Esta página se ha traducido con Cloud Translation API.

RawOp

clase abstracta pública RawOp

Subclases directas conocidas

Abortar , Abs <T extiende TNumber >, AccumulateN <T extiende TType >, AccumulatorApplyGradient , AccumulatorNumAccumulated , AccumulatorSetGlobalStep , AccumulatorTakeGradient <T extiende TType >, Acos <T extiende TType >, Acosh <T extiende TType >, Agregar <T extiende TType >, AddManySparseToTensorsMap , AddN <T extiende TType >, AddSparseToTensorsMap , AjustarContrast <T extiende TNumber >, AjustaHue <T extiende TNumber > y 1152 más.

Abortar	Lanza una excepción para cancelar el proceso cuando se llama.
Abs <T extiende TNumber >	Calcula el valor absoluto de un tensor.
AcumularN <T extiende TType >	Devuelve la suma por elementos de una lista de tensores.
AcumuladorAplicarGradiente	Aplica un gradiente a un acumulador determinado.
AcumuladorNumAcumulado	Devuelve el número de gradientes agregados en los acumuladores dados.
AcumuladorSetGlobalStep	Actualiza el acumulador con un nuevo valor para global_step.
AccumulatorTakeGradient <T extiende TType >	Extrae el gradiente promedio en el ConditionalAccumulator dado.
Acos <T extiende TType >	Calcula acos de x por elementos.
Acosh <T extiende TType >	Calcula el coseno hiperbólico inverso de x por elementos.
Añadir <T extiende TType >	Devuelve x + y por elementos.
AgregarManySparseToTensorsMap	Agregue un `N`-minibatch `SparseTensor` a un `SparseTensorsMap`, devuelva `N` identificadores.
AgregarN <T extiende TType >	Agregue todos los elementos tensores de entrada.
Agregar mapa disperso a tensores	Agregue un `SparseTensor` a un `SparseTensorsMap` y devuelva su identificador.
AjustarContraste <T extiende TNumber >	Ajusta el contraste de una o más imágenes.
AjustarHue <T extiende TNumber >	Ajusta el tono de una o más imágenes.
AjustarSaturación <T extiende TNumber >	Ajusta la saturación de una o más imágenes.
Todo	Calcula el "y lógico" de los elementos en las dimensiones de un tensor.
Todos los candidatosSampler	Genera etiquetas para el muestreo de candidatos con una distribución de unigramas aprendida.
AllReduce <T extiende TNumber >	Reduce mutuamente múltiples tensores de idéntico tipo y forma.
AllToAll <T extiende TType >	Una operación para intercambiar datos entre réplicas de TPU.
Ángulo <U extiende TNumber >	Devuelve el argumento de un número complejo.
Iterador anónimo	Un contenedor para un recurso iterador.
AnónimoMemoriaCaché
AnónimoMultiDeviceIterator	Un contenedor para un recurso iterador multidispositivo.
AnónimoRandomSeedGenerator
Generador de semillas anónimo
Cualquier	Calcula el "o lógico" de elementos en las dimensiones de un tensor.
ApplyAdaMax <T extiende TType >	Actualice '*var' según el algoritmo AdamMax.
ApplyAdadelta <T extiende TType >	Actualice '*var' según el esquema adadelta.
ApplyAdagrad <T extiende TType >	Actualice '*var' según el esquema adagrad.
ApplyAdagradDa <T extiende TType >	Actualice '*var' según el esquema de adagrad proximal.
ApplyAdagradV2 <T extiende TType >	Actualice '*var' según el esquema adagrad.
ApplyAdam <T extiende TType >	Actualice '*var' según el algoritmo de Adam.
ApplyAddSign <T extiende TType >	Actualice '*var' según la actualización de AddSign.
ApplyCenteredRmsProp <T extiende TType >	Actualice '*var' según el algoritmo RMSProp centrado.
ApplyFtrl <T extiende TType >	Actualice '*var' según el esquema Ftrl-proximal.
ApplyGradientDescent <T extiende TType >	Actualice 'var' restándole 'alfa' 'delta'.
ApplyMomentum <T extiende TType >	Actualice '*var' según el esquema de impulso.
ApplyPowerSign <T extiende TType >	Actualice '*var' según la actualización de AddSign.
ApplyProximalAdagrad <T extiende TType >	Actualice 'var' y 'accum' según FOBOS con la tasa de aprendizaje de Adagrad.
ApplyProximalGradientDescent <T extiende TType >	Actualice '*var' como algoritmo FOBOS con tasa de aprendizaje fija.
ApplyRmsProp <T extiende TType >	Actualice '*var' según el algoritmo RMSProp.
AproximadamenteIgual	Devuelve el valor de verdad de abs(xy) <tolerancia por elementos.
ArgMax <V extiende TNumber >	Devuelve el índice con el valor más grande en todas las dimensiones de un tensor.
ArgMin <V extiende TNumber >	Devuelve el índice con el valor más pequeño entre las dimensiones de un tensor.
Como cadena	Convierte cada entrada del tensor dado en cadenas.
Asin <T extiende TType >	Calcula el seno inverso trigonométrico de x por elementos.
Asinh <T extiende TType >	Calcula el seno hiperbólico inverso de x por elementos.
Afirmar cardinalidadConjunto de datos
Afirmar el siguiente conjunto de datos
Afirmar que	Afirma que la condición dada es verdadera.
Asignar <T extiende TType >	Actualice 'ref' asignándole 'valor'.
AsignarAgregar <T extiende TType >	Actualice 'ref' agregándole 'valor'.
AsignarAgregarVariableOp	Agrega un valor al valor actual de una variable.
AssignSub <T extiende TType >	Actualice 'ref' restándole 'valor'.
AsignarSubVariableOp	Resta un valor del valor actual de una variable.
AsignarVariableOp	Asigna un nuevo valor a una variable.
Atan <T extiende TType >	Calcula la tangente inversa trigonométrica de x por elementos.
Atan2 <T extiende TNumber >	Calcula la arcotangente de `y/x` por elementos, respetando los signos de los argumentos.
Atanh <T extiende TType >	Calcula la tangente hiperbólica inversa de x por elementos.
Audioespectrograma	Produce una visualización de datos de audio a lo largo del tiempo.
AudioResumen	Genera un búfer de protocolo "Resumen" con audio.
Conjunto de datos AutoShard	Crea un conjunto de datos que fragmenta el conjunto de datos de entrada.
AvgPool <T extiende TNumber >	Realiza una agrupación promedio en la entrada.
AvgPool3d <T extiende TNumber >	Realiza una agrupación promedio 3D en la entrada.
AvgPool3dGrad <T extiende TNumber >	Calcula los gradientes de la función de agrupación promedio.
AvgPoolGrad <T extiende TNumber >	Calcula los gradientes de la función de agrupación promedio.
BandPart <T extiende TType >	Copie un tensor poniendo a cero todo lo que está fuera de una banda central en cada matriz más interna.
BandedTriangularSolve <T extiende TType >
Barrera	Define una barrera que persiste en diferentes ejecuciones de gráficos.
BarreraCerrar	Cierra la barrera dada.
BarreraIncompletaTamaño	Calcula el número de elementos incompletos en la barrera dada.
BarreraInsertarMuchos	Para cada clave, asigna el valor respectivo al componente especificado.
BarreraListoTamaño	Calcula el número de elementos completos en la barrera dada.
barreratomarmuchos	Toma el número dado de elementos completos de una barrera.
Lote	Agrupa todos los tensores de entrada por lotes de forma no determinista.
BatchCholesky <T extiende TNumber >
BatchCholeskyGrad <T extiende TNumber >
Conjunto de datos por lotes	Crea un conjunto de datos que agrupa elementos `batch_size` desde `input_dataset`.
LoteFft
LoteFft2d
LoteFft3d
loteIfft
loteIfft2d
loteIfft3d
BatchMatMul <T extiende TType >	Multiplica porciones de dos tensores en lotes.
BatchMatrixBandPart <T extiende TType >
BatchMatrixDeterminant <T extiende TType >
BatchMatrixDiag <T extiende TType >
BatchMatrixDiagPart <T extiende TType >
BatchMatrixInverse <T extiende TNumber >
BatchMatrixSetDiag <T extiende TType >
BatchMatrixSolve <T extiende TNumber >
BatchMatrixSolveLs <T extiende TNumber >
BatchMatrixTriangularSolve <T extiende TNumber >
BatchNormWithGlobalNormalization <T extiende TType >	Normalización por lotes.
BatchNormWithGlobalNormalizationGrad <T extiende TType >	Gradientes para normalización por lotes.
BatchSelfAdjointEig <T extiende TNumber >
BatchSvd <T extiende TType >
BatchToSpace <T extiende TType >	BatchToSpace para tensores 4-D de tipo T.
BatchToSpaceNd <T extiende TType >	BatchToSpace para tensores ND de tipo T.
BesselI0 <T extiende TNumber >
BesselI0e <T extiende TNumber >
BesselI1 <T extiende TNumber >
BesselI1e <T extiende TNumber >
BesselJ0 <T extiende TNumber >
BesselJ1 <T extiende TNumber >
BesselK0 <T extiende TNumber >
BesselK0e <T extiende TNumber >
BesselK1 <T extiende TNumber >
BesselK1e <T extiende TNumber >
BesselY0 <T extiende TNumber >
BesselY1 <T extiende TNumber >
Betainc <T extiende TNumber >	Calcular la integral beta incompleta regularizada \\(I_x(a, b)\\).
BiasAdd <T extiende TType >	Agrega "sesgo" al "valor".
BiasAddGrad <T extiende TType >	La operación hacia atrás para "BiasAdd" en el tensor de "sesgo".
Bincount <T extiende TNumber >	Cuenta el número de apariciones de cada valor en una matriz de números enteros.
Bitcast <U extiende TType >	Transmite un tensor de un tipo a otro sin copiar datos.
Bit a bitY <T extiende TNumber >	Elementwise calcula el AND bit a bit de `x` e `y`.
Bit a bitO <T extiende TNumber >	Elementwise calcula el OR bit a bit de `x` e `y`.
BitwiseXor <T extiende TNumber >	Elementwise calcula el XOR bit a bit de `x` e `y`.
BlockLSTM <T extiende TNumber >	Calcula la propagación directa de la celda LSTM para todos los pasos de tiempo.
BlockLSTMGrad <T extiende TNumber >	Calcula la propagación hacia atrás de la celda LSTM para toda la secuencia de tiempo.
Árboles impulsadosEstadísticas agregadas	Agrega el resumen de estadísticas acumuladas para el lote.
ImpulsadoárbolesBucketize	Divida cada característica en grupos según los límites del grupo.
ImpulsadoÁrbolesCalcularMejorCaracterísticaDividir	Calcula las ganancias de cada función y devuelve la mejor información dividida posible para la función.
ImpulsadoÁrbolesCalcularMejorCaracterísticaSplitV2	Calcula las ganancias para cada característica y devuelve la mejor información dividida posible para cada nodo.
Árboles impulsadosCalcularmejoresgananciasporcaracterística	Calcula las ganancias de cada función y devuelve la mejor información dividida posible para la función.
Árboles impulsadosCentroBias	Calcula el prior a partir de los datos de entrenamiento (el sesgo) y completa el primer nodo con el prior de los logits.
Árboles impulsadosCrear conjunto	Crea un modelo de conjunto de árboles y le devuelve un identificador.
BoostedTreesCreateQuantileStreamResource	Cree el recurso para secuencias cuantiles.
Árboles impulsadosDeserializeEnsemble	Deserializa una configuración de conjunto de árbol serializado y reemplaza el árbol actual conjunto.
ImpulsadoTreesEnsembleResourceHandleOp	Crea un identificador para BoostedTreesEnsembleResource
BoostedTreesEjemploDebugOutputs	Resultados de depuración/interpretabilidad del modelo para cada ejemplo.
ImpulsadoÁrbolesFlushCuantilResúmenes	Elimine los resúmenes cuantiles de cada recurso de flujo cuantil.
Árboles impulsadosGetEnsembleStates	Recupera el token de sello de recursos del conjunto de árboles, la cantidad de árboles y las estadísticas de crecimiento.
ImpulsadoTreesMakeQuantileResúmenes	Realiza el resumen de cuantiles del lote.
BoostedTreesMakeStatsResumen	Realiza el resumen de estadísticas acumuladas del lote.
Árboles impulsadosPredecir	Ejecuta múltiples predictores de conjuntos de regresión aditiva en instancias de entrada y calcula los logits.
BoostedTreesQuantileStreamResourceAgregarResúmenes	Agregue los resúmenes cuantiles a cada recurso de flujo cuantil.
BoostedTreesQuantileStreamResourceDeserialize	Deserialice los límites del depósito y el indicador listo en el QuantileAccumulator actual.
ImpulsadoTreesQuantileStreamResourceFlush	Vacíe los resúmenes de un recurso de flujo cuantil.
BoostedTreesQuantileStreamResourceGetBucketBoundaries	Genere los límites del depósito para cada característica en función de los resúmenes acumulados.
ImpulsadoTreesQuantileStreamResourceHandleOp	Crea un identificador para BoostedTreesQuantileStreamResource.
ImpulsadoTreesSerializeEnsemble	Serializa el conjunto de árboles en un proto.
BoostedTreesSparseAggregateStats	Agrega el resumen de estadísticas acumuladas para el lote.
ImpulsadoÁrbolesEscasoCalcularMejorCaracterísticaDividir	Calcula las ganancias de cada función y devuelve la mejor información dividida posible para la función.
ImpulsadoÁrbolesEntrenamientoPredecir	Ejecuta múltiples predictores de conjuntos de regresión aditiva en instancias de entrada y calcula la actualización de los logits almacenados en caché.
Conjunto de actualización de árboles impulsados	Actualiza el conjunto de árboles agregando una capa al último árbol que se está cultivando o iniciando un nuevo árbol.
ImpulsadoTreesUpdateEnsembleV2	Actualiza el conjunto de árboles agregando una capa al último árbol que se está cultivando. o iniciando un nuevo árbol.
BroadcastDynamicShape <T extiende TNumber >	Devuelve la forma de s0 op s1 con transmisión.
BroadcastGradientArgs <T extiende TNumber >	Devuelve los índices de reducción para calcular gradientes de s0 op s1 con transmisión.
BroadcastHelper <T extiende TType >	Operador auxiliar para realizar transmisiones estilo XLA Transmite `lhs` y `rhs` con el mismo rango, agregando dimensiones de tamaño 1 a cualquiera de los `lhs` y `rhs` que tenga el rango inferior, utilizando las reglas de transmisión de XLA para operadores binarios.
BroadcastRecv <T extiende TType >	Recibe un valor tensor transmitido desde otro dispositivo.
BroadcastSend <T extiende TType >	Transmite un valor de tensor a uno o más dispositivos.
BroadcastTo <T extiende TType >	Transmita una matriz para obtener una forma compatible.
Bucketizar	Divide las 'entradas' en función de los 'límites'.
BytesProducidoEstadísticasConjunto de datos	Registra el tamaño de bytes de cada elemento de `input_dataset` en un StatsAggregator.
CSRSparseMatrixComponents <T extiende TType >	Lee en voz alta los componentes de CSR en el "índice" del lote.
CSRSparseMatrixToDense <T extiende TType >	Convierta un CSRSparseMatrix (posiblemente por lotes) en denso.
CSRSparseMatrixToSparseTensor <T extiende TType >	Convierte un CSRSparesMatrix (posiblemente por lotes) en un SparseTensor.
Conjunto de datos CSV
CSVDatasetV2
CTCLossV2	Calcula la pérdida de CTC (probabilidad logarítmica) para cada entrada de lote.
Conjunto de datos de caché	Crea un conjunto de datos que almacena en caché elementos de `input_dataset`.
CachéDatasetV2
Cast <U extiende TType >	Transfiera x de tipo SrcT a y de DstT.
Ceil <T extiende TNumber >	Devuelve el entero más pequeño por elementos no menor que x.
CheckNumerics <T extiende TNumber >	Comprueba un tensor en busca de valores NaN, -Inf y +Inf.
Cholesky <T extiende TType >	Calcula la descomposición de Cholesky de una o más matrices cuadradas.
CholeskyGrad <T extiende TNumber >	Calcula el gradiente retropropagado en modo inverso del algoritmo de Cholesky.
Elija el conjunto de datos más rápido
ClipByValue <T extiende TType >	Recorta los valores del tensor a un mínimo y un máximo especificados.
CerrarResumenEscritor
Salida de clúster <T extiende TType >	Operador que conecta la salida de un cálculo XLA a otros nodos del gráfico de consumo.
CollectiveGather <T extiende TNumber >	Acumula mutuamente múltiples tensores de idéntico tipo y forma.
CollectivePermute <T extiende TType >	Una operación para permutar tensores entre instancias de TPU replicadas.
Supresión combinada no máxima	Selecciona con avidez un subconjunto de cuadros delimitadores en orden descendente de puntuación, Esta operación realiza non_max_suppression en las entradas por lote, en todas las clases.
Comparar y Bitpack	Compare los valores de "entrada" con el "umbral" y empaquete los bits resultantes en un "uint8".
Resultado de compilación	Devuelve el resultado de una compilación de TPU.
CompileSucceededAssert	Afirma que la compilación fue exitosa.
Complejo <U extiende TType >	Convierte dos números reales en un número complejo.
ComplexAbs <U extiende TNumber >	Calcula el valor absoluto complejo de un tensor.
ComprimirElemento	Comprime un elemento del conjunto de datos.
ComputarGolpes Accidentales	Calcula los identificadores de las posiciones en sampled_candidates que coinciden con true_labels.
Calcular tamaño de lote	Calcula el tamaño de lote estático de un conjunto de datos sin lotes parciales.
Concat <T extiende TType >	Concatena tensores a lo largo de una dimensión.
Concatenar conjunto de datos	Crea un conjunto de datos que concatena `input_dataset` con `another_dataset`.
Acumulador condicional	Un acumulador condicional para agregar gradientes.
Configurar TPU distribuido	Configura las estructuras centralizadas para un sistema TPU distribuido.
ConfigurarTPUEmbedding	Configura TPUEmbedding en un sistema TPU distribuido.
Conj <T extiende TType >	Devuelve el conjugado complejo de un número complejo.
ConjugateTranspose <T extiende TType >	Mezcla las dimensiones de x según una permutación y conjuga el resultado.
Constante <T extiende TType >	Un operador que produce un valor constante.
ConsumirMutexBloquear	Esta operación consume un bloqueo creado por "MutexLock".
ControlDisparador	No hace nada.
Conv <T extiende TType >	Envuelve el operador XLA ConvGeneralDilated, documentado en https://www.tensorflow.org/rendimiento/xla/operación_semantics#conv_convolution.
Conv2d <T extiende TNumber >	Calcula una convolución 2-D dados los tensores de "entrada" y "filtro" 4-D.
Conv2dBackpropFilter <T extiende TNumber >	Calcula los gradientes de convolución con respecto al filtro.
Conv2dBackpropInput <T extiende TNumber >	Calcula los gradientes de convolución con respecto a la entrada.
Conv3d <T extiende TNumber >	Calcula una convolución 3D dados los tensores de "entrada" y "filtro" 5D.
Conv3dBackpropFilter <T extiende TNumber >	Calcula los gradientes de convolución 3-D con respecto al filtro.
Conv3dBackpropInput <U extiende TNumber >	Calcula los gradientes de convolución 3-D con respecto a la entrada.
Copiar <T extiende TType >	Copie un tensor de CPU a CPU o de GPU a GPU.
CopyHost <T extiende TType >	Copia un tensor al host.
Porque <T extiende TType >	Calcula el cos de x por elementos.
Cosh <T extiende TType >	Calcula el coseno hiperbólico de x por elementos.
CountUpTo <T extiende TNumber >	Incrementa 'ref' hasta que alcanza el 'límite'.
CrearResumenDbWriter
CrearResumenFileWriter
Recortar y cambiar tamaño	Extrae cultivos del tensor de imagen de entrada y les cambia el tamaño.
CropAndResizeGradBoxes	Calcula el gradiente de crop_and_resize op con el tensor de cuadros de entrada.
CropAndResizeGradImage <T extiende TNumber >	Calcula el gradiente de la operación crop_and_resize con el tensor de la imagen de entrada.
Cruz <T extiende TNumber >	Calcule el producto cruzado por pares.
CrossReplicaSum <T extiende TNumber >	Una operación para sumar entradas entre instancias de TPU replicadas.
CtcBeamSearchDecoder <T extiende TNumber >	Realiza decodificación de búsqueda de haz en los logits dados en la entrada.
CtcGreedyDecoder <T extiende TNumber >	Realiza una decodificación codiciosa en los logits dados en las entradas.
CtcLoss <T extiende TNumber >	Calcula la pérdida de CTC (probabilidad logarítmica) para cada entrada de lote.
CudnnRNN <T extiende TNumber >	Un RNN respaldado por cuDNN.
CudnnRNNBackprop <T extiende TNumber >	Paso de respaldo de CudnnRNNV3.
CudnnRNNCanonicalToParams <T extiende TNumber >	Convierte los parámetros CudnnRNN de forma canónica a forma utilizable.
CudnnRNNParamsToCanonical <T extiende TNumber >	Recupera los parámetros de CudnnRNN en forma canónica.
CudnnRnnParamsSize <U extiende TNumber >	Calcula el tamaño de los pesos que puede utilizar un modelo Cudnn RNN.
Cumprod <T extiende TType >	Calcule el producto acumulativo del tensor "x" a lo largo del "eje".
Cumsum <T extiende TType >	Calcule la suma acumulada del tensor "x" a lo largo del "eje".
CumulativeLogsumexp <T extiende TNumber >	Calcule el producto acumulativo del tensor "x" a lo largo del "eje".
DataFormatDimMap <T extiende TNumber >	Devuelve el índice de dimensión en el formato de datos de destino dado el de el formato de datos de origen.
DataFormatVecPermute <T extiende TNumber >	Permuta el tensor de entrada de `src_format` a `dst_format`.
Servicio de datosConjunto de datos
Conjunto de datosCardinalidad	Devuelve la cardinalidad de `input_dataset`.
Conjunto de datos de gráfico	Crea un conjunto de datos a partir del `graph_def` dado.
Conjunto de datos a gráfico	Devuelve un GraphDef serializado que representa `input_dataset`.
Conjunto de datos a elemento único	Genera el elemento único del conjunto de datos dado.
Conjunto de datosToTFRecord	Escribe el conjunto de datos dado en el archivo dado usando el formato TFRecord.
Conjunto de datos a TfRecord	Escribe el conjunto de datos dado en el archivo dado usando el formato TFRecord.
Dawsn <T extiende TNumber >
DebugGradientIdentity <T extiende TType >	Operación de identidad para la depuración de gradientes.
DebugGradientRefIdentity <T extiende TType >	Operación de identidad para la depuración de gradientes.
DebugIdentity <T extiende TType >	Identidad de depuración V2 Op.
DepurarNanCount	Depurar operación de contador de valor NaN.
DebugNumericsSummary <U extiende TNumber >	Resumen numérico de depuración V2 Op.
Decodificar y recortar Jpeg	Decodifica y recorta una imagen codificada en JPEG en un tensor uint8.
DecodificarBase64	Decodifica cadenas codificadas en base64 seguras para la web.
DecodificarBmp	Decodifica el primer fotograma de una imagen codificada en BMP en un tensor uint8.
DecodificarComprimido	Descomprimir cadenas.
DecodificarCsv	Convierte registros CSV a tensores.
DecodificarGif	Decodifica los fotogramas de una imagen codificada en GIF en un tensor uint8.
DecodeImage <T extiende TNumber >	Función para decode_bmp, decode_gif, decode_jpeg y decode_png.
DecodificarJpeg	Decodifica una imagen codificada en JPEG en un tensor uint8.
DecodeJsonEjemplo	Convierta registros de ejemplo codificados en JSON en cadenas de búfer de protocolo binario.
DecodePaddedRaw <T extiende TNumber >	Reinterpretar los bytes de una cadena como un vector de números.
DecodePng <T extiende TNumber >	Decodifica una imagen codificada en PNG en un tensor uint8 o uint16.
DecodificarProto	La operación extrae campos de un mensaje de buffer de protocolo serializado en tensores.
DecodeRaw <T extiende TType >	Reinterpretar los bytes de una cadena como un vector de números.
DecodificarWav	Decodifica un archivo PCM WAV de 16 bits en un tensor flotante.
DeepCopy <T extiende TType >	Hace una copia de `x`.
EliminarIterador	Un contenedor para un recurso iterador.
Eliminar memoria caché
Eliminar MultiDeviceIterator	Un contenedor para un recurso iterador.
EliminarRandomSeedGenerator
Eliminar generador de semillas
EliminarSessionTensor	Elimina el tensor especificado por su identificador en la sesión.
DenseBincount <U extiende TNumber >	Cuenta el número de apariciones de cada valor en una matriz de números enteros.
DenseCountSparseOutput <U extiende TNumber >	Realiza un recuento de contenedores de salida dispersa para una entrada tf.tensor.
DenseToCSRSparseMatrix	Convierte un tensor denso en un CSRSparseMatrix (posiblemente por lotes).
DenseToDenseSetOperation <T extiende TType >	Aplica la operación de conjunto a lo largo de la última dimensión de 2 entradas "Tensor".
Conjunto de datos DenseToSparseBatch	Crea un conjunto de datos que agrupa elementos de entrada en un SparseTensor.
DenseToSparseSetOperation <T extiende TType >	Aplica la operación de conjunto a lo largo de la última dimensión de "Tensor" y "SparseTensor".
DepthToSpace <T extiende TType >	DepthToSpace para tensores de tipo T.
DepthwiseConv2dNative <T extiende TNumber >	Calcula una convolución en profundidad 2-D dados los tensores de "entrada" y "filtro" 4-D.
DepthwiseConv2dNativeBackpropFilter <T extiende TNumber >	Calcula los gradientes de convolución en profundidad con respecto al filtro.
DepthwiseConv2dNativeBackpropInput <T extiende TNumber >	Calcula los gradientes de convolución en profundidad con respecto a la entrada.
Descuantificar	Toma la entrada uint32 empaquetada y la descomprime en uint8 para realizar Descuantificación en el dispositivo.
DeserializarIterador	Convierte el tensor variante dado en un iterador y lo almacena en el recurso dado.
DeserializeManySparse <T extiende TType >	Deserializar y concatenar `SparseTensors` desde un minibatch serializado.
DeserializeSparse <U extiende TType >	Deserializar objetos `SparseTensor`.
DestruirRecursoOp	Elimina el recurso especificado por el identificador.
DestroyTemporaryVariable <T extiende TType >	Destruye la variable temporal y devuelve su valor final.
Det <T extiende TType >	Calcula el determinante de una o más matrices cuadradas.
Índice de dispositivo	Devuelve el índice del dispositivo que ejecuta la operación.
Digamma <T extiende TNumber >	Calcula Psi, la derivada de Lgamma (el logaritmo del valor absoluto de `Gamma(x)`), por elementos.
Dilatación2d <T extiende TNumber >	Calcula la dilatación en escala de grises de los tensores de "entrada" 4-D y de "filtro" 3-D.
