Package ai.djl.nn.norm

Class LayerNorm.Builder

java.lang.Object

ai.djl.nn.norm.LayerNorm.Builder

Enclosing class:

LayerNorm

public static class LayerNorm.Builder
extends Object

The Builder to construct a LayerNorm.

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	Builder()

Method Summary

Modifier and Type	Method	Description
LayerNorm.Builder	axis(int... axis)	List the axis over which the mean and variance will be calculated (alternative to normalizedShape).
LayerNorm	build()	Builds a LayerNorm block.
LayerNorm.Builder	optCenter(boolean val)	If True, add offset of `beta` to normalized tensor.
LayerNorm.Builder	optEpsilon(float val)	Sets the epsilon value to prevent division by 0.
LayerNorm.Builder	optScale(boolean val)	If True, multiply result by `gamma`.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

Builder

protected Builder()

Method Details

axis

public LayerNorm.Builder axis(int... axis)

List the axis over which the mean and variance will be calculated (alternative to normalizedShape).

Parameters:

axis - input axis over which the mean and variance will be calculated (if null all existing dimensions)

Returns:

this Builder

optCenter

public LayerNorm.Builder optCenter(boolean val)

If True, add offset of `beta` to normalized tensor. Defaults to True.

Parameters:

val - True or False on whether to add and train offset value

Returns:

this Builder

optScale

public LayerNorm.Builder optScale(boolean val)

If True, multiply result by `gamma`. Defaults to True;

Parameters:

val - True or False on whether to add and train scale value

Returns:

this Builder

optEpsilon

public LayerNorm.Builder optEpsilon(float val)

Sets the epsilon value to prevent division by 0.

Parameters:

val - the epsilon value

Returns:

this Builder

build

public LayerNorm build()

Builds a LayerNorm block.

Returns:

the LayerNorm block