Exactly, my model has 10 output neurons, and I am ignoring half of them in these two steps. A little explanation is below.
Assume that prior to training the model, there will be a total of ten classes (the size of the target one-hot vector is 10). However, in step 1, I show only the first five classes (the one-hot vector’s last 5 values remain 0, and the first five change depending on the input class) and train and save the model.
In step 2, I reload the model (which has already learned the first five classes) and learn the remaining classes without learning the previously learned classes (the one-hot vector first 5 values always remain 0, and the last five change depending on the input class), retraining and saving it.
Then, I expect the model to know all ten classes at this point, but in my experiment, it forgets the step 1 learned classes.