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Threshold Network for 3-input XNOR Gate
XNOR3(a,b,c) = 1 when even number of inputs are 1 (0 or 2)
Built as: XOR(XNOR(a,b), c)
"""
import torch
from safetensors.torch import load_file
class ThresholdXNOR3:
def __init__(self, weights_dict):
self.w = weights_dict
def __call__(self, a, b, c):
# First XNOR: a XNOR b (1 when a=b)
inp1 = torch.tensor([float(a), float(b)])
n1 = int((inp1 * self.w['xnor1.layer1.n1.weight']).sum() + self.w['xnor1.layer1.n1.bias'] >= 0)
n2 = int((inp1 * self.w['xnor1.layer1.n2.weight']).sum() + self.w['xnor1.layer1.n2.bias'] >= 0)
h1 = torch.tensor([float(n1), float(n2)])
xnor_ab = int((h1 * self.w['xnor1.layer2.weight']).sum() + self.w['xnor1.layer2.bias'] >= 0)
# Second XOR: xnor_ab XOR c
inp2 = torch.tensor([float(xnor_ab), float(c)])
n3 = int((inp2 * self.w['xor2.layer1.n1.weight']).sum() + self.w['xor2.layer1.n1.bias'] >= 0)
n4 = int((inp2 * self.w['xor2.layer1.n2.weight']).sum() + self.w['xor2.layer1.n2.bias'] >= 0)
h2 = torch.tensor([float(n3), float(n4)])
out = int((h2 * self.w['xor2.layer2.weight']).sum() + self.w['xor2.layer2.bias'] >= 0)
return float(out)
@classmethod
def from_safetensors(cls, path="model.safetensors"):
return cls(load_file(path))
if __name__ == "__main__":
weights = load_file("model.safetensors")
model = ThresholdXNOR3(weights)
print("3-input XNOR Gate Truth Table:")
print("-" * 30)
correct = 0
for a in [0, 1]:
for b in [0, 1]:
for c in [0, 1]:
out = int(model(a, b, c))
# XNOR3 = even parity = NOT XOR3
expected = 1 - (a ^ b ^ c)
status = "OK" if out == expected else "FAIL"
if out == expected:
correct += 1
print(f"XNOR3({a}, {b}, {c}) = {out} [{status}]")
print(f"\nTotal: {correct}/8 correct")
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