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Add partial embedding updates to Parikh model, fix dropout, other corrections.

This commit is contained in:
Matthew Honnibal 2016-11-18 06:32:12 -06:00
parent 80f473dfb8
commit ff5ab75f5e
3 changed files with 65 additions and 30 deletions
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2
examples/keras_parikh_entailment/__main__.py
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@ -93,7 +93,7 @@ def read_snli(path):
nr_hidden=("Number of hidden units", "option", "H", int),
dropout=("Dropout level", "option", "d", float),
learn_rate=("Learning rate", "option", "e", float),
batch_size=("Batch size for neural network training", "option", "b", float),
batch_size=("Batch size for neural network training", "option", "b", int),
nr_epoch=("Number of training epochs", "option", "i", int),
tree_truncate=("Truncate sentences by tree distance", "flag", "T", bool),
gru_encode=("Encode sentences with bidirectional GRU", "flag", "E", bool),

73
examples/keras_parikh_entailment/keras_decomposable_attention.py
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@ -3,8 +3,10 @@
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
from keras.layers import Lambda, Activation, Dropout, Embedding, TimeDistributed
from keras.layers import Bidirectional, GRU, LSTM
from keras.layers.noise import GaussianNoise
from keras.layers.advanced_activations import ELU
import keras.backend as K
from keras.models import Sequential, Model, model_from_json
from keras.regularizers import l2
@ -20,13 +22,13 @@ def build_model(vectors, shape, settings):
ids2 = Input(shape=(max_length,), dtype='int32', name='words2')
# Construct operations, which we'll chain together.
embed = _StaticEmbedding(vectors, max_length, nr_hidden)
embed = _StaticEmbedding(vectors, max_length, nr_hidden, dropout=0.2, nr_tune=5000)
if settings['gru_encode']:
encode = _BiRNNEncoding(max_length, nr_hidden)
attend = _Attention(max_length, nr_hidden)
encode = _BiRNNEncoding(max_length, nr_hidden, dropout=settings['dropout'])
attend = _Attention(max_length, nr_hidden, dropout=settings['dropout'])
align = _SoftAlignment(max_length, nr_hidden)
compare = _Comparison(max_length, nr_hidden)
entail = _Entailment(nr_hidden, nr_class)
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)
@ -59,15 +61,26 @@ def build_model(vectors, shape, settings):
class _StaticEmbedding(object):
def __init__(self, vectors, max_length, nr_out):
def __init__(self, vectors, max_length, nr_out, nr_tune=1000, dropout=0.0):
self.nr_out = nr_out
self.max_length = max_length
self.embed = Embedding(
vectors.shape[0],
vectors.shape[1],
input_length=max_length,
weights=[vectors],
name='embed',
trainable=False,
dropout=0.0)
trainable=False)
self.tune = Embedding(
nr_tune,
nr_out,
input_length=max_length,
weights=None,
name='tune',
trainable=True,
dropout=dropout)
self.mod_ids = Lambda(lambda sent: sent % (nr_tune-1)+1,
output_shape=(self.max_length,))
self.project = TimeDistributed(
Dense(
@ -77,23 +90,37 @@ class _StaticEmbedding(object):
name='project'))
def __call__(self, sentence):
return self.project(self.embed(sentence))
def get_output_shape(shapes):
print(shapes)
return shapes[0]
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)),
# output_shape=(self.max_length, self.nr_out))
pretrained = self.project(self.embed(sentence))
vectors = merge([pretrained, tuning], mode='sum')
return vectors
class _BiRNNEncoding(object):
def __init__(self, max_length, nr_out):
def __init__(self, max_length, nr_out, dropout=0.0):
self.model = Sequential()
self.model.add(Bidirectional(GRU(int(nr_out/2), return_sequences=True),
input_shape=(max_length, nr_out)))
self.model.add(Bidirectional(LSTM(nr_out, return_sequences=True,
dropout_W=dropout, dropout_U=dropout),
input_shape=(max_length, nr_out)))
self.model.add(TimeDistributed(Dense(nr_out, activation='relu', init='he_normal')))
self.model.add(TimeDistributed(Dropout(0.2)))
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'):
def __init__(self, max_length, nr_hidden, dropout=0.0, L2=0.0, activation='relu'):
self.max_length = max_length
self.model = Sequential()
self.model.add(Dropout(dropout, input_shape=(nr_hidden,)))
self.model.add(
Dense(nr_hidden, name='attend1',
init='he_normal', W_regularizer=l2(L2),
@ -134,18 +161,17 @@ class _SoftAlignment(object):
class _Comparison(object):
def __init__(self, words, nr_hidden, L2=1e-6, dropout=0.2):
def __init__(self, words, nr_hidden, L2=0.0, dropout=0.0):
self.words = words
self.model = Sequential()
self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,)))
self.model.add(Dense(nr_hidden, name='compare1',
init='he_normal', W_regularizer=l2(L2),
input_shape=(nr_hidden*2,)))
init='he_normal', W_regularizer=l2(L2)))
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):
@ -156,13 +182,16 @@ class _Comparison(object):
class _Entailment(object):
def __init__(self, nr_hidden, nr_out, dropout=0.2, L2=1e-4):
def __init__(self, nr_hidden, nr_out, dropout=0.0, L2=0.0):
self.model = Sequential()
self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,)))
self.model.add(Dense(nr_hidden, name='entail1',
init='he_normal', W_regularizer=l2(L2),
input_shape=(nr_hidden*2,)))
init='he_normal', W_regularizer=l2(L2)))
self.model.add(Activation('relu'))
self.model.add(Dropout(dropout))
self.model.add(Dense(nr_hidden, name='entail2',
init='he_normal', W_regularizer=l2(L2)))
self.model.add(Activation('relu'))
self.model.add(Dense(nr_out, name='entail_out', activation='softmax',
W_regularizer=l2(L2), init='zero'))

