FFNeuralGraph

Instance Constructors

new FFNeuralGraph(baseGraph: FramedGraph[Graph], act: List[String], hidden: Int = 1)

baseGraph
The base graph object, FFNeuralGraph encapsulates an existing graph object of type FramedGraph and builds upon it by defining a set of behaviours expected from Neural Network graphs
act
A list of Strings containing the activations for each layer. Options for activation functions are: 1) "logsig" or "sigmoid" 2) "tansig" 3) "linear" 4) "recLinear"
hidden
The number of hidden layers in the network.

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

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

Definition Classes
AnyRef → Any
val activations: List[String]
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

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

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

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
val forwardPass: (DenseVector[Double]) ⇒ DenseVector[Double]

Perform a forward pass through the network to calculate the predicted output.
Perform a forward pass through the network to calculate the predicted output.

Definition Classes
FFNeuralGraph → NeuralGraph
val g: FramedGraph[Graph]

Attributes
protected
Definition Classes
FFNeuralGraph → NeuralGraph
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getLayer(layer: Int): Iterable[Neuron]

Get as a scala Iterable the neurons for a particular layer.
Get as a scala Iterable the neurons for a particular layer.
layer
The layer number, can vary from 0 (input layer) to hidden_layers + 1 (output layer)
returns
The neurons in the particular layer as Neuron objects
def getLayerSynapses(layer: Int): Iterable[Synapse]

Get as a scala Iterable the synapses between layer l and l-1.
Get as a scala Iterable the synapses between layer l and l-1.
layer
The layer number, can vary from 1 (input layer synapses) to hidden_layers + 1 (output layer synapses)
returns
The respective synapses as Synapse objects
def getSynapsesAsMatrix(layer: Int): DenseMatrix[Double]

Get as a breeze DenseMatrix the synapses between layer l and l-1.
Get as a breeze DenseMatrix the synapses between layer l and l-1.
layer
The layer number, can vary from 1 (input layer synapses) to hidden_layers + 1 (output layer synapses)
returns
The respective synapses as elements of a matrix
def hashCode(): Int

Definition Classes
AnyRef → Any
val hidden_layers: Int
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val num_inputs: Int
val num_outputs: Int
def predictBatch(procInputs: List[List[Double]]): Map[Int, Map[Int, Double]]

Perform a forward pass through the network to calculate the predicted output for a batch of input points.
Perform a forward pass through the network to calculate the predicted output for a batch of input points.
procInputs
The input batch as a List of Lists where each level of the top level List represents an input node. On the other hand each element of the lower level list represents a particular dimension of a particular data point in the data set.
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

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

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

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object FFNeuralGraph | package graph

class FFNeuralGraph extends NeuralGraph[FramedGraph[Graph], DenseVector[Double], DenseVector[Double]]

Instance Constructors

new FFNeuralGraph(baseGraph: FramedGraph[Graph], act: List[String], hidden: Int = 1)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val activations: List[String]

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

val forwardPass: (DenseVector[Double]) ⇒ DenseVector[Double]

val g: FramedGraph[Graph]

final def getClass(): Class[_]

def getLayer(layer: Int): Iterable[Neuron]

def getLayerSynapses(layer: Int): Iterable[Synapse]

def getSynapsesAsMatrix(layer: Int): DenseMatrix[Double]

def hashCode(): Int

val hidden_layers: Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val num_inputs: Int

val num_outputs: Int

def predictBatch(procInputs: List[List[Double]]): Map[Int, Map[Int, Double]]

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

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from NeuralGraph[FramedGraph[Graph], DenseVector[Double], DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

Ungrouped