class TensorflowClassifier extends Serializable with ClassifierMetrics
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- TensorflowClassifier
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Instance Constructors
- new TensorflowClassifier(tensorflow: TensorflowWrapper, encoder: ClassifierDatasetEncoder, testEncoder: Option[ClassifierDatasetEncoder], verboseLevel: nlp.annotators.ner.Verbose.Value)
Value Members
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final
def
!=(arg0: Any): Boolean
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final
def
##(): Int
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final
def
==(arg0: Any): Boolean
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def
aggregatedMetrics(labels: Seq[String], truePositives: Map[String, Int], falsePositives: Map[String, Int], falseNegatives: Map[String, Int], extended: Boolean, enableOutputLogs: Boolean, outputLogsPath: String, uuid: String = ...): (Float, Float)
- Definition Classes
- ClassifierMetrics
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final
def
asInstanceOf[T0]: T0
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- Any
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def
calcStat(tp: Int, fp: Int, fn: Int): (Float, Float, Float)
- Definition Classes
- ClassifierMetrics
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def
clone(): AnyRef
- Attributes
- protected[lang]
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- @throws( ... ) @native()
- val encoder: ClassifierDatasetEncoder
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final
def
eq(arg0: AnyRef): Boolean
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def
equals(arg0: Any): Boolean
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def
finalize(): Unit
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final
def
getClass(): Class[_]
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def
getLogName: String
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def
hashCode(): Int
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- @native()
- def internalPredict(inputs: Array[Array[Float]], numClasses: Int, configProtoBytes: Option[Array[Byte]] = None): Array[Int]
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final
def
isInstanceOf[T0]: Boolean
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def
log(value: ⇒ String, minLevel: Level): Unit
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val
logger: Logger
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- protected
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- Logging
- def measure(labeled: Array[(Array[Float], Array[Int])], sourceData: String, extended: Boolean = false, enableOutputLogs: Boolean = false, outputLogsPath: String, batchSize: Int = 100): (Float, Float)
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final
def
ne(arg0: AnyRef): Boolean
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final
def
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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- Definition Classes
- Logging
- def predict(docs: Seq[(Int, Seq[Annotation])], configProtoBytes: Option[Array[Byte]] = None): Seq[Annotation]
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
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- val tensorflow: TensorflowWrapper
- val testEncoder: Option[ClassifierDatasetEncoder]
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def
toString(): String
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- def train(inputs: (Array[Array[Float]], Array[String]), testInputs: Option[(Array[Array[Float]], Array[String])], classNum: Int, lr: Float = 5e-3f, batchSize: Int = 64, dropout: Float = 0.5f, startEpoch: Int = 0, endEpoch: Int = 10, configProtoBytes: Option[Array[Byte]] = None, validationSplit: Float = 0.0f, evaluationLogExtended: Boolean = false, enableOutputLogs: Boolean = false, outputLogsPath: String, uuid: String = ...): Unit
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val
verboseLevel: nlp.annotators.ner.Verbose.Value
- Definition Classes
- TensorflowClassifier → Logging
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final
def
wait(): Unit
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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