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README.md

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+---
+license: mit
+tags:
+- pytorch
+- safetensors
+- threshold-logic
+- neuromorphic
+---
+
+# threshold-xnor4
+
+4-input XNOR gate. Outputs 1 when an even number of inputs are 1 (0, 2, or 4).
+
+## Architecture
+
+Tree structure: XNOR4(a,b,c,d) = XNOR(XNOR(a,b), XNOR(c,d))
+
+```
+a,b ──► XNOR ──┐
+               ├──► XNOR ──► output
+c,d ──► XNOR ──┘
+```
+
+## Parameters
+
+| | |
+|---|---|
+| Neurons | 9 |
+| Layers | 4 |
+| Parameters | 27 |
+| Magnitude | 27 |
+
+## Usage
+
+```python
+from safetensors.torch import load_file
+
+w = load_file('model.safetensors')
+# See model.py for forward pass
+```
+
+## License
+
+MIT

config.json

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+{
+  "name": "threshold-xnor4",
+  "description": "4-input XNOR gate as threshold circuit",
+  "inputs": 4,
+  "outputs": 1,
+  "neurons": 9,
+  "layers": 4,
+  "parameters": 27
+}

create_safetensors.py

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+import torch
+from safetensors.torch import save_file
+
+# XNOR4 = XNOR(XNOR(a,b), XNOR(c,d))
+# Tree structure using 3 XNOR gates
+# Each XNOR: NOR + AND -> OR
+
+def make_xnor_weights(prefix):
+    return {
+        f'{prefix}.layer1.n1.weight': torch.tensor([-1.0, -1.0]),  # NOR
+        f'{prefix}.layer1.n1.bias': torch.tensor([0.0]),
+        f'{prefix}.layer1.n2.weight': torch.tensor([1.0, 1.0]),    # AND
+        f'{prefix}.layer1.n2.bias': torch.tensor([-2.0]),
+        f'{prefix}.layer2.weight': torch.tensor([1.0, 1.0]),       # OR
+        f'{prefix}.layer2.bias': torch.tensor([-1.0]),
+    }
+
+weights = {}
+weights.update(make_xnor_weights('xnor1'))  # XNOR(a,b)
+weights.update(make_xnor_weights('xnor2'))  # XNOR(c,d)
+weights.update(make_xnor_weights('xnor3'))  # XNOR(xnor_ab, xnor_cd)
+
+save_file(weights, 'model.safetensors')
+
+mag = sum(t.abs().sum().item() for t in weights.values())
+print(f'Created model.safetensors')
+print(f'  magnitude: {mag:.0f}')
+print(f'  parameters: {sum(t.numel() for t in weights.values())}')

model.py

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+"""
+Threshold Network for 4-input XNOR Gate
+
+XNOR4(a,b,c,d) = 1 when even number of inputs are 1 (0, 2, or 4)
+Built as: XNOR(XNOR(a,b), XNOR(c,d))
+"""
+
+import torch
+from safetensors.torch import load_file
+
+
+def xnor2(x, y, w, prefix):
+    """2-input XNOR using NOR + AND -> OR structure."""
+    inp = torch.tensor([float(x), float(y)])
+    n1 = int((inp * w[f'{prefix}.layer1.n1.weight']).sum() + w[f'{prefix}.layer1.n1.bias'] >= 0)
+    n2 = int((inp * w[f'{prefix}.layer1.n2.weight']).sum() + w[f'{prefix}.layer1.n2.bias'] >= 0)
+    h = torch.tensor([float(n1), float(n2)])
+    return int((h * w[f'{prefix}.layer2.weight']).sum() + w[f'{prefix}.layer2.bias'] >= 0)
+
+
+class ThresholdXNOR4:
+    def __init__(self, weights_dict):
+        self.w = weights_dict
+
+    def __call__(self, a, b, c, d):
+        # Tree structure: XNOR(XNOR(a,b), XNOR(c,d))
+        xnor_ab = xnor2(a, b, self.w, 'xnor1')
+        xnor_cd = xnor2(c, d, self.w, 'xnor2')
+        result = xnor2(xnor_ab, xnor_cd, self.w, 'xnor3')
+        return float(result)
+
+    @classmethod
+    def from_safetensors(cls, path="model.safetensors"):
+        return cls(load_file(path))
+
+
+if __name__ == "__main__":
+    weights = load_file("model.safetensors")
+    model = ThresholdXNOR4(weights)
+
+    print("4-input XNOR Gate Truth Table:")
+    print("-" * 35)
+    correct = 0
+    for a in [0, 1]:
+        for b in [0, 1]:
+            for c in [0, 1]:
+                for d in [0, 1]:
+                    out = int(model(a, b, c, d))
+                    # XNOR4 = even parity = NOT XOR4
+                    expected = 1 - (a ^ b ^ c ^ d)
+                    status = "OK" if out == expected else "FAIL"
+                    if out == expected:
+                        correct += 1
+                    print(f"XNOR4({a},{b},{c},{d}) = {out}  [{status}]")
+    print(f"\nTotal: {correct}/16 correct")

model.safetensors

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+oid sha256:8a85f9a18d2b2f26d401efc40f3f410be757d5a379fe7a50114b7d3992d34f94
+size 1452