Hi Pappu,
I understand that you aim to improve your validation set accuracy of the LSTM based deep neural network. You can try out the following approaches to improve your accuracy metric:
1) You can normalize your input features in the range [0, 1] to stabilize the training process and improve convergence.
2) You can use dropouts in your network to prevent overfitting and get a generalized model. You can refer to below MathWorks documentation to understand more about dropouts.
3) Instead of using a fixed learning rate, you can use Adam optimizer which utilizes adaptive learning rate based on the gradient magnitude. You can refer to below MathWorks documentation to understand more about Adam optimizer.
I hope this helps.
