Hi everyone, I have an issue with getting the right indices for predicted classes of the probabilities returned by topk function of pytorch.
My output classes are just 2 (cancerous and noncancerous) and the directories are labeled as (0 for cancerous and 1 for noncancerous).
Unfortunately, after getting my output from model.forward(), applying the softmax function and applying the topk function, I get reasonable probalities but the indices tend to be [436, 600] (out of range) instead of [0,1] which represents my class directories.
I have tried a number way to detect the source of these large index values, but couldn’t have an answer. Help on this would be appreciated.
The screenshop below is a typical example of what I am talking about, just that mine is only 2 classes.
Did you change your network’s final layer to only have a two-dimensional output?
Without evidence to the contrary (i.e. the tensor you input to topk and the result) I would venture that it is more likely that something is unexpected about what you feed into topk than with topk itself.
Next, the topk receives the resulting output probabilities and returns the best 2 probabilities with their indices in a sorted order. Just that the returned indices tend to be out of range. Here is the sample snapshot:
