def build(self, input_shape):
super(ConditionalRandomField, self).build(input_shape)
output_dim = input_shape[-1]
self._trans = self.add_weight(name='trans',
shape=(output_dim, output_dim),
initializer='glorot_uniform',
trainable=True)
if self.lr_multiplier != 1:
K.set_value(self._trans, K.eval(self._trans) / self.lr_multiplier)
def build(self, input_shape):
super(MaximumEntropyMarkovModel, self).build(input_shape)
output_dim = input_shape[-1]
if self.hidden_dim is None:
self._trans = self.add_weight(name='trans',
shape=(output_dim, output_dim),
initializer='glorot_uniform',
trainable=True)
if self.lr_multiplier != 1:
K.set_value(self._trans,
K.eval(self._trans) / self.lr_multiplier)
else:
self._l_trans = self.add_weight(name='l_trans',
shape=(output_dim,
self.hidden_dim),
initializer='glorot_uniform',
trainable=True)
self._r_trans = self.add_weight(name='r_trans',
shape=(output_dim,
self.hidden_dim),
initializer='glorot_uniform',
trainable=True)
if self.lr_multiplier != 1:
K.set_value(self._l_trans,
K.eval(self._l_trans) / self.lr_multiplier)
K.set_value(self._r_trans,
K.eval(self._r_trans) / self.lr_multiplier)