def __init__(self, args, emb_layer, nclasses=2):
super(Model, self).__init__()
self.args = args
self.drop = nn.Dropout(args.dropout)
self.emb_layer = emb_layer
if args.cnn:
self.encoder = modules.CNN_Text(
emb_layer.n_d,
widths = [3,4,5]
)
d_out = 300
elif args.lstm:
self.encoder = nn.LSTM(
emb_layer.n_d,
args.d,
args.depth,
dropout = args.dropout,
)
d_out = args.d
else:
self.encoder = MF.SRU(
emb_layer.n_d,
args.d,
args.depth,
dropout = args.dropout,
use_tanh = 1,
)
d_out = args.d
self.out = nn.Linear(d_out, nclasses)
def __init__(self,
embedding,
hidden_size=150,
depth=1,
dropout=0.3,
cnn=False,
nclasses=2):
super(Model, self).__init__()
self.cnn = cnn
self.drop = nn.Dropout(dropout)
self.emb_layer = modules.EmbeddingLayer(
embs=dataloader.load_embedding(embedding))
self.word2id = self.emb_layer.word2id
if cnn:
self.encoder = modules.CNN_Text(self.emb_layer.n_d,
widths=[3, 4, 5],
filters=hidden_size)
d_out = 3 * hidden_size
else:
self.encoder = nn.LSTM(
self.emb_layer.n_d,
hidden_size // 2,
depth,
dropout=dropout,
# batch_first=True,
bidirectional=True)
d_out = hidden_size
# else:
# self.encoder = SRU(
# emb_layer.n_d,
# args.d,
# args.depth,
# dropout = args.dropout,
# )
# d_out = args.d
self.out = nn.Linear(d_out, nclasses)