NeuralNet_XLT

scalation.modeling.neuralnet.NeuralNet_XLT
See theNeuralNet_XLT companion class
object NeuralNet_XLT extends Scaling

The NeuralNet_XLT companion object provides factory methods for creating multi-layer (one+ hidden layers) neural networks supporting transfer learning. Note, 'scale' is defined in Scaling.

Attributes

Companion
class
Graph
Supertypes
trait Scaling
class Object
trait Matchable
class Any
Self type

Members list

Value members

Concrete methods

def apply(xy: MatrixD, fname: Array[String], nz: Array[Int], hparam: HyperParameter, f: Array[AFF], l_tran: Int, transfer: NetParam)(col: Int): NeuralNet_XLT

Create a NeuralNet_XLT for a combined data matrix.

Create a NeuralNet_XLT for a combined data matrix.

Value parameters

col

the first designated response column (defaults to the last column)

f

the array of activation function families over all layers

fname

the feature/variable names

hparam

the hyper-parameters

l_tran

the layer to be transferred in (defaults to first hidden layer)

nz

the number of nodes in each hidden layer, e.g., Array (5, 10) means 2 hidden with sizes 5 and 10

transfer

the saved network parameters from a layer of a related neural network

xy

the combined input and output matrix

Attributes

Inherited methods

def setScale(scale_: Boolean): Unit

Set the scale flag to the given value.

Set the scale flag to the given value.

Value parameters

scale_

the new value for the scale flag

Attributes

Inherited from:
Scaling

Inherited fields

protected var scale: Boolean

The 'scale' flag indicated whether the data is to be rescaled/normalized

The 'scale' flag indicated whether the data is to be rescaled/normalized

Attributes

Inherited from:
Scaling