用pytorch写一段代码:以西储大学CWRU轴承故障的振动信号数据库作为模型的训练集,故障类型标签包括:正常、内圈故障、外圈故障和滚动体故障四类。首先将一维故障信号转换为二维灰度图作为CNN模型的输...
时间: 2024-05-10 08:19:05 浏览: 18
抱歉,我作为一个语言模型,并不能在物理上执行代码。但是,我可以提供一些代码示例,供您参考。
首先,您需要导入所需的库:
```
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.transforms import ToTensor, Compose
from torchvision.datasets import ImageFolder
```
然后,您需要定义转换函数将一维信号转换为二维灰度图:
```
def signal_to_image(signal):
image = signal.reshape(1, -1)
image = (image - image.min()) / (image.max() - image.min())
image = image.reshape(1, 1, -1)
return image
```
接下来,您需要定义一个自定义数据集类来加载数据:
```
class SignalDataset(ImageFolder):
def __getitem__(self, index):
path, target = self.imgs[index]
signal = np.loadtxt(path)
image = signal_to_image(signal)
return image, target
```
然后,您需要定义CNN模型:
```
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.fc1 = nn.Linear(64 * 32 * 32, 128)
self.fc2 = nn.Linear(128, 4)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 64 * 32 * 32)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
```
接下来,您需要定义训练循环:
```
def train(model, train_loader, criterion, optimizer):
model.train()
running_loss = 0.0
for inputs, targets in train_loader:
inputs = inputs.to(device)
targets = targets.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0)
epoch_loss = running_loss / len(train_loader.dataset)
return epoch_loss
```
最后,您需要定义测试循环:
```
def test(model, test_loader, criterion):
model.eval()
running_loss = 0.0
corrects = 0
for inputs, targets in test_loader:
inputs = inputs.to(device)
targets = targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
running_loss += loss.item() * inputs.size(0)
preds = outputs.argmax(dim=1)
corrects += torch.sum(preds == targets.data)
epoch_loss = running_loss / len(test_loader.dataset)
epoch_acc = corrects.double() / len(test_loader.dataset)
return epoch_loss, epoch_acc
```
最后,您需要加载数据集并开始训练:
```
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
transform = Compose([ToTensor()])
train_dataset = SignalDataset('path/to/train/dataset', transform=transform)
test_dataset = SignalDataset('path/to/test/dataset', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
model = CNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())
for epoch in range(10):
train_loss = train(model, train_loader, criterion, optimizer)
test_loss, test_acc = test(model, test_loader, criterion)
print(f'Epoch {epoch + 1}: Train Loss = {train_loss:.4f}, Test Loss = {test_loss:.4f}, Test Acc = {test_acc:.4f}')
```
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