在Keras中进行模型的集成通常有两种方法:平均集成和堆叠集成。
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
# 创建多个不同的模型
model1 = Sequential()
model1.add(Dense(64, input_dim=10, activation='relu'))
model1.add(Dropout(0.5))
model1.add(Dense(1, activation='sigmoid'))
model2 = Sequential()
model2.add(Dense(64, input_dim=10, activation='relu'))
model2.add(Dropout(0.5))
model2.add(Dense(1, activation='sigmoid'))
# 训练模型
# 在测试集上进行预测
pred1 = model1.predict(test_data)
pred2 = model2.predict(test_data)
# 计算平均值
final_pred = (pred1 + pred2) / 2
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Input
from keras.models import Model
# 创建多个不同的模型
input_layer = Input(shape=(10,))
model1 = Dense(64, activation='relu')(input_layer)
model1 = Dropout(0.5)(model1)
output1 = Dense(1, activation='sigmoid')(model1)
model1 = Model(inputs=input_layer, outputs=output1)
model2 = Dense(64, activation='relu')(input_layer)
model2 = Dropout(0.5)(model2)
output2 = Dense(1, activation='sigmoid')(model2)
model2 = Model(inputs=input_layer, outputs=output2)
# 训练模型
# 在测试集上进行预测
pred1 = model1.predict(test_data)
pred2 = model2.predict(test_data)
# 创建元模型
input_layer = Input(shape=(2,))
meta_model = Dense(64, activation='relu')(input_layer)
meta_model = Dense(1, activation='sigmoid')(meta_model)
meta_model = Model(inputs=input_layer, outputs=meta_model)
# 使用预测结果作为特征输入到元模型中进行训练
meta_features = np.concatenate([pred1, pred2], axis=1)
meta_model.fit(meta_features, true_labels)
这是一个简单的堆叠集成示例代码,你可以根据自己的需求和模型进行自定义和调整。