| | """
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| | Threshold Network for Half Adder
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| |
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| | Adds two 1-bit inputs, producing sum (XOR) and carry (AND) outputs.
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| | Sum uses 2-layer XOR, Carry uses single AND neuron.
|
| | """
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| |
|
| | import torch
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| | from safetensors.torch import load_file
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| |
|
| |
|
| | def heaviside(x):
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| | return (x >= 0).float()
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| |
|
| |
|
| | class ThresholdHalfAdder:
|
| | """
|
| | Half adder: sum = a XOR b, carry = a AND b
|
| | """
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| |
|
| | def __init__(self, weights_dict):
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| | self.weights = weights_dict
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| |
|
| | def __call__(self, a, b):
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| | inputs = torch.tensor([float(a), float(b)])
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| |
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| |
|
| | or_out = heaviside((inputs * self.weights['sum.layer1.or.weight']).sum() +
|
| | self.weights['sum.layer1.or.bias'])
|
| | nand_out = heaviside((inputs * self.weights['sum.layer1.nand.weight']).sum() +
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| | self.weights['sum.layer1.nand.bias'])
|
| | layer1 = torch.tensor([or_out, nand_out])
|
| | sum_out = heaviside((layer1 * self.weights['sum.layer2.weight']).sum() +
|
| | self.weights['sum.layer2.bias'])
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| |
|
| |
|
| | carry_out = heaviside((inputs * self.weights['carry.weight']).sum() +
|
| | self.weights['carry.bias'])
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| |
|
| | return int(sum_out.item()), int(carry_out.item())
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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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| |
|
| | if __name__ == "__main__":
|
| | model = ThresholdHalfAdder.from_safetensors("model.safetensors")
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| |
|
| | print("Half Adder Truth Table:")
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| | print("-" * 30)
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| | print("a | b | sum | carry")
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| | print("-" * 30)
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| | for a in [0, 1]:
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| | for b in [0, 1]:
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| | s, c = model(a, b)
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| | expected_s = a ^ b
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| | expected_c = a & b
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| | status = "OK" if (s == expected_s and c == expected_c) else "FAIL"
|
| | print(f"{a} | {b} | {s} | {c} [{status}]")
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| |
|