Dilation2dBackpropFilter <T extiende TNumber >	Calcula el gradiente de dilatación morfológica 2-D con respecto al filtro.
Dilation2dBackpropInput <T extiende TNumber >	Calcula el gradiente de dilatación morfológica 2-D con respecto a la entrada.
Conjunto de datos de intercalado dirigido	Un sustituto de `InterleaveDataset` en una lista fija de `N` conjuntos de datos.
Div <T extiende TType >	Devuelve x/y por elementos.
DivNoNan <T extiende TType >	Devuelve 0 si el denominador es cero.
Punto <T extiende TType >	Envuelve el operador XLA DotGeneral, documentado en https://www.tensorflow.org/rendimiento/xla/operación_semantics#dotgeneral.
DrawBoundingBoxes <T extiende TNumber >	Dibuja cuadros delimitadores en un lote de imágenes.
Contador De Iteraciones Ficticias
caché de memoria ficticia
Generador De Semillas Ficticias
Partición dinámica <T extiende TType >	Divide los "datos" en tensores "num_partitions" usando índices de "particiones".
DynamicSlice <T extiende TType >	Envuelve el operador XLA DynamicSlice, documentado en https://www.tensorflow.org/rendimiento/xla/operación_semantics#dynamicslice.
DynamicStitch <T extiende TType >	Intercale los valores de los tensores de "datos" en un solo tensor.
DynamicUpdateSlice <T extiende TType >	Envuelve el operador XLA DynamicUpdateSlice, documentado en https://www.tensorflow.org/rendimiento/xla/operación_semantics#dynamicupdateslice.
Editar distancia	Calcula la distancia de edición de Levenshtein (posiblemente normalizada).
Eig <U extiende TType >	Calcula la descomposición propia de una o más matrices cuadradas.
Einsum <T extiende TType >	Una operación que admite operaciones básicas einsum con 2 entradas y 1 salida.
Elu <T extiende TNumber >	Calcula lineal exponencial: `exp(características) - 1` si < 0, `características` en caso contrario.
EluGrad <T extiende TNumber >	Calcula gradientes para la operación lineal exponencial (Elu).
IncrustarActivaciones	Una operación que permite la diferenciación de las incrustaciones de TPU.
Vacío <T extiende TType >	Crea un tensor con la forma dada.
Lista de tensores vacía	Crea y devuelve una lista de tensores vacía.
VacíoTensorMap	Crea y devuelve un mapa tensorial vacío.
CodificarBase64	Codifique cadenas en formato base64 seguro para la web.
CodificarJpeg	Codifica una imagen en formato JPEG.
CodificarJpegCalidadVariable	JPEG codifica la imagen de entrada con la calidad de compresión proporcionada.
CodificarPng	Codifica una imagen en formato PNG.
codificarproto	La operación serializa los mensajes de protobuf proporcionados en los tensores de entrada.
codificarWav	Codifique datos de audio utilizando el formato de archivo WAV.
Poner en colaTPUEmbeddingIntegerBatch	Una operación que pone en cola una lista de tensores por lotes de entrada en TPUEmbedding.
Poner en colaTPUEmbeddingRaggedTensorBatch	Facilita la portabilidad de código que utiliza tf.nn.embedding_lookup().
Poner en colaTPUEmbeddingSparseBatch	Una operación que pone en cola los índices de entrada de TPUEmbedding desde un SparseTensor.
Poner en colaTPUEmbeddingSparseTensorBatch	Facilita la portabilidad de código que utiliza tf.nn.embedding_lookup_sparse().
GarantizarShape <T extiende TType >	Garantiza que la forma del tensor coincida con la forma esperada.
Ingrese <T extiende TType >	Crea o encuentra un marco secundario y pone "datos" a disposición del marco secundario.
Igual	Devuelve el valor de verdad de (x == y) por elementos.
Erf <T extiende TNumber >	Calcula la función de error de Gauss de `x` por elementos.
Erfc <T extiende TNumber >	Calcula la función de error complementaria de `x` por elementos.
Norma Euclidiana <T extiende TType >	Calcula la norma euclidiana de elementos en las dimensiones de un tensor.
Ejecutar	Op que carga y ejecuta un programa TPU en un dispositivo TPU.
Ejecutar y actualizar variables	Op que ejecuta un programa con actualizaciones de variables locales opcionales.
Salir <T extiende TType >	Sale del marco actual a su marco principal.
Exp <T extiende TType >	Calcula el exponencial de x por elementos.
ExpandDims <T extiende TType >	Inserta una dimensión de 1 en la forma de un tensor.
Expint <T extiende TNumber >
Expm1 <T extiende TType >	Calcula `exp(x) - 1` por elementos.
Extraer vislumbre	Extrae un vistazo del tensor de entrada.
ExtractImagePatches <T extiende TType >	Extraiga "parches" de "imágenes" y colóquelos en la dimensión de salida de "profundidad".
ExtractJpegShape <T extiende TNumber >	Extraiga la información de forma de una imagen codificada en JPEG.
ExtractVolumePatches <T extiende TNumber >	Extraiga los `parches` de la `entrada` y colóquelos en la dimensión de salida `"profundidad"`.
Hecho	Genera un hecho sobre factoriales.
FakeQuantWithMinMaxArgs	Cuantifique falsamente el tensor de 'entradas', escriba float en el tensor de 'salidas' del mismo tipo.
FakeQuantWithMinMaxArgsGradient	Calcule gradientes para una operación FakeQuantWithMinMaxArgs.
FakeQuantWithMinMaxVars	Cuantifique falsamente el tensor de 'entradas' de tipo float mediante escalares flotantes globales Cuantifique falsamente el tensor de "entradas" de tipo flotante mediante los escalares flotantes globales "min" y "max" al tensor de "salidas" de la misma forma que las "entradas".
FakeQuantWithMinMaxVarsGradient	Calcule gradientes para una operación FakeQuantWithMinMaxVars.
FakeQuantWithMinMaxVarsPerChannel	Cuantifique falsamente el tensor de 'entradas' de tipo flotante mediante flotadores por canal Cuantifique falsamente el tensor de `entradas` de tipo flotante por canal y una de las formas: `[d]`, `[b, d]` `[b, h, w, d]` mediante flotantes por canal ` min` y `max` de forma `[d]` al tensor de `outputs` de la misma forma que `inputs`.
FakeQuantWithMinMaxVarsPerChannelGradient	Calcule gradientes para una operación FakeQuantWithMinMaxVarsPerChannel.
Fft <T extiende TType >	Transformada rápida de Fourier.
Fft2d <T extiende TType >	Transformada rápida de Fourier 2D.
Fft3d <T extiende TType >	Transformada rápida de Fourier 3D.
FifoQueue	Una cola que produce elementos en orden de primero en entrar, primero en salir.
Rellenar <U extiende TType >	Crea un tensor lleno de un valor escalar.
Filtrar por último componente conjunto de datos	Crea un conjunto de datos que contiene elementos del primer componente de `input_dataset` que tienen verdadero en el último componente.
Huella dactilar	Genera valores de huellas dactilares.
Conjunto de datos de registro de longitud fija
Lector de registros de longitud fija	Un lector que genera registros de longitud fija desde un archivo.
FijoUnigramCandidateSampler	Genera etiquetas para el muestreo de candidatos con una distribución de unigramas aprendida.
Piso <T extiende TNumber >	Devuelve el entero más grande por elementos no mayor que x.
FloorDiv <T extiende TType >	Devuelve x // y por elementos.
FloorMod <T extiende TNumber >	Devuelve el resto de la división por elementos.
FlushResumenEscritor
FractionalAvgPool <T extiende TNumber >	Realiza una agrupación promedio fraccionaria en la entrada.
FractionalAvgPoolGrad <T extiende TNumber >	Calcula el gradiente de la función FractionalAvgPool.
FractionalMaxPool <T extiende TNumber >	Realiza una agrupación máxima fraccionaria en la entrada.
FractionalMaxPoolGrad <T extiende TNumber >	Calcula el gradiente de la función FractionalMaxPool.
FresnelCos <T extiende TNumber >
FresnelSin <T extiende TNumber >
FusedBatchNorm <T extiende TNumber , U extiende TNumber >	Normalización por lotes.
FusedBatchNormGrad <T extiende TNumber , U extiende TNumber >	Gradiente para normalización por lotes.
FusedPadConv2d <T extiende TNumber >	Realiza un relleno como preproceso durante una convolución.
FusedResizeAndPadConv2d <T extiende TNumber >	Realiza un cambio de tamaño y relleno como preproceso durante una convolución.
GRUBlockCell <T extiende TNumber >	Calcula la propagación directa de la celda GRU durante 1 paso de tiempo.
GRUBlockCellGrad <T extiende TNumber >	Calcula la retropropagación de la celda GRU durante 1 paso de tiempo.
Reunir <T extiende TType >	Envuelve el operador XLA Gather documentado en https://www.tensorflow.org/xla/ Operation_semantics#gather
GatherNd <T extiende TType >	Reúna sectores de "params" en un tensor con la forma especificada por "índices".
GatherV2 <T extiende TNumber >	Acumula mutuamente múltiples tensores de idéntico tipo y forma.
Generar propuestas de cuadro delimitador	Esta operación produce una región de intereses a partir de cuadros delimitadores dados (bbox_deltas) codificados con anclajes wrt de acuerdo con la ecuación 2 en arXiv:1506.01497 La operación selecciona los cuadros de puntuación superiores `pre_nms_topn`, los decodifica con respecto a los anclajes, aplica supresión no máxima en cuadros superpuestos con un valor de intersección sobre unión (iou) superior a `nms_threshold`, descartando cuadros donde el lado más corto es menor que ` tamaño_mínimo`.
GenerarVocabRemapping	Dada una ruta a archivos de vocabulario nuevos y antiguos, devuelve un tensor de reasignación de longitud `num_new_vocab`, donde `remapping[i]` contiene el número de fila en el vocabulario antiguo que corresponde a la fila `i` en el nuevo vocabulario (comenzando en la línea `new_vocab_offset` y hasta las entidades `num_new_vocab`), o `- 1` si la entrada `i` en el vocabulario nuevo no está en el vocabulario antiguo.
Obtener identificador de sesión	Almacene el tensor de entrada en el estado de la sesión actual.
GetSessionTensor <T extiende TType >	Obtenga el valor del tensor especificado por su identificador.
Mayor que	Devuelve el valor de verdad de (x > y) por elementos.
MayorIgual	Devuelve el valor de verdad de (x >= y) por elementos.
GuaranteeConst <T extiende TType >	Da una garantía al tiempo de ejecución de TF de que el tensor de entrada es una constante.
Tabla hash	Crea una tabla hash no inicializada.
HistogramFixedWidth <U extiende TNumber >	Devuelve histograma de valores.
HistogramaResumen	Genera un búfer de protocolo "Resumen" con un histograma.
HsvToRgb <T extiende TNumber >	Convierta una o más imágenes de HSV a RGB.
Identidad <T extiende TType >	Devuelve un tensor con la misma forma y contenido que el tensor o valor de entrada.
IdentidadN	Devuelve una lista de tensores con las mismas formas y contenidos que la entrada. tensores.
Lector de identidad	Un lector que genera el trabajo en cola como clave y valor.
Ifft <T extiende TType >	Transformada rápida inversa de Fourier.
Ifft2d <T extiende TType >	Transformada rápida de Fourier inversa 2D.
Ifft3d <T extiende TType >	Transformada rápida de Fourier 3D inversa.
Igamma <T extiende TNumber >	Calcule la función Gamma incompleta regularizada inferior `P (a, x)`.
IgammaGradA <T extiende TNumber >	Calcula el gradiente de `igamma (a, x)` wrt `a`.
Igammac <t extiende tNumber >	Calcule la función gamma incompleta superior regularizada `Q (A, X)`.
Ignorrorsdataset	Crea un conjunto de datos que contiene los elementos de `input_dataset` ignorando los errores.
Imag <u extiende tnumber >	Devuelve la parte imaginaria de un número complejo.
ImageProjectIVetransformv2 <t extiende tNumber >	Aplica la transformación dada a cada una de las imágenes.
ImageProjectIVetransformv3 <t extiende tNumber >	Aplica la transformación dada a cada una de las imágenes.
Imagesummary	Emite un búfer de protocolo `summary` con imágenes.
ImmutableConst <t extiende ttype >	Devuelve el tensor inmutable de la región de la memoria.
Importación
Intopk	Dice si los objetivos están en las principales predicciones `K`.
Infeeddequeue <t extiende ttype >	Un marcador de posición OP por un valor que se alimentará en el cálculo.
Confeeddequeuetuple	Obtiene múltiples valores de FEED como una tupla XLA.
Infreedenqueue	Un OP que alimenta un solo valor tensor en el cálculo.
InfeedenqueuePrelinealizedBuffer	Un OP que enqueuza el tampón prelinealizar en la TPU Feed.
Infreedenqueuetuple	Alimenta múltiples valores de tensor en el cálculo como una tupla XLA.
inicio
Inicializable	Inicializador de tabla que toma dos tensores para claves y valores respectivamente.
Inicializetable de una dataSet
InicializetableFromTextFile	Inicializa una tabla de un archivo de texto.
Inplaceadd <t extiende ttype >	Agrega V a las filas especificadas de x.
Inplacesub <t extiende ttype >	Resta `v` en filas especificadas de` x`.
InplaceUpdate <t extiende ttype >	Actualizaciones de filas especificadas 'i' con valores 'v'.
Inv <t extiende ttype >	Calcula el inverso de una o más matrices invertibles cuadradas o sus adjuntos (transposiciones conjugadas).
Invgrad <t extiende ttype >	Calcula el gradiente para el inverso de `x` wrt su entrada.
Invertir <t extiende tnumber >	Invertir (flip) cada bit de tipos compatibles; Por ejemplo, el valor de tipo `Uint8` 01010101 se convierte en 10101010.
Invertpermutation <t extiende tNumber >	Calcula la permutación inversa de un tensor.
Irfft <u extiende tnumber >	Transformación rápida de Fourier de valor real inversa.
Irfft2d <u extiende tnumber >	Transformación rápida de Fourier rápida de valor real en 2D.
Irfft3d <u extiende tnumber >	Transformación rápida de Fourier de valor real en 3D inversa.
Isboostedtreesensembleinitialized	Comprueba si se ha inicializado un conjunto de árbol.
IsBOOSTREDTREESQUANTIREMESTRESECRESCEINITICIDADO	Comprueba si se ha inicializado una transmisión cuantil.
Esfinito	Devoluciones qué elementos de X son finitos.
Esinf	Devoluciones que elementos de x son inf.
Isnan	Devoluciones qué elementos de X son nan.
Isvariableinitializado	Comprueba si se ha inicializado un tensor.
IsotonicRegression <u extiende tnumber >	Resuelve un lote de problemas de regresión isotónica.
Iterador
IteratorFromStringHandle
IteratorgetDevice	Devuelve el nombre del dispositivo en el que se ha colocado 'Resource'.
IteratorgetNext	Obtiene la siguiente salida del iterador dado.
IteratorgetNextAsoptional	Obtiene la siguiente salida del iterador dado como una variante opcional.
IteratorgetNextSync	Obtiene la siguiente salida del iterador dado.
IteratortoStringHandle	Convierte el `recurse_handle" dado que representa un iterador en una cadena.
Unirse	Se une a las cadenas en la lista dada de tensores de cadena en un tensor; con el separador dado (el valor predeterminado es un separador vacío).
KMC2ChainInitalización	Devuelve el índice de un punto de datos que debe agregarse al conjunto de semillas.
KeyValuesort <t extiende tnumber , u extiende ttype >	Envuelve el operador de clasificación XLA, documentado en https://www.tensorflow.org/performance/xla/operation_semantics#sort.
Kmeansplusplusinitialización	Selecciona filas de entrada num_to_sample usando el criterio KMeans ++.
Kthorderstatistic	Calcula la estadística de orden KTH de un conjunto de datos.
L2loss <t extiende tNumber >	L2 Pérdida.
Lmdbdataset	Crea un conjunto de datos que emite los pares de valor clave en uno o más archivos LMDB.
LSTMBLOCKCELL <t extiende tNumber >	Calcula la propagación de avance de la célula LSTM durante 1 paso de tiempo.
LSTMBLOCKCELLGRAD <t extiende tnumber >	Calcula la propagación hacia atrás de la célula LSTM para 1 tiempo de tiempo.
LatencyStatsdataset	Registra la latencia de producir elementos `input_dataset` en un statsaggregator.
LeakyRelu <t extiende tNumber >	Calcula rectificado lineal: `max (características, características * alfa)`.
LeakyRelugrad <t extiende tnumber >	Calcula los gradientes lineales rectificados para una operación LeakyRelu.
Learnunigramcandidatesampler	Genera etiquetas para el muestreo de candidatos con una distribución unigram aprendida.
El desplazamiento de izquierda <t extiende tnumber >	Elementwise calcula el cambio de izquierda bit a bit de `x` y` y`.
Menos	Devuelve el valor de verdad del elemento (x <y).
Menos igual	Devuelve el valor de verdad del elemento (x <= y) en cuanto al elemento.
Lgamma <t extiende tnumber >	Calcula el registro del valor absoluto de `gamma (x)` elemento en cuanto a elemento.
Linspace <t extiende tNumber >	Genera valores en un intervalo.
Lmdbdataset
LMDBRADER	Un lector que genera los registros de un archivo LMDB.
Loadandremapmatrix	Carga un 2-D (matriz) `tensor` con el nombre` old_tensor_name` desde el punto de control en `ckpt_path` y potencialmente reordenan sus filas y columnas utilizando los remappes especificados.
LoadtpUbbeddingadamparameters	Cargar los parámetros de incrustación de Adam.
LoadtpUbbeddingadamparametersgradaccumdebug	Cargue los parámetros de incrustación de Adam con soporte de depuración.
LoadtpUbbeddingadadeltaparameters	Cargar los parámetros de incrustación de adadelta.
LoadtpUmbeddingadadeltaparametersgradaccumdebug	Cargue los parámetros de Adadelta con soporte de depuración.
LoadtpUbbeddingdadagradparameters	Cargar los parámetros de incrustación de Adagrad.
LoadtpUbbeddingdadagradparametersgradaccumdebug	Cargue los parámetros de incrustación de Adagrad con soporte de depuración.
LoadtpUbbeddingcenteredrmspropparameters	Parámetros de incrustación RMSPRP centrados en la carga.
LoadtpUbbeddingftrlParameters	Carga de parámetros de incrustación FTRL.
LoadtpUmbeddingftrlParametersgradaccumdebug	Cargar los parámetros de incrustación FTRL con soporte de depuración.
LoadtpUbbeddingmdladagradlightParameters	Cargue los parámetros de incrustación de luz MDL Adagrad.
LoadtpUbbeddingMomentumarameters	Carga de parámetros de incrustación de impulso.
LoadtpUmbeddingMomentumMetersgradaccumdebug	Cargue los parámetros de incrustación de impulso con soporte de depuración.
LoadtpUbbeddingproximaladagradparameters	Carga de parámetros de incrustación proximal de Adagrad.
LoadtpUbbeddingproximaladagradparametersgradaccumdebug	Cargue los parámetros de incrustación proximal de Adagrad con soporte de depuración.
LoadtpUbbeddingproximalyogiparameters
LoadtpUmbeddingproximalyogiparametersgradaccumdebug
LoadtpUmbeddingrmspropparameters	Cargar parámetros de incrustación RMSProp.
LoadtpUmbeddingrmspropparametersgradaccumdebug	Cargar los parámetros de incrustación RMSProp con soporte de depuración.
LoadtpUbbeddingstoCasticgradientDescentParameters	Carga de parámetros de incrustación SGD.
LoadtpUbbeddingstoCasticgradientDescentParametersgradaccumdebug	Carga de parámetros de incrustación SGD.
LocalResponsenormalización <t extiende tnumber >	Normalización de la respuesta local.
LocalResponsenormalizationGrad <t extiende tnumber >	Gradientes para la normalización de la respuesta local.
Log <t extiende ttype >	Calcula el logaritmo natural de X Elemento.
Log1p <t extiende ttype >	Calcula el logaritmo natural del elemento (1 + x) en cuanto al elemento.
LogMatrixDeterminant <t extiende ttype >	Calcula el signo y el registro del valor absoluto del determinante de una o más matrices cuadradas.
Logsoftmax <t extiende tnumber >	Calcula las activaciones de registro de Softmax.
Loguniformcandidatesampler	Genera etiquetas para el muestreo candidato con una distribución uniforme log.
Lógico y	Devuelve el valor de verdad de X e Y Elemento.
Nota lógica	Devuelve el valor de verdad de `no x` elemento en cuanto a elemento.
Lógico	Devuelve el valor de verdad de X o Y Elemento.
LookUptableExport <t extiende ttype , u extiende ttype >	Emite todas las claves y valores en la tabla.
Lookuptablefind <u extiende ttype >	Busca claves en una tabla, genera los valores correspondientes.
LoveUptableImport	Reemplaza el contenido de la tabla con las teclas y valores especificados.
Lookuptableinsert	Actualiza la tabla para asociar las claves con valores.
LookUptableRemove	Elimina las claves y sus valores asociados de una tabla.
LookUptablesize	Calcula el número de elementos en la tabla dada.
Bucle	Reenvía la entrada a la salida.
Más bajo	Convierte todos los caracteres mayúsculas en sus respectivos reemplazos en minúsculas.
Lowerbound <u extiende tnumber >	Aplica Lower_Bound (Sorted_Search_Values, valores) a lo largo de cada fila.
Lu <t extiende ttype , u extiende tnumber >	Calcula la descomposición de Lu de una o más matrices cuadradas.
Makiterator	Hace un nuevo iterador del 'DataSet' dado y lo almacena en 'Iterator`.
Makeunique	Haga que todos los elementos en la dimensión no por lotes sean únicos, pero \ "Cerrar \" a su valor inicial.
Mapa	OP elimina todos los elementos en el contenedor subyacente.
Mapincompletesize	OP devuelve el número de elementos incompletos en el contenedor subyacente.
Mapa	OP se asoma a los valores en la clave especificada.
Mapsizar	OP devuelve el número de elementos en el contenedor subyacente.
Escenario de mapas	Etapa (clave, valores) en el contenedor subyacente que se comporta como un hashtable.
Mapunstage	OP elimina y devuelve los valores asociados con la clave del contenedor subyacente.
Mapunstagenokey	OP elimina y devuelve un aleatorio (clave, valor) del contenedor subyacente.
Matmul <t extiende ttype >	Multiplique la matriz "A" por la matriz "B".
Perfiles	Devuelve el conjunto de archivos que coinciden con uno o más patrones de globas.
Matchingfilesdataset
Matrixdiag <t extiende ttype >	Devuelve un tensor diagonal lotado con valores diagonales lotes dados.
MatrixdiaGPart <t extiende ttype >	Devuelve la parte diagonal de un tensor por lotes.
MatrixdiaGPartv3 <t extiende ttype >	Devuelve la parte diagonal de un tensor por lotes.
MatrixdiaGv3 <t extiende ttype >	Devuelve un tensor diagonal lotado con valores diagonales lotes dados.
Matrixlogarithm <t extiende ttype >	Calcula el logaritmo de matriz de una o más matrices cuadradas: \\(log(exp(A)) = A\\) Este OP solo se define para matrices complejas.
Matrixsetdiag <t extiende ttype >	Devuelve un tensor de matriz por parte con nuevos valores diagonales lotes.
MatrixSOlvels <t extiende ttype >	Resuelve uno o más problemas de mínimos cuadrados lineales.
Max <t extiende ttype >	Calcula el máximo de elementos a través de las dimensiones de un tensor.
MaxintraopParallelismdataSet	Crea un conjunto de datos que anula el paralelismo máximo intraopp.
Maxpool <t extiende ttype >	Realiza una agrupación máxima en la entrada.
Maxpool3d <t extiende tNumber >	Realiza una agrupación máxima 3D en la entrada.
Maxpool3dgrad <u extiende tnumber >	Calcula los gradientes de la función de agrupación MAX 3D.
Maxpool3dgradgrad <t extiende tNumber >	Calcula los gradientes de segundo orden de la función MaxPooling.
Maxpoolgrad <t extiende tNumber >	Calcula los gradientes de la función maxpooling.
Maxpoolgradgrad <t extiende tnumber >	Calcula los gradientes de segundo orden de la función MaxPooling.
Maxpoolgradgradwithargmax <t extiende tnumber >	Calcula los gradientes de segundo orden de la función MaxPooling.
Maxpoolgradwithargmax <t extiende tNumber >	Calcula los gradientes de la función maxpooling.
Maxpoolwithargmax <t extiende tnumber , u extiende tnumber >	Realiza una acumulación máxima en la entrada y emite valores máximos e índices.
Máximo <t extiende tNumber >	Devuelve el máximo de x e y (es decir
Media <t extiende ttype >	Calcula la media de los elementos a través de las dimensiones de un tensor.
Fusionar <t extiende ttype >	Reenvía el valor de un tensor disponible de `entradas 'a` salida'.
Fusionero	Fusiona resúmenes.
Mergev2checkpoints	Formato V2 específico: fusiona los archivos de metadatos de puntos de control fragmentados.
MFCC	Transforma un espectrograma en una forma útil para el reconocimiento de voz.
Min <t extiende ttype >	Calcula el mínimo de elementos a través de las dimensiones de un tensor.
Mínimo <t extiende tNumber >	Devuelve el min de x e y (es decir
Mirrorpad <t extiende ttype >	Pads un tensor con valores reflejados.
Mirrorpadgrad <t extiende ttype >	Gradiente OP para `Mirrorpad` op.
Mlirpassthroughop	Envuelve un cálculo MLIR arbitrario expresado como un módulo con una función main ().
Mod <t extiende tnumber >	Devuelve el resto de la división.
Modelo	Transformación de identidad que modela el rendimiento.
Mul <t extiende ttype >	Devuelve x * y elemento en cuanto al elemento.
Mulnonan <t extiende ttype >	Devuelve x * y elemento en cuanto al elemento.
Multideviciterator	Crea un recurso multideviceiterator.
MultideviceIterator de mano	Genera un recurso multideviceiterator a partir de su mango de cadena provisto.
MultideviceiteratorgetNextFromShard	Obtiene el siguiente elemento para el número de fragmentos provisto.
MultideviceIteratorInit	Inicializa el iterador de dispositivo múltiple con el conjunto de datos dado.
MultideviceiteratortoStringHandle	Produce un mango de cadena para el multideviceiterator dado.
Multinomial <u extiende tnumber >	Dibuja muestras de una distribución multinomial.
MutableSeShashtable	Crea una mesa de hash vacía que usa tensores como la tienda de respaldo.
Machacable	Crea una mesa de hash vacía.
Mutablehashtableftensors	Crea una mesa de hash vacía.
Mutex	Crea un recurso mutex que puede ser bloqueado por 'mutexlock'.
Mutexlock	Bloquea un recurso mutex.
