
tensorflow::ops::IdentityN	Returns a list of tensors with the same shapes and contents as the input.
tensorflow::ops::IdentityReader	A Reader that outputs the queued work as both the key and value.
tensorflow::ops::Igamma	Compute the lower regularized incomplete Gamma function `P(a, x)`.
tensorflow::ops::Igammac	Compute the upper regularized incomplete Gamma function `Q(a, x)`.
tensorflow::ops::Imag	Returns the imaginary part of a complex number.
tensorflow::ops::ImmutableConst	Returns immutable tensor from memory region.
tensorflow::ops::InTopK	Says whether the targets are in the top `K` predictions.
tensorflow::ops::InTopKV2	Says whether the targets are in the top `K` predictions.
tensorflow::ops::InplaceAdd	Adds v into specified rows of x.
tensorflow::ops::InplaceSub	Subtracts `v` into specified rows of `x`.
tensorflow::ops::InplaceUpdate	Updates specified rows 'i' with values 'v'.
tensorflow::ops::Inv	Computes the reciprocal of x element-wise.
tensorflow::ops::InvertPermutation	Computes the inverse permutation of a tensor.
tensorflow::ops::IsFinite	Returns which elements of x are finite.
tensorflow::ops::IsInf	Returns which elements of x are Inf.
tensorflow::ops::IsNan	Returns which elements of x are NaN.
tensorflow::ops::IsVariableInitialized	Checks whether a tensor has been initialized.
tensorflow::ops::L2Loss	L2 Loss.
tensorflow::ops::LMDBReader	A Reader that outputs the records from a LMDB file.
tensorflow::ops::LRN	Local Response Normalization.
tensorflow::ops::LearnedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
tensorflow::ops::Less	Returns the truth value of (x < y) element-wise.
tensorflow::ops::LessEqual	Returns the truth value of (x <= y) element-wise.
tensorflow::ops::Lgamma	Computes the log of the absolute value of `Gamma(x)` element-wise.
tensorflow::ops::Log	Computes natural logarithm of x element-wise.
tensorflow::ops::Log1p	Computes natural logarithm of (1 + x) element-wise.
tensorflow::ops::LogSoftmax	Computes log softmax activations.
tensorflow::ops::LogUniformCandidateSampler	Generates labels for candidate sampling with a log-uniform distribution.
tensorflow::ops::LogicalAnd	Returns the truth value of x AND y element-wise.
tensorflow::ops::LogicalNot	Returns the truth value of `NOT x` element-wise.
tensorflow::ops::LogicalOr	Returns the truth value of x OR y element-wise.
tensorflow::ops::LoopCond	Forwards the input to the output.
tensorflow::ops::MapClear	Op removes all elements in the underlying container.
tensorflow::ops::MapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
tensorflow::ops::MapPeek	Op peeks at the values at the specified key.
tensorflow::ops::MapSize	Op returns the number of elements in the underlying container.
tensorflow::ops::MapStage	Stage (key, values) in the underlying container which behaves like a hashtable.
tensorflow::ops::MapUnstage	Op removes and returns the values associated with the key.
tensorflow::ops::MapUnstageNoKey	Op removes and returns a random (key, value)
tensorflow::ops::MatMul	Multiply the matrix "a" by the matrix "b".
tensorflow::ops::MatchingFiles	Returns the set of files matching one or more glob patterns.
tensorflow::ops::MatrixBandPart	Copy a tensor setting everything outside a central band in each innermost matrix to zero.
tensorflow::ops::MatrixDiag	Returns a batched diagonal tensor with a given batched diagonal values.
tensorflow::ops::MatrixDiagPart	Returns the batched diagonal part of a batched tensor.
tensorflow::ops::MatrixDiagPartV2	Returns the batched diagonal part of a batched tensor.
tensorflow::ops::MatrixDiagPartV3	Returns the batched diagonal part of a batched tensor.
tensorflow::ops::MatrixDiagV2	Returns a batched diagonal tensor with given batched diagonal values.
tensorflow::ops::MatrixDiagV3	Returns a batched diagonal tensor with given batched diagonal values.
tensorflow::ops::MatrixSetDiag	Returns a batched matrix tensor with new batched diagonal values.
tensorflow::ops::MatrixSetDiagV2	Returns a batched matrix tensor with new batched diagonal values.
tensorflow::ops::MatrixSetDiagV3	Returns a batched matrix tensor with new batched diagonal values.
tensorflow::ops::Max	Computes the maximum of elements across dimensions of a tensor.
tensorflow::ops::MaxPool	Performs max pooling on the input.
tensorflow::ops::MaxPool3D	Performs 3D max pooling on the input.
tensorflow::ops::MaxPool3DGrad	Computes gradients of 3D max pooling function.
tensorflow::ops::MaxPool3DGradGrad	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGrad	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGradV2	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradGradWithArgmax	Computes second-order gradients of the maxpooling function.
