def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
phs = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
nhs = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
with tf.name_scope('triple_loss'):
self.triple_loss = limited_loss(
phs,
prs,
pts,
nhs,
nrs,
nts,
self.args.pos_margin,
self.args.neg_margin,
self.args.loss_norm,
balance=self.args.neg_margin_balance)
self.triple_optimizer = generate_optimizer(self.triple_loss,
self.args.learning_rate,
opt=self.args.optimizer)
def _define_embed_graph(self):
with tf.name_scope('triple_placeholder'):
# create placeholders for all positive and negative samples?? -> head, relation, tail
self.pos_hs = tf.placeholder(tf.int32, shape=[None])
self.pos_rs = tf.placeholder(tf.int32, shape=[None])
self.pos_ts = tf.placeholder(tf.int32, shape=[None])
self.neg_hs = tf.placeholder(tf.int32, shape=[None])
self.neg_rs = tf.placeholder(tf.int32, shape=[None])
self.neg_ts = tf.placeholder(tf.int32, shape=[None])
with tf.name_scope('triple_lookup'):
# get embeddings from the initialized random variables,
# should given a listen of id but we give just none here, don't quite understood it
phs = tf.nn.embedding_lookup(self.ent_embeds, self.pos_hs)
prs = tf.nn.embedding_lookup(self.rel_embeds, self.pos_rs)
pts = tf.nn.embedding_lookup(self.ent_embeds, self.pos_ts)
nhs = tf.nn.embedding_lookup(self.ent_embeds, self.neg_hs)
nrs = tf.nn.embedding_lookup(self.rel_embeds, self.neg_rs)
nts = tf.nn.embedding_lookup(self.ent_embeds, self.neg_ts)
with tf.name_scope('triple_loss'):
# Compute loss and use optimizer (Adagrad for BootEA) to apply gradients
self.triple_loss = limited_loss(phs, prs, pts, nhs, nrs, nts,
self.args.pos_margin, self.args.neg_margin,
self.args.loss_norm, balance=self.args.neg_margin_balance)
self.triple_optimizer = generate_optimizer(self.triple_loss, self.args.learning_rate, opt=self.args.optimizer)