import logging
import os
import keras.backend as K
from keras.layers import Dense, Activation, Embedding, Input
from keras.models import Model
from keras.constraints import MaxNorm
from my_layers import Attention, Average, WeightedSum, WeightedAspectEmb, MaxMargin
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s')
logger = logging.getLogger(__name__)
def create_model(args, maxlen, vocab):
def ortho_reg(weight_matrix):
### orthogonal regularization for aspect embedding matrix ###
w_n = K.l2_normalize(weight_matrix, axis=-1)
reg = K.sum(K.square(K.dot(w_n, K.transpose(w_n)) - K.eye(w_n.get_shape().as_list()[0])))
return args.ortho_reg * reg
vocab_size = len(vocab)
if args.emb_name:
from w2vEmbReader import W2VEmbReader as EmbReader
emb_reader = EmbReader(os.path.join("..", "preprocessed_data", args.domain), args.emb_name)
aspect_matrix = emb_reader.get_aspect_matrix(args.aspect_size)
args.aspect_size = emb_reader.aspect_size
args.emb_dim = emb_reader.emb_dim
##### Inputs #####
sentence_input = Input(shape=(maxlen,), dtype='int32', name='sentence_input')
neg_input = Input(shape=(args.neg_size, maxlen), dtype='int32', name='neg_input')
##### Construct word embedding layer #####
word_emb = Embedding(vocab_size, args.emb_dim,
mask_zero=True, name='word_emb',
embeddings_constraint=MaxNorm(10))
##### Compute sentence representation #####
e_w = word_emb(sentence_input)
y_s = Average()(e_w)
att_weights = Attention(name='att_weights',
W_constraint=MaxNorm(10),
b_constraint=MaxNorm(10))([e_w, y_s])
z_s = WeightedSum()([e_w, att_weights])
##### Compute representations of negative instances #####
e_neg = word_emb(neg_input)
z_n = Average()(e_neg)
##### Reconstruction #####
p_t = Dense(args.aspect_size)(z_s)
p_t = Activation('softmax', name='p_t')(p_t)
r_s = WeightedAspectEmb(args.aspect_size, args.emb_dim, name='aspect_emb',
W_constraint=MaxNorm(10),
W_regularizer=ortho_reg)(p_t)
##### Loss #####
loss = MaxMargin(name='max_margin')([z_s, z_n, r_s])
model = Model(inputs=[sentence_input, neg_input], outputs=[loss])
### Word embedding and aspect embedding initialization ######
if args.emb_name:
from w2vEmbReader import W2VEmbReader as EmbReader
logger.info('Initializing word embedding matrix')
embs = model.get_layer('word_emb').embeddings
K.set_value(embs, emb_reader.get_emb_matrix_given_vocab(vocab, K.get_value(embs)))
logger.info('Initializing aspect embedding matrix as centroid of kmean clusters')
K.set_value(model.get_layer('aspect_emb').W, aspect_matrix)
return model
