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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
from ..tokens import Token, Doc
from ..gold import GoldParse
from ..util import compounding
from ..syntax.nonproj import projectivize
from ..matcher import Matcher
from collections import defaultdict, Counter
@ -36,7 +37,7 @@ lang.ja.Japanese.Defaults.use_janome = False
random.seed(0)
numpy.random.seed(0)
def minibatch_by_words(items, size=5000):
def minibatch_by_words(items, size):
random.shuffle(items)
if isinstance(size, int):
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)
batch_sizes = compounding(config.batch_size //10, config.batch_size, 1.001)
for i in range(config.nr_epoch):
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 = {}
n_train_words = sum(len(doc) for doc in docs)
with tqdm.tqdm(total=n_train_words, leave=False) as pbar: