LearningModel
A generic interface representing a machine learning model within a LearningEngine.
This abstraction allows a LearningEngine implementation (like :karl-kldl) to be designed to work with different model architectures (e.g., MLP, RNN, Transformer) that conform to this contract. It defines the essential operations of a model: prediction, training, and state management.
Functions
Initializes the model's internal structure and parameters. This is where the actual model graph (e.g., a KotlinDL Sequential model) would be built.
Loads the model's learned parameters (e.g., weights) from a serialized state.
Performs inference on a given input feature vector.
Retrieves the model's current learned parameters in a serializable format.
Performs a single training step on the model.