Struct cogent::core::Trainer

pub struct Trainer<'a> { /* fields omitted */ }

To practicaly implement optional setting of training hyperparameters.

Methods

impl<'a> Trainer<'a>

pub fn evaluation_data(
    &mut self,
    evaluation_data: EvaluationData
) -> &mut Trainer<'a>

Sets evaluation_data.

evaluation_data determines how to set the evaluation data.

pub fn halt_condition(
    &mut self,
    halt_condition: HaltCondition
) -> &mut Trainer<'a>

Sets halt_condition.

halt_condition sets after which Iteration/Duration or reached accuracy to stop training.

pub fn log_interval(
    &mut self,
    log_interval: MeasuredCondition
) -> &mut Trainer<'a>

Sets log_interval.

log_interval sets some amount of Iterations/Duration to print the cost and accuracy of the neural net.

pub fn batch_size(&mut self, batch_size: Proportion) -> &mut Trainer<'a>

Sets batch_size.

pub fn learning_rate(&mut self, learning_rate: f32) -> &mut Trainer<'a>

Sets learning_rate.

pub fn lambda(&mut self, lambda: f32) -> &mut Trainer<'a>

Sets lamdba (otherwise known as regulation parameter).

lamdba is the regularization paramter.

pub fn early_stopping_condition(
    &mut self,
    early_stopping_condition: MeasuredCondition
) -> &mut Trainer<'a>

Sets early_stopping_condition.

early_stopping_condition sets some amount of Iterations/Duration to stop after without notable cost improvement.

pub fn evaluation_min_change(
    &mut self,
    evaluation_min_change: Proportion
) -> &mut Trainer<'a>

Sets evaluation_min_change.

Minimum change required to log positive evaluation change.

pub fn learning_rate_decay(
    &mut self,
    learning_rate_decay: f32
) -> &mut Trainer<'a>

Sets learning_rate_decay.

learning_rate_decay is the mulipliers by which to decay the learning rate.

pub fn learning_rate_interval(
    &mut self,
    learning_rate_interval: MeasuredCondition
) -> &mut Trainer<'a>

Sets learning_rate_interval.

pub fn checkpoint_interval(
    &mut self,
    checkpoint_interval: MeasuredCondition
) -> &mut Trainer<'a>

Sets checkpoint_interval.

checkpoint_interval sets how often (if at all) to serialize and output neural network to .txt file.

pub fn name(&mut self, name: &'a str) -> &mut Trainer<'a>

Sets name

name sets what to pretend to checkpoint files. Used to differentiate between nets when checkpointing multiple.

pub fn tracking(&mut self) -> &mut Trainer<'a>

Sets tracking.

tracking determines whether to output percentage progress during backpropgation.

pub fn go(&mut self)

Begins training.