What should be the ideal constraint for calculating weighted loss?

We can calculate weighted loss by defining a custom loss like this:

custom_loss = l_1 * loss1 + l_2 * loss2 + …. + l_N * lossN

I wanted to know that, should the constraint for l_i’s be

l_1 + l_2 + … + l_N = 1

or it could be greater than 1?

What are the pros and cons for choosing a value larger than 1?

A weight constraint is an update to the network that checks the size of the weights, and if the size exceeds a predefined limit, the weights are rescaled so that their size is below the limit or between a range.

You are talking about L1 and L2 weight regularization. I was talking about weighted loss :slight_smile:

I wanted to know about the design of loss function as shown in this post (for example) python - Keras/Tensorflow: Combined Loss function for single output - Stack Overflow