以下是使用Python和Pytorch框架编写的DNN神经网络代码，用于根据基因表达量预测患者是否患病的情况。

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 from bayes_opt import BayesianOptimization

读取数据

data = pd.read_excel('C:\Users\lenovo\Desktop\HIV\DNN\data1.xlsx', header=0) X = data.iloc[:, 1:].values y = data.iloc[:, 0].values

数据归一化处理

X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)

数据集类

class GeneDataset(Dataset): def init(self, X, y): self.X = torch.tensor(X, dtype=torch.float32) self.y = torch.tensor(y, dtype=torch.float32)

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

def __getitem__(self, index):
    return self.X[index], self.y[index]

神经网络模型类

class GeneNet(nn.Module): def init(self, input_size, hidden_size, output_size, p_dropout): super(GeneNet, self).init() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, output_size) self.dropout = nn.Dropout(p=p_dropout) self.sigmoid = nn.Sigmoid()

def forward(self, x):
    x = self.fc1(x)
    x = self.dropout(x)
    x = self.sigmoid(x)
    x = self.fc2(x)
    return x

定义训练函数

def train(model, dataloader, optimizer, criterion): model.train() total_loss = 0.0 total_correct = 0.0 for i, (inputs, labels) in enumerate(dataloader): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels.unsqueeze(1)) loss.backward() optimizer.step() total_loss += loss.item() * inputs.size(0) total_correct += torch.sum((outputs > 0.5) == labels.unsqueeze(1)).item() return total_loss / len(dataloader.dataset), total_correct / len(dataloader.dataset)

定义贝叶斯优化函数

def optimize(hidden_size, p_dropout): hidden_size = int(hidden_size) p_dropout = max(0.0, min(1.0, p_dropout)) net = GeneNet(input_size=16, hidden_size=hidden_size, output_size=1, p_dropout=p_dropout) criterion = nn.BCEWithLogitsLoss() optimizer = optim.Adam(net.parameters(), lr=0.01) dataset = GeneDataset(X, y) dataloader = DataLoader(dataset, batch_size=16, shuffle=True) for epoch in range(50): train_loss, train_acc = train(net, dataloader, optimizer, criterion) print('Epoch:', epoch+1, 'Train Loss:', train_loss, 'Train Acc:', train_acc) return train_acc

对神经网络模型进行贝叶斯优化

optimizer = BayesianOptimization( f=optimize, pbounds={'hidden_size': (10, 50), 'p_dropout': (0.0, 0.5)} ) optimizer.maximize(init_points=5, n_iter=20)

输出最优参数

print('Optimal Parameters:', optimizer.max['params'])