Hi @ptrblck
I did some training yesterday and the NN seems to be learning, the loss keeps going down steadily and appears to be converging.
Epoch 1/100
100% 5/5 [00:05<00:00,  1.80s/it, loss=2.325]
100% 5/5 [00:05<00:00,  1.90s/it, loss=2.281]
100% 5/5 [00:05<00:00,  1.78s/it, loss=2.242]


100% 5/5 [00:04<00:00,  1.41s/it, loss=0.075]
100% 5/5 [00:05<00:00,  1.95s/it, loss=0.072]

Epoch 100/100
100% 5/5 [00:04<00:00,  1.50s/it, loss=0.070]

I’ll now need to look at the output masks and see if it is working correctly.
I must admit, I don’t quite follow what’s happening here. How does the class to index mapping work? I understand the part about taking the 10 classes and running it through the softmax to work as an activation for the 10 classes, but what happens to a batch of say [4, 10, 24, 24], do we just drop the batches when doing F.softmax(target, dim=1) > 0.5 ?
Don’t we somehow lose the correspondence between the loss per channel, when doing this, during back propagation? i.e. the loss is finally a single scalar value, but is the framework able to keep track of the loss per channel during back propagation?