| import torch | |
| from safetensors.torch import load_file | |
| def load_model(path='model.safetensors'): | |
| return load_file(path) | |
| def xor2(a, b, prefix, w): | |
| or_out = int(a * w[f'{prefix}.or.weight'][0] + b * w[f'{prefix}.or.weight'][1] + w[f'{prefix}.or.bias'] >= 0) | |
| nand_out = int(a * w[f'{prefix}.nand.weight'][0] + b * w[f'{prefix}.nand.weight'][1] + w[f'{prefix}.nand.bias'] >= 0) | |
| return int(or_out * w[f'{prefix}.and.weight'][0] + nand_out * w[f'{prefix}.and.weight'][1] + w[f'{prefix}.and.bias'] >= 0) | |
| def parity4(a, b, c, d, weights): | |
| xor_ab = xor2(a, b, 'xor_ab', weights) | |
| xor_cd = xor2(c, d, 'xor_cd', weights) | |
| return xor2(xor_ab, xor_cd, 'xor_final', weights) | |
| if __name__ == '__main__': | |
| w = load_model() | |
| print('parity4 truth table:') | |
| for i in range(16): | |
| a, b, c, d = (i >> 3) & 1, (i >> 2) & 1, (i >> 1) & 1, i & 1 | |
| print(f' parity({a},{b},{c},{d}) = {parity4(a, b, c, d, w)}') | |