Ncclalleduce <t extiende tNumber >	Emite un tensor que contiene la reducción en todos los tensores de entrada.
Ncclbroadcast <t extiende tnumber >	Envía `entrada` a todos los dispositivos que están conectados a la salida.
Nccleduce <t extiende tnumber >	Reduce `Entrada` de` num_devices` usando `reducción 'a un solo dispositivo.
Ndtri <t extiende tnumber >
Neweighbors más cercanos	Selecciona los centros K más cercanos para cada punto.
Neg <t extiende ttype >	Calcula el valor negativo numérico en cuanto al elemento.
Negra	Entrenamiento a través de muestreo negativo.
NextAfter <t extiende tnumber >	Devuelve el siguiente valor representable de `x1` en la dirección de` x2`, elemento.
NextIteration <t extiende ttype >	Hace que su aporte esté disponible para la próxima iteración.
Noop	No hace nada.
No deterministas <u extiende ttype >	No determinan los enteros no deterministas.
No maxsupresión <t extiende tNumber >	Selecciona con codicia un subconjunto de cuadros delimitadores en orden descendente de puntaje, podando las cajas que tienen una alta intersección-sobre unión (IOU) se superponen con cajas previamente seleccionadas.
No MaxsupresionWithoverLaps	Selecciona con codicia un subconjunto de cuadros delimitadores en orden descendente de puntaje, podando cajas que tienen superposiciones altas con cajas previamente seleccionadas.
NoerializedAdataSet
Nota	Devuelve el valor de verdad del elemento (x! = Y).
NthElement <t extiende tNumber >	Encuentra valores de la estadística de `` n`-th orden para la última dimensión.
Onehot <u extiende ttype >	Devuelve un tensor único.
Onely <t extiende ttype >	Devuelve un tensor de los que tiene la misma forma y tipo que x.
OptimizedataSet	Crea un conjunto de datos aplicando optimizaciones a `input_dataset`.
OptimizedAtAsetv2	Crea un conjunto de datos aplicando optimizaciones relacionadas a `input_dataset`.
Opcional a partir de	Construye una variante opcional a partir de una tupla de tensores.
OpcionalgetValue	Devuelve el valor almacenado en una variante opcional o plantea un error si no existe ninguno.
Opcionalhasvalue	Devuelve verdadero si y solo si la variante opcional dada tiene un valor.
Opcionalnone	Crea una variante opcional sin valor.
Ordenado	OP elimina todos los elementos en el contenedor subyacente.
Ordenado mapinompletesize	OP devuelve el número de elementos incompletos en el contenedor subyacente.
Ordenadomappeek	OP se asoma a los valores en la clave especificada.
Mapas ordenado	OP devuelve el número de elementos en el contenedor subyacente.
Mapa de orden ordenado	Etapa (clave, valores) en el contenedor subyacente que se comporta como un ordenado Contenedor asociativo.
Ordenado mapu	OP elimina y devuelve los valores asociados con la clave del contenedor subyacente.
Ordenadomapunstagenokey	OP elimina y devuelve el elemento (clave, valor) con el más pequeño Clave del contenedor subyacente.
Ordinalselector	Un selector de núcleo de TPU op.
Outfeeddequeue <t extiende ttype >	Recupera un solo tensor de la salida de cálculo.
OutfeeddequeUetuple	Recupere múltiples valores de la salida de cálculo.
OutFeeddequeUetuplev2	Recupere múltiples valores de la salida de cálculo.
Outfeeddequeuev2 <t extiende ttype >	Recupera un solo tensor de la salida de cálculo.
Outfeedenqueue	Enoue un tensor en la salida de cálculo.
OutfeedenqueUetuple	Enoue múltiples valores de tensor en la salida de cálculo.
Pad <t extiende ttype >	Envuelve el operador de la almohadilla XLA, documentado en https://www.tensorflow.org/performance/xla/operation_semantics#pad.
Paddedbatchdataset	Crea un conjunto de datos que por lotes y los elementos `batch_size` de la entrada.
Relleno	Una cola que produce elementos en el primer orden de primera salida.
Paraleloconcat <t extiende ttype >	Concatena una lista de tensores `n` a lo largo de la primera dimensión.
ParallelDynamicStitch <t extiende ttype >	Interleve los valores de los tensores `data` en un solo tensor.
ParameterizedTruncatedNormal <u extiende tNumber >	Emite valores aleatorios de una distribución normal.
Parsexample	Transforma un vector de TF.Enmample Protos (como cadenas) en tensores tipados.
ParseexampledataSet	Transforma `input_dataSet` que contiene` Ejemplo` Protos como vectores de dt_string en un conjunto de datos de objetos `tensor` o` sparsetensor` que representan las características analizadas.
ParsesequenceExample	Transforma un vector de tf.io.SequenceExample Protos (como cadenas) en tensores tipados.
ParsesingleyExample	Transforma un proto -EXample (como una cadena) en tensores tipados.
ParsesingleseQuencexample	Transforma un cerebro escalar. SECHENCEEXAMHEPRO (como cadenas) en tensores tipados.
Parsetensor <t extiende ttype >	Transforma un tensorflow serializado. TensorProto Proto en un tensor.
PartitionedInput <t extiende ttype >	Un OP que agrupa una lista de entradas divididas juntas.
ParticionedOutput <t extiende ttype >	Un OP que demultiplexa un tensor para ser fisionado por XLA a una lista de divididos Salidas fuera del cálculo XLA.
El marcador de posición <t extiende ttype >	Un marcador de posición OP por un valor que se alimentará en el cálculo.
Deforzador de posiciónWithDefault <t extiende ttype >	Un marcador de posición OP que pasa a través de `entrada` cuando su salida no se alimenta.
Polygamma <t extiende tnumber >	Calcule la función Polygamma \\(\psi^{(n)}(x)\\).
Cuenta de la población	Calcula el recuento de población en cuanto a elementos (también conocido como
POW <t extiende ttype >	Calcula la potencia de un valor a otro.
Prefetchdataset	Crea un conjunto de datos que prefetea asincrónicamente elementos de `input_dataset`.
Prelinealarizar	Un OP que linealiza un valor de tensor a un tensor de variante opaca.
Prelinealetuple	Un OP que linealiza múltiples valores de tensor a un tensor de variante opaca.
Preventgradient <t extiende ttype >	Una identidad OP que desencadena un error si se solicita un gradiente.
Imprimir	Imprime una cadena escalar.
Cola de prioridad	Una cola que produce elementos ordenados por el valor del primer componente.
PrivatethreadpooldataSet	Crea un conjunto de datos que utiliza un grupo de subprocesos personalizado para calcular `input_dataset`.
Prod <t extiende ttype >	Calcula el producto de elementos a través de las dimensiones de un tensor.
Qr <t extiende ttype >	Calcula las descomposiciones QR de una o más matrices.
Cuantizar <t extiende ttype >	Cuantite el tensor de 'entrada' de tipo flotante al tensor de 'salida' del tipo 't'.
CuantizeSandDequantize <t extiende tnumber >	Cuantiza luego desquantado un tensor.
CuantizeSandDequantizeV3 <t extiende tnumber >	Cuantiza luego desquantado un tensor.
CuantizeSandDequantizeV4 <t extiende tNumber >	Devuelve el gradiente de `cuantización.quantizeandDequantizeV4`.
CuantizeSandDequantizeV4grad <t extiende tnumber >	Devuelve el gradiente de `cuantizeandDequantizeV4`.
QuantizeDownandshrinkRange <u extiende ttype >	Convierta el tensor de 'entrada' cuantificado en una 'salida' de menor precisión ', utilizando el Distribución real de los valores para maximizar el uso de la profundidad de broca inferior y ajustar el mínimo de salida y los rangos máximos en consecuencia.
CuantizedAdd <v extiende ttype >	Devuelve el elemento X + Y, trabajando en buffers cuantificados.
Cuantizedavgpool <t extiende ttype >	Produce el grupo promedio del tensor de entrada para tipos cuantificados.
CuantizedBatchNormwithGlobalNormalization <u extiende ttype >	Normalización de lotes cuantificados.
Cuantizedbiasadd <v extiende ttype >	Agrega 'sesgo' tensor al tensor 'entrada' para tipos cuantificados.
CuantizedConcat <t extiende ttype >	Concatena tensores cuantizados a lo largo de una dimensión.
CuantizedConv2Dandrelu <V extiende ttype >
CuantizedConv2DandreluandRequantize <V extiende ttype >
CuantizedConv2DandRequantize <V extiende ttype >
CuantizedConv2DperChannel <v extiende ttype >	Calcula cuantizedConv2d por canal.
CuantizedConv2dwithbias <v extiende ttype >
CuantizedConv2dwithbiasandrelu <v extiende ttype >
CuantizedConv2dwithbiasandreluandRequantize <w extiende ttype >
CuantizedConv2dWithbiasandRequantize <w extiende ttype >
CuantizedConv2dwithbiassignedSumandreluandRequantize <x extiende ttype >
CuantizedConv2dwithbiassumandrelu <v extiende ttype >
CuantizedConv2dwithbiassumandreluandRequantize <x extiende ttype >
CuantizedConv2d <v extiende ttype >	Calcula una convolución 2D dada la entrada 4D cuantificada y los tensores de filtro.
CuantizedDepthwiseConv2d <v extiende ttype >	Calcula cuantificada profundamente conv2d.
CuantizedDepthwiseConv2dwithbias <v extiende ttype >	Calcula cuantificada profundamente conv2d con sesgo.
CuantizedDepthwiseConv2dwithbiasandrelu <v extiende ttype >	Calcula cuantificada profundamente conv2d con sesgo y relu.
CuantizedDepthwiseConv2dwithbiasandreluandRequantize <w extiende ttype >	Calcula cuantificada con mayor conv2d con sesgo, relu y requerido.
Cuantizedinstancenorm <t extiende ttype >	Normalización de instancia cuantificada.
CuantizedMatmul <V extiende ttype >	Realice una multiplicación de matriz cuantificada de `a` por la matriz` b`.
CuantizedMatmulwithbias <w extiende ttype >	Realiza una multiplicación de matriz cuantificada de `A` por la matriz` B` con BIAS ADD.
CuantizedMatmulwithbias yDequantize <w extiende tnumber >
CuantizedMatmulwithbiasandrelu <V extiende ttype >	Realice una multiplicación de matriz cuantificada de `a` por la matriz` b` con sesgo add y relu fusion.
CuantizadomatmulwithbiasandreluandRequantize <w extiende ttype >	Realice una multiplicación de matriz cuantificada de `a` por la matriz` b` con sesgo agregar y relu y requerir fusión.
CuantizadomatmulwithbiasyRquantize <w extiende ttype >
Cuantizedmaxpool <t extiende ttype >	Produce el grupo máximo del tensor de entrada para tipos cuantificados.
Cuantizedmul <v extiende ttype >	Devuelve X * Y Elemento, trabajando en buffers cuantificados.
Cuantizedrelu <u extiende ttype >	Calcula lineal rectificado cuantificado: `max (características, 0)`
Cuantizedrelu6 <u extiende ttype >	Calcula cuantificada rectificado lineal 6: `min (max (características, 0), 6)`
Cuantizedrelux <u extiende ttype >	Calcula el lineal rectificado cuantificado: `min (max (características, 0), max_value)`
CuantizedReshape <t extiende ttype >	Reestructura un tensor cuantificado según la remodelación op.
CuantizedResizeBilineal <t extiende ttype >	Redice las `imágenes 'cuantiadas a` size' utilizando interpolación bilineal cuantificada.
Reza	Cierra la cola dada.
Cola	Dequea una tupla de uno o más tensores de la cola dada.
Cola	Dequeues `n` tuplas de uno o más tensores de la cola dada.
Queuedequeueupto	Dequeues `n` tuplas de uno o más tensores de la cola dada.
Réplica	Enqueues una tupla de uno o más tensores en la cola dada.
Queueenqueuemany	Enqueues cero o más tuplas de uno o más tensores en la cola dada.
Cola	Devuelve verdadero si la cola está cerrada.
Cola	Calcula el número de elementos en la cola dada.
RaggedBinDount <u extiende tnumber >	Cuenta el número de ocurrencias de cada valor en una matriz entera.
RaggedCountSparseOutput <u extiende tnumber >	Realiza un contado de basura de salida escasa para una entrada de tensor irregular.
RaggedCross <t extiende ttype , u extiende tnumber >	Genera una característica de una lista de tensores y la devuelve como un tensor tragado.
Raggedgather <t extiende tnumber , u extiende ttype >	Recopile rebanadas irregulares del eje `params`` 0` de acuerdo con 'índices'.
RaggedRange <u extiende tNumber , t extiende tNumber >	Devuelve un `RaggedTensor` que contiene las secuencias especificadas de números.
RaggedTensorFromVariant <u extiende tnumber , t extiende ttype >	Decodifica un tensor `variante` en un` raggedtensor`.
RaggedTensortoSparse <u extiende ttype >	Convierte un `RaggedTensor` en un` sparsetensor` con los mismos valores.
Raggedtensortotensor <u extiende ttype >	Cree un tensor denso a partir de un tensor irregular, posiblemente alterando su forma.
Desagüe	Codifica un `RaggedTensor` en un tensor` variante`.
Raggedtensortovariantgradient <u extiende ttype >	Helper solía calcular el gradiente de 'RaggedTensortovariant`.
RandomCrop <t extiende tNumber >	Corte al azar 'Imagen`.
DataSet Random	Crea un conjunto de datos que devuelve números pseudorandom.
RandomGamma <u extiende tnumber >	Emite valores aleatorios de la distribución (s) gamma descritas por alfa.
RandomGammaGrad <t extiende tNumber >	Calcula la derivada de una muestra aleatoria gamma WRT
Randompoisson <v extiende tnumber >	Emite valores aleatorios de la distribución (s) de Poisson descritas por tasa.
RandomShuffle <t extiende ttype >	Baraja aleatoriamente un tensor a lo largo de su primera dimensión.
Random shufflequeue	Una cola que aleatoriza el orden de los elementos.
Random standardnormal <u extiende tnumber >	Emite valores aleatorios de una distribución normal.
Randomuniform <u extiende tnumber >	Emite valores aleatorios de una distribución uniforme.
Randomuniformint <u extiende tnumber >	Emite enteros aleatorios de una distribución uniforme.
Rango <t extiende tnumber >	Crea una secuencia de números.
RangedataSet	Crea un conjunto de datos con un rango de valores.
Rango	Devuelve el rango de un tensor.
Archivo de lectura	Lee y emite todo el contenido del nombre de archivo de entrada.
ReadVariableP <t extiende ttype >	Lee el valor de una variable.
ReadernumRecordSproduced	Devuelve el número de registros que este lector ha producido.
ReadernumworkUnitsCompleted	Devuelve el número de unidades de trabajo que este lector ha terminado de procesar.
Readerread	Devuelve el siguiente registro (clave, par de valor) producido por un lector.
Readerreadupto	Devuelve los pares de `num_records` (clave, valor) producidos por un lector.
ReaderReset	Restaurar a un lector a su estado limpio inicial.
ReadErRestorestate	Restaurar a un lector a un estado previamente guardado.
LecterserializeState	Producir un tensor de cadena que codifique el estado de un lector.
Real <u extiende tnumber >	Devuelve la parte real de un número complejo.
RealDiv <t extiende ttype >	Devuelve el elemento X / Y para tipos reales.
Rebatchdataset	Crea un conjunto de datos que cambia el tamaño del lote.
RebatchDataSetv2	Crea un conjunto de datos que cambia el tamaño del lote.
Reciprocal <t extiende ttype >	Calcula el recíproco de x elemento en cuanto a elemento.
Reciprocalgrad <t extiende ttype >	Calcula el gradiente para el inverso de `x` wrt su entrada.
Grabación de grabación	Emite registros aleatorios.
Recv <t extiende ttype >	Recibe el tensor nombrado de otro cálculo XLA.
RecvtpUbbeddingactivations	Un OP que recibe activaciones de incrustación en la TPU.
Reducir <t extiende tnumber >	Reduce mutuamente múltiples tensores de tipo y forma idénticos.
Reducido	Calcula el "lógico y" de los elementos a través de las dimensiones de un tensor.
Reductora	Calcula el "lógico o" de los elementos a través de las dimensiones de un tensor.
Reducejo	Se une a un tensor de cuerda a través de las dimensiones dadas.
ReduceMax <t extiende ttype >	Calcula el máximo de elementos a través de las dimensiones de un tensor.
Reducemin <t extiende ttype >	Calcula el mínimo de elementos a través de las dimensiones de un tensor.
Reduce <t extiende ttype >	Calcula el producto de elementos a través de las dimensiones de un tensor.
ReduceSum <t extiende ttype >	Calcula la suma de elementos a través de las dimensiones de un tensor.
Reducev2 <t extiende tNumber >	Reduce mutuamente múltiples tensores de tipo y forma idénticos.
Refentador <t extiende ttype >	Crea o encuentra un marco infantil, y pone a 'datos' a disposición del marco infantil.
Refexit <t extiende ttype >	Sale del marco actual a su marco principal.
Refidentidad <t extiende ttype >	Devuelva el mismo tensor de referencia que el tensor de referencia de entrada.
Refmerge <t extiende ttype >	Reenvía el valor de un tensor disponible de `entradas 'a` salida'.
RefNextiteration <t extiende ttype >	Hace que su aporte esté disponible para la próxima iteración.
Refselect <t extiende ttype >	Reenvía el elemento `índice`th de` entradas 'a `salida'.
Refswitch <t extiende ttype >	Reenvía el tensor de referencia `datos` al puerto de salida determinado por` pred`.
Regexfullmatch	Compruebe si la entrada coincide con el patrón regex.
Regla	Reemplaza las coincidencias de la expresión regular `Pattern` en` Entrada` con la cadena de reemplazo proporcionada en 'Rewrite`.
RegistroDataSet	Registra un conjunto de datos con el servicio TF.Data.
Relu <t extiende ttype >	Calcula rectificado lineal: `max (características, 0)`.
Relu6 <t extiende tnumber >	Calcula lineal rectificado 6: `min (max (características, 0), 6)`.
Relu6grad <t extiende tnumber >	Calcula los gradientes lineales rectificados para una operación RELU6.
Relugrad <t extiende tnumber >	Calcula los gradientes lineales rectificados para una operación RELU.
RemotefusedGraphexecute	Ejecutar un subgráfico en un procesador remoto.
Repetirdataset	Crea un conjunto de datos que emite las salidas de tiempos `input_dataset`` Count`.
Réplica	ID de réplica.
ReplicateMetadata	Metadatos que indican cómo se debe replicar el cálculo de TPU.
ReplicatedInput <t extiende ttype >	Conecta N entradas a un cálculo de TPU replicado por N-Way.
ReplicatedOutput <t extiende ttype >	Conecta N salidas de un cálculo de TPU replicado por N-Way.
Ranización de rango	Calcula un rango que cubre los valores reales presentes en un tensor cuantificado.
RequantizationRangeperchannel	Calcula el rango de requisitos por canal.
Requerir <u extiende ttype >	Convierte el tensor de `Entrada` cuantificada en una` `salida '' de menor precisión.
Requantizeperchannel <u extiende ttype >	Requerir la entrada con valores MIN y MAX conocidos por canal.
Remodelar <t extiende ttype >	Remodelan un tensor.
Resizearea	Redice las `imágenes` a` size 'utilizando la interpolación del área.
Ensicebicúcico	Redice las `imágenes` a` size 'usando interpolación bicúbica.
Alicebicubicgrad <t extiende tnumber >	Calcula el gradiente de interpolación bicúbica.
Cambiar el tamaño	Redice las `imágenes` a` size` usando interpolación bilineal.
RESEDBILINEArGRAD <t extiende tNumber >	Calcula el gradiente de interpolación bilineal.
Resizenearestneighbor <t extiende tnumber >	Redice las `imágenes 'a` size' usando la interpolación vecina más cercana.
Resizenearestneighborgrad <t extiende tnumber >	Calcula el gradiente de la interpolación vecina más cercana.
ResourceAcCumulatorApplygradient	Aplica un gradiente a un acumulador dado.
Resoaccumulatornumacumulado	Devuelve el número de gradientes agregados en los acumuladores dados.
ResourceAcCumulatorSetGlobalstep	Actualiza el acumulador con un nuevo valor para global_step.
ResourceAcCumulatortAkeGradient <t extiende ttype >	Extrae el gradiente promedio en el condicionalacumulador dado.
ResourceApplyAdamax	Actualizar '*var' según el algoritmo Adamax.
ResourceApplyadadelta	Actualizar '*var' según el esquema Adadelta.
ResourceApplyadagrad	Actualizar '*var' según el esquema de Adagrad.
ResourceApplyadagradda	Actualizar '*var' de acuerdo con el esquema proximal de Adagrad.
ResourceApplyAdam	Actualizar '*var' según el algoritmo Adam.
ResourceApplyAdamWithamSgrad	Actualizar '*var' según el algoritmo Adam.
ResourceApplyaddsign	Actualizar '*var' según la actualización addSign.
Recursclepplycenteredrmsprop	Actualizar '*var' según el algoritmo RMSPROP centrado.
ResourceApplyftrl	Actualizar '*var' según el esquema FTRL-Proximal.
Recursos de recursos	Actualizar ' * var' restando 'alfa' * 'delta' de él.
Resontageplykeramomentum	Actualizar '*var' según el esquema de impulso.
Resonta	Actualizar '*var' según el esquema de impulso.
Recurso de recursos	Actualizar '*var' según la actualización addSign.
ResourceApplyProximaladaGrad	Actualizar 'var' y 'acum' según Fobos con tasa de aprendizaje de Adagrad.
ResontajepplyproximalgradientDescente	Actualizar '*var' como algoritmo Fobos con tasa de aprendizaje fija.
ResourceApplyrmspop	Actualizar '*var' según el algoritmo RMSPROP.
ResourceConditionalAccumulator	A conditional accumulator for aggregating gradients.
ResourceCountUpTo <T extends TNumber >	Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather <U extends TType >	Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGatherNd <U extends TType >
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	Applies sparse addition to individual values or slices in a Variable.
ResourceScatterNdMax
ResourceScatterNdMin
ResourceScatterNdSub	Applies sparse subtraction to individual values or slices in a Variable.
ResourceScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
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().
ResourceSparseApplyAdagrad	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyAdagradDa	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradV2	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyCenteredRmsProp	Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyFtrl	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyKerasMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyProximalAdagrad	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
ResourceSparseApplyProximalGradientDescent	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyRmsProp	Update '*var' according to the RMSProp algorithm.
ResourceStridedSliceAssign	Assign `value` to the sliced l-value reference of `ref`.
Restaurar	Restores tensors from a V2 checkpoint.
RestoreSlice <T extends TType >	Restores a tensor from checkpoint files.
RetrieveTPUEmbeddingADAMParameters	Retrieve ADAM embedding parameters.
RetrieveTPUEmbeddingADAMParametersGradAccumDebug	Retrieve ADAM embedding parameters with debug support.
RetrieveTPUEmbeddingAdadeltaParameters	Retrieve Adadelta embedding parameters.
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug	Retrieve Adadelta embedding parameters with debug support.
RetrieveTPUEmbeddingAdagradParameters	Retrieve Adagrad embedding parameters.
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug	Retrieve Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingCenteredRMSPropParameters	Retrieve centered RMSProp embedding parameters.
RetrieveTPUEmbeddingFTRLParameters	Retrieve FTRL embedding parameters.
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug	Retrieve FTRL embedding parameters with debug support.
RetrieveTPUEmbeddingMDLAdagradLightParameters	Retrieve MDL Adagrad Light embedding parameters.
RetrieveTPUEmbeddingMomentumParameters	Retrieve Momentum embedding parameters.
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug	Retrieve Momentum embedding parameters with debug support.
RetrieveTPUEmbeddingProximalAdagradParameters	Retrieve proximal Adagrad embedding parameters.
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug	Retrieve proximal Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingProximalYogiParameters
RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug
RetrieveTPUEmbeddingRMSPropParameters	Retrieve RMSProp embedding parameters.
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug	Retrieve RMSProp embedding parameters with debug support.
RetrieveTPUEmbeddingStochasticGradientDescentParameters	Retrieve SGD embedding parameters.
RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug	Retrieve SGD embedding parameters with debug support.
Reverse <T extends TType >	Reverses specific dimensions of a tensor.
ReverseSequence <T extends TType >	Reverses variable length slices.
Rfft <U extends TType >	Real-valued fast Fourier transform.
Rfft2d <U extends TType >	2D real-valued fast Fourier transform.
Rfft3d <U extends TType >	3D real-valued fast Fourier transform.
RgbToHsv <T extends TNumber >	Converts one or more images from RGB to HSV.
RightShift <T extends TNumber >	Elementwise computes the bitwise right-shift of `x` and `y`.
Rint <T extends TNumber >	Returns element-wise integer closest to x.
RngReadAndSkip	Advance the counter of a counter-based RNG.
RngSkip	Advance the counter of a counter-based RNG.
Roll <T extends TType >	Rolls the elements of a tensor along an axis.
Round <T extends TType >	Rounds the values of a tensor to the nearest integer, element-wise.
RPC	Perform batches of RPC requests.
Rsqrt <T extends TType >	Computes reciprocal of square root of x element-wise.
RsqrtGrad <T extends TType >	Computes the gradient for the rsqrt of `x` wrt its input.
SampleDistortedBoundingBox <T extends TNumber >	Generate a single randomly distorted bounding box for an image.
SamplingDataset	Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
Ahorrar	Saves tensors in V2 checkpoint format.
SaveSlices	Saves input tensors slices to disk.
ScalarSummary	Outputs a `Summary` protocol buffer with scalar values.
