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Fix loggers

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This commit is contained in:
Matthew Honnibal 2020-10-03 19:26:10 +02:00
parent 4fccd2ceaf
commit b305f2ff5a
2 changed files with 12 additions and 8 deletions
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8
spacy/training/loggers.py
View File

@ -29,8 +29,8 @@ def console_logger(progress_bar: bool = False):
table_header = [col.upper() for col in table_header]
table_widths = [3, 6] + loss_widths + score_widths + [6]
table_aligns = ["r" for _ in table_widths]
stdout.write(msg.row(table_header, widths=table_widths))
stdout.write(msg.row(["-" * width for width in table_widths]))
stdout.write(msg.row(table_header, widths=table_widths) + "\n")
stdout.write(msg.row(["-" * width for width in table_widths]) + "\n")
progress = None
def log_step(info: Optional[Dict[str, Any]]) -> None:
@ -75,7 +75,9 @@ def console_logger(progress_bar: bool = False):
)
if progress is not None:
progress.close()
stdout.write(msg.row(data, widths=table_widths, aligns=table_aligns))
stdout.write(
msg.row(data, widths=table_widths, aligns=table_aligns) + "\n"
)
if progress_bar:
# Set disable=None, so that it disables on non-TTY
progress = tqdm.tqdm(

12
spacy/training/loop.py
View File

@ -69,10 +69,10 @@ def train(
eval_frequency=T["eval_frequency"],
exclude=frozen_components,
)
stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}"))
stdout.write(msg.info(f"Pipeline: {nlp.pipe_names}") + "\n")
if frozen_components:
stdout.write(msg.info(f"Frozen components: {frozen_components}"))
stdout.write(msg.info(f"Initial learn rate: {optimizer.learn_rate}"))
stdout.write(msg.info(f"Frozen components: {frozen_components}") + "\n")
stdout.write(msg.info(f"Initial learn rate: {optimizer.learn_rate}") + "\n")
with nlp.select_pipes(disable=frozen_components):
log_step, finalize_logger = train_logger(nlp, stdout, stderr)
try:
@ -93,7 +93,7 @@ def train(
msg.warn(
f"Aborting and saving the final best model. "
f"Encountered exception: {str(e)}"
)
) + "\n"
)
raise e
finally:
@ -106,7 +106,9 @@ def train(
else:
nlp.to_disk(final_model_path)
# This will only run if we don't hit an error
stdout.write(msg.good("Saved pipeline to output directory", final_model_path))
stdout.write(
msg.good("Saved pipeline to output directory", final_model_path) + "\n"
)
def train_while_improving(