tensorflow::ops::MaxPoolGradV2	Computes gradients of the maxpooling function.
tensorflow::ops::MaxPoolV2	Performs max pooling on the input.
tensorflow::ops::MaxPoolWithArgmax	Performs max pooling on the input and outputs both max values and indices.
tensorflow::ops::Maximum	Returns the max of x and y (i.e.
tensorflow::ops::Mean	Computes the mean of elements across dimensions of a tensor.
tensorflow::ops::Merge	Forwards the value of an available tensor from `inputs` to `output`.
tensorflow::ops::MergeSummary	Merges summaries.
tensorflow::ops::MergeV2Checkpoints	V2 format specific: merges the metadata files of sharded checkpoints.
tensorflow::ops::Min	Computes the minimum of elements across dimensions of a tensor.
tensorflow::ops::Minimum	Returns the min of x and y (i.e.
tensorflow::ops::MirrorPad	Pads a tensor with mirrored values.
tensorflow::ops::Mod	Returns element-wise remainder of division.
tensorflow::ops::MulNoNan	Returns x * y element-wise.
tensorflow::ops::Multinomial	Draws samples from a multinomial distribution.
tensorflow::ops::Multiply	Returns x * y element-wise.
tensorflow::ops::Ndtri	TODO: add doc.
tensorflow::ops::Negate	Computes numerical negative value element-wise.
tensorflow::ops::NextAfter	Returns the next representable value of `x1` in the direction of `x2`, element-wise.
tensorflow::ops::NextIteration	Makes its input available to the next iteration.
tensorflow::ops::NoOp	Does nothing.
tensorflow::ops::NonMaxSuppression	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NonMaxSuppressionV2	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NonMaxSuppressionV3	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NonMaxSuppressionV4	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NonMaxSuppressionV5	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NonMaxSuppressionWithOverlaps	Greedily selects a subset of bounding boxes in descending order of score,.
tensorflow::ops::NotEqual	Returns the truth value of (x != y) element-wise.
tensorflow::ops::NthElement	Finds values of the `n`-th order statistic for the last dimension.
tensorflow::ops::OneHot	Returns a one-hot tensor.
tensorflow::ops::OnesLike	Returns a tensor of ones with the same shape and type as x.
tensorflow::ops::OrderedMapClear	Op removes all elements in the underlying container.
tensorflow::ops::OrderedMapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
tensorflow::ops::OrderedMapPeek	Op peeks at the values at the specified key.
tensorflow::ops::OrderedMapSize	Op returns the number of elements in the underlying container.
tensorflow::ops::OrderedMapStage	Stage (key, values) in the underlying container which behaves like a ordered.
tensorflow::ops::OrderedMapUnstage	Op removes and returns the values associated with the key.
tensorflow::ops::OrderedMapUnstageNoKey	Op removes and returns the (key, value) element with the smallest.
tensorflow::ops::Pad	Pads a tensor with zeros.
tensorflow::ops::PadV2	Pads a tensor.
tensorflow::ops::PaddingFIFOQueue	A queue that produces elements in first-in first-out order.
tensorflow::ops::ParallelConcat	Concatenates a list of `N` tensors along the first dimension.
tensorflow::ops::ParallelDynamicStitch	Interleave the values from the `data` tensors into a single tensor.
tensorflow::ops::ParameterizedTruncatedNormal	Outputs random values from a normal distribution.
tensorflow::ops::ParseExample	Transforms a vector of brain.Example protos (as strings) into typed tensors.
tensorflow::ops::ParseExampleV2	Transforms a vector of tf.Example protos (as strings) into typed tensors.
tensorflow::ops::ParseSequenceExample	Transforms a vector of brain.SequenceExample protos (as strings) into typed tensors.
tensorflow::ops::ParseSequenceExampleV2	Transforms a vector of tf.io.SequenceExample protos (as strings) into typed tensors.
tensorflow::ops::ParseSingleExample	Transforms a tf.Example proto (as a string) into typed tensors.
tensorflow::ops::ParseSingleSequenceExample	Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
tensorflow::ops::ParseTensor	Transforms a serialized tensorflow.TensorProto proto into a Tensor.
tensorflow::ops::Placeholder	A placeholder op for a value that will be fed into the computation.
tensorflow::ops::PlaceholderWithDefault	A placeholder op that passes through `input` when its output is not fed.
tensorflow::ops::Polygamma	Compute the polygamma function \(^{(n)}(x)\).
tensorflow::ops::Pow	Computes the power of one value to another.