ScaleAndTranslate
ScaleAndTranslateGrad <T extends TNumber >
ScatterAdd <T extends TType >	Adds sparse updates to a variable reference.
ScatterDiv <T extends TType >	Divides a variable reference by sparse updates.
ScatterMax <T extends TNumber >	Reduces sparse updates into a variable reference using the `max` operation.
ScatterMin <T extends TNumber >	Reduces sparse updates into a variable reference using the `min` operation.
ScatterMul <T extends TType >	Multiplies sparse updates into a variable reference.
ScatterNd <U extends TType >	Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd <T extends TType >	Applies sparse addition to individual values or slices in a Variable.
ScatterNdMax <T extends TType >	Computes element-wise maximum.
ScatterNdMin <T extends TType >	Computes element-wise minimum.
ScatterNdNonAliasingAdd <T extends TType >	Applies sparse addition to `input` using individual values or slices from `updates` according to indices `indices`.
ScatterNdSub <T extends TType >	Applies sparse subtraction to individual values or slices in a Variable.
ScatterNdUpdate <T extends TType >	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
ScatterSub <T extends TType >	Subtracts sparse updates to a variable reference.
ScatterUpdate <T extends TType >	Applies sparse updates to a variable reference.
SdcaFprint	Computes fingerprints of the input strings.
SdcaOptimizer	Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for linear models with L1 + L2 regularization.
SdcaShrinkL1	Applies L1 regularization shrink step on the parameters.
SegmentMax <T extends TNumber >	Computes the maximum along segments of a tensor.
SegmentMean <T extends TType >	Computes the mean along segments of a tensor.
SegmentMin <T extends TNumber >	Computes the minimum along segments of a tensor.
SegmentProd <T extends TType >	Computes the product along segments of a tensor.
SegmentSum <T extends TType >	Computes the sum along segments of a tensor.
Select <T extends TType >
SelfAdjointEig <T extends TType >	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
Selu <T extends TNumber >	Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise.
SeluGrad <T extends TNumber >	Computes gradients for the scaled exponential linear (Selu) operation.
Enviar	Sends the named tensor to another XLA computation.
SendTPUEmbeddingGradients	Performs gradient updates of embedding tables.
SerializeIterator	Converts the given `resource_handle` representing an iterator to a variant tensor.
SerializeManySparse <U extends TType >	Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
SerializeSparse <U extends TType >	Serialize a `SparseTensor` into a `[3]` `Tensor` object.
SerializeTensor	Transforms a Tensor into a serialized TensorProto proto.
SetDiff1d <T extends TType , U extends TNumber >	Computes the difference between two lists of numbers or strings.
SetSize	Number of unique elements along last dimension of input `set`.
SetStatsAggregatorDataset
Shape <U extends TNumber >	Returns the shape of a tensor.
ShapeN <U extends TNumber >	Returns shape of tensors.
ShardDataset	Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
ShardedFilename	Generate a sharded filename.
ShardedFilespec	Generate a glob pattern matching all sharded file names.
Sharding <T extends TType >	An op which shards the input based on the given sharding attribute.
ShuffleAndRepeatDataset
ShuffleDataset
ShutdownDistributedTPU	Shuts down a running distributed TPU system.
Sigmoid <T extends TType >	Computes sigmoid of `x` element-wise.
SigmoidGrad <T extends TType >	Computes the gradient of the sigmoid of `x` wrt its input.
Sign <T extends TType >	Returns an element-wise indication of the sign of a number.
Sin <T extends TType >	Computes sine of x element-wise.
Sinh <T extends TType >	Computes hyperbolic sine of x element-wise.
Size <U extends TNumber >	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.
SleepDataset
Slice <T extends TType >	Return a slice from 'input'.
SlidingWindowDataset	Creates a dataset that passes a sliding window over `input_dataset`.
Snapshot <T extends TType >	Returns a copy of the input tensor.
SobolSample <T extends TNumber >	Generates points from the Sobol sequence.
Softmax <T extends TNumber >	Computes softmax activations.
SoftmaxCrossEntropyWithLogits <T extends TNumber >	Computes softmax cross entropy cost and gradients to backpropagate.
Softplus <T extends TNumber >	Computes softplus: `log(exp(features) + 1)`.
SoftplusGrad <T extends TNumber >	Computes softplus gradients for a softplus operation.
Softsign <T extends TNumber >	Computes softsign: `features / (abs(features) + 1)`.
SoftsignGrad <T extends TNumber >	Computes softsign gradients for a softsign operation.
Solve <T extends TType >	Solves systems of linear equations.
Sort <T extends TType >	Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort .
SpaceToBatch <T extends TType >	SpaceToBatch for 4-D tensors of type T.
SpaceToBatchNd <T extends TType >	SpaceToBatch for ND tensors of type T.
SpaceToDepth <T extends TType >	SpaceToDepth for tensors of type T.
SparseAccumulatorApplyGradient	Applies a sparse gradient to a given accumulator.
SparseAccumulatorTakeGradient <T extends TType >	Extracts the average sparse gradient in a SparseConditionalAccumulator.
SparseAdd <T extends TType >	Adds two `SparseTensor` objects to produce another `SparseTensor`.
SparseAddGrad <T extends TType >	The gradient operator for the SparseAdd op.
SparseApplyAdadelta <T extends TType >	var: Should be from a Variable().
SparseApplyAdagrad <T extends TType >	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
SparseApplyAdagradDa <T extends TType >	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
SparseApplyCenteredRmsProp <T extends TType >	Update '*var' according to the centered RMSProp algorithm.
SparseApplyFtrl <T extends TType >	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyMomentum <T extends TType >	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
SparseApplyProximalAdagrad <T extends TType >	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
SparseApplyProximalGradientDescent <T extends TType >	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyRmsProp <T extends TType >	Update '*var' according to the RMSProp algorithm.
SparseBincount <U extends TNumber >	Counts the number of occurrences of each value in an integer array.
SparseConcat <T extends TType >	Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator	A conditional accumulator for aggregating sparse gradients.
SparseCountSparseOutput <U extends TNumber >	Performs sparse-output bin counting for a sparse tensor input.
SparseCross	Generates sparse cross from a list of sparse and dense tensors.
SparseCrossHashed	Generates sparse cross from a list of sparse and dense tensors.
SparseDenseCwiseAdd <T extends TType >	Adds up a SparseTensor and a dense Tensor, using these special rules: (1) Broadcasts the dense side to have the same shape as the sparse side, if eligible; (2) Then, only the dense values pointed to by the indices of the SparseTensor participate in the cwise addition.
SparseDenseCwiseDiv <T extends TType >	Component-wise divides a SparseTensor by a dense Tensor.
SparseDenseCwiseMul <T extends TType >	Component-wise multiplies a SparseTensor by a dense Tensor.
SparseFillEmptyRows <T extends TType >	Fills empty rows in the input 2-D `SparseTensor` with a default value.
SparseFillEmptyRowsGrad <T extends TType >	The gradient of SparseFillEmptyRows.
SparseMatMul	Multiply matrix "a" by matrix "b".
SparseMatrixAdd	Sparse addition of two CSR matrices, C = alpha * A + beta * B.
SparseMatrixMatMul <T extends TType >	Matrix-multiplies a sparse matrix with a dense matrix.
SparseMatrixMul	Element-wise multiplication of a sparse matrix with a dense tensor.
SparseMatrixNNZ	Returns the number of nonzeroes of `sparse_matrix`.
SparseMatrixOrderingAMD	Computes the Approximate Minimum Degree (AMD) ordering of `input`.
SparseMatrixSoftmax	Calculates the softmax of a CSRSparseMatrix.
SparseMatrixSoftmaxGrad	Calculates the gradient of the SparseMatrixSoftmax op.
SparseMatrixSparseCholesky	Computes the sparse Cholesky decomposition of `input`.
SparseMatrixSparseMatMul	Sparse-matrix-multiplies two CSR matrices `a` and `b`.
SparseMatrixTranspose	Transposes the inner (matrix) dimensions of a CSRSparseMatrix.
SparseMatrixZeros	Creates an all-zeros CSRSparseMatrix with shape `dense_shape`.
SparseReduceMax <T extends TNumber >	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse <T extends TNumber >	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceSum <T extends TType >	Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse <T extends TType >	Computes the sum of elements across dimensions of a SparseTensor.
SparseReorder <T extends TType >	Reorders a SparseTensor into the canonical, row-major ordering.
SparseReshape	Reshapes a SparseTensor to represent values in a new dense shape.
SparseSegmentMean <T extends TNumber >	Computes the mean along sparse segments of a tensor.
SparseSegmentMeanGrad <T extends TNumber >	Computes gradients for SparseSegmentMean.
SparseSegmentMeanWithNumSegments <T extends TNumber >	Computes the mean along sparse segments of a tensor.
SparseSegmentSqrtN <T extends TNumber >	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSqrtNGrad <T extends TNumber >	Computes gradients for SparseSegmentSqrtN.
SparseSegmentSqrtNWithNumSegments <T extends TNumber >	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSum <T extends TNumber >	Computes the sum along sparse segments of a tensor.
SparseSegmentSumWithNumSegments <T extends TNumber >	Computes the sum along sparse segments of a tensor.
SparseSlice <T extends TType >	Slice a `SparseTensor` based on the `start` and `size`.
SparseSliceGrad <T extends TType >	The gradient operator for the SparseSlice op.
SparseSoftmax <T extends TNumber >	Applies softmax to a batched ND `SparseTensor`.
SparseSoftmaxCrossEntropyWithLogits <T extends TNumber >	Computes softmax cross entropy cost and gradients to backpropagate.
SparseSparseMaximum <T extends TNumber >	Returns the element-wise max of two SparseTensors.
SparseSparseMinimum <T extends TType >	Returns the element-wise min of two SparseTensors.
SparseSplit <T extends TType >	Split a `SparseTensor` into `num_split` tensors along one dimension.
SparseTensorDenseAdd <U extends TType >	Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
SparseTensorDenseMatMul <U extends TType >	Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
SparseTensorSliceDataset	Creates a dataset that splits a SparseTensor into elements row-wise.
SparseTensorToCSRSparseMatrix	Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.
SparseToDense <U extends TType >	Converts a sparse representation into a dense tensor.
SparseToSparseSetOperation <T extends TType >	Applies set operation along last dimension of 2 `SparseTensor` inputs.
Spence <T extends TNumber >
Split <T extends TType >	Splits a tensor into `num_split` tensors along one dimension.
SplitV <T extends TType >	Splits a tensor into `num_split` tensors along one dimension.
SqlDataset	Creates a dataset that executes a SQL query and emits rows of the result set.
Sqrt <T extends TType >	Computes square root of x element-wise.
SqrtGrad <T extends TType >	Computes the gradient for the sqrt of `x` wrt its input.
Sqrtm <T extends TType >	Computes the matrix square root of one or more square matrices: matmul(sqrtm(A), sqrtm(A)) = A The input matrix should be invertible.
Square <T extends TType >	Computes square of x element-wise.
SquaredDifference <T extends TType >	Returns conj(x - y)(x - y) element-wise.
Squeeze <T extends TType >	Removes dimensions of size 1 from the shape of a tensor.
Stack <T extends TType >	Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Escenario	Stage values similar to a lightweight Enqueue.
StageClear	Op removes all elements in the underlying container.
StagePeek	Op peeks at the values at the specified index.
StageSize	Op returns the number of elements in the underlying container.
StatefulRandomBinomial <V extends TNumber >
StatefulStandardNormal <U extends TType >	Outputs random values from a normal distribution.
StatefulTruncatedNormal <U extends TType >	Outputs random values from a truncated normal distribution.
StatefulUniform <U extends TType >	Outputs random values from a uniform distribution.
StatefulUniformFullInt <U extends TType >	Outputs random integers from a uniform distribution.
StatefulUniformInt <U extends TType >	Outputs random integers from a uniform distribution.
StatelessMultinomial <V extends TNumber >	Draws samples from a multinomial distribution.
StatelessParameterizedTruncatedNormal <V extends TNumber >
StatelessRandomBinomial <W extends TNumber >	Outputs deterministic pseudorandom random numbers from a binomial distribution.
StatelessRandomGamma <V extends TNumber >	Outputs deterministic pseudorandom random numbers from a gamma distribution.
StatelessRandomGetKeyCounterAlg	Picks the best algorithm based on device, and scrambles seed into key and counter.
StatelessRandomNormal <V extends TNumber >	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomNormalV2 <U extends TNumber >	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomPoisson <W extends TNumber >	Outputs deterministic pseudorandom random numbers from a Poisson distribution.
StatelessRandomUniform <V extends TNumber >	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessRandomUniformFullInt <V extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformFullIntV2 <U extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformInt <V extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformIntV2 <U extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformV2 <U extends TNumber >	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessSampleDistortedBoundingBox <T extends TNumber >	Generate a randomly distorted bounding box for an image deterministically.
StatelessTruncatedNormal <V extends TNumber >	Outputs deterministic pseudorandom values from a truncated normal distribution.
StatelessTruncatedNormalV2 <U extends TNumber >	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.
StatsAggregatorHandle
StatsAggregatorSetSummaryWriter	Set a summary_writer_interface to record statistics using given stats_aggregator.
StatsAggregatorSummary	Produces a summary of any statistics recorded by the given statistics manager.
StopGradient <T extends TType >	Stops gradient computation.
StridedSlice <T extends TType >	Return a strided slice from `input`.
StridedSliceAssign <T extends TType >	Assign `value` to the sliced l-value reference of `ref`.
StridedSliceGrad <U extends TType >	Returns the gradient of `StridedSlice`.
StringFormat	Formats a string template using a list of tensors.
StringLength	String lengths of `input`.
StringNGrams <T extends TNumber >	Creates ngrams from ragged string data.
StringSplit	Split elements of `source` based on `sep` into a `SparseTensor`.
Banda	Strip leading and trailing whitespaces from the Tensor.
Sub <T extends TType >	Returns x - y element-wise.
Substr	Return substrings from `Tensor` of strings.
Sum <T extends TType >	Computes the sum of elements across dimensions of a tensor.
SummaryWriter
Svd <T extends TType >	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
SwitchCond <T extends TType >	Forwards `data` to the output port determined by `pred`.
TPUCompilationResult	Returns the result of a TPU compilation.
TPUEmbeddingActivations	An op enabling differentiation of TPU Embeddings.
TPUReplicateMetadata	Metadata indicating how the TPU computation should be replicated.
TPUReplicatedInput <T extends TType >	Connects N inputs to an N-way replicated TPU computation.
TPUReplicatedOutput <T extends TType >	Connects N outputs from an N-way replicated TPU computation.
TakeDataset	Creates a dataset that contains `count` elements from the `input_dataset`.
TakeManySparseFromTensorsMap <T extends TType >	Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
Tan <T extends TType >	Computes tan of x element-wise.
Tanh <T extends TType >	Computes hyperbolic tangent of `x` element-wise.
TanhGrad <T extends TType >	Computes the gradient for the tanh of `x` wrt its input.
TemporaryVariable <T extends TType >	Returns a tensor that may be mutated, but only persists within a single step.
TensorArray	An array of Tensors of given size.
TensorArrayClose	Delete the TensorArray from its resource container.
TensorArrayConcat <T extends TType >	Concat the elements from the TensorArray into value `value`.
TensorArrayGather <T extends TType >	Gather specific elements from the TensorArray into output `value`.
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 <T extends TType >
TensorArrayRead <T extends TType >	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.
TensorDiag <T extends TType >	Returns a diagonal tensor with a given diagonal values.
TensorDiagPart <T extends TType >	Returns the diagonal part of the tensor.
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
TensorForestTreeSerialize	Serializes the tree handle to a proto
TensorForestTreeSize	Get the number of nodes in a tree
TensorListConcat <U extends TType >	Concats all tensors in the list along the 0th dimension.
TensorListConcatLists
TensorListElementShape <T extends TNumber >	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 <T extends TType >	Creates a Tensor by indexing into the TensorList.
TensorListGetItem <T extends TType >
TensorListLength	Returns the number of tensors in the input tensor list.
TensorListPopBack <T extends TType >	Returns the last element of the input list as well as a list with all but that element.
TensorListPushBack	Returns a 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.
TensorListResize	Resizes the list.
TensorListScatter	Creates a TensorList by indexing into a Tensor.
TensorListScatterIntoExistingList	Scatters tensor at indices in an input list.
TensorListSetItem
TensorListSplit	Splits a tensor into a list.
TensorListStack <T extends TType >	Stacks all tensors in the list.
TensorMapErase	Returns a tensor map with item from given key erased.
TensorMapHasKey	Returns whether the given key exists in the map.
TensorMapInsert	Returns a map that is the 'input_handle' with the given key-value pair inserted.
TensorMapLookup <U extends TType >	Returns the value from a given key in a tensor map.
TensorMapSize	Returns the number of tensors in the input tensor map.
TensorMapStackKeys <T extends TType >	Returns a Tensor stack of all keys in a tensor map.
TensorScatterNdAdd <T extends TType >	Adds sparse `updates` to an existing tensor according to `indices`.
TensorScatterNdMax <T extends TType >
TensorScatterNdMin <T extends TType >
TensorScatterNdSub <T extends TType >	Subtracts sparse `updates` from an existing tensor according to `indices`.
TensorScatterNdUpdate <T extends TType >	Scatter `updates` into an existing tensor according to `indices`.
TensorSliceDataset	Creates a dataset that emits each dim-0 slice of `components` once.
TensorStridedSliceUpdate <T extends TType >	Assign `value` to the sliced l-value reference of `input`.
TensorSummary	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'.
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.
ThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ThreadPoolHandle	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Tile <T extends TType >	Constructs a tensor by tiling a given tensor.
TileGrad <T extends TType >	Returns the gradient of `Tile`.
Marca de tiempo	Provides the time since epoch in seconds.
ToBool	Converts a tensor to a scalar predicate.
ToHashBucket	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketFast	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketStrong	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToNumber <T extends TNumber >	Converts each string in the input Tensor to the specified numeric type.
TopK <T extends TNumber >	Finds values and indices of the `k` largest elements for the last dimension.
TopKUnique	Returns the TopK unique values in the array in sorted order.
TopKWithUnique	Returns the TopK values in the array in sorted order.
Transpose <T extends TType >	Shuffle dimensions of x according to a permutation.
TriangularSolve <T extends TType >	Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.
TridiagonalMatMul <T extends TType >	Calculate product with tridiagonal matrix.
TridiagonalSolve <T extends TType >	Solves tridiagonal systems of equations.
TruncateDiv <T extends TType >	Returns x / y element-wise for integer types.
TruncateMod <T extends TNumber >	Returns element-wise remainder of division.
TruncatedNormal <U extends TNumber >	Outputs random values from a truncated normal distribution.
TryRpc	Perform batches of RPC requests.
Unbatch <T extends TType >	Reverses the operation of Batch for a single output Tensor.
UnbatchDataset	A dataset that splits the elements of its input into multiple elements.
UnbatchGrad <T extends TType >	Gradient of Unbatch.
UncompressElement	Uncompresses a compressed dataset element.
UnicodeDecode <T extends TNumber >	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeDecodeWithOffsets <T extends TNumber >	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeEncode	Encode a tensor of ints into unicode strings.
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.
UniformCandidateSampler	Generates labels for candidate sampling with a uniform distribution.
Unique <T extends TType , V extends TNumber >	Finds unique elements along an axis of a tensor.
UniqueDataset	Creates a dataset that contains the unique elements of `input_dataset`.
UniqueWithCounts <T extends TType , V extends TNumber >	Finds unique elements along an axis of a tensor.
UnravelIndex <T extends TNumber >	Converts an array of flat indices into a tuple of coordinate arrays.
UnsortedSegmentJoin	Joins the elements of `inputs` based on `segment_ids`.
UnsortedSegmentMax <T extends TNumber >	Computes the maximum along segments of a tensor.
UnsortedSegmentMin <T extends TNumber >	Computes the minimum along segments of a tensor.
UnsortedSegmentProd <T extends TType >	Computes the product along segments of a tensor.
UnsortedSegmentSum <T extends TType >	Computes the sum along segments of a tensor.
Unstack <T extends TType >	Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
fuera del escenario	Op is similar to a lightweight Dequeue.
UnwrapDatasetVariant
Superior	Converts all lowercase characters into their respective uppercase replacements.
UpperBound <U extends TNumber >	Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp	Creates a handle to a Variable resource.
VarIsInitializedOp	Checks whether a resource handle-based variable has been initialized.
Variable <T extends TType >	Holds state in the form of a tensor that persists across steps.
VariableShape <T extends TNumber >	Returns the shape of the variable pointed to by `resource`.
Dónde	Returns locations of nonzero / true values in a tensor.
WholeFileReader	A Reader that outputs the entire contents of a file as a value.
WindowDataset	Combines (nests of) input elements into a dataset of (nests of) windows.
WorkerHeartbeat	Worker heartbeat op.
WrapDatasetVariant
WriteAudioSummary	Writes an audio summary.
WriteFile	Writes contents to the file at input filename.
WriteGraphSummary	Writes a graph summary.
WriteHistogramSummary	Writes a histogram summary.
WriteImageSummary	Writes an image summary.
WriteRawProtoSummary	Writes a serialized proto summary.
WriteScalarSummary	Writes a scalar summary.
WriteSummary	Writes a tensor summary.
Xdivy <T extends TType >	Returns 0 if x == 0, and x / y otherwise, elementwise.
XlaRecvFromHost <T extends TType >	An op to receive a tensor from the host.
XlaSendToHost	An op to send a tensor to the host.
XlaSetBound	Set a bound for the given input value as a hint to Xla compiler, returns the same value.
XlaSpmdFullToShardShape <T extends TType >	An op used by XLA SPMD partitioner to switch from automatic partitioning to manual partitioning.
XlaSpmdShardToFullShape <T extends TType >	An op used by XLA SPMD partitioner to switch from manual partitioning to automatic partitioning.
Xlog1py <T extends TType >	Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
Xlogy <T extends TType >	Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
ZerosLike <T extends TType >	Returns a tensor of zeros with the same shape and type as x.
Zeta <T extends TNumber >	Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
ZipDataset	Creates a dataset that zips together `input_datasets`.
erfinv <T extends TNumber >

