AbstractActivationLayer

Base class for all layer class representing activation functions.

By default, it is marked as not trainable layer with no extra parameters and weights but having the activation on it.

By default, it defines returning the output with the same shape as the input Operand.

Parameters

name

Layer name. Would be changed if empty during model compilation.

AbstractActivationLayer

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.

forward

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

Applies the activation functions to the input to produce the 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.

hasActivation

Returns True, if layer has internal activation function.

inboundLayers

Returns inbound layers.

name

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

outboundLayers

Returns outbound layers.

outputShape

Output data tensor shape.

parentModel

Model where this layer is used.

ELU

LeakyReLU

PReLU

ReLU

Softmax

ThresholdedReLU

ActivationLayer