Hi Vellamike,
I still don’t get why you would want to do that.
CrossEntropy simply compares the scores that your model outputs against a one-hot encoded vector where the 1 is in the index corresponding to the true label.
If your input are sequences of length 10, then you need to build a model that accepts 10 inputs and apply a tranformation into 3 outputs, which will be your feature vector or scores for that classes. Then you can apply softmax to normalize them.
Now is when you use CELoss to compute the diferrence between the output of your model and the true labels.
I hope it helps.
Pablo