A base class for Op implementations that are backed by a single Operation .

Each operation registered in the TensorFlow core is provided as a RawOp . Custom operations working with only a single operation may also derive from this class.

Public Methods

final boolean	equals (Object obj)
final int	hashCode ()
Operación	op () Return this unit of computation as a single `Operation` .
final String	toString ()

Inherited Methods

From class java.lang.Object

booleano	equals (Object arg0)
final Class<?>	getClass ()
entero	hashCode ()
final void	notify ()
final void	notifyAll ()
Cadena	toString ()
final void	wait (long arg0, int arg1)
final void	wait (long arg0)
final void	esperar ()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env () Return the execution environment this op was created in.
abstract Operation	op () Return this unit of computation as a single `Operation` .

Public Methods

public final boolean equals (Object obj)

public final int hashCode ()

public Operation op ()

Return this unit of computation as a single Operation .

For a raw op, the returned value correspond to the TensorFlow operation wrapped by this op.

For a composite op, the returned value usually correspond to the last operation invoked in a chain or to a NoOp grouping one or more operations as a single unit of computation.

Devoluciones

an Operation

public final String toString ()

public abstract class RawOp

Known Direct Subclasses

Abort , Abs <T extends TNumber >, AccumulateN <T extends TType >, AccumulatorApplyGradient , AccumulatorNumAccumulated , AccumulatorSetGlobalStep , AccumulatorTakeGradient <T extends TType >, Acos <T extends TType >, Acosh <T extends TType >, Add <T extends TType >, AddManySparseToTensorsMap , AddN <T extends TType >, AddSparseToTensorsMap , AdjustContrast <T extends TNumber >, AdjustHue <T extends TNumber >, and 1152 others.

