org.deeplearning4j.scalnet.models

Model

trait Model extends Logging

Abstract base class for neural net architectures.

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  1. abstract def compile(lossFunction: LossFunction, optimizer: OptimizationAlgorithm, updater: Updater): Unit

    Compile neural net architecture.

    Compile neural net architecture. Call immediately before training.

    lossFunction

    loss function to use

    optimizer

    optimization algorithm to use

Concrete 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
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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. final def asInstanceOf[T0]: T0

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  7. def buildModelConfig(optimizer: OptimizationAlgorithm, updater: Updater, miniBatch: Boolean, biasInit: Double, seed: Long): Builder

    Build model configuration from optimizer and seed.

    Build model configuration from optimizer and seed.

    optimizer

    optimization algorithm to use in model

    seed

    seed to use

    returns

    NeuralNetConfiguration.Builder

  8. def buildOutput(lossFunction: LossFunction): Unit

    Make last layer of architecture an output layer using the provided loss function.

    Make last layer of architecture an output layer using the provided loss function.

    lossFunction

    loss function to use

  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  12. def evaluate(dataset: DataSet, numClasses: Int): Evaluation

    Evaluate model against an iterator over data set

    Evaluate model against an iterator over data set

    dataset

    data set

    numClasses

    output size

    returns

    Evaluation instance

  13. def evaluate(dataset: DataSet): Evaluation

    Evaluate model against an iterator over data set

    Evaluate model against an iterator over data set

    dataset

    data set

    returns

    Evaluation instance

  14. def evaluate(iter: DataSetIterator, numClasses: Int): Evaluation

    Evaluate model against an iterator over data set

    Evaluate model against an iterator over data set

    iter

    iterator over data set

    numClasses

    output size

    returns

    Evaluation instance

  15. def evaluate(iter: DataSetIterator): Evaluation

    Evaluate model against an iterator over data set

    Evaluate model against an iterator over data set

    iter

    iterator over data set

    returns

    Evaluation instance

  16. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  17. def fit(dataset: DataSet, nbEpoch: Int, listeners: List[TrainingListener]): Unit

    Fit neural net to data.

    Fit neural net to data.

    dataset

    data set

    nbEpoch

    number of epochs to train

    listeners

    callbacks for monitoring training

  18. def fit(iter: DataSetIterator, nbEpoch: Int, listeners: List[TrainingListener]): 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

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

    Definition Classes
    AnyRef → Any
  20. def getLayers: List[Node]

  21. def getNetwork: MultiLayerNetwork

  22. def hashCode(): Int

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

    Definition Classes
    Any
  24. var layers: List[Node]

    Attributes
    protected
  25. lazy val logger: Logger

    Attributes
    protected
    Definition Classes
    Logging
  26. var model: MultiLayerNetwork

    Attributes
    protected
  27. final def ne(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef
  29. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  30. 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

  31. 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

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

    Definition Classes
    AnyRef
  33. def toJson: String

  34. def toString(): String

    Definition Classes
    Model → AnyRef → Any
  35. def toYaml: String

  36. final def wait(): Unit

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

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

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    @throws( ... )

Inherited from Logging

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