explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-25 09:26:27 +03:00
Code Issues Packages Projects Releases Wiki Activity

Improve train CLI

Browse Source
This commit is contained in:
Matthew Honnibal 2017-06-04 20:18:37 -05:00
parent a053b1218e
commit c52fde40f4
1 changed files with 8 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
spacy/cli/train.py
View File

@ -18,6 +18,7 @@ from ..gold import GoldCorpus, minibatch
from ..util import prints from ..util import prints
from .. import util from .. import util
from .. import displacy from .. import displacy
from ..compat import json_dumps
@plac.annotations( @plac.annotations(
@ -44,7 +45,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
train_path = util.ensure_path(train_data) train_path = util.ensure_path(train_data)
dev_path = util.ensure_path(dev_data) dev_path = util.ensure_path(dev_data)
if not output_path.exists(): if not output_path.exists():
prints(output_path, title="Output directory not found", exits=1) output_path.mkdir()
if not train_path.exists(): if not train_path.exists():
prints(train_path, title="Training data not found", exits=1) prints(train_path, title="Training data not found", exits=1)
if dev_path and not dev_path.exists(): if dev_path and not dev_path.exists():
@ -74,7 +75,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
else: else:
nlp = lang_class(pipeline=pipeline) nlp = lang_class(pipeline=pipeline)
corpus = GoldCorpus(train_path, dev_path, limit=n_sents) corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
n_train_docs = corpus.count_train() n_train_words = corpus.count_train()
optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu) optimizer = nlp.begin_training(lambda: corpus.train_tuples, device=use_gpu)
@ -83,7 +84,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
for i in range(n_iter): for i in range(n_iter):
if resume: if resume:
i += 20 i += 20
with tqdm.tqdm(total=corpus.count_train(), leave=False) as pbar: with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
train_docs = corpus.train_docs(nlp, projectivize=True, train_docs = corpus.train_docs(nlp, projectivize=True,
gold_preproc=False, max_length=0) gold_preproc=False, max_length=0)
losses = {} losses = {}
@ -91,7 +92,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
docs, golds = zip(*batch) docs, golds = zip(*batch)
nlp.update(docs, golds, sgd=optimizer, nlp.update(docs, golds, sgd=optimizer,
drop=next(dropout_rates), losses=losses) drop=next(dropout_rates), losses=losses)
pbar.update(len(docs)) pbar.update(sum(len(doc) for doc in docs))
with nlp.use_params(optimizer.averages): with nlp.use_params(optimizer.averages):
util.set_env_log(False) util.set_env_log(False)
@ -105,6 +106,9 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
corpus.dev_docs( corpus.dev_docs(
nlp_loaded, nlp_loaded,
gold_preproc=False)) gold_preproc=False))
acc_loc =(output_path / ('model%d' % i) / 'accuracy.json')
with acc_loc.open('w') as file_:
file_.write(json_dumps(scorer.scores))
util.set_env_log(True) util.set_env_log(True)
print_progress(i, losses, scorer.scores) print_progress(i, losses, scorer.scores)
finally: finally: