Hello,
I am trying to obtain a wave model which requires simultaneous solutions of multiple ODE’s.
My input is has 2 input features. First one is Stimuli which only initiates the wave, and mostly 0’s.
However, my model can’t produce a specific part of the wave.
As you can see, most of the wave is predicted very accurately.
What I tried is,
- Changing the hidden dimension
- Changins the sequence length
- Changing the batch size
- Adding additional FC layers
- Increasing and decreasing LSTM Layer numbers
- Using MSE and L1 Loss functions
- Increasing data sizes
When I try to make my model more complex (such as increasing number of layers & size of hidden dimension) It tends to overfit,
Exact opposite effect occurs when model is simplified or regularization is added.
How can it learn the area after initial sharp increase ? Is there a known approach for this ? I am at a loss at this point.
Thank you kindly.