Stop Guessing Which Weights Your Neural Network Actually Learned: Deterministic Initialization That Tracks Every Change

The Problem Nobody Talks About You've spent hours training your neural network. The loss converged, metrics look good, and you're ready to deploy. But here's a question you probably can't answer: Which weights actually learned during training? With standard initialization methods (PyTorch's kaiming_normal_, TensorFlow's he_normal), the answer is: you have no idea. Once those random values are generated, they're gone forever. You can't tell which weights changed by 0.001 and which changed by...