i've checked for papers that do exactly what you want.

so as I assumed this data is time sensitive and therefor you need an additional temporal dimension.

this model needs to be more complex in order to solve this problem.

i suggest reading this:

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01736-y

​

BTW: have you tried grid search for finding the right hyperparametrs?

oh and your model does improve..

have you increased the data set size??

​

https://www.freecodecamp.org/news/how-to-format-code-in-markdown/

i can try to help you though, i worked as a deep learning engineer in computer vision:

1. do you mean the dimension of 1 sample is [2000, 5] ? that is a very weird shape for an image. usually they have a shape of [h, w, 3] and [h, w, 4] for video data - a temporal additional dimension is added
2. what do you want this model should be classifying ? so far it sounds more trivial - but depending on the object it might be a bit more complex.
3. the more complex your task -> more complex your model must be -> a larger data set you will need
4. how are the labels distributed in your data set ?
5. do you use adversarial attacks for robustness ? don't do that at the beginning.
6. are you sure that a cnn is the proper model for signal classification ?
7. how do you want to represent your dataset ? what should be the 3rd axes represent as an information ?
8. btw dropouts makes it also more difficult for the model to overfit. you use this so the model learns to generalize
9. i think the model is way to complex when the task is actually trivial. but i never did any signal classification
10. the use of sigmoid can lead to exploding gradients

no, that was an example code to show you how much better the code is readable when you use markdowns..

DLPlotter is a library i am building in the moment.. :)

try using markdowns:

​

        plotter = DLPlotter()     # add this line
        model = MyModel()
        ...
        total_loss = 0
        for epoch in range(5):
            for step, (x, y) in enumerate(loader):
                ...
                output = model(x)
                loss = loss_func(output, y)
                total_loss += loss.item()
                ...
        config = dict(lr=0.001, batch_size=64, ...)
        plotter.collect_parameter("exp001"", config, total_loss / (5 * len(loader))     # add this line
        plotter.construct()     # add this line

too me it also sounds like a bad learning rate. have you checked the distribution of your weights for each layer in each step?

P.S: try hyperparameter optimization methods like grid search or baysian. in that way you get faster an answer to your question..