Hi Josep!



 titoniubo:

Can you recommend me any tutorial where Pytorch is used for Logistic Regression where the input is different from an image?
All tutorials I find use MNIST dataset.


I don’t know offhand of a tutorial that is pytorch and logistic regression,
but doesn’t use the MNIST dataset.
But it doesn’t matter.  If you look at one of your logistic-regression
tutorials (if it’s done right) you will see that it ignores the spatial
structure of the MNIST image.  You just need to use your 20 variables
in lieu of the ~1000 variable (pixels) in the MNIST image (and adjust
the number of input variable in your model accordingly).


I try to use Pytorch for a normal mulit-class classification problem, where I have over 20 variables and 1 target variable (5 class)


Just to be clear, you will be doing a multinomial (multiclass)
logistic regression (in contrast to binary).  And (in the interest of
working through a practice problem) you’ll be using a sledge
hammer (pytorch) to perform an old-school logistic regression,
rather than using the power of neural networks (using pytorch)
to build what would likely be a much more capable classifier.
(There’s nothing the matter with this – I just want to make sure you
know that doing logistic regression with pytorch is a toy problem.)
Best.
K. Frank