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docs/torch/torch.nn/torch.nn.modules/torch.nn.modules.normalization

torch.nn.modules.normalization

package torch.nn.modules.normalization

Members list

Type members

Classlikes

final class GroupNorm[ParamType <: FloatNN | ComplexNN](numGroups: Int, numChannels: Int, eps: Double, affine: Boolean)(implicit evidence$1: Default[ParamType]) extends HasWeight[ParamType], TensorModule[ParamType]

Applies Group Normalization over a mini-batch of inputs

Applies Group Normalization over a mini-batch of inputs

Value parameters

affine

a boolean value that when set to true, this module has learnable per-channel affine parameters initialized to ones (for weights) and zeros (for biases)

eps

a value added to the denominator for numerical stability

numChannels

number of channels expected in input

numGroups

number of groups to separate the channels into

Attributes

Source
GroupNorm.scala
Supertypes
trait TensorModule[ParamType]
trait Tensor[ParamType] => Tensor[ParamType]
trait HasWeight[ParamType]
class Module
class Object
trait Matchable
class Any
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final class LayerNorm[ParamType <: DType](normalizedShape: Seq[Int] | Int, eps: Double, elementwiseAffine: Boolean)(implicit evidence$1: Default[ParamType]) extends TensorModule[ParamType]

Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization // TODO Add docs

Applies Layer Normalization over a mini-batch of inputs as described in the paper Layer Normalization // TODO Add docs

Attributes

Source
LayerNorm.scala
Supertypes
trait TensorModule[ParamType]
trait Tensor[ParamType] => Tensor[ParamType]
class Module
class Object
trait Matchable
class Any
Show all
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