RnnLM

Instance Constructors

new RnnLM(w2i: Map[String, Int], c2i: Map[Char, Int], wordRnnStateSize: Int, charRnnStateSize: Int, lmLabelCount: Int, parameters: ParameterCollection, wordLookupParameters: LookupParameter, charLookupParameters: LookupParameter, charFwRnnBuilder: RnnBuilder, charBwRnnBuilder: RnnBuilder, wordFwRnnBuilder: RnnBuilder, wordBwRnnBuilder: RnnBuilder, fwO: Parameter, bwO: Parameter)

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val bwO: Parameter
val c2i: Map[Char, Int]
val charBwRnnBuilder: RnnBuilder
val charFwRnnBuilder: RnnBuilder
val charLookupParameters: LookupParameter
val charRnnStateSize: Int
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def dimensions: Int

Definition Classes
RnnLM → LM
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
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def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val fwO: Parameter
final def getClass(): Class[_]

Definition Classes
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def hashCode(): Int

Definition Classes
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final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val lmLabelCount: Int
def mkEmbedding(word: String): Expression

Creates an overall word embedding by concatenating word and character embeddings
def mkEmbeddings(words: Iterable[String], doDropout: Boolean): Iterable[Expression]

Definition Classes
RnnLM → LM
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val parameters: ParameterCollection
def perplexity(emissionScoresForSeq: ExpressionVector, words: Array[String], t2i: Map[String, Int]): Double

Computes perplexity for this sentence
def reportPerplexity(devFileName: String, t2i: Map[String, Int]): Unit
def save(baseModelName: String): Unit
def saveX2i(printWriter: PrintWriter): Unit

Definition Classes
RnnLM → LM
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def trainLM(trainFile: String, devFileOpt: Option[String], lmLabelCount: Int, logCheckpoint: Int, saveCheckpoint: Int, batchSize: Int): Unit

Pretrain this LM for next word prediction, in both directions
Pretrain this LM for next word prediction, in both directions
trainFile
File with one sentence per line; each sentence must be pre-tokenized
devFileOpt
Optional development file to report perplexity once in a while
lmLabelCount
How many prediction labels to use
logCheckpoint
When to printout the cummulative loss
saveCheckpoint
When to save a model
batchSize
Batch size in sentences; each actual sentences counts as 2 due to the bi traversal
val w2i: Map[String, Int]
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val wordBwRnnBuilder: RnnBuilder
val wordFwRnnBuilder: RnnBuilder
val wordLookupParameters: LookupParameter
val wordRnnStateSize: Int

Related Docs: object RnnLM | package lm

class RnnLM extends LM

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val bwO: Parameter

val c2i: Map[Char, Int]

val charBwRnnBuilder: RnnBuilder

val charFwRnnBuilder: RnnBuilder

val charLookupParameters: LookupParameter

val charRnnStateSize: Int

def clone(): AnyRef

def dimensions: Int

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

val fwO: Parameter

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val lmLabelCount: Int

def mkEmbedding(word: String): Expression

def mkEmbeddings(words: Iterable[String], doDropout: Boolean): Iterable[Expression]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val parameters: ParameterCollection

def perplexity(emissionScoresForSeq: ExpressionVector, words: Array[String], t2i: Map[String, Int]): Double

def reportPerplexity(devFileName: String, t2i: Map[String, Int]): Unit

def save(baseModelName: String): Unit

def saveX2i(printWriter: PrintWriter): Unit

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

def toString(): String

def trainLM(trainFile: String, devFileOpt: Option[String], lmLabelCount: Int, logCheckpoint: Int, saveCheckpoint: Int, batchSize: Int): Unit

val w2i: Map[String, Int]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

val wordBwRnnBuilder: RnnBuilder

val wordFwRnnBuilder: RnnBuilder

val wordLookupParameters: LookupParameter

val wordRnnStateSize: Int

Inherited from LM

Inherited from AnyRef

Inherited from Any

Ungrouped