def __init__(self,
eprojs,
odim,
dlayers,
dunits,
sos,
eos,
att,
verbose=0,
char_list=None,
labeldist=None,
lsm_weight=0.):
super(Decoder, self).__init__()
with self.init_scope():
self.embed = DL.EmbedID(odim, dunits)
self.lstm0 = L.StatelessLSTM(dunits + eprojs, dunits)
for l in six.moves.range(1, dlayers):
setattr(self, 'lstm%d' % l, L.StatelessLSTM(dunits, dunits))
self.output = L.Linear(dunits, odim)
self.loss = None
self.att = att
self.dlayers = dlayers
self.dunits = dunits
self.sos = sos
self.eos = eos
self.verbose = verbose
self.char_list = char_list
# for label smoothing
self.labeldist = labeldist
self.vlabeldist = None
self.lsm_weight = lsm_weight
def __init__(self, n_vocab, n_layers, n_units):
super(RNNLM, self).__init__()
with self.init_scope():
self.embed = DL.EmbedID(n_vocab, n_units)
self.lstm = chainer.ChainList(
*[L.StatelessLSTM(n_units, n_units) for _ in range(n_layers)])
self.lo = L.Linear(n_units, n_vocab)
for param in self.params():
param.data[...] = np.random.uniform(-0.1, 0.1, param.data.shape)
self.n_layers = n_layers
