非常抱歉,我的回答还是有误。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, 2