Enum opencv::ml::ANN_MLP_TrainFlags

#[repr(C)]
pub enum ANN_MLP_TrainFlags {
    UPDATE_WEIGHTS,
    NO_INPUT_SCALE,
    NO_OUTPUT_SCALE,
}

Train options

Variants

UPDATE_WEIGHTS

Update the network weights, rather than compute them from scratch. In the latter case the weights are initialized using the Nguyen-Widrow algorithm.

NO_INPUT_SCALE

Do not normalize the input vectors. If this flag is not set, the training algorithm normalizes each input feature independently, shifting its mean value to 0 and making the standard deviation equal to 1. If the network is assumed to be updated frequently, the new training data could be much different from original one. In this case, you should take care of proper normalization.

NO_OUTPUT_SCALE

Do not normalize the output vectors. If the flag is not set, the training algorithm normalizes each output feature independently, by transforming it to the certain range depending on the used activation function.

Trait Implementations

impl Clone for ANN_MLP_TrainFlags

fn clone(&self) -> ANN_MLP_TrainFlags

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ANN_MLP_TrainFlags

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

Formats the value using the given formatter.

impl PartialEq<ANN_MLP_TrainFlags> for ANN_MLP_TrainFlags

fn eq(&self, other: &ANN_MLP_TrainFlags) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Copy for ANN_MLP_TrainFlags

impl Eq for ANN_MLP_TrainFlags

impl StructuralEq for ANN_MLP_TrainFlags

impl StructuralPartialEq for ANN_MLP_TrainFlags