Trait opencv::prelude::ANN_MLPTrait

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pub trait ANN_MLPTrait: ANN_MLPTraitConst + StatModelTrait {
Show 17 methods    // Required method
    fn as_raw_mut_ANN_MLP(&mut self) -> *mut c_void;

    // Provided methods
    fn set_train_method(
        &mut self,
        method: i32,
        param1: f64,
        param2: f64
    ) -> Result<()> { ... }
    fn set_activation_function(
        &mut self,
        typ: i32,
        param1: f64,
        param2: f64
    ) -> Result<()> { ... }
    fn set_layer_sizes(
        &mut self,
        _layer_sizes: &impl ToInputArray
    ) -> Result<()> { ... }
    fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()> { ... }
    fn set_backprop_weight_scale(&mut self, val: f64) -> Result<()> { ... }
    fn set_backprop_momentum_scale(&mut self, val: f64) -> Result<()> { ... }
    fn set_rprop_dw0(&mut self, val: f64) -> Result<()> { ... }
    fn set_rprop_dw_plus(&mut self, val: f64) -> Result<()> { ... }
    fn set_rprop_dw_minus(&mut self, val: f64) -> Result<()> { ... }
    fn set_rprop_dw_min(&mut self, val: f64) -> Result<()> { ... }
    fn set_rprop_dw_max(&mut self, val: f64) -> Result<()> { ... }
    fn set_anneal_initial_t(&mut self, val: f64) -> Result<()> { ... }
    fn set_anneal_final_t(&mut self, val: f64) -> Result<()> { ... }
    fn set_anneal_cooling_ratio(&mut self, val: f64) -> Result<()> { ... }
    fn set_anneal_ite_per_step(&mut self, val: i32) -> Result<()> { ... }
    fn set_anneal_energy_rng(&mut self, rng: &RNG) -> Result<()> { ... }
}

Expand description

Mutable methods for crate::ml::ANN_MLP

Required Methods§

source

fn as_raw_mut_ANN_MLP(&mut self) -> *mut c_void

Provided Methods§

source

fn set_train_method( &mut self, method: i32, param1: f64, param2: f64 ) -> Result<()>

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.

fn set_activation_function( &mut self, typ: i32, param1: f64, param2: f64 ) -> Result<()>

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, $inline formula$ . Default value is 0.
param2: The second parameter of the activation function, $inline formula$ . Default value is 0.

fn set_layer_sizes(&mut self, _layer_sizes: &impl ToInputArray) -> Result<()>

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.

fn set_term_criteria(&mut self, val: TermCriteria) -> Result<()>

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).

fn set_backprop_weight_scale(&mut self, val: f64) -> Result<()>

BPROP: Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1.

fn set_backprop_momentum_scale(&mut self, val: f64) -> Result<()>

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.