20
examples/keras_parikh_entailment/spacy_hook.py
View File

@ -1,5 +1,6 @@
from keras.models import model_from_json
import numpy
import numpy.random
class KerasSimilarityShim(object):
@ -31,16 +32,16 @@ class KerasSimilarityShim(object):
return scores[0]
def get_embeddings(vocab):
max_rank = max(lex.rank+1 for lex in vocab if lex.has_vector)
vectors = numpy.ndarray((max_rank+1, vocab.vectors_length), dtype='float32')
def get_embeddings(vocab, nr_unk=100):
nr_vector = max(lex.rank for lex in vocab) + 1
vectors = numpy.zeros((nr_vector+nr_unk+2, vocab.vectors_length), dtype='float32')
for lex in vocab:
if lex.has_vector:
vectors[lex.rank + 1] = lex.vector
vectors[lex.rank+1] = lex.vector / lex.vector_norm
return vectors
def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100):
def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100, nr_unk=100):
Xs = numpy.zeros((len(docs), max_length), dtype='int32')
for i, doc in enumerate(docs):
if tree_truncate:
@ -50,17 +51,22 @@ def get_word_ids(docs, rnn_encode=False, tree_truncate=False, max_length=100):
words = []
while len(words) <= max_length and queue:
word = queue.pop(0)
if rnn_encode or (word.has_vector and not word.is_punct and not word.is_space):
if rnn_encode or (not word.is_punct and not word.is_space):
words.append(word)
if tree_truncate:
queue.extend(list(word.lefts))
queue.extend(list(word.rights))
words.sort()
for j, token in enumerate(words):
Xs[i, j] = token.rank + 1
if token.has_vector:
Xs[i, j] = token.rank+1
else:
Xs[i, j] = (token.shape % (nr_unk-1))+2
j += 1
if j >= max_length:
break
else:
Xs[i, len(words)] = 1
return Xs