initialize
Initializes the machine learning engine with optional state recovery capabilities.
This method performs a comprehensive initialization sequence that prepares the neural network for training and inference. It handles both fresh initialization scenarios and sophisticated state recovery from previous sessions, ensuring seamless continuity of the learning experience across application restarts.
Initialization Phases:
Thread Safety Verification: Ensures atomic initialization through compare-and-set operations
Scope Assignment: Establishes the coroutine context for all asynchronous ML operations
State Recovery: Attempts to restore previous learning state if available
Model Preparation: Initializes neural network architecture and parameters
Validation: Verifies the engine is ready for training and inference operations
State Recovery Logic: When a saved state is provided, the engine attempts to restore:
Neural network weights and biases
Training history and interaction patterns
Learning rate schedules and optimization state
Model architecture parameters and configurations
Error Handling Strategy: If state restoration fails due to corruption, version incompatibility, or structural changes, the engine gracefully falls back to fresh initialization while logging detailed error information for debugging purposes.
Thread Safety Guarantees: The initialization process is fully atomic and thread-safe. Multiple concurrent calls will be handled safely, with subsequent calls being ignored if initialization has already completed successfully.
Parameters
Previously saved learning state for continuity across sessions. When null, initializes with a fresh, untrained model.
Scope for managing all asynchronous ML operations. Must remain active throughout the engine's lifecycle.
See also
For the counterpart state persistence operation
For detailed state recovery implementation
Throws
If initialization fails due to resource constraints
If saved state is corrupted and cannot be restored