Embedding

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

Accepts a tensor of Shape() and returns a tensor of Shape(, embeddingSize)

Embedding

fun Embedding(numEmbeddings: Int, embeddingSize: Int, random: Random, initializer: (Shape, Random) -> FloatTensor = Initializer.gaussian())

Embedding

cpu

open override fun cpu(): Embedding

equals

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

extractTangent

getSingleInput

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

gpu

open override fun gpu(): Embedding

hashCode

open override fun hashCode(): Int

invoke

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

operator fun invoke(input: IntTensor): DTensor

load

open override fun load(from: ByteBuffer): Embedding

store

open override fun store(into: ByteBuffer): ByteBuffer

open fun to(device: Device): OnDevice

trainingStep

withTrainables

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

wrap

open override fun wrap(wrapper: Wrapper): Embedding

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

trainables

open override val trainables: List<Trainable<*>>

trainableWeights