在定义贝叶斯优化函数中，需要先定义神经网络模型和优化器，所以需要将以下代码：

定义神经网络模型

class DNN(nn.Module): def init(self, input_dim, hidden_dim, output_dim): super(DNN, self).init() self.fc1 = nn.Linear(input_dim, hidden_dim[0]) self.fc2 = nn.Linear(hidden_dim[0], hidden_dim[1]) self.fc3 = nn.Linear(hidden_dim[1], hidden_dim[2]) self.fc4 = nn.Linear(hidden_dim[2], output_dim) self.dropout = nn.Dropout(p=0.5) self.attention = nn.MultiheadAttention(hidden_dim[2], 1)

def forward(self, x):
    x = nn.functional.relu(self.fc1(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc2(x))
    x = self.dropout(x)
    x = nn.functional.relu(self.fc3(x))
    x = self.dropout(x)
    x, _ = self.attention(x, x, x)
    x = nn.functional.sigmoid(self.fc4(x))
    return x

定义损失函数和优化器

criterion = nn.BCELoss() optimizer = optim.Adam(model.parameters())

改为：

定义神经网络模型和优化器

def create_model(lr, hidden_dim1, hidden_dim2, hidden_dim3): model = DNN(input_dim=X.shape[1], hidden_dim=[hidden_dim1, hidden_dim2, hidden_dim3], output_dim=1) optimizer = optim.Adam(model.parameters(), lr=lr) return model, optimizer

然后在定义贝叶斯优化函数中调用该函数即可：

定义贝叶斯优化函数

@use_named_args(space) def objective(lr, batch_size, hidden_dim1, hidden_dim2, hidden_dim3): model, optimizer = create_model(lr, hidden_dim1, hidden_dim2, hidden_dim3) # 省略后续代码