source/model.py

from __future__ import annotations

import torch
from torch import nn


class LinearCodec(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.encoder = nn.Linear(2, 2)
        self.decoder = nn.Linear(2, 2)

    def encode(self, observations: torch.Tensor) -> torch.Tensor:
        return self.encoder(observations)

    def forward(self, observations: torch.Tensor) -> torch.Tensor:
        return self.decoder(self.encode(observations))


class CoordinatePredictor(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.network = nn.Sequential(
            nn.Linear(1, 24),
            nn.Tanh(),
            nn.Linear(24, 24),
            nn.Tanh(),
            nn.Linear(24, 1),
        )

    def forward(self, coordinate: torch.Tensor) -> torch.Tensor:
        return self.network(coordinate)


def parameter_count(module: nn.Module) -> int:
    return sum(parameter.numel() for parameter in module.parameters())