def __call__(self, *cshsx):
"""Returns new cell state and output of Child-Sum TreeLSTM.
Args:
cshsx (list of :class:`~chainer.Variable`): Variable arguments
which include all cell vectors and all output vectors of
variable children, and an input vector.
Returns:
tuple of ~chainer.Variable: Returns
:math:`(c_{new}, h_{new})`, where :math:`c_{new}` represents
new cell state vector, and :math:`h_{new}` is new output
vector.
"""
cs = cshsx[:len(cshsx) // 2]
hs = cshsx[len(cshsx) // 2:-1]
x = cshsx[-1]
assert (len(cshsx) % 2 == 1)
assert (len(cs) == len(hs))
if x is None:
if any(c is not None for c in cs):
base = [c for c in cs if c is not None][0]
elif any(h is not None for h in hs):
base = [h for h in hs if h is not None][0]
else:
raise ValueError('All inputs (cs, hs, x) are None.')
batchsize, dtype = base.shape[0], base.dtype
x = self.xp.zeros((batchsize, self.in_size), dtype=dtype)
W_x_in = self.W_x(x)
W_x_aio_in, W_x_f_in = split_axis.split_axis(W_x_in,
[3 * self.state_size],
axis=1)
if len(hs) == 0:
aio_in = W_x_aio_in
a, i, o = split_axis.split_axis(aio_in, 3, axis=1)
c = sigmoid.sigmoid(i) * tanh.tanh(a)
h = sigmoid.sigmoid(o) * tanh.tanh(c)
return c, h
hs = self._pad_zero_nodes(hs, (x.shape[0], self.state_size),
dtype=x.dtype)
cs = self._pad_zero_nodes(cs, (x.shape[0], self.state_size),
dtype=x.dtype)
aio_in = self.W_h_aio(sum(hs)) + W_x_aio_in
W_h_fs_in = concat.concat(split_axis.split_axis(self.W_h_f(
concat.concat(hs, axis=0)),
len(hs),
axis=0),
axis=1)
f_in = W_h_fs_in + \
concat.concat([W_x_f_in] * len(hs), axis=1)
tree_lstm_in = concat.concat([aio_in, f_in], axis=1)
return tree_lstm.tree_lstm(*(cs + (tree_lstm_in, )))
def forward(self, *cshsx):
"""Returns new cell state and output of Child-Sum TreeLSTM.
Args:
cshsx (list of :class:`~chainer.Variable`): Variable arguments
which include all cell vectors and all output vectors of
variable children, and an input vector.
Returns:
tuple of ~chainer.Variable: Returns
:math:`(c_{new}, h_{new})`, where :math:`c_{new}` represents
new cell state vector, and :math:`h_{new}` is new output
vector.
"""
cs = cshsx[:len(cshsx) // 2]
hs = cshsx[len(cshsx) // 2:-1]
x = cshsx[-1]
assert(len(cshsx) % 2 == 1)
assert(len(cs) == len(hs))
if x is None:
if any(c is not None for c in cs):
base = [c for c in cs if c is not None][0]
elif any(h is not None for h in hs):
base = [h for h in hs if h is not None][0]
else:
raise ValueError('All inputs (cs, hs, x) are None.')
batchsize, dtype = base.shape[0], base.dtype
x = self.xp.zeros(
(batchsize, self.in_size), dtype=dtype)
W_x_in = self.W_x(x)
W_x_aio_in, W_x_f_in = split_axis.split_axis(
W_x_in, [3 * self.state_size], axis=1)
if len(hs) == 0:
aio_in = W_x_aio_in
a, i, o = split_axis.split_axis(aio_in, 3, axis=1)
c = sigmoid.sigmoid(i) * tanh.tanh(a)
h = sigmoid.sigmoid(o) * tanh.tanh(c)
return c, h
hs = self._pad_zero_nodes(
hs, (x.shape[0], self.state_size), dtype=x.dtype)
cs = self._pad_zero_nodes(
cs, (x.shape[0], self.state_size), dtype=x.dtype)
aio_in = self.W_h_aio(sum(hs)) + W_x_aio_in
W_h_fs_in = concat.concat(split_axis.split_axis(
self.W_h_f(concat.concat(hs, axis=0)), len(hs), axis=0),
axis=1)
f_in = W_h_fs_in + \
concat.concat([W_x_f_in] * len(hs), axis=1)
tree_lstm_in = concat.concat([aio_in, f_in], axis=1)
return tree_lstm.tree_lstm(*(cs + (tree_lstm_in, )))
def forward(self, *cshsx):
"""Returns new cell state and output of N-ary TreeLSTM.
Args:
cshsx (list of :class:`~chainer.Variable`): Arguments which include
all cell vectors and all output vectors of fixed-length
children, and an input vector. The number of arguments must be
same as ``n_ary * 2 + 1``.
Returns:
tuple of ~chainer.Variable: Returns :math:`(c_{new}, h_{new})`,
where :math:`c_{new}` represents new cell state vector,
and :math:`h_{new}` is new output vector.
"""
assert(len(cshsx) == self.n_ary * 2 + 1)
cs = cshsx[:self.n_ary]
hs = cshsx[self.n_ary:-1]
x = cshsx[-1]
if x is None:
if any(c is not None for c in cs):
base = [c for c in cs if c is not None][0]
elif any(h is not None for h in hs):
base = [h for h in hs if h is not None][0]
else:
raise ValueError('All inputs (cs, hs, x) are None.')
batchsize, dtype = base.shape[0], base.dtype
x = self.xp.zeros(
(batchsize, self.in_size), dtype=dtype)
tree_lstm_in = self.W_x(x)
for i, h in enumerate(hs, start=1):
if h is not None:
tree_lstm_in += getattr(self, 'W_h{}'.format(i))(h)
cs = self._pad_zero_nodes(
cs, (x.shape[0], self.state_size), dtype=x.dtype)
return tree_lstm.tree_lstm(*(cs + (tree_lstm_in, )))
def __call__(self, *cshsx):
"""Returns new cell state and output of N-ary TreeLSTM.
Args:
cshsx (list of :class:`~chainer.Variable`): Arguments which include
all cell vectors and all output vectors of fixed-length
children, and an input vector. The number of arguments must be
same as ``n_ary * 2 + 1``.
Returns:
tuple of ~chainer.Variable: Returns :math:`(c_{new}, h_{new})`,
where :math:`c_{new}` represents new cell state vector,
and :math:`h_{new}` is new output vector.
"""
assert (len(cshsx) == self.n_ary * 2 + 1)
cs = cshsx[:self.n_ary]
hs = cshsx[self.n_ary:-1]
x = cshsx[-1]
if x is None:
if any(c is not None for c in cs):
base = [c for c in cs if c is not None][0]
elif any(h is not None for h in hs):
base = [h for h in hs if h is not None][0]
else:
raise ValueError('All inputs are None.')
batchsize, dtype = base.shape[0], base.dtype
x = self.xp.zeros((batchsize, self.in_size), dtype=dtype)
tree_lstm_in = self.W_x(x)
for i, h in enumerate(hs, start=1):
if h is not None:
tree_lstm_in += getattr(self, 'W_h{}'.format(i))(h)
cs = self._pad_zero_nodes(cs, (x.shape[0], self.state_size),
dtype=x.dtype)
return tree_lstm.tree_lstm(*(cs + (tree_lstm_in, )))