C# Class Encog.Engine.Network.Flat.FlatNetwork

Implements a flat (vector based) neural network in the Encog Engine. This is meant to be a very highly efficient feedforward, or simple recurrent, neural network. It uses a minimum of objects and is designed with one principal in mind-- SPEED. Readability, c reuse, object oriented programming are all secondary in consideration. Vector based neural networks are also very good for GPU processing. The flat network classes will make use of the GPU if you have enabled GPU processing. See the Encog class for more info.
Inheritance: IEngineNeuralNetwork
Afficher le fichier Open project: encog/encog-silverlight-core Class Usage Examples

Méthodes publiques

Méthode Description
CalculateError ( IEngineIndexableSet data ) : double

Calculate the error for this neural network. The error is calculated using root-mean-square(RMS).
ClearConnectionLimit ( ) : void

Clear the connection limit.
ClearContext ( ) : void

Clear any context neurons.
Clone ( ) : Object

Clone the network.
CloneFlatNetwork ( FlatNetwork result ) : void

Clone a flat network.
Compute ( double input, double output ) : void

Calculate the output for the given input.
DecodeNetwork ( double data ) : void

Dec the specified data into the weights of the neural network. This method performs the opposite of encNetwork.
EncodeNetwork ( ) : double[]

Enc the neural network to an array of doubles. This includes the network weights. To read this into a neural network, use the decNetwork method.
FlatNetwork ( ) : Encog.Engine

Default constructor.
FlatNetwork ( FlatLayer layers ) : Encog.Engine

Create a flat network from an array of layers.
FlatNetwork ( int input, int hidden1, int hidden2, int output, bool tanh ) : Encog.Engine

Construct a flat neural network.
HasSameActivationFunction ( ) : Type

Neural networks with only one type of activation function offer certain optimization options. This method determines if only a single activation function is used.
Init ( FlatLayer layers ) : void

Construct a flat network.
Randomize ( ) : void

Perform a simple randomization of the weights of the neural network between -1 and 1.
Randomize ( double hi, double lo ) : void

Perform a simple randomization of the weights of the neural network between the specified hi and lo.

Méthodes protégées

Méthode Description
ComputeLayer ( int currentLayer ) : void

Calculate a layer.

Method Details

CalculateError() public méthode

Calculate the error for this neural network. The error is calculated using root-mean-square(RMS).
public CalculateError ( IEngineIndexableSet data ) : double
data IEngineIndexableSet The training set.
Résultat double

ClearConnectionLimit() public méthode

Clear the connection limit.
public ClearConnectionLimit ( ) : void
Résultat void

ClearContext() public méthode

Clear any context neurons.
public ClearContext ( ) : void
Résultat void

Clone() public méthode

Clone the network.
public Clone ( ) : Object
Résultat Object

CloneFlatNetwork() public méthode

Clone a flat network.
public CloneFlatNetwork ( FlatNetwork result ) : void
result FlatNetwork The cloned flat network.
Résultat void

Compute() public méthode

Calculate the output for the given input.
public Compute ( double input, double output ) : void
input double The input.
output double Output will be placed here.
Résultat void

ComputeLayer() protected méthode

Calculate a layer.
protected ComputeLayer ( int currentLayer ) : void
currentLayer int The layer to calculate.
Résultat void

DecodeNetwork() public méthode

Dec the specified data into the weights of the neural network. This method performs the opposite of encNetwork.
public DecodeNetwork ( double data ) : void
data double The data to be decd.
Résultat void

EncodeNetwork() public méthode

Enc the neural network to an array of doubles. This includes the network weights. To read this into a neural network, use the decNetwork method.
public EncodeNetwork ( ) : double[]
Résultat double[]

FlatNetwork() public méthode

Default constructor.
public FlatNetwork ( ) : Encog.Engine
Résultat Encog.Engine

FlatNetwork() public méthode

Create a flat network from an array of layers.
public FlatNetwork ( FlatLayer layers ) : Encog.Engine
layers FlatLayer The layers.
Résultat Encog.Engine

FlatNetwork() public méthode

Construct a flat neural network.
public FlatNetwork ( int input, int hidden1, int hidden2, int output, bool tanh ) : Encog.Engine
input int Neurons in the input layer.
hidden1 int Neurons in the first hidden layer. Zero for no first hiddenlayer.
hidden2 int Neurons in the second hidden layer. Zero for no second hiddenlayer.
output int Neurons in the output layer.
tanh bool True if this is a tanh activation, false for sigmoid.
Résultat Encog.Engine

HasSameActivationFunction() public méthode

Neural networks with only one type of activation function offer certain optimization options. This method determines if only a single activation function is used.
public HasSameActivationFunction ( ) : Type
Résultat System.Type

Init() public méthode

Construct a flat network.
public Init ( FlatLayer layers ) : void
layers FlatLayer The layers of the network to create.
Résultat void

Randomize() public méthode

Perform a simple randomization of the weights of the neural network between -1 and 1.
public Randomize ( ) : void
Résultat void

Randomize() public méthode

Perform a simple randomization of the weights of the neural network between the specified hi and lo.
public Randomize ( double hi, double lo ) : void
hi double The network high.
lo double The network low.
Résultat void