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

Package-level declarations

Types

interface AVGPOOL2D<T : DType, V> : NetworkDslItem

class AvgPool2dImpl<T : DType, V>(val executionContext: ExecutionContext, initialKernelSize: Pair<Int, Int>, initialStride: Pair<Int, Int>, initialPadding: Pair<Int, Int>, initialCountIncludePad: Boolean, id: String) : AVGPOOL2D<T, V>

BiasScope

interface BiasScope<T : DType, V> : TensorCreationScope<T, V>

Scope for bias initialization with implicit shape context.

BiasScopeImpl

class BiasScopeImpl<T : DType, V>(val executionContext: ExecutionContext, val shape: Shape, val dtype: KClass<T>) : BiasScope<T, V>

Implementation of BiasScope for bias initialization.

CONV1D

interface CONV1D<T : DType, V> : NetworkDslItem, WandBTensorValueContext<T, V>

Conv1dImpl

class Conv1dImpl<T : DType, V>(val executionContext: ExecutionContext, initialInChannels: Int, initialOutChannels: Int, initialKernelSize: Int, initialStride: Int, initialPadding: Int, initialDilation: Int, initialGroups: Int, initialBias: Boolean, id: String, kClass: KClass<T>) : CONV1D<T, V>

CONV2D

interface CONV2D<T : DType, V> : NetworkDslItem, WandBTensorValueContext<T, V>

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>

CONV3D

interface CONV3D<T : DType, V> : NetworkDslItem, WandBTensorValueContext<T, V>

Conv3dImpl

class Conv3dImpl<T : DType, V>(val executionContext: ExecutionContext, initialInChannels: Int, initialOutChannels: Int, initialKernelSize: Triple<Int, Int, Int>, initialStride: Triple<Int, Int, Int>, initialPadding: Triple<Int, Int, Int>, initialDilation: Triple<Int, Int, Int>, initialGroups: Int, initialBias: Boolean, id: String, kClass: KClass<T>) : CONV3D<T, V>

DENSE

interface DENSE<T : DType, V> : NetworkDslItem, WandBTensorValueContext<T, V>

DenseImpl

class DenseImpl<T : DType, V>(val executionContext: ExecutionContext, inputDimension: Int, _outputDimension: Int, id: String, kClass: KClass<T>) : DENSE<T, V>

FLATTEN

interface FLATTEN<T : DType, V> : NetworkDslItem

FlattenImpl

class FlattenImpl<T : DType, V>(val executionContext: ExecutionContext, var startDim: Int = 1, var endDim: Int = -1, id: String) : FLATTEN<T, V>

MAXPOOL2D

interface MAXPOOL2D<T : DType, V> : NetworkDslItem

MaxPool2dImpl

class MaxPool2dImpl<T : DType, V>(val executionContext: ExecutionContext, initialKernelSize: Pair<Int, Int>, initialStride: Pair<Int, Int>, initialPadding: Pair<Int, Int>, id: String) : MAXPOOL2D<T, V>

NetworkBuilder

class NetworkBuilder<T : DType, V>

NetworkDsl

@DslMarker

annotation class NetworkDsl

NetworkDslItem

interface NetworkDslItem

NeuralNetworkDsl

interface NeuralNetworkDsl<T : DType, V> : NetworkDslItem

Core DSL interface for building neural networks with generic tensor types. This interface provides a fluent API for constructing neural network architectures with support for different data types and precision levels.

NeuralNetworkDslImpl

class NeuralNetworkDslImpl<T : DType, V>(val executionContext: ExecutionContext, kClass: KClass<T>) : NeuralNetworkDsl<T, V>

PrecisionResolver

interface PrecisionResolver<V>

StageImpl

class StageImpl<T : DType, V>(val executionContext: ExecutionContext, id: String, kClass: KClass<T>) : NeuralNetworkDsl<T, V>

TrainingConfig

class TrainingConfig<T : DType, V>

Experimental DSL for configuring and running training. This is kept internal/experimental to avoid early API lock-in.

TrainingRunner

class TrainingRunner<T : DType, V>(val model: Module<T, V>, val loss: Loss, val optimizer: Optimizer)

Runner that holds training components and provides methods to execute training steps.

UPSAMPLE2D

interface UPSAMPLE2D<T : DType, V> : NetworkDslItem

Upsample2dImpl

class Upsample2dImpl<T : DType, V>(val executionContext: ExecutionContext, initialScale: Pair<Int, Int>, initialMode: UpsampleMode, initialAlignCorners: Boolean, id: String) : UPSAMPLE2D<T, V>

WandBTensorValueContext

interface WandBTensorValueContext<T : DType, V>

WeightsScope

interface WeightsScope<T : DType, V> : TensorCreationScope<T, V>

Scope for weights initialization with implicit shape context.

WeightsScopeImpl

class WeightsScopeImpl<T : DType, V>(val executionContext: ExecutionContext, val shape: Shape, val dtype: KClass<T>) : WeightsScope<T, V>

Implementation of WeightsScope for weights initialization.

Functions

elu

fun <T : DType, V> NeuralNetworkDsl<T, V>.elu(alpha: Float = 1.0f, id: String = "")

Adds a elu activation layer to the network.

gelu

fun <T : DType, V> NeuralNetworkDsl<T, V>.gelu(id: String = "")

Adds a gelu activation layer to the network.

leakyRelu

fun <T : DType, V> NeuralNetworkDsl<T, V>.leakyRelu(negativeSlope: Float = 0.01f, id: String = "")

Adds a leakyRelu activation layer to the network.

relu

fun <T : DType, V> NeuralNetworkDsl<T, V>.relu(id: String = "")

Adds a relu activation layer to the network.

sequential

inline fun <T : DType, V> sequential(content: NeuralNetworkDsl<T, V>.() -> Unit): Module<T, V>

Generic network builder function that creates a neural network with specified data type and value type.

inline fun <T : DType, V> sequential(executionContext: ExecutionContext, content: NeuralNetworkDsl<T, V>.() -> Unit): Module<T, V>

Overload that wires both tensor factory and ops from an ExecutionContext.

sigmoid

fun <T : DType, V> NeuralNetworkDsl<T, V>.sigmoid(id: String = "")

Adds a sigmoid activation layer to the network.

silu

fun <T : DType, V> NeuralNetworkDsl<T, V>.silu(id: String = "")

Adds a silu activation layer to the network.

training

fun <T : DType, V> training(block: TrainingConfig<T, V>.() -> Unit): TrainingRunner<T, V>

Experimental entry point for the training DSL.