Optuna是一個Python庫，用于在訓(xùn)練機(jī)器學(xué)習(xí)模型時自動選擇最佳的超參數(shù)。它通過使用隨機(jī)梯度下降（SGD）算法來優(yōu)化模型的超參數(shù)，從而加速模型的訓(xùn)練過程并提高模型的性能。

要使用Optuna進(jìn)行模型訓(xùn)練，首先需要安裝Optuna庫?？梢允褂靡韵旅畎惭b：

pip install optuna

接下來，可以使用以下代碼示例來演示如何使用Optuna進(jìn)行模型訓(xùn)練：

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

# 定義模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.fc = nn.Linear(128 * 8 * 8, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 128 * 8 * 8)
        x = F.relu(self.fc(x))
        return x

# 定義數(shù)據(jù)加載器和數(shù)據(jù)集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

trainset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)

testset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = DataLoader(testset, batch_size=64, shuffle=False)

# 定義損失函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = SGD(net.parameters(), lr=0.001, momentum=0.9)

# 使用Optuna進(jìn)行模型訓(xùn)練
optuna_tuner = optuna.create_tuner("mnist_tuner")
optuna_tuner.register_param('learning_rate', p=0.001)
optuna_tuner.register_param('momentum', p=0.9)
optuna_tuner.register_param('epochs', p=10)
optuna_tuner.register_param('batch_size', p=64)

best_score = None
best_params = None
for params in optuna_tuner.searchspace():
    tuner = optuna.create_tuner("my_tuner")
    tuner.register_param('learning_rate', p=params['learning_rate'])
    tuner.register_param('momentum', p=params['momentum'])
    tuner.register_param('epochs', p=params['epochs'])
    tuner.register_param('batch_size', p=params['batch_size'])
    tuner.maximize(fn=lambda: criterion(net(torch.tensor(trainloader).to(device)), torch.tensor(testloader).to(device)))
    score = tuner.get_result()
    if best_score is None or score > best_score:
        best_score = score
        best_params = params

print("Best parameters found: ", best_params)
print("Best score on validation set: ", best_score)

在這個示例中，我們定義了一個卷積神經(jīng)網(wǎng)絡(luò)（CNN）模型，并使用Optuna進(jìn)行超參數(shù)搜索以找到最佳學(xué)習(xí)率、動量、迭代次數(shù)和批處理大小。最后，我們打印出找到的最佳參數(shù)和驗證集上的最佳分?jǐn)?shù)。