com.intel.analytics.bigdl.visualization

TrainSummary

class TrainSummary extends Summary

Train logger for tensorboard. Use optimize.setTrainSummary to enable train logger. Then the log will be saved to logDir/appName/train.

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Instance Constructors

  1. new TrainSummary(logDir: String, appName: String)

    logDir

    log dir.

    appName

    application Name.

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

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

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

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    Any
  6. def addHistogram[T](tag: String, value: Tensor[T], step: Long)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): TrainSummary.this.type

    Add a histogram summary.

    Add a histogram summary.

    tag

    tag name.

    value

    a tensor.

    step

    current step.

    returns

    this

    Definition Classes
    Summary
  7. def addScalar(tag: String, value: Float, step: Long): TrainSummary.this.type

    Add a scalar summary.

    Add a scalar summary.

    tag

    tag name.

    value

    tag value.

    step

    current step.

    returns

    this

    Definition Classes
    Summary
  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def close(): Unit

    Close this logger.

    Close this logger.

    Definition Classes
    Summary
  11. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  14. val folder: String

    Attributes
    protected
  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  16. def getSummaryTrigger(tag: String): Option[Trigger]

    Get a trigger by tag name.

    Get a trigger by tag name.

    tag
    returns

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  19. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  20. final def notify(): Unit

    Definition Classes
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  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  22. def readScalar(tag: String): Array[(Long, Float, Double)]

    Read scalar values to an array of triple by tag name.

    Read scalar values to an array of triple by tag name. First element of the triple is step, second is value, third is wallClockTime.

    tag

    tag name. Supported tag names is "LearningRate", "Loss", "Throughput"

    returns

    an array of triple.

    Definition Classes
    TrainSummarySummary
  23. def setSummaryTrigger(tag: String, trigger: Trigger): TrainSummary.this.type

    Supported tag name are LearningRate, Loss, Throughput, Parameters.

    Supported tag name are LearningRate, Loss, Throughput, Parameters. Parameters contains weight, bias, gradWeight, gradBias, and some running status(eg. runningMean and runningVar in BatchNormalization).

    Notice: By default, we record LearningRate, Loss and Throughput each iteration, while recording parameters is disabled. The reason is getting parameters from workers is a heavy operation when the model is very big.

    tag

    tag name

    trigger

    trigger

    returns

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

    Definition Classes
    AnyRef
  25. def toString(): String

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  26. final def wait(): Unit

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    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit

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    @throws( ... )
  28. final def wait(arg0: Long): Unit

    Definition Classes
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    @throws( ... )
  29. val writer: FileWriter

    Attributes
    protected
    Definition Classes
    TrainSummarySummary

Inherited from Summary

Inherited from AnyRef

Inherited from Any

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