Enum TrainAlgorithm

pub enum TrainAlgorithm {
    Incremental(IncrementalParams),
    Batch(BatchParams),
    Rprop(RpropParams),
    Quickprop(QuickpropParams),
}

The Training algorithms used when training on fann_train_data with functions like fann_train_on_data or fann_train_on_file. The incremental training alters the weights after each time it is presented an input pattern, while batch only alters the weights once after it has been presented to all the patterns.

Variants

Incremental(IncrementalParams)

Standard backpropagation algorithm, where the weights are updated after each training pattern. This means that the weights are updated many times during a single epoch and some problems will train very fast, while other more advanced problems will not train very well.

Batch(BatchParams)

Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. This means that the weights are only updated once during an epoch. For this reason some problems will train slower with this algorithm. But since the mean square error is calculated more correctly than in incremental training, some problems will reach better solutions.

Rprop(RpropParams)

A more advanced batch training algorithm which achieves good results for many problems. Rprop is adaptive and therefore does not use the learning_rate. Some other parameters can, however, be set to change the way Rprop works, but it is only recommended for users with a deep understanding of the algorithm. The original RPROP training algorithm is described by [Riedmiller and Braun, 1993], but the algorithm used here is a variant, iRPROP, described by [Igel and Husken, 2000].

Quickprop(QuickpropParams)

A more advanced batch training algorithm which achieves good results for many problems. The quickprop training algorithm uses the learning_rate parameter along with other more advanced parameters, but it is only recommended to change these for users with a deep understanding of the algorithm. Quickprop is described by [Fahlman, 1988].

Trait Implementations

impl Clone for TrainAlgorithm

fn clone(&self) -> TrainAlgorithm

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainAlgorithm

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

Formats the value using the given formatter.

impl Default for TrainAlgorithm

fn default() -> TrainAlgorithm

Returns the “default value” for a type.

impl PartialEq for TrainAlgorithm

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl Copy for TrainAlgorithm

impl StructuralPartialEq for TrainAlgorithm