ANN_MLP (JavaCPP Presets for OpenCV 4.3.0-1.5.3 API)

java.lang.Object
- org.bytedeco.javacpp.Pointer
- - org.bytedeco.opencv.opencv_core.Algorithm
  - - org.bytedeco.opencv.opencv_ml.AbstractStatModel
    - - org.bytedeco.opencv.opencv_ml.StatModel
      - org.bytedeco.opencv.opencv_ml.ANN_MLP

All Implemented Interfaces:

AutoCloseable
```
@Namespace(value="cv::ml")
 @Properties(inherit=opencv_ml.class)
public class ANN_MLP
extends StatModel
```
\brief 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 Also:

ml_intro_ann

Nested Class Summary
- Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer
  Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator, Pointer.ReferenceCounter

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`ANNEAL` enum cv::ml::ANN_MLP::TrainingMethods
`static int`	`BACKPROP` enum cv::ml::ANN_MLP::TrainingMethods
`static int`	`GAUSSIAN` enum cv::ml::ANN_MLP::ActivationFunctions
`static int`	`IDENTITY` enum cv::ml::ANN_MLP::ActivationFunctions
`static int`	`LEAKYRELU` enum cv::ml::ANN_MLP::ActivationFunctions
`static int`	`NO_INPUT_SCALE` enum cv::ml::ANN_MLP::TrainFlags
`static int`	`NO_OUTPUT_SCALE` enum cv::ml::ANN_MLP::TrainFlags
`static int`	`RELU` enum cv::ml::ANN_MLP::ActivationFunctions
`static int`	`RPROP` enum cv::ml::ANN_MLP::TrainingMethods
`static int`	`SIGMOID_SYM` enum cv::ml::ANN_MLP::ActivationFunctions
`static int`	`UPDATE_WEIGHTS` enum cv::ml::ANN_MLP::TrainFlags

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

Fields inherited from class org.bytedeco.javacpp.Pointer
address, capacity, limit, position

Constructor Summary

Constructors
Constructor and Description

ANN_MLP(Pointer p)
Pointer cast constructor.

Constructors
Constructor and Description
`ANN_MLP(Pointer p)` Pointer cast constructor.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static ANN_MLP`	`create()` \brief Creates empty model
`double`	`getAnnealCoolingRatio()`
`double`	`getAnnealFinalT()`
`double`	`getAnnealInitialT()`
`int`	`getAnnealItePerStep()`
`double`	`getBackpropMomentumScale()`
`double`	`getBackpropWeightScale()`
`Mat`	`getLayerSizes()` Integer vector specifying the number of neurons in each layer including the input and output layers.
`double`	`getRpropDW0()`
`double`	`getRpropDWMax()`
`double`	`getRpropDWMin()`
`double`	`getRpropDWMinus()`
`double`	`getRpropDWPlus()`
`TermCriteria`	`getTermCriteria()`
`int`	`getTrainMethod()` Returns current training method
`Mat`	`getWeights(int layerIdx)`
`static ANN_MLP`	`load(BytePointer filepath)` \brief Loads and creates a serialized ANN from a file Use ANN::save to serialize and store an ANN to disk.
`static ANN_MLP`	`load(String filepath)`
`void`	`setActivationFunction(int type)`
`void`	`setActivationFunction(int type, double param1, double param2)` Initialize the activation function for each neuron.
`void`	`setAnnealCoolingRatio(double val)` \copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio
`void`	`setAnnealEnergyRNG(RNG rng)` \brief Set/initialize anneal RNG
`void`	`setAnnealFinalT(double val)` \copybrief getAnnealFinalT @see getAnnealFinalT
`void`	`setAnnealInitialT(double val)` \copybrief getAnnealInitialT @see getAnnealInitialT
`void`	`setAnnealItePerStep(int val)` \copybrief getAnnealItePerStep @see getAnnealItePerStep
`void`	`setBackpropMomentumScale(double val)` \copybrief getBackpropMomentumScale @see getBackpropMomentumScale
`void`	`setBackpropWeightScale(double val)` \copybrief getBackpropWeightScale @see getBackpropWeightScale
`void`	`setLayerSizes(GpuMat _layer_sizes)`
`void`	`setLayerSizes(Mat _layer_sizes)` Integer vector specifying the number of neurons in each layer including the input and output layers.
`void`	`setLayerSizes(UMat _layer_sizes)`
`void`	`setRpropDW0(double val)` \copybrief getRpropDW0 @see getRpropDW0
`void`	`setRpropDWMax(double val)` \copybrief getRpropDWMax @see getRpropDWMax
`void`	`setRpropDWMin(double val)` \copybrief getRpropDWMin @see getRpropDWMin
`void`	`setRpropDWMinus(double val)` \copybrief getRpropDWMinus @see getRpropDWMinus
`void`	`setRpropDWPlus(double val)` \copybrief getRpropDWPlus @see getRpropDWPlus
`void`	`setTermCriteria(TermCriteria val)` \copybrief getTermCriteria @see getTermCriteria
`void`	`setTrainMethod(int method)`
`void`	`setTrainMethod(int method, double param1, double param2)` Sets training method and common parameters.

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

Methods inherited from class org.bytedeco.opencv.opencv_ml.AbstractStatModel
loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.opencv.opencv_core.Algorithm
clear, getDefaultName, position, read, save, save, write, write, write

Methods inherited from class org.bytedeco.javacpp.Pointer
address, asBuffer, asByteBuffer, availablePhysicalBytes, calloc, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, formatBytes, free, hashCode, isNull, isNull, limit, limit, malloc, maxBytes, maxPhysicalBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, parseBytes, physicalBytes, position, put, realloc, referenceCount, releaseReference, retainReference, setNull, sizeof, toString, totalBytes, totalPhysicalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object
clone, finalize, getClass, notify, notifyAll, wait, wait, wait

- Field Detail
  - BACKPROP
```
public static final int BACKPROP
```
    enum cv::ml::ANN_MLP::TrainingMethods
    
    See Also:
    
    Constant Field Values
  - RPROP
```
public static final int RPROP
```
    enum cv::ml::ANN_MLP::TrainingMethods
    
    See Also:
    
    Constant Field Values
  - ANNEAL
```
public static final int ANNEAL
```
    enum cv::ml::ANN_MLP::TrainingMethods
    
    See Also:
    
    Constant Field Values
  - IDENTITY
```
public static final int IDENTITY
```
    enum cv::ml::ANN_MLP::ActivationFunctions
    
    See Also:
    
    Constant Field Values
  - SIGMOID_SYM
```
public static final int SIGMOID_SYM
```
    enum cv::ml::ANN_MLP::ActivationFunctions
    
    See Also:
    
    Constant Field Values
  - GAUSSIAN
```
public static final int GAUSSIAN
```
    enum cv::ml::ANN_MLP::ActivationFunctions
    
    See Also:
    
    Constant Field Values
  - RELU
```
public static final int RELU
```
    enum cv::ml::ANN_MLP::ActivationFunctions
    
    See Also:
    
    Constant Field Values
  - LEAKYRELU
```
public static final int LEAKYRELU
```
    enum cv::ml::ANN_MLP::ActivationFunctions
    
    See Also:
    
    Constant Field Values
  - UPDATE_WEIGHTS
```
public static final int UPDATE_WEIGHTS
```
    enum cv::ml::ANN_MLP::TrainFlags
    
    See Also:
    
    Constant Field Values
  - NO_INPUT_SCALE
```
public static final int NO_INPUT_SCALE
```
    enum cv::ml::ANN_MLP::TrainFlags
    
    See Also:
    
    Constant Field Values
  - NO_OUTPUT_SCALE
```
public static final int NO_OUTPUT_SCALE
```
    enum cv::ml::ANN_MLP::TrainFlags
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - ANN_MLP
```
public ANN_MLP(Pointer p)
```
    Pointer cast constructor. Invokes Pointer.Pointer(Pointer).
- Method Detail
  - 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)
```
  - getTrainMethod
```
public int getTrainMethod()
```
    Returns current training method
  - 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)
```
  - setLayerSizes
```
public void setLayerSizes(@ByVal
                          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 Also:
    
    getLayerSizes
  - setLayerSizes
```
public void setLayerSizes(@ByVal
                          UMat _layer_sizes)
```
  - setLayerSizes
```
public void setLayerSizes(@ByVal
                          GpuMat _layer_sizes)
```
  - getLayerSizes
```
@ByVal
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 Also:
    
    setLayerSizes
  - getTermCriteria
```
@ByVal
public TermCriteria getTermCriteria()
```
    See Also:
    
    setTermCriteria
  - setTermCriteria
```
public void setTermCriteria(@ByVal
                            TermCriteria val)
```
    \copybrief getTermCriteria @see getTermCriteria
  - getBackpropWeightScale
```
public double getBackpropWeightScale()
```
    See Also:
    
    setBackpropWeightScale
  - setBackpropWeightScale
```
public void setBackpropWeightScale(double val)
```
    \copybrief getBackpropWeightScale @see getBackpropWeightScale
  - getBackpropMomentumScale
```
public double getBackpropMomentumScale()
```
    See Also:
    
    setBackpropMomentumScale
  - setBackpropMomentumScale
```
public void setBackpropMomentumScale(double val)
```
    \copybrief getBackpropMomentumScale @see getBackpropMomentumScale
  - getRpropDW0
```
public double getRpropDW0()
```
    See Also:
    
    setRpropDW0
  - setRpropDW0
```
public void setRpropDW0(double val)
```
    \copybrief getRpropDW0 @see getRpropDW0
  - getRpropDWPlus
```
public double getRpropDWPlus()
```
    See Also:
    
    setRpropDWPlus
  - setRpropDWPlus
```
public void setRpropDWPlus(double val)
```
    \copybrief getRpropDWPlus @see getRpropDWPlus
  - getRpropDWMinus
```
public double getRpropDWMinus()
```
    See Also:
    
    setRpropDWMinus
  - setRpropDWMinus
```
public void setRpropDWMinus(double val)
```
    \copybrief getRpropDWMinus @see getRpropDWMinus
  - getRpropDWMin
```
public double getRpropDWMin()
```
    See Also:
    
    setRpropDWMin
  - setRpropDWMin
```
public void setRpropDWMin(double val)
```
    \copybrief getRpropDWMin @see getRpropDWMin
  - getRpropDWMax
```
public double getRpropDWMax()
```
    See Also:
    
    setRpropDWMax
  - setRpropDWMax
```
public void setRpropDWMax(double val)
```
    \copybrief getRpropDWMax @see getRpropDWMax
  - getAnnealInitialT
```
public double getAnnealInitialT()
```
    See Also:
    
    setAnnealInitialT
  - setAnnealInitialT
```
public void setAnnealInitialT(double val)
```
    \copybrief getAnnealInitialT @see getAnnealInitialT
  - getAnnealFinalT
```
public double getAnnealFinalT()
```
    See Also:
    
    setAnnealFinalT
  - setAnnealFinalT
```
public void setAnnealFinalT(double val)
```
    \copybrief getAnnealFinalT @see getAnnealFinalT
  - getAnnealCoolingRatio
```
public double getAnnealCoolingRatio()
```
    See Also:
    
    setAnnealCoolingRatio
  - setAnnealCoolingRatio
```
public void setAnnealCoolingRatio(double val)
```
    \copybrief getAnnealCoolingRatio @see getAnnealCoolingRatio
  - getAnnealItePerStep
```
public int getAnnealItePerStep()
```
    See Also:
    
    setAnnealItePerStep
  - setAnnealItePerStep
```
public void setAnnealItePerStep(int val)
```
    \copybrief getAnnealItePerStep @see getAnnealItePerStep
  - setAnnealEnergyRNG
```
public void setAnnealEnergyRNG(@Const @ByRef
                               RNG rng)
```
    \brief Set/initialize anneal RNG
  - getWeights
```
@ByVal
public Mat getWeights(int layerIdx)
```
  - create
```
@opencv_core.Ptr
public static ANN_MLP create()
```
    \brief Creates empty model
    Use StatModel::train to train the model, Algorithm::load\(filename) to load the pre-trained model. Note that the train method has optional flags: ANN_MLP::TrainFlags.
  - load
```
@opencv_core.Ptr
public static ANN_MLP load(@opencv_core.Str
                                            BytePointer filepath)
```
    \brief 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
  - load
```
@opencv_core.Ptr
public static ANN_MLP load(@opencv_core.Str
                                            String filepath)
```

Class ANN_MLP

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Field Summary

Fields inherited from class org.bytedeco.opencv.opencv_ml.StatModel

Fields inherited from class org.bytedeco.javacpp.Pointer

Constructor Summary

Method Summary

Methods inherited from class org.bytedeco.opencv.opencv_ml.StatModel

Methods inherited from class org.bytedeco.opencv.opencv_ml.AbstractStatModel

Methods inherited from class org.bytedeco.opencv.opencv_core.Algorithm

Methods inherited from class org.bytedeco.javacpp.Pointer

Methods inherited from class java.lang.Object

Field Detail

BACKPROP

RPROP

ANNEAL

IDENTITY

SIGMOID_SYM

GAUSSIAN

RELU

LEAKYRELU

UPDATE_WEIGHTS

NO_INPUT_SCALE

NO_OUTPUT_SCALE

Constructor Detail

ANN_MLP

Method Detail

setTrainMethod

setTrainMethod

getTrainMethod

setActivationFunction

setActivationFunction

setLayerSizes

setLayerSizes

setLayerSizes

getLayerSizes

getTermCriteria

setTermCriteria

getBackpropWeightScale

setBackpropWeightScale

getBackpropMomentumScale

setBackpropMomentumScale

getRpropDW0

setRpropDW0

getRpropDWPlus

setRpropDWPlus

getRpropDWMinus

setRpropDWMinus

getRpropDWMin

setRpropDWMin

getRpropDWMax

setRpropDWMax

getAnnealInitialT

setAnnealInitialT

getAnnealFinalT

setAnnealFinalT

getAnnealCoolingRatio

setAnnealCoolingRatio

getAnnealItePerStep

setAnnealItePerStep

setAnnealEnergyRNG

getWeights

create

load

load