In deep learning, especially in the field of natural language processing, the Attention mechanism has become a very important method. Its core idea is to allocate different weights based on the relevance of each element in the input sequence to the current element, thereby achieving dynamic focus on the input sequence. In the Attention mechanism, Q, K, and V represent Query, Key, and Value, respectively. In the self-attention mechanism, Q, K, and V are obtained through linear transformations of the input sequence (such as the word embedding vectors). Specifically, we first need to define three weight matrices W_Q, W_K, and W_V. These weight matrices are parameters learned during the training process. For each element in the input sequence (such as the word embedding vector x_i), we calculate the product with the weight matrices W_Q, W_K, and W_V to obtain Q_i, K_i, and V_i:
Q_i = x_i * W_Q
K_i = x_i * W_K
V_i = x_i * W_V
In practical applications, such as in the Transformer model, to increase the model’s expressive capability, a multi-head attention mechanism is typically used. In this case, we will have multiple sets of Q, K, and V weight matrices corresponding to different attention “heads.” After calculating Q, K, and V, we can measure the similarity between the i-th element and the j-th element in the input sequence by calculating the dot product of Q_i and K_j (or performing scaled dot product), and then normalize these similarities to obtain the attention weights. Finally, we perform a weighted sum of V_j based on these attention weights to get the attention output for the current element. This process can be expressed as:
Attention(Q, K, V) = softmax(Q * K^T / sqrt(d_k)) * V
where d_k is the dimension of Q and K.
