| from __future__ import annotations |
|
|
| import torch |
| from torch import nn |
|
|
|
|
| class TemporalMLP(nn.Module): |
| def __init__(self, input_dim: int, output_dim: int = 2, hidden: tuple[int, ...] = (256, 256, 128), dropout: float = 0.05): |
| super().__init__() |
| layers: list[nn.Module] = [] |
| last = input_dim |
| for width in hidden: |
| layers.extend([nn.Linear(last, width), nn.LayerNorm(width), nn.GELU(), nn.Dropout(dropout)]) |
| last = width |
| layers.append(nn.Linear(last, output_dim)) |
| self.net = nn.Sequential(*layers) |
|
|
| def forward(self, x: torch.Tensor) -> torch.Tensor: |
| return self.net(x) |
|
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|
