| | """
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| | Threshold Network for 4-input XOR Gate (Magnitude 21)
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| |
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| | Cascade of three magnitude-7 optimal XORs.
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| | """
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| |
|
| | import torch
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| | from safetensors.torch import load_file
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| |
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| |
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| | def xor2(x1, x2, w, prefix):
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| | inp = torch.tensor([float(x1), float(x2)])
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| | n1 = int((inp * w[f'{prefix}.layer1.n1.weight']).sum() + w[f'{prefix}.layer1.n1.bias'] >= 0)
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| | n2 = int((inp * w[f'{prefix}.layer1.n2.weight']).sum() + w[f'{prefix}.layer1.n2.bias'] >= 0)
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| | h = torch.tensor([float(n1), float(n2)])
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| | return int((h * w[f'{prefix}.layer2.weight']).sum() + w[f'{prefix}.layer2.bias'] >= 0)
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| |
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| |
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| | class ThresholdXOR4:
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| | def __init__(self, weights_dict):
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| | self.w = weights_dict
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| |
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| | def __call__(self, a, b, c, d):
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| | xor_ab = xor2(a, b, self.w, 'xor1')
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| | xor_abc = xor2(xor_ab, c, self.w, 'xor2')
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| | xor_abcd = xor2(xor_abc, d, self.w, 'xor3')
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| | return float(xor_abcd)
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| |
|
| | @classmethod
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| | def from_safetensors(cls, path="model.safetensors"):
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| | return cls(load_file(path))
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| |
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| |
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| | if __name__ == "__main__":
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| | weights = load_file("model.safetensors")
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| | model = ThresholdXOR4(weights)
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| |
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| | print("4-input XOR Gate (mag21):")
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| | correct = 0
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| | for a in [0, 1]:
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| | for b in [0, 1]:
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| | for c in [0, 1]:
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| | for d in [0, 1]:
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| | out = int(model(a, b, c, d))
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| | expected = a ^ b ^ c ^ d
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| | if out == expected:
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| | correct += 1
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| | status = "OK" if out == expected else "FAIL"
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| | print(f" XOR4({a},{b},{c},{d}) = {out} [{status}]")
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| | print(f"Total: {correct}/16")
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| |
|
