Class

ai.minxiao.ds4s.core.dl4j.embed

D2V

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class D2V extends Serializable

D2V Doc2Vec (Paragraph Vector): Doc2Vec (ParagraphVectors)

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@SerialVersionUID()
Note

Quoc Le, Tomas Mikolov. Distributed Representations of Sentences and Documents. In ICML, 2014.

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reference: Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. In ICML, 2014.

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Instance Constructors

  1. new D2V(minWordFrequency: Int = 5, layerSize: Int = 100, windowSize: Int = 5, epochs: Int = 1, batchSize: Int = 16, iterations: Int = 1, sampling: Double = 0, learningRate: Double = 1E-1, minLearningRate: Double = 1E-6, useAdaGrad: Boolean = false, tokenizerFactory: TokenizerFactory = new DefaultTokenizerFactory, tokenPreProcessor: TokenPreProcess = new CommonPreprocessor, lookupTable: Option[InMemoryLookupTable[VocabWord]] = None, vocabCache: Option[AbstractCache[VocabWord]] = None, seed: Long = 2018L, trainWordVectors: Boolean = true, labelsSource: Option[LabelsSource] = None)

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    minWordFrequency

    min word frequency, default=5

    layerSize

    word vector dimensionality, default=100

    windowSize

    window size, default=5

    epochs

    training epochs, default=1

    batchSize

    batch size, default=12

    sampling

    sampling rate, default=0(no sampling)

    learningRate

    learning rate, default=1E-1

    minLearningRate

    mini learning rate, default=1E-6

    useAdaGrad

    whether to use AdaGrad, default=false

    tokenizerFactory

    tokenizer factory, default=new DefaultTokenizerFactory

    tokenPreProcessor

    token preprocessor, default=new CommonPreprocessor

    seed

    seed for random generator, default=2018

    trainWordVectors

    whether to train word vectors together with document vectors, default=true (PV-DM), false for PV-DBOW

    labelsSource

    label source, default=None

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def build(): ParagraphVectors

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  6. def clone(): AnyRef

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  7. final def eq(arg0: AnyRef): Boolean

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  8. def equals(arg0: Any): Boolean

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  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final val iterations: Int

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  14. final def ne(arg0: AnyRef): Boolean

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. final def synchronized[T0](arg0: ⇒ T0): T0

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  18. def toString(): String

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  19. final def wait(): Unit

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  20. final def wait(arg0: Long, arg1: Int): Unit

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  21. final def wait(arg0: Long): Unit

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