tensorflow::ops::PreventGradient	An identity op that triggers an error if a gradient is requested.
tensorflow::ops::Print	Prints a list of tensors.
tensorflow::ops::PrintV2	Prints a string scalar.
tensorflow::ops::PriorityQueue	A queue that produces elements sorted by the first component value.
tensorflow::ops::Prod	Computes the product of elements across dimensions of a tensor.
tensorflow::ops::QuantizeAndDequantizeV2	Quantizes then dequantizes a tensor.
tensorflow::ops::QuantizeAndDequantizeV3	Quantizes then dequantizes a tensor.
tensorflow::ops::QuantizeDownAndShrinkRange	Convert the quantized 'input' tensor into a lower-precision 'output', using the.
tensorflow::ops::QuantizeV2	Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
tensorflow::ops::QuantizedAdd	Returns x + y element-wise, working on quantized buffers.
tensorflow::ops::QuantizedAvgPool	Produces the average pool of the input tensor for quantized types.
tensorflow::ops::QuantizedBatchNormWithGlobalNormalization	Quantized Batch normalization.
tensorflow::ops::QuantizedBiasAdd	Adds Tensor 'bias' to Tensor 'input' for Quantized types.
tensorflow::ops::QuantizedConcat	Concatenates quantized tensors along one dimension.
tensorflow::ops::QuantizedConv2D	Computes a 2D convolution given quantized 4D input and filter tensors.
tensorflow::ops::QuantizedInstanceNorm	Quantized Instance normalization.
tensorflow::ops::QuantizedMatMul	Perform a quantized matrix multiplication of `a` by the matrix `b`.
tensorflow::ops::QuantizedMaxPool	Produces the max pool of the input tensor for quantized types.
tensorflow::ops::QuantizedMul	Returns x * y element-wise, working on quantized buffers.
tensorflow::ops::QuantizedRelu	Computes Quantized Rectified Linear: `max(features, 0)`
tensorflow::ops::QuantizedRelu6	Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
tensorflow::ops::QuantizedReluX	Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
tensorflow::ops::QuantizedResizeBilinear	Resize quantized `images` to `size` using quantized bilinear interpolation.
tensorflow::ops::QueueClose	Closes the given queue.
tensorflow::ops::QueueDequeue	Dequeues a tuple of one or more tensors from the given queue.
tensorflow::ops::QueueDequeueMany	Dequeues `n` tuples of one or more tensors from the given queue.
tensorflow::ops::QueueDequeueUpTo	Dequeues `n` tuples of one or more tensors from the given queue.
tensorflow::ops::QueueEnqueue	Enqueues a tuple of one or more tensors in the given queue.
tensorflow::ops::QueueEnqueueMany	Enqueues zero or more tuples of one or more tensors in the given queue.
tensorflow::ops::QueueIsClosed	Returns true if queue is closed.
tensorflow::ops::QueueIsClosedV2	Returns true if queue is closed.
tensorflow::ops::QueueSize	Computes the number of elements in the given queue.
tensorflow::ops::RGBToHSV	Converts one or more images from RGB to HSV.
tensorflow::ops::RaggedBincount	Counts the number of occurrences of each value in an integer array.
tensorflow::ops::RandomGamma	Outputs random values from the Gamma distribution(s) described by alpha.
tensorflow::ops::RandomNormal	Outputs random values from a normal distribution.
tensorflow::ops::RandomPoissonV2	Outputs random values from the Poisson distribution(s) described by rate.
tensorflow::ops::RandomShuffle	Randomly shuffles a tensor along its first dimension.
tensorflow::ops::RandomShuffleQueue	A queue that randomizes the order of elements.
tensorflow::ops::RandomUniform	Outputs random values from a uniform distribution.
tensorflow::ops::RandomUniformInt	Outputs random integers from a uniform distribution.
tensorflow::ops::Range	Creates a sequence of numbers.
tensorflow::ops::ReadFile	Reads and outputs the entire contents of the input filename.
tensorflow::ops::ReaderNumRecordsProduced	Returns the number of records this Reader has produced.
tensorflow::ops::ReaderNumWorkUnitsCompleted	Returns the number of work units this Reader has finished processing.
tensorflow::ops::ReaderRead	Returns the next record (key, value pair) produced by a Reader.
tensorflow::ops::ReaderReadUpTo	Returns up to `num_records` (key, value) pairs produced by a Reader.
tensorflow::ops::ReaderReset	Restore a Reader to its initial clean state.
tensorflow::ops::ReaderRestoreState	Restore a reader to a previously saved state.
tensorflow::ops::ReaderSerializeState	Produce a string tensor that encodes the state of a Reader.
tensorflow::ops::Real	Returns the real part of a complex number.
tensorflow::ops::RealDiv	Returns x / y element-wise for real types.
tensorflow::ops::Reciprocal	Computes the reciprocal of x element-wise.
