Keras - Add attention mechanism to an LSTM model duplicate

Question

Can you explain with the help of python programming that Keras - Add attention mechanism to an LSTM model [duplicate]

Answer 1

Adding an attention mechanism to an LSTM model in Keras enhances sequence processing by dynamically weighting relevant time steps, improving model interpretability and performance.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses an LSTM Layer to process sequential data.
• Applies a Self-Attention Mechanism to emphasize key time steps.
• Computes Context Vectors by dynamically weighting LSTM outputs.
• Aggregates Important Features using a mean operation over attention.
• Uses a Dense Sigmoid Layer for binary classification.

Hence, integrating an attention mechanism into an LSTM model in Keras improves sequence learning by allowing the network to focus on the most critical time steps.