keras实现CNN-bilstm-attention layer回归预测

以下是Keras实现CNN-BiLSTM-Attention Layer回归预测的示例代码：

from keras.layers import Input, Dense, Conv1D, MaxPooling1D, Dropout, Bidirectional, LSTM, Flatten, Activation, Multiply
from keras.models import Model

# 定义输入
inputs = Input(shape=(timesteps, features))

# 定义CNN层
conv1 = Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')(inputs)
maxpool1 = MaxPooling1D(pool_size=2)(conv1)
conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(maxpool1)
maxpool2 = MaxPooling1D(pool_size=2)(conv2)

# 定义BiLSTM层
lstm = Bidirectional(LSTM(units=128, return_sequences=True))(maxpool2)

# 定义Attention层
attention = Dense(1, activation='tanh')(lstm)
attention = Flatten()(attention)
attention = Activation('softmax')(attention)
attention = Multiply()([lstm, attention])
attention = Lambda(lambda x: K.sum(x, axis=1))(attention)

# 定义输出层
output = Dense(units=1)(attention)

# 定义模型
model = Model(inputs=inputs, outputs=output)

# 编译模型
model.compile(optimizer='adam', loss='mean_squared_error')

# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))

其中，timesteps是时间步长，features是每个时间步长的特征数。模型包括了CNN层、BiLSTM层和Attention层，将它们按顺序添加到模型中即可。最后编译模型并训练。