tensorflow::ops::RecordInput	Emits randomized records.
tensorflow::ops::ReduceJoin	Joins a string Tensor across the given dimensions.
tensorflow::ops::RefNextIteration	Makes its input available to the next iteration.
tensorflow::ops::RefSelect	Forwards the `index`th element of `inputs` to `output`.
tensorflow::ops::RefSwitch	Forwards the ref tensor `data` to the output port determined by `pred`.
tensorflow::ops::RegexFullMatch	Check if the input matches the regex pattern.
tensorflow::ops::RegexReplace	Replaces matches of the `pattern` regular expression in `input` with the replacement string provided in `rewrite`.
tensorflow::ops::Relu	Computes rectified linear: `max(features, 0)`.
tensorflow::ops::Relu6	Computes rectified linear 6: `min(max(features, 0), 6)`.
tensorflow::ops::RequantizationRange	Computes a range that covers the actual values present in a quantized tensor.
tensorflow::ops::Requantize	Converts the quantized `input` tensor into a lower-precision `output`.
tensorflow::ops::ResizeArea	Resize `images` to `size` using area interpolation.
tensorflow::ops::ResizeBicubic	Resize `images` to `size` using bicubic interpolation.
tensorflow::ops::ResizeBilinear	Resize `images` to `size` using bilinear interpolation.
tensorflow::ops::ResizeNearestNeighbor	Resize `images` to `size` using nearest neighbor interpolation.
tensorflow::ops::ResourceApplyAdadelta	Update '*var' according to the adadelta scheme.
tensorflow::ops::ResourceApplyAdagrad	Update '*var' according to the adagrad scheme.
tensorflow::ops::ResourceApplyAdagradDA	Update '*var' according to the proximal adagrad scheme.
tensorflow::ops::ResourceApplyAdam	Update '*var' according to the Adam algorithm.
tensorflow::ops::ResourceApplyAdamWithAmsgrad	Update '*var' according to the Adam algorithm.
tensorflow::ops::ResourceApplyAddSign	Update '*var' according to the AddSign update.
tensorflow::ops::ResourceApplyCenteredRMSProp	Update '*var' according to the centered RMSProp algorithm.
tensorflow::ops::ResourceApplyFtrl	Update '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ResourceApplyFtrlV2	Update '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ResourceApplyGradientDescent	Update 'var' by subtracting 'alpha' 'delta' from it.
tensorflow::ops::ResourceApplyKerasMomentum	Update '*var' according to the momentum scheme.
tensorflow::ops::ResourceApplyMomentum	Update '*var' according to the momentum scheme.
tensorflow::ops::ResourceApplyPowerSign	Update '*var' according to the AddSign update.
tensorflow::ops::ResourceApplyProximalAdagrad	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
tensorflow::ops::ResourceApplyProximalGradientDescent	Update '*var' as FOBOS algorithm with fixed learning rate.
tensorflow::ops::ResourceApplyRMSProp	Update '*var' according to the RMSProp algorithm.
tensorflow::ops::ResourceCountUpTo	Increments variable pointed to by 'resource' until it reaches 'limit'.
tensorflow::ops::ResourceScatterNdAdd	Applies sparse addition to individual values or slices in a Variable.
tensorflow::ops::ResourceScatterNdMax	TODO: add doc.
tensorflow::ops::ResourceScatterNdMin	TODO: add doc.
tensorflow::ops::ResourceScatterNdSub	Applies sparse subtraction to individual values or slices in a Variable.
tensorflow::ops::ResourceScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given.
tensorflow::ops::ResourceSparseApplyAdadelta	var: Should be from a Variable().
tensorflow::ops::ResourceSparseApplyAdagrad	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
tensorflow::ops::ResourceSparseApplyAdagradDA	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
tensorflow::ops::ResourceSparseApplyCenteredRMSProp	Update '*var' according to the centered RMSProp algorithm.
tensorflow::ops::ResourceSparseApplyFtrl	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ResourceSparseApplyFtrlV2	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::ResourceSparseApplyKerasMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
tensorflow::ops::ResourceSparseApplyMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
tensorflow::ops::ResourceSparseApplyProximalAdagrad	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
tensorflow::ops::ResourceSparseApplyProximalGradientDescent	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
tensorflow::ops::ResourceSparseApplyRMSProp	Update '*var' according to the RMSProp algorithm.
tensorflow::ops::Restore	Restores a tensor from checkpoint files.
tensorflow::ops::RestoreSlice	Restores a tensor from checkpoint files.
tensorflow::ops::RestoreV2	Restores tensors from a V2 checkpoint.
