Object/Class

io.github.mandar2812.dynaml.optimization

BackPropagation

Related Docs: class BackPropagation | package optimization

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object BackPropagation extends Serializable

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  8. def finalize(): Unit

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  9. final def getClass(): Class[_]

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  11. final def isInstanceOf[T0]: Boolean

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  12. val logger: Logger

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. val processDataToNeuronBuffers: BifurcationPipe[Stream[(DenseVector[Double], DenseVector[Double])], List[List[Double]], List[List[Double]]]

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    Processes the raw data into neuron buffers, represented as a DynaML pipe

  17. var rho: Double

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  18. def run[T](nPoints: Long, regParam: Double, numIterations: Int, miniBatchFraction: Double, stepSize: Double, momentum: Double, initialP: FFNeuralGraph, ParamOutEdges: T, transform: DataPipe[T, Stream[(DenseVector[Double], DenseVector[Double])]], sparsityWeight: Double = 0.0): FFNeuralGraph

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    Carry out the iterative backpropagation algorithm to determine synapse weights in a feed-forward neural network graph

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

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  20. def toString(): String

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  21. final def wait(): Unit

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  22. final def wait(arg0: Long, arg1: Int): Unit

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  23. final def wait(arg0: Long): Unit

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