PReLU

class PReLU(alphaInitializer: Initializer, alphaRegularizer: Regularizer?, sharedAxes: IntArray?, name: String) : AbstractActivationLayer

Parametric Rectified Linear Unit.

It follows:

f(x) = alpha * x     if x < 0
f(x) = x             if x >= 0

where alpha is a learnable weight and has the same shape as x (i.e. input).

Constructors

PReLU

fun PReLU(alphaInitializer: Initializer = Zeros(), alphaRegularizer: Regularizer? = null, sharedAxes: IntArray? = null, name: String = "")

Functions

build

open override fun build(tf: Ops, kGraph: KGraph, inputShape: Shape)

Extend this function to define variables in layer.

buildFromInboundLayers

fun buildFromInboundLayers(tf: Ops, kGraph: KGraph)

Extend this function to define variables in layer.

computeOutputShape

open override fun computeOutputShape(inputShape: Shape): Shape

Computes output shape, based on inputShape and Layer type.

computeOutputShapeFromInboundLayers

open fun computeOutputShapeFromInboundLayers(): TensorShape

Computes output shape, based on input shapes of inbound layers.

forward

open override fun forward(tf: Ops, input: Operand<Float>): Operand<Float>

Applies the activation functions to the input to produce the output.

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

Builds main layer input transformation with tf. Depends on Layer type.

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

Builds main layer input transformation with tf. Depends on Layer type.

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.

toString

open override fun toString(): String

Properties

alphaInitializer

val alphaInitializer: Initializer

Initializer instance for the weights.

alphaRegularizer

val alphaRegularizer: Regularizer? = null

Regularizer instance for the weights.

hasActivation

open override val hasActivation: Boolean

Returns True, if layer has internal activation function.

inboundLayers

var inboundLayers: MutableList<Layer>

Returns inbound layers.

isTrainable

var isTrainable: Boolean = true

True, if layer's weights could be changed during training. If false, layer's weights are frozen and could be changed during the training.

name

var name: String

outboundLayers

var outboundLayers: MutableList<Layer>

Returns outbound layers.

outputShape

lateinit var outputShape: TensorShape

Output data tensor shape.

paramCount

open override val paramCount: Int

Returns amount of neurons.

parentModel

var parentModel: TrainableModel? = null

Model where this layer is used.

sharedAxes

val sharedAxes: IntArray? = null

The axes along which to share learnable parameters.

weights

open override var weights: Map<String, Array<*>>

Layer's weights.