class TensorflowAssertion extends Logging
Created by jose on 15/03/18.
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Instance Constructors
- new TensorflowAssertion(tensorflow: TensorflowWrapper, encoder: AssertionDatasetEncoder, batchSize: Int, verboseLevel: nlp.annotators.ner.Verbose.Value)
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
asInstanceOf[T0]: T0
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- val batchSize: Int
- def calcStat(tp: Int, fp: Int, fn: Int): (Float, Float, Float)
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def
clone(): AnyRef
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- protected[lang]
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- @throws( ... ) @native()
- val encoder: AssertionDatasetEncoder
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final
def
eq(arg0: AnyRef): Boolean
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getClass(): Class[_]
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def
getLogName: String
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- Logging
- def getPiecesTags(tokenTags: TextSentenceLabels, sentence: WordpieceEmbeddingsSentence): Array[String]
- def getPiecesTags(tokenTags: Array[TextSentenceLabels], sentences: Array[WordpieceEmbeddingsSentence]): Array[Array[String]]
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def
hashCode(): Int
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isInstanceOf[T0]: Boolean
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def
log(value: ⇒ String, minLevel: Level): Unit
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- Logging
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val
logger: Logger
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- protected
- Definition Classes
- Logging
- def measure(labeled: Array[(WordpieceEmbeddingsSentence, AssertionAnnotationWithLabel)], batchSize: Int = 100, outputLogsPath: String, embeddingsDim: Int, maxLen: Int, configProtoBytes: Option[Array[Byte]] = None, enableOutputLogs: Boolean = false, extended: Boolean = true, includeConfidence: Boolean = false, description: String = "", uuid: String = Identifiable.randomUID("annotator"), scopeWindow: (Int, Int)): Unit
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notify(): Unit
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final
def
notifyAll(): Unit
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def
outputLog(value: ⇒ String, uuid: String, shouldLog: Boolean, outputLogsPath: String): Unit
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- Logging
- def predict(chunks: Array[IndexedChunk], maxLen: Int, embeddingsDim: Int, configProtoBytes: Option[Array[Byte]] = None, includeConfidence: Boolean = false, scopeWindow: (Int, Int)): Array[AssertionAnnotationWithLabel]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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- def tagsForTokens(labels: Array[(String, Option[Double])], pieces: WordpieceEmbeddingsSentence): Array[(String, Option[Double])]
- def tagsForTokens(labels: Array[Array[(String, Option[Double])]], pieces: Array[WordpieceEmbeddingsSentence]): Array[Array[(String, Option[Double])]]
- val tensorflow: TensorflowWrapper
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def
toString(): String
- Definition Classes
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- def train(trainDataset: Array[(WordpieceEmbeddingsSentence, AssertionAnnotationWithLabel)], lr: Float, batchSize: Int, dropout: Float, startEpoch: Int, endEpoch: Int, maxLen: Int, embeddingsDim: Int, graphFileName: String = "", test: Array[(WordpieceEmbeddingsSentence, AssertionAnnotationWithLabel)] = Array.empty, configProtoBytes: Option[Array[Byte]] = None, validationSplit: Float = 0.0f, includeConfidence: Boolean = false, enableOutputLogs: Boolean = false, outputLogsPath: String, uuid: String = Identifiable.randomUID("annotator"), scopeWindow: (Int, Int)): Unit
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val
verboseLevel: nlp.annotators.ner.Verbose.Value
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- TensorflowAssertion → Logging
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final
def
wait(): Unit
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wait(arg0: Long): Unit
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