Struct MAMLModel 

pub struct MAMLModel { /* private fields */ }

Concrete implementations of meta-learning models would go here For brevity, I’ll include basic stubs

Implementations

impl MAMLModel

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

Trait Implementations

impl MetaLearningModel for MAMLModel

fn forward(&mut self, _examples: &ExampleSet) -> Result<f64, TrustformersError>

fn compute_accuracy( &self, _examples: &ExampleSet, ) -> Result<f64, TrustformersError>

fn compute_gradients( &self, _loss: f64, ) -> Result<ModelGradients, TrustformersError>

fn apply_gradients( &mut self, _gradients: &ModelGradients, _lr: f64, ) -> Result<(), TrustformersError>

fn get_parameters(&self) -> Result<ModelParameters, TrustformersError>

fn set_parameters( &mut self, _params: ModelParameters, ) -> Result<(), TrustformersError>

fn embed(&self, example: &Example) -> Result<Tensor, TrustformersError>

fn compute_second_order_gradients( &self, _initial_params: &ModelParameters, _loss: f64, ) -> Result<ModelGradients, TrustformersError>

fn compute_first_order_gradients( &self, _loss: f64, ) -> Result<ModelGradients, TrustformersError>

fn compute_relation( &self, _emb1: &Tensor, _emb2: &Tensor, ) -> Result<f64, TrustformersError>

fn write_to_memory( &mut self, _example: &Example, ) -> Result<(), TrustformersError>

fn read_from_memory( &self, _example: &Example, ) -> Result<MemoryOutput, TrustformersError>

fn predict_from_memory( &self, _memory_output: &MemoryOutput, ) -> Result<MemoryPrediction, TrustformersError>

fn clear_memory(&mut self) -> Result<(), TrustformersError>

fn get_learning_rates(&self) -> Result<Vec<f64>, TrustformersError>

fn apply_gradients_with_lr( &mut self, _gradients: &ModelGradients, _learning_rates: &[f64], ) -> Result<(), TrustformersError>

fn compute_lr_gradients( &self, _loss: f64, ) -> Result<Vec<f64>, TrustformersError>

fn get_meta_learner_state(&self) -> Result<MetaLearnerState, TrustformersError>

fn apply_learned_algorithm( &self, _support_set: &ExampleSet, _state: &MetaLearnerState, ) -> Result<ModelParameters, TrustformersError>

fn evaluate_with_params( &self, _examples: &ExampleSet, _params: &ModelParameters, ) -> Result<f64, TrustformersError>

fn compute_accuracy_with_params( &self, _examples: &ExampleSet, _params: &ModelParameters, ) -> Result<f64, TrustformersError>

fn compute_meta_learner_gradients( &self, _loss: f64, ) -> Result<ModelGradients, TrustformersError>

fn get_lstm_state(&self) -> Result<LSTMState, TrustformersError>

fn lstm_update( &self, _gradients: &ModelGradients, _state: &LSTMState, _step: usize, ) -> Result<(ModelUpdates, LSTMState), TrustformersError>

fn apply_lstm_updates( &mut self, _updates: &ModelUpdates, ) -> Result<(), TrustformersError>

fn compute_lstm_gradients( &self, _loss: f64, ) -> Result<ModelGradients, TrustformersError>