在这段代码的gru1_out后面加入多头注意力机制def LSTNet(trainX1,trainX2,trainY,config): input1 = Input(shape=(trainX1.shape[1], trainX1

你可以按照以下方式在 `gru1out` 后面添加多头注意力机制： ```python 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) attention1_output = MultiHeadAttention(num_heads=8, key_dim=64)(conv1out, conv1out) attention1_output = LayerNormalization()(attention1_output + conv1out) gru1out = CuDNNGRU(64, return_sequences=True)(attention1_output) attention2_output = MultiHeadAttention(num_heads=8, key_dim=64)(gru1out, gru1out) attention2_output = LayerNormalization()(attention2_output + gru1out) gru1out = Dropout(config.dropout)(attention2_output) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2out = conv2(input2) attention3_output = MultiHeadAttention(num_heads=8, key_dim=64)(conv2out, conv2out) attention3_output = LayerNormalization()(attention3_output + conv2out) gru2out = CuDNNGRU(64)(attention3_output) 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