Closed tumbleintoyourheart closed 5 years ago
what do logits , probas and predicted_levels mean?
The logits are basically the inverse of the probabilities -- the name comes from logistic regression referring to the "natural logarithm of the odds" (log-odds). In the case of neural networks, people usually call the values before applying the last sigmoid or softmax activation the logits.
The "probas" are the class-membership probabilities then. The "P"'s here:
Predicted levels are the binarized version of the probabilities. If the predicted probability is greater than 0.5 (more than 50%), it's 1, and 0 otherwise.
why predicted_label = sum(predict_levels)
The answer is basically also in the figure above, but to illustrate that with an example:
If you have 5 classes and the true label is 3, you may predict
Then, the predicted labels is 1+1+1+0=3.
Hope that helps!
A very concise explanation. I appreciate it. My respect, Sir. Do you want me to close the issue or keep it open?
Glad to hear that the explanation was helpful. I guess we can close this then.
Dear Sir,
First of all thanks for sharing this great repo
I have some questions. Hopefully, this will not bother you
In
single-image-prediction/afad_coral.py
, can you explainlogits
,probas
andpredicted_levels
mean?predicted_label
=sum(predict_levels)
?I have had read the paper but still not been able to clarify those
Thank you for your time Sir
Originally posted by @tumbleintoyourheart in https://github.com/Raschka-research-group/coral-cnn/issues/6#issuecomment-528066010