Neural Networks - Loss and Accuracy correlation
Log loss has the nice property that it is a differentiable function. Accuracy might be more important and is definitely more interpretable but is not directly usable in the training of the network due to the backpropagation algorithm that requires the loss function to be differentiable. When your preferred loss is not directly optimizable (like the accuracy) you use a loss function that behaves similarly to proxy the true metric. In case of binary classification you would use a sigmoid at the end and a log loss to approximate accuracy. They are highly correlated.