Abortar	Raise a exception to abort the process when called.
Abs <T extends TNumber >	Computes the absolute value of a tensor.
AccumulateN <T extends TType >	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 <T extends TType >	Extracts the average gradient in the given ConditionalAccumulator.
Acos <T extends TType >	Computes acos of x element-wise.
Acosh <T extends TType >	Computes inverse hyperbolic cosine of x element-wise.
Add <T extends TType >	Returns x + y element-wise.
AddManySparseToTensorsMap	Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
AddN <T extends TType >	Add all input tensors element wise.
AddSparseToTensorsMap	Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
AdjustContrast <T extends TNumber >	Adjust the contrast of one or more images.
AdjustHue <T extends TNumber >	Adjust the hue of one or more images.
AdjustSaturation <T extends TNumber >	Adjust the saturation of one or more images.
Todo	Computes the "logical and" of elements across dimensions of a tensor.
AllCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
AllReduce <T extends TNumber >	Mutually reduces multiple tensors of identical type and shape.
AllToAll <T extends TType >	An Op to exchange data across TPU replicas.
Angle <U extends TNumber >	Returns the argument of a complex number.
AnonymousIterator	A container for an iterator resource.
AnonymousMemoryCache
AnonymousMultiDeviceIterator	A container for a multi device iterator resource.
AnonymousRandomSeedGenerator
AnonymousSeedGenerator
Cualquier	Computes the "logical or" of elements across dimensions of a tensor.
ApplyAdaMax <T extends TType >	Update '*var' according to the AdaMax algorithm.
ApplyAdadelta <T extends TType >	Update '*var' according to the adadelta scheme.
ApplyAdagrad <T extends TType >	Update '*var' according to the adagrad scheme.
ApplyAdagradDa <T extends TType >	Update '*var' according to the proximal adagrad scheme.
ApplyAdagradV2 <T extends TType >	Update '*var' according to the adagrad scheme.
ApplyAdam <T extends TType >	Update '*var' according to the Adam algorithm.
ApplyAddSign <T extends TType >	Update '*var' according to the AddSign update.
ApplyCenteredRmsProp <T extends TType >	Update '*var' according to the centered RMSProp algorithm.
ApplyFtrl <T extends TType >	Update '*var' according to the Ftrl-proximal scheme.
ApplyGradientDescent <T extends TType >	Update 'var' by subtracting 'alpha' 'delta' from it.
ApplyMomentum <T extends TType >	Update '*var' according to the momentum scheme.
ApplyPowerSign <T extends TType >	Update '*var' according to the AddSign update.
ApplyProximalAdagrad <T extends TType >	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ApplyProximalGradientDescent <T extends TType >	Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyRmsProp <T extends TType >	Update '*var' according to the RMSProp algorithm.
ApproximateEqual	Returns the truth value of abs(xy) < tolerance element-wise.
ArgMax <V extends TNumber >	Returns the index with the largest value across dimensions of a tensor.
ArgMin <V extends TNumber >	Returns the index with the smallest value across dimensions of a tensor.
AsString	Converts each entry in the given tensor to strings.
Asin <T extends TType >	Computes the trignometric inverse sine of x element-wise.
Asinh <T extends TType >	Computes inverse hyperbolic sine of x element-wise.
AssertCardinalityDataset
AssertNextDataset
AssertThat	Asserts that the given condition is true.
Assign <T extends TType >	Update 'ref' by assigning 'value' to it.
AssignAdd <T extends TType >	Update 'ref' by adding 'value' to it.
AssignAddVariableOp	Adds a value to the current value of a variable.
AssignSub <T extends TType >	Update 'ref' by subtracting 'value' from it.
AssignSubVariableOp	Subtracts a value from the current value of a variable.
AssignVariableOp	Assigns a new value to a variable.
Atan <T extends TType >	Computes the trignometric inverse tangent of x element-wise.
Atan2 <T extends TNumber >	Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
Atanh <T extends TType >	Computes inverse hyperbolic tangent of x element-wise.
AudioSpectrogram	Produces a visualization of audio data over time.
AudioSummary	Outputs a `Summary` protocol buffer with audio.
AutoShardDataset	Creates a dataset that shards the input dataset.
AvgPool <T extends TNumber >	Performs average pooling on the input.
AvgPool3d <T extends TNumber >	Performs 3D average pooling on the input.
AvgPool3dGrad <T extends TNumber >	Computes gradients of average pooling function.
AvgPoolGrad <T extends TNumber >	Computes gradients of the average pooling function.
BandPart <T extends TType >	Copy a tensor setting everything outside a central band in each innermost matrix to zero.
BandedTriangularSolve <T extends TType >
Barrera	Defines a barrier that persists across different graph executions.
BarrierClose	Closes the given barrier.
BarrierIncompleteSize	Computes the number of incomplete elements in the given barrier.
BarrierInsertMany	For each key, assigns the respective value to the specified component.
BarrierReadySize	Computes the number of complete elements in the given barrier.
BarrierTakeMany	Takes the given number of completed elements from a barrier.
Lote	Batches all input tensors nondeterministically.
BatchCholesky <T extends TNumber >
BatchCholeskyGrad <T extends TNumber >
BatchDataset	Creates a dataset that batches `batch_size` elements from `input_dataset`.
BatchFft
BatchFft2d
BatchFft3d
BatchIfft
BatchIfft2d
BatchIfft3d
BatchMatMul <T extends TType >	Multiplies slices of two tensors in batches.
BatchMatrixBandPart <T extends TType >
BatchMatrixDeterminant <T extends TType >
BatchMatrixDiag <T extends TType >
BatchMatrixDiagPart <T extends TType >
BatchMatrixInverse <T extends TNumber >
BatchMatrixSetDiag <T extends TType >
BatchMatrixSolve <T extends TNumber >
BatchMatrixSolveLs <T extends TNumber >
BatchMatrixTriangularSolve <T extends TNumber >
BatchNormWithGlobalNormalization <T extends TType >	Batch normalization.
BatchNormWithGlobalNormalizationGrad <T extends TType >	Gradients for batch normalization.
BatchSelfAdjointEig <T extends TNumber >
BatchSvd <T extends TType >
BatchToSpace <T extends TType >	BatchToSpace for 4-D tensors of type T.
BatchToSpaceNd <T extends TType >	BatchToSpace for ND tensors of type T.
BesselI0 <T extends TNumber >
BesselI0e <T extends TNumber >
BesselI1 <T extends TNumber >
BesselI1e <T extends TNumber >
BesselJ0 <T extends TNumber >
BesselJ1 <T extends TNumber >
BesselK0 <T extends TNumber >
BesselK0e <T extends TNumber >
BesselK1 <T extends TNumber >
BesselK1e <T extends TNumber >
BesselY0 <T extends TNumber >
BesselY1 <T extends TNumber >
Betainc <T extends TNumber >	Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
BiasAdd <T extends TType >	Adds `bias` to `value`.
BiasAddGrad <T extends TType >	The backward operation for "BiasAdd" on the "bias" tensor.
Bincount <T extends TNumber >	Counts the number of occurrences of each value in an integer array.
Bitcast <U extends TType >	Bitcasts a tensor from one type to another without copying data.
BitwiseAnd <T extends TNumber >	Elementwise computes the bitwise AND of `x` and `y`.
BitwiseOr <T extends TNumber >	Elementwise computes the bitwise OR of `x` and `y`.
BitwiseXor <T extends TNumber >	Elementwise computes the bitwise XOR of `x` and `y`.
BlockLSTM <T extends TNumber >	Computes the LSTM cell forward propagation for all the time steps.
BlockLSTMGrad <T extends TNumber >	Computes the LSTM cell backward propagation for the entire time sequence.
BoostedTreesAggregateStats	Aggregates the summary of accumulated stats for the batch.
BoostedTreesBucketize	Bucketize each feature based on bucket boundaries.
BoostedTreesCalculateBestFeatureSplit	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCalculateBestFeatureSplitV2	Calculates gains for each feature and returns the best possible split information for each node.
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.
BoostedTreesDeserializeEnsemble	Deserializes a serialized tree ensemble config and replaces current tree conjunto.
BoostedTreesEnsembleResourceHandleOp	Creates a handle to a BoostedTreesEnsembleResource
BoostedTreesExampleDebugOutputs	Debugging/model interpretability outputs for each example.
BoostedTreesFlushQuantileSummaries	Flush the quantile summaries from each quantile stream resource.
BoostedTreesGetEnsembleStates	Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
BoostedTreesMakeQuantileSummaries	Makes the summary of quantiles for the batch.
BoostedTreesMakeStatsSummary	Makes the summary of accumulated stats for the batch.
BoostedTreesPredict	Runs multiple additive regression ensemble predictors on input instances and computes the logits.
BoostedTreesQuantileStreamResourceAddSummaries	Add the quantile summaries to each quantile stream resource.
BoostedTreesQuantileStreamResourceDeserialize	Deserialize bucket boundaries and ready flag into current QuantileAccumulator.
BoostedTreesQuantileStreamResourceFlush	Flush the summaries for a quantile stream resource.
BoostedTreesQuantileStreamResourceGetBucketBoundaries	Generate the bucket boundaries for each feature based on accumulated summaries.
BoostedTreesQuantileStreamResourceHandleOp	Creates a handle to a BoostedTreesQuantileStreamResource.
BoostedTreesSerializeEnsemble	Serializes the tree ensemble to a proto.
BoostedTreesSparseAggregateStats	Aggregates the summary of accumulated stats for the batch.
BoostedTreesSparseCalculateBestFeatureSplit	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesTrainingPredict	Runs multiple additive regression ensemble predictors on input instances and computes the update to cached logits.
BoostedTreesUpdateEnsemble	Updates the tree ensemble by either adding a layer to the last tree being grown or by starting a new tree.
BoostedTreesUpdateEnsembleV2	Updates the tree ensemble by adding a layer to the last tree being grown or by starting a new tree.
BroadcastDynamicShape <T extends TNumber >	Return the shape of s0 op s1 with broadcast.
BroadcastGradientArgs <T extends TNumber >	Return the reduction indices for computing gradients of s0 op s1 with broadcast.
BroadcastHelper <T extends TType >	Helper operator for performing XLA-style broadcasts Broadcasts `lhs` and `rhs` to the same rank, by adding size 1 dimensions to whichever of `lhs` and `rhs` has the lower rank, using XLA's broadcasting rules for binary operators.
BroadcastRecv <T extends TType >	Receives a tensor value broadcast from another device.
BroadcastSend <T extends TType >	Broadcasts a tensor value to one or more other devices.
BroadcastTo <T extends TType >	Broadcast an array for a compatible shape.
Bucketize	Bucketizes 'input' based on 'boundaries'.
BytesProducedStatsDataset	Records the bytes size of each element of `input_dataset` in a StatsAggregator.
CSRSparseMatrixComponents <T extends TType >	Reads out the CSR components at batch `index`.
CSRSparseMatrixToDense <T extends TType >	Convert a (possibly batched) CSRSparseMatrix to dense.
CSRSparseMatrixToSparseTensor <T extends TType >	Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.
CSVDataset
CSVDatasetV2
CTCLossV2	Calculates the CTC Loss (log probability) for each batch entry.
CacheDataset	Creates a dataset that caches elements from `input_dataset`.
CacheDatasetV2
Cast <U extends TType >	Cast x of type SrcT to y of DstT.
Ceil <T extends TNumber >	Returns element-wise smallest integer not less than x.
CheckNumerics <T extends TNumber >	Checks a tensor for NaN, -Inf and +Inf values.
Cholesky <T extends TType >	Computes the Cholesky decomposition of one or more square matrices.
CholeskyGrad <T extends TNumber >	Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
ChooseFastestDataset
ClipByValue <T extends TType >	Clips tensor values to a specified min and max.
CloseSummaryWriter
ClusterOutput <T extends TType >	Operator that connects the output of an XLA computation to other consumer graph nodes.
CollectiveGather <T extends TNumber >	Mutually accumulates multiple tensors of identical type and shape.
CollectivePermute <T extends TType >	An Op to permute tensors across replicated TPU instances.
CombinedNonMaxSuppression	Greedily selects a subset of bounding boxes in descending order of score, This operation performs non_max_suppression on the inputs per batch, across all classes.
CompareAndBitpack	Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
CompilationResult	Returns the result of a TPU compilation.
CompileSucceededAssert	Asserts that compilation succeeded.
Complex <U extends TType >	Converts two real numbers to a complex number.
ComplexAbs <U extends TNumber >	Computes the complex absolute value of a tensor.
CompressElement	Compresses a dataset element.
ComputeAccidentalHits	Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeBatchSize	Computes the static batch size of a dataset sans partial batches.
Concat <T extends TType >	Concatenates tensors along one dimension.
ConcatenateDataset	Creates a dataset that concatenates `input_dataset` with `another_dataset`.
ConditionalAccumulator	A conditional accumulator for aggregating gradients.
ConfigureDistributedTPU	Sets up the centralized structures for a distributed TPU system.
ConfigureTPUEmbedding	Sets up TPUEmbedding in a distributed TPU system.
Conj <T extends TType >	Returns the complex conjugate of a complex number.
ConjugateTranspose <T extends TType >	Shuffle dimensions of x according to a permutation and conjugate the result.
Constant <T extends TType >	An operator producing a constant value.
ConsumeMutexLock	This op consumes a lock created by `MutexLock`.
ControlTrigger	No hace nada.
Conv <T extends TType >	Wraps the XLA ConvGeneralDilated operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution .
Conv2d <T extends TNumber >	Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2dBackpropFilter <T extends TNumber >	Computes the gradients of convolution with respect to the filter.
Conv2dBackpropInput <T extends TNumber >	Computes the gradients of convolution with respect to the input.
Conv3d <T extends TNumber >	Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3dBackpropFilter <T extends TNumber >	Computes the gradients of 3-D convolution with respect to the filter.
Conv3dBackpropInput <U extends TNumber >	Computes the gradients of 3-D convolution with respect to the input.
Copy <T extends TType >	Copy a tensor from CPU-to-CPU or GPU-to-GPU.
CopyHost <T extends TType >	Copy a tensor to host.
Cos <T extends TType >	Computes cos of x element-wise.
Cosh <T extends TType >	Computes hyperbolic cosine of x element-wise.
CountUpTo <T extends TNumber >	Increments 'ref' until it reaches 'limit'.
CreateSummaryDbWriter
CreateSummaryFileWriter
CropAndResize	Extracts crops from the input image tensor and resizes them.
CropAndResizeGradBoxes	Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradImage <T extends TNumber >	Computes the gradient of the crop_and_resize op wrt the input image tensor.
Cross <T extends TNumber >	Compute the pairwise cross product.
CrossReplicaSum <T extends TNumber >	An Op to sum inputs across replicated TPU instances.
CtcBeamSearchDecoder <T extends TNumber >	Performs beam search decoding on the logits given in input.
CtcGreedyDecoder <T extends TNumber >	Performs greedy decoding on the logits given in inputs.
CtcLoss <T extends TNumber >	Calculates the CTC Loss (log probability) for each batch entry.
CudnnRNN <T extends TNumber >	A RNN backed by cuDNN.
CudnnRNNBackprop <T extends TNumber >	Backprop step of CudnnRNNV3.
CudnnRNNCanonicalToParams <T extends TNumber >	Converts CudnnRNN params from canonical form to usable form.
CudnnRNNParamsToCanonical <T extends TNumber >	Retrieves CudnnRNN params in canonical form.
CudnnRnnParamsSize <U extends TNumber >	Computes size of weights that can be used by a Cudnn RNN model.
Cumprod <T extends TType >	Compute the cumulative product of the tensor `x` along `axis`.
Cumsum <T extends TType >	Compute the cumulative sum of the tensor `x` along `axis`.
CumulativeLogsumexp <T extends TNumber >	Compute the cumulative product of the tensor `x` along `axis`.
DataFormatDimMap <T extends TNumber >	Returns the dimension index in the destination data format given the one in the source data format.
DataFormatVecPermute <T extends TNumber >	Permute input tensor from `src_format` to `dst_format`.
DataServiceDataset
DatasetCardinality	Returns the cardinality of `input_dataset`.
DatasetFromGraph	Creates a dataset from the given `graph_def`.
DatasetToGraph	Returns a serialized GraphDef representing `input_dataset`.
DatasetToSingleElement	Outputs the single element from the given dataset.
DatasetToTFRecord	Writes the given dataset to the given file using the TFRecord format.
DatasetToTfRecord	Writes the given dataset to the given file using the TFRecord format.
Dawsn <T extends TNumber >
DebugGradientIdentity <T extends TType >	Identity op for gradient debugging.
DebugGradientRefIdentity <T extends TType >	Identity op for gradient debugging.
DebugIdentity <T extends TType >	Debug Identity V2 Op.
DebugNanCount	Debug NaN Value Counter Op.
DebugNumericsSummary <U extends TNumber >	Debug Numeric Summary V2 Op.
DecodeAndCropJpeg	Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeBase64	Decode web-safe base64-encoded strings.
DecodeBmp	Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeCompressed	Decompress strings.
DecodeCsv	Convert CSV records to tensors.
DecodeGif	Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
DecodeImage <T extends TNumber >	Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.
DecodeJpeg	Decode a JPEG-encoded image to a uint8 tensor.
DecodeJsonExample	Convert JSON-encoded Example records to binary protocol buffer strings.
DecodePaddedRaw <T extends TNumber >	Reinterpret the bytes of a string as a vector of numbers.
DecodePng <T extends TNumber >	Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodeProto	The op extracts fields from a serialized protocol buffers message into tensors.
DecodeRaw <T extends TType >	Reinterpret the bytes of a string as a vector of numbers.
DecodeWav	Decode a 16-bit PCM WAV file to a float tensor.
DeepCopy <T extends TType >	Makes a copy of `x`.
DeleteIterator	A container for an iterator resource.
DeleteMemoryCache
DeleteMultiDeviceIterator	A container for an iterator resource.
DeleteRandomSeedGenerator
DeleteSeedGenerator
DeleteSessionTensor	Delete the tensor specified by its handle in the session.
DenseBincount <U extends TNumber >	Counts the number of occurrences of each value in an integer array.
DenseCountSparseOutput <U extends TNumber >	Performs sparse-output bin counting for a tf.tensor input.
DenseToCSRSparseMatrix	Converts a dense tensor to a (possibly batched) CSRSparseMatrix.
DenseToDenseSetOperation <T extends TType >	Applies set operation along last dimension of 2 `Tensor` inputs.
DenseToSparseBatchDataset	Creates a dataset that batches input elements into a SparseTensor.
DenseToSparseSetOperation <T extends TType >	Applies set operation along last dimension of `Tensor` and `SparseTensor`.
DepthToSpace <T extends TType >	DepthToSpace for tensors of type T.
DepthwiseConv2dNative <T extends TNumber >	Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNativeBackpropFilter <T extends TNumber >	Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropInput <T extends TNumber >	Computes the gradients of depthwise convolution with respect to the input.
Dequantize	Takes the packed uint32 input and unpacks the input to uint8 to do Dequantization on device.
DeserializeIterator	Converts the given variant tensor to an iterator and stores it in the given resource.
DeserializeManySparse <T extends TType >	Deserialize and concatenate `SparseTensors` from a serialized minibatch.
DeserializeSparse <U extends TType >	Deserialize `SparseTensor` objects.
DestroyResourceOp	Deletes the resource specified by the handle.
DestroyTemporaryVariable <T extends TType >	Destroys the temporary variable and returns its final value.
Det <T extends TType >	Computes the determinant of one or more square matrices.
DeviceIndex	Return the index of device the op runs.
Digamma <T extends TNumber >	Computes Psi, the derivative of Lgamma (the log of the absolute value of `Gamma(x)`), element-wise.
Dilation2d <T extends TNumber >	Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
Dilation2dBackpropFilter <T extends TNumber >	Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2dBackpropInput <T extends TNumber >	Computes the gradient of morphological 2-D dilation with respect to the input.
DirectedInterleaveDataset	A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
Div <T extends TType >	Returns x / y element-wise.
DivNoNan <T extends TType >	Returns 0 if the denominator is zero.
Dot <T extends TType >	Wraps the XLA DotGeneral operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral .
DrawBoundingBoxes <T extends TNumber >	Draw bounding boxes on a batch of images.
DummyIterationCounter
DummyMemoryCache
DummySeedGenerator
DynamicPartition <T extends TType >	Partitions `data` into `num_partitions` tensors using indices from `partitions`.
DynamicSlice <T extends TType >	Wraps the XLA DynamicSlice operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice .
DynamicStitch <T extends TType >	Interleave the values from the `data` tensors into a single tensor.
DynamicUpdateSlice <T extends TType >	Wraps the XLA DynamicUpdateSlice operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice .
EditDistance	Computes the (possibly normalized) Levenshtein Edit Distance.
Eig <U extends TType >	Computes the eigen decomposition of one or more square matrices.
Einsum <T extends TType >	An op which supports basic einsum op with 2 inputs and 1 output.
Elu <T extends TNumber >	Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
EluGrad <T extends TNumber >	Computes gradients for the exponential linear (Elu) operation.
EmbeddingActivations	An op enabling differentiation of TPU Embeddings.
Empty <T extends TType >	Creates a tensor with the given shape.
EmptyTensorList	Creates and returns an empty tensor list.
EmptyTensorMap	Creates and returns an empty tensor map.
EncodeBase64	Encode strings into web-safe base64 format.
EncodeJpeg	JPEG-encode an image.
EncodeJpegVariableQuality	JPEG encode input image with provided compression quality.
EncodePng	PNG-encode an image.
EncodeProto	The op serializes protobuf messages provided in the input tensors.
EncodeWav	Encode audio data using the WAV file format.
EnqueueTPUEmbeddingIntegerBatch	An op that enqueues a list of input batch tensors to TPUEmbedding.
EnqueueTPUEmbeddingRaggedTensorBatch	Eases the porting of code that uses tf.nn.embedding_lookup().
EnqueueTPUEmbeddingSparseBatch	An op that enqueues TPUEmbedding input indices from a SparseTensor.
EnqueueTPUEmbeddingSparseTensorBatch	Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
EnsureShape <T extends TType >	Ensures that the tensor's shape matches the expected shape.
Enter <T extends TType >	Creates or finds a child frame, and makes `data` available to the child frame.
Igual	Returns the truth value of (x == y) element-wise.
Erf <T extends TNumber >	Computes the Gauss error function of `x` element-wise.
Erfc <T extends TNumber >	Computes the complementary error function of `x` element-wise.
EuclideanNorm <T extends TType >	Computes the euclidean norm of elements across dimensions of a tensor.
Ejecutar	Op that loads and executes a TPU program on a TPU device.
ExecuteAndUpdateVariables	Op that executes a program with optional in-place variable updates.
Exit <T extends TType >	Exits the current frame to its parent frame.
Exp <T extends TType >	Computes exponential of x element-wise.
ExpandDims <T extends TType >	Inserts a dimension of 1 into a tensor's shape.
Expint <T extends TNumber >
Expm1 <T extends TType >	Computes `exp(x) - 1` element-wise.
ExtractGlimpse	Extracts a glimpse from the input tensor.
ExtractImagePatches <T extends TType >	Extract `patches` from `images` and put them in the "depth" output dimension.
ExtractJpegShape <T extends TNumber >	Extract the shape information of a JPEG-encoded image.
ExtractVolumePatches <T extends TNumber >	Extract `patches` from `input` and put them in the `"depth"` output dimension.
Hecho	Output a fact about factorials.
FakeQuantWithMinMaxArgs	Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgsGradient	Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxVars	Fake-quantize the 'inputs' tensor of type float via global float scalars Fake-quantize the `inputs` tensor of type float via global float scalars `min` and `max` to `outputs` tensor of same shape as `inputs`.
FakeQuantWithMinMaxVarsGradient	Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsPerChannel	Fake-quantize the 'inputs' tensor of type float via per-channel floats Fake-quantize the `inputs` tensor of type float per-channel and one of the shapes: `[d]`, `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]` to `outputs` tensor of same shape as `inputs`.
FakeQuantWithMinMaxVarsPerChannelGradient	Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
Fft <T extends TType >	Transformada rápida de Fourier.
Fft2d <T extends TType >	2D fast Fourier transform.
Fft3d <T extends TType >	3D fast Fourier transform.
FifoQueue	A queue that produces elements in first-in first-out order.
Fill <U extends TType >	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.
Huella dactilar	Generates fingerprint values.
FixedLengthRecordDataset
FixedLengthRecordReader	A Reader that outputs fixed-length records from a file.
FixedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
Floor <T extends TNumber >	Returns element-wise largest integer not greater than x.
FloorDiv <T extends TType >	Returns x // y element-wise.
FloorMod <T extends TNumber >	Returns element-wise remainder of division.
FlushSummaryWriter
FractionalAvgPool <T extends TNumber >	Performs fractional average pooling on the input.
FractionalAvgPoolGrad <T extends TNumber >	Computes gradient of the FractionalAvgPool function.
FractionalMaxPool <T extends TNumber >	Performs fractional max pooling on the input.
FractionalMaxPoolGrad <T extends TNumber >	Computes gradient of the FractionalMaxPool function.
FresnelCos <T extends TNumber >
FresnelSin <T extends TNumber >
FusedBatchNorm <T extends TNumber , U extends TNumber >	Batch normalization.
FusedBatchNormGrad <T extends TNumber , U extends TNumber >	Gradient for batch normalization.
FusedPadConv2d <T extends TNumber >	Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2d <T extends TNumber >	Performs a resize and padding as a preprocess during a convolution.
GRUBlockCell <T extends TNumber >	Computes the GRU cell forward propagation for 1 time step.
GRUBlockCellGrad <T extends TNumber >	Computes the GRU cell back-propagation for 1 time step.
Gather <T extends TType >	Wraps the XLA Gather operator documented at https://www.tensorflow.org/xla/operation_semantics#gather
GatherNd <T extends TType >	Gather slices from `params` into a Tensor with shape specified by `indices`.
GatherV2 <T extends TNumber >	Mutually accumulates multiple tensors of identical type and shape.
GenerateBoundingBoxProposals	This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497 The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors, applies non-maximal suppression on overlapping boxes with higher than `nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter side is less than `min_size`.
GenerateVocabRemapping	Given a path to new and old vocabulary files, returns a remapping Tensor of length `num_new_vocab`, where `remapping[i]` contains the row number in the old vocabulary that corresponds to row `i` in the new vocabulary (starting at line `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` in the new vocabulary is not in the old vocabulary.
GetSessionHandle	Store the input tensor in the state of the current session.
GetSessionTensor <T extends TType >	Get the value of the tensor specified by its handle.
Mayor que	Returns the truth value of (x > y) element-wise.
GreaterEqual	Returns the truth value of (x >= y) element-wise.
GuaranteeConst <T extends TType >	Gives a guarantee to the TF runtime that the input tensor is a constant.
HashTable	Creates a non-initialized hash table.
HistogramFixedWidth <U extends TNumber >	Return histogram of values.
HistogramSummary	Outputs a `Summary` protocol buffer with a histogram.
HsvToRgb <T extends TNumber >	Convert one or more images from HSV to RGB.
Identity <T extends TType >	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 tensores.
IdentityReader	A Reader that outputs the queued work as both the key and value.
Ifft <T extends TType >	Inverse fast Fourier transform.
Ifft2d <T extends TType >	Inverse 2D fast Fourier transform.
Ifft3d <T extends TType >	Inverse 3D fast Fourier transform.
Igamma <T extends TNumber >	Compute the lower regularized incomplete Gamma function `P(a, x)`.
IgammaGradA <T extends TNumber >	Computes the gradient of `igamma(a, x)` wrt `a`.
Igammac <T extends TNumber >	Compute the upper regularized incomplete Gamma function `Q(a, x)`.
IgnoreErrorsDataset	Creates a dataset that contains the elements of `input_dataset` ignoring errors.
Imag <U extends TNumber >	Returns the imaginary part of a complex number.
