hiqmat_nisa1:

My training exapmles are 8161 and validtion samples are 900.


Awesome that you’ve implemented augmenting!
Main Suggestion: Given that the dataset is very small, reducing the overall complexity of your model should help to avoid overfitting. Perhaps try simply dropping one of the convolutional/sequential layers from your model and see what happens . (Note that to do this you’ll probably have to adjust the corresponding input layer of the LSTM.)
Side Note: Adjusting the Learning Rate α — and to a lesser extent, weight decay λ — in your call to optim.Adam() will help if you are having convergence issues, but it seems that your model is doing okay in that regard. (Commonly, λ ranges from 1e-5 to 1e-6.) However, given the spike around epoch 15 it may still be worth looking into if the main suggestion doesn’t help.