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