DBN-LSTM网络

DBN-LSTM网络是一种深度学习网络，它结合了深度置信网络（DBN）和长短期记忆网络（LSTM）。DBN用于无监督特征提取，LSTM用于回归分析。这种网络结构可以用于时间序列预测等任务。下面是一个简单的DBN-LSTM网络的实现示例：

from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM, TimeDistributed
from keras.layers.convolutional import Conv1D, MaxPooling1D
from keras.layers.convolutional_recurrent import ConvLSTM2D
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Flatten

# 构建DBN-LSTM网络
model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_steps, n_features)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=64, validation_data=(X_test, y_test))