The relative importance of each input in the sequence is determined by a scoring function, which is typically a dot product or a similarity function that measures the similarity between the hidden state of the LSTM and each input in the sequence. The scoring function calculates a score for each input, indicating how much attention should be given to that input in generating the output.

Once the scores are calculated, they are normalized using a softmax function, which converts them into a probability distribution. This distribution represents the relative importance of each input in the sequence, with higher scores indicating higher importance. The softmax function ensures that the scores sum up to 1, allowing the model to allocate attention across all inputs in a meaningful way.

The normalized scores are then used to compute the weighted sum of the inputs, where each input is multiplied by its corresponding attention weight. 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 dynamically adjust the weights during each time step, allowing it to focus on different inputs depending on the context and task requirements. This way, the LSTM can selectively attend to the most relevant inputs, improving its ability to capture long-term dependencies and improving its performance on various sequence-to-sequence tasks.
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