Enable us to use sklearn to do cv for functional api #9320
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fchollet
reviewed
Feb 6, 2018
keras/wrappers/scikit_learn.py
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| if isinstance(self.model, Sequential): | ||
| classes = self.model.predict_classes(x, **kwargs) | ||
| else: | ||
| classes = np.round(self.model.predict(x, **kwargs)).astype(int) |
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Rounding predictions to int does not, in fact, predict classes (unless in the specific case of a binary classification problem). Please check the implementation of predict_classes on Sequential.
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Thanks for taking time to review the commit! I modified the code again referring to the implementation of "predict_classes" on "Sequential".
fchollet
reviewed
Feb 8, 2018
keras/wrappers/scikit_learn.py
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| @@ -247,7 +252,8 @@ def predict_proba(self, x, **kwargs): | |||
| (instead of `(n_sample, 1)` as in Keras). | |||
| """ | |||
| kwargs = self.filter_sk_params(Sequential.predict_proba, kwargs) | |||
| probs = self.model.predict_proba(x, **kwargs) | |||
| # check if the model is sequential or functional | |||
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fchollet
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Feb 8, 2018
ahundt
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Feb 16, 2018
* 'master' of github.com:fchollet/keras: (57 commits) Minor README edit Speed up Travis tests (keras-team#9386) fix typo (keras-team#9391) Fix style issue in docstring Prepare 2.1.4 release. Fix activity regularizer + model composition test Corrected copyright years (keras-team#9375) Change default interpolation from nearest to bilinear. (keras-team#8849) a capsule cnn on cifar-10 (keras-team#9193) Enable us to use sklearn to do cv for functional api (keras-team#9320) Add support for stateful metrics. (keras-team#9253) The type of list keys was float (keras-team#9324) Fix mnist sklearn wrapper example (keras-team#9317) keras-team#9287 Fix most of the file-handle resource leaks. (keras-team#9309) Pass current learning rate to schedule() in LearningRateScheduler (keras-team#8865) Simplify with from six.moves import input (keras-team#9216) fixed RemoteMonitor: Json to handle np.float32 and np.int32 types (keras-team#9261) Update tweet length from 140 to 280 in docs Add `depthconv_conv2d` tests (keras-team#9225) Remove `force` option in progbar ...
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E.g. This modification can enable us to do cross validation for models created by functional api with sklearn.model_selection.cross_validate.