ECE Increasing

[austinmw]

Hi,

I ran this with a very simple 10 layer CNN model I trained on MNIST using pytorch lightning.

orig_model = pl_module.model
val_loader = trainer.datamodule.val_dataloader()
scaled_model = ModelWithTemperature(orig_model)
scaled_model.set_temperature(val_loader)

But the ECE ends up increasing instead of decreasing:

Before temperature - NLL: 0.645, ECE: 0.271
Optimal temperature: 1.229
After temperature - NLL: 0.779, ECE: 0.351

Any idea why this could be?

[Liel-leman]

same for me :
Before temperature - NLL: 0.058, ECE: 0.002
Optimal temperature: 1.316
After temperature - NLL: 0.061, ECE: 0.010

[dwil2444]

Check if Model output is logits vector or softmax probs @NoSleepDeveloper @austinmw

[RobbenRibery]

same applies for me, the model is output logit vector, not softmax

[zhangyx0417]

I'm wondering if I could use ECE as optimization goal rather than NLL, if the overhead is not large? (Since there is problem above)

[RobbenRibery]

I don't think ECE is differenable bro

[RobbenRibery]

But that being siad, NLL is the metric that we should minise in order to make P(Y=y^|y^=f(x)) = f(x) [perfectly calibrated model, you may think the output probs follow a categorical distribution paramertirsed by f(x) ]

[tomgwasira]

Try increasing the learning rate or increasing max_iter. Your optimisation needs to converge. In the __init__ function of ModelWithTemperature create an empty list to store the loss i.e.

self.loss = []

then before return loss in the eval function, append loss to the list

self.loss.append(loss.item())

After your call to set_temperature, plot the values in the self.loss list and see if the loss was minimised. The loss curve should taper off to some value that's somewhat constant after convergence.

[MengyuanChen21]

After the optimization has converged, I still fail to get decreasing ECE.

I wonder, is it possible for us to get the optimal temperature by optimizing NLL loss on the validation set? I think it is a little strange.