tensorflow::ops::Rint	Returns element-wise integer closest to x.
tensorflow::ops::Round	Rounds the values of a tensor to the nearest integer, element-wise.
tensorflow::ops::Rsqrt	Computes reciprocal of square root of x element-wise.
tensorflow::ops::SampleDistortedBoundingBox	Generate a single randomly distorted bounding box for an image.
tensorflow::ops::SampleDistortedBoundingBoxV2	Generate a single randomly distorted bounding box for an image.
tensorflow::ops::Save	Saves the input tensors to disk.
tensorflow::ops::SaveSlices	Saves input tensors slices to disk.
tensorflow::ops::SaveV2	Saves tensors in V2 checkpoint format.
tensorflow::ops::ScalarSummary	Outputs a `Summary` protocol buffer with scalar values.
tensorflow::ops::ScaleAndTranslate	TODO: add doc.
tensorflow::ops::ScatterAdd	Adds sparse updates to a variable reference.
tensorflow::ops::ScatterDiv	Divides a variable reference by sparse updates.
tensorflow::ops::ScatterMax	Reduces sparse updates into a variable reference using the `max` operation.
tensorflow::ops::ScatterMin	Reduces sparse updates into a variable reference using the `min` operation.
tensorflow::ops::ScatterMul	Multiplies sparse updates into a variable reference.
tensorflow::ops::ScatterNdAdd	Applies sparse addition to individual values or slices in a Variable.
tensorflow::ops::ScatterNdSub	Applies sparse subtraction to individual values or slices in a Variable.
tensorflow::ops::ScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given.
tensorflow::ops::ScatterSub	Subtracts sparse updates to a variable reference.
tensorflow::ops::ScatterUpdate	Applies sparse updates to a variable reference.
tensorflow::ops::SegmentMax	Computes the maximum along segments of a tensor.
tensorflow::ops::SegmentMean	Computes the mean along segments of a tensor.
tensorflow::ops::SegmentMin	Computes the minimum along segments of a tensor.
tensorflow::ops::SegmentProd	Computes the product along segments of a tensor.
tensorflow::ops::SegmentSum	Computes the sum along segments of a tensor.
tensorflow::ops::SelectV2	TODO: add doc.
tensorflow::ops::Selu	Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)`
tensorflow::ops::SerializeManySparse	Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]Tensor` object.
tensorflow::ops::SerializeSparse	Serialize a `SparseTensor` into a `[3]Tensor` object.
tensorflow::ops::SerializeTensor	Transforms a Tensor into a serialized TensorProto proto.
tensorflow::ops::SetDiff1D	Computes the difference between two lists of numbers or strings.
tensorflow::ops::ShardedFilename	Generate a sharded filename.
tensorflow::ops::ShardedFilespec	Generate a glob pattern matching all sharded file names.
tensorflow::ops::Sigmoid	Computes sigmoid of `x` element-wise.
tensorflow::ops::Sign	Returns an element-wise indication of the sign of a number.
tensorflow::ops::Sin	Computes sine of x element-wise.
tensorflow::ops::Sinh	Computes hyperbolic sine of x element-wise.
tensorflow::ops::Softmax	Computes softmax activations.
tensorflow::ops::SoftmaxCrossEntropyWithLogits	Computes softmax cross entropy cost and gradients to backpropagate.
tensorflow::ops::Softplus	Computes softplus: `log(exp(features) + 1)`.
tensorflow::ops::Softsign	Computes softsign: `features / (abs(features) + 1)`.
tensorflow::ops::SparseAccumulatorApplyGradient	Applies a sparse gradient to a given accumulator.
tensorflow::ops::SparseAccumulatorTakeGradient	Extracts the average sparse gradient in a SparseConditionalAccumulator.
tensorflow::ops::SparseAdd	Adds two `SparseTensor` objects to produce another `SparseTensor`.
tensorflow::ops::SparseAddGrad	The gradient operator for the SparseAdd op.
tensorflow::ops::SparseApplyAdadelta	var: Should be from a Variable().
tensorflow::ops::SparseApplyAdagrad	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
tensorflow::ops::SparseApplyAdagradDA	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
tensorflow::ops::SparseApplyCenteredRMSProp	Update '*var' according to the centered RMSProp algorithm.
tensorflow::ops::SparseApplyFtrl	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::SparseApplyFtrlV2	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
tensorflow::ops::SparseApplyMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
tensorflow::ops::SparseApplyProximalAdagrad	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
tensorflow::ops::SparseApplyProximalGradientDescent	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
tensorflow::ops::SparseApplyRMSProp	Update '*var' according to the RMSProp algorithm.
tensorflow::ops::SparseBincount	Counts the number of occurrences of each value in an integer array.
