Commit Graph

4 Commits

Author SHA1 Message Date
Vijay Janapa Reddi
2ab842bd35 📚 Consolidate module documentation into single source
- Replaced 3 overlapping documentation files with 1 authoritative source
- Set modules/source/08_optimizers/optimizers_dev.py as reference implementation
- Created comprehensive module-rules.md with complete patterns and examples
- Added living-example approach: use actual working code as template
- Removed redundant files: module-structure-design.md, module-quick-reference.md, testing-design.md
- Updated cursor rules to point to consolidated documentation
- All module development now follows single source of truth
2025-07-13 19:35:16 -04:00
Vijay Janapa Reddi
2282af2d10 🔬 Standardize Unit Test terminology across all modules
 Updated modules to use consistent testing format:
- 08_optimizers: 'Testing X...' → '🔬 Unit Test: X...'
- 07_autograd: 'Testing X...' → '🔬 Unit Test: X...'
- 02_activations: 'Testing X...' → '🔬 Unit Test: X...'
- 03_layers: 'Testing X...' → '🔬 Unit Test: X...'

🎯 Now all modules follow tensor_dev.py format:
-  Consistent '🔬 Unit Test: [Component]...' format
-  Maintains visual consistency across all modules
-  Clear identification of unit test sections
-  Professional and educational presentation

📊 Status: All 9 modules (00-08) now use unified testing terminology
2025-07-13 17:30:36 -04:00
Vijay Janapa Reddi
9f38cc435c Complete 08_optimizers module implementation
🔥 Core Features Implemented:
- Gradient descent step function with proper parameter updates
- SGD optimizer with momentum and weight decay
- Adam optimizer with adaptive learning rates and bias correction
- StepLR learning rate scheduler with step-based decay
- Complete training integration with real convergence examples

🧪 Testing & Validation:
- All unit tests passing for each optimizer component
- Learning rate scheduler timing fixed and working correctly
- Training integration demonstrates SGD vs Adam convergence
- Comprehensive test suite covering all functionality

�� Educational Structure:
- Follows TinyTorch NBDev patterns with solution markers
- Step-by-step implementation guidance with TODO blocks
- Mathematical foundations with intuitive explanations
- Real-world training examples showing optimizer behavior
- Complete documentation and README

 Results:
- SGD achieves perfect convergence: w=2.000, b=1.000
- Adam achieves good convergence: w=1.598, b=1.677
- All tests pass, module ready for student use
- Sets foundation for future 09_training module
2025-07-13 17:23:07 -04:00
Vijay Janapa Reddi
e5f1daf740 Remove module-level tests directories, keep only main tests/ for exported package validation
- Remove all tests/ directories under modules/source/
- Keep main tests/ directory for testing exported functionality
- Update status command to check tests in main tests/ directory
- Update documentation to reflect new test structure
- Reduce maintenance burden by eliminating duplicate test systems
- Focus on inline NBGrader tests for development, main tests for package validation
2025-07-13 17:14:14 -04:00