def encoding_layer(self, vecs: tf.Variable, num_words: tf.Variable,
reuse: bool) -> tf.Variable:
with tf.variable_scope('encoding', reuse=reuse):
encoded_vecs, _, _ = bi_gru_layer([self.conf_layer_size] * 3, vecs,
num_words, self.apply_dropout,
self.conf_rnn_parallelity)
return encoded_vecs
def match_par_qu_layer(self):
with tf.variable_scope('alignment_par_qu') as scope:
rnn_cell = MatchRNNCell(GRUCell(self.conf_layer_size), self.qu_encoded, self.conf_att_size)
outputs, final_state = dynamic_rnn(rnn_cell, self.par_encoded, self.par_num_words,
parallel_iterations=self.conf_rnn_parallelity,
scope=scope, swap_memory=True, dtype=tf.float32)
with tf.variable_scope('encoding'):
outputs, _, _ = bi_gru_layer([self.conf_layer_size], self.apply_dropout(outputs), self.par_num_words,
self.apