Struct MultiTaskNetwork 

pub struct MultiTaskNetwork<T: FloatBounds> { /* private fields */ }

Multi-task neural network model

Implementations

impl<T: FloatBounds + ScalarOperand + Sum> MultiTaskNetwork<T>

pub fn new(config: MultiTaskConfig<T>) -> Self

Create a new multi-task network

pub fn build_architecture(&mut self) -> NeuralResult<()>

Build the network architecture based on configuration

pub fn forward_task( &mut self, input: &Array2<T>, task_name: &str, training: bool, ) -> NeuralResult<Array2<T>>

Forward pass for a specific task

pub fn forward_all_tasks( &mut self, input: &Array2<T>, training: bool, ) -> NeuralResult<HashMap<String, Array2<T>>>

Forward pass for all tasks

pub fn compute_multi_task_loss( &self, predictions: &HashMap<String, Array2<T>>, targets: &HashMap<String, Array2<T>>, ) -> NeuralResult<T>

Compute multi-task loss

pub fn update_task_weights( &mut self, task_losses: &[T], _epoch: usize, ) -> NeuralResult<()>

Update task weights based on strategy

pub fn config(&self) -> &MultiTaskConfig<T>

Get configuration

pub fn task_names(&self) -> &[String]

Get task names

pub fn num_tasks(&self) -> usize

Get number of tasks

pub fn is_fitted(&self) -> bool

Check if model is fitted

pub fn set_fitted(&mut self, fitted: bool)

Set fitted state

pub fn task_weights(&self) -> &[T]

Get current task weights

Trait Implementations

impl<T: FloatBounds> Debug for MultiTaskNetwork<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.