org.deeplearning4j.scalnet.models

Sequential

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)

Value Members

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

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

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

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. def add(layer: Node): Unit

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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

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

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

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

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

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

    Definition Classes
    AnyRef → Any
  15. def getNetwork: MultiLayerNetwork

  16. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  17. def inputShape: List[Int]

  18. final def isInstanceOf[T0]: Boolean

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

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    AnyRef
  20. final def notify(): Unit

    Definition Classes
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  21. final def notifyAll(): Unit

    Definition Classes
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  22. def predict(x: INDArray): INDArray

    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
  23. def predict(x: DataSet): INDArray

    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
  24. val rngSeed: Long

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

    Definition Classes
    AnyRef
  26. def toJson: String

    Definition Classes
    SequentialModel
  27. def toString(): String

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

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

    Definition Classes
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    Annotations
    @throws( ... )
  30. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
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    @throws( ... )
  31. final def wait(arg0: Long): Unit

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    @throws( ... )

Inherited from Model

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