org.deeplearning4j.scalnet.models

NeuralNet

class NeuralNet extends Model

Simple DL4J-style sequential neural net architecture with one input node and one output node for each node in computational graph.

Wraps DL4J MultiLayerNetwork. Enforces DL4J model construction pattern: adds pre-processing layers automatically but requires user to specify output layer explicitly.

Linear Supertypes
Model, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. NeuralNet
  2. Model
  3. AnyRef
  4. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new NeuralNet(inputType: Option[InputType] = scala.None, 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

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

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

    Definition Classes
    Any
  6. def add(layer: Node): Unit

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def buildModelConfig(optimizer: Optimizer, 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

    Definition Classes
    Model
  9. 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

    Definition Classes
    Model
  10. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  11. def compile(lossFunction: LossFunction, optimizer: Optimizer = defaultOptimizer): 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
    NeuralNetModel
  12. val defaultEpochs: Int

    Attributes
    protected
    Definition Classes
    Model
  13. val defaultOptimizer: SGD

    Attributes
    protected
    Definition Classes
    Model
  14. final def eq(arg0: AnyRef): Boolean

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

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

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  17. def fit(iter: DataSetIterator, nbEpoch: Int = defaultEpochs, 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
    NeuralNetModel
  18. final def getClass(): Class[_]

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

    Definition Classes
    Model
  20. def getNetwork: MultiLayerNetwork

    Definition Classes
    Model
  21. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  22. val inputType: Option[InputType]

  23. final def isInstanceOf[T0]: Boolean

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

    Attributes
    protected
    Definition Classes
    Model
  25. var model: MultiLayerNetwork

    Attributes
    protected
    Definition Classes
    Model
  26. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  29. 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
  30. 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
    Model
  31. val rngSeed: Long

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

    Definition Classes
    AnyRef
  33. def toJson: String

    Definition Classes
    Model
  34. def toString(): String

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

    Definition Classes
    Model
  36. final def wait(): Unit

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  38. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Model

Inherited from AnyRef

Inherited from Any

Ungrouped