Rooba
/
spaCy
mirror of https://github.com/explosion/spaCy.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Set vectors in chainer example

This commit is contained in:
Matthew Honnibal 2016-11-19 18:42:58 -06:00
parent b701a08249
commit 1ed40682a3
1 changed files with 15 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
examples/chainer_sentiment.py
View File

@ -3,6 +3,9 @@ import plac
import random
import six
import cProfile
import pstats
import pathlib
import cPickle as pickle
from itertools import izip
@ -81,7 +84,7 @@ class SentimentModel(Chain):
def __init__(self, nlp, shape, **settings):
Chain.__init__(self,
embed=_Embed(shape['nr_vector'], shape['nr_dim'], shape['nr_hidden'],
initialW=lambda arr: set_vectors(arr, nlp.vocab)),
set_vectors=lambda arr: set_vectors(arr, nlp.vocab)),
encode=_Encode(shape['nr_hidden'], shape['nr_hidden']),
attend=_Attend(shape['nr_hidden'], shape['nr_hidden']),
predict=_Predict(shape['nr_hidden'], shape['nr_class']))
@ -95,11 +98,11 @@ class SentimentModel(Chain):
class _Embed(Chain):
def __init__(self, nr_vector, nr_dim, nr_out):
def __init__(self, nr_vector, nr_dim, nr_out, set_vectors=None):
Chain.__init__(self,
embed=L.EmbedID(nr_vector, nr_dim),
embed=L.EmbedID(nr_vector, nr_dim, initialW=set_vectors),
project=L.Linear(None, nr_out, nobias=True))
#self.embed.unchain_backward()
self.embed.W.volatile = False
def __call__(self, sentence):
return [self.project(self.embed(ts)) for ts in F.transpose(sentence)]
@ -214,7 +217,6 @@ def set_vectors(vectors, vocab):
vectors[lex.rank + 1] = lex.vector
else:
lex.norm = 0
vectors.unchain_backwards()
return vectors
@ -223,7 +225,9 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
by_sentence=True):
nlp = spacy.load('en', entity=False)
if 'nr_vector' not in lstm_shape:
lstm_shape['nr_vector'] = max(lex.rank+1 for lex in vocab if lex.has_vector)
lstm_shape['nr_vector'] = max(lex.rank+1 for lex in nlp.vocab if lex.has_vector)
if 'nr_dim' not in lstm_shape:
lstm_shape['nr_dim'] = nlp.vocab.vectors_length
print("Make model")
model = Classifier(SentimentModel(nlp, lstm_shape, **lstm_settings))
print("Parsing texts...")
@ -240,7 +244,7 @@ def train(train_texts, train_labels, dev_texts, dev_labels,
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
updater = chainer.training.StandardUpdater(train_iter, optimizer, device=0)
trainer = chainer.training.Trainer(updater, (20, 'epoch'), out='result')
trainer = chainer.training.Trainer(updater, (1, 'epoch'), out='result')
trainer.extend(extensions.Evaluator(dev_iter, model, device=0))
trainer.extend(extensions.LogReport())
@ -305,11 +309,14 @@ def main(model_dir, train_dir, dev_dir,
dev_labels = xp.asarray(dev_labels, dtype='i')
lstm = train(train_texts, train_labels, dev_texts, dev_labels,
{'nr_hidden': nr_hidden, 'max_length': max_length, 'nr_class': 2,
'nr_vector': 2000, 'nr_dim': 32},
'nr_vector': 5000},
{'dropout': 0.5, 'lr': learn_rate},
{},
nb_epoch=nb_epoch, batch_size=batch_size)
if __name__ == '__main__':
#cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
#s = pstats.Stats("Profile.prof")
#s.strip_dirs().sort_stats("time").print_stats()
plac.call(main)