alphago_sl.py

import torch
import torch.nn.functional as F
import torch.optim as optim

from alphago.data.dataset import GoDataSet
from alphago.networks import AlphaGoNet

if torch.cuda.is_available():
    device = torch.device("cuda")
else:
    device = torch.device("cpu")

BOARD_SIZE = 9
training_dataset = GoDataSet(game="go9", encoder="oneplane", no_of_games=8)
test_dataset = GoDataSet(
    game="go9", encoder="oneplane", no_of_games=100, avoid=training_dataset.games
)
train_loader = torch.utils.data.DataLoader(
    training_dataset, batch_size=64, shuffle=True
)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=512, shuffle=True)


def train(model, device, train_loader, optimizer, epoch):
    losses = []
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output, _ = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        losses.append(loss.item())
        if batch_idx % 1000 == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch,
                    batch_idx * len(data),
                    len(train_loader.dataset),
                    100.0 * batch_idx / len(train_loader),
                    loss.item(),
                )
            )
    return losses


def test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output, _ = model(data)
            test_loss += F.nll_loss(output, target, reduction="sum").item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    print(
        "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.1f}%)\n".format(
            test_loss,
            correct,
            len(test_loader.dataset),
            100.0 * correct / len(test_loader.dataset),
        )
    )
    return float(correct) / len(test_loader.dataset)


model = AlphaGoNet((1, BOARD_SIZE, BOARD_SIZE))
model.load_state_dict(torch.load("experiment/weights/go2.pth"))
if torch.cuda.is_available():
    model = model.cuda()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.5)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=25, gamma=0.1)
losses = []
accuracies = []
for epoch in range(0, 10):
    # losses.extend(train(model, device, train_loader, optimizer, epoch))
    accuracies.append(test(model, device, train_loader))
    scheduler.step()

torch.save(model.state_dict(), "experiment/weights/go1.pth")