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Clean up spacy.cli.train

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This commit is contained in:
Matthew Honnibal 2017-05-25 16:16:30 -05:00
parent b9cea9cd93
commit 702fe74a4d
1 changed files with 22 additions and 29 deletions
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51
spacy/cli/train.py
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@ -14,7 +14,7 @@ from timeit import default_timer as timer
from ..tokens.doc import Doc
from ..scorer import Scorer
from ..gold import GoldParse, merge_sents
from ..gold import GoldCorpus
from ..gold import GoldCorpus, minibatch
from ..util import prints
from .. import util
from .. import displacy
@ -53,44 +53,38 @@ def train(_, lang, output_dir, train_data, dev_data, n_iter=20, n_sents=0,
if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
if no_entities and 'entities' in pipeline: pipeline.remove('entities')
# Take dropout and batch size as generators of values -- dropout
# starts high and decays sharply, to force the optimizer to explore.
# Batch size starts at 1 and grows, so that we make updates quickly
# at the beginning of training.
dropout_rates = util.decaying(util.env_opt('dropout_from', 0.0),
util.env_opt('dropout_to', 0.0),
util.env_opt('dropout_decay', 0.0))
batch_sizes = util.compounding(util.env_opt('batch_from', 1),
util.env_opt('batch_to', 64),
util.env_opt('batch_compound', 1.001))
nlp = lang_class(pipeline=pipeline)
corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
dropout = util.env_opt('dropout', 0.0)
dropout_decay = util.env_opt('dropout_decay', 0.0)
orig_dropout = dropout
n_train_docs = corpus.count_train()
optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu)
n_train_docs = corpus.count_train()
batch_size = float(util.env_opt('min_batch_size', 4))
max_batch_size = util.env_opt('max_batch_size', 64)
batch_accel = util.env_opt('batch_accel', 1.001)
print("Itn.\tDep. Loss\tUAS\tNER P.\tNER R.\tNER F.\tTag %\tToken %")
for i in range(n_iter):
with tqdm.tqdm(total=n_train_docs) as pbar:
train_docs = corpus.train_docs(nlp, shuffle=i, projectivize=True,
gold_preproc=False)
with tqdm.tqdm(total=corpus.count_train()) as pbar:
train_docs = corpus.train_docs(nlp, projectivize=True,
gold_preproc=False, shuffle=i)
losses = {}
idx = 0
while idx < n_train_docs:
batch = list(cytoolz.take(int(batch_size), train_docs))
if not batch:
break
for batch in minibatch(train_docs, size=batch_sizes):
docs, golds = zip(*batch)
nlp.update(docs, golds, drop=dropout, sgd=optimizer, losses=losses)
nlp.update(docs, golds, sgd=optimizer,
drop=next(dropout_rates), losses=losses)
pbar.update(len(docs))
idx += len(docs)
batch_size *= batch_accel
batch_size = min(batch_size, max_batch_size)
dropout = linear_decay(orig_dropout, dropout_decay, i*n_train_docs+idx)
with nlp.use_params(optimizer.averages):
start = timer()
scorer = nlp.evaluate(corpus.dev_docs(nlp, gold_preproc=False))
end = timer()
n_words = scorer.tokens.tp + scorer.tokens.fn
assert n_words != 0
wps = n_words / (end-start)
print_progress(i, losses, scorer.scores, wps=wps)
print_progress(i, losses, scorer.scores)
with (output_path / 'model.bin').open('wb') as file_:
with nlp.use_params(optimizer.averages):
dill.dump(nlp, file_, -1)
@ -118,7 +112,6 @@ def print_progress(itn, losses, dev_scores, wps=0.0):
tpl = '\t'.join((
'{:d}',
'{dep_loss:.3f}',
'{tag_loss:.3f}',
'{uas:.3f}',
'{ents_p:.3f}',
'{ents_r:.3f}',