AFAIK f-score is ill-suited as a loss function for training a network. F-score is better suited to judge a classifier’s calibration, but does not hold enough information for the neural network to improve its predictions.
Loss functions 35 are differentiable so that they can propagate gradients throughout the network using the chain rule (see “backpropagation”).