---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
---

# threshold-exactly2outof3

Exactly 2 of 3 inputs high.

## Function

exactly2outof3(a, b, c) = 1 if (a + b + c) == 2, else 0

## Truth Table

| a | b | c | sum | out |
|---|---|---|-----|-----|
| 0 | 0 | 0 | 0 | 0 |
| 0 | 0 | 1 | 1 | 0 |
| 0 | 1 | 0 | 1 | 0 |
| 0 | 1 | 1 | 2 | 1 |
| 1 | 0 | 0 | 1 | 0 |
| 1 | 0 | 1 | 2 | 1 |
| 1 | 1 | 0 | 2 | 1 |
| 1 | 1 | 1 | 3 | 0 |

## Architecture

Two layers (not linearly separable):

**Layer 1:**
- N1: weights [1, 1, 1], bias -2 (fires when sum >= 2)
- N2: weights [-1, -1, -1], bias 2 (fires when sum <= 2)

**Layer 2:**
- AND: weights [1, 1], bias -2 (fires when both N1 and N2 fire)

## Parameters

| | |
|---|---|
| Inputs | 3 |
| Outputs | 1 |
| Neurons | 3 |
| Layers | 2 |
| Parameters | 11 |
| Magnitude | 14 |

## Usage

```python
from safetensors.torch import load_file
import torch

w = load_file('model.safetensors')

def exactly2of3(a, b, c):
    inp = torch.tensor([float(a), float(b), float(c)])
    l1 = (inp @ w['layer1.weight'].T + w['layer1.bias'] >= 0).float()
    out = (l1 @ w['layer2.weight'].T + w['layer2.bias'] >= 0).float()
    return int(out.item())

print(exactly2of3(0, 1, 1))  # 1 (sum=2)
print(exactly2of3(1, 1, 1))  # 0 (sum=3)
```

## License

MIT