explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2025-07-11 08:42:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

Make some updates for new-Keras. Still broken

Browse Source
This commit is contained in:
Matthew Honnibal 2017-11-05 18:49:35 +01:00
parent 320008352b
commit a78939bea8
1 changed files with 29 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

56
examples/keras_parikh_entailment/keras_decomposable_attention.py
View File

@ -2,10 +2,12 @@
# Practical state-of-the-art text similarity with spaCy and Keras # Practical state-of-the-art text similarity with spaCy and Keras
import numpy import numpy
from keras.layers import InputSpec, Layer, Input, Dense, merge from keras.layers.merge import Add, Concatenate
from keras.layers import InputSpec, Layer, Input, Dense
from keras.layers import Lambda, Activation, Dropout, Embedding, TimeDistributed from keras.layers import Lambda, Activation, Dropout, Embedding, TimeDistributed
from keras.layers import Bidirectional, GRU, LSTM from keras.layers import Bidirectional, GRU, LSTM
from keras.layers.noise import GaussianNoise from keras.layers.noise import GaussianNoise
from keras.layers import SpatialDropout1D
from keras.layers.advanced_activations import ELU from keras.layers.advanced_activations import ELU
import keras.backend as K import keras.backend as K
from keras.models import Sequential, Model, model_from_json from keras.models import Sequential, Model, model_from_json
@ -13,7 +15,6 @@ from keras.regularizers import l2
from keras.optimizers import Adam from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import GlobalAveragePooling1D, GlobalMaxPooling1D from keras.layers.pooling import GlobalAveragePooling1D, GlobalMaxPooling1D
from keras.layers import Merge
def build_model(vectors, shape, settings): def build_model(vectors, shape, settings):
@ -79,8 +80,8 @@ class _StaticEmbedding(object):
input_length=max_length, input_length=max_length,
weights=None, weights=None,
name='tune', name='tune',
trainable=True, trainable=True)
dropout=dropout) self.drop_ids = SpatialDropout1D(dropout)
self.mod_ids = Lambda(lambda sent: sent % (nr_tune-1)+1, self.mod_ids = Lambda(lambda sent: sent % (nr_tune-1)+1,
output_shape=(self.max_length,)) output_shape=(self.max_length,))
@ -88,7 +89,7 @@ class _StaticEmbedding(object):
Dense( Dense(
nr_out, nr_out,
activation=None, activation=None,
bias=False, use_bias=False,
name='project')) name='project'))
def __call__(self, sentence): def __call__(self, sentence):
@ -96,12 +97,12 @@ class _StaticEmbedding(object):
print(shapes) print(shapes)
return shapes[0] return shapes[0]
mod_sent = self.mod_ids(sentence) mod_sent = self.mod_ids(sentence)
tuning = self.tune(mod_sent) tuning = self.drop_ids(self.tune(mod_sent))
#tuning = merge([tuning, mod_sent], #tuning = merge([tuning, mod_sent],
# mode=lambda AB: AB[0] * (K.clip(K.cast(AB[1], 'float32'), 0, 1)), # mode=lambda AB: AB[0] * (K.clip(K.cast(AB[1], 'float32'), 0, 1)),
# output_shape=(self.max_length, self.nr_out)) # output_shape=(self.max_length, self.nr_out))
pretrained = self.project(self.embed(sentence)) pretrained = self.project(self.embed(sentence))
vectors = merge([pretrained, tuning], mode='sum') vectors = Add()([pretrained, tuning])
return vectors return vectors
@ -109,9 +110,10 @@ class _BiRNNEncoding(object):
def __init__(self, max_length, nr_out, dropout=0.0): def __init__(self, max_length, nr_out, dropout=0.0):
self.model = Sequential() self.model = Sequential()
self.model.add(Bidirectional(LSTM(nr_out, return_sequences=True, self.model.add(Bidirectional(LSTM(nr_out, return_sequences=True,
dropout_W=dropout, dropout_U=dropout), dropout=dropout, recurrent_dropout=dropout),
input_shape=(max_length, nr_out))) input_shape=(max_length, nr_out)))
self.model.add(TimeDistributed(Dense(nr_out, activation='relu', init='he_normal'))) self.model.add(TimeDistributed(Dense(nr_out, activation='relu',
kernel_initializer='he_normal')))
self.model.add(TimeDistributed(Dropout(0.2))) self.model.add(TimeDistributed(Dropout(0.2)))
def __call__(self, sentence): def __call__(self, sentence):
@ -125,21 +127,20 @@ class _Attention(object):
self.model.add(Dropout(dropout, input_shape=(nr_hidden,))) self.model.add(Dropout(dropout, input_shape=(nr_hidden,)))
self.model.add( self.model.add(
Dense(nr_hidden, name='attend1', Dense(nr_hidden, name='attend1',
