| """Load model.safetensors and run the gate network (self-contained).""" | |
| import torch | |
| from safetensors.torch import load_file | |
| GATES = ("AND", "OR", "XOR", "NAND", "NOR", "XNOR", "NOT", "BUF", "CNOT", "SWAP") | |
| W = load_file("model.safetensors") | |
| def forward(gate: str, a: int, b: int) -> torch.Tensor: | |
| x = torch.zeros(12) | |
| x[GATES.index(gate)] = 1.0 | |
| x[-2], x[-1] = a, b | |
| for i in (0, 2, 4): | |
| x = torch.tanh(x @ W[f"net.{i}.weight"].T + W[f"net.{i}.bias"]) | |
| return torch.sigmoid(x @ W["net.6.weight"].T + W["net.6.bias"]) | |
| if __name__ == "__main__": | |
| for gate in GATES: | |
| p1, p2 = forward(gate, 1, 0).tolist() | |
| print(f"{gate:4} a=1 b=0 -> ({int(p1 >= 0.5)}, {int(p2 >= 0.5)})") | |