pytorch抑郁症预测模型代码

PyTorch 抑郁症预测模型可以分为以下几个部分：数据预处理、模型构建、模型训练和模型预测。其中，数据预处理包括数据读取、数据清洗和特征提取。模型构建包括构建神经网络模型，定义损失函数和优化器。模型训练包括对模型进行训练、评估和保存。模型预测包括使用训练好的模型进行新数据的预测。

以下是一个简单的 PyTorch 抑郁症预测模型代码示例：

# 数据预处理
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

data = pd.read_csv('depression_data.csv')
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=0)

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 模型构建
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(8, 16)
        self.fc2 = nn.Linear(16, 16)
        self.fc3 = nn.Linear(16, 1)

    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()
criterion = nn.BCELoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)

# 模型训练
for epoch in range(100):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(trainloader)))

# 模型预测
outputs = net(X_test)
predicted = (outputs > 0.5).float()
accuracy = (predicted == y_test).sum().item() / len(y_test)
print('Accuracy: %.2f %%' % (accuracy * 100))

这是一个简单的抑郁症预测模型，仅作为示例。实际应用中，需要根据具体的数据集和任务进行调整和优化。