I work in the DL area with previous background in systems. The field is diverse and rapidly changing. I have sort of come to the conclusion that - for me - it is best to learn the application of the technology in a few chosen domains (say NLP, image recognition) rather than chasing everything that is happening.

Got back to a totally crazy week at work. Finally got time to spend on my project. I think I need to simplify my inputs and give MFCC another try before jumping into CNNs

I don’t follow the computation. How 45.5k bits?

I tried a model with [512,512,512] (perceptrons in each layer) and that performed very poorly < 0.2 accuracy.

Certainly. I've got to travel a couple of days but Tue after work I'll be back on this.

Yes you are correct. I am flattening (1723, 13) shape data.

I will try out CNN as well.

The data is MFCCs created from audio files. Sort of like this - https://www.youtube.com/watch?v=szyGiObZymo

I tried that as well with similar results when adding L2+dropout

I see. I’ll try increasing the data used. My fear is that it may lead to a some categories having much less data than others.

L2 0.001 and Dropout 0.1

For creating test and val I used test_train_spilt from sklearn

I'll I manually examine it.

But in general shouldn't the distribution be OK?

inputs_train, inputs_test, targets_train, targets_test = train_test_split(inputs, targets, test_size=0.1)

Thank you for the response. I am breaking my data to train and validation sets. Do you mean another set for test?

The baseline is overfitting - test accuracy is really high and val accuracy is much lower. That is why I added L2+Dropout

Since the validation accuracy is still very low (52%) should I not focus on improving that?