Convert a binary neural network classifier to one verses all classifier I have a neural network model (implemented from scratch) which gives me some continuous outputs and I have used a sigmoid layer, in the end, to convert it into a binary classifier. But my original problem is a multiclass problem.
I tried to use the softmax layer but somehow that cannot be applied to my model architecture as the output is not in that format (I get some continuous value as output).

Thus I realized that an alternative way would be to use one versus all classifier but I am not able to figure out how to put that on top of my binary classifier.
Please forgive me if my concepts are a bit weak, but I would really be thankful if someone could help me with this.

Like, how can I convert the binary classifier to a multiclass classifier while keeping the original architecture and what kind of loss function should be used? Would that be different from the binary classifier loss function?

Thanks in advance.
You may keep N neurons and Sigmoid and treat is like multi-label though your data will always be multi-class. So it should learn like multi-class

Create your own One-vs-One

Have 3 copy of the same model
Fit with A|B, B|C and C|A respectively
For new data, you need the average of two for each Class probability


Use scikit-learn Classifier

OneVsOneClassifier Link
OneVsRestClassifier Link