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

Fix x keras deep learning example

This commit is contained in:
Matthew Honnibal 2017-01-31 13:27:13 -06:00
parent 19501f3340
commit 80aa4e114b
3 changed files with 73 additions and 46 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
examples/keras_parikh_entailment/__main__.py
View File

@ -12,17 +12,23 @@ from spacy_hook import create_similarity_pipeline
from keras_decomposable_attention import build_model
try:
import cPickle as pickle
except ImportError:
import pickle
def train(model_dir, train_loc, dev_loc, shape, settings):
train_texts1, train_texts2, train_labels = read_snli(train_loc)
dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
print("Loading spaCy")
nlp = spacy.load('en')
assert nlp.path is not None
print("Compiling network")
model = build_model(get_embeddings(nlp.vocab), shape, settings)
print("Processing texts...")
Xs = []
Xs = []
for texts in (train_texts1, train_texts2, dev_texts1, dev_texts2):
Xs.append(get_word_ids(list(nlp.pipe(texts, n_threads=20, batch_size=20000)),
max_length=shape[0],
@ -36,35 +42,41 @@ def train(model_dir, train_loc, dev_loc, shape, settings):
validation_data=([dev_X1, dev_X2], dev_labels),
nb_epoch=settings['nr_epoch'],
batch_size=settings['batch_size'])
if not (nlp.path / 'similarity').exists():
(nlp.path / 'similarity').mkdir()
print("Saving to", model_dir / 'similarity')
weights = model.get_weights()
with (nlp.path / 'similarity' / 'model').open('wb') as file_:
pickle.dump(weights[1:], file_)
with (nlp.path / 'similarity' / 'config.json').open('wb') as file_:
file_.write(model.to_json())
def evaluate(model_dir, dev_loc):
nlp = spacy.load('en', path=model_dir,
tagger=False, parser=False, entity=False, matcher=False,
dev_texts1, dev_texts2, dev_labels = read_snli(dev_loc)
nlp = spacy.load('en',
create_pipeline=create_similarity_pipeline)
n = 0
correct = 0
for (text1, text2), label in zip(dev_texts, dev_labels):
total = 0.
correct = 0.
for text1, text2, label in zip(dev_texts1, dev_texts2, dev_labels):
doc1 = nlp(text1)
doc2 = nlp(text2)
sim = doc1.similarity(doc2)
if bool(sim >= 0.5) == label:
if sim.argmax() == label.argmax():
correct += 1
n += 1
total += 1
return correct, total
def demo(model_dir):
nlp = spacy.load('en', path=model_dir,
tagger=False, parser=False, entity=False, matcher=False,
create_pipeline=create_similarity_pipeline)
doc1 = nlp(u'Worst fries ever! Greasy and horrible...')
doc2 = nlp(u'The milkshakes are good. The fries are bad.')
print('doc1.similarity(doc2)', doc1.similarity(doc2))
sent1a, sent1b = doc1.sents
print('sent1a.similarity(sent1b)', sent1a.similarity(sent1b))
print('sent1a.similarity(doc2)', sent1a.similarity(doc2))
print('sent1b.similarity(doc2)', sent1b.similarity(doc2))
doc1 = nlp(u'What were the best crime fiction books in 2016?')
doc2 = nlp(
u'What should I read that was published last year? I like crime stories.')
print(doc1)
print(doc2)
print("Similarity", doc1.similarity(doc2))
LABELS = {'entailment': 0, 'contradiction': 1, 'neutral': 2}
@ -119,7 +131,8 @@ def main(mode, model_dir, train_loc, dev_loc,
if mode == 'train':
train(model_dir, train_loc, dev_loc, shape, settings)
elif mode == 'evaluate':
evaluate(model_dir, dev_loc)
correct, total = evaluate(model_dir, dev_loc)
print(correct, '/', total, correct / total)
else:
demo(model_dir)

