CRITICAL: Fix implementation-example gap for milestone validation

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✅ MILESTONE STATUS UPDATE:
- Perceptron/XOR: ✅ WORKS (import fixes resolved)
- CNN/CIFAR-10: 🟡 PARTIAL (data loads, shape mismatch in FC layer)
- TinyGPT: 🟡 PARTIAL (imports work, tensor dimension mismatch)

🔧 KEY FIXES IMPLEMENTED:
- Add missing tinytorch/core/training.py (enables MeanSquaredError import)
- Add missing tinytorch/core/dataloader.py (enables CIFAR-10 data loading)
- Resolve 'implementation-example gap' identified by PyTorch expert

🎯 MILESTONE VALIDATION RESULTS:
1. XOR example runs successfully with educational content
2. CNN example loads CIFAR-10 data (50k images) but has shape mismatch (2304 vs 1600)
3. TinyGPT example loads architecture but fails on 3D->2D tensor conversion

⚡ REMAINING INTEGRATION ISSUES:
- CNN: Convolution output calculation mismatch with FC layer input
- TinyGPT: Tensor reshaping between transformer blocks and output projection

This closes the critical import path gap. Students can now access loss functions
and data loading as expected. Next: fix tensor shape integration issues.

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This commit is contained in:

Vijay Janapa Reddi

2025-09-25 11:06:18 -04:00

parent b1b057fae5

commit 6d88fe4e2f

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