First of all, I realized if I need to perform binary predictions, I have to create at least two classes through performing a one-hot-encoding. Is this correct? However, is binary cross-entropy only for predictions with only one class? If I were to use a categorical cross-entropy loss, which is typically found in most libraries (like TensorFlow), would there be a significant difference?
In fact, what are the exact differences between a categorical and binary cross-entropy? I have never seen an implementation of binary cross-entropy in TensorFlow, so I thought perhaps the categorical one works just as fine.