def forward(self, c, h, x):
"""Returns new cell state and updated output of LSTM.
Args:
c (~chainer.Variable): Cell states of LSTM units.
h (~chainer.Variable): Output at the previous time step.
x (~chainer.Variable): A new batch from the input sequence.
Returns:
tuple of ~chainer.Variable: Returns ``(c_new, h_new)``, where
``c_new`` represents new cell state, and ``h_new`` is updated
output of LSTM units.
"""
if self.upward.W.array is None:
in_size = x.size // x.shape[0]
with chainer.using_device(self.device):
self.upward._initialize_params(in_size)
self._initialize_params()
lstm_in = self.upward(x)
if h is not None:
lstm_in += self.lateral(h)
if c is None:
xp = self.xp
with chainer.using_device(self.device):
c = variable.Variable(
xp.zeros((x.shape[0], self.state_size), dtype=x.dtype))
return lstm.lstm(c, lstm_in)
def _lstm(x, h, c, w, b):
xw = _stack_weight([w[2], w[0], w[1], w[3]])
hw = _stack_weight([w[6], w[4], w[5], w[7]])
xb = _stack_weight([b[2], b[0], b[1], b[3]])
hb = _stack_weight([b[6], b[4], b[5], b[7]])
lstm_in = linear.linear(x, xw, xb) + linear.linear(h, hw, hb)
c_bar, h_bar = lstm.lstm(c, lstm_in)
return h_bar, c_bar
def forward(self, x):
"""Updates the internal state and returns the LSTM outputs.
Args:
x (~chainer.Variable): A new batch from the input sequence.
Returns:
~chainer.Variable: Outputs of updated LSTM units.
"""
if self.upward.W.array is None:
with chainer.using_device(self.device):
in_size = utils.size_of_shape(x.shape[1:])
self.upward._initialize_params(in_size)
self._initialize_params()
batch = x.shape[0]
lstm_in = self.upward(x)
h_rest = None
if self.h is not None:
h_size = self.h.shape[0]
if batch == 0:
h_rest = self.h
elif h_size < batch:
msg = ('The batch size of x must be equal to or less than '
'the size of the previous state h.')
raise TypeError(msg)
elif h_size > batch:
h_update, h_rest = split_axis.split_axis(self.h, [batch],
axis=0)
lstm_in += self.lateral(h_update)
else:
lstm_in += self.lateral(self.h)
if self.c is None:
with chainer.using_device(self.device):
self.c = variable.Variable(
self.xp.zeros((batch, self.state_size), dtype=x.dtype))
self.c, y = lstm.lstm(self.c, lstm_in)
if h_rest is None:
self.h = y
elif len(y.array) == 0:
self.h = h_rest
else:
self.h = concat.concat([y, h_rest], axis=0)
return y