Derived classes of the FeedforwardClosedloopLearning class for special functionality. More...

#include <fcl_util.h>

Inheritance diagram for FeedforwardClosedloopLearningWithFilterbank:

Collaboration diagram for FeedforwardClosedloopLearningWithFilterbank:

Public Member Functions
	FeedforwardClosedloopLearningWithFilterbank (int num_of_inputs, int *num_of_neurons_per_layer_array, int num_layers, int num_filtersInput, double minT, double maxT)
	FeedforwardClosedloopLearning with Filterbank at each input. More...

	~FeedforwardClosedloopLearningWithFilterbank ()
	Destructor.

void	doStep (double input, double error)
	Performs the simulation step. More...

void	doStep (double input, int n1, double error, int n2)
	Python wrapper function. More...

double	getFilterOutput (int inputIdx, int filterIdx)

int	getNFiltersPerInput ()

Public Member Functions inherited from FeedforwardClosedloopLearning
	FeedforwardClosedloopLearning (int num_of_inputs, int *num_of_neurons_per_layer_array, int _num_layers)
	Constructor: FCL without any filters. More...

	~FeedforwardClosedloopLearning ()
	Destructor De-allocated any memory. More...

void	doStep (double input, double error)
	Performs the simulation step. More...

void	doStep (double input, int n1, double error, int n2)
	Python wrapper function. More...

double	getOutput (int index)
	Gets the output from one of the output neurons. More...

void	setLearningRate (double learningRate)
	Sets globally the learning rate. More...

void	setLearningRateDiscountFactor (double _learningRateDiscountFactor)
	Sets how the learnign rate increases or decreases from layer to layer. More...

void	setDecay (double decay)
	Sets a typical weight decay scaled with the learning rate. More...

void	setMomentum (double momentum)
	Sets the global momentum for all layers. More...

void	setActivationFunction (Neuron::ActivationFunction _activationFunction)
	Sets the activation function of the Neuron. More...

void	initWeights (double max=0.001, int initBias=1, Neuron::WeightInitMethod weightInitMethod=Neuron::MAX_OUTPUT_RANDOM)
	Inits the weights in all layers. More...

void	seedRandom (int s)
	Seeds the random number generator. More...

void	setBias (double _bias)
	Sets globally the bias. More...

int	getNumLayers ()
	Gets the total number of layers. More...

Layer *	getLayer (int i)
	Gets a pointer to a layer. More...

Layer *	getOutputLayer ()
	Gets the output layer. More...

int	getNumInputs ()
	Gets the number of inputs. More...

Layer **	getLayers ()
	Returns all Layers. More...

bool	saveModel (const char *name)
	Saves the whole network. More...

bool	loadModel (const char *name)
	Loads the while network. More...

Detailed Description

Derived classes of the FeedforwardClosedloopLearning class for special functionality.

Constructor & Destructor Documentation

◆ FeedforwardClosedloopLearningWithFilterbank()

FeedforwardClosedloopLearningWithFilterbank::FeedforwardClosedloopLearningWithFilterbank	(	int	num_of_inputs,
		int *	num_of_neurons_per_layer_array,
		int	num_layers,
		int	num_filtersInput,
		double	minT,
		double	maxT
	)

FeedforwardClosedloopLearning with Filterbank at each input.

Constructor: FCL with a filter bank at the input Every input feeds internally into a has a filter bank of num_filtersInput filters. This allows for a temporal distribution of the inputs.

Parameters

num_of_inputs	Number of inputs in the input layer
num_of_neurons_per_layer_array	Number of neurons in each layer
_num_layers	Number of layer (needs to match with array above)
num_filtersInput	Number of filters at the input layer, 0 = no filterbank
num_filters	Number of filters in the hiddel layers (usually zero)
_minT	Minimum/first temporal duration of the 1st filter
_maxT	Maximum/last temporal duration of the last filter

Member Function Documentation

◆ doStep() [1/2]

void FeedforwardClosedloopLearningWithFilterbank::doStep	(	double *	input,
		double *	error
	)

Performs the simulation step.

Parameters

input	Array with the input values
error	Array of the error signals

◆ doStep() [2/2]

void FeedforwardClosedloopLearningWithFilterbank::doStep	(	double *	input,
		int	n1,
		double *	error,
		int	n2
	)

Python wrapper function.

Not public.

The documentation for this class was generated from the following file: