AbstractUpSampling

abstract class AbstractUpSampling(sizeInternal: IntArray, interpolationInternal: InterpolationMethod, name: String) : Layer

Abstract UpSampling layer used as the base layer for all the upsampling layers.

Constructors

AbstractUpSampling

fun AbstractUpSampling(sizeInternal: IntArray, interpolationInternal: InterpolationMethod, name: String)

Functions

build

open override fun build(tf: Ops, input: Operand<Float>, isTraining: Operand<Boolean>, numberOfLosses: Operand<Float>?): Operand<Float>

open fun build(tf: Ops, input: List<Operand<Float>>, isTraining: Operand<Boolean>, numberOfLosses: Operand<Float>?): Operand<Float>

Extend this function to define variables in the layer and compute layer output.

invoke

operator fun invoke(vararg layers: Layer): Layer

Important part of functional API. It takes layers as input and saves them to the inboundLayers of the given layer.

Properties

hasActivation

open override val hasActivation: Boolean

Returns True, if layer has internal activation function.

inboundLayers

var inboundLayers: MutableList<Layer>

Returns inbound layers.

interpolationInternal

val interpolationInternal: InterpolationMethod

Interpolation method used for filling values (only applicable to 2D data for the moment).

name

var name: String

Layer name. A new name is generated during model compilation when provided name is empty.

outboundLayers

var outboundLayers: MutableList<Layer>

Returns outbound layers.

outputShape

lateinit var outputShape: TensorShape

Output data tensor shape.

parentModel

var parentModel: GraphTrainableModel? = null

Model where this layer is used.

sizeInternal

val sizeInternal: IntArray

UpSampling size factors; currently, they are not used in the implementation of this abstract class and each subclassed layer uses its own copy of the upsampling size factors.

Inheritors

UpSampling1D

UpSampling2D

UpSampling3D