Multiple output layers and multiple losses handling

Hi, I wanted a network that can take in data and have 2 sets of predictions. My code is written in Gluon. In my custom block, I simply added another Dense layer, and return the two results.
For example, in my custom block,

self.out1 = gluon.nn.Dense(5)
self.out2 = gluon.nn.Dense(19)

and in forward:

x1 = self.out1(x)
x2 = self.out2(x)
return x1, x2

I created 2 loss functions with softmax loss like this:

loss_func_1 = gluon.loss.SoftmaxCrossEntropyLoss()
loss_func_2 = gluon.loss.SoftmaxCrossEntropyLoss()

And in back propagation:

loss = loss_1 + loss_2

This is not giving me meaningful result. Can anyone tell me the right way to handle the multiple outputs and back propagation?

The codes seem fine with me. However, you may need a hyperparameter to balance the two objectives and tune the hyperparameter until you are happy with the results.

Thanks for your reply! My problem is actually solved.