AvgPool1D

class AvgPool1D(poolSize: IntArray, strides: IntArray, padding: ConvPadding, name: String) : Layer

Average pooling operation for 1D temporal data (e.g. audio, time-series).

Downsamples the input by taking the average over a temporal window of size poolSize.

Since

0.3

Constructors

AvgPool1D

fun AvgPool1D(poolSize: Int = 2, strides: Int = 2, padding: ConvPadding = ConvPadding.VALID, name: String = "")

AvgPool1D

fun AvgPool1D(poolSize: IntArray = intArrayOf(1, 2, 1), strides: IntArray = intArrayOf(1, 2, 1), padding: ConvPadding = ConvPadding.VALID, 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.

toString

open override fun toString(): String

Properties

hasActivation

open override 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.

padding

val padding: ConvPadding

Padding strategy; can be either of ConvPadding.VALID which means no padding, or ConvPadding.SAME which means padding the input equally such that the output has the same dimension as the input.

parentModel

var parentModel: GraphTrainableModel? = null

Model where this layer is used.

poolSize

val poolSize: IntArray

Size of the temporal pooling window for each dimension of input.

strides

val strides: IntArray

The amount of shift for pooling window per each input dimension in each pooling step.