scalation.modeling.forecasting.neuralforecasting.LayerNorm
case class LayerNorm(
atransform:
Boolean = ...,
eps:
Double = ...)
The LayerNorm class will, in computing the output, normalize by subtracting the mean and dividing by the standard deviation.
Value parameters
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atransform
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whether to apply an affine transformation to standard normalization
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eps
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the small value to prevent division by zero
Attributes
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See also
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pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html#torch.nn.LayerNorm
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Graph
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Supertypes
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trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
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Members list
Forward pass: calculate the output of this layer.
Forward pass: calculate the output of this layer.
Value parameters
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x
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the m by nx input matrix (full or batch)
Attributes
Reset the weight and bias.
Reset the weight and bias.
Value parameters
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b_
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the new bias
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w_
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the new weight
Attributes
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
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Inherited from:
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Product
An iterator over all the elements of this product.
An iterator over all the elements of this product.
Attributes
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Returns
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in the default implementation, an Iterator[Any]
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Inherited from:
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Product