ImageProjectiveTransformV2 <T extends TNumber >	Applies the given transform to each of the images.
ImageProjectiveTransformV3 <T extends TNumber >	Applies the given transform to each of the images.
ImageSummary	Outputs a `Summary` protocol buffer with images.
ImmutableConst <T extends TType >	Returns immutable tensor from memory region.
ImportEvent
InTopK	Says whether the targets are in the top `K` predictions.
InfeedDequeue <T extends TType >	A placeholder op for a value that will be fed into the computation.
InfeedDequeueTuple	Fetches multiple values from infeed as an XLA tuple.
InfeedEnqueue	An op which feeds a single Tensor value into the computation.
InfeedEnqueuePrelinearizedBuffer	An op which enqueues prelinearized buffer into TPU infeed.
InfeedEnqueueTuple	Feeds multiple Tensor values into the computation as an XLA tuple.
inicio
InitializeTable	Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromDataset
InitializeTableFromTextFile	Initializes a table from a text file.
InplaceAdd <T extends TType >	Adds v into specified rows of x.
InplaceSub <T extends TType >	Subtracts `v` into specified rows of `x`.
InplaceUpdate <T extends TType >	Updates specified rows 'i' with values 'v'.
Inv <T extends TType >	Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes).
InvGrad <T extends TType >	Computes the gradient for the inverse of `x` wrt its input.
Invert <T extends TNumber >	Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.
InvertPermutation <T extends TNumber >	Computes the inverse permutation of a tensor.
Irfft <U extends TNumber >	Inverse real-valued fast Fourier transform.
Irfft2d <U extends TNumber >	Inverse 2D real-valued fast Fourier transform.
Irfft3d <U extends TNumber >	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.
IsotonicRegression <U extends TNumber >	Solves a batch of isotonic regression problems.
Iterador
IteratorFromStringHandle
IteratorGetDevice	Returns the name of the device on which `resource` has been placed.
IteratorGetNext	Gets the next output from the given iterator .
IteratorGetNextAsOptional	Gets the next output from the given iterator as an Optional variant.
IteratorGetNextSync	Gets the next output from the given iterator.
IteratorToStringHandle	Converts the given `resource_handle` representing an iterator to a string.
Unirse	Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator).
KMC2ChainInitialization	Returns the index of a data point that should be added to the seed set.
KeyValueSort <T extends TNumber , U extends TType >	Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort .
KmeansPlusPlusInitialization	Selects num_to_sample rows of input using the KMeans++ criterion.
KthOrderStatistic	Computes the Kth order statistic of a data set.
L2Loss <T extends TNumber >	L2 Loss.
LMDBDataset	Creates a dataset that emits the key-value pairs in one or more LMDB files.
LSTMBlockCell <T extends TNumber >	Computes the LSTM cell forward propagation for 1 time step.
LSTMBlockCellGrad <T extends TNumber >	Computes the LSTM cell backward propagation for 1 timestep.
LatencyStatsDataset	Records the latency of producing `input_dataset` elements in a StatsAggregator.
LeakyRelu <T extends TNumber >	Computes rectified linear: `max(features, features * alpha)`.
LeakyReluGrad <T extends TNumber >	Computes rectified linear gradients for a LeakyRelu operation.
LearnedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
LeftShift <T extends TNumber >	Elementwise computes the bitwise left-shift of `x` and `y`.
Menos	Returns the truth value of (x < y) element-wise.
LessEqual	Returns the truth value of (x <= y) element-wise.
Lgamma <T extends TNumber >	Computes the log of the absolute value of `Gamma(x)` element-wise.
LinSpace <T extends TNumber >	Generates values in an interval.
LmdbDataset
LmdbReader	A Reader that outputs the records from a LMDB file.
LoadAndRemapMatrix	Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint at `ckpt_path` and potentially reorders its rows and columns using the specified remappings.
LoadTPUEmbeddingADAMParameters	Load ADAM embedding parameters.
LoadTPUEmbeddingADAMParametersGradAccumDebug	Load ADAM embedding parameters with debug support.
LoadTPUEmbeddingAdadeltaParameters	Load Adadelta embedding parameters.
LoadTPUEmbeddingAdadeltaParametersGradAccumDebug	Load Adadelta parameters with debug support.
LoadTPUEmbeddingAdagradParameters	Load Adagrad embedding parameters.
LoadTPUEmbeddingAdagradParametersGradAccumDebug	Load Adagrad embedding parameters with debug support.
LoadTPUEmbeddingCenteredRMSPropParameters	Load centered RMSProp embedding parameters.
LoadTPUEmbeddingFTRLParameters	Load FTRL embedding parameters.
LoadTPUEmbeddingFTRLParametersGradAccumDebug	Load FTRL embedding parameters with debug support.
LoadTPUEmbeddingMDLAdagradLightParameters	Load MDL Adagrad Light embedding parameters.
LoadTPUEmbeddingMomentumParameters	Load Momentum embedding parameters.
LoadTPUEmbeddingMomentumParametersGradAccumDebug	Load Momentum embedding parameters with debug support.
LoadTPUEmbeddingProximalAdagradParameters	Load proximal Adagrad embedding parameters.
LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug	Load proximal Adagrad embedding parameters with debug support.
LoadTPUEmbeddingProximalYogiParameters
LoadTPUEmbeddingProximalYogiParametersGradAccumDebug
LoadTPUEmbeddingRMSPropParameters	Load RMSProp embedding parameters.
LoadTPUEmbeddingRMSPropParametersGradAccumDebug	Load RMSProp embedding parameters with debug support.
LoadTPUEmbeddingStochasticGradientDescentParameters	Load SGD embedding parameters.
LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug	Load SGD embedding parameters.
LocalResponseNormalization <T extends TNumber >	Local Response Normalization.
LocalResponseNormalizationGrad <T extends TNumber >	Gradients for Local Response Normalization.
Log <T extends TType >	Computes natural logarithm of x element-wise.
Log1p <T extends TType >	Computes natural logarithm of (1 + x) element-wise.
LogMatrixDeterminant <T extends TType >	Computes the sign and the log of the absolute value of the determinant of one or more square matrices.
LogSoftmax <T extends TNumber >	Computes log softmax activations.
LogUniformCandidateSampler	Generates labels for candidate sampling with a log-uniform distribution.
LogicalAnd	Returns the truth value of x AND y element-wise.
LogicalNot	Returns the truth value of `NOT x` element-wise.
LogicalOr	Returns the truth value of x OR y element-wise.
LookupTableExport <T extends TType , U extends TType >	Outputs all keys and values in the table.
LookupTableFind <U extends TType >	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.
Más bajo	Converts all uppercase characters into their respective lowercase replacements.
LowerBound <U extends TNumber >	Applies lower_bound(sorted_search_values, values) along each row.
Lu <T extends TType , U extends TNumber >	Computes the LU decomposition of one or more square matrices.
MakeIterator	Makes a new iterator from the given `dataset` and stores it in `iterator`.
MakeUnique	Make all elements in the non-Batch dimension unique, but \"close\" to their initial value.
MapClear	Op removes all elements in the underlying container.
MapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
MapPeek	Op peeks at the values at the specified key.
MapSize	Op returns the number of elements in the underlying container.
MapStage	Stage (key, values) in the underlying container which behaves like a hashtable.
MapUnstage	Op removes and returns the values associated with the key from the underlying container.
MapUnstageNoKey	Op removes and returns a random (key, value) from the underlying container.
MatMul <T extends TType >	Multiply the matrix "a" by the matrix "b".
MatchingFiles	Returns the set of files matching one or more glob patterns.
MatchingFilesDataset
MatrixDiag <T extends TType >	Returns a batched diagonal tensor with given batched diagonal values.
MatrixDiagPart <T extends TType >	Returns the batched diagonal part of a batched tensor.
MatrixDiagPartV3 <T extends TType >	Returns the batched diagonal part of a batched tensor.
MatrixDiagV3 <T extends TType >	Returns a batched diagonal tensor with given batched diagonal values.
MatrixLogarithm <T extends TType >	Computes the matrix logarithm of one or more square matrices: \\(log(exp(A)) = A\\) This op is only defined for complex matrices.
MatrixSetDiag <T extends TType >	Returns a batched matrix tensor with new batched diagonal values.
MatrixSolveLs <T extends TType >	Solves one or more linear least-squares problems.
Max <T extends TType >	Computes the maximum of elements across dimensions of a tensor.
MaxIntraOpParallelismDataset	Creates a dataset that overrides the maximum intra-op parallelism.
MaxPool <T extends TType >	Performs max pooling on the input.
MaxPool3d <T extends TNumber >	Performs 3D max pooling on the input.
MaxPool3dGrad <U extends TNumber >	Computes gradients of 3D max pooling function.
MaxPool3dGradGrad <T extends TNumber >	Computes second-order gradients of the maxpooling function.
MaxPoolGrad <T extends TNumber >	Computes gradients of the maxpooling function.
MaxPoolGradGrad <T extends TNumber >	Computes second-order gradients of the maxpooling function.
MaxPoolGradGradWithArgmax <T extends TNumber >	Computes second-order gradients of the maxpooling function.
MaxPoolGradWithArgmax <T extends TNumber >	Computes gradients of the maxpooling function.
MaxPoolWithArgmax <T extends TNumber , U extends TNumber >	Performs max pooling on the input and outputs both max values and indices.
Maximum <T extends TNumber >	Returns the max of x and y (ie
Mean <T extends TType >	Computes the mean of elements across dimensions of a tensor.
Merge <T extends TType >	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.
Mfcc	Transforms a spectrogram into a form that's useful for speech recognition.
Min <T extends TType >	Computes the minimum of elements across dimensions of a tensor.
Minimum <T extends TNumber >	Returns the min of x and y (ie
MirrorPad <T extends TType >	Pads a tensor with mirrored values.
MirrorPadGrad <T extends TType >	Gradient op for `MirrorPad` op.
MlirPassthroughOp	Wraps an arbitrary MLIR computation expressed as a module with a main() function.
Mod <T extends TNumber >	Returns element-wise remainder of division.
ModelDataset	Identity transformation that models performance.
Mul <T extends TType >	Returns x * y element-wise.
MulNoNan <T extends TType >	Returns x * y element-wise.
MultiDeviceIterator	Creates a MultiDeviceIterator resource.
MultiDeviceIteratorFromStringHandle	Generates a MultiDeviceIterator resource from its provided string handle.
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 <U extends TNumber >	Draws samples from a multinomial distribution.
MutableDenseHashTable	Creates an empty hash table that uses tensors as the backing store.
MutableHashTable	Creates an empty hash table.
MutableHashTableOfTensors	Creates an empty hash table.
Mutex	Creates a Mutex resource that can be locked by `MutexLock`.
MutexLock	Locks a mutex resource.
NcclAllReduce <T extends TNumber >	Outputs a tensor containing the reduction across all input tensors.
NcclBroadcast <T extends TNumber >	Sends `input` to all devices that are connected to the output.
NcclReduce <T extends TNumber >	Reduces `input` from `num_devices` using `reduction` to a single device.
Ndtri <T extends TNumber >
NearestNeighbors	Selects the k nearest centers for each point.
Neg <T extends TType >	Computes numerical negative value element-wise.
NegTrain	Training via negative sampling.
NextAfter <T extends TNumber >	Returns the next representable value of `x1` in the direction of `x2`, element-wise.
NextIteration <T extends TType >	Makes its input available to the next iteration.
NoOp	No hace nada.
NonDeterministicInts <U extends TType >	Non-deterministically generates some integers.
NonMaxSuppression <T extends TNumber >	Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
NonMaxSuppressionWithOverlaps	Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high overlaps with previously selected boxes.
NonSerializableDataset
NotEqual	Returns the truth value of (x != y) element-wise.
NthElement <T extends TNumber >	Finds values of the `n`-th order statistic for the last dimension.
OneHot <U extends TType >	Returns a one-hot tensor.
OnesLike <T extends TType >	Returns a tensor of ones with the same shape and type as x.
OptimizeDataset	Creates a dataset by applying optimizations to `input_dataset`.
OptimizeDatasetV2	Creates a dataset by applying related 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.
OrderedMapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
OrderedMapPeek	Op peeks at the values at the specified key.
OrderedMapSize	Op returns the number of elements in the underlying container.
OrderedMapStage	Stage (key, values) in the underlying container which behaves like a ordered associative container.
OrderedMapUnstage	Op removes and returns the values associated with the key from the underlying container.
OrderedMapUnstageNoKey	Op removes and returns the (key, value) element with the smallest key from the underlying container.
OrdinalSelector	A TPU core selector Op.
OutfeedDequeue <T extends TType >	Retrieves a single tensor from the computation outfeed.
OutfeedDequeueTuple	Retrieve multiple values from the computation outfeed.
OutfeedDequeueTupleV2	Retrieve multiple values from the computation outfeed.
OutfeedDequeueV2 <T extends TType >	Retrieves a single tensor from the computation outfeed.
OutfeedEnqueue	Enqueue a Tensor on the computation outfeed.
OutfeedEnqueueTuple	Enqueue multiple Tensor values on the computation outfeed.
Pad <T extends TType >	Wraps the XLA Pad operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#pad .
PaddedBatchDataset	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.
ParallelConcat <T extends TType >	Concatenates a list of `N` tensors along the first dimension.
ParallelDynamicStitch <T extends TType >	Interleave the values from the `data` tensors into a single tensor.
ParameterizedTruncatedNormal <U extends TNumber >	Outputs random values from a normal distribution.
ParseExample	Transforms a vector of tf.Example protos (as strings) into typed tensors.
ParseExampleDataset	Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
ParseSequenceExample	Transforms a vector of tf.io.SequenceExample protos (as strings) into typed tensors.
ParseSingleExample	Transforms a tf.Example proto (as a string) into typed tensors.
ParseSingleSequenceExample	Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
ParseTensor <T extends TType >	Transforms a serialized tensorflow.TensorProto proto into a Tensor.
PartitionedInput <T extends TType >	An op that groups a list of partitioned inputs together.
PartitionedOutput <T extends TType >	An op that demultiplexes a tensor to be sharded by XLA to a list of partitioned outputs outside the XLA computation.
Placeholder <T extends TType >	A placeholder op for a value that will be fed into the computation.
PlaceholderWithDefault <T extends TType >	A placeholder op that passes through `input` when its output is not fed.
Polygamma <T extends TNumber >	Compute the polygamma function \\(\psi^{(n)}(x)\\).
PopulationCount	Computes element-wise population count (aka
Pow <T extends TType >	Computes the power of one value to another.
PrefetchDataset	Creates a dataset that asynchronously prefetches elements from `input_dataset`.
Prelinearize	An op which linearizes one Tensor value to an opaque variant tensor.
PrelinearizeTuple	An op which linearizes multiple Tensor values to an opaque variant tensor.
PreventGradient <T extends TType >	An identity op that triggers an error if a gradient is requested.
Imprimir	Prints a string scalar.
Cola de prioridad	A queue that produces elements sorted by the first component value.
PrivateThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Prod <T extends TType >	Computes the product of elements across dimensions of a tensor.
Qr <T extends TType >	Computes the QR decompositions of one or more matrices.
Quantize <T extends TType >	Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizeAndDequantize <T extends TNumber >	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3 <T extends TNumber >	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV4 <T extends TNumber >	Returns the gradient of `quantization.QuantizeAndDequantizeV4`.
QuantizeAndDequantizeV4Grad <T extends TNumber >	Returns the gradient of `QuantizeAndDequantizeV4`.
QuantizeDownAndShrinkRange <U extends TType >	Convert the quantized 'input' tensor into a lower-precision 'output', using the actual distribution of the values to maximize the usage of the lower bit depth and adjusting the output min and max ranges accordingly.
QuantizedAdd <V extends TType >	Returns x + y element-wise, working on quantized buffers.
QuantizedAvgPool <T extends TType >	Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization <U extends TType >	Quantized Batch normalization.
QuantizedBiasAdd <V extends TType >	Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConcat <T extends TType >	Concatenates quantized tensors along one dimension.
QuantizedConv2DAndRelu <V extends TType >
QuantizedConv2DAndReluAndRequantize <V extends TType >
QuantizedConv2DAndRequantize <V extends TType >
QuantizedConv2DPerChannel <V extends TType >	Computes QuantizedConv2D per channel.
QuantizedConv2DWithBias <V extends TType >
QuantizedConv2DWithBiasAndRelu <V extends TType >
QuantizedConv2DWithBiasAndReluAndRequantize <W extends TType >
QuantizedConv2DWithBiasAndRequantize <W extends TType >
QuantizedConv2DWithBiasSignedSumAndReluAndRequantize <X extends TType >
QuantizedConv2DWithBiasSumAndRelu <V extends TType >
QuantizedConv2DWithBiasSumAndReluAndRequantize <X extends TType >
QuantizedConv2d <V extends TType >	Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedDepthwiseConv2D <V extends TType >	Computes quantized depthwise Conv2D.
QuantizedDepthwiseConv2DWithBias <V extends TType >	Computes quantized depthwise Conv2D with Bias.
QuantizedDepthwiseConv2DWithBiasAndRelu <V extends TType >	Computes quantized depthwise Conv2D with Bias and Relu.
QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize <W extends TType >	Computes quantized depthwise Conv2D with Bias, Relu and Requantize.
QuantizedInstanceNorm <T extends TType >	Quantized Instance normalization.
QuantizedMatMul <V extends TType >	Perform a quantized matrix multiplication of `a` by the matrix `b`.
QuantizedMatMulWithBias <W extends TType >	Performs a quantized matrix multiplication of `a` by the matrix `b` with bias add.
QuantizedMatMulWithBiasAndDequantize <W extends TNumber >
QuantizedMatMulWithBiasAndRelu <V extends TType >	Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion.
QuantizedMatMulWithBiasAndReluAndRequantize <W extends TType >	Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu and requantize fusion.
QuantizedMatMulWithBiasAndRequantize <W extends TType >
QuantizedMaxPool <T extends TType >	Produces the max pool of the input tensor for quantized types.
QuantizedMul <V extends TType >	Returns x * y element-wise, working on quantized buffers.
QuantizedRelu <U extends TType >	Computes Quantized Rectified Linear: `max(features, 0)`
QuantizedRelu6 <U extends TType >	Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
QuantizedReluX <U extends TType >	Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
QuantizedReshape <T extends TType >	Reshapes a quantized tensor as per the Reshape op.
QuantizedResizeBilinear <T extends TType >	Resize quantized `images` to `size` using quantized bilinear interpolation.
QueueClose	Closes the given queue.
QueueDequeue	Dequeues a tuple of one or more tensors from the given queue.
QueueDequeueMany	Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueUpTo	Dequeues `n` tuples of one or more tensors from the given queue.
QueueEnqueue	Enqueues a tuple of one or more tensors in the given queue.
QueueEnqueueMany	Enqueues zero or more tuples of one or more tensors in the given queue.
QueueIsClosed	Returns true if queue is closed.
QueueSize	Computes the number of elements in the given queue.
RaggedBincount <U extends TNumber >	Counts the number of occurrences of each value in an integer array.
RaggedCountSparseOutput <U extends TNumber >	Performs sparse-output bin counting for a ragged tensor input.
RaggedCross <T extends TType , U extends TNumber >	Generates a feature cross from a list of tensors, and returns it as a RaggedTensor.
RaggedGather <T extends TNumber , U extends TType >	Gather ragged slices from `params` axis `0` according to `indices`.
RaggedRange <U extends TNumber , T extends TNumber >	Returns a `RaggedTensor` containing the specified sequences of numbers.
RaggedTensorFromVariant <U extends TNumber , T extends TType >	Decodes a `variant` Tensor into a `RaggedTensor`.
RaggedTensorToSparse <U extends TType >	Converts a `RaggedTensor` into a `SparseTensor` with the same values.
RaggedTensorToTensor <U extends TType >	Create a dense tensor from a ragged tensor, possibly altering its shape.
RaggedTensorToVariant	Encodes a `RaggedTensor` into a `variant` Tensor.
RaggedTensorToVariantGradient <U extends TType >	Helper used to compute the gradient for `RaggedTensorToVariant`.
RandomCrop <T extends TNumber >	Randomly crop `image`.
RandomDataset	Creates a Dataset that returns pseudorandom numbers.
RandomGamma <U extends TNumber >	Outputs random values from the Gamma distribution(s) described by alpha.
RandomGammaGrad <T extends TNumber >	Computes the derivative of a Gamma random sample wrt
RandomPoisson <V extends TNumber >	Outputs random values from the Poisson distribution(s) described by rate.
RandomShuffle <T extends TType >	Randomly shuffles a tensor along its first dimension.
RandomShuffleQueue	A queue that randomizes the order of elements.
RandomStandardNormal <U extends TNumber >	Outputs random values from a normal distribution.
RandomUniform <U extends TNumber >	Outputs random values from a uniform distribution.
RandomUniformInt <U extends TNumber >	Outputs random integers from a uniform distribution.
Range <T extends TNumber >	Creates a sequence of numbers.
RangeDataset	Creates a dataset with a range of values.
Rango	Returns the rank of a tensor.
ReadFile	Reads and outputs the entire contents of the input filename.
ReadVariableOp <T extends TType >	Reads the value of a variable.
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.
Real <U extends TNumber >	Returns the real part of a complex number.
RealDiv <T extends TType >	Returns x / y element-wise for real types.
RebatchDataset	Creates a dataset that changes the batch size.
RebatchDatasetV2	Creates a dataset that changes the batch size.
Reciprocal <T extends TType >	Computes the reciprocal of x element-wise.
ReciprocalGrad <T extends TType >	Computes the gradient for the inverse of `x` wrt its input.
RecordInput	Emits randomized records.
Recv <T extends TType >	Receives the named tensor from another XLA computation.
RecvTPUEmbeddingActivations	An op that receives embedding activations on the TPU.
Reduce <T extends TNumber >	Mutually reduces multiple tensors of identical type and shape.
ReduceAll	Computes the "logical and" of elements across dimensions of a tensor.
ReduceAny	Computes the "logical or" of elements across dimensions of a tensor.
ReduceJoin	Joins a string Tensor across the given dimensions.
ReduceMax <T extends TType >	Computes the maximum of elements across dimensions of a tensor.
ReduceMin <T extends TType >	Computes the minimum of elements across dimensions of a tensor.
ReduceProd <T extends TType >	Computes the product of elements across dimensions of a tensor.
ReduceSum <T extends TType >	Computes the sum of elements across dimensions of a tensor.
ReduceV2 <T extends TNumber >	Mutually reduces multiple tensors of identical type and shape.
RefEnter <T extends TType >	Creates or finds a child frame, and makes `data` available to the child frame.
RefExit <T extends TType >	Exits the current frame to its parent frame.
RefIdentity <T extends TType >	Return the same ref tensor as the input ref tensor.
RefMerge <T extends TType >	Forwards the value of an available tensor from `inputs` to `output`.
RefNextIteration <T extends TType >	Makes its input available to the next iteration.
RefSelect <T extends TType >	Forwards the `index`th element of `inputs` to `output`.
RefSwitch <T extends TType >	Forwards the ref tensor `data` to the output port determined by `pred`.
RegexFullMatch	Check if the input matches the regex pattern.
RegexReplace	Replaces matches of the `pattern` regular expression in `input` with the replacement string provided in `rewrite`.
RegisterDataset	Registers a dataset with the tf.data service.
Relu <T extends TType >	Computes rectified linear: `max(features, 0)`.
Relu6 <T extends TNumber >	Computes rectified linear 6: `min(max(features, 0), 6)`.
Relu6Grad <T extends TNumber >	Computes rectified linear 6 gradients for a Relu6 operation.
ReluGrad <T extends TNumber >	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.
ReplicaId	Replica ID.
ReplicateMetadata	Metadata indicating how the TPU computation should be replicated.
ReplicatedInput <T extends TType >	Connects N inputs to an N-way replicated TPU computation.
ReplicatedOutput <T extends TType >	Connects N outputs from an N-way replicated TPU computation.
RequantizationRange	Computes a range that covers the actual values present in a quantized tensor.
RequantizationRangePerChannel	Computes requantization range per channel.
Requantize <U extends TType >	Converts the quantized `input` tensor into a lower-precision `output`.
RequantizePerChannel <U extends TType >	Requantizes input with min and max values known per channel.
Reshape <T extends TType >	Reshapes a tensor.
ResizeArea	Resize `images` to `size` using area interpolation.
ResizeBicubic	Resize `images` to `size` using bicubic interpolation.
ResizeBicubicGrad <T extends TNumber >	Computes the gradient of bicubic interpolation.
ResizeBilinear	Resize `images` to `size` using bilinear interpolation.
ResizeBilinearGrad <T extends TNumber >	Computes the gradient of bilinear interpolation.
ResizeNearestNeighbor <T extends TNumber >	Resize `images` to `size` using nearest neighbor interpolation.
ResizeNearestNeighborGrad <T extends TNumber >	Computes the gradient of nearest neighbor interpolation.
ResourceAccumulatorApplyGradient	Applies a gradient to a given accumulator.
ResourceAccumulatorNumAccumulated	Returns the number of gradients aggregated in the given accumulators.
ResourceAccumulatorSetGlobalStep	Updates the accumulator with a new value for global_step.
ResourceAccumulatorTakeGradient <T extends TType >	Extracts the average gradient in the given ConditionalAccumulator.
ResourceApplyAdaMax	Update '*var' according to the AdaMax algorithm.
ResourceApplyAdadelta	Update '*var' according to the adadelta scheme.
ResourceApplyAdagrad	Update '*var' according to the adagrad scheme.
ResourceApplyAdagradDa	Update '*var' according to the proximal adagrad scheme.
ResourceApplyAdam	Update '*var' according to the Adam algorithm.
ResourceApplyAdamWithAmsgrad	Update '*var' according to the Adam algorithm.
ResourceApplyAddSign	Update '*var' according to the AddSign update.
ResourceApplyCenteredRmsProp	Update '*var' according to the centered RMSProp algorithm.
ResourceApplyFtrl	Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyGradientDescent	Update 'var' by subtracting 'alpha' 'delta' from it.
ResourceApplyKerasMomentum	Update '*var' according to the momentum scheme.
ResourceApplyMomentum	Update '*var' according to the momentum scheme.
ResourceApplyPowerSign	Update '*var' according to the AddSign update.
ResourceApplyProximalAdagrad	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ResourceApplyProximalGradientDescent	Update '*var' as FOBOS algorithm with fixed learning rate.
ResourceApplyRmsProp	Update '*var' according to the RMSProp algorithm.
ResourceConditionalAccumulator	A conditional accumulator for aggregating gradients.
ResourceCountUpTo <T extends TNumber >	Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather <U extends TType >	Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGatherNd <U extends TType >
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	Applies sparse addition to individual values or slices in a Variable.
ResourceScatterNdMax
ResourceScatterNdMin
ResourceScatterNdSub	Applies sparse subtraction to individual values or slices in a Variable.
ResourceScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
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().
ResourceSparseApplyAdagrad	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyAdagradDa	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradV2	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyCenteredRmsProp	Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyFtrl	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyKerasMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyProximalAdagrad	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
ResourceSparseApplyProximalGradientDescent	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyRmsProp	Update '*var' according to the RMSProp algorithm.
ResourceStridedSliceAssign	Assign `value` to the sliced l-value reference of `ref`.
