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spaCy
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* Wire hyperparameters to script interface

This commit is contained in:
Matthew Honnibal 2015-06-27 04:18:01 +02:00
parent ebe630cc8d
commit da793073d0
1 changed files with 27 additions and 9 deletions
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36
bin/parser/nn_train.py
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@ -84,7 +84,8 @@ def _merge_sents(sents):
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0, seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
verbose=False, verbose=False,
eta=0.01, mu=0.9, n_hidden=100, word_vec_len=10, pos_vec_len=10): eta=0.01, mu=0.9, n_hidden=100,
nv_word=10, nv_tag=10, nv_label=10):
dep_model_dir = path.join(model_dir, 'deps') dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos') pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner') ner_model_dir = path.join(model_dir, 'ner')
@ -99,8 +100,15 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
os.mkdir(ner_model_dir) os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir) setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed, Config.write(dep_model_dir, 'config',
labels=Language.ParserTransitionSystem.get_labels(gold_tuples)) seed=seed,
features=feat_set,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
vector_lengths=(nv_word, nv_tag, nv_label),
hidden_nodes=n_hidden,
eta=eta,
mu=mu
)
Config.write(ner_model_dir, 'config', features='ner', seed=seed, Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples), labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
beam_width=0) beam_width=0)
@ -110,16 +118,17 @@ def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
nlp = Language(data_dir=model_dir) nlp = Language(data_dir=model_dir)
def make_model(n_classes, input_spec, model_dir): def make_model(n_classes, (words, tags, labels), model_dir):
print input_spec n_in = (nv_word * len(words)) + \
n_in = sum(n_cols * len(fields) for (n_cols, fields) in input_spec) (nv_tag * len(tags)) + \
(nv_label * len(labels))
print 'Compiling' print 'Compiling'
debug, train_func, predict_func = compile_theano_model(n_classes, n_hidden, debug, train_func, predict_func = compile_theano_model(n_classes, n_hidden,
n_in, 0.0, 0.0) n_in, 0.0, 0.0)
print 'Done' print 'Done'
return TheanoModel( return TheanoModel(
n_classes, n_classes,
input_spec, ((nv_word, words), (nv_tag, tags), (nv_label, labels)),
train_func, train_func,
predict_func, predict_func,
model_loc=model_dir, model_loc=model_dir,
@ -226,14 +235,23 @@ def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
n_sents=("Number of training sentences", "option", "n", int), n_sents=("Number of training sentences", "option", "n", int),
n_iter=("Number of training iterations", "option", "i", int), n_iter=("Number of training iterations", "option", "i", int),
verbose=("Verbose error reporting", "flag", "v", bool), verbose=("Verbose error reporting", "flag", "v", bool),
debug=("Debug mode", "flag", "d", bool),
nv_word=("Word vector length", "option", "W", int),
nv_tag=("Tag vector length", "option", "T", int),
nv_label=("Label vector length", "option", "L", int),
nv_hidden=("Hidden nodes length", "option", "H", int),
eta=("Learning rate", "option", "E", float),
mu=("Momentum", "option", "M", float),
) )
def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False, def main(train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
debug=False, corruption_level=0.0, gold_preproc=False, beam_width=1, corruption_level=0.0, gold_preproc=False,
nv_word=10, nv_tag=10, nv_label=10, nv_hidden=10,
eta=0.1, mu=0.9,
eval_only=False): eval_only=False):
gold_train = list(read_json_file(train_loc)) gold_train = list(read_json_file(train_loc))
nlp = train(English, gold_train, model_dir, nlp = train(English, gold_train, model_dir,
feat_set='embed', feat_set='embed',
nv_word=nv_word, nv_tag=nv_tag, nv_label=nv_label,
gold_preproc=gold_preproc, n_sents=n_sents, gold_preproc=gold_preproc, n_sents=n_sents,
corruption_level=corruption_level, n_iter=n_iter, corruption_level=corruption_level, n_iter=n_iter,
verbose=verbose) verbose=verbose)