EmbeddingBag

class EmbeddingBag(trainableWeights: TrainableTensor, reduction: EmbeddingBag.Companion.Reduction) : TrainableLayer<EmbeddingBag>

A trainable embedding table with size vocabSize x embeddingSize

Parameters

numEmbeddings

the size of the vocabulary/number of embedding vectors

embeddingSize

the size of each embedding vector

reduction

the reduction mode used to reduce each bag

This layer is equivalent to Embedding followed by a reduction (e.g. sum) across each bag of embeddings along the 0th axis.

Constructors

EmbeddingBag

fun EmbeddingBag(numEmbeddings: Int, embeddingSize: Int, reduction: EmbeddingBag.Companion.Reduction, random: Random, initializer: (Shape, Random) -> FloatTensor = Initializer.gaussian())

EmbeddingBag

fun EmbeddingBag(trainableWeights: TrainableTensor, reduction: EmbeddingBag.Companion.Reduction)

Types

Companion

object Companion

Functions

cpu

open override fun cpu(): EmbeddingBag

equals

open operator override fun equals(other: Any?): Boolean

extractTangent

open override fun extractTangent(output: DTensor, extractor: (DTensor, DTensor) -> DTensor): TrainableComponent.Companion.Tangent

getSingleInput

open fun getSingleInput(inputs: Array<out DTensor>): DTensor

Helper to check that the layer was called with a single input. Returns that input if successful, else errors.

gpu

open override fun gpu(): EmbeddingBag

hashCode

open override fun hashCode(): Int

invoke

open operator override fun invoke(vararg inputs: DTensor): DTensor

operator fun invoke(indices: IntTensor, bagOffsets: IntTensor): DTensor

load

open override fun load(from: ByteBuffer): EmbeddingBag

store

open override fun store(into: ByteBuffer): ByteBuffer

open fun to(device: Device): OnDevice

trainingStep

open override fun trainingStep(optim: Optimizer<*>, tangent: Trainable.Tangent): EmbeddingBag

withTrainables

open override fun withTrainables(trainables: List<Trainable<*>>): EmbeddingBag

wrap

open override fun wrap(wrapper: Wrapper): EmbeddingBag

The wrap function should return the same static type it is declared on.

Properties

reduction

val reduction: EmbeddingBag.Companion.Reduction

trainables

open override val trainables: List<TrainableTensor>

trainableWeights

val trainableWeights: TrainableTensor