RMSNorm

scalation.modeling.forecasting.neuralforecasting.RMSNorm
case class RMSNorm()

The RMSNorm class will, in computing the output, normalize by dividing by the Root Mean Square (RMS).

Attributes

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Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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Members list

Value members

Concrete methods

def apply(u: VectorD): VectorD

Forward pass: calculate the output of this layer given the values sent to all its neurons: u = W x where W is a weight matrix

Forward pass: calculate the output of this layer given the values sent to all its neurons: u = W x where W is a weight matrix

Value parameters

u

the input vector to the given layer

Attributes

Inherited methods

def productElementNames: Iterator[String]

An iterator over the names of all the elements of this product.

An iterator over the names of all the elements of this product.

Attributes

Inherited from:
Product
def productIterator: Iterator[Any]

An iterator over all the elements of this product.

An iterator over all the elements of this product.

Attributes

Returns

in the default implementation, an Iterator[Any]

Inherited from:
Product