| # Pruned Variant | |
| Minimal subnetwork extracted from the full model via neuron ablation analysis. | |
| ## Architecture | |
| | Layer | Full | Pruned | | |
| |-------|------|--------| | |
| | Hidden 1 | 32 | 11 | | |
| | Hidden 2 | 16 | 3 | | |
| | Parameters | 833 | 139 | | |
| ## Pruning Method | |
| Systematic single-neuron ablation identified which neurons are critical (their removal degrades accuracy) versus redundant (their removal has no effect). Only 11 of 32 Layer-1 neurons and 3 of 16 Layer-2 neurons were critical. The minimal subnetwork retains only these neurons. | |
| ## Verification | |
| Correctness is proven in Coq for all 256 inputs, identical to the full network. See `PrunedThresholdLogic.v` in the [GitHub repository](https://github.com/CharlesCNorton/threshold-logic-verified). | |
| ## Usage | |
| ```python | |
| from model import PrunedThresholdNetwork | |
| model = PrunedThresholdNetwork.from_safetensors('model.safetensors') | |
| ``` | |