diff --git a/spacy/training/loggers.py b/spacy/training/loggers.py index f0ca7064a..467f1e36b 100644 --- a/spacy/training/loggers.py +++ b/spacy/training/loggers.py @@ -41,19 +41,10 @@ def console_logger(progress_bar: bool = False): if progress is not None: progress.update(1) return - try: - losses = [ - "{0:.2f}".format(float(info["losses"][pipe_name])) - for pipe_name in logged_pipes - ] - except KeyError as e: - raise KeyError( - Errors.E983.format( - dict="scores (losses)", - key=str(e), - keys=list(info["losses"].keys()), - ) - ) from None + losses = [ + "{0:.2f}".format(float(info["losses"][pipe_name])) + for pipe_name in logged_pipes if pipe_name in info["losses"] + ] scores = [] for col in score_cols: diff --git a/spacy/training/loop.py b/spacy/training/loop.py index 0d4414964..8f0aea6d4 100644 --- a/spacy/training/loop.py +++ b/spacy/training/loop.py @@ -184,7 +184,7 @@ def train_while_improving( and hasattr(proc, "model") and proc.model not in (True, False, None) ): - proc.model.finish_update(optimizer) + proc.finish_update(optimizer) optimizer.step_schedules() if not (step % eval_frequency): if optimizer.averages: @@ -287,7 +287,8 @@ def update_meta( if metric is not None: nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0) for pipe_name in nlp.pipe_names: - nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name] + if pipe_name in info["losses"]: + nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name] def create_before_to_disk_callback(