File size: 1,078 Bytes
25743ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
---
license: mit
tags:
- pytorch
- safetensors
- threshold-logic
- neuromorphic
---
# threshold-majority3
3-input majority gate. Outputs 1 when at least 2 of 3 inputs are high.
## Function
majority3(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 | 1 |
## 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 majority3(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(majority3(0, 1, 1)) # 1
print(majority3(0, 0, 1)) # 0
```
## License
MIT
|