The Attention mechanism for LSTM networks works by assigning a weight to each input in the sequence, indicating its relative importance to the output. This weight is calculated based on a score function that compares the hidden state of the LSTM with each input in the sequence. The scores are then normalized to produce a probability distribution over the inputs, which is used to compute the weighted sum of the inputs. This weighted sum is then concatenated with the output of the LSTM at the current time step, and passed through a fully connected layer to produce the final output. The Attention mechanism allows the LSTM to selectively focus on different parts of the input sequence at different times, giving it greater flexibility in capturing long-term dependencies and improving its performance on tasks such as machine translation or speech recognition.
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