From the first lesson, when I usually do a `learn.fit_one_cycle(...)`

metrics `train_loss`

, `valid_loss`

, `error_rate`

are printed out.

But if I’m loading a model from a previously saved model, how do I calculate just the metrics again, or directly print them if they are stored with the model? I don’t want to retrain, the model, I just want to print out the metrics for the loaded model. And if I’m understanding it correctly, calling `fit`

or `fit_one_cycle`

will retrain the model for an epoch.

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Isn’t there a way to just re-calculate them on the data set. Maybe as a part of the ClassificationInterpretation object ?

I had the same problem and used the confusion matrix from the ClassificationInterpretation object to calculate the error_rate

Can you share your code here?

yeah, sure. just use this line:

round(1-sum(interp.confusion_matrix().diagonal())/interp.confusion_matrix().sum(),6)

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Has anyone figured out how to do it in fast ai, or that if its possible?