ANN_MLP (OpenCV 4.1.2 Java documentation)

java.lang.Object
- org.opencv.core.Algorithm
- - org.opencv.ml.StatModel
  - - org.opencv.ml.ANN_MLP

```
public class ANN_MLP
extends StatModel
```
Artificial Neural Networks - Multi-Layer Perceptrons. Unlike many other models in ML that are constructed and trained at once, in the MLP model these steps are separated. First, a network with the specified topology is created using the non-default constructor or the method ANN_MLP::create. All the weights are set to zeros. Then, the network is trained using a set of input and output vectors. The training procedure can be repeated more than once, that is, the weights can be adjusted based on the new training data. Additional flags for StatModel::train are available: ANN_MLP::TrainFlags. SEE: REF: ml_intro_ann

Field Summary

Fields
Modifier and Type	Field and Description
`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`

Fields inherited from class org.opencv.ml.StatModel
COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Fields inherited from class org.opencv.core.Algorithm
nativeObj

Constructor Summary

Constructors
Modifier Constructor and Description

protected ANN_MLP(long addr)

Constructors
Modifier	Constructor and Description
`protected`	`ANN_MLP(long addr)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
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.

Methods inherited from class org.opencv.ml.StatModel
calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

Methods inherited from class org.opencv.core.Algorithm
clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object
clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - IDENTITY
```
public static final int IDENTITY
```
    See Also:
    
    Constant Field Values
  - SIGMOID_SYM
```
public static final int SIGMOID_SYM
```
    See Also:
    
    Constant Field Values
  - GAUSSIAN
```
public static final int GAUSSIAN
```
    See Also:
    
    Constant Field Values
  - RELU
```
public static final int RELU
```
    See Also:
    
    Constant Field Values
  - LEAKYRELU
```
public static final int LEAKYRELU
```
    See Also:
    
    Constant Field Values
  - UPDATE_WEIGHTS
```
public static final int UPDATE_WEIGHTS
```
    See Also:
    
    Constant Field Values
  - NO_INPUT_SCALE
```
public static final int NO_INPUT_SCALE
```
    See Also:
    
    Constant Field Values
  - NO_OUTPUT_SCALE
```
public static final int NO_OUTPUT_SCALE
```
    See Also:
    
    Constant Field Values
  - BACKPROP
```
public static final int BACKPROP
```
    See Also:
    
    Constant Field Values
  - RPROP
```
public static final int RPROP
```
    See Also:
    
    Constant Field Values
  - ANNEAL
```
public static final int ANNEAL
```
    See Also:
    
    Constant Field Values
- Constructor Detail
  - ANN_MLP
```
protected ANN_MLP(long addr)
```
- Method Detail
  - __fromPtr__
```
public static ANN_MLP __fromPtr__(long addr)
```
  - getLayerSizes
```
public Mat getLayerSizes()
```
    Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. SEE: setLayerSizes
    
    Returns:
    
    automatically generated
  - getWeights
```
public Mat getWeights(int layerIdx)
```
  - create
```
public 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. Note that the train method has optional flags: ANN_MLP::TrainFlags.
    
    Returns:
    
    automatically generated
  - load
```
public 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. Load the ANN from this file again, by calling this function with the path to the file.
    
    Parameters:
    
    filepath - path to serialized ANN
    
    Returns:
    
    automatically generated
  - getTermCriteria
```
public TermCriteria getTermCriteria()
```
    SEE: setTermCriteria
    
    Returns:
    
    automatically generated
  - getAnnealCoolingRatio
```
public double getAnnealCoolingRatio()
```
    SEE: setAnnealCoolingRatio
    
    Returns:
    
    automatically generated
  - getAnnealFinalT
```
public double getAnnealFinalT()
```
    SEE: setAnnealFinalT
    
    Returns:
    
    automatically generated
  - getAnnealInitialT
```
public double getAnnealInitialT()
```
    SEE: setAnnealInitialT
    
    Returns:
    
    automatically generated
  - getBackpropMomentumScale
```
public double getBackpropMomentumScale()
```
    SEE: setBackpropMomentumScale
    
    Returns:
    
    automatically generated
  - getBackpropWeightScale
```
public double getBackpropWeightScale()
```
    SEE: setBackpropWeightScale
    
    Returns:
    
    automatically generated
  - getRpropDW0
```
public double getRpropDW0()
```
    SEE: setRpropDW0
    
    Returns:
    
    automatically generated
  - getRpropDWMax
```
public double getRpropDWMax()
```
    SEE: setRpropDWMax
    
    Returns:
    
    automatically generated
  - getRpropDWMin
```
public double getRpropDWMin()
```
    SEE: setRpropDWMin
    
    Returns:
    
    automatically generated
  - getRpropDWMinus
```
public double getRpropDWMinus()
```
    SEE: setRpropDWMinus
    
    Returns:
    
    automatically generated
  - getRpropDWPlus
```
public double getRpropDWPlus()
```
    SEE: setRpropDWPlus
    
    Returns:
    
    automatically generated
  - getAnnealItePerStep
```
public int getAnnealItePerStep()
```
    SEE: setAnnealItePerStep
    
    Returns:
    
    automatically generated
  - getTrainMethod
```
public int getTrainMethod()
```
    Returns current training method
    
