2025 / UC Berkeley (ME249)
First-Principles vs Keras Neural Networks
Benchmarking custom optimization against RMSprop
Overview
Compared a hand-coded Newton-style solver against a Keras implementation on the same physical system dataset to understand convergence behavior and error profiles.
Problem
How do custom first-principles optimization routines compare to modern deep learning optimizers for the same regression task?
Role
Course project (ME249)
Timeline
Oct 2025
Tools
Python / NumPy / TensorFlow/Keras
Tags
ML / Regression / Optimization / Scientific ML
Data
- - Physical system dataset provided in course notebooks
- - Identical initialization and activation functions across pipelines
Approach
- - Implemented a Newton-style update loop with manual step scheduling
- - Trained a Keras model with RMSprop using matched architecture
- - Compared parameter drift and prediction error across runs
Evaluation
- - Normalized MAE reported ~0.012 for RMSprop vs higher baseline
- - Prediction error stayed within ~1.1°C across test cases
Results
- - Keras converged faster with lower sensitivity to initialization
- - First-principles solver offered transparent gradient auditing
Deployment & Monitoring
- - Reusable scripts for reproducible training runs
- - Logs document sensitivity to starting weights
Limitations
- - Small dataset with controlled test cases
- - Single architecture comparison
Gallery
Keras predictions vs data
First-principles predictions vs data
Repro Steps
- - Run keras_training.py or first_principles.py in project2
- - Outputs saved to project2/out
Next Steps
- - Expand to larger datasets and additional optimizers
- - Add regularization to stabilize first-principles updates