Trait opencv::prelude::ANN_MLPTraitConst

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pub trait ANN_MLPTraitConst: StatModelTraitConst {
Show 16 methods    // Required method
    fn as_raw_ANN_MLP(&self) -> *const c_void;

    // Provided methods
    fn get_train_method(&self) -> Result<i32> { ... }
    fn get_layer_sizes(&self) -> Result<Mat> { ... }
    fn get_term_criteria(&self) -> Result<TermCriteria> { ... }
    fn get_backprop_weight_scale(&self) -> Result<f64> { ... }
    fn get_backprop_momentum_scale(&self) -> Result<f64> { ... }
    fn get_rprop_dw0(&self) -> Result<f64> { ... }
    fn get_rprop_dw_plus(&self) -> Result<f64> { ... }
    fn get_rprop_dw_minus(&self) -> Result<f64> { ... }
    fn get_rprop_dw_min(&self) -> Result<f64> { ... }
    fn get_rprop_dw_max(&self) -> Result<f64> { ... }
    fn get_anneal_initial_t(&self) -> Result<f64> { ... }
    fn get_anneal_final_t(&self) -> Result<f64> { ... }
    fn get_anneal_cooling_ratio(&self) -> Result<f64> { ... }
    fn get_anneal_ite_per_step(&self) -> Result<i32> { ... }
    fn get_weights(&self, layer_idx: i32) -> Result<Mat> { ... }
}

Expand description

Constant methods for crate::ml::ANN_MLP

Required Methods§

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fn as_raw_ANN_MLP(&self) -> *const c_void

Provided Methods§

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fn get_train_method(&self) -> Result<i32>

Returns current training method

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fn get_layer_sizes(&self) -> Result<Mat>

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

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fn get_term_criteria(&self) -> Result<TermCriteria>

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 also

setTermCriteria

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fn get_backprop_weight_scale(&self) -> Result<f64>

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

§See also

setBackpropWeightScale

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fn get_backprop_momentum_scale(&self) -> Result<f64>

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.