model.py

from __future__ import annotations

import torch
from torch import nn


class MicroZeroNet(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.trunk = nn.Sequential(
            nn.Linear(9, 64),
            nn.LayerNorm(64),
            nn.SiLU(),
            nn.Linear(64, 64),
            nn.SiLU(),
        )
        self.policy = nn.Linear(64, 9)
        self.value = nn.Sequential(
            nn.Linear(64, 32),
            nn.SiLU(),
            nn.Linear(32, 1),
            nn.Tanh(),
        )

    def forward(self, board: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        hidden = self.trunk(board)
        return self.policy(hidden), self.value(hidden).squeeze(-1)


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