Feedforward Closedloop Learning
FeedforwardClosedloopLearningWithFilterbank Class Reference

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...
 
LayergetLayer (int i)
 Gets a pointer to a layer. More...
 
LayergetOutputLayer ()
 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_inputsNumber of inputs in the input layer
num_of_neurons_per_layer_arrayNumber of neurons in each layer
_num_layersNumber of layer (needs to match with array above)
num_filtersInputNumber of filters at the input layer, 0 = no filterbank
num_filtersNumber of filters in the hiddel layers (usually zero)
_minTMinimum/first temporal duration of the 1st filter
_maxTMaximum/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
inputArray with the input values
errorArray 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: