ml_ANN_MLP
import { ml_ANN_MLP } from '@banou/opencv-wasm'Use after await initOpenCV(). See the initialization and named imports guide.
Native object: release it with using or delete(). Factories can return null; check before calling methods. Inherits ml_StatModel.
Gradient Boosted Trees *
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class CV_EXPORTS_W GBTrees : public DTrees
{
public:
struct CV_EXPORTS_W_MAP Params : public DTrees::Params
{
CV_PROP_RW int weakCount;
CV_PROP_RW int lossFunctionType;
CV_PROP_RW float subsamplePortion;
CV_PROP_RW float shrinkage;
Params();
Params( int lossFunctionType, int weakCount, float shrinkage,
float subsamplePortion, int maxDepth, bool useSurrogates );
};
enum {SQUARED_LOSS=0, ABSOLUTE_LOSS, HUBER_LOSS=3, DEVIANCE_LOSS};
virtual void setK(int k) = 0;
virtual float predictSerial( InputArray samples,
OutputArray weakResponses, int flags) const = 0;
static Ptr<GBTrees> create(const Params& p);
};
Artificial Neural Networks (ANN) *
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Multi-Layer Perceptrons //////////////////////////////
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: ml_intro_ann
Constructors and members
static 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.
create(): ml_ANN_MLP | null;The ml_ANN_MLP | null result.
static load
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.
load(filepath: EmbindString): ml_ANN_MLP | null;filepathpath to serialized ANN
The ml_ANN_MLP | null result.
clone
Create another handle to the same native object. This retains the object without copying its pixels or algorithm state; dispose both handles separately.
clone(): this;The this result.
setTrainMethod
Sets training method and common parameters.
setTrainMethod(method: number, param1: number, param2: number): void;3 available overloads
setTrainMethod(method: number): void;setTrainMethod(method: number, param1: number): void;setTrainMethod(method: number, param1: number, param2: number): void;methodDefault value is ANN_MLP::RPROP. See ANN_MLP::TrainingMethods.
param1passed to setRpropDW0 for ANN_MLP::RPROP and to setBackpropWeightScale for ANN_MLP::BACKPROP and to initialT for ANN_MLP::ANNEAL.
param2passed to setRpropDWMin for ANN_MLP::RPROP and to setBackpropMomentumScale for ANN_MLP::BACKPROP and to finalT for ANN_MLP::ANNEAL.
getTrainMethod
Returns current training method
getTrainMethod(): number;The number result.
setActivationFunction
Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.
setActivationFunction(type_: number, param1: number, param2: number): void;3 available overloads
setActivationFunction(type_: number): void;setActivationFunction(type_: number, param1: number): void;setActivationFunction(type_: number, param1: number, param2: number): void;type_The type of activation function. See ANN_MLP::ActivationFunctions.
param1The first parameter of the activation function,
\alpha. Default value is 0.param2The second parameter of the activation function,
\beta. Default value is 0.
setLayerSizes
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
setLayerSizes(_layer_sizes: Mat): void;_layer_sizeslayer sizes argument (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
getLayerSizes(): Mat;The Mat result. Release returned native handles with using or delete(), including handles nested in results.
getTermCriteria
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01). See: setTermCriteria
getTermCriteria(): TermCriteria;The TermCriteria result.
setTermCriteria
Termination criteria of the training algorithm. You can specify the maximum number of iterations (maxCount) and/or how much the error could change between the iterations to make the algorithm continue (epsilon). Default value is TermCriteria(TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, 0.01). See: setTermCriteria See: getTermCriteria
setTermCriteria(val: TermCriteria): void;valval argument (TermCriteria).
getBackpropWeightScale
BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1. See: setBackpropWeightScale
getBackpropWeightScale(): number;The number result.
setBackpropWeightScale
BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1. See: setBackpropWeightScale See: getBackpropWeightScale
setBackpropWeightScale(val: number): void;valval argument (number).
getBackpropMomentumScale
BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1. See: setBackpropMomentumScale
getBackpropMomentumScale(): number;The number result.
setBackpropMomentumScale
BPROP: Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1. See: setBackpropMomentumScale See: getBackpropMomentumScale
setBackpropMomentumScale(val: number): void;valval argument (number).
getRpropDW0
RPROP: Initial value \Delta_0 of update-values \Delta_{ij}.
Default value is 0.1.
See: setRpropDW0
getRpropDW0(): number;The number result.
setRpropDW0
RPROP: Initial value \Delta_0 of update-values \Delta_{ij}.
Default value is 0.1.
See: setRpropDW0 See: getRpropDW0
setRpropDW0(val: number): void;valval argument (number).
getRpropDWPlus
RPROP: Increase factor \eta^+.
It must be >1. Default value is 1.2.
See: setRpropDWPlus
getRpropDWPlus(): number;The number result.
setRpropDWPlus
RPROP: Increase factor \eta^+.
It must be >1. Default value is 1.2.
See: setRpropDWPlus See: getRpropDWPlus
setRpropDWPlus(val: number): void;valval argument (number).
getRpropDWMinus
RPROP: Decrease factor \eta^-.
It must be <1. Default value is 0.5.
See: setRpropDWMinus
getRpropDWMinus(): number;The number result.
setRpropDWMinus
RPROP: Decrease factor \eta^-.
It must be <1. Default value is 0.5.
See: setRpropDWMinus See: getRpropDWMinus
setRpropDWMinus(val: number): void;valval argument (number).
getRpropDWMin
RPROP: Update-values lower limit \Delta_{min}.
It must be positive. Default value is FLT_EPSILON.
See: setRpropDWMin
getRpropDWMin(): number;The number result.
setRpropDWMin
RPROP: Update-values lower limit \Delta_{min}.
It must be positive. Default value is FLT_EPSILON.
See: setRpropDWMin See: getRpropDWMin
setRpropDWMin(val: number): void;valval argument (number).
getRpropDWMax
RPROP: Update-values upper limit \Delta_{max}.
It must be >1. Default value is 50.
See: setRpropDWMax
getRpropDWMax(): number;The number result.
setRpropDWMax
RPROP: Update-values upper limit \Delta_{max}.
It must be >1. Default value is 50.
See: setRpropDWMax See: getRpropDWMax
setRpropDWMax(val: number): void;valval argument (number).
getAnnealInitialT
ANNEAL: Update initial temperature. It must be >=0. Default value is 10. See: setAnnealInitialT
getAnnealInitialT(): number;The number result.
setAnnealInitialT
ANNEAL: Update initial temperature. It must be >=0. Default value is 10. See: setAnnealInitialT See: getAnnealInitialT
setAnnealInitialT(val: number): void;valval argument (number).
getAnnealFinalT
ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1. See: setAnnealFinalT
getAnnealFinalT(): number;The number result.
setAnnealFinalT
ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1. See: setAnnealFinalT See: getAnnealFinalT
setAnnealFinalT(val: number): void;valval argument (number).
getAnnealCoolingRatio
ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95. See: setAnnealCoolingRatio
getAnnealCoolingRatio(): number;The number result.
setAnnealCoolingRatio
ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95. See: setAnnealCoolingRatio See: getAnnealCoolingRatio
setAnnealCoolingRatio(val: number): void;valval argument (number).
getAnnealItePerStep
ANNEAL: Update iteration per step. It must be >0 . Default value is 10. See: setAnnealItePerStep
getAnnealItePerStep(): number;The number result.
setAnnealItePerStep
ANNEAL: Update iteration per step. It must be >0 . Default value is 10. See: setAnnealItePerStep See: getAnnealItePerStep
setAnnealItePerStep(val: number): void;valval argument (number).
getWeights
Return the weights configured on this ml_ANN_MLP object.
getWeights(layerIdx: number): Mat;layerIdxlayer idx argument (number).
The Mat result. Release returned native handles with using or delete(), including handles nested in results.
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