| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def canalizing(x2, x1, x0, weights): | |
| """Canalizing function: if x0=1, output=0 regardless of x1,x2.""" | |
| inp = torch.tensor([float(x2), float(x1), float(x0)]) | |
| not_x0 = int((inp @ weights['not_x0.weight'].T + weights['not_x0.bias'] >= 0).item()) | |
| or_x1_x2 = int((inp @ weights['or_x1_x2.weight'].T + weights['or_x1_x2.bias'] >= 0).item()) | |
| l1 = torch.tensor([float(not_x0), float(or_x1_x2)]) | |
| y = int((l1 @ weights['y.weight'].T + weights['y.bias'] >= 0).item()) | |
| return y | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('Canalizing Function (x0 canalizes to 0):') | |
| for i in range(8): | |
| x2, x1, x0 = (i >> 2) & 1, (i >> 1) & 1, i & 1 | |
| y = canalizing(x2, x1, x0, w) | |
| print(f' {x2}{x1}{x0} -> {y}') | |