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
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. PositionalNeuralModel
  2. ParserModel
  3. ParserExtractable
  4. Model
  5. Model
  6. SerializableLogging
  7. Serializable
  8. Serializable
  9. AnyRef
  10. Any
  1. Hide All
  2. Show All
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
    @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. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  23. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  24. val index: SegmentedIndex[Feature, Index[Feature]]

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

  25. def inferenceFromWeights(weights: DenseVector[Double], forTrain: Boolean): Inference
  26. def inferenceFromWeights(weights: DenseVector[Double]): Inference
    Definition Classes
    PositionalNeuralModel → Model
  27. def initialValueForFeature(f: Feature): Double
    Definition Classes
    PositionalNeuralModel → Model
  28. def initialWeightVector(initWeightsScale: Double, initializerSpec: String, trulyRandom: Boolean = false): DenseVector[Double]
  29. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  30. val lexicon: Lexicon[L, W]
    Definition Classes
    PositionalNeuralModel → ParserExtractable
  31. def logger: LazyLogger
    Attributes
    protected
    Definition Classes
    SerializableLogging
  32. val maybeSparseSurfaceFeaturizer: Option[IndexedSpanFeaturizer[L, L2, W]]
  33. def mergeWeightsForEnsembling(x1: DenseVector[Double], x2: DenseVector[Double]): DenseVector[Double]
  34. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  35. final def notify(): Unit
    Definition Classes
    AnyRef
  36. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  37. def numFeatures: Int
    Definition Classes
    Model
  38. 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
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def toString(): String
    Definition Classes
    AnyRef → Any
  41. val topology: RuleTopology[L]
    Definition Classes
    PositionalNeuralModel → ParserExtractable
  42. val transforms: IndexedSeq[OutputTransform[Array[Int], DenseVector[Double]]]
  43. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  45. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. def weightsCacheName: String
    Attributes
    protected
    Definition Classes
    Model

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