Conv1DTranspose

class Conv1DTranspose(filters: Int, kernelLength: Int, strides: IntArray, dilations: IntArray, activation: Activations, kernelInitializer: Initializer, biasInitializer: Initializer, kernelRegularizer: Regularizer?, biasRegularizer: Regularizer?, activityRegularizer: Regularizer?, padding: ConvPadding, outputPadding: IntArray?, useBias: Boolean, name: String) : ConvTranspose, NoGradients

1D convolution transpose layer.

This is an operation going in the opposite direction of a normal convolution: it transforms a tensor shaped like an output of some convolution into tensor that has the shape of the input.

This layer expects input data of size (N, L, C) where

N - batch size
L - length of signal sequence
C - number of channels

Note: dilation values greater than 1 are not supported on cpu (see https://github.com/tensorflow/tensorflow/issues/28264).

Since

0.4

Parameters

name

custom layer name

Constructors

Conv1DTranspose

fun Conv1DTranspose(filters: Int = 3, kernelLength: Int = 3, strides: IntArray = intArrayOf(1, 1, 1), dilations: IntArray = intArrayOf(1, 1, 1), activation: Activations = Activations.Relu, kernelInitializer: Initializer = HeNormal(), biasInitializer: Initializer = HeUniform(), kernelRegularizer: Regularizer? = null, biasRegularizer: Regularizer? = null, activityRegularizer: Regularizer? = null, padding: ConvPadding = ConvPadding.SAME, outputPadding: IntArray? = null, useBias: Boolean = true, name: String = "")

Functions

build

open override 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

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

open override val activation: Activations

activation function

activityRegularizer

open override val activityRegularizer: Regularizer? = null

regularizer function applied to the output of the layer

biasInitializer

open override val biasInitializer: Initializer

initializer for the bias

biasRegularizer

open override val biasRegularizer: Regularizer? = null

regularizer for the bias

dilations

open override val dilations: IntArray

dilations of the convolution for each dimension of the input tensor (three numbers). Currently, dilation values greater than 1 are not supported on cpu.

dimensions

val dimensions: Int

dimensionality of this convolution operation

filters

open override val filters: Int = 3

dimensionality of the output space (i.e. the number of filters in the convolution)

hasActivation

open override val hasActivation: Boolean

Returns True, if layer has internal activation function.

inboundLayers

var inboundLayers: MutableList<Layer>

Returns inbound layers.