New Challenges in Version 2.0
The evolution to hybrid encoding and expanded architecture introduced new technical challenges that required innovative solutions.
Challenge
Implementing the hybrid encoding required fundamentally rethinking the input representation. The network needed to simultaneously process sorted positional information (4 neurons) and binary presence flags (26 neurons), creating a 30-neuron input layer. This expansion risked exceeding the calculator's 24KB RAM limit and required careful architectural calibration.
Solution
I developed a comprehensive encoding function that extracts unique letters, sorts them alphabetically, and generates both positional encodings (normalized 0-1 values for the first 4 letters) and binary presence flags (26 binary values indicating which letters A-Z are present). The implementation required reducing the hidden layer from 60 to 50 neurons to accommodate the expanded input while maintaining 1.5 KB of remaining RAM for program execution overhead.
Challenge
The transition from 4-60-12 to 30-50-12 architecture required precise memory calculations. With 30×50 = 1,500 weights in matrix [I], 50×12 = 600 weights in matrix [J], plus 62 bias values, the total parameter count significantly increased despite reducing hidden neurons. Every neuron added or removed had cascading effects on memory consumption and computational requirements.
Solution
Through systematic experimentation, I determined that 50 hidden neurons provided optimal performance while maintaining the necessary memory overhead. This configuration uses approximately 22.6 KB of the available 24 KB RAM, leaving 1.5 KB for program execution. The architecture represents the maximum complexity achievable within the calculator's constraints while supporting the hybrid encoding breakthrough.
Challenge
The hybrid encoding's increased dimensionality and expressiveness required re-evaluating the entire training strategy. Initial attempts with the v1.0 training parameters (learning rate 0.01, 500K epochs) achieved only 84% accuracy, significantly below the target. The network appeared to plateau, suggesting the learning rate and decay schedule needed adjustment for the new architecture.
Solution
Through extensive hyperparameter tuning, I discovered that a higher learning rate (0.02) with a decay factor of 0.3 enabled the network to better explore the expanded parameter space. This counter-intuitive finding—that more aggressive learning improved performance—broke through the 84% plateau and achieved 96.24% accuracy on comprehensive robustness testing. The training still required 500,000 epochs to fully converge.
Challenge
The hybrid encoding's claims of handling both scrambled words and adjacent-key typos required rigorous validation. A simple test set was insufficient—the network needed evaluation across multiple perturbation types including full scrambles, single-letter substitutions, dropped letters, and multi-intensity adjacent-key typos. Designing and implementing a comprehensive test suite that validated all these capabilities proved complex.
Solution
I developed a comprehensive test suite comprising 186 test cases spanning five categories: 12 original words (100%), 36 scrambled variants (100%), 36 single-letter adjacent substitutions (95%+), 24 dropped-letter cases (98%+), and 78 multi-intensity adjacent-key typos. This systematic evaluation validated the hybrid encoding's effectiveness across all perturbation types and identified edge cases where the network struggled, informing potential future improvements.
Challenge
Understanding why the hybrid encoding worked so effectively required visualization tools that could reveal the internal representations. The 30-50-12 architecture's complexity made manual inspection impractical. I needed automated tools to generate encoding heatmaps, prediction confidence charts, weight distribution analyses, and architectural diagrams.
Solution
I developed a comprehensive visualization suite using matplotlib and numpy that generates encoding heatmaps showing both sorted positional and binary presence layers, prediction comparison charts displaying confidence levels across perturbation types, weight distribution histograms, and architectural flow diagrams. These visualizations provided crucial insights into how the network processes hybrid encodings and guided refinements to the architecture and training strategy.
Encoding innovation beats brute-force capacity expansion: The 30-50-12 architecture with hybrid encoding outperformed hypothetical larger networks with naive encoding, demonstrating that better representation enables smaller, more efficient models.
Architectural constraints drive innovation: The severe memory limitations forced creative solutions like hybrid encoding that might not have been discovered in unconstrained environments.
Counter-intuitive hyperparameters: Higher learning rates (0.02 vs 0.01) proved more effective for the hybrid architecture, challenging conventional wisdom about training stability.
Comprehensive testing is essential: The 186-test validation suite revealed edge cases and limitations that simpler testing would have missed, providing crucial insights for understanding network capabilities and constraints.