def predict(self, X):
        X = check_tensor(X, dtype=np.float32, n_dim=4)
        res = self._predict_proba(X)[:, :, 0, 0]
        indices = np.argsort(res, axis=1)
        indices = indices[:, -self.top_n:]
        if self.output_strings:
            class_strings = np.empty_like(indices,
                                          dtype=object)
            for index, value in enumerate(indices.flat):
                class_strings.flat[index] = get_overfeat_class_label(value)
            return class_strings
        else:
            return indices
Pad cell_bytes to uniform length.
dataset/ETHPy150Open sklearn-theano/sklearn-theano/sklearn_theano/feature_extraction/overfeat.py/OverfeatClassifier.predict--crossover with--dataset/ETHPy150Open nskinkel/oppy/oppy/cell/fixedlen.py/FixedLenCell.padCellBytes