def predict_proba(self, x, batch_size=32, verbose=1):
        if self.model is None:
            raise Exception('The model needs to be compiled before being used.')
        preds = self.predict(x, batch_size, verbose)
        if preds.min() < 0. or preds.max() > 1.:
            warnings.warn('Network returning invalid probability values. '
                          'The last layer might not normalize predictions '
                          'into probabilities '
                          '(like softmax or sigmoid would).')
        return preds
Function used internally to send static files from the static
dataset/ETHPy150Open fchollet/keras/keras/models.py/Sequential.predict_proba--crossover with--dataset/ETHPy150Open hhstore/flask-annotated/flask-0.5/flask/helpers.py/_PackageBoundObject.send_static_file