def __init__(self, emb_size, max_his):
super().__init__()
self.max_his = max_his
self.p_embeddings = nn.Embedding(max_his + 1, emb_size)
self.transformer = layers.TransformerLayer(d_model=emb_size,
d_ff=emb_size,
n_heads=1,
kq_same=False)
def __init__(self, emb_size, max_his, num_layers=2, num_heads=2):
super().__init__()
self.p_embeddings = nn.Embedding(max_his + 1, emb_size)
self.transformer_block = nn.ModuleList([
layers.TransformerLayer(d_model=emb_size,
d_ff=emb_size,
n_heads=num_heads)
for _ in range(num_layers)
])
def _define_params(self):
self.i_embeddings = nn.Embedding(self.item_num, self.emb_size)
self.p_embeddings = nn.Embedding(self.max_his + 1, self.emb_size)
self.transformer_block = nn.ModuleList([
layers.TransformerLayer(d_model=self.emb_size,
d_ff=self.emb_size,
n_heads=self.num_heads,
dropout=self.dropout,
kq_same=False)
for _ in range(self.num_layers)
])
def __init__(self, k, item_num, emb_size, attn_size, max_his, add_pos):
super(MultiInterestExtractor, self).__init__()
self.max_his = max_his
self.add_pos = add_pos
self.i_embeddings = nn.Embedding(item_num, emb_size)
if self.add_pos:
self.p_embeddings = nn.Embedding(max_his + 1, emb_size)
self.W1 = nn.Linear(emb_size, attn_size)
self.W2 = nn.Linear(attn_size, k)
self.transformer = layers.TransformerLayer(d_model=emb_size,
d_ff=emb_size,
n_heads=1,
kq_same=False)