生成代码 利用pytorch实现疲劳驾驶预测

以下是用PyTorch实现疲劳驾驶预测的示例代码：

1. 数据预处理

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 读取数据
data = pd.read_csv('data.csv')

# 划分特征和标签
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征标准化
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 转换为PyTorch的张量
X_train = torch.from_numpy(X_train).float()
X_test = torch.from_numpy(X_test).float()
y_train = torch.from_numpy(y_train).float()
y_test = torch.from_numpy(y_test).float()

2. 构建模型

import torch.nn as nn
import torch.nn.functional as F

# 构建神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(8, 64)  # 输入层到隐藏层
        self.fc2 = nn.Linear(64, 32)  # 隐藏层到隐藏层
        self.fc3 = nn.Linear(32, 1)  # 隐藏层到输出层

    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = torch.sigmoid(self.fc3(x))
        return x

net = Net()

3. 训练模型

import torch.optim as optim

# 定义损失函数和优化器
criterion = nn.BCELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

# 训练模型
for epoch in range(1000):
    optimizer.zero_grad()
    outputs = net(X_train)
    loss = criterion(outputs, y_train.unsqueeze(1))
    loss.backward()
    optimizer.step()

    if epoch % 100 == 0:
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 1000, loss.item()))

4. 评估模型

# 预测测试集结果
with torch.no_grad():
    y_pred = net(X_test)
    y_pred = (y_pred > 0.5).float()

# 计算准确率
accuracy = (y_pred == y_test.unsqueeze(1)).float().mean()
print('Accuracy: {:.2f}%'.format(accuracy.item() * 100))

这是一个简单的示例，实际中需要根据具体情况进行调整和优化。