case class OutputEmbeddingTransform[FV](numOutputs: Int, outputDim: Int, innerTransform: Transform[FV, DenseVector[Double]], coarsenerForInitialization: Option[(Int) ⇒ Int] = None) extends OutputTransform[FV, DenseVector[Double]] with Product with Serializable
Output embedding technique described in section 6 of http://www.eecs.berkeley.edu/~gdurrett/papers/durrett-klein-acl2015.pdf Basically learns a dictionary for the output as well as an affine transformation in order to produce the vector that gets combined with the input in the final bilinear product.
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- OutputEmbeddingTransform
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Instance Constructors
Type Members
- case class OutputLayer (embeddings: DenseMatrix[Double], bias: DenseVector[Double], innerLayer: Layer) extends OutputTransform.OutputLayer[FV, DenseVector[Double]] with Product with Serializable
Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
- Definition Classes
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
- def clipEmbeddingNorms(weights: DenseVector[Double]): Unit
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def
clipHiddenWeightVectors(weights: DenseVector[Double], norm: Double, outputLayer: Boolean): Unit
- Definition Classes
- OutputEmbeddingTransform → OutputTransform
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def
clone(): AnyRef
- Attributes
- protected[java.lang]
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- @throws( ... )
- val coarsenerForInitialization: Option[(Int) ⇒ Int]
- def displayEmbeddingNorms(weights: DenseVector[Double]): Unit
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
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def
extractLayer(dv: DenseVector[Double], forTrain: Boolean): OutputLayer
- Definition Classes
- OutputTransform
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def
extractLayerAndPenultimateLayer(weights: DenseVector[Double], forTrain: Boolean): (OutputLayer, Layer)
- Definition Classes
- OutputEmbeddingTransform → OutputTransform
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def
finalize(): Unit
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- protected[java.lang]
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final
def
getClass(): Class[_]
- Definition Classes
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def
getInterestingWeightIndicesForGradientCheck(offset: Int): Seq[Int]
- Definition Classes
- OutputEmbeddingTransform → OutputTransform
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val
index: SegmentedIndex[Feature, Index[Feature]]
- Definition Classes
- OutputEmbeddingTransform → OutputTransform
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def
initialWeightVector(initWeightsScale: Double, rng: Random, outputLayer: Boolean, spec: String): DenseVector[Double]
- Definition Classes
- OutputEmbeddingTransform → OutputTransform
- val innerTransform: Transform[FV, DenseVector[Double]]
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
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final
def
notify(): Unit
- Definition Classes
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final
def
notifyAll(): Unit
- Definition Classes
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- val numOutputs: Int
- val outputDim: Int
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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