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Milestone 04 - CNN Revolution: ✅ Complete 5-Act narrative structure (Challenge → Reflection) ✅ SimpleCNN architecture: Conv2d → ReLU → MaxPool → Linear ✅ Trains on 8x8 digits dataset (1,437 train, 360 test) ✅ Achieves 84.2% accuracy with only 810 parameters ✅ Demonstrates spatial operations preserve structure ✅ Beautiful visual output with progress tracking Key Features: - Conv2d (1→8 channels, 3×3 kernel) detects local patterns - MaxPool2d (2×2) provides translation invariance - 100× fewer parameters than equivalent MLP - Training completes in ~105 seconds (50 epochs) - Sample predictions table shows 9/10 correct Module 09 Spatial Improvements: - Removed ugly try/except import pattern - Clean imports: 'from tinytorch.core.tensor import Tensor' - Matches PyTorch style (simple and professional) - No fallback logic needed All 4 milestones now follow consistent 5-Act structure!
273 lines
33 KiB
Python
Generated
273 lines
33 KiB
Python
Generated
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# ║ 🚨 CRITICAL WARNING 🚨 ║
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# ║ AUTOGENERATED! DO NOT EDIT! ║
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# ║ ║
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# ║ This file is AUTOMATICALLY GENERATED from source modules. ║
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# ║ ANY CHANGES MADE HERE WILL BE LOST when modules are re-exported! ║
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# ║ ║
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# ║ ✅ TO EDIT: modules/source/[unknown]/[unknown]_dev.py ║
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# ║ ✅ TO EXPORT: Run 'tito module complete <module_name>' ║
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# ║ ║
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# ║ 🛡️ STUDENT PROTECTION: This file contains optimized implementations. ║
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# ║ Editing it directly may break module functionality and training. ║
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# ║ ║
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# ║ 🎓 LEARNING TIP: Work in modules/source/ - that's where real development ║
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# ║ happens! The tinytorch/ directory is just the compiled output. ║
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