用MultiHeadAttention函数直接将多头注意力机制应用到gru上

非常抱歉，我之前的回答有误。在Keras中，`MultiHeadAttention`函数是用于实现Transformer模型中的多头注意力机制的，而不是直接将多头注意力机制应用到GRU上。 如果要在LSTNet中加入多头注意力机制，可以参考以下代码： ```python from keras.layers import Input, Conv1D, CuDNNGRU, Dropout, concatenate, Dense, Lambda, add, Activation, Permute, Reshape, multiply 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, return_sequences=True)(conv1out) gru1out = Dropout(config.dropout)(gru1out) input2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) conv2out = conv2(input2) gru2out = CuDNNGRU(64, return_sequences=True)(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) query = Lambda(lambda x: K.reshape(x, (-1, trainX1.shape[1], head_num, attention_size // head_num)))(query) query = Lambda(lambda x: K.permute_dimensions(x, (0,