mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
239 lines
8.1 KiB
Python
239 lines
8.1 KiB
Python
# Semantic similarity with decomposable attention (using spaCy and Keras)
|
|
# Practical state-of-the-art text similarity with spaCy and Keras
|
|
import numpy
|
|
|
|
from keras.layers import InputSpec, Layer, Input, Dense, merge
|
|
from keras.layers import Activation, Dropout, Embedding, TimeDistributed
|
|
from keras.layers import Bidirectional, GRU
|
|
import keras.backend as K
|
|
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
|
|
|
|
|
|
def build_model(vectors, shape, settings):
|
|
'''Compile the model.'''
|
|
max_length, nr_hidden, nr_class = shape
|
|
# Declare inputs.
|
|
ids1 = Input(shape=(max_length,), dtype='int32', name='words1')
|
|
ids2 = Input(shape=(max_length,), dtype='int32', name='words2')
|
|
|
|
# Construct operations, which we'll chain together.
|
|
embed = _StaticEmbedding(vectors, max_length, nr_hidden)
|
|
if settings['gru_encode']:
|
|
encode = _BiRNNEncoding(max_length, nr_hidden)
|
|
attend = _Attention(max_length, nr_hidden)
|
|
align = _SoftAlignment(max_length, nr_hidden)
|
|
compare = _Comparison(max_length, nr_hidden)
|
|
entail = _Entailment(nr_hidden, nr_class)
|
|
|
|
# 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)
|
|
|
|
attention = attend(sent1, sent2) # Shape: (i, j)
|
|
|
|
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])
|
|
|
|
# ...Compile it...
|
|
model.compile(
|
|
optimizer=Adam(lr=settings['lr']),
|
|
loss='categorical_crossentropy',
|
|
metrics=['accuracy'])
|
|
# ...And return it for training.
|
|
return model
|
|
|
|
|
|
class _StaticEmbedding(object):
|
|
def __init__(self, vectors, max_length, nr_out):
|
|
self.embed = Embedding(
|
|
vectors.shape[0],
|
|
vectors.shape[1],
|
|
input_length=max_length,
|
|
weights=[vectors],
|
|
name='embed',
|
|
trainable=False,
|
|
dropout=0.0)
|
|
|
|
self.project = TimeDistributed(
|
|
Dense(
|
|
nr_out,
|
|
activation=None,
|
|
bias=False,
|
|
name='project'))
|
|
|
|
def __call__(self, sentence):
|
|
return self.project(self.embed(sentence))
|
|
|
|
|
|
class _BiRNNEncoding(object):
|
|
def __init__(self, max_length, nr_out):
|
|
self.model = Sequential()
|
|
self.model.add(Bidirectional(GRU(int(nr_out/2), return_sequences=True),
|
|
input_shape=(max_length, nr_out)))
|
|
|
|
def __call__(self, sentence):
|
|
return self.model(sentence)
|
|
|
|
|
|
class _Attention(object):
|
|
def __init__(self, max_length, nr_hidden, dropout=0.0, L2=1e-4, activation='relu'):
|
|
self.max_length = max_length
|
|
self.model = Sequential()
|
|
self.model.add(
|
|
Dense(nr_hidden, name='attend1',
|
|
init='he_normal', W_regularizer=l2(L2),
|
|
input_shape=(nr_hidden,), activation='relu'))
|
|
self.model.add(Dropout(dropout))
|
|
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)))
|
|
return K.permute_dimensions(att_ji,(0, 2, 1))
|
|
|
|
|
|
return merge(
|
|
[self.model(sent1), self.model(sent2)],
|
|
mode=_outer,
|
|
output_shape=(self.max_length, self.max_length))
|
|
|
|
|
|
class _SoftAlignment(object):
|
|
def __init__(self, max_length, nr_hidden):
|
|
self.max_length = max_length
|
|
self.nr_hidden = nr_hidden
|
|
|
|
def __call__(self, sentence, attention, transpose=False):
|
|
