2 Commits

Author	SHA1	Message	Date
Vijay Janapa Reddi	e82bc8ba97	Complete comprehensive system validation and cleanup ... 🎯 Major Accomplishments: • ✅ All 15 module dev files validated and unit tests passing • ✅ Comprehensive integration tests (11/11 pass) • ✅ All 3 examples working with PyTorch-like API (XOR, MNIST, CIFAR-10) • ✅ Training capability verified (4/4 tests pass, XOR shows 35.8% improvement) • ✅ Clean directory structure (modules/source/ → modules/) 🧹 Repository Cleanup: • Removed experimental/debug files and old logos • Deleted redundant documentation (API_SIMPLIFICATION_COMPLETE.md, etc.) • Removed empty module directories and backup files • Streamlined examples (kept modern API versions only) • Cleaned up old TinyGPT implementation (moved to examples concept) 📊 Validation Results: • Module unit tests: 15/15 ✅ • Integration tests: 11/11 ✅ • Example validation: 3/3 ✅ • Training validation: 4/4 ✅ 🔧 Key Fixes: • Fixed activations module requires_grad test • Fixed networks module layer name test (Dense → Linear) • Fixed spatial module Conv2D weights attribute issues • Updated all documentation to reflect new structure 📁 Structure Improvements: • Simplified modules/source/ → modules/ (removed unnecessary nesting) • Added comprehensive validation test suites • Created VALIDATION_COMPLETE.md and WORKING_MODULES.md documentation • Updated book structure to reflect ML evolution story 🚀 System Status: READY FOR PRODUCTION All components validated, examples working, training capability verified. Test-first approach successfully implemented and proven.	2025-09-23 10:00:33 -04:00
Vijay Janapa Reddi	5c44b5f260	Organize package with nn and optim modules ... Stage 5 of TinyTorch API simplification: - Created tinytorch.nn package with PyTorch-compatible interface - Added Module base class in nn.modules for automatic parameter registration - Added functional module with relu, flatten, max_pool2d operations - Created tinytorch.optim package exposing Adam and SGD optimizers - Updated main __init__.py to export nn and optim modules - Linear and Conv2d now available through clean nn interface Students can now write PyTorch-like code: import tinytorch.nn as nn import tinytorch.nn.functional as F model = nn.Linear(784, 10) x = F.relu(model(x))	2025-09-23 08:10:47 -04:00