Enhance LSTM Model with Multiple LSTM and Dense Layers

[deepanshubaghel]

Is this a unique feature?

• I have checked "open" AND "closed" issues and this is not a duplicate

Is your feature request related to a problem/unavailable functionality? Please describe.

Yes, the current implementation of the LSTM model is limited to a single layer, which may not fully capture the complexity of long-term dependencies and temporal patterns in sequential data. This limitation can result in suboptimal performance for tasks that require deeper learning capabilities. Adding multiple LSTM layers and dense layers aims to overcome this limitation, enhancing the model’s ability to generalize and produce more accurate predictions.

Proposed Solution

I propose enhancing the existing LSTM model by stacking 2-3 LSTM layers to capture both short-term and long-term dependencies in sequential data. Dense layers will be added after the LSTM stack for better feature extraction, and dropout regularization will be applied to prevent overfitting. Additionally, we will tune hyperparameters such as LSTM units, dropout rates, and layer configurations to optimize performance.

Do you want to work on this issue?

Yes

If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)

I will modify the existing LSTM model using TensorFlow/Keras by stacking multiple LSTM layers and adding Dense layers for better feature extraction. The implementation will also include dropout regularization to prevent overfitting.

Please assign me this issue .
@rohitinu6

[rohitinu6]

@deepanshubaghel , Please ensure to star this repo,
All the best

[github-actions]

✅ This issue has been successfully closed. Thank you for your contribution and helping us improve the project! If you have any more ideas or run into other issues, feel free to open a new one. Happy coding! 🚀