class TensorflowGenericClassifier extends Serializable with Logging
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Instance Constructors
- new TensorflowGenericClassifier(model: TensorflowWrapper, verboseLevel: nlp.annotators.ner.Verbose.Value = Verbose.All, outputLogsPath: Option[String] = None)
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finalize(): Unit
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final
def
getClass(): Class[_]
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def
getLogName: String
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- def getTFModel: TensorflowWrapper
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def
hashCode(): Int
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def
internalPredict(features: Array[Array[Float]], labels: Array[Array[Float]], configProtoBytes: Option[Array[Byte]] = None, classWeights: Array[Float]): (Float, Float)
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isInstanceOf[T0]: Boolean
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def
log(value: ⇒ String, minLevel: Level): Unit
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def
logMessage(message: String, uuid: String): Unit
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val
logger: Logger
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- val model: TensorflowWrapper
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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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- val outputLogsPath: Option[String]
- def predict(features: Array[Array[Float]], configProtoBytes: Option[Array[Byte]] = None, allowedLabelIds: Option[Array[Int]] = None): (Array[Long], Array[Float], Array[Array[Float]])
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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- def train(features: Array[Array[Float]], labels: Array[Array[Float]], batchSize: Int, epochsNumber: Int, learningRate: Float = 0.001f, validationSplit: Float = 0.0f, classWeights: Array[Float], dropout: Float = 0.0f, configProtoBytes: Option[Array[Byte]] = None, uuid: String = Identifiable.randomUID("annotator"), resumeTraining: Boolean = false): Unit
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val
verboseLevel: nlp.annotators.ner.Verbose.Value
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final
def
wait(): Unit
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wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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