tensorflow::ops::SparseConcat	Concatenates a list of `SparseTensor` along the specified dimension.
tensorflow::ops::SparseConditionalAccumulator	A conditional accumulator for aggregating sparse gradients.
tensorflow::ops::SparseCross	Generates sparse cross from a list of sparse and dense tensors.
tensorflow::ops::SparseCrossHashed	Generates sparse cross from a list of sparse and dense tensors.
tensorflow::ops::SparseCrossV2	Generates sparse cross from a list of sparse and dense tensors.
tensorflow::ops::SparseDenseCwiseAdd	Adds up a SparseTensor and a dense Tensor, using these special rules:
tensorflow::ops::SparseDenseCwiseDiv	Component-wise divides a SparseTensor by a dense Tensor.
tensorflow::ops::SparseDenseCwiseMul	Component-wise multiplies a SparseTensor by a dense Tensor.
tensorflow::ops::SparseFillEmptyRows	Fills empty rows in the input 2-D `SparseTensor` with a default value.
tensorflow::ops::SparseFillEmptyRowsGrad	The gradient of SparseFillEmptyRows.
tensorflow::ops::SparseMatMul	Multiply matrix "a" by matrix "b".
tensorflow::ops::SparseReduceMax	Computes the max of elements across dimensions of a SparseTensor.
tensorflow::ops::SparseReduceMaxSparse	Computes the max of elements across dimensions of a SparseTensor.
tensorflow::ops::SparseReduceSum	Computes the sum of elements across dimensions of a SparseTensor.
tensorflow::ops::SparseReduceSumSparse	Computes the sum of elements across dimensions of a SparseTensor.
tensorflow::ops::SparseReorder	Reorders a SparseTensor into the canonical, row-major ordering.
tensorflow::ops::SparseReshape	Reshapes a SparseTensor to represent values in a new dense shape.
tensorflow::ops::SparseSegmentMean	Computes the mean along sparse segments of a tensor.
tensorflow::ops::SparseSegmentMeanGrad	Computes gradients for SparseSegmentMean.
tensorflow::ops::SparseSegmentMeanWithNumSegments	Computes the mean along sparse segments of a tensor.
tensorflow::ops::SparseSegmentSqrtN	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
tensorflow::ops::SparseSegmentSqrtNGrad	Computes gradients for SparseSegmentSqrtN.
tensorflow::ops::SparseSegmentSqrtNWithNumSegments	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
tensorflow::ops::SparseSegmentSum	Computes the sum along sparse segments of a tensor.
tensorflow::ops::SparseSegmentSumWithNumSegments	Computes the sum along sparse segments of a tensor.
tensorflow::ops::SparseSlice	Slice a `SparseTensor` based on the `start` and `size`.
tensorflow::ops::SparseSliceGrad	The gradient operator for the SparseSlice op.
tensorflow::ops::SparseSoftmax	Applies softmax to a batched N-D `SparseTensor`.
tensorflow::ops::SparseSoftmaxCrossEntropyWithLogits	Computes softmax cross entropy cost and gradients to backpropagate.
tensorflow::ops::SparseSparseMaximum	Returns the element-wise max of two SparseTensors.
tensorflow::ops::SparseSparseMinimum	Returns the element-wise min of two SparseTensors.
tensorflow::ops::SparseSplit	Split a `SparseTensor` into `num_split` tensors along one dimension.
tensorflow::ops::SparseTensorDenseAdd	Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
tensorflow::ops::SparseTensorDenseMatMul	Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
tensorflow::ops::Sqrt	Computes square root of x element-wise.
tensorflow::ops::Square	Computes square of x element-wise.
tensorflow::ops::SquaredDifference	Returns (x - y)(x - y) element-wise.
tensorflow::ops::Stack	Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
tensorflow::ops::Stage	Stage values similar to a lightweight Enqueue.
tensorflow::ops::StageClear	Op removes all elements in the underlying container.
tensorflow::ops::StagePeek	Op peeks at the values at the specified index.
tensorflow::ops::StageSize	Op returns the number of elements in the underlying container.
tensorflow::ops::StringFormat	Formats a string template using a list of tensors.
tensorflow::ops::StringJoin	Joins the strings in the given list of string tensors into one tensor;.
tensorflow::ops::StringLength	String lengths of `input`.
tensorflow::ops::StringLower	Converts all uppercase characters into their respective lowercase replacements.
tensorflow::ops::StringNGrams	Creates ngrams from ragged string data.
tensorflow::ops::StringSplit	Split elements of `input` based on `delimiter` into a `SparseTensor`.
tensorflow::ops::StringSplitV2	Split elements of `source` based on `sep` into a `SparseTensor`.
tensorflow::ops::StringStrip	Strip leading and trailing whitespaces from the Tensor.
