conv1out = conv1(input1) gru1out = CuDNNGRU(64)(conv1out) gru1out = Dropout(config.dropout)(gru1out) conv2out = conv2(in

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小小草
小小草 LV1 2023年9月23日 23:40 发表
可以使用Keras中的MultiHeadAttention层来实现多头注意力机制。具体步骤如下: 1. 导入MultiHeadAttention层:`from tensorflow.keras.layers import MultiHeadAttention` 2. 在conv1out和conv2out之后分别添加一个MultiHeadAttention层,代码如下: ``` attn1 = MultiHeadAttention(num_heads=8, key_dim=64) attn1_out = attn1(gru1out, gru1out) attn1_out = Dropout(config.dropout)(attn1_out) attn2 = MultiHeadAttention(num_heads=8, key_dim=64) attn2_out = attn2(gru2out, gru2out) attn2_out = Dropout(config.dropout)(attn2_out) ``` 其中,num_heads表示头的数量,key_dim表示每个头的维度。 3. 将attn1_out和attn2_out拼接起来,代码如下: ``` attn_out = concatenate([attn1_out, attn2_out]) ``` 4. 将attn_out作为输入,继续进行后续操作,代码如下: ``` output = Dense(trainY.shape[1])(attn_out) ``` 完整代码如下: ``` from tensorflow.keras.layers import MultiHeadAttention conv1out = conv1(input1) gru1out = CuDNNGRU(64)(conv1out) gru1out = Dropout(config.dropout)(gru1out) attn1 = MultiHeadAttention(num_heads=8, key_dim=64) attn1_out = attn1(gru1out, gru1out) attn1_out = Dropout(config.dropout)(attn1_out) conv2out = conv2(input2) gru2out = CuDNNGRU(64)(conv2out) gru2out = Dropout(config.dropout)(gru2out) attn2 = MultiHeadAttention(num_heads=8, key_dim=64) attn2_out = attn2(gru2out, gru2out) attn2_out = Dropout(config.dropout)(attn2_out) attn_out = concatenate([attn1_out, attn2_out]) output = Dense(trainY.shape[1])(attn_out)
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