abodh_ltd:

How do you think I can use data augmentation techniques here?


That’s a tricky question, as I’m not familiar with the data.
You would have to narrow down certain properties if a specific class, e.g. M=9, and compare how the waveforms differ for this particular class.
Are they shifted or scaled slightly? Do they have more “noise” in the signal?
Note that you would have to avoid such aggressive augmentation techniques that your data sample might be pushed to another class.
Also, knowledge about the underlying physical process might be useful.
I.e. how was the data created and what kind of “noise” might the recording setup experience?



 abodh_ltd:

What do you think about these plots? Is log(loss) the right thing to look even if I am using torch.nn.MSE() as my loss metric? I am not sure if I plotted the loss loss correctly. I just used np.log(losses) where I got losses from MSE.


Yes, the log should be fine for visualization purposes and helps a bit in seeming the decreasing behavior for small (close to 0) values.
The loss curves look generally good and it seems your model is still learning.
Do you see some overfitting after a while or a static loss?