<filename>Aspect_Extraction/model.py
import logging

import keras.backend as K
from keras.layers import Dense, Activation, Embedding, Input
from keras.models import Model

from my_layers import Attention, Average, WeightedSum, WeightedAspectEmb, MaxMargin
from w2v_emb_reader import W2VEmbReader as EmbReader

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 = weight_matrix / K.cast(K.epsilon() + K.sqrt(K.sum(K.square(weight_matrix), axis=-1, keepdims=True)),
                                     K.floatx())
        reg = K.sum(K.square(K.dot(w_n, K.transpose(w_n)) - K.eye(w_n.shape[0].value)))

        return args.ortho_reg * reg

    vocab_size = len(vocab)

    # 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')

    # Compute sentence representation
    e_w = word_emb(sentence_input)
    y_s = Average()(e_w)
    att_weights = Attention(name='att_weights')([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_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_path:
        emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
        logger.info('Initializing word embedding matrix')
        K.set_value(
            model.get_layer('word_emb').embeddings,
            emb_reader.get_emb_matrix_given_vocab(vocab, K.get_value(model.get_layer('word_emb').embeddings)))
        logger.info('Initializing aspect embedding matrix as centroid of kmean clusters')
        K.set_value(
            model.get_layer('aspect_emb').W,
            emb_reader.get_aspect_matrix(args.aspect_size))

    return model
<filename>Aspect_Extraction/model.py
import logging

import keras.backend as K
from keras.layers import Dense, Activation, Embedding, Input
from keras.models import Model

from my_layers import Attention, Average, WeightedSum, WeightedAspectEmb, MaxMargin
from w2v_emb_reader import W2VEmbReader as EmbReader

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 = weight_matrix / K.cast(K.epsilon() + K.sqrt(K.sum(K.square(weight_matrix), axis=-1, keepdims=True)),
                                     K.floatx())
        reg = K.sum(K.square(K.dot(w_n, K.transpose(w_n)) - K.eye(w_n.shape[0].value)))

        return args.ortho_reg * reg

    vocab_size = len(vocab)

    # 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')

    # Compute sentence representation
    e_w = word_emb(sentence_input)
    y_s = Average()(e_w)
    att_weights = Attention(name='att_weights')([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_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_path:
        emb_reader = EmbReader(args.emb_path, emb_dim=args.emb_dim)
        logger.info('Initializing word embedding matrix')
        K.set_value(
            model.get_layer('word_emb').embeddings,
            emb_reader.get_emb_matrix_given_vocab(vocab, K.get_value(model.get_layer('word_emb').embeddings)))
        logger.info('Initializing aspect embedding matrix as centroid of kmean clusters')
        K.set_value(
            model.get_layer('aspect_emb').W,
            emb_reader.get_aspect_matrix(args.aspect_size))

    return model
# orthogonal regularization for aspect embedding matrix # Inputs # Construct word embedding layer # Compute sentence representation # Compute representations of negative instances # Reconstruction # Loss # Word embedding and aspect embedding initialization