Minimal working example for TabNetRegressor/Classifier
SurtaiHan opened this issue · comments
Hi, I'm trying to make a minimal working example to understand how to use TabNet.
Currently, with the features as the identity matrix of size 3, and the target values as a vector [3,4,5], the final fitted model seems to predict negative values.
If the current behavior is a bug, please provide the steps to reproduce.
The full code to reproduce the issue is shown below, along with the current output.
Expected behavior
I expect it to correctly produce [3,4,5] on the given input because it seems like this should be learnable even with a linear regression. Although I'm not sure if this expectation is unrealistic for DNNs.
Screenshots
Other relevant information:
poetry version:
python version:
Operating System:
Additional tools:
Additional context
I'm also a bit confused why loss=0 makes sense alongside a non-zero mse.
Seems like when batch_size is greater than the number of datapoints, loss is 0. When I set batch size to 2, it works for the classifier case.
During training, drop_last=True for the dataloader, so if your entire dataset is smaller than your batch size then the model is never trained at all. loss=0 is because it is instanciated with the value 0.
that worked, thanks!