def _normalize_attention(attmat):
|
|
att = attmat[0]
|
|
mat = attmat[1]
|
|
if transpose:
|
|
att = K.permute_dimensions(att,(0, 2, 1))
|
|
# 3d softmax
|
|
e = K.exp(att - K.max(att, axis=-1, keepdims=True))
|
|
s = K.sum(e, axis=-1, keepdims=True)
|
|
sm_att = e / s
|
|
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=1e-6, dropout=0.2):
|
|
self.words = words
|
|
self.model = Sequential()
|
|
self.model.add(Dense(nr_hidden, name='compare1',
|
|
init='he_normal', W_regularizer=l2(L2),
|
|
input_shape=(nr_hidden*2,)))
|
|
self.model.add(Activation('relu'))
|
|
self.model.add(Dropout(dropout))
|
|
self.model.add(Dense(nr_hidden, name='compare2',
|
|
W_regularizer=l2(L2), init='he_normal'))
|
|
self.model.add(Activation('relu'))
|
|
self.model.add(Dropout(dropout))
|
|
self.model = TimeDistributed(self.model)
|
|
|
|
def __call__(self, sent, align, **kwargs):
|
|
result = self.model(merge([sent, align], mode='concat')) # Shape: (i, n)
|
|
result = _GlobalSumPooling1D()(result, mask=self.words)
|
|
return result
|
|
|
|
|
|
class _Entailment(object):
|
|
def __init__(self, nr_hidden, nr_out, dropout=0.2, L2=1e-4):
|
|
self.model = Sequential()
|
|
self.model.add(Dense(nr_hidden, name='entail1',
|
|
init='he_normal', W_regularizer=l2(L2),
|
|
input_shape=(nr_hidden*2,)))
|
|
self.model.add(Activation('relu'))
|
|
self.model.add(Dropout(dropout))
|
|
self.model.add(Dense(nr_out, name='entail_out', activation='softmax',
|
|
W_regularizer=l2(L2), init='zero'))
|
|
|
|
def __call__(self, feats1, feats2):
|
|
features = merge([feats1, feats2], mode='concat')
|
|
return self.model(features)
|
|
|
|
|
|
class _GlobalSumPooling1D(Layer):
|
|
'''Global sum pooling operation for temporal data.
|
|
|
|
# Input shape
|
|
3D tensor with shape: `(samples, steps, features)`.
|
|
|
|
# Output shape
|
|
2D tensor with shape: `(samples, features)`.
|
|
'''
|
|
def __init__(self, **kwargs):
|
|
super(_GlobalSumPooling1D, self).__init__(**kwargs)
|
|
self.input_spec = [InputSpec(ndim=3)]
|
|
|
|
def get_output_shape_for(self, input_shape):
|
|
return (input_shape[0], input_shape[2])
|
|
|
|
def call(self, x, mask=None):
|
|
if mask is not None:
|
|
return K.sum(x * K.clip(mask, 0, 1), axis=1)
|
|
else:
|
|
return K.sum(x, axis=1)
|
|
|
|
|
|
def test_build_model():
|
|
vectors = numpy.ndarray((100, 8), dtype='float32')
|
|
shape = (10, 16, 3)
|
|
settings = {'lr': 0.001, 'dropout': 0.2, 'gru_encode':True}
|
|
model = build_model(vectors, shape, settings)
|
|
|
|
|
|
def test_fit_model():
|
|
|
|
def _generate_X(nr_example, length, nr_vector):
|
|
X1 = numpy.ndarray((nr_example, length), dtype='int32')
|
|
X1 *= X1 < nr_vector
|
|
X1 *= 0 <= X1
|
|
X2 = numpy.ndarray((nr_example, length), dtype='int32')
|
|
X2 *= X2 < nr_vector
|
|
X2 *= 0 <= X2
|
|
return [X1, X2]
|
|
|
|
def _generate_Y(nr_example, nr_class):
|
|
ys = numpy.zeros((nr_example, nr_class), dtype='int32')
|
|
for i in range(nr_example):
|
|
ys[i, i % nr_class] = 1
|
|
return ys
|
|
|
|
vectors = numpy.ndarray((100, 8), dtype='float32')
|
|
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])
|
|
dev_Y = _generate_Y(15, shape[2])
|
|
|
|
model.fit(train_X, train_Y, validation_data=(dev_X, dev_Y), nb_epoch=5,
|
|
batch_size=4)
|
|
|
|
|
|
|
|
|
|
__all__ = [build_model]
|