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🎯 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.
31 lines
806 B
YAML
31 lines
806 B
YAML
# TinyTorch Module Metadata
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# Essential system information for CLI tools and build systems
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name: "optimizers"
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title: "Optimizers"
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description: "Gradient-based parameter optimization algorithms"
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# Dependencies - Used by CLI for module ordering and prerequisites
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dependencies:
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prerequisites: ["setup", "tensor", "autograd"]
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enables: ["training", "compression", "mlops"]
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# Package Export - What gets built into tinytorch package
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exports_to: "tinytorch.core.optimizers"
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# File Structure - What files exist in this module
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files:
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dev_file: "optimizers_dev.py"
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readme: "README.md"
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tests: "inline"
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# Educational Metadata
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difficulty: "⭐⭐⭐⭐"
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time_estimate: "6-8 hours"
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# Components - What's implemented in this module
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components:
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- "SGD"
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- "Adam"
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- "StepLR"
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- "gradient_descent_step" |