The attention mechanism is used in LSTM networks to selectively focus on certain parts of the input sequence when making predictions. This can be particularly useful in cases where the input sequence is very long or the relevant information for making a prediction is distributed across different parts of the sequence.

In an LSTM network, the following operations are performed with the input sequence in mind:
1. It is processed through the LSTM layers to obtain a sequence of hidden states.
2. For each hidden state, a set of attention weights are computed that determine how much attention to give to that state.

It is important to know that attention weights are usually computed by taking the dot product between a set of training parameters (called 'attention weights') and the 'hidden state'.
This produces a scalar value for each 'hidden state', which is then normalised over all hidden states using the 'softmax' function to produce a set of 'attention weights' that add up to one.

Once the ''attention weights'' are computed, the weighted sum of the ''hidden states'' is taken to obtain a context vector' that represents the portion of the input sequence that is most appropriate for making a prediction.
Finally, the ''context vector'' is connected to the output of the LSTM layers and fed to the final prediction layer.

Overall, the Attention mechanism allows the LSTM network to selectively focus on the most relevant parts of the input sequence, which can improve its ability to make accurate predictions.
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