Class

org.deeplearning4j.scalnet.models

Sequential

Related Doc: package models

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class Sequential extends Model

Class for keras-style simple sequential neural net architectures with one input node and one output node for each node in computational graph.

Wraps DL4J MultiLayerNetwork. Enforces keras model construction pattern: preprocessing (reshaping) layers should be explicitly provided by the user, while last layer is treated implicitly as an output layer.

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Instance Constructors

  1. new Sequential(rngSeed: Long = 0)

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Value Members

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

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  2. final def ##(): Int

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

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  4. def add(layer: Node): Unit

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )
  7. def compile(lossFunction: LossFunction = null, optimizer: Optimizer = SGD(lr = 0.01)): Unit

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    Compile neural net architecture.

    Compile neural net architecture. Call immediately before training.

    lossFunction

    loss function to use

    optimizer

    optimization algorithm to use

    Definition Classes
    SequentialModel
  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  11. def fit(iter: DataSetIterator, nbEpoch: Int = 10, listeners: List[IterationListener]): Unit

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    Fit neural net to data.

    Fit neural net to data.

    iter

    iterator over data set

    nbEpoch

    number of epochs to train

    listeners

    callbacks for monitoring training

    Definition Classes
    SequentialModel
  12. final def getClass(): Class[_]

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  13. def getNetwork: MultiLayerNetwork

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  14. def hashCode(): Int

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  15. def inputShape: List[Int]

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  16. final def isInstanceOf[T0]: Boolean

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  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def predict(x: INDArray): INDArray

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    Use neural net to make prediction on input x

    Use neural net to make prediction on input x

    x

    input represented as INDArray

    Definition Classes
    SequentialModel
  21. def predict(x: DataSet): INDArray

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    Use neural net to make prediction on input x.

    Use neural net to make prediction on input x.

    x

    input represented as DataSet

    Definition Classes
    Model
  22. val rngSeed: Long

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

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    Definition Classes
    AnyRef
  24. def toJson: String

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    Definition Classes
    SequentialModel
  25. def toString(): String

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    Definition Classes
    SequentialModel → AnyRef → Any
  26. def toYaml: String

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    Definition Classes
    SequentialModel
  27. final def wait(): Unit

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

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

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Inherited from Model

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