Binary classification and numerical labels
If you’re going to have more than two labels, you need to go with a softmax activation and a loss for multi class classification, ie cross entropy loss.
Also, be cautious for multi-class versus multi-label (below).

Multi-class
One-of-many classification. Each sample can belong to ONE of $C$ classes. The model will have $C$ output neurons that can be gathered in a vector $s$ (Scores). The target (ground truth) vector $t$ will be a one-hot vector with a positive class and $C−1$ negative classes. This task is treated as a single classification problem of samples in one of $C$ classes.
Multi-label
Each sample can belong to more than one class. The model will have as well
$C$ output neurons. The target vector $t$ can have more than a positive class, so it will be a vector of 0s and 1s with $C$ dimensionality. This task is treated as $C$ different binary $(C′=2,t′=0 \ or \ t′=1)$ and independent classification problems, where each output neuron decides if a sample belongs to a class or not.
source: https://gombru.github.io/2018/05/23/cross_entropy_loss/