使用hyperopt优化pytorch神经网络模型

Hyperopt是一种基于贝叶斯优化的超参数优化工具，可以用于优化PyTorch神经网络模型的超参数。下面是一个简单的示例：

首先，安装必要的库：

pip install hyperopt
pip install torch

然后，定义一个目标函数，该函数接受超参数并返回模型的性能指标。在这个例子中，我们将使用CIFAR-10数据集，使用交叉熵损失函数和Adam优化器。

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from hyperopt import fmin, tpe, hp, STATUS_OK

def train_model(params):
    # 定义超参数
    lr = params['lr']
    dropout_prob = params['dropout_prob']
    hidden_size = params['hidden_size']
    batch_size = params['batch_size']
    epochs = params['epochs']

    # 加载CIFAR-10数据集
    transform = transforms.Compose([transforms.ToTensor(),
                                    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
    train_set = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
    test_set = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)
    test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False)

    # 定义模型
    class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.conv1 = nn.Conv2d(3, 6, 5)
            self.pool = nn.MaxPool2d(2, 2)
            self.conv2 = nn.Conv2d(6, 16, 5)
            self.fc1 = nn.Linear(16 * 5 * 5, hidden_size)
            self.fc2 = nn.Linear(hidden_size, 10)
            self.dropout = nn.Dropout(p=dropout_prob)

        def forward(self, x):
            x = self.pool(torch.relu(self.conv1(x)))
            x = self.pool(torch.relu(self.conv2(x)))
            x = x.view(-1, 16 * 5 * 5)
            x = self.dropout(torch.relu(self.fc1(x)))
            x = self.fc2(x)
            return x

    net = Net()

    # 训练模型
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(net.parameters(), lr=lr)

    for epoch in range(epochs):
        running_loss = 0.0
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data

            optimizer.zero_grad()

            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        print('Epoch %d loss: %.3f' % (epoch + 1, running_loss / len(train_loader)))

    # 测试模型
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    accuracy = 100 * correct / total
    print('Accuracy: %.2f%%' % accuracy)

    return {'loss': -accuracy, 'status': STATUS_OK}

接下来，定义超参数的搜索空间：

space = {
    'lr': hp.choice('lr', [0.001, 0.01, 0.1]),
    'dropout_prob': hp.uniform('dropout_prob', 0.0, 0.5),
    'hidden_size': hp.choice('hidden_size', [64, 128, 256]),
    'batch_size': hp.choice('batch_size', [32, 64, 128]),
    'epochs': hp.choice('epochs', [10, 20, 30])
}

最后，使用Hyperopt运行优化：

best = fmin(train_model, space, algo=tpe.suggest, max_evals=100)
print(best)

这将运行100个模型训练，使用TPE算法搜索最佳超参数组合，并返回最佳超参数组合。