pytorch轴承故障分类代码

A：以下是一个基于PyTorch的轴承故障分类代码示例：

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import ImageFolder

# 数据集文件夹路径
train_data_path = './train'
valid_data_path = './valid'

# 超参数
learning_rate = 0.001
num_epochs = 10
batch_size = 8

# 数据预处理
transform_data = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])

# 加载训练集和验证集
train_dataset = ImageFolder(root=train_data_path, transform=transform_data)
valid_dataset = ImageFolder(root=valid_data_path, transform=transform_data)

# 创建数据加载器
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
valid_loader = DataLoader(dataset=valid_dataset, batch_size=batch_size, shuffle=False)

# 定义卷积神经网络
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.fc = nn.Sequential(
            nn.Linear(64 * 56 * 56, 1024),
            nn.ReLU(),
            nn.Dropout(0.5),
            nn.Linear(1024, 5)
        )

    def forward(self, x):
        out = self.conv(x)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 实例化卷积神经网络
model = CNN()

# GPU加速
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

# 定义loss函数和optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)

        # 前向传播和计算loss
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和更新参数
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # 每100次迭代打印一次信息
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

    # 模型验证
    correct = 0
    total = 0
    for images, labels in valid_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    accuracy = correct / total
    print('Epoch [{}/{}], Validation Accuracy: {:.4f}'.format(epoch+1, num_epochs, accuracy))

# 保存模型
torch.save(model.state_dict(), 'model.ckpt')

上述代码使用了一个简单的卷积神经网络，并使用交叉熵作为损失函数。训练过程中还包括了模型验证和模型参数的保存。这是一个简单的示例，实际应用中可以根据具体情况进行修改和优化。