case class LayerNorm(scale: Constant, bias: Constant, eps: Double, normalizedDim: List[Int]) extends Module with Product with Serializable
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def
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Alias of forward
Alias of forward
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def
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- val bias: Constant
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- val eps: Double
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forward[S](x: Variable)(implicit arg0: Sc[S]): Variable
The implementation of the function.
The implementation of the function.
In addition of
xit can also use all thestate to compute its value.- Definition Classes
- LayerNorm → GenericModule
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def
getClass(): Class[_]
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final
def
gradients(loss: Variable, zeroGrad: Boolean = true): Seq[Option[STen]]
Computes the gradient of loss with respect to the parameters.
Computes the gradient of loss with respect to the parameters.
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def
learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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final
def
ne(arg0: AnyRef): Boolean
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- val normalizedDim: List[Int]
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notify(): Unit
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notifyAll(): Unit
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def
parameters: Seq[(Constant, PTag)]
Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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- val scale: Constant
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val
state: List[(Constant, LeafTag with Product with Serializable)]
List of optimizable, or non-optimizable, but stateful parameters
List of optimizable, or non-optimizable, but stateful parameters
Stateful means that the state is carried over the repeated forward calls.
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final
def
zeroGrad(): Unit
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