故障检测代码pytorch
时间: 2023-09-24 17:08:51 浏览: 23
当然,我可以为你提供一个简单的示例代码来进行故障检测。以下是一个使用PyTorch框架的故障检测代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
# 定义自定义数据集类
class FaultDetectionDataset(Dataset):
def __init__(self, data, labels):
self.data = data
self.labels = labels
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
# 定义故障检测模型类
class FaultDetectionModel(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(FaultDetectionModel, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out
# 准备数据
data = [[0.2, 0.1, 0.3], [0.3, 0.2, 0.4], [0.1, 0.4, 0.2], [0.4, 0.3, 0.5]]
labels = [0, 1, 0, 1]
train_dataset = FaultDetectionDataset(data, labels)
train_loader = DataLoader(train_dataset, batch_size=2, shuffle=True)
# 定义模型和优化器
input_size = len(data[0])
hidden_size = 10
num_classes = 2
model = FaultDetectionModel(input_size, hidden_size, num_classes)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)
# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
for batch_data, batch_labels in train_loader:
optimizer.zero_grad()
outputs = model(batch_data)
loss = criterion(outputs, batch_labels)
loss.backward()
optimizer.step()
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')
# 使用训练好的模型进行预测
test_data = [[0.1, 0.2, 0.4], [0.3, 0.1, 0.5]]
test_dataset = FaultDetectionDataset(test_data, labels=None)
test_loader = DataLoader(test_dataset, batch_size=1)
model.eval()
with torch.no_grad():
for test_batch_data, _ in test_loader:
predictions = model(test_batch_data)
_, predicted_labels = torch.max(predictions.data, 1)
print(f'Predicted Labels: {predicted_labels}')
```
这是一个简单的故障检测代码示例,你可以根据自己的数据和需求进行适当的修改和扩展。希望对你有帮助!