11/25/2023 0 Comments Sparse categorical cross entropy![]() ![]() the number of categories is large to the prediction output becomes overwhelming.you don’t care at all about other close-enough predictions, when your classes are mutually exclusive, i.e.There are a number of situations to use scce, including: when each sample belongs exactly to one class) and categorical crossentropy when one sample can have multiple classes or labels are soft probabilities (like 0.5, 0.3, 0.2). Many categorical models produce scce output because you save space, but lose A LOT of information (for example, in the 2nd example, index 2 was also very close.) I generally prefer cce output for model reliability. Use sparse categorical crossentropy when your classes are mutually exclusive (e.g. Sparse Categorical Cross-entropy and multi-hot categorical cross-entropy use the same equation and should have the same output. In the case of scce, the target index might be, and the model may predict Following is the definition of cross-entropy when the number of classes is larger than 2.In the case of cce, the one-hot target might be and the model may predict (probably inaccurate, given that it gives more probability to the first class).Ĭonsider now a classification problem with 3 classes. What does fromlogitsTrue do in SparseCategoricalcrossEntropy loss function Ask Question Asked 3 years, 3 months ago Modified 11 months ago Viewed 44k times 44 In the documentation it has been mentioned that ypred needs to be in the range of -inf to inf when fromlogitsTrue. In the case of scce, the target index may be and the model may predict.In the case of cce, the one-hot target may be and the model may predict (probably right).I think this is the one used by PytrochĬonsider a classification problem with 5 categories (or classes). sparse_categorical_crossentropy ( scce) produces a category index of the most likely matching category.categorical_crossentropy ( cce) produces a one-hot array containing the probable match for each category,.A bit late but I was trying to understand how Pytorch loss work and came across this post, on the other hand the difference is Simply: ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |