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ddaaeb2 | 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 | from __future__ import annotations
import torch
from torch import nn
class SequenceRegressor(nn.Module):
def __init__(self, cell: str) -> None:
super().__init__()
self.cell = cell
if cell == "rnn":
hidden = 67
self.recurrent = nn.RNN(
2, hidden, nonlinearity="tanh", batch_first=True
)
elif cell == "lstm":
hidden = 32
self.recurrent = nn.LSTM(2, hidden, batch_first=True)
elif cell == "gru":
hidden = 36
self.recurrent = nn.GRU(2, hidden, batch_first=True)
else:
raise ValueError(f"Unknown recurrent cell: {cell}")
self.readout = nn.Linear(hidden, 1)
def forward(self, sequence: torch.Tensor) -> torch.Tensor:
hidden, _ = self.recurrent(sequence)
return self.readout(hidden[:, -1]).squeeze(-1)
def parameter_count(module: nn.Module) -> int:
return sum(parameter.numel() for parameter in module.parameters())
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