public class ANN_MLP extends StatModel
Modifier and Type | Field and Description |
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static int |
ANNEAL |
static int |
BACKPROP |
static int |
GAUSSIAN |
static int |
IDENTITY |
static int |
LEAKYRELU |
static int |
NO_INPUT_SCALE |
static int |
NO_OUTPUT_SCALE |
static int |
RELU |
static int |
RPROP |
static int |
SIGMOID_SYM |
static int |
UPDATE_WEIGHTS |
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL
Modifier | Constructor and Description |
---|---|
protected |
ANN_MLP(long addr) |
Modifier and Type | Method and Description |
---|---|
static ANN_MLP |
__fromPtr__(long addr) |
static ANN_MLP |
create()
Creates empty model
Use StatModel::train to train the model, Algorithm::load<ANN_MLP>(filename) to load the pre-trained model.
|
protected void |
finalize() |
double |
getAnnealCoolingRatio()
SEE: setAnnealCoolingRatio
|
double |
getAnnealFinalT()
SEE: setAnnealFinalT
|
double |
getAnnealInitialT()
SEE: setAnnealInitialT
|
int |
getAnnealItePerStep()
SEE: setAnnealItePerStep
|
double |
getBackpropMomentumScale()
SEE: setBackpropMomentumScale
|
double |
getBackpropWeightScale()
SEE: setBackpropWeightScale
|
Mat |
getLayerSizes()
Integer vector specifying the number of neurons in each layer including the input and output layers.
|
double |
getRpropDW0()
SEE: setRpropDW0
|
double |
getRpropDWMax()
SEE: setRpropDWMax
|
double |
getRpropDWMin()
SEE: setRpropDWMin
|
double |
getRpropDWMinus()
SEE: setRpropDWMinus
|
double |
getRpropDWPlus()
SEE: setRpropDWPlus
|
TermCriteria |
getTermCriteria()
SEE: setTermCriteria
|
int |
getTrainMethod()
Returns current training method
|
Mat |
getWeights(int layerIdx) |
static ANN_MLP |
load(String filepath)
Loads and creates a serialized ANN from a file
Use ANN::save to serialize and store an ANN to disk.
|
void |
setActivationFunction(int type)
Initialize the activation function for each neuron.
|
void |
setActivationFunction(int type,
double param1)
Initialize the activation function for each neuron.
|
void |
setActivationFunction(int type,
double param1,
double param2)
Initialize the activation function for each neuron.
|
void |
setAnnealCoolingRatio(double val)
getAnnealCoolingRatio SEE: getAnnealCoolingRatio
|
void |
setAnnealFinalT(double val)
getAnnealFinalT SEE: getAnnealFinalT
|
void |
setAnnealInitialT(double val)
getAnnealInitialT SEE: getAnnealInitialT
|
void |
setAnnealItePerStep(int val)
getAnnealItePerStep SEE: getAnnealItePerStep
|
void |
setBackpropMomentumScale(double val)
getBackpropMomentumScale SEE: getBackpropMomentumScale
|
void |
setBackpropWeightScale(double val)
getBackpropWeightScale SEE: getBackpropWeightScale
|
void |
setLayerSizes(Mat _layer_sizes)
Integer vector specifying the number of neurons in each layer including the input and output layers.
|
void |
setRpropDW0(double val)
getRpropDW0 SEE: getRpropDW0
|
void |
setRpropDWMax(double val)
getRpropDWMax SEE: getRpropDWMax
|
void |
setRpropDWMin(double val)
getRpropDWMin SEE: getRpropDWMin
|
void |
setRpropDWMinus(double val)
getRpropDWMinus SEE: getRpropDWMinus
|
void |
setRpropDWPlus(double val)
getRpropDWPlus SEE: getRpropDWPlus
|
void |
setTermCriteria(TermCriteria val)
getTermCriteria SEE: getTermCriteria
|
void |
setTrainMethod(int method)
Sets training method and common parameters.
|
void |
setTrainMethod(int method,
double param1)
Sets training method and common parameters.
|
void |
setTrainMethod(int method,
double param1,
double param2)
Sets training method and common parameters.
|
calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train
clear, getDefaultName, getNativeObjAddr, save
public static final int IDENTITY
public static final int SIGMOID_SYM
public static final int GAUSSIAN
public static final int RELU
public static final int LEAKYRELU
public static final int UPDATE_WEIGHTS
public static final int NO_INPUT_SCALE
public static final int NO_OUTPUT_SCALE
public static final int BACKPROP
public static final int RPROP
public static final int ANNEAL
public static ANN_MLP __fromPtr__(long addr)
public void setTrainMethod(int method, double param1, double param2)
method
- Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.param1
- passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.param2
- passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.public void setTrainMethod(int method, double param1)
method
- Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.param1
- passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.public void setTrainMethod(int method)
method
- Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.public int getTrainMethod()
public void setActivationFunction(int type, double param1, double param2)
type
- The type of activation function. See ANN_MLP::ActivationFunctions.param1
- The first parameter of the activation function, \(\alpha\). Default value is 0.param2
- The second parameter of the activation function, \(\beta\). Default value is 0.public void setActivationFunction(int type, double param1)
type
- The type of activation function. See ANN_MLP::ActivationFunctions.param1
- The first parameter of the activation function, \(\alpha\). Default value is 0.public void setActivationFunction(int type)
type
- The type of activation function. See ANN_MLP::ActivationFunctions.public void setLayerSizes(Mat _layer_sizes)
_layer_sizes
- automatically generatedpublic Mat getLayerSizes()
public TermCriteria getTermCriteria()
public void setTermCriteria(TermCriteria val)
val
- automatically generatedpublic double getBackpropWeightScale()
public void setBackpropWeightScale(double val)
val
- automatically generatedpublic double getBackpropMomentumScale()
public void setBackpropMomentumScale(double val)
val
- automatically generatedpublic double getRpropDW0()
public void setRpropDW0(double val)
val
- automatically generatedpublic double getRpropDWPlus()
public void setRpropDWPlus(double val)
val
- automatically generatedpublic double getRpropDWMinus()
public void setRpropDWMinus(double val)
val
- automatically generatedpublic double getRpropDWMin()
public void setRpropDWMin(double val)
val
- automatically generatedpublic double getRpropDWMax()
public void setRpropDWMax(double val)
val
- automatically generatedpublic double getAnnealInitialT()
public void setAnnealInitialT(double val)
val
- automatically generatedpublic double getAnnealFinalT()
public void setAnnealFinalT(double val)
val
- automatically generatedpublic double getAnnealCoolingRatio()
public void setAnnealCoolingRatio(double val)
val
- automatically generatedpublic int getAnnealItePerStep()
public void setAnnealItePerStep(int val)
val
- automatically generatedpublic Mat getWeights(int layerIdx)
public static ANN_MLP create()
public static ANN_MLP load(String filepath)
filepath
- path to serialized ANNCopyright © 2024. All rights reserved.