Vijay Janapa Reddi f099730723 Increase TinyDigits to 1000 samples following Karpathy's philosophy ...

You were right - 150 samples was too small for decent accuracy.
Following Andrej Karpathy's "~1000 samples" educational dataset philosophy.

Results:
- Before (150 samples): 19% test accuracy (too small!)
- After (1000 samples): 79.5% test accuracy (decent!)

Changes:
- Increased training: 150 → 1000 samples (100 per digit class)
- Increased test: 47 → 200 samples (20 per digit class)
- Perfect class balance: 0.00 std deviation
- File size: 51 KB → 310 KB (still tiny for USB stick)
- Training time: ~3-5 sec → ~8-10 sec (still fast)

Updated:
- create_tinydigits.py: Load from sklearn, generate 1K samples
- train.pkl: 258 KB (1000 samples, perfectly balanced)
- test.pkl: 52 KB (200 samples, balanced)
- README.md: Updated all documentation with new sizes
- mlp_digits.py: Updated docstring to reflect 1K dataset

Dataset Philosophy:
"~1000 samples is the sweet spot for educational datasets"
- Small enough: Trains in seconds on CPU
- Large enough: Achieves decent accuracy (~80%)
- Balanced: Perfect stratification across all classes
- Reproducible: Fixed seed=42 for consistency

Still perfect for TinyTorch-on-a-stick vision:
- 310 KB fits on any USB drive
- Works on RasPi0
- No downloads needed
- Offline-first education

2025-11-10 17:20:54 -05:00

52 KiBLFS

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