likelihood loss is computed in a way as below, if the predicated probabilities is [0.4, 0.6, 0.9, 0.1], while the truth labels are [0, 1, 1, 0]. The likelihood loss would be computed as (0.6) * (0.6) * (0.9) * (0.9) = 0.2916. Since the model outputs probabilities for TRUE (or 1) only, when the ground truth label is 0 we take (1-p) as the probability.

log loss or cross entropy loss is more complicated to understand.