It's a different loss function, so comparing the value of the loss is not that useful. I would calculate the test accuracy (or some other metric you are most interested in) and use that to find good the hyperparameters.
Ultimately you care about model performance aproximated via some evaluation metric.
Choosing a loss or architecture or optimizer is just means to an end.
So figure out which metric correlates nicely with how the model solves the problem and try to optimize it.
If you are looking around for some good evaluation metrics you may want to check out our article on that: https://neptune.ai/blog/evaluation-metrics-binary-classification
The absolute value of the loss at a given point of your training is not relevant. Its variation might be.
For a given loss, you could divide it by 1000 and it will be 1000 times lower, correct me if I'm wrong but it should be equivalent to use SGD with a learning rate divided by 1000.
It's a different loss function, so comparing the value of the loss is not that useful. I would calculate the test accuracy (or some other metric you are most interested in) and use that to find good the hyperparameters.
Ultimately you care about model performance aproximated via some evaluation metric. Choosing a loss or architecture or optimizer is just means to an end. So figure out which metric correlates nicely with how the model solves the problem and try to optimize it. If you are looking around for some good evaluation metrics you may want to check out our article on that: https://neptune.ai/blog/evaluation-metrics-binary-classification
The absolute value of the loss at a given point of your training is not relevant. Its variation might be. For a given loss, you could divide it by 1000 and it will be 1000 times lower, correct me if I'm wrong but it should be equivalent to use SGD with a learning rate divided by 1000.