Package ai.djl.nn.norm
Class LayerNorm.Builder
- java.lang.Object
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- ai.djl.nn.norm.LayerNorm.Builder
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Constructor Summary
Constructors Modifier Constructor Description protected
Builder()
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Method Summary
All Methods Instance Methods Concrete Methods 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 aLayerNorm
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`.
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Method Detail
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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
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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
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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
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optEpsilon
public LayerNorm.Builder optEpsilon(float val)
Sets the epsilon value to prevent division by 0.- Parameters:
val
- the epsilon value- Returns:
- this Builder
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