在这段代码的基础上，在gru后面加入多头注意力机制（用MultiHeadAttention）def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trai

可以在GRU层后面加入多头注意力机制，使用Keras的MultiHeadAttention实现，代码如下： ```python from keras.layers import Input, Conv1D, CuDNNGRU, Dropout, concatenate, Dense, Lambda, add, Activation, Permute, Reshape, multiply, MultiHeadAttention 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 = CuDNNGRU(64)(conv1out) gru1out = Dropout(config.dropout)(gru1out) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2out = conv2(input2) gru2out = CuDNNGRU(64)(conv2out) gru2out = Dropout(config.dropout)(gru2out) # 多头注意力机制 head_num = 4 attention_size = gru1out.shape[-1] query = Dense(attention_size)(gru1out) key = Dense(attention_size)(gru2out) value = Dense(attention_size)(gru2out) attention_output = MultiHeadAttention(num_heads=head_num, key_dim=attention_size // head_num)(query, key, value) attention_output = Dropout(config.dropout)(attention_output) gru_out = concatenate([gru1out, attention_output]) 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