skainet-lang-core/sk.ainet.lang.nn.dsl/Conv2dImpl

Conv2dImpl

class Conv2dImpl<T : DType, V>(val executionContext: ExecutionContext, initialInChannels: Int, initialOutChannels: Int, initialKernelSize: Pair<Int, Int>, initialStride: Pair<Int, Int>, initialPadding: Pair<Int, Int>, initialDilation: Pair<Int, Int>, initialGroups: Int, initialBias: Boolean, id: String, kClass: KClass<T>) : CONV2D<T, V> (source)

Constructors

constructor(executionContext: ExecutionContext, initialInChannels: Int, initialOutChannels: Int, initialKernelSize: Pair<Int, Int>, initialStride: Pair<Int, Int>, initialPadding: Pair<Int, Int>, initialDilation: Pair<Int, Int>, initialGroups: Int, initialBias: Boolean, id: String, kClass: KClass<T>)

Properties

open override var bias: Boolean

open override val biasShape: Shape

open override var dilation: Pair<Int, Int>

executionContext

open override val executionContext: ExecutionContext

open override var groups: Int

open override var inChannels: Int

open override var kernelSize: Pair<Int, Int>

open override var outChannels: Int

open override var padding: Pair<Int, Int>

open override var stride: Pair<Int, Int>

open override var trainable: Boolean

open override val weightsShape: Shape

Functions

open override fun bias(initBlock: BiasScope<T, V>.(Shape) -> Tensor<T, V>)

fun create(): Conv2d<T, V>

open override fun kernelSize(size: Int)

open override fun padding(size: Int)

open override fun stride(size: Int)

open override fun weights(initBlock: WeightsScope<T, V>.(Shape) -> Tensor<T, V>)