Restaurar	Restores tensors from a V2 checkpoint.
RestoreSlice <T extends TType >	Restores a tensor from checkpoint files.
RetrieveTPUEmbeddingADAMParameters	Retrieve ADAM embedding parameters.
RetrieveTPUEmbeddingADAMParametersGradAccumDebug	Retrieve ADAM embedding parameters with debug support.
RetrieveTPUEmbeddingAdadeltaParameters	Retrieve Adadelta embedding parameters.
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug	Retrieve Adadelta embedding parameters with debug support.
RetrieveTPUEmbeddingAdagradParameters	Retrieve Adagrad embedding parameters.
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug	Retrieve Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingCenteredRMSPropParameters	Retrieve centered RMSProp embedding parameters.
RetrieveTPUEmbeddingFTRLParameters	Retrieve FTRL embedding parameters.
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug	Retrieve FTRL embedding parameters with debug support.
RetrieveTPUEmbeddingMDLAdagradLightParameters	Retrieve MDL Adagrad Light embedding parameters.
RetrieveTPUEmbeddingMomentumParameters	Retrieve Momentum embedding parameters.
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug	Retrieve Momentum embedding parameters with debug support.
RetrieveTPUEmbeddingProximalAdagradParameters	Retrieve proximal Adagrad embedding parameters.
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug	Retrieve proximal Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingProximalYogiParameters
RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug
RetrieveTPUEmbeddingRMSPropParameters	Retrieve RMSProp embedding parameters.
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug	Retrieve RMSProp embedding parameters with debug support.
RetrieveTPUEmbeddingStochasticGradientDescentParameters	Retrieve SGD embedding parameters.
RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug	Retrieve SGD embedding parameters with debug support.
Reverse <T extends TType >	Reverses specific dimensions of a tensor.
ReverseSequence <T extends TType >	Reverses variable length slices.
Rfft <U extends TType >	Real-valued fast Fourier transform.
Rfft2d <U extends TType >	2D real-valued fast Fourier transform.
Rfft3d <U extends TType >	3D real-valued fast Fourier transform.
RgbToHsv <T extends TNumber >	Converts one or more images from RGB to HSV.
RightShift <T extends TNumber >	Elementwise computes the bitwise right-shift of `x` and `y`.
Rint <T extends TNumber >	Returns element-wise integer closest to x.
RngReadAndSkip	Advance the counter of a counter-based RNG.
RngSkip	Advance the counter of a counter-based RNG.
Roll <T extends TType >	Rolls the elements of a tensor along an axis.
Round <T extends TType >	Rounds the values of a tensor to the nearest integer, element-wise.
RPC	Perform batches of RPC requests.
Rsqrt <T extends TType >	Computes reciprocal of square root of x element-wise.
RsqrtGrad <T extends TType >	Computes the gradient for the rsqrt of `x` wrt its input.
SampleDistortedBoundingBox <T extends TNumber >	Generate a single randomly distorted bounding box for an image.
SamplingDataset	Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
Ahorrar	Saves tensors in V2 checkpoint format.
SaveSlices	Saves input tensors slices to disk.
ScalarSummary	Outputs a `Summary` protocol buffer with scalar values.
ScaleAndTranslate
ScaleAndTranslateGrad <T extends TNumber >
ScatterAdd <T extends TType >	Adds sparse updates to a variable reference.
ScatterDiv <T extends TType >	Divides a variable reference by sparse updates.
ScatterMax <T extends TNumber >	Reduces sparse updates into a variable reference using the `max` operation.
ScatterMin <T extends TNumber >	Reduces sparse updates into a variable reference using the `min` operation.
ScatterMul <T extends TType >	Multiplies sparse updates into a variable reference.
ScatterNd <U extends TType >	Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd <T extends TType >	Applies sparse addition to individual values or slices in a Variable.
ScatterNdMax <T extends TType >	Computes element-wise maximum.
ScatterNdMin <T extends TType >	Computes element-wise minimum.
ScatterNdNonAliasingAdd <T extends TType >	Applies sparse addition to `input` using individual values or slices from `updates` according to indices `indices`.
ScatterNdSub <T extends TType >	Applies sparse subtraction to individual values or slices in a Variable.
ScatterNdUpdate <T extends TType >	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
ScatterSub <T extends TType >	Subtracts sparse updates to a variable reference.
ScatterUpdate <T extends TType >	Applies sparse updates to a variable reference.
SdcaFprint	Computes fingerprints of the input strings.
SdcaOptimizer	Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for linear models with L1 + L2 regularization.
SdcaShrinkL1	Applies L1 regularization shrink step on the parameters.
SegmentMax <T extends TNumber >	Computes the maximum along segments of a tensor.
SegmentMean <T extends TType >	Computes the mean along segments of a tensor.
SegmentMin <T extends TNumber >	Computes the minimum along segments of a tensor.
SegmentProd <T extends TType >	Computes the product along segments of a tensor.
SegmentSum <T extends TType >	Computes the sum along segments of a tensor.
Select <T extends TType >
SelfAdjointEig <T extends TType >	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
Selu <T extends TNumber >	Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise.
SeluGrad <T extends TNumber >	Computes gradients for the scaled exponential linear (Selu) operation.
Enviar	Sends the named tensor to another XLA computation.
SendTPUEmbeddingGradients	Performs gradient updates of embedding tables.
SerializeIterator	Converts the given `resource_handle` representing an iterator to a variant tensor.
SerializeManySparse <U extends TType >	Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
SerializeSparse <U extends TType >	Serialize a `SparseTensor` into a `[3]` `Tensor` object.
SerializeTensor	Transforms a Tensor into a serialized TensorProto proto.
SetDiff1d <T extends TType , U extends TNumber >	Computes the difference between two lists of numbers or strings.
SetSize	Number of unique elements along last dimension of input `set`.
SetStatsAggregatorDataset
Shape <U extends TNumber >	Returns the shape of a tensor.
ShapeN <U extends TNumber >	Returns shape of tensors.
ShardDataset	Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
ShardedFilename	Generate a sharded filename.
ShardedFilespec	Generate a glob pattern matching all sharded file names.
Sharding <T extends TType >	An op which shards the input based on the given sharding attribute.
ShuffleAndRepeatDataset
ShuffleDataset
ShutdownDistributedTPU	Shuts down a running distributed TPU system.
Sigmoid <T extends TType >	Computes sigmoid of `x` element-wise.
SigmoidGrad <T extends TType >	Computes the gradient of the sigmoid of `x` wrt its input.
Sign <T extends TType >	Returns an element-wise indication of the sign of a number.
Sin <T extends TType >	Computes sine of x element-wise.
Sinh <T extends TType >	Computes hyperbolic sine of x element-wise.
Size <U extends TNumber >	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.
SleepDataset
Slice <T extends TType >	Return a slice from 'input'.
SlidingWindowDataset	Creates a dataset that passes a sliding window over `input_dataset`.
Snapshot <T extends TType >	Returns a copy of the input tensor.
SobolSample <T extends TNumber >	Generates points from the Sobol sequence.
Softmax <T extends TNumber >	Computes softmax activations.
SoftmaxCrossEntropyWithLogits <T extends TNumber >	Computes softmax cross entropy cost and gradients to backpropagate.
Softplus <T extends TNumber >	Computes softplus: `log(exp(features) + 1)`.
SoftplusGrad <T extends TNumber >	Computes softplus gradients for a softplus operation.
Softsign <T extends TNumber >	Computes softsign: `features / (abs(features) + 1)`.
SoftsignGrad <T extends TNumber >	Computes softsign gradients for a softsign operation.
Solve <T extends TType >	Solves systems of linear equations.
Sort <T extends TType >	Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort .
SpaceToBatch <T extends TType >	SpaceToBatch for 4-D tensors of type T.
SpaceToBatchNd <T extends TType >	SpaceToBatch for ND tensors of type T.
SpaceToDepth <T extends TType >	SpaceToDepth for tensors of type T.
SparseAccumulatorApplyGradient	Applies a sparse gradient to a given accumulator.
SparseAccumulatorTakeGradient <T extends TType >	Extracts the average sparse gradient in a SparseConditionalAccumulator.
SparseAdd <T extends TType >	Adds two `SparseTensor` objects to produce another `SparseTensor`.
SparseAddGrad <T extends TType >	The gradient operator for the SparseAdd op.
SparseApplyAdadelta <T extends TType >	var: Should be from a Variable().
SparseApplyAdagrad <T extends TType >	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
SparseApplyAdagradDa <T extends TType >	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
SparseApplyCenteredRmsProp <T extends TType >	Update '*var' according to the centered RMSProp algorithm.
SparseApplyFtrl <T extends TType >	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyMomentum <T extends TType >	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
SparseApplyProximalAdagrad <T extends TType >	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
SparseApplyProximalGradientDescent <T extends TType >	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyRmsProp <T extends TType >	Update '*var' according to the RMSProp algorithm.
SparseBincount <U extends TNumber >	Counts the number of occurrences of each value in an integer array.
SparseConcat <T extends TType >	Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator	A conditional accumulator for aggregating sparse gradients.
SparseCountSparseOutput <U extends TNumber >	Performs sparse-output bin counting for a sparse tensor input.
SparseCross	Generates sparse cross from a list of sparse and dense tensors.
SparseCrossHashed	Generates sparse cross from a list of sparse and dense tensors.
SparseDenseCwiseAdd <T extends TType >	Adds up a SparseTensor and a dense Tensor, using these special rules: (1) Broadcasts the dense side to have the same shape as the sparse side, if eligible; (2) Then, only the dense values pointed to by the indices of the SparseTensor participate in the cwise addition.
SparseDenseCwiseDiv <T extends TType >	Component-wise divides a SparseTensor by a dense Tensor.
SparseDenseCwiseMul <T extends TType >	Component-wise multiplies a SparseTensor by a dense Tensor.
SparseFillEmptyRows <T extends TType >	Fills empty rows in the input 2-D `SparseTensor` with a default value.
SparseFillEmptyRowsGrad <T extends TType >	The gradient of SparseFillEmptyRows.
SparseMatMul	Multiply matrix "a" by matrix "b".
SparseMatrixAdd	Sparse addition of two CSR matrices, C = alpha * A + beta * B.
SparseMatrixMatMul <T extends TType >	Matrix-multiplies a sparse matrix with a dense matrix.
SparseMatrixMul	Element-wise multiplication of a sparse matrix with a dense tensor.
SparseMatrixNNZ	Returns the number of nonzeroes of `sparse_matrix`.
SparseMatrixOrderingAMD	Computes the Approximate Minimum Degree (AMD) ordering of `input`.
SparseMatrixSoftmax	Calculates the softmax of a CSRSparseMatrix.
SparseMatrixSoftmaxGrad	Calculates the gradient of the SparseMatrixSoftmax op.
SparseMatrixSparseCholesky	Computes the sparse Cholesky decomposition of `input`.
SparseMatrixSparseMatMul	Sparse-matrix-multiplies two CSR matrices `a` and `b`.
SparseMatrixTranspose	Transposes the inner (matrix) dimensions of a CSRSparseMatrix.
SparseMatrixZeros	Creates an all-zeros CSRSparseMatrix with shape `dense_shape`.
SparseReduceMax <T extends TNumber >	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse <T extends TNumber >	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceSum <T extends TType >	Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse <T extends TType >	Computes the sum of elements across dimensions of a SparseTensor.
SparseReorder <T extends TType >	Reorders a SparseTensor into the canonical, row-major ordering.
SparseReshape	Reshapes a SparseTensor to represent values in a new dense shape.
SparseSegmentMean <T extends TNumber >	Computes the mean along sparse segments of a tensor.
SparseSegmentMeanGrad <T extends TNumber >	Computes gradients for SparseSegmentMean.
SparseSegmentMeanWithNumSegments <T extends TNumber >	Computes the mean along sparse segments of a tensor.
SparseSegmentSqrtN <T extends TNumber >	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSqrtNGrad <T extends TNumber >	Computes gradients for SparseSegmentSqrtN.
SparseSegmentSqrtNWithNumSegments <T extends TNumber >	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSum <T extends TNumber >	Computes the sum along sparse segments of a tensor.
SparseSegmentSumWithNumSegments <T extends TNumber >	Computes the sum along sparse segments of a tensor.
SparseSlice <T extends TType >	Slice a `SparseTensor` based on the `start` and `size`.
SparseSliceGrad <T extends TType >	The gradient operator for the SparseSlice op.
SparseSoftmax <T extends TNumber >	Applies softmax to a batched ND `SparseTensor`.
SparseSoftmaxCrossEntropyWithLogits <T extends TNumber >	Computes softmax cross entropy cost and gradients to backpropagate.
SparseSparseMaximum <T extends TNumber >	Returns the element-wise max of two SparseTensors.
SparseSparseMinimum <T extends TType >	Returns the element-wise min of two SparseTensors.
SparseSplit <T extends TType >	Split a `SparseTensor` into `num_split` tensors along one dimension.
SparseTensorDenseAdd <U extends TType >	Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
SparseTensorDenseMatMul <U extends TType >	Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
SparseTensorSliceDataset	Creates a dataset that splits a SparseTensor into elements row-wise.
SparseTensorToCSRSparseMatrix	Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.
SparseToDense <U extends TType >	Converts a sparse representation into a dense tensor.
SparseToSparseSetOperation <T extends TType >	Applies set operation along last dimension of 2 `SparseTensor` inputs.
Spence <T extends TNumber >
Split <T extends TType >	Splits a tensor into `num_split` tensors along one dimension.
SplitV <T extends TType >	Splits a tensor into `num_split` tensors along one dimension.
SqlDataset	Creates a dataset that executes a SQL query and emits rows of the result set.
Sqrt <T extends TType >	Computes square root of x element-wise.
SqrtGrad <T extends TType >	Computes the gradient for the sqrt of `x` wrt its input.
Sqrtm <T extends TType >	Computes the matrix square root of one or more square matrices: matmul(sqrtm(A), sqrtm(A)) = A The input matrix should be invertible.
Square <T extends TType >	Computes square of x element-wise.
SquaredDifference <T extends TType >	Returns conj(x - y)(x - y) element-wise.
Squeeze <T extends TType >	Removes dimensions of size 1 from the shape of a tensor.
Stack <T extends TType >	Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Escenario	Stage values similar to a lightweight Enqueue.
StageClear	Op removes all elements in the underlying container.
StagePeek	Op peeks at the values at the specified index.
StageSize	Op returns the number of elements in the underlying container.
StatefulRandomBinomial <V extends TNumber >
StatefulStandardNormal <U extends TType >	Outputs random values from a normal distribution.
StatefulTruncatedNormal <U extends TType >	Outputs random values from a truncated normal distribution.
StatefulUniform <U extends TType >	Outputs random values from a uniform distribution.
StatefulUniformFullInt <U extends TType >	Outputs random integers from a uniform distribution.
StatefulUniformInt <U extends TType >	Outputs random integers from a uniform distribution.
StatelessMultinomial <V extends TNumber >	Draws samples from a multinomial distribution.
StatelessParameterizedTruncatedNormal <V extends TNumber >
StatelessRandomBinomial <W extends TNumber >	Outputs deterministic pseudorandom random numbers from a binomial distribution.
StatelessRandomGamma <V extends TNumber >	Outputs deterministic pseudorandom random numbers from a gamma distribution.
StatelessRandomGetKeyCounterAlg	Picks the best algorithm based on device, and scrambles seed into key and counter.
StatelessRandomNormal <V extends TNumber >	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomNormalV2 <U extends TNumber >	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomPoisson <W extends TNumber >	Outputs deterministic pseudorandom random numbers from a Poisson distribution.
StatelessRandomUniform <V extends TNumber >	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessRandomUniformFullInt <V extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformFullIntV2 <U extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformInt <V extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformIntV2 <U extends TNumber >	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformV2 <U extends TNumber >	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessSampleDistortedBoundingBox <T extends TNumber >	Generate a randomly distorted bounding box for an image deterministically.
StatelessTruncatedNormal <V extends TNumber >	Outputs deterministic pseudorandom values from a truncated normal distribution.
StatelessTruncatedNormalV2 <U extends TNumber >	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.
StatsAggregatorHandle
StatsAggregatorSetSummaryWriter	Set a summary_writer_interface to record statistics using given stats_aggregator.
StatsAggregatorSummary	Produces a summary of any statistics recorded by the given statistics manager.
StopGradient <T extends TType >	Stops gradient computation.
StridedSlice <T extends TType >	Return a strided slice from `input`.
StridedSliceAssign <T extends TType >	Assign `value` to the sliced l-value reference of `ref`.
StridedSliceGrad <U extends TType >	Returns the gradient of `StridedSlice`.
StringFormat	Formats a string template using a list of tensors.
StringLength	String lengths of `input`.
StringNGrams <T extends TNumber >	Creates ngrams from ragged string data.
StringSplit	Split elements of `source` based on `sep` into a `SparseTensor`.
Banda	Strip leading and trailing whitespaces from the Tensor.
Sub <T extends TType >	Returns x - y element-wise.
Substr	Return substrings from `Tensor` of strings.
Sum <T extends TType >	Computes the sum of elements across dimensions of a tensor.
SummaryWriter
Svd <T extends TType >	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
SwitchCond <T extends TType >	Forwards `data` to the output port determined by `pred`.
TPUCompilationResult	Returns the result of a TPU compilation.
TPUEmbeddingActivations	An op enabling differentiation of TPU Embeddings.
TPUReplicateMetadata	Metadata indicating how the TPU computation should be replicated.
TPUReplicatedInput <T extends TType >	Connects N inputs to an N-way replicated TPU computation.
TPUReplicatedOutput <T extends TType >	Connects N outputs from an N-way replicated TPU computation.
TakeDataset	Creates a dataset that contains `count` elements from the `input_dataset`.
TakeManySparseFromTensorsMap <T extends TType >	Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
Tan <T extends TType >	Computes tan of x element-wise.
Tanh <T extends TType >	Computes hyperbolic tangent of `x` element-wise.
TanhGrad <T extends TType >	Computes the gradient for the tanh of `x` wrt its input.
TemporaryVariable <T extends TType >	Returns a tensor that may be mutated, but only persists within a single step.
TensorArray	An array of Tensors of given size.
TensorArrayClose	Delete the TensorArray from its resource container.
TensorArrayConcat <T extends TType >	Concat the elements from the TensorArray into value `value`.
TensorArrayGather <T extends TType >	Gather specific elements from the TensorArray into output `value`.
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 <T extends TType >
TensorArrayRead <T extends TType >	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.
TensorDiag <T extends TType >	Returns a diagonal tensor with a given diagonal values.
TensorDiagPart <T extends TType >	Returns the diagonal part of the tensor.
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
TensorForestTreeSerialize	Serializes the tree handle to a proto
TensorForestTreeSize	Get the number of nodes in a tree
TensorListConcat <U extends TType >	Concats all tensors in the list along the 0th dimension.
TensorListConcatLists
TensorListElementShape <T extends TNumber >	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 <T extends TType >	Creates a Tensor by indexing into the TensorList.
TensorListGetItem <T extends TType >
TensorListLength	Returns the number of tensors in the input tensor list.
TensorListPopBack <T extends TType >	Returns the last element of the input list as well as a list with all but that element.
TensorListPushBack	Returns a 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.
TensorListResize	Resizes the list.
TensorListScatter	Creates a TensorList by indexing into a Tensor.
TensorListScatterIntoExistingList	Scatters tensor at indices in an input list.
TensorListSetItem
TensorListSplit	Splits a tensor into a list.
TensorListStack <T extends TType >	Stacks all tensors in the list.
TensorMapErase	Returns a tensor map with item from given key erased.
TensorMapHasKey	Returns whether the given key exists in the map.
TensorMapInsert	Returns a map that is the 'input_handle' with the given key-value pair inserted.
TensorMapLookup <U extends TType >	Returns the value from a given key in a tensor map.
TensorMapSize	Returns the number of tensors in the input tensor map.
TensorMapStackKeys <T extends TType >	Returns a Tensor stack of all keys in a tensor map.
TensorScatterNdAdd <T extends TType >	Adds sparse `updates` to an existing tensor according to `indices`.
TensorScatterNdMax <T extends TType >
TensorScatterNdMin <T extends TType >
TensorScatterNdSub <T extends TType >	Subtracts sparse `updates` from an existing tensor according to `indices`.
TensorScatterNdUpdate <T extends TType >	Scatter `updates` into an existing tensor according to `indices`.
TensorSliceDataset	Creates a dataset that emits each dim-0 slice of `components` once.
TensorStridedSliceUpdate <T extends TType >	Assign `value` to the sliced l-value reference of `input`.
TensorSummary	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'.
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.
ThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ThreadPoolHandle	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Tile <T extends TType >	Constructs a tensor by tiling a given tensor.
TileGrad <T extends TType >	Returns the gradient of `Tile`.
Marca de tiempo	Provides the time since epoch in seconds.
ToBool	Converts a tensor to a scalar predicate.
ToHashBucket	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketFast	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketStrong	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToNumber <T extends TNumber >	Converts each string in the input Tensor to the specified numeric type.
TopK <T extends TNumber >	Finds values and indices of the `k` largest elements for the last dimension.
TopKUnique	Returns the TopK unique values in the array in sorted order.
TopKWithUnique	Returns the TopK values in the array in sorted order.
Transpose <T extends TType >	Shuffle dimensions of x according to a permutation.
TriangularSolve <T extends TType >	Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.
TridiagonalMatMul <T extends TType >	Calculate product with tridiagonal matrix.
TridiagonalSolve <T extends TType >	Solves tridiagonal systems of equations.
TruncateDiv <T extends TType >	Returns x / y element-wise for integer types.
TruncateMod <T extends TNumber >	Returns element-wise remainder of division.
TruncatedNormal <U extends TNumber >	Outputs random values from a truncated normal distribution.
TryRpc	Perform batches of RPC requests.
Unbatch <T extends TType >	Reverses the operation of Batch for a single output Tensor.
UnbatchDataset	A dataset that splits the elements of its input into multiple elements.
UnbatchGrad <T extends TType >	Gradient of Unbatch.
UncompressElement	Uncompresses a compressed dataset element.
UnicodeDecode <T extends TNumber >	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeDecodeWithOffsets <T extends TNumber >	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeEncode	Encode a tensor of ints into unicode strings.
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.
UniformCandidateSampler	Generates labels for candidate sampling with a uniform distribution.
Unique <T extends TType , V extends TNumber >	Finds unique elements along an axis of a tensor.
UniqueDataset	Creates a dataset that contains the unique elements of `input_dataset`.
UniqueWithCounts <T extends TType , V extends TNumber >	Finds unique elements along an axis of a tensor.
UnravelIndex <T extends TNumber >	Converts an array of flat indices into a tuple of coordinate arrays.
UnsortedSegmentJoin	Joins the elements of `inputs` based on `segment_ids`.
UnsortedSegmentMax <T extends TNumber >	Computes the maximum along segments of a tensor.
UnsortedSegmentMin <T extends TNumber >	Computes the minimum along segments of a tensor.
UnsortedSegmentProd <T extends TType >	Computes the product along segments of a tensor.
UnsortedSegmentSum <T extends TType >	Computes the sum along segments of a tensor.
Unstack <T extends TType >	Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
fuera del escenario	Op is similar to a lightweight Dequeue.
UnwrapDatasetVariant
Superior	Converts all lowercase characters into their respective uppercase replacements.
UpperBound <U extends TNumber >	Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp	Creates a handle to a Variable resource.
VarIsInitializedOp	Checks whether a resource handle-based variable has been initialized.
Variable <T extends TType >	Holds state in the form of a tensor that persists across steps.
VariableShape <T extends TNumber >	Returns the shape of the variable pointed to by `resource`.
Dónde	Returns locations of nonzero / true values in a tensor.
WholeFileReader	A Reader that outputs the entire contents of a file as a value.
WindowDataset	Combines (nests of) input elements into a dataset of (nests of) windows.
WorkerHeartbeat	Worker heartbeat op.
WrapDatasetVariant
WriteAudioSummary	Writes an audio summary.
WriteFile	Writes contents to the file at input filename.
WriteGraphSummary	Writes a graph summary.
WriteHistogramSummary	Writes a histogram summary.
WriteImageSummary	Writes an image summary.
WriteRawProtoSummary	Writes a serialized proto summary.
WriteScalarSummary	Writes a scalar summary.
WriteSummary	Writes a tensor summary.
Xdivy <T extends TType >	Returns 0 if x == 0, and x / y otherwise, elementwise.
XlaRecvFromHost <T extends TType >	An op to receive a tensor from the host.
XlaSendToHost	An op to send a tensor to the host.
XlaSetBound	Set a bound for the given input value as a hint to Xla compiler, returns the same value.
XlaSpmdFullToShardShape <T extends TType >	An op used by XLA SPMD partitioner to switch from automatic partitioning to manual partitioning.
XlaSpmdShardToFullShape <T extends TType >	An op used by XLA SPMD partitioner to switch from manual partitioning to automatic partitioning.
Xlog1py <T extends TType >	Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
Xlogy <T extends TType >	Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
ZerosLike <T extends TType >	Returns a tensor of zeros with the same shape and type as x.
Zeta <T extends TNumber >	Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
ZipDataset	Creates a dataset that zips together `input_datasets`.
erfinv <T extends TNumber >

A base class for Op implementations that are backed by a single Operation .

Each operation registered in the TensorFlow core is provided as a RawOp . Custom operations working with only a single operation may also derive from this class.

Public Methods

final boolean	equals (Object obj)
final int	hashCode ()
Operación	op () Return this unit of computation as a single `Operation` .
final String	toString ()

Inherited Methods

From class java.lang.Object

booleano	equals (Object arg0)
final Class<?>	getClass ()
entero	hashCode ()
final void	notify ()
final void	notifyAll ()
Cadena	toString ()
final void	wait (long arg0, int arg1)
final void	wait (long arg0)
final void	esperar ()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env () Return the execution environment this op was created in.
abstract Operation	op () Return this unit of computation as a single `Operation` .

Public Methods

public final boolean equals (Object obj)

public final int hashCode ()

public Operation op ()

Return this unit of computation as a single Operation .

For a raw op, the returned value correspond to the TensorFlow operation wrapped by this op.

For a composite op, the returned value usually correspond to the last operation invoked in a chain or to a NoOp grouping one or more operations as a single unit of computation.

Devoluciones

an Operation