api/org.diffkt.model/Dense

Dense

class Dense : TrainableLayerSingleInput<Dense>

Densely-connected layer

Constructors

Dense

fun Dense(numInputs: Int, numOutputs: Int, random: Random, bias: Boolean = true, activation: Activation = defaultActivation, weightInit: (Shape, Random) -> FloatTensor = defaultInit(numInputs), biasInit: (Shape, Random) -> FloatTensor = defaultInit(numInputs))

Full public constructor

Dense

fun Dense(numInputs: Int, numOutputs: Int, activation: Activation, random: Random, weightInit: (Shape, Random) -> FloatTensor = defaultInit(numInputs), biasInit: (Shape, Random) -> FloatTensor = defaultInit(numInputs))

Convenience constructor where bias is always true.

Types

Companion

object Companion

Functions

apply

fun apply(input: DTensor): DTensor

cpu

open override fun cpu(): Dense

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(): Dense

hashCode

open override fun hashCode(): Int

invoke

open operator override fun invoke(input: DTensor): DTensor

abstract operator fun invoke(vararg inputs: DTensor): DTensor

load

open override fun load(from: ByteBuffer): Dense

store

open override fun store(into: ByteBuffer): ByteBuffer

to

open fun to(device: Device): OnDevice

trainingStep

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

withTrainables

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

wrap

open override fun wrap(wrapper: Wrapper): Dense

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

Properties

activation

val activation: Activation

b

bias

val bias: Boolean

trainables

open override val trainables: List<TrainableTensor>

w