def score(self, X, y, **kwargs):
        kwargs = self.filter_sk_params(Sequential.evaluate, kwargs)
        outputs = self.model.evaluate(X, y, **kwargs)
        if type(outputs) is not list:
            outputs = [outputs]
        for name, output in zip(self.model.metrics_names, outputs):
            if name == 'acc':
                return output
        raise Exception('The model is not configured to compute accuracy. '
                        'You should pass `metrics=["accuracy"]` to '
                        'the `model.compile()` method.')
Returns the mean accuracy on the given test data and labels.
dataset/ETHPy150Open fchollet/keras/keras/wrappers/scikit_learn.py/KerasClassifier.score