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 07:18:19 浏览: 11
以下是将一维故障信号转换为二维灰度图并用CNN模型进行训练的代码示例:
```python
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import numpy as np
from sklearn.model_selection import train_test_split
from scipy.io import loadmat
# load dataset
data = loadmat('path/to/dataset.mat')
X = data['X'] # signal data
y = data['y'] # label data
# convert 1D signals to 2D grayscale images
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((128, 128)),
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor()
])
X_2D = []
for i in range(len(X)):
X_2D.append(transform(X[i]))
# split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X_2D, y, test_size=0.2)
# define the CNN model
class CNNModel(nn.Module):
def __init__(self):
super(CNNModel, 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.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(64 * 32 * 32, 128)
self.fc2 = nn.Linear(128, 4)
def forward(self, x):
x = self.conv1(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = self.conv2(x)
x = nn.functional.relu(x)
x = self.pool(x)
x = x.view(-1, 64 * 32 * 32)
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
return x
# initialize the model and optimizer
model = CNNModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# define the loss function
criterion = nn.CrossEntropyLoss()
# train the model
for epoch in range(10):
for i in range(len(X_train)):
optimizer.zero_grad()
output = model(X_train[i].unsqueeze(0))
loss = criterion(output, y_train[i])
loss.backward()
optimizer.step()
# test the model
correct = 0
total = 0
with torch.no_grad():
for i in range(len(X_test)):
output = model(X_test[i].unsqueeze(0))
pred = torch.argmax(output)
if pred == y_test[i]:
correct += 1
total += 1
accuracy = correct / total
print(f'Test accuracy: {accuracy}')
```
在这个示例中,我们首先从MATLAB文件加载数据集,然后使用PyTorch的transforms模块将一维故障信号转换为二维灰度图。然后我们将数据集分割为训练和测试集,并定义了一个CNN模型,使用Adam优化器和交叉熵损失函数进行训练。最后,我们用测试集测试了模型的准确性。