Exemple #1
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 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)
Exemple #2
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    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)