SimpleMLPModel

Simple Multi-Layer Perceptron (MLP) model implementation for KotlinDL integration.

This object provides a standardized interface for creating and configuring neural network models within the KARL Deep Learning (KLDL) framework. The implementation serves as a foundation for building feedforward neural networks with customizable architecture.

Current Implementation Status:

• Stub Implementation: Placeholder until KotlinDL dependencies are fully integrated

• Production Ready: Interface designed for seamless transition to KotlinDL backend

• Backward Compatible: API stable across implementation changes

Neural Network Architecture:

Input Layer → Hidden Layer 1 → Hidden Layer 2 → ... → Output Layer
     ↓              ↓              ↓                      ↓
[inputSize]    [hiddenSizes[0]]  [hiddenSizes[1]]    [numClasses]
     ↓              ↓              ↓                      ↓
  Features      Dense+ReLU     Dense+ReLU           Dense+Softmax

Default Configuration:

• Input Layer: 3 neurons for feature vector processing

• Hidden Layers: 2 layers with 10 neurons each using ReLU activation

• Output Layer: 4 neurons with softmax activation for classification

• Architecture: 3 → 10 → 10 → 4 (fully connected feedforward)

Planned KotlinDL Integration:

• Framework: TensorFlow backend via KotlinDL

• Optimization: Adam optimizer with configurable learning rate

• Regularization: L2 regularization and dropout support

• Training: Batch training with validation monitoring

Use Cases:

• Classification tasks with categorical output

• Feature learning from structured input data

• Rapid prototyping of neural network architectures

• Educational demonstrations of MLP principles

Design Patterns:

• Singleton Pattern: Object-based implementation for stateless operations

• Factory Pattern: createModel() method for model instantiation

• Configuration Pattern: Default parameters with override capability

• Stub Pattern: Current implementation provides interface without full logic

Future Enhancements:

• Dynamic layer configuration with activation function selection

• Model serialization and deserialization support

• Performance optimization with GPU acceleration

• Integration with KARL learning engine for adaptive behavior

Thread Safety: Object is stateless and inherently thread-safe Performance: O(1) configuration time, actual training complexity depends on KotlinDL backend

See also