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"""
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Pruned Threshold Network for Parity Computation
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A minimal ternary threshold network (8->11->3->1) that computes 8-bit parity.
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Pruned from the original 8->32->16->1 architecture with 83.3% parameter reduction.
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"""
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import torch
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import torch.nn as nn
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class PrunedThresholdNetwork(nn.Module):
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"""
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Pruned binary threshold network with ternary weights.
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Architecture: 8 -> 11 -> 3 -> 1
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Weights: {-1, 0, +1}
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Activation: Heaviside (x >= 0 -> 1, else 0)
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"""
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def __init__(self, n_bits=8, hidden1=11, hidden2=3):
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super().__init__()
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self.n_bits = n_bits
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self.hidden1 = hidden1
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self.hidden2 = hidden2
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self.layer1_weight = nn.Parameter(torch.zeros(hidden1, n_bits))
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self.layer1_bias = nn.Parameter(torch.zeros(hidden1))
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self.layer2_weight = nn.Parameter(torch.zeros(hidden2, hidden1))
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self.layer2_bias = nn.Parameter(torch.zeros(hidden2))
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self.output_weight = nn.Parameter(torch.zeros(1, hidden2))
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self.output_bias = nn.Parameter(torch.zeros(1))
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def forward(self, x):
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"""Forward pass with Heaviside activation."""
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x = x.float()
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x = (torch.nn.functional.linear(x, self.layer1_weight, self.layer1_bias) >= 0).float()
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x = (torch.nn.functional.linear(x, self.layer2_weight, self.layer2_bias) >= 0).float()
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x = (torch.nn.functional.linear(x, self.output_weight, self.output_bias) >= 0).float()
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return x.squeeze(-1)
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@classmethod
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def from_safetensors(cls, path):
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"""Load model from SafeTensors file."""
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from safetensors.torch import load_file
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weights = load_file(path)
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hidden1 = weights['layer1.weight'].shape[0]
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hidden2 = weights['layer2.weight'].shape[0]
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n_bits = weights['layer1.weight'].shape[1]
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model = cls(n_bits=n_bits, hidden1=hidden1, hidden2=hidden2)
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model.layer1_weight.data = weights['layer1.weight']
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model.layer1_bias.data = weights['layer1.bias']
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model.layer2_weight.data = weights['layer2.weight']
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model.layer2_bias.data = weights['layer2.bias']
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model.output_weight.data = weights['output.weight']
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model.output_bias.data = weights['output.bias']
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return model
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def parity(x):
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"""Ground truth parity function."""
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return (x.sum(dim=-1) % 2).float()
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if __name__ == '__main__':
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model = PrunedThresholdNetwork.from_safetensors('model.safetensors')
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all_inputs = torch.tensor([[int(b) for b in format(i, '08b')] for i in range(256)], dtype=torch.float32)
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outputs = model(all_inputs)
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expected = parity(all_inputs)
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correct = (outputs == expected).sum().item()
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print(f'Accuracy: {correct}/256 ({100*correct/256:.1f}%)')
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