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Tidy up train-from-config a bit

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This commit is contained in:
Matthw Honnibal 2020-05-20 12:56:27 +02:00
parent fda7355508
commit 60e8da4813
1 changed files with 24 additions and 14 deletions
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36
spacy/cli/train_from_config.py
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@ -193,10 +193,11 @@ def train_from_config(
optimizer, optimizer,
train_batches, train_batches,
evaluate, evaluate,
training["dropout"], dropout=training["dropout"],
training["patience"], accumulate_gradient=training["accumulate_gradient"],
training["eval_frequency"], patience=training.get("patience", 0),
training["accumulate_gradient"] max_steps=training.get("max_steps", 0),
eval_frequency=training["eval_frequency"],
) )
msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}") msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
@ -214,17 +215,17 @@ def train_from_config(
progress = tqdm.tqdm(total=training["eval_frequency"], leave=False) progress = tqdm.tqdm(total=training["eval_frequency"], leave=False)
finally: finally:
if output_path is not None: if output_path is not None:
with nlp.use_params(optimizer.averages):
final_model_path = output_path / "model-final" final_model_path = output_path / "model-final"
if optimizer.averages:
with nlp.use_params(optimizer.averages):
nlp.to_disk(final_model_path)
else:
nlp.to_disk(final_model_path) nlp.to_disk(final_model_path)
msg.good("Saved model to output directory", final_model_path) msg.good("Saved model to output directory", final_model_path)
# with msg.loading("Creating best model..."):
# best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
# msg.good("Created best model", best_model_path)
def create_train_batches(nlp, corpus, cfg): def create_train_batches(nlp, corpus, cfg):
is_first = True epochs_todo = cfg.get("max_epochs", 0)
while True: while True:
train_examples = list(corpus.train_dataset( train_examples = list(corpus.train_dataset(
nlp, nlp,
@ -240,6 +241,11 @@ def create_train_batches(nlp, corpus, cfg):
batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"]) batches = util.minibatch_by_words(train_examples, size=cfg["batch_size"])
for batch in batches: for batch in batches:
yield batch yield batch
epochs_todo -= 1
# We intentionally compare exactly to 0 here, so that max_epochs < 1
# will not break.
if epochs_todo == 0:
break
def create_evaluation_callback(nlp, optimizer, corpus, cfg): def create_evaluation_callback(nlp, optimizer, corpus, cfg):
@ -270,8 +276,8 @@ def create_evaluation_callback(nlp, optimizer, corpus, cfg):
def train_while_improving( def train_while_improving(
nlp, optimizer, train_data, evaluate, dropout, patience, eval_frequency, nlp, optimizer, train_data, evaluate, *, dropout, eval_frequency,
accumulate_gradient accumulate_gradient=1, patience=0, max_steps=0
): ):
"""Train until an evaluation stops improving. Works as a generator, """Train until an evaluation stops improving. Works as a generator,
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`, with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
@ -281,6 +287,7 @@ def train_while_improving(
Positional arguments: Positional arguments:
nlp: The spaCy pipeline to evaluate. nlp: The spaCy pipeline to evaluate.
optimizer: The optimizer callable.
train_data (Iterable[Batch]): A generator of batches, with the training train_data (Iterable[Batch]): A generator of batches, with the training
data. Each batch should be a Sized[Tuple[Input, Annot]]. The training data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
data iterable needs to take care of iterating over the epochs and data iterable needs to take care of iterating over the epochs and
@ -344,9 +351,12 @@ def train_while_improving(
yield batch, info, is_best_checkpoint yield batch, info, is_best_checkpoint
if is_best_checkpoint is not None: if is_best_checkpoint is not None:
losses = {} losses = {}
# Stop if no improvement in `patience` updates # Stop if no improvement in `patience` updates (if specified)
best_score, best_step = max(results) best_score, best_step = max(results)
if (step - best_step) >= patience: if patience and (step - best_step) >= patience:
break
# Stop if we've exhausted our max steps (if specified)
if max_steps and (step * accumulate_gradient) >= max_steps:
break break