Struct MetaLearningFramework 

pub struct MetaLearningFramework<T: Float + Debug + Send + Sync + 'static> { /* private fields */ }

Meta-Learning Framework for Learned Optimizers

Implementations

impl<T: Float + Default + Clone + Send + Sync + Sum + for<'a> Sum<&'a T> + ScalarOperand + Debug> MetaLearningFramework<T>

pub fn new(config: MetaLearningConfig) -> Result<Self>

Create a new meta-learning framework

pub async fn meta_train( &mut self, tasks: Vec<MetaTask<T>>, num_epochs: usize, ) -> Result<MetaTrainingResults<T>>

Perform meta-training

pub fn adapt_to_task( &mut self, task: &MetaTask<T>, meta_parameters: &HashMap<String, Array1<T>>, ) -> Result<TaskAdaptationResult<T>>

Adapt to new task

pub fn few_shot_learning( &mut self, support_set: &TaskDataset<T>, query_set: &TaskDataset<T>, meta_parameters: &HashMap<String, Array1<T>>, ) -> Result<FewShotResult<T>>

Perform few-shot learning

pub fn transfer_to_domain( &mut self, source_tasks: &[MetaTask<T>], target_tasks: &[MetaTask<T>], meta_parameters: &HashMap<String, Array1<T>>, ) -> Result<TransferLearningResult<T>>

Transfer learning to new domain

pub fn continual_learning( &mut self, task_sequence: &[MetaTask<T>], meta_parameters: &mut HashMap<String, Array1<T>>, ) -> Result<ContinualLearningResult<T>>

Continual learning across task sequence

pub fn multi_task_learning( &mut self, tasks: &[MetaTask<T>], meta_parameters: &mut HashMap<String, Array1<T>>, ) -> Result<MultiTaskResult<T>>

Multi-task learning

pub fn get_meta_learning_statistics(&self) -> MetaLearningStatistics<T>

Get meta-learning statistics