Struct Learner 

pub struct Learner<LC>
where
    LC: LearningComponentsTypes,
{ /* private fields */ }

Learner struct encapsulating all components necessary to train a Neural Network model.

Implementations

impl<B, LR, M, O> Learner<LearningComponentsMarker<B, LR, M, O>>

where B: AutodiffBackend, LR: LrScheduler + 'static, M: TrainStep + AutodiffModule<B> + Display + 'static, <M as AutodiffModule<B>>::InnerModule: InferenceStep, O: Optimizer<M, B> + 'static,

pub fn new( model: M, optim: O, lr_scheduler: LR, ) -> Learner<LearningComponentsMarker<B, LR, M, O>>

Create a learner.

impl<LC> Learner<LC>

where LC: LearningComponentsTypes,

pub fn fork( &mut self, device: &<<LC as LearningComponentsTypes>::Backend as Backend>::Device, )

Fork the learner’s model to the given device.

pub fn model(&self) -> <LC as LearningComponentsTypes>::TrainingModel

Returns the current model.

pub fn lr_current(&self) -> f64

Returns the current learning rate.

pub fn lr_step(&mut self)

Executes a step of the learning rate scheduler.

pub fn train_step( &self, item: <<LC as LearningComponentsTypes>::TrainingModel as TrainStep>::Input, ) -> TrainOutput<<<LC as LearningComponentsTypes>::TrainingModel as TrainStep>::Output>

Runs a step of the model for training, which executes the forward and backward passes.

Arguments

• item - The input for the model.

Returns

The output containing the model output and the gradients.

pub fn optimizer_step(&mut self, grads: GradientsParams)

Optimize the current module with the provided gradients and learning rate.

Arguments

• optim: Optimizer used for learning.
• lr: The learning rate used for this step.
• grads: The gradients of each parameter in the current model.

pub fn optimizer_step_multi(&mut self, grads: MultiGradientsParams)

Optimize the current module with the provided gradients and learning rate.

Arguments

• optim: Optimizer used for learning.
• lr: The learning rate used for this step.
• grads: Multiple gradients associated to each parameter in the current model.

pub fn load_model( &mut self, record: <<LC as LearningComponentsTypes>::TrainingModel as Module<<LC as LearningComponentsTypes>::Backend>>::Record, )

Load the module state from a record.

pub fn load_optim( &mut self, record: <<LC as LearningComponentsTypes>::Optimizer as Optimizer<<LC as LearningComponentsTypes>::TrainingModel, <LC as LearningComponentsTypes>::Backend>>::Record, )

Load the state of the learner’s optimizer as a record.

pub fn load_scheduler( &mut self, record: <<LC as LearningComponentsTypes>::LrScheduler as LrScheduler>::Record<<LC as LearningComponentsTypes>::Backend>, )

Load the state of the learner’s scheduler as a record.

Trait Implementations

impl<LC> Clone for Learner<LC>

where LC: LearningComponentsTypes,

fn clone(&self) -> Learner<LC>

Returns a duplicate of the value.

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

Performs copy-assignment from source.