How backpropagation through gradient descent represents the error after each forward pass
To get total error before back propagating - it is common to take an average of all the forward-pass errors. This is what's done in RNN such as LSTM.

In the case of linear regression and logistic regression, The traditional Mean Squared Error Function can produce such a value.

In essence, this value is represented by an average of errors: $Q(w) = 1/n{\sum_{i=1}^n Q_i(w)}$

Also, as a reminder, speaking of an actual backpropagation - from wikipedia:

When used to minimize the above function, a standard (or "batch") gradient descent method would perform the following iterations:

$$w:=w - {\eta}\nabla Q(w) = w - \eta{\sum_{i=1}^n}\nabla Q_i(w)/n$$

notice the $/n$ When used with the ${\sum_{i=1}^n}$ it results in the average of all gradients

:= means 'becomes qual to'

$\eta$ is the learning rate