贝叶斯优化lstm神经网络分类代码

### 贝叶斯优化 LSTM 神经网络分类示例

为了展示如何利用贝叶斯优化来改进LSTM神经网络分类模型，在此提供一段Python代码作为实例。这段代码展示了创建一个简单的LSTM模型并应用贝叶斯优化来进行超参数调整的过程。

from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from sklearn.model_selection import train_test_split
import numpy as np
from skopt.space import Real, Integer
from skopt.utils import use_named_args
from skopt import gp_minimize


def create_lstm_model(units=50, dropout_rate=0.2):
    model = Sequential()
    
    # 添加LSTM层
    model.add(LSTM(units=units, return_sequences=True))
    model.add(Dropout(dropout_rate))

    # 输出层
    model.add(Dense(1, activation='sigmoid'))
    
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

# 定义搜索空间中的超参数范围
dim_units = Integer(low=32, high=256, name='units')
dim_dropout_rate = Real(low=0.1, high=0.5, prior='log-uniform', name='dropout_rate')

dimensions = [dim_units,
              dim_dropout_rate]

@use_named_args(dimensions=dimensions)
def fitness(units, dropout_rate):

    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)

    # 创建模型
    model = create_lstm_model(units=int(units), dropout_rate=float(dropout_rate))

    history = model.fit(
        X_train, 
        y_train,
        epochs=epochs,
        batch_size=batch_size,
        validation_data=(X_val, y_val),
        verbose=verbose_level
    )

    accuracy = max(history.history['val_accuracy'])

    del model
    
    return -accuracy  # 返回负数因为gp_minimize会最小化目标函数

search_result = gp_minimize(func=fitness,
                            dimensions=dimensions,
                            n_calls=11,
                            noise=0.01,
                            random_state=7)

print('Best parameters:', search_result.x)

上述代码片段中，`create_lstm_model()` 函数负责构建具有指定单元数量和丢弃率的LSTM模型[^1]。通过 `skopt.gp_minimize` 实现了对这些超参数的自动调优过程，其中定义了一个适合二元分类任务的目标函数 `fitness()` 来评估不同配置下的性能表现，并最终输出最佳找到的最佳组合[^2]。