    Returns:
    
    automatically generated
  - setActivationFunction
```
public void setActivationFunction(int type,
                                  double param1,
                                  double param2)
```
    Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
    
    Parameters:
    
    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.
  - setActivationFunction
```
public void setActivationFunction(int type,
                                  double param1)
```
    Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
    
    Parameters:
    
    type - The type of activation function. See ANN_MLP::ActivationFunctions.
    
    param1 - The first parameter of the activation function, \(\alpha\). Default value is 0.
  - setActivationFunction
```
public void setActivationFunction(int type)
```
    Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
    
    Parameters:
    
    type - The type of activation function. See ANN_MLP::ActivationFunctions.
  - setAnnealCoolingRatio
```
public void setAnnealCoolingRatio(double val)
```
    getAnnealCoolingRatio SEE: getAnnealCoolingRatio
    
    Parameters:
    
    val - automatically generated
  - setAnnealFinalT
```
public void setAnnealFinalT(double val)
```
    getAnnealFinalT SEE: getAnnealFinalT
    
    Parameters:
    
    val - automatically generated
  - setAnnealInitialT
```
public void setAnnealInitialT(double val)
```
    getAnnealInitialT SEE: getAnnealInitialT
    
    Parameters:
    
    val - automatically generated
  - setAnnealItePerStep
```
public void setAnnealItePerStep(int val)
```
    getAnnealItePerStep SEE: getAnnealItePerStep
    
    Parameters:
    
    val - automatically generated
  - setBackpropMomentumScale
```
public void setBackpropMomentumScale(double val)
```
    getBackpropMomentumScale SEE: getBackpropMomentumScale
    
    Parameters:
    
    val - automatically generated
  - setBackpropWeightScale
```
public void setBackpropWeightScale(double val)
```
    getBackpropWeightScale SEE: getBackpropWeightScale
    
    Parameters:
    
    val - automatically generated
  - setLayerSizes
```
public void setLayerSizes(Mat _layer_sizes)
```
    Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer. Default value is empty Mat. SEE: getLayerSizes
    
    Parameters:
    
    _layer_sizes - automatically generated
  - setRpropDW0
```
public void setRpropDW0(double val)
```
    getRpropDW0 SEE: getRpropDW0
    
    Parameters:
    
    val - automatically generated
  - setRpropDWMax
```
public void setRpropDWMax(double val)
```
    getRpropDWMax SEE: getRpropDWMax
    
    Parameters:
    
    val - automatically generated
  - setRpropDWMin
```
public void setRpropDWMin(double val)
```
    getRpropDWMin SEE: getRpropDWMin
    
    Parameters:
    
    val - automatically generated
  - setRpropDWMinus
```
public void setRpropDWMinus(double val)
```
    getRpropDWMinus SEE: getRpropDWMinus
    
    Parameters:
    
    val - automatically generated
  - setRpropDWPlus
```
public void setRpropDWPlus(double val)
```
    getRpropDWPlus SEE: getRpropDWPlus
    
    Parameters:
    
    val - automatically generated
  - setTermCriteria
```
public void setTermCriteria(TermCriteria val)
```
    getTermCriteria SEE: getTermCriteria
    
    Parameters:
    
    val - automatically generated
  - setTrainMethod
```
public void setTrainMethod(int method,
                           double param1,
                           double param2)
```
    Sets training method and common parameters.
    
    Parameters:
    
    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.
  - setTrainMethod
```
public void setTrainMethod(int method,
                           double param1)
```
    Sets training method and common parameters.
    
    Parameters:
    
    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.
  - setTrainMethod
```
public void setTrainMethod(int method)
```
    Sets training method and common parameters.
    
    Parameters:
    
    method - Default value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
  - finalize
```
protected void finalize()
                 throws Throwable
```
    Overrides:
    
    finalize in class StatModel
    
    Throws:
    
    Throwable

Class ANN_MLP

Field Summary

Fields inherited from class org.opencv.ml.StatModel

Fields inherited from class org.opencv.core.Algorithm

Constructor Summary

Method Summary

Methods inherited from class org.opencv.ml.StatModel

Methods inherited from class org.opencv.core.Algorithm

Methods inherited from class java.lang.Object

Field Detail

IDENTITY

SIGMOID_SYM

GAUSSIAN

RELU

LEAKYRELU

UPDATE_WEIGHTS

NO_INPUT_SCALE

NO_OUTPUT_SCALE

BACKPROP

RPROP

ANNEAL

Constructor Detail

ANN_MLP

Method Detail

__fromPtr__

getLayerSizes

getWeights

create

load

getTermCriteria

getAnnealCoolingRatio

getAnnealFinalT

getAnnealInitialT

getBackpropMomentumScale

getBackpropWeightScale

getRpropDW0

getRpropDWMax

getRpropDWMin

getRpropDWMinus

getRpropDWPlus

getAnnealItePerStep

getTrainMethod

setActivationFunction

setActivationFunction

setActivationFunction

setAnnealCoolingRatio

setAnnealFinalT

setAnnealInitialT

setAnnealItePerStep

setBackpropMomentumScale

setBackpropWeightScale

setLayerSizes

setRpropDW0

setRpropDWMax

setRpropDWMin

setRpropDWMinus

setRpropDWPlus

setTermCriteria

setTrainMethod

setTrainMethod

setTrainMethod

finalize

fromPtr