能不能在这段代码的基础上，将gru换成bigru，并在bigru后面加入多头注意力机制:def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trainX

好的，可以将CuDNNGRU替换为Bidirectional(CuDNNGRU)，并在其后面添加MultiHeadAttention层。具体代码如下： from keras.layers import Input, Conv1D, Dropout, concatenate, Dense, Lambda, Activation, CuDNNGRU, Bidirectional from keras.models import Model from keras_self_attention import SeqSelfAttention def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) conv1 = Conv1D(filters=48, kernel_size=6, strides=1, activation='relu') # for input1 # It's a probelm that I can't find any way to use the same Conv1D layer to train the two inputs, conv2 = Conv1D(filters=48, kernel_size=6 , strides=1, activation='relu') # for input2 conv2.set_weights(conv1.get_weights()) # at least use same weight conv1out = conv1(input1) gru1out = Bidirectional(CuDNNGRU(64, return_sequences=True))(conv1out) gru1out = SeqSelfAttention(attention_activation='sigmoid')(gru1out) gru1out = Dropout(config.dropout)(gru1out) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2out = conv2(input2) gru2out = Bidirectional(CuDNNGRU(64, return_sequences=True))(conv2out) gru2out = SeqSelfAttention(attention_activation='sigmoid')(gru2out) gru2out = Dropout(config.dropout)(gru2out) gru_out = concatenate([gru1out,gru2out]) output = Dense(trainY.shape[1])(gru_out) #highway 使用Dense模拟AR自回归过程，为预测添加线性成份，同时使输出可以响应输入的尺度变化。 highway_window = config.highway_window #截取近3个窗口的时间维 保留了所有的输入维度 z = Lambda