def __init__(self, n_vocab, n_layers, n_units, typ="lstm"):
super(RNNLM, self).__init__()
with self.init_scope():
self.embed = DL.EmbedID(n_vocab, n_units)
self.rnn = chainer.ChainList(
*[L.StatelessLSTM(n_units, n_units) for _ in range(n_layers)]) if typ == "lstm" \
else chainer.ChainList(*[L.StatelessGRU(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
self.n_units = n_units
self.typ = typ
def __init__(
self,
eprojs,
odim,
dtype,
dlayers,
dunits,
sos,
eos,
att,
verbose=0,
char_list=None,
labeldist=None,
lsm_weight=0.0,
sampling_probability=0.0,
):
super(Decoder, self).__init__()
with self.init_scope():
self.embed = DL.EmbedID(odim, dunits)
self.rnn0 = (
L.StatelessLSTM(dunits + eprojs, dunits)
if dtype == "lstm"
else L.StatelessGRU(dunits + eprojs, dunits)
)
for l in six.moves.range(1, dlayers):
setattr(
self,
"rnn%d" % l,
L.StatelessLSTM(dunits, dunits)
if dtype == "lstm"
else L.StatelessGRU(dunits, dunits),
)
self.output = L.Linear(dunits, odim)
self.dtype = dtype
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
self.sampling_probability = sampling_probability