b05b32dad4671f8e3575baf775f82be03617e1e7,elephas/spark_model.py,SparkModel,_evaluate,#SparkModel#Any#,209

Before Change

            return [model.evaluate(x_test, y_test)]
        if self.num_workers:
            rdd = rdd.repartition(self.num_workers)
        results = rdd.mapPartitions(partial(_evaluate, yaml_model, optimizer, loss, custom_objects)).mean()
        return results

After Change

            return results.mean()
        else:
            // if we do have metrics, we want to return a list of [loss value, metric value] - to match the keras API
            loss_value = results.map(lambda x: x[0]).mean()
            metric_value = results.map(lambda x: x[1]).mean()
            return [loss_value, metric_value]

Instances