Struct ANN_MLP

pub struct ANN_MLP { /* private fields */ }

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]

Implementations

impl ANN_MLP

pub fn create() -> Result<Ptr<ANN_MLP>>

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.

pub fn load(filepath: &str) -> Result<Ptr<ANN_MLP>>

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

Trait Implementations

impl ANN_MLPTrait for ANN_MLP

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

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

Sets training method and common parameters.

fn set_train_method_def(&mut self, method: i32) -> Result<()>

Sets training method and common parameters.

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.

fn set_activation_function_def(&mut self, typ: i32) -> Result<()>

Initialize the activation function for each neuron. Currently the default and the only fully supported activation function is ANN_MLP::SIGMOID_SYM.

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.

fn set_rprop_dw0(&mut self, val: f64) -> Result<()>

RPROP: Initial value inline formula of update-values inline formula. Default value is 0.1.

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

RPROP: Increase factor inline formula. It must be >1. Default value is 1.2.

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

RPROP: Decrease factor inline formula. It must be <1. Default value is 0.5.

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

RPROP: Update-values lower limit inline formula. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values upper limit inline formula. It must be >1. Default value is 50.

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

ANNEAL: Update initial temperature. It must be >=0. Default value is 10.

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

ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1.

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

ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95.

fn set_anneal_ite_per_step(&mut self, val: i32) -> Result<()>

ANNEAL: Update iteration per step. It must be >0 . Default value is 10.

fn set_anneal_energy_rng(&mut self, rng: &impl RNGTraitConst) -> Result<()>

Set/initialize anneal RNG

impl ANN_MLPTraitConst for ANN_MLP

fn as_raw_ANN_MLP(&self) -> *const c_void

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

Returns current training method

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.

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).

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.

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.

fn get_rprop_dw0(&self) -> Result<f64>

RPROP: Initial value inline formula of update-values inline formula. Default value is 0.1.

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

RPROP: Increase factor inline formula. It must be >1. Default value is 1.2.

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

RPROP: Decrease factor inline formula. It must be <1. Default value is 0.5.

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

RPROP: Update-values lower limit inline formula. It must be positive. Default value is FLT_EPSILON.

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

RPROP: Update-values upper limit inline formula. It must be >1. Default value is 50.

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

ANNEAL: Update initial temperature. It must be >=0. Default value is 10.

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

ANNEAL: Update final temperature. It must be >=0 and less than initialT. Default value is 0.1.

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

ANNEAL: Update cooling ratio. It must be >0 and less than 1. Default value is 0.95.

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

ANNEAL: Update iteration per step. It must be >0 . Default value is 10.

fn get_weights(&self, layer_idx: i32) -> Result<Mat>

impl AlgorithmTrait for ANN_MLP

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

fn clear(&mut self) -> Result<()>

Clears the algorithm state

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

Reads algorithm parameters from a file storage

impl AlgorithmTraitConst for ANN_MLP

fn as_raw_Algorithm(&self) -> *const c_void

fn write(&self, fs: &mut impl FileStorageTrait) -> Result<()>

Stores algorithm parameters in a file storage

fn write_1(&self, fs: &mut impl FileStorageTrait, name: &str) -> Result<()>

Stores algorithm parameters in a file storage

fn write_with_name(&self, fs: &Ptr<FileStorage>, name: &str) -> Result<()>

@deprecated

fn write_with_name_def(&self, fs: &Ptr<FileStorage>) -> Result<()>

👎Deprecated:

Note

Deprecated: ## Note This alternative version of AlgorithmTraitConst::write_with_name function uses the following default values for its arguments:

fn empty(&self) -> Result<bool>

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

fn save(&self, filename: &str) -> Result<()>

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

fn get_default_name(&self) -> Result<String>

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

impl Boxed for ANN_MLP

unsafe fn from_raw(ptr: <ANN_MLP as OpenCVFromExtern>::ExternReceive) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> <ANN_MLP as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> <ANN_MLP as OpenCVTypeExternContainer>::ExternSend

Return the underlying raw pointer.

fn as_raw_mut( &mut self, ) -> <ANN_MLP as OpenCVTypeExternContainer>::ExternSendMut

Return the underlying mutable raw pointer

impl Debug for ANN_MLP

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Drop for ANN_MLP

fn drop(&mut self)

Executes the destructor for this type.

impl From<ANN_MLP> for Algorithm

fn from(s: ANN_MLP) -> Self

Converts to this type from the input type.

impl From<ANN_MLP> for StatModel

fn from(s: ANN_MLP) -> Self

Converts to this type from the input type.

impl StatModelTrait for ANN_MLP

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

fn train_with_data( &mut self, train_data: &Ptr<TrainData>, flags: i32, ) -> Result<bool>

Trains the statistical model

fn train_with_data_def(&mut self, train_data: &Ptr<TrainData>) -> Result<bool>

Trains the statistical model

fn train( &mut self, samples: &impl ToInputArray, layout: i32, responses: &impl ToInputArray, ) -> Result<bool>

Trains the statistical model

impl StatModelTraitConst for ANN_MLP

fn as_raw_StatModel(&self) -> *const c_void

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

Returns the number of variables in training samples

fn empty(&self) -> Result<bool>

fn is_trained(&self) -> Result<bool>

Returns true if the model is trained

fn is_classifier(&self) -> Result<bool>

Returns true if the model is classifier

fn calc_error( &self, data: &Ptr<TrainData>, test: bool, resp: &mut impl ToOutputArray, ) -> Result<f32>

Computes error on the training or test dataset

fn predict( &self, samples: &impl ToInputArray, results: &mut impl ToOutputArray, flags: i32, ) -> Result<f32>

Predicts response(s) for the provided sample(s)

fn predict_def(&self, samples: &impl ToInputArray) -> Result<f32>

Predicts response(s) for the provided sample(s)

impl TryFrom<StatModel> for ANN_MLP

type Error = Error

The type returned in the event of a conversion error.

fn try_from(s: StatModel) -> Result<Self>

Performs the conversion.

impl Send for ANN_MLP