606a61abf04e3108949022ae1bcea975b2adb560,allennlp/training/trainer.py,Trainer,train,#Trainer#,724

Before Change

            else:
                best_validation_metric = max(validation_metric_per_epoch)
            metrics.update({f"best_validation_{k}": v for k, v in best_epoch_val_metrics.items()})
            metrics["best_epoch"] = [i for i, value in enumerate(validation_metric_per_epoch)
                                     if value == best_validation_metric][-1]
        return metrics

    def _is_best_so_far(self,
                        this_epoch_val_metric: float,

After Change

                // Update all the best_ metrics.
                // (Otherwise they just stay the same as they were.)
                metrics["best_epoch"] = epoch
                for key, value in val_metrics.items():
                    metrics["best_validation_" + key] = value

            if self._serialization_dir:
                dump_metrics(os.path.join(self._serialization_dir, f"metrics_epoch_{epoch}.json"), metrics)

            if self._learning_rate_scheduler:

Instances