编写使用余弦退火算法自适应学习率优化CNN模型的pytorch环境下开发的程序

这里提供一个使用余弦退火算法自适应学习率优化CNN模型的PyTorch程序。

首先，导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim

from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor

然后，定义一个简单的CNN模型：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

接下来，定义训练函数：

def train(model, device, train_loader, optimizer, epoch):
    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 = nn.functional.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 100 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

然后，定义测试函数：

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 += nn.functional.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: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

最后，定义主函数：

def main():
    # 设置设备
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 加载数据集
    train_dataset = MNIST('./data', train=True, download=True, transform=ToTensor())
    test_dataset = MNIST('./data', train=False, download=True, transform=ToTensor())
    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=True)

    # 定义模型
    model = CNN().to(device)

    # 定义优化器
    optimizer = optim.SGD(model.parameters(), lr=0.1)

    # 训练模型
    for epoch in range(1, 11):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)

        # 更新学习率
        optimizer.param_groups[0]['lr'] = 0.1 * (1 + math.cos(math.pi * epoch / 10))

if __name__ == '__main__':
    main()

在主函数中，我们使用余弦退火算法来自适应地更新学习率。具体来说，我们将学习率初始化为0.1，然后在每个epoch中，根据当前epoch数使用余弦函数计算新的学习率，然后将其更新到优化器中的参数组中。这样，我们可以在训练过程中自适应地调整学习率，从而更好地优化模型。