File size: 1,158 Bytes
42c7ef8 | 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 | from __future__ import annotations
import torch
from torch import nn
class ChronosMicroGRU(nn.Module):
def __init__(
self,
channels: int = 6,
hidden_size: int = 24,
horizon: int = 8,
) -> None:
super().__init__()
self.channels = channels
self.horizon = horizon
self.recurrent = nn.GRU(
input_size=channels,
hidden_size=hidden_size,
batch_first=True,
)
self.head = nn.Sequential(
nn.Linear(hidden_size, 48),
nn.GELU(),
nn.Linear(48, horizon * channels * 2),
)
def forward(self, context: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
_, hidden = self.recurrent(context)
output = self.head(hidden[-1]).reshape(
len(context),
self.horizon,
self.channels,
2,
)
mean = output[..., 0]
log_variance = torch.clamp(output[..., 1], min=-6, max=3)
return mean, log_variance
def parameter_count(model: nn.Module) -> int:
return sum(parameter.numel() for parameter in model.parameters())
|