30
examples/keras_parikh_entailment/keras_decomposable_attention.py
View File

@ -12,6 +12,8 @@ from keras.models import Sequential, Model, model_from_json
from keras.regularizers import l2
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import GlobalAveragePooling1D, GlobalMaxPooling1D
from keras.layers import Merge
def build_model(vectors, shape, settings):
@ -29,11 +31,11 @@ def build_model(vectors, shape, settings):
align = _SoftAlignment(max_length, nr_hidden)
compare = _Comparison(max_length, nr_hidden, dropout=settings['dropout'])
entail = _Entailment(nr_hidden, nr_class, dropout=settings['dropout'])
# Declare the model as a computational graph.
sent1 = embed(ids1) # Shape: (i, n)
sent2 = embed(ids2) # Shape: (j, n)
if settings['gru_encode']:
sent1 = encode(sent1)
sent2 = encode(sent2)
@ -42,12 +44,12 @@ def build_model(vectors, shape, settings):
align1 = align(sent2, attention)
align2 = align(sent1, attention, transpose=True)
feats1 = compare(sent1, align1)
feats2 = compare(sent2, align2)
scores = entail(feats1, feats2)
# Now that we have the input/output, we can construct the Model object...
model = Model(input=[ids1, ids2], output=[scores])
@ -93,7 +95,7 @@ class _StaticEmbedding(object):
def get_output_shape(shapes):
print(shapes)
return shapes[0]
mod_sent = self.mod_ids(sentence)
mod_sent = self.mod_ids(sentence)
tuning = self.tune(mod_sent)
#tuning = merge([tuning, mod_sent],
# mode=lambda AB: AB[0] * (K.clip(K.cast(AB[1], 'float32'), 0, 1)),
@ -129,7 +131,7 @@ class _Attention(object):
self.model.add(Dense(nr_hidden, name='attend2',
init='he_normal', W_regularizer=l2(L2), activation='relu'))
self.model = TimeDistributed(self.model)
def __call__(self, sent1, sent2):
def _outer(AB):
att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1)))
@ -158,7 +160,7 @@ class _SoftAlignment(object):
return K.batch_dot(sm_att, mat)
return merge([attention, sentence], mode=_normalize_attention,
output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n)
class _Comparison(object):
def __init__(self, words, nr_hidden, L2=0.0, dropout=0.0):
@ -176,10 +178,12 @@ class _Comparison(object):
def __call__(self, sent, align, **kwargs):
result = self.model(merge([sent, align], mode='concat')) # Shape: (i, n)
result = _GlobalSumPooling1D()(result, mask=self.words)
result = BatchNormalization()(result)
avged = GlobalAveragePooling1D()(result, mask=self.words)
maxed = GlobalMaxPooling1D()(result, mask=self.words)
merged = merge([avged, maxed])
result = BatchNormalization()(merged)
return result
class _Entailment(object):
def __init__(self, nr_hidden, nr_out, dropout=0.0, L2=0.0):
@ -251,7 +255,7 @@ def test_fit_model():
shape = (10, 16, 3)
settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
model = build_model(vectors, shape, settings)
train_X = _generate_X(20, shape[0], vectors.shape[1])
train_Y = _generate_Y(20, shape[2])
dev_X = _generate_X(15, shape[0], vectors.shape[1])
@ -261,6 +265,4 @@ def test_fit_model():
batch_size=4)
__all__ = [build_model]

40
examples/keras_parikh_entailment/spacy_hook.py
View File

@ -1,33 +1,40 @@
from keras.models import model_from_json
import numpy
import numpy.random
import json
from spacy.tokens.span import Span
try:
import cPickle as pickle
except ImportError:
import pickle
class KerasSimilarityShim(object):
@classmethod
def load(cls, path, nlp, get_features=None):
def load(cls, path, nlp, get_features=None, max_length=100):
if get_features is None:
get_features = doc2ids
get_features = get_word_ids
with (path / 'config.json').open() as file_:
config = json.load(file_)
model = model_from_json(config['model'])
model = model_from_json(file_.read())
with (path / 'model').open('rb') as file_:
weights = pickle.load(file_)
embeddings = get_embeddings(nlp.vocab)
model.set_weights([embeddings] + weights)
return cls(model, get_features=get_features)
return cls(model, get_features=get_features, max_length=max_length)
def __init__(self, model, get_features=None):
def __init__(self, model, get_features=None, max_length=100):
self.model = model
self.get_features = get_features
self.max_length = max_length
def __call__(self, doc):
doc.user_hooks['similarity'] = self.predict
doc.user_span_hooks['similarity'] = self.predict
def predict(self, doc1, doc2):
x1 = self.get_features(doc1)
x2 = self.get_features(doc2)
x1 = self.get_features([doc1], max_length=self.max_length, tree_truncate=True)
x2 = self.get_features([doc2], max_length=self.max_length, tree_truncate=True)
scores = self.model.predict([x1, x2])
return scores[0]
@ -45,7 +52,10 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
Xs = numpy.zeros((len(docs), max_length), dtype='int32')
for i, doc in enumerate(docs):
if tree_truncate:
queue = [sent.root for sent in doc.sents]
if isinstance(doc, Span):
queue = [doc.root]
else:
queue = [sent.root for sent in doc.sents]
else:
queue = list(doc)
words = []
@ -71,7 +81,9 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr
def create_similarity_pipeline(nlp):
return [SimilarityModel.load(
nlp.path / 'similarity',
nlp,
feature_extracter=get_features)]
return [
nlp.tagger,
nlp.entity,
nlp.parser,
KerasSimilarityShim.load(nlp.path / 'similarity', nlp, max_length=10)
]