class PositionalNeuralModel[L, L2, W] extends ParserModel[L, W] with Serializable

Main neural CRF parser class.

Annotations
@SerialVersionUID()
Linear Supertypes
ParserModel[L, W], ParserExtractable[L, W], Model[TreeInstance[L, W]], Model[TreeInstance[L, W]], SerializableLogging, Serializable, Serializable, AnyRef, Any
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Inherited
  1. PositionalNeuralModel
  2. ParserModel
  3. ParserExtractable
  4. Model
  5. Model
  6. SerializableLogging
  7. Serializable
  8. Serializable
  9. AnyRef
  10. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PositionalNeuralModel(annotator: (BinarizedTree[L], IndexedSeq[W]) ⇒ BinarizedTree[IndexedSeq[L2]], constrainer: Factory[L, W], topology: RuleTopology[L], lexicon: Lexicon[L, W], refinedTopology: RuleTopology[L2], refinements: GrammarRefinements[L, L2], labelFeaturizer: RefinedFeaturizer[L, W, Feature], surfaceFeaturizer: Word2VecSurfaceFeaturizerIndexed[W], depFeaturizer: Word2VecDepFeaturizerIndexed[W], transforms: IndexedSeq[OutputTransform[Array[Int], DenseVector[Double]]], maybeSparseSurfaceFeaturizer: Option[IndexedSpanFeaturizer[L, L2, W]], depTransforms: Seq[OutputTransform[Array[Int], DenseVector[Double]]], decoupledTransforms: Seq[OutputTransform[Array[Int], DenseVector[Double]]])

Type Members

  1. type ExpectedCounts = StandardExpectedCounts[Feature]
    Definition Classes
    Model → Model
  2. type Inference = PositionalNeuralModel.Inference[L, L2, W]
    Definition Classes
    PositionalNeuralModel → ParserModel → Model
  3. type Marginal = ParseMarginal[L, W]
    Definition Classes
    ParserModel → Model
  4. type Scorer = GrammarAnchoring[L, W]
    Definition Classes
    ParserModel → Model

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def accumulateCounts(inf: Inference, s: Scorer, d: TreeInstance[L, W], m: Marginal, accum: ExpectedCounts, scale: Double): Unit
    Definition Classes
    PositionalNeuralModel → Model
  5. final def accumulateCounts(inf: Inference, d: TreeInstance[L, W], accum: ExpectedCounts, scale: Double): Unit
    Definition Classes
    Model
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def cacheFeatureWeights(weights: DenseVector[Double], suffix: String = ""): Unit

    Caches the weights using the cache broker.

    Caches the weights using the cache broker.

    Definition Classes
    Model
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate() @throws( ... )
  9. def cloneModelForEnsembling: PositionalNeuralModel[L, L2, W]
  10. val constrainer: Factory[L, W]
    Definition Classes
    PositionalNeuralModel → ParserExtractable
  11. val decoupledTransforms: Seq[OutputTransform[Array[Int], DenseVector[Double]]]
  12. val depTransforms: Seq[OutputTransform[Array[Int], DenseVector[Double]]]
  13. def emptyCounts: StandardExpectedCounts[Feature]
    Definition Classes
    Model → Model
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. final def expectedCounts(inf: Inference, d: TreeInstance[L, W], scale: Double = 1.0): ExpectedCounts
    Definition Classes
    Model
  17. def expectedCountsToObjective(ecounts: ExpectedCounts): (Double, DenseVector[Double])
    Definition Classes
    Model → Model
  18. def extractParser(weights: DenseVector[Double], trainExs: Seq[TreeInstance[L, W]])(implicit deb: Debinarizer[L]): Parser[L, W]

    When doing batch normalization, we need to normalize the test network

  19. def extractParser(weights: DenseVector[Double])(implicit deb: Debinarizer[L]): Parser[L, W]
    Definition Classes
    ParserModel → ParserExtractable
  20. def featureIndex: Index[Feature]

    Models have features, and this defines the mapping from indices in the weight vector to features.

    Models have features, and this defines the mapping from indices in the weight vector to features.

    Definition Classes
    PositionalNeuralModel → Model
  21. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  22. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  23. val index: SegmentedIndex[Feature, Index[Feature]]

    Models have features, and this defines the mapping from indices in the weight vector to features.

  24. def inferenceFromWeights(weights: DenseVector[Double], forTrain: Boolean): Inference
  25. def inferenceFromWeights(weights: DenseVector[Double]): Inference
    Definition Classes
    PositionalNeuralModel → Model
  26. def initialValueForFeature(f: Feature): Double
    Definition Classes
    PositionalNeuralModel → Model
  27. def initialWeightVector(initWeightsScale: Double, initializerSpec: String, trulyRandom: Boolean = false): DenseVector[Double]
  28. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  29. val lexicon: Lexicon[L, W]
    Definition Classes
    PositionalNeuralModel → ParserExtractable
  30. def logger: LazyLogger
    Attributes
    protected
    Definition Classes
    SerializableLogging
  31. val maybeSparseSurfaceFeaturizer: Option[IndexedSpanFeaturizer[L, L2, W]]
  32. def mergeWeightsForEnsembling(x1: DenseVector[Double], x2: DenseVector[Double]): DenseVector[Double]
  33. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  34. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  35. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  36. def numFeatures: Int
    Definition Classes
    Model
  37. def readCachedFeatureWeights(suffix: String = ""): Option[DenseVector[Double]]

    just saves feature weights to disk as a serialized counter.

    just saves feature weights to disk as a serialized counter. The file is prefix.ser.gz

    Definition Classes
    Model
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. val topology: RuleTopology[L]
    Definition Classes
    PositionalNeuralModel → ParserExtractable
  41. val transforms: IndexedSeq[OutputTransform[Array[Int], DenseVector[Double]]]
  42. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  44. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. def weightsCacheName: String
    Attributes
    protected
    Definition Classes
    Model

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @Deprecated @deprecated @throws( classOf[java.lang.Throwable] )
    Deprecated

    (Since version ) see corresponding Javadoc for more information.

Inherited from ParserModel[L, W]

Inherited from ParserExtractable[L, W]

Inherited from Model[TreeInstance[L, W]]

Inherited from Model[TreeInstance[L, W]]

Inherited from SerializableLogging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped