如何使用AdaBoost增强基于Keras的神经网络？

from sklearn.ensemble import AdaBoostClassifier
from keras.models import Sequential
from keras.layers import Dense

# 准备数据集
# ...

# 构建基本分类器
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 初始化权重
sample_weights = np.ones(len(X_train)) / len(X_train)

# 迭代训练
for i in range(num_iterations):
    # 训练基本分类器
    model.fit(X_train, y_train, sample_weight=sample_weights)
    
    # 预测并计算错误率
    y_pred = model.predict(X_train)
    error = np.sum(sample_weights * (y_pred != y_train))
    
    # 计算分类器权重
    classifier_weight = 0.5 * np.log((1 - error) / error)
    
    # 更新样本权重
    sample_weights *= np.exp(-classifier_weight * y_train * y_pred)
    sample_weights /= np.sum(sample_weights)
    
# 组合分类器
ada_model = AdaBoostClassifier(base_estimator=model, n_estimators=num_iterations)

# 预测
y_pred = ada_model.predict(X_test)