def __init__(self, ntoken, ninp, dropout, name="", cuda=False):
super(_netW, self).__init__()
if cuda:
self.word_embed = nn.Embedding(ntoken, ninp, padding_idx=0).cuda()
self.Linear = share_Linear(self.word_embed.weight).cuda()
else:
self.word_embed = nn.Embedding(ntoken, ninp, padding_idx=0).cpu()
self.Linear = share_Linear(self.word_embed.weight).cpu()
self.init_weights()
self.d = dropout
self.name = name
def __init__(self, ntoken, ninp, dropout, pretrained_wemb):
super(_netW, self).__init__()
#self.word_embed = nn.Embedding(ntoken+1, ninp).cuda()
self.word_embed = nn.Embedding(ntoken + 1, ninp, padding_idx=0).cuda()
#pdb.set_trace()
self.word_embed.weight.data.copy_(torch.from_numpy(pretrained_wemb))
self.Linear = share_Linear(self.word_embed.weight).cuda()
#self.init_weights()
self.d = dropout
def __init__(self, ntoken, ninp, dropout):
super(_netW, self).__init__()
self.word_embed = nn.Embedding(ntoken+1, ninp).cuda()
self.Linear = share_Linear(self.word_embed.weight).cuda()
self.init_weights()
self.d = dropout
def __init__(self, ntoken, ninp, dropout):
super(_netW, self).__init__()
self.word_embed = nn.Embedding(ntoken+1, ninp).cuda()
self.Linear = share_Linear(self.word_embed.weight).cuda()
self.init_weights()
self.d = dropout
