BatchNorm2d

A training version of batch normalization provided for compatibility with existing code.

Constructors

BatchNorm2d

fun BatchNorm2d(numFeatures: Int, momentum: Float = 0.1f)

cpu

open override fun cpu(): BatchNormTraining

equals

open operator override fun equals(other: Any?): Boolean

extractTangent

getSingleInput

Helper to check that the layer was called with a single input. Returns that input if successful, else errors.

gpu

open override fun gpu(): BatchNormTraining

hashCode

open override fun hashCode(): Int

invoke

open operator override fun invoke(input: DTensor): DTensor

abstract operator fun invoke(vararg inputs: DTensor): DTensor

load

store

open override fun store(into: ByteBuffer): ByteBuffer

open fun to(device: Device): OnDevice

trainingStep

withTrainables

wrap

open override fun wrap(wrapper: Wrapper): BatchNormTraining

The wrap function should return the same static type it is declared on.

inferenceMode

Freeze the batch norm transform that was used during training, returning an affine transform to be used for inference.

stats

open override val stats: Pair<DTensor, DTensor>

The computed running mean and variance.

trainables

open override val trainables: List<Trainable<*>>