def forward(self, xs, ys, reduce='mean', transpose=False):
"""Computes negative log-likelihood of linear-chain CRF
Args:
xs (list of Variable): Input vector for each label
ys (list of Variable): Expected output labels.
transpose (bool): If ``True``, input/output sequences
will be sorted in descending order of length.
Returns:
~chainer.Variable: A variable holding the average negative
log-likelihood of the input sequences.
.. seealso:: See :func:`~chainer.frunctions.crf1d` for more detail.
"""
if transpose:
indices = argsort_list_descent(xs)
xs = permutate_list(xs, indices, inv=False)
ys = permutate_list(ys, indices, inv=False)
trans_x = transpose_sequence.transpose_sequence(xs)
trans_y = transpose_sequence.transpose_sequence(ys)
loss = crf1d.crf1d(self.cost, trans_x, trans_y, reduce)
else:
loss = crf1d.crf1d(self.cost, xs, ys, reduce)
return loss
def forward(self, xs, ys, reduce='mean', transpose=False):
"""Computes negative log-likelihood of linear-chain CRF
Args:
xs (list of Variable): Input vector for each label
ys (list of Variable): Expected output labels.
transpose (bool): If ``True``, input/output sequences
will be sorted in descending order of length.
Returns:
~chainer.Variable: A variable holding the average negative
log-likelihood of the input sequences.
.. seealso:: See :func:`~chainer.frunctions.crf1d` for more detail.
"""
if transpose:
indices = argsort_list_descent(xs)
xs = permutate_list(xs, indices, inv=False)
ys = permutate_list(ys, indices, inv=False)
trans_x = transpose_sequence.transpose_sequence(xs)
trans_y = transpose_sequence.transpose_sequence(ys)
loss = crf1d.crf1d(self.cost, trans_x, trans_y, reduce)
else:
loss = crf1d.crf1d(self.cost, xs, ys, reduce)
return loss
def forward(self, xs, ys, reduce='mean'):
return crf1d.crf1d(self.cost, xs, ys, reduce)
def __call__(self, xs, ys):
return crf1d.crf1d(self.cost, xs, ys)
def __call__(self, xs, ys, reduce='mean'):
return crf1d.crf1d(self.cost, xs, ys, reduce)
def forward(self, xs, ys, reduce='mean'):
return crf1d.crf1d(self.cost, xs, ys, reduce)
def __call__(self, xs, ys):
return crf1d.crf1d(self.cost, xs, ys)
def __call__(self, xs, ys, reduce='mean'):
return crf1d.crf1d(self.cost, xs, ys, reduce)