C# 클래스 numl.Supervised.NeuralNetwork.NetworkOps

A network.

공개 메소드들

메소드	설명
AddConnections ( this network, Neuron node, IEnumerable parentNodes, IEnumerable childNodes, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network	Adds new connections for the specified node for the parent and child nodes.
Constrain ( this network, int layer = -1, bool constrain = true ) : numl.Supervised.NeuralNetwork.Network	Constrains the weights in the specified layer from being updated. This prevents weights in pretrained layers from being updated.
Create ( this network, Descriptor d, Matrix X, Vector y, IFunction activationFunction, IFunction outputFunction = null ) : numl.Supervised.NeuralNetwork.Network	Creates a new deep neural network based on the supplied inputs and layers.
Create ( this network, Descriptor d, Matrix x, Vector y, IFunction activationFunction, IFunction outputFunction = null, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network	Defaults.
Create ( this network, int inputLayer, int outputLayer, IFunction activationFunction, IFunction outputFunction = null, Func fnNodeInitializer = null, Func fnWeightInitializer = null, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network	Creates a new fully connected deep neural network based on the supplied size and depth parameters.
GetBiases ( this network, int layer ) : Vector	Gets a bias input vector for the specified layer. Each item is the bias weight on the connecting node in the next layer.
GetWeights ( this network, int layer, bool includeBiases = false ) : Matrix	Gets the weight values as an [i x j] weight matrix. Where i represents the node in the next layer (layer + 1) and j represents the node in the specified layer.
LinkNodes ( this network, IEnumerable nodes, IEnumerable edges ) : numl.Supervised.NeuralNetwork.Network	Links a Network from nodes and edges.
Prune ( this network, bool backwards = true, int layers = 1 ) : numl.Supervised.NeuralNetwork.Network	Prunes the network in the given direction for the specified number of layers.
Reindex ( this network ) : numl.Supervised.NeuralNetwork.Network	Reindexes each node's layer and label in the network, starting from 0 (input layer).
Stack ( this network, bool removeInputs = false, bool removeOutputs = false, bool addBiases = true, bool constrain = true ) : numl.Supervised.NeuralNetwork.Network	Stacks the given networks in order, on top of the current network, to create a fully connected deep neural network. This is useful in building pretrained multi-layered neural networks, where each layer is partially trained prior to stacking.

메소드 상세

AddConnections() 공개 정적인 메소드

Adds new connections for the specified node for the parent and child nodes.

public static AddConnections ( this network, Neuron node, IEnumerable parentNodes, IEnumerable childNodes, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network
network	this	Current network.
node	Neuron	Neuron being added.
parentNodes	IEnumerable	Parent nodes that this neuron is connected with.
childNodes	IEnumerable	Child nodes that this neuron is connected to.
epsilon	double	Weight initialization parameter.
리턴	numl.Supervised.NeuralNetwork.Network

Constrain() 공개 정적인 메소드

Constrains the weights in the specified layer from being updated. This prevents weights in pretrained layers from being updated.

public static Constrain ( this network, int layer = -1, bool constrain = true ) : numl.Supervised.NeuralNetwork.Network
network	this	Current network.
layer	int	The layer of weights to constrain. To prevent all weights from being changed specify the global value of -1.
constrain	bool	Sets the value to true/false.
리턴	numl.Supervised.NeuralNetwork.Network

Create() 공개 정적인 메소드

Creates a new deep neural network based on the supplied inputs and layers.

public static Create ( this network, Descriptor d, Matrix X, Vector y, IFunction activationFunction, IFunction outputFunction = null ) : numl.Supervised.NeuralNetwork.Network
network	this
d	Descriptor	Descriptor object.
X	Matrix	Training examples
y	numl.Math.LinearAlgebra.Vector	Training labels
activationFunction	IFunction	Activation Function for each output layer.
outputFunction	IFunction	Ouput Function for each output layer.
리턴	numl.Supervised.NeuralNetwork.Network

Create() 공개 정적인 메소드

Defaults.

public static Create ( this network, Descriptor d, Matrix x, Vector y, IFunction activationFunction, IFunction outputFunction = null, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network
network	this
d	Descriptor	The Descriptor to process.
x	Matrix	The Vector to process.
y	numl.Math.LinearAlgebra.Vector	The Vector to process.
activationFunction	IFunction	The activation.
outputFunction	IFunction	The ouput function for hidden nodes (Optional).
epsilon	double	epsilon
리턴	numl.Supervised.NeuralNetwork.Network

