from chainer.functions.loss import crf1d
from chainer import link
[docs]class CRF1d(link.Link):
"""Linear-chain conditional random field loss layer.
This link wraps the :func:`~chainer.functions.crf1d` function.
It holds a transition cost matrix as a parameter.
Args:
n_label (int): Number of labels.
.. seealso:: :func:`~chainer.functions.crf1d` for more detail.
Attributes:
cost (~chainer.Variable): Transition cost parameter.
"""
def __init__(self, n_label):
super(CRF1d, self).__init__(cost=(n_label, n_label))
self.cost.data[...] = 0
def __call__(self, xs, ys, reduce='mean'):
return crf1d.crf1d(self.cost, xs, ys, reduce)
[docs] def argmax(self, xs):
"""Computes a state that maximizes a joint probability.
Args:
xs (list of Variable): Input vector for each label.
Returns:
tuple: A tuple of :class:`~chainer.Variable` representing each
log-likelihood and a list representing the argmax path.
.. seealso:: See :func:`~chainer.frunctions.crf1d_argmax` for more
detail.
"""
return crf1d.argmax_crf1d(self.cost, xs)