I know that is better avoid loop in
Keras custom loss function, but I think I have to do it.
The problem is the following: I'm trying to implement a loss function that compute a loss value for multiple bunches of data and then aggregate this values in an unique value.
For example I have 6 data entry, so in my
Keras loss I'll have 6
y_true and 6
I want to compute 2 loss value: one for the first 3 elements and one for the last 3 elements.
Example of hypothetical code:
def custom_loss(y_true, y_pred): start_range = 0 losses =  for index in range(0,2): end_range = start_range + 3 y_true_bunch = y_true[start_range:end_range] y_pred_bunch = y_pred[start_range:end_range] loss_value = ...some processing on bunches... losses.append(loss_value) start_range = end_range final_loss = ...aggregate loss_value... return final_loss
Is it possible to achieve something like this? I need to process the whole dataset and calculate loss for multiple bunches and then aggregate all bunches value in a single value.