Create() 공개 정적인 메소드

Creates a new fully connected deep neural network based on the supplied size and depth parameters.

public static Create ( this network, int inputLayer, int outputLayer, IFunction activationFunction, IFunction outputFunction = null, Func fnNodeInitializer = null, Func fnWeightInitializer = null, double epsilon = double.NaN ) : numl.Supervised.NeuralNetwork.Network
network	this
inputLayer	int	Neurons in the input layer.
outputLayer	int	Neurons in the output layer.
activationFunction	IFunction	Activation function for the hidden and output layers.
outputFunction	IFunction	(Optional) Output function of the the Nodes in the output layer (overrides the Activation function).
fnNodeInitializer	Func	(Optional) Function to call for initializing new Nodes - supplying parameters for the layer and node index.
fnWeightInitializer	Func	(Optional) Function to call for initializing the weights of each connection (including bias nodes). /// Where int1 = Source layer (0 is input layer), int2 = Source Node, int3 = Target node in the next layer.
epsilon	double	Weight initialization parameter for random weight selection. Weight will be in the range of: -epsilon to +epsilon.
리턴	numl.Supervised.NeuralNetwork.Network

GetBiases() 공개 정적인 메소드

Gets a bias input vector for the specified layer. Each item is the bias weight on the connecting node in the next layer.

public static GetBiases ( this network, int layer ) : Vector
network	this	Current network.
layer	int	Forward layer of biases and their weights. The layer should be between 0 (first hidden layer) and the last hidden layer.
리턴	numl.Math.LinearAlgebra.Vector

GetWeights() 공개 정적인 메소드

Gets the weight values as an [i x j] weight matrix. Where i represents the node in the next layer (layer + 1) and j represents the node in the specified layer.

public static GetWeights ( this network, int layer, bool includeBiases = false ) : Matrix
network	this	Current network.
layer	int	The layer to retrieve weights for. The layer should be between 0 and the last hidden layer.
includeBiases	bool	Indicates whether bias weights are included in the output.
리턴	Matrix

LinkNodes() 공개 정적인 메소드

Links a Network from nodes and edges.

public static LinkNodes ( this network, IEnumerable nodes, IEnumerable edges ) : numl.Supervised.NeuralNetwork.Network
network	this
nodes	IEnumerable	An array of nodes in the network
edges	IEnumerable	An array of edges between the nodes in the network.
리턴	numl.Supervised.NeuralNetwork.Network

Prune() 공개 정적인 메소드

Prunes the network in the given direction for the specified number of layers.

public static Prune ( this network, bool backwards = true, int layers = 1 ) : numl.Supervised.NeuralNetwork.Network
network	this	Current network.
backwards	bool	If true, removes layer by layer in reverse order (i.e. output layer first).
layers	int	Number of layers to prune from the network.
리턴	numl.Supervised.NeuralNetwork.Network

Reindex() 공개 정적인 메소드

Reindexes each node's layer and label in the network, starting from 0 (input layer).

public static Reindex ( this network ) : numl.Supervised.NeuralNetwork.Network
network	this	Network to reindex.
리턴	numl.Supervised.NeuralNetwork.Network

Stack() 공개 정적인 메소드

Stacks the given networks in order, on top of the current network, to create a fully connected deep neural network.

This is useful in building pretrained multi-layered neural networks, where each layer is partially trained prior to stacking.

public static Stack ( this network, bool removeInputs = false, bool removeOutputs = false, bool addBiases = true, bool constrain = true ) : numl.Supervised.NeuralNetwork.Network
network	this	Current network.
removeInputs	bool	If true, the input nodes in additional layers are removed prior to stacking. /// This will link the previous network's output layer with the hidden units of the next layer. ///
removeOutputs	bool	If true, output nodes in the input and middle layers are removed prior to stacking. /// This will link the previous network's hidden or output layer with the input or hidden units (when is true) in the next layer. ///
addBiases	bool	If true, missing bias nodes are automatically added within new hidden layers.
constrain	bool	If true, the weights within each network are constrained leaving the new interconnecting network weights for training.
리턴	numl.Supervised.NeuralNetwork.Network