Comments (3)
To show the loss for each epoch, the _fit()
method should return the loss for the batch, so the average loss for each epoch can be computed in the fit()
method.
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It seems like a good opportunity to add callbacks to the estimator, so that, for example, any metric in addition to the loss value can be calculated and printed. This would replace the verbose
parameter.
What do you think?
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This issue is closed because it is related to issue #66
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Related Issues (14)
- [API] `distributions` module should be renamed to `soft_labelling` HOT 2
- [API] Standardisation of the interface of the functions of the module distributions HOT 3
- distributions module docstrings should be improved
- Imports in test files should be absolute
- Avoid default value for `num_classes` parameter in Unimodal Loss Functions HOT 1
- PytorchEstimator predict and predict_proba interface changes
- Warnings in tutorials
- [ENH, DOC] Displaying descriptions of the class attributes.
- [ENH, DOC] Documentation generation steps
- [MNT] `label_smoothing` parameter of `CrossEntropyLoss` should not be exposed in soft labelling loss functions HOT 1
- [API, MNT] `PytorchEstimator` deprecation
- [BUG] Numerical instability in CLM activation layer
- [MNT] `matplotlib` and `seaborn` can be removed from dependencies list
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