tensorflow::ops::StringToHashBucket	Converts each string in the input Tensor to its hash mod by a number of buckets.
tensorflow::ops::StringToHashBucketFast	Converts each string in the input Tensor to its hash mod by a number of buckets.
tensorflow::ops::StringToHashBucketStrong	Converts each string in the input Tensor to its hash mod by a number of buckets.
tensorflow::ops::StringToNumber	Converts each string in the input Tensor to the specified numeric type.
tensorflow::ops::StringUpper	Converts all lowercase characters into their respective uppercase replacements.
tensorflow::ops::Substr	Return substrings from `Tensor` of strings.
tensorflow::ops::Subtract	Returns x - y element-wise.
tensorflow::ops::Sum	Computes the sum of elements across dimensions of a tensor.
tensorflow::ops::Switch	Forwards `data` to the output port determined by `pred`.
tensorflow::ops::TFRecordReader	A Reader that outputs the records from a TensorFlow Records file.
tensorflow::ops::TakeManySparseFromTensorsMap	Converts a sparse representation into a dense tensor.
tensorflow::ops::Tan	Computes tan of x element-wise.
tensorflow::ops::Tanh	Computes hyperbolic tangent of `x` element-wise.
tensorflow::ops::TemporaryVariable	Returns a tensor that may be mutated, but only persists within a single step.
tensorflow::ops::TensorArray	An array of Tensors of given size.
tensorflow::ops::TensorArrayClose	Delete the TensorArray from its resource container.
tensorflow::ops::TensorArrayConcat	Concat the elements from the TensorArray into value `value`.
tensorflow::ops::TensorArrayGather	Gather specific elements from the TensorArray into output `value`.
tensorflow::ops::TensorArrayGrad	Creates a TensorArray for storing the gradients of values in the given handle.
tensorflow::ops::TensorArrayGradWithShape	Creates a TensorArray for storing multiple gradients of values in the given handle.
tensorflow::ops::TensorArrayRead	Read an element from the TensorArray into output `value`.
tensorflow::ops::TensorArrayScatter	Scatter the data from the input value into specific TensorArray elements.
tensorflow::ops::TensorArraySize	Get the current size of the TensorArray.
tensorflow::ops::TensorArraySplit	Split the data from the input value into TensorArray elements.
tensorflow::ops::TensorArrayWrite	Push an element onto the tensor_array.
tensorflow::ops::TensorSummary	Outputs a `Summary` protocol buffer with a tensor.
tensorflow::ops::TensorSummaryV2	Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
tensorflow::ops::TextLineReader	A Reader that outputs the lines of a file delimited by ' '.
tensorflow::ops::Timestamp	Provides the time since epoch in seconds.
tensorflow::ops::TopK	Finds values and indices of the `k` largest elements for the last dimension.
tensorflow::ops::TruncateDiv	Returns x / y element-wise for integer types.
tensorflow::ops::TruncateMod	Returns element-wise remainder of division.
tensorflow::ops::TruncatedNormal	Outputs random values from a truncated normal distribution.
tensorflow::ops::UnicodeScript	Determine the script codes of a given tensor of Unicode integer code points.
tensorflow::ops::UnicodeTranscode	Transcode the input text from a source encoding to a destination encoding.
tensorflow::ops::UniformCandidateSampler	Generates labels for candidate sampling with a uniform distribution.
tensorflow::ops::UnsortedSegmentJoin	Joins the elements of `inputs` based on `segment_ids`.
tensorflow::ops::UnsortedSegmentMax	Computes the maximum along segments of a tensor.
tensorflow::ops::UnsortedSegmentMin	Computes the minimum along segments of a tensor.
tensorflow::ops::UnsortedSegmentProd	Computes the product along segments of a tensor.
tensorflow::ops::UnsortedSegmentSum	Computes the sum along segments of a tensor.
tensorflow::ops::Unstage	Op is similar to a lightweight Dequeue.
tensorflow::ops::Variable	Holds state in the form of a tensor that persists across steps.
tensorflow::ops::Where	Reshapes a quantized tensor as per the Reshape op.
tensorflow::ops::Where3	Selects elements from `x` or `y`, depending on `condition`.
tensorflow::ops::WholeFileReader	A Reader that outputs the entire contents of a file as a value.
tensorflow::ops::WriteFile	Writes contents to the file at input filename.
tensorflow::ops::Xdivy	Returns 0 if x == 0, and x / y otherwise, elementwise.
tensorflow::ops::Xlog1py	Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
tensorflow::ops::Xlogy	Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
tensorflow::ops::ZerosLike	Returns a tensor of zeros with the same shape and type as x.
tensorflow::ops::Zeta	Compute the Hurwitz zeta function \((x, q)\).

