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 5.0. You can't identify the "dead" neurons that never activated. And you certainly can't safely prune your model without risking quality loss. I built a solution that fixes this — and it revealed something surprising. The "Aha!" Moment After implementing deterministic weight initialization with full addressability, I ran a simple experiment: # Initialize a 6,100-parameter network gen = DeterministicNoiseGenerator ( seed = 42 ) for layer_id , layer in enumerate ( network ): layer . weights = gen .…