def __init__(self,
node_type,
path_patterns,
max_id,
dim,
sparse_feature_dims,
feature_ids,
feature_embedding_dim=16,
walk_len=3,
left_win_size=1,
right_win_size=1,
num_negs=5,
gamma=5,
*args,
**kwargs):
super(LsHNE, self).__init__(node_type, path_patterns, max_id, *args,
**kwargs)
self.node_type = node_type
self.path_patterns = path_patterns
self.max_id = max_id
self.dim = dim
self.walk_len = walk_len
self.left_win_size = left_win_size
self.right_win_size = right_win_size
self.num_negs = num_negs
self.view_num = len(path_patterns)
if self.view_num < 1:
raise ValueError('View Number must be bigger than 1, got{}'.format(
self.view_num))
if not isinstance(sparse_feature_dims, list):
raise TypeError(
'Expect list for sparse feature dimsgot {}.'.format(
type(sparse_feature_dims).__name__))
self.sparse_feature_dims = sparse_feature_dims
self.feature_ids = feature_ids
self.feature_embedding_dim = feature_embedding_dim
self.raw_fdim = feature_embedding_dim * len(feature_ids)
self.feature_embedding_layer = []
for d in sparse_feature_dims:
self.feature_embedding_layer.append(
layers.SparseEmbedding(d,
feature_embedding_dim,
combiner="sum"))
self.hidden_layer = [{}] * self.view_num
for i in range(0, self.view_num):
self.hidden_layer[i]['src'] = layers.Dense(256)
self.hidden_layer[i]['tar'] = layers.Dense(256)
self.out_layer = [{}] * self.view_num
for i in range(0, self.view_num):
self.out_layer[i]['src'] = layers.Dense(self.dim)
self.out_layer[i]['tar'] = layers.Dense(self.dim)
self.att_vec = tf.get_variable(
'att_vec',
shape=[self.view_num, self.dim],
initializer=tf.truncated_normal_initializer(stddev=0.1))
self.gamma = gamma
def create_sparse_embeddings(feature_dims):
sparse_embeddings = [
layers.SparseEmbedding(feature_dim, 16)
for feature_dim in feature_dims
]
return sparse_embeddings