| | """
|
| | Threshold Network for 4-input OR Gate
|
| | """
|
| |
|
| | import torch
|
| | from safetensors.torch import load_file
|
| |
|
| |
|
| | class ThresholdOR4:
|
| | """
|
| | 4-input OR gate implemented as a threshold neuron.
|
| |
|
| | Circuit: output = (w1*x1 + w2*x2 + w3*x3 + w4*x4 + bias >= 0)
|
| | With weights=[1,1,1,1], bias=-1: any single input reaches threshold.
|
| | """
|
| |
|
| | def __init__(self, weights_dict):
|
| | self.weight = weights_dict['weight']
|
| | self.bias = weights_dict['bias']
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| |
|
| | def __call__(self, x1, x2, x3, x4):
|
| | inputs = torch.tensor([float(x1), float(x2), float(x3), float(x4)])
|
| | weighted_sum = (inputs * self.weight).sum() + self.bias
|
| | return (weighted_sum >= 0).float()
|
| |
|
| | @classmethod
|
| | def from_safetensors(cls, path="model.safetensors"):
|
| | return cls(load_file(path))
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| |
|
| |
|
| | def forward(x, weights):
|
| | """
|
| | Forward pass with Heaviside activation.
|
| |
|
| | Args:
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| | x: Input tensor of shape [..., 4]
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| | weights: Dict with 'weight' and 'bias' tensors
|
| |
|
| | Returns:
|
| | OR(x[0], x[1], x[2], x[3])
|
| | """
|
| | x = torch.as_tensor(x, dtype=torch.float32)
|
| | weighted_sum = (x * weights['weight']).sum(dim=-1) + weights['bias']
|
| | return (weighted_sum >= 0).float()
|
| |
|
| |
|
| | if __name__ == "__main__":
|
| | weights = load_file("model.safetensors")
|
| | model = ThresholdOR4(weights)
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| |
|
| | print("4-input OR Gate Truth Table:")
|
| | print("-" * 35)
|
| | correct = 0
|
| | for x1 in [0, 1]:
|
| | for x2 in [0, 1]:
|
| | for x3 in [0, 1]:
|
| | for x4 in [0, 1]:
|
| | out = int(model(x1, x2, x3, x4).item())
|
| | expected = x1 | x2 | x3 | x4
|
| | status = "OK" if out == expected else "FAIL"
|
| | if out == expected:
|
| | correct += 1
|
| | print(f"OR4({x1}, {x2}, {x3}, {x4}) = {out} [{status}]")
|
| | print(f"\nTotal: {correct}/16 correct")
|
| |
|
