使用贝叶斯优化优化神经网络模型

为了对神经网络模型进行贝叶斯优化，我们需要使用第三方库BayesianOptimization。首先需要安装该库：

pip install bayesian-optimization

接下来，我们需要对模型的超参数进行定义和搜索范围的设定。在这里，我们对第一个模型的超参数进行优化，搜索范围如下：

hidden_dim: 隐藏层神经元的个数，范围为[64, 256]
dropout: dropout的概率，范围为[0.2, 0.5]

代码如下：

# 导入必要的库
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from bayes_opt import BayesianOptimization


# 读取Excel表格
data = pd.read_excel('C:\Users\lenovo\Desktop\HIV\GSE6740GSE50011基因降低\output_data.xlsx')
# 数据标准化
data.iloc[:, 1:] = (data.iloc[:, 1:] - data.iloc[:, 1:].mean()) / data.iloc[:, 1:].std()

# 划分数据集
x_train = torch.tensor(data.iloc[:, 1:].values, dtype=torch.float32)
y_train = torch.tensor(data.iloc[:, 0].values, dtype=torch.long)
num_features = x_train.shape[1]


# 定义第一个模型
class Model1(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim, dropout):
        super(Model1, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, x):
        out = nn.functional.relu(self.fc1(x))
        out = self.dropout(out)
        out = nn.functional.relu(self.fc2(out))
        out = self.dropout(out)
        out = self.fc3(out)
        return out


# 定义超参数搜索范围
pbounds = {'hidden_dim': (64, 256), 'dropout': (0.2, 0.5)}

# 定义优化函数
def optimize_model1(hidden_dim, dropout):
    # 初始化模型
    input_dim = num_features
    output_dim = 4
    model1 = Model1(input_dim, int(hidden_dim), output_dim, dropout)

    # 定义损失函数和优化器
    criterion1 = nn.CrossEntropyLoss()
    optimizer1 = optim.Adam(model1.parameters(), lr=0.001)

    # 训练模型
    num_epochs1 = 2000
    for epoch in range(num_epochs1):
        model1.train()
        optimizer1.zero_grad()
        outputs = model1(x_train)
        loss = criterion1(outputs, y_train)
        loss.backward()
        optimizer1.step()
        _, predicted = torch.max(outputs.data, 1)
        accuracy = (predicted == y_train).sum().item() / y_train.size(0)

    # 返回准确率
    return accuracy


# 初始化贝叶斯优化器并开始优化
optimizer = BayesianOptimization(
    f=optimize_model1,
    pbounds=pbounds,
    random_state=1,
)

optimizer.maximize(init_points=5, n_iter=20)

最后，我们可以通过调用optimizer.max方法来输出优化结果，包括最优超参数和准确率。