# Displaying network error as a single value

I've been writing a neural network from scratch. I've completed the feedforward, backpropagation, and mini-batch gradient descent methods, so I can train the network. Other neural networks I've worked with usually display the error/loss after each batch as a single decimal value, and I'd like to implement this functionality but I'm not sure how.

I understand squared error is given by $$(y - \hat{y})^2$$, and that for an output layer with $$m$$ neurons, you should have an error vector of size $$m$$. However, how is the error vector displayed as one value?