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| Output | Weights [d, s2, s1, s0] | Bias |
|---|---|---|
| y0 | [1, -1, -1, -1] | -1 |
| y1 | [1, -1, -1, +1] | -2 |
| y2 | [1, -1, +1, -1] | -2 |
| y3 | [1, -1, +1, +1] | -3 |
| y4 | [1, +1, -1, -1] | -2 |
| y5 | [1, +1, -1, +1] | -3 |
| y6 | [1, +1, +1, -1] | -3 |
| y7 | [1, +1, +1, +1] | -4 |
| Inputs | 4 (1 data + 3 select) |
| Outputs | 8 |
| Neurons | 8 |
| Layers | 1 |
| Parameters | 40 |
| Magnitude | 52 |
1from safetensors.torch import load_file
2import torch
3
4w = load_file('model.safetensors')
5
6def demux8(d, s2, s1, s0):
7 inp = torch.tensor([float(d), float(s2), float(s1), float(s0)])
8 return [int((inp * w[f'y{i}.weight']).sum() + w[f'y{i}.bias'] >= 0)
9 for i in range(8)]
10
11# Route d=1 to output 5 (s=101)
12print(demux8(1, 1, 0, 1)) # [0, 0, 0, 0, 0, 1, 0, 0]