Issue
I’ve model with two output layers, age and gender prediction layers. I want to assign different weight values for each output layer’s loss. I’ve the following line of code to do so.
model.compile(loss=[losses.mean_squared_error,losses.categorical_crossentropy], optimizer='sgd',loss_weights=[1,10])
My question is what is the effect of loss weights on performance of a model? How can I configure the loss weights so that the model can perform better on age prediction?
Solution
As stated in the book Deep Learning with Python by François Chollet:
The mean squared error (MSE) loss used for the age-regression task
typically takes a value around 3–5, whereas the crossentropy loss used
for the gender-classification task can be as low as 0.1. In such a
situation, to balance the contribution of the different losses, you
can assign a weight of 10 to the crossentropy loss and a weight of
0.25 to the MSE loss.
Answered By – Grigorios Kalliatakis
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