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

# threshold-atmost2outof3

At most 2 of 3 inputs high. Equivalent to 3-input NAND gate.

## Function

atmost2outof3(a, b, c) = 1 if (a + b + c) <= 2, else 0

Equivalently: NAND(a, b, c) = NOT(a AND b AND c)

## Truth Table

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

## Architecture

Single neuron: weights [-1, -1, -1], bias 2

Fires when: -a - b - c + 2 >= 0, i.e., sum <= 2

## Parameters

| | |
|---|---|
| Inputs | 3 |
| Outputs | 1 |
| Neurons | 1 |
| Layers | 1 |
| Parameters | 4 |
| Magnitude | 5 |

## Usage

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

w = load_file('model.safetensors')

def atmost2of3(a, b, c):
    inp = torch.tensor([float(a), float(b), float(c)])
    return int((inp @ w['neuron.weight'].T + w['neuron.bias'] >= 0).item())

print(atmost2of3(1, 1, 0))  # 1 (sum=2)
print(atmost2of3(1, 1, 1))  # 0 (sum=3)
```

## License

MIT