def init_embedding(self):
self.user_embedding = var_init(
'user_embedding', [self.n_users, self.emb_dim],
tf.random_normal_initializer(mean=0.0, stddev=0.01))
self.train_visual_embedding = tf.Variable(tf.constant(
0.0, shape=[self.n_items + 1, 512]),
trainable=False,
name='train_visual_emb')
self.item_category_embedding = var_init(
'item_category_embedding', [self.n_cates, self.emb_dim],
tf.random_normal_initializer(mean=0.0, stddev=0.01))
def init_embedding(self):
self.user_embedding = var_init(
'user_embedding', [self.n_users, self.emb_dim],
tf.random_normal_initializer(mean=0.0, stddev=0.01))
item_category_embedding = var_init(
'item_category_embedding', [self.n_cates, self.emb_dim],
tf.random_normal_initializer(mean=0.0, stddev=0.01))
self.item_category_embedding = tf.concat(
[item_category_embedding,
tf.zeros((1, self.emb_dim))], axis=0)
self.visual_feature = tf.Variable(tf.constant(
0.0, shape=[self.n_items + 1, 4000]),
trainable=False,
name='visual_feat')
def init_embedding(self):
category_embedding = var_init('category_embedding',
[512, self.cate_dim],
tf.random_normal_initializer())
self.category_embedding = tf.concat(
[category_embedding,
tf.zeros((1, self.cate_dim))], axis=0)
self.train_visual_emb = tf.Variable(tf.constant(0.0,
shape=[984984, 512]),
trainable=False,
name='train_visual_emb')