def on_epoch(
self,
trainer: "GradientDescentTrainer",
metrics: Dict[str, Any],
epoch: int,
is_primary: bool = True,
**kwargs,
) -> None:
if not is_primary:
return None
assert self.trainer is not None
train_metrics, val_metrics = get_train_and_validation_metrics(metrics)
self.log_epoch(
train_metrics,
val_metrics,
epoch,
)
def on_epoch(
self,
trainer: "GradientDescentTrainer",
metrics: Dict[str, Any],
epoch: int,
is_primary: bool = True,
**kwargs,
) -> None:
if not is_primary:
return None
train_metrics, val_metrics = get_train_and_validation_metrics(metrics)
metric_names = set(train_metrics.keys())
if val_metrics is not None:
metric_names.update(val_metrics.keys())
val_metrics = val_metrics or {}
dual_message_template = "%s | %8.3f | %8.3f"
no_val_message_template = "%s | %8.3f | %8s"
no_train_message_template = "%s | %8s | %8.3f"
header_template = "%s | %-10s"
name_length = max(len(x) for x in metric_names)
logger.info(header_template, "Training".rjust(name_length + 13),
"Validation")
for name in sorted(metric_names):
train_metric = train_metrics.get(name)
val_metric = val_metrics.get(name)
if val_metric is not None and train_metric is not None:
logger.info(dual_message_template, name.ljust(name_length),
train_metric, val_metric)
elif val_metric is not None:
logger.info(no_train_message_template, name.ljust(name_length),
"N/A", val_metric)
elif train_metric is not None:
logger.info(no_val_message_template, name.ljust(name_length),
train_metric, "N/A")