Usually I would recommend to scale down the problem a bit and check for possible bugs in the code.
Since your model is apparently not learning the target (I’m not sure what it represents), I would suggest to use just a small part of your training dataset and try to overfit your model.
If your model successfully learns to predict e.g. 1 or 10 samples, you could try to carefully use more samples etc. and try to find out what goes wrong at which point.
However, if your model can’t even predict a single sample, there are most likely some issues in the code or e.g. the architecture is not suitable for this problem.