#include <onelayerffnn.h>
Inherits FeedForwardNN.
Inheritance diagram for OneLayerFFNN:


Public Member Functions | |
| OneLayerFFNN (double eps, double factor_bias=0.1) | |
| Uses linear activation function. | |
| OneLayerFFNN (double eps, double factor_bias, ActivationFunction actfun, ActivationFunction dactfun) | |
| virtual | ~OneLayerFFNN () |
| virtual void | init (unsigned int inputDim, unsigned int outputDim, double unit_map=0.0, RandGen *randGen=0) |
| initialisation of the network with the given number of input and output units | |
| virtual const matrix::Matrix | process (const matrix::Matrix &input) |
| passive processing of the input | |
| virtual const matrix::Matrix | learn (const matrix::Matrix &input, const matrix::Matrix &nom_output, double learnRateFactor=1) |
| performs learning and returns the network output before learning | |
| virtual unsigned int | getInputDim () const |
| returns the number of input neurons | |
| virtual unsigned int | getOutputDim () const |
| returns the number of output neurons | |
| virtual const matrix::Matrix & | getWeights () const |
| virtual const matrix::Matrix & | getBias () const |
| virtual void | damp (double damping) |
| damps the weights and the biases by multiplying (1-damping) | |
| bool | store (FILE *f) const |
| stores the layer binary into file stream | |
| bool | restore (FILE *f) |
| restores the layer binary from file stream | |
| virtual paramkey | getName () const |
| return the name of the object | |
| virtual paramval | getParam (const paramkey key) const |
| virtual bool | setParam (const paramkey key, paramval val) |
| virtual paramlist | getParamList () const |
| The list of all parameters with there value as allocated lists. | |
| OneLayerFFNN | ( | double | eps, | |
| double | factor_bias = 0.1 | |||
| ) | [inline] |
Uses linear activation function.
| eps | learning rate | |
| factor_bias | learning rate factor for bias learning |
| OneLayerFFNN | ( | double | eps, | |
| double | factor_bias, | |||
| ActivationFunction | actfun, | |||
| ActivationFunction | dactfun | |||
| ) | [inline] |
| eps | learning rate | |
| factor_bias | learning rate factor for bias learning | |
| actfun | callback activation function (see FeedForwardNN) | |
| dactfun | callback for first derivative of the activation function |
| virtual ~OneLayerFFNN | ( | ) | [inline, virtual] |
| virtual void damp | ( | double | damping | ) | [inline, virtual] |
| virtual const matrix::Matrix& getBias | ( | ) | const [inline, virtual] |
| virtual unsigned int getInputDim | ( | ) | const [inline, virtual] |
| virtual paramkey getName | ( | ) | const [inline, virtual] |
| virtual unsigned int getOutputDim | ( | ) | const [inline, virtual] |
| virtual paramlist getParamList | ( | ) | const [inline, virtual] |
The list of all parameters with there value as allocated lists.
Reimplemented from Configurable.
| virtual const matrix::Matrix& getWeights | ( | ) | const [inline, virtual] |
| void init | ( | unsigned int | inputDim, | |
| unsigned int | outputDim, | |||
| double | unit_map = 0.0, |
|||
| RandGen * | randGen = 0 | |||
| ) | [virtual] |
initialisation of the network with the given number of input and output units
Implements AbstractModel.
| const Matrix learn | ( | const matrix::Matrix & | input, | |
| const matrix::Matrix & | nom_output, | |||
| double | learnRateFactor = 1 | |||
| ) | [virtual] |
| const Matrix process | ( | const matrix::Matrix & | input | ) | [virtual] |
| bool restore | ( | FILE * | f | ) | [virtual] |
| bool store | ( | FILE * | f | ) | const [virtual] |
1.4.7