def embedding_layer(self):
self.input_layer()
# 随机初始化
self.positions_up_embed = load_random_embedding(len(self.all_word_index[position_index]), self.embedding_size, self.positons_up)
self.positions_down_embed = load_random_embedding(len(self.all_word_index[position_index]), self.embedding_size, self.positons_down)
self.poses_up_embed = load_random_embedding(len(self.all_word_index[poses_index]), self.embedding_size, self.poses_up)
self.poses_down_embed = load_random_embedding(len(self.all_word_index[poses_index]), self.embedding_size, self.poses_down)
self.sts_up_embed = load_random_embedding(len(self.all_word_index[poses_index]), self.embedding_size, self.sts_up)
self.sts_down_embed = load_random_embedding(len(self.all_word_index[poses_index]), self.embedding_size, self.sts_down)
# glove
embedding_maxtrix = load_glove_embedding(
word_index=self.all_word_index[word_index],
file='',
trimmed_filename=self.embedding_path,
load=True,
dim=self.embedding_size
)
glove_w2v = tf.Variable(embedding_maxtrix, dtype=tf.float32, name='glove_w2v')
self.word_up_embed = tf.nn.embedding_lookup(glove_w2v, self.word_up)
self.word_down_embed = tf.nn.embedding_lookup(glove_w2v, self.word_down)
# dropout [b_s, max_len, 300]
self.word_up_embed = tf.nn.dropout(self.word_up_embed, self.dropout_keep_prob)
self.word_down_embed = tf.nn.dropout(self.word_down_embed, self.dropout_keep_prob)
self.positions_up_embed = tf.nn.dropout(self.positions_up_embed, self.dropout_keep_prob)
self.positions_down_embed = tf.nn.dropout(self.positions_down_embed, self.dropout_keep_prob)
self.poses_up_embed = tf.nn.dropout(self.poses_up_embed, self.dropout_keep_prob)
self.poses_down_embed = tf.nn.dropout(self.poses_down_embed, self.dropout_keep_prob)
self.sts_up_embed = tf.nn.dropout(self.sts_up_embed, self.dropout_keep_prob)
self.sts_down_embed = tf.nn.dropout(self.sts_down_embed, self.dropout_keep_prob)
# 合成 [b_s, max_len, 300]
self.up_compose = tf.add(
tf.add(self.word_up_embed, self.positions_up_embed),
tf.add(self.sts_up_embed, self.poses_up_embed)
)
self.down_compose = tf.add(
tf.add(self.word_down_embed, self.positions_down_embed),
tf.add(self.sts_down_embed, self.poses_down_embed)
)
def embedding_layer(self):
self.input_layer()
# 随机初始化
self.positions_up_embed = load_random_embedding(
len(self.all_word_index[position_index]), self.embedding_size,
self.positons_up)
self.positions_down_embed = load_random_embedding(
len(self.all_word_index[position_index]), self.embedding_size,
self.positons_down)
self.poses_up_embed = load_random_embedding(
len(self.all_word_index[poses_index]), self.embedding_size,
self.poses_up)
self.poses_down_embed = load_random_embedding(
len(self.all_word_index[poses_index]), self.embedding_size,
self.poses_down)
self.sts_up_embed = load_random_embedding(
len(self.all_word_index[poses_index]), self.embedding_size,
self.sts_up)
self.sts_down_embed = load_random_embedding(
len(self.all_word_index[poses_index]), self.embedding_size,
self.sts_down)
# glove
embedding_maxtrix = load_glove_embedding(
word_index=self.all_word_index[word_index],
file='',
trimmed_filename=self.embedding_path,
load=True,
dim=self.embedding_size)
glove_w2v = tf.Variable(embedding_maxtrix,
dtype=tf.float32,
name='glove_w2v')
self.word_up_embed = tf.nn.embedding_lookup(glove_w2v, self.word_up)
self.word_down_embed = tf.nn.embedding_lookup(glove_w2v,
self.word_down)
# 讲aspect词向量求平均然后拓展开来
self.asp_embed = tf.nn.embedding_lookup(
glove_w2v, self.aspects) # [b_s, asp_max_len, 300]
self.asp_embed = tf.reduce_mean(
self.asp_embed, axis=1,
keepdims=True) / self.asp_max_len # [b_s, 1, 300]
self.asp_embed = tf.multiply(
tf.constant(1, tf.float32,
[self.batch_size, self.max_len, self.embedding_size]),
self.asp_embed / self.asp_max_len)
# dropout [b_s, max_len, 300]
self.word_up_embed = tf.nn.dropout(self.word_up_embed,
self.dropout_keep_prob)
self.word_down_embed = tf.nn.dropout(self.word_down_embed,
self.dropout_keep_prob)
self.positions_up_embed = tf.nn.dropout(self.positions_up_embed,
self.dropout_keep_prob)
self.positions_down_embed = tf.nn.dropout(self.positions_down_embed,
self.dropout_keep_prob)
self.poses_up_embed = tf.nn.dropout(self.poses_up_embed,
self.dropout_keep_prob)
self.poses_down_embed = tf.nn.dropout(self.poses_down_embed,
self.dropout_keep_prob)
self.sts_up_embed = tf.nn.dropout(self.sts_up_embed,
self.dropout_keep_prob)
self.sts_down_embed = tf.nn.dropout(self.sts_down_embed,
self.dropout_keep_prob)
# 合成 [b_s, max_len, 300]
self.up_compose = tf.add(
tf.add(self.word_up_embed, self.positions_up_embed),
tf.add(self.sts_up_embed, self.poses_up_embed))
self.down_compose = tf.add(
tf.add(self.word_down_embed, self.positions_down_embed),
tf.add(self.sts_down_embed, self.poses_down_embed))