Categorical cross-entropy loss is usually used in settings where the target in one-hot encoded. Suppose I have a problem where there are 300 possible outcomes, and thus my final fully connected layer will have 300 neurons and the output (after softmax) is expected to be probabilities for each class such that their sum is equal to one.
My question is: how to interpret the value of the categorical cross-entropy loss? What does it mean to have a categorical cross-entropy loss of 5 or 0.9 or 0.1?
Does the interpretation of the loss get affected with the number of possible classes?