Layer

fun Layer(name: String)

build

abstract fun build(tf: Ops, inputShape: Shape)

Extend this function to define variables in layer.

buildFromInboundLayers

fun buildFromInboundLayers(tf: Ops)

Extend this function to define variables in layer.

computeOutputShape

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

abstract 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.

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

abstract val hasActivation: Boolean

Returns True, if layer has internal activation function.

inboundLayers

var inboundLayers: MutableList<Layer>

Returns inbound layers.

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.

AbstractActivationLayer

AbstractConv

SeparableConv2D

Dense

Input

AbstractMerge

BatchNorm

AvgPool1D

AvgPool2D

AvgPool3D

GlobalAvgPool1D

GlobalAvgPool2D

GlobalAvgPool3D

GlobalMaxPool1D

GlobalMaxPool2D

GlobalMaxPool3D

MaxPool1D

MaxPool2D

MaxPool3D

Dropout

AbstractCropping

AbstractUpSampling

AbstractZeroPadding

Flatten

Permute

RepeatVector

Reshape

freeze

fun Layer.freeze()

Freezes layer weights, so they won't be changed during training.

isTrainable

val Layer.isTrainable: Boolean

Returns true if this Layer is trainable, false otherwise. Always returns false for layers not implementing TrainableLayer.

paramCount

val Layer.paramCount: Int

Returns the number of parameters in this layer. If layer is not a ParametrizedLayer, returns zero.

unfreeze

fun Layer.unfreeze()

Unfreezes layer weights, allowing to change them during training.

weights

var Layer.weights: Map<String, Array<*>>

Weights of this layer. Require parent model to be set on the layer.

Layer

Constructors

Functions

Properties

Inheritors

Extensions