init='he_normal', W_regularizer=l2(L2), kernel_initializer='he_normal', kernel_regularizer=l2(L2),
input_shape=(nr_hidden,), activation='relu')) input_shape=(nr_hidden,), activation='relu'))
self.model.add(Dropout(dropout)) self.model.add(Dropout(dropout))
self.model.add(Dense(nr_hidden, name='attend2', self.model.add(Dense(nr_hidden, name='attend2',
init='he_normal', W_regularizer=l2(L2), activation='relu')) kernel_initializer='he_normal', kernel_regularizer=l2(L2), activation='relu'))
self.model = TimeDistributed(self.model) self.model = TimeDistributed(self.model)
def __call__(self, sent1, sent2): def __call__(self, sent1, sent2):
def _outer(AB): def _outer(AB):
att_ji = K.batch_dot(AB[1], K.permute_dimensions(AB[0], (0, 2, 1))) 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 K.permute_dimensions(att_ji,(0, 2, 1))
return merge( return Lambda(_outer,
[self.model(sent1), self.model(sent2)], output_shape=(self.max_length, self.max_length))(
mode=_outer, [self.model(sent1), self.model(sent2)])
output_shape=(self.max_length, self.max_length))
class _SoftAlignment(object): class _SoftAlignment(object):
@ -158,8 +159,9 @@ class _SoftAlignment(object):
s = K.sum(e, axis=-1, keepdims=True) s = K.sum(e, axis=-1, keepdims=True)
sm_att = e / s sm_att = e / s
return K.batch_dot(sm_att, mat) return K.batch_dot(sm_att, mat)
return merge([attention, sentence], mode=_normalize_attention, return Lambda(_normalize_attention,
output_shape=(self.max_length, self.nr_hidden)) # Shape: (i, n) output_shape=(self.max_length, self.nr_hidden))(
[attention, sentence]) # Shape: (i, n)
class _Comparison(object): class _Comparison(object):
@ -168,19 +170,19 @@ class _Comparison(object):
self.model = Sequential() self.model = Sequential()
self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,))) self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,)))
self.model.add(Dense(nr_hidden, name='compare1', self.model.add(Dense(nr_hidden, name='compare1',
init='he_normal', W_regularizer=l2(L2))) kernel_initializer='he_normal', kernel_regularizer=l2(L2)))
self.model.add(Activation('relu')) self.model.add(Activation('relu'))
self.model.add(Dropout(dropout)) self.model.add(Dropout(dropout))
self.model.add(Dense(nr_hidden, name='compare2', self.model.add(Dense(nr_hidden, name='compare2',
W_regularizer=l2(L2), init='he_normal')) kernel_regularizer=l2(L2), kernel_initializer='he_normal'))
self.model.add(Activation('relu')) self.model.add(Activation('relu'))
self.model = TimeDistributed(self.model) self.model = TimeDistributed(self.model)
def __call__(self, sent, align, **kwargs): def __call__(self, sent, align, **kwargs):
result = self.model(merge([sent, align], mode='concat')) # Shape: (i, n) result = self.model(Concatenate()([sent, align])) # Shape: (i, n)
avged = GlobalAveragePooling1D()(result, mask=self.words) avged = GlobalAveragePooling1D()(result) # mask=self.words)
maxed = GlobalMaxPooling1D()(result, mask=self.words) maxed = GlobalMaxPooling1D()(result) # mask=self.words)
merged = merge([avged, maxed]) merged = Concatenate()([avged, maxed])
result = BatchNormalization()(merged) result = BatchNormalization()(merged)
return result return result
@ -190,17 +192,17 @@ class _Entailment(object):
self.model = Sequential() self.model = Sequential()
self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,))) self.model.add(Dropout(dropout, input_shape=(nr_hidden*2,)))
self.model.add(Dense(nr_hidden, name='entail1', self.model.add(Dense(nr_hidden, name='entail1',
init='he_normal', W_regularizer=l2(L2))) kernel_initializer='he_normal', kernel_regularizer=l2(L2)))
self.model.add(Activation('relu')) self.model.add(Activation('relu'))
self.model.add(Dropout(dropout)) self.model.add(Dropout(dropout))
self.model.add(Dense(nr_hidden, name='entail2', self.model.add(Dense(nr_hidden, name='entail2',
init='he_normal', W_regularizer=l2(L2))) kernel_initializer='he_normal', kernel_regularizer=l2(L2)))
self.model.add(Activation('relu')) self.model.add(Activation('relu'))
self.model.add(Dense(nr_out, name='entail_out', activation='softmax', self.model.add(Dense(nr_out, name='entail_out', activation='softmax',
W_regularizer=l2(L2), init='zero')) kernel_regularizer=l2(L2), kernel_initializer='zero'))
def __call__(self, feats1, feats2): def __call__(self, feats1, feats2):
features = merge([feats1, feats2], mode='concat') features = Concatenate()([feats1, feats2])
return self.model(features) return self.model(features)