Class/Object

com.intel.analytics.bigdl.optim

Predictor

Related Docs: object Predictor | package optim

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class Predictor[T] extends Serializable

Predictor for distributed data

NOTE: The predictClass, predict and predictImage will call the relevant methods of object Predictor. Why we do this? Because every these methods uses the ClassTag T. If we do these jobs in the methods of classPredictor, when we do mapPartition, Spark will find all used values and do serialization. The T is the argument of constructor, the serialization will package the whole Predictor class, which contains themodel. It will send a duplicate model to the workers. So we should move these methods to object Predictor.

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. def predict(dataSet: RDD[Sample[T]], batchSize: Int = 1, shareBuffer: Boolean = false): RDD[Activity]

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  16. def predictClass(dataSet: RDD[Sample[T]], batchSize: Int = 1): RDD[Int]

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  17. def predictImage(imageFrame: DistributedImageFrame, outputLayer: String = null, shareBuffer: Boolean = false, predictKey: String = ImageFeature.predict): DistributedImageFrame

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    model predict DistributedImageFrame, return imageFrame with predicted tensor

    model predict DistributedImageFrame, return imageFrame with predicted tensor

    imageFrame

    imageFrame that contains images

    outputLayer

    if outputLayer is not null, the output of layer that matches outputLayer will be used as predicted output

    shareBuffer

    whether to share same memory for each batch predict results

    predictKey

    key to store predicted result

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

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