def forward(self, state, x):
if state is None:
h = [
to_device(self, self.zero_state(x.size(0)))
for n in six.moves.range(self.n_layers)
]
state = {'h': h}
if self.typ == "lstm":
c = [
to_device(self, self.zero_state(x.size(0)))
for n in six.moves.range(self.n_layers)
]
state = {'c': c, 'h': h}
h = [None] * self.n_layers
emb = self.embed(x)
if self.typ == "lstm":
c = [None] * self.n_layers
h[0], c[0] = self.rnn[0](self.dropout[0](emb),
(state['h'][0], state['c'][0]))
for n in six.moves.range(1, self.n_layers):
h[n], c[n] = self.rnn[n](self.dropout[n](h[n - 1]),
(state['h'][n], state['c'][n]))
state = {'c': c, 'h': h}
else:
h[0] = self.rnn[0](self.dropout[0](emb), state['h'][0])
for n in six.moves.range(1, self.n_layers):
h[n] = self.rnn[n](self.dropout[n](h[n - 1]), state['h'][n])
state = {'h': h}
y = self.lo(self.dropout[-1](h[-1]))
return state, y
def forward(self, state, x):
"""Forward neural networks."""
if state is None:
h = [to_device(x, self.zero_state(x.size(0))) for n in range(self.n_layers)]
state = {"h": h}
if self.typ == "lstm":
c = [
to_device(x, self.zero_state(x.size(0)))
for n in range(self.n_layers)
]
state = {"c": c, "h": h}
h = [None] * self.n_layers
if self.embed_drop is not None:
emb = self.embed_drop(self.embed(x))
else:
emb = self.embed(x)
if self.typ == "lstm":
c = [None] * self.n_layers
h[0], c[0] = self.rnn[0](
self.dropout[0](emb), (state["h"][0], state["c"][0])
)
for n in range(1, self.n_layers):
h[n], c[n] = self.rnn[n](
self.dropout[n](h[n - 1]), (state["h"][n], state["c"][n])
)
state = {"c": c, "h": h}
else:
h[0] = self.rnn[0](self.dropout[0](emb), state["h"][0])
for n in range(1, self.n_layers):
h[n] = self.rnn[n](self.dropout[n](h[n - 1]), state["h"][n])
state = {"h": h}
y = self.lo(self.dropout[-1](h[-1]))
return state, y