def __init__(self, num_features, padding_idx=0, rnn_class='lstm',
emb_size=128, hidden_size=128, num_layers=2, dropout=0.1,
bidir_input=False, share_output=True,
attn_type='none', attn_length=-1):
super().__init__()
if padding_idx != 0:
raise RuntimeError('This module\'s output layer needs to be fixed '
'if you want a padding_idx other than zero.')
self.dropout = dropout
self.layers = num_layers
self.hsz = hidden_size
self.lt = nn.Embedding(num_features, emb_size, padding_idx=padding_idx)
self.rnn = rnn_class(emb_size, hidden_size, num_layers,
dropout=dropout, batch_first=True)
# rnn output to embedding
self.o2e = nn.Linear(hidden_size, emb_size)
# embedding to scores, use custom linear to possibly share weights
shared_weight = self.lt.weight if share_output else None
self.e2s = Linear(emb_size, num_features, bias=False,
shared_weight=shared_weight)
self.shared = shared_weight is not None
self.attn_type = attn_type
self.attention = AttentionLayer(attn_type=attn_type,
hidden_size=hidden_size,
emb_size=emb_size,
bidirectional=bidir_input,
attn_length=attn_length)
评论列表
文章目录
