Pytorch中使用一维CNN网络对齿轮csv有标签数据集故障分类代码

以下是使用PyTorch实现一维CNN网络对齿轮故障分类的代码：

首先，导入必要的库和模块：

import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split

然后，定义一个自定义的数据集类 GearDataset，用于加载齿轮故障的CSV数据集。该类继承于PyTorch提供的Dataset类，需要重写其中的__len__和__getitem__方法。

class GearDataset(Dataset):
    def __init__(self, data, labels):
        self.data = data
        self.labels = labels

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        return torch.tensor(self.data[idx], dtype=torch.float32), torch.tensor(self.labels[idx], dtype=torch.long)

接下来，定义一个一维CNN网络类 GearCNN，该网络类包含两个卷积层和一个全连接层。其中，每个卷积层包括一个卷积层、一个ReLU激活函数和一个最大池化层。

class GearCNN(nn.Module):
    def __init__(self):
        super(GearCNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(1, 16, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(2)
        )
        self.conv2 = nn.Sequential(
            nn.Conv1d(16, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool1d(2)
        )
        self.fc = nn.Linear(32*6, 3)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(-1, 32*6)
        x = self.fc(x)
        return x

然后，读取齿轮故障的CSV数据集，并将其分成训练集和测试集。

data = pd.read_csv('gear_fault_dataset.csv', header=None)
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，将训练集和测试集转换为GearDataset类的实例，并使用DataLoader类加载数据。

train_dataset = GearDataset(X_train, y_train)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

test_dataset = GearDataset(X_test, y_test)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

然后，定义损失函数和优化器。

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

最后，训练模型并测试模型性能。

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = GearCNN().to(device)

num_epochs = 100

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(train_loader):
        inputs = inputs.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(inputs.unsqueeze(1))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print('Epoch [%d/%d], Loss: %.4f' % (epoch+1, num_epochs, running_loss))

# Test the model
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for inputs, labels in test_loader:
        inputs = inputs.to(device)
        labels = labels.to(device)

        outputs = model(inputs.unsqueeze(1))
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy of the model on the %d test samples: %d %%' % (len(test_dataset), 100 * correct / total))

这就是使用PyTorch实现一维CNN网络对齿轮故障分类的完整代码。