Train_loss, valid_loss, error_rate on a saved/loaded model

Part 1 (2019)
1

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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3

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

4

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

5

Can you share your code here?

6

yeah, sure. just use this line:

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

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7

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