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Add evaluation_loss to the estimator base class. #16888
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leezu
reviewed
Nov 22, 2019
|
Could you also add a test case to https://github.com/apache/incubator-mxnet/blob/master/tests/python/unittest/test_gluon_estimator.py to prevent future regressions of this feature |
leezu
reviewed
Nov 25, 2019
| @@ -59,6 +59,8 @@ class Estimator(object): | |||
| Trainer to apply optimizer on network parameters. | |||
| context : Context or list of Context | |||
| Device(s) to run the training on. | |||
| evaluation_loss: gluon.loss.loss | |||
| Loss (objective) function to calculate during evaluation. | |||
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It's not clear that evaluation_loss will use loss if it is None. Could you improve the description here?
leezu
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Nov 25, 2019
ptrendx
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Nov 25, 2019
* Add evaluation_loss to the estimator base class. * Update the base estimator class to support the separate evaluation loss. * Add evaluation loss to the base estimator class. * Add unittest for evaluation loss in the test_evaluation function * Update estimator.py * Update estimator.py
ptrendx
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Nov 26, 2019
* refactor and reduce float types for some functions, also add bitwise_xor (#16827) * Mixed precison binary op backward (use in) for numpy (#16791) * mixed precison binary op backward * reduce unix cpu runtime * Add evaluation_loss to the estimator base class. (#16888) * Add evaluation_loss to the estimator base class. * Update the base estimator class to support the separate evaluation loss. * Add evaluation loss to the base estimator class. * Add unittest for evaluation loss in the test_evaluation function * Update estimator.py * Update estimator.py
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Description
[Bug_fix] Add evaluation loss member in estimator class. The purpose of add the evaluation loss is to decouple the training loss with the evaluation loss in the fit_batch() and evaluate_batch() methods. Please refer to the issue for further information: #16879