Hi Luis!



 Luis_Costa:

Do you have any idea what am I doing wrong?


No, not really.
I also don’t have any idea what you are trying to do.
Could you explain in a few sentences the problem you are working
on?  What is your input data?  It sounds like you are working on a
classification problem.  What are your class labels?


The output from the model, of one image is:
tensor([[3.8711e-05, 4.7626e-06, 7.6847e-07,  ..., 1.1946e-06, 2.9188e-06,
         1.8121e-06],
        [1.7615e-06, 3.0756e-06, 2.0859e-06,  ..., 5.7203e-06, 7.6894e-06,
         3.1614e-06],
        [1.8942e-06, 2.1382e-06, 1.1094e-06,  ..., 2.8606e-06, 5.4414e-06,
         1.8292e-06],
...



Well, this can’t be the tensor you were passing through softmax()
that gave you a result tensor whose elements were all 0.0039.
Here’s one point of confusion:


with this sape: torch.Size([256, 256])
this what I get doing logits[0].shape.


Does this mean that [256, 256] is the shape of the output of your
model?  Or is [ 256, 256] the shape of output_of_your_model[0]?
What is your batch size?  What is the shape of the actual output of your
model?  What is your loss function?  What is the shape of your labels?
Best.
K. Frank