Typedefs

Mul

Multiply Mul

Neg

Negate Neg

ReduceAll

All ReduceAll

ReduceAny

Any ReduceAny

ReduceMax

Max ReduceMax

ReduceMean

Mean ReduceMean

ReduceMin

Min ReduceMin

ReduceProd

Prod ReduceProd

ReduceSum

Sum ReduceSum

Sub

Subtract Sub

Functions

AsNodeOut

NodeBuilder::NodeOut AsNodeOut(
  const Scope & scope,
  const Input & inp
)

AsNodeOutList

std::vector< NodeBuilder::NodeOut > AsNodeOutList(
  const Scope & scope,
  const InputList & inp
)

AudioSummary

 AudioSummary(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input tag,
  ::tensorflow::Input tensor,
  ::tensorflow::Input sample_rate
)

AudioSummary

 AudioSummary(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input tag,
  ::tensorflow::Input tensor,
  ::tensorflow::Input sample_rate,
  const AudioSummary::Attrs & attrs
)

BadColor

TF_MUST_USE_RESULT Attrs BadColor(
  const TensorProto & x
)

Color to use for pixels with non-finite values.

Defaults to Tensor

Const

Output Const(
  const Scope & scope,
  const Input::Initializer & val
)

Const

Output Const(
  const Scope & scope,
  const T & v,
  const TensorShape shape
)

Const

Output Const(
  const Scope & scope,
  const std::initializer_list< T > & v,
  const TensorShape shape
)

ConstFromProto

Output ConstFromProto(
  const Scope & scope,
  const TensorProto & proto
)

ImageSummary

 ImageSummary(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input tag,
  ::tensorflow::Input tensor
)

ImageSummary

 ImageSummary(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input tag,
  ::tensorflow::Input tensor,
  const ImageSummary::Attrs & attrs
)

MaxImages

Attrs MaxImages(
  int64 x
)

MaxOutputs

Attrs MaxOutputs(
  int64 x
)

node

::tensorflow::Node * node() const

range

image **If max_images is greater the summary value tags are *generated sequentially as *tag *tag etc **The bad_color argument is the color to use in the generated images for *non finite input values It is a uint8 D tensor of length channels *Each element must be in the range(
  It represents the value of a *pixel in the output image
).Non-finite values in the input tensor are *replaced by this tensor in the output image.The default value is the color *red.**Arguments

Max number of batch elements to generate images for.

Defaults to 3

tensorflow::ops

Summary

Typedefs

Functions

Classes

Typedefs

Mul

Neg

ReduceAll

ReduceAny

ReduceMax

ReduceMean

ReduceMin

ReduceProd

ReduceSum

Sub

Functions

AsNodeOut

AsNodeOutList

AudioSummary

AudioSummary

BadColor

Const

Const

Const

ConstFromProto

ImageSummary

ImageSummary

MaxImages

MaxOutputs

node

range