[MRG] added variation_stop parameter to multylayer perceptron #6518
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usopp
changed the title
amueller
requested changes
Oct 7, 2016
| Count of interations to attempt before stopping if score | ||
| is not improving on the train set or on the validation fraction | ||
| if early_stopping is True. | ||
| Shold be 2 or more |
| Count of interations to attempt before stopping if score | ||
| is not improving on the train set or on the validation fraction | ||
| if early_stopping is True. | ||
| Shold be 2 or more |
| @@ -1152,6 +1163,12 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin): | |||
| early stopping. Must be between 0 and 1. | |||
| Only used if early_stopping is True | |||
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| variation_stop : int, optional, default 2 | |||
| Count of interations to attempt before stopping if score | |||
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@amueller Done |
|
A test to ensure:
would be useful. |
|
I think #9457 makes this redundant. |
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To the issue #6512
As described in book of Simon Haykin "Neural Networks - A Comprehensive Foundation", pp. 237-240, we should find an early stopping point if we use a validation set. But curve of a score on validation set may have it's local minimums and we will end falling in them if we make only 2 iteration after it before stopping. So i added count of iterations made before stop after best score as a parameter. Got an improvemen on MNIST in a few percent with 50 iterations.