import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# ハイパーパラメータ
batch_size = 64
learning_rate = 0.001
num_epochs = 20

# STL-10のデータセットとデータローダーの準備
transform = transforms.Compose([
    transforms.Resize((96, 96)),  # STL-10の画像サイズに合わせる
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),  # 正規化
])

train_dataset = datasets.STL10(root='./data', split='train', download=False, transform=transform)
test_dataset = datasets.STL10(root='./data', split='test', download=False, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

########################## ここにモデルの class を書いてください． ############################


#########################################################################################

# モデル、損失関数、オプティマイザの設定
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet().to(device)
print(model)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=2e-5)

# 学習ループ
def train_model():
    model.train()
    for epoch in range(num_epochs):
        total_loss = 0
        for images, labels in train_loader:
            images, labels = images.to(device), labels.to(device)
            
            optimizer.zero_grad()
            outputs = model(images)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
        print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(train_loader):.4f}")

# テストループ
def test_model():
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print(f"Test Accuracy: {100 * correct / total:.2f}%")

# 実行
if __name__ == "__main__":
    train_model()
    test_model()