Comments (4)
originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-11-21T20:54:13.437Z on GitLab
Also we probably shouldn't be naming factory functions for loss functions get_XXX_loss
.
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originally posted by Bas Nijholt (@basnijholt) at 2018-12-07T19:21:26.066Z on GitLab
@anton-akhmerov I think we addressed these points (except the second one) recently.
I don't really understand what you mean with
- We should test whether a learner provides a correct input to the loss function. For example if we say that
Learner2D
passes an interpolation instance to the loss, we should try and runLearner2D
with the loss that verifies that its input is indeed an instance of interpolation. We did not realize this, butloss
is a part of the learner's public API.
Should we just check the data type? Is that what you mean? If so, why would this be useful?
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-12-07T20:31:19.691Z on GitLab
I think we addressed these points (except the second one) recently.
I cannot confirm that learners clearly document the loss format.
-
Learner1D
-
Learner2D
. I may be overly nitpicky here, but the description seems rather vague. Also I think it should go into the parameters section and not the notes. -
LearnerND
Did I miss any learner with customizable loss?
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-12-07T20:32:56.682Z on GitLab
Should we just check the data type? Is that what you mean? If so, why would this be useful?
I think that makes sense for the purpose of API stability.
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Related Issues (20)
- make triangulation tests stronger with more randomness HOT 1
- learner tests fail HOT 2
- use a ItemSortedDict for the loss in the LearnerND
- divide by zero warnings in LearnerND
- Issues that can potentially be closed
- Improvements to plotting of the LearnerND
- Learner.load does not raise an exception if the provided filename was not found HOT 5
- Specify an API for defining the scale of point
- (LearnerND) use direct neighbours in loss
- (LearnerND) add advanced usage example HOT 1
- Document and test loss function signatures HOT 4
- Balancing learner does not work with Integrator learner HOT 5
- make triangulation tests stronger with more randomness HOT 1
- learner tests fail HOT 4
- deprecate Learner2D HOT 3
- use a ItemSortedDict for the loss in the LearnerND
- suggested points lie outside of domain HOT 2
- use a ItemSortedDict for the loss in the LearnerND
- Specify an API for defining the scale of point
- Runners should tell learner about remaining points at end of run HOT 1
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