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* Switch beam_sgd to best_first_sgd

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This commit is contained in:
Matthew Honnibal 2016-02-26 02:29:23 +00:00
parent afed99b3c8
commit fca5367aac
1 changed files with 12 additions and 52 deletions
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64
bin/tagger/conll_train.py
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@ -26,7 +26,7 @@ from spacy.tagger import Tagger
class GoldSents(object):
def __init__(self, tokenizer, sents, n=5000):
def __init__(self, tokenizer, sents, n=10000):
self.tokenizer = tokenizer
self.sents = sents
self.n = n
@ -62,22 +62,21 @@ def _parse_line(line):
return id_, word, pos
def beam_sgd(tagger, train_data, check_data):
def best_first_sgd(tagger, train_data, check_data):
print(tagger.model.widths)
print("Itn.\tTrain\tPrev\tNew")
queue = [(score_model(check_data, tagger), 0, tagger)]
workers = [None] * 100
queue = [[score_model(check_data, tagger), 0, tagger]]
limit = 4
while True:
for prev_score, i, tagger in list(queue):
#prev_score, i, tagger = max(queue)
train_acc, new_model = get_new_model(train_data, tagger)
new_score = score_model(check_data, new_model)
queue.append((new_score, i+1, new_model))
print('%d:\t%.3f\t%.3f\t%.3f\t%.4f' % (i, train_acc, prev_score, new_score,
tagger.model.eta))
queue.sort(reverse=True)
queue = queue[:limit]
prev_score, i, tagger = queue[0]
queue[0][0] *= 0.999
train_acc, new_model = get_new_model(train_data, tagger)
new_score = score_model(check_data, new_model)
queue.append([new_score, i+1, new_model])
print('%d:\t%.3f\t%.3f\t%.3f\t%.4f' % (i+1, train_acc, prev_score, new_score,
tagger.model.eta))
return max(queue)
@ -93,7 +92,7 @@ def score_model(gold_sents, tagger):
def get_new_model(gold_sents, tagger):
learn_rate = numpy.random.normal(loc=tagger.model.learn_rate, scale=0.001)
learn_rate = numpy.random.normal(loc=tagger.model.eta, scale=0.0001)
if learn_rate < 0.0001:
learn_rate = 0.0001
@ -137,46 +136,7 @@ def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
heldout_sents = GoldSents(nlp.tokenizer, heldout_sents)
tagger = Tagger.blank(nlp.vocab, [], **model_args)
return beam_sgd(tagger, train_sents, heldout_sents)
#prev_score = 0.0
#variance = 0.001
#last_good_learn_rate = nlp.tagger.model.eta
#n = 0
#total = 0
#acc = 0
#last_model = (nlp.tagger.model.weights, nlp.tagger.model.embeddings)
#while True:
# words, gold_tags = random.choice(train_sents)
# tokens = nlp.tokenizer.tokens_from_list(words)
# acc += nlp.tagger.train(tokens, gold_tags)
# total += len(tokens)
# n += 1
# if n and n % 20000 == 0:
# dev_score = score_model(nlp, heldout_sents)
# eval_score = score_model(nlp, dev_sents)
# if dev_score >= prev_score:
# last_model = (nlp.tagger.model.weights, nlp.tagger.model.embeddings)
# prev_score = dev_score
# variance = 0.001
# last_good_learn_rate = nlp.tagger.model.eta
# nlp.tagger.model.eta *= 1.01
#
# else:
# nlp.tagger.model.weights = last_model[0]
# nlp.tagger.model.embeddings = last_model[1]
# new_eta = numpy.random.normal(loc=last_good_learn_rate, scale=variance)
# if new_eta >= 0.0001:
# nlp.tagger.model.eta = new_eta
# else:
# nlp.tagger.model.eta = 0.0001
# print('X:\t%.3f\t%.3f\t%.3f\t%.4f' % (acc/total, dev_score, eval_score, nlp.tagger.model.eta))
# variance *= 1.1
# prev_score *= 0.9999
# acc = 0.0
# total = 0.0
#nlp.end_training(data_dir=model_dir)
#return nlp
return best_first_sgd(tagger, train_sents, heldout_sents)
@plac.annotations(