| """
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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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|
|
|
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| def heaviside(x):
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| return (x >= 0).float()
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|
|
|
|
| class ThresholdHalfAdder:
|
| """
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| Half adder: sum = a XOR b, carry = a AND b
|
| """
|
|
|
| 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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|
|
|
|
| or_out = heaviside((inputs * self.weights['sum.layer1.or.weight']).sum() +
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| self.weights['sum.layer1.or.bias'])
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| nand_out = heaviside((inputs * self.weights['sum.layer1.nand.weight']).sum() +
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| self.weights['sum.layer1.nand.bias'])
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| layer1 = torch.tensor([or_out, nand_out])
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| sum_out = heaviside((layer1 * self.weights['sum.layer2.weight']).sum() +
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| self.weights['sum.layer2.bias'])
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|
|
|
|
| carry_out = heaviside((inputs * self.weights['carry.weight']).sum() +
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| 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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|
|
|
|
| if __name__ == "__main__":
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| 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"
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| print(f"{a} | {b} | {s} | {c} [{status}]")
|
|
|
