DepthwiseConv2D

class DepthwiseConv2D(kernelSize: LongArray, strides: LongArray, dilations: LongArray, activation: Activations, depthMultiplier: Int, depthwiseInitializer: Initializer, biasInitializer: Initializer, depthwiseRegularizer: Regularizer?, biasRegularizer: Regularizer?, activityRegularizer: Regularizer?, padding: ConvPadding, useBias: Boolean, name: String) : AbstractConv, NoGradients

Depthwise separable 2D convolution. (e.g. spatial convolution over images).

Depthwise Separable convolutions consist of performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depthMultiplier argument controls how many output channels are generated per input channel in the depthwise step.

Since

0.2

Constructors

DepthwiseConv2D

fun DepthwiseConv2D(kernelSize: LongArray = longArrayOf(3, 3), strides: LongArray = longArrayOf(1, 1, 1, 1), dilations: LongArray = longArrayOf(1, 1, 1, 1), activation: Activations = Activations.Relu, depthMultiplier: Int = 1, depthwiseInitializer: Initializer = HeNormal(), biasInitializer: Initializer = HeUniform(), depthwiseRegularizer: Regularizer? = null, biasRegularizer: Regularizer? = null, activityRegularizer: Regularizer? = null, padding: ConvPadding = ConvPadding.SAME, useBias: Boolean = true, name: String = "")

Creates DepthwiseConv2D object.

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>, 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

activation

val activation: Activations

Activation function.

activityRegularizer

val activityRegularizer: Regularizer? = null

Regularizer function applied to the output of the layer (its "activation").

biasInitializer

val biasInitializer: Initializer

An initializer for the bias vector.

biasRegularizer

val biasRegularizer: Regularizer? = null

Regularizer function applied to the bias vector.

biasShapeArray

val biasShapeArray: LongArray

Returns the shape of bias weights.

depthMultiplier

val depthMultiplier: Int = 1

The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to numberOfChannels * depthMultiplier.

depthwiseInitializer

val depthwiseInitializer: Initializer

An initializer for the depthwise kernel matrix.

depthwiseRegularizer

val depthwiseRegularizer: Regularizer? = null

Regularizer function applied to the depthwise kernel matrix.

dilations

val dilations: LongArray

Four numbers, specifying the dilation rate to use for dilated convolution for each dimension of input tensor.

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.

kernelShapeArray

val kernelShapeArray: LongArray

Returns the shape of kernel weights.

kernelSize

val kernelSize: LongArray

Two long numbers, specifying the height and width of the 2D convolution window.

name

var name: String

outboundLayers

var outboundLayers: MutableList<Layer>

Returns outbound layers.

outputShape

lateinit var outputShape: TensorShape

Output data tensor shape.

padding

val padding: ConvPadding

The padding method, either 'valid' or 'same' or 'full'.

paramCount

open override val paramCount: Int

Returns amount of neurons.

parentModel

var parentModel: TrainableModel? = null

Model where this layer is used.

strides

val strides: LongArray

Strides of the pooling operation for each dimension of input tensor. NOTE: Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.

useBias

val useBias: Boolean = true

If true the layer uses a bias vector.

weights

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

Layer's weights.