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Use increasing batch sizes in ud-train

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This commit is contained in:
Matthew Honnibal 2018-03-14 20:15:28 +01:00
parent 7b755414eb
commit d7ce6527fb
1 changed files with 4 additions and 2 deletions
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6
spacy/cli/ud_train.py
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@ -13,6 +13,7 @@ import spacy
import spacy.util import spacy.util
from ..tokens import Token, Doc from ..tokens import Token, Doc
from ..gold import GoldParse from ..gold import GoldParse
from ..util import compounding
from ..syntax.nonproj import projectivize from ..syntax.nonproj import projectivize
from ..matcher import Matcher from ..matcher import Matcher
from collections import defaultdict, Counter from collections import defaultdict, Counter
@ -36,7 +37,7 @@ lang.ja.Japanese.Defaults.use_janome = False
random.seed(0) random.seed(0)
numpy.random.seed(0) numpy.random.seed(0)
def minibatch_by_words(items, size=5000): def minibatch_by_words(items, size):
random.shuffle(items) random.shuffle(items)
if isinstance(size, int): if isinstance(size, int):
size_ = itertools.repeat(size) size_ = itertools.repeat(size)
@ -368,9 +369,10 @@ def main(ud_dir, parses_dir, config, corpus, limit=0):
optimizer = initialize_pipeline(nlp, docs, golds, config) optimizer = initialize_pipeline(nlp, docs, golds, config)
batch_sizes = compounding(config.batch_size //10, config.batch_size, 1.001)
for i in range(config.nr_epoch): for i in range(config.nr_epoch):
docs = [nlp.make_doc(doc.text) for doc in docs] docs = [nlp.make_doc(doc.text) for doc in docs]
batches = minibatch_by_words(list(zip(docs, golds)), size=config.batch_size) batches = minibatch_by_words(list(zip(docs, golds)), size=batch_sizes)
losses = {} losses = {}
n_train_words = sum(len(doc) for doc in docs) n_train_words = sum(len(doc) for doc in docs)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar: with tqdm.tqdm(total=n_train_words, leave=False) as pbar: