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)
self.trans = self.lr_multiplier * self.trans
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)
self.l_trans = self.lr_multiplier * self.l_trans
K.set_value(self.r_trans,
K.eval(self.r_trans) / self.lr_multiplier)
self.r_trans = self.lr_multiplier * self.r_trans
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)
self.trans = self.lr_multiplier * self.trans
