Enum fann_sys::fann_train_enum

#[repr(C)]
pub enum fann_train_enum {
    FANN_TRAIN_INCREMENTAL = 0,
    FANN_TRAIN_BATCH = 1,
    FANN_TRAIN_RPROP = 2,
    FANN_TRAIN_QUICKPROP = 3,
}

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

FANN_TRAIN_INCREMENTAL = 0

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. For this reason some problems will train very fast with this algorithm, while other more advanced problems will not train very well.

FANN_TRAIN_BATCH = 1

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 with this algorithm.

FANN_TRAIN_RPROP = 2

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

FANN_TRAIN_QUICKPROP = 3

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 advanced parameters for users with insight in how the quickprop training algorithm works. The quickprop training algorithm is described by [Fahlman, 1988].

Trait Implementations

impl Clone for fann_train_enum

fn clone(&self) -> fann_train_enum

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Copy for fann_train_enum