api/org.diffkt.model/Conv2d

Conv2d

open class Conv2d(filterShape: Shape, horizontalStride: Int, verticalStride: Int, activation: Activation, paddingStyle: Convolve.PaddingStyle, trainableFilter: TrainableTensor) : TrainableLayerSingleInput<Conv2d>

Constructors

Conv2d

fun Conv2d(filterShape: Shape, horizontalStride: Int, verticalStride: Int, activation: Activation = defaultActivation, padding: Convolve.Padding2D, random: Random, weightInit: (Shape, Random) -> FloatTensor = defaultInit)

Conv2d

fun Conv2d(filterShape: Shape, horizontalStride: Int, verticalStride: Int, activation: Activation = defaultActivation, paddingStyle: Convolve.PaddingStyle, random: Random, weightInit: (Shape, Random) -> FloatTensor = defaultInit)

Types

Companion

object Companion

Functions

cpu

open override fun cpu(): Conv2d

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(): Conv2d

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): Conv2d

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): Conv2d

withTrainables

open override fun withTrainables(trainables: List<Trainable<*>>): Conv2d

wrap

open override fun wrap(wrapper: Wrapper): Conv2d

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

Properties

activation

val activation: Activation

filterShape

val filterShape: Shape

horizontalStride

val horizontalStride: Int

paddingStyle

val paddingStyle: Convolve.PaddingStyle

trainables

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

verticalStride

val verticalStride: Int

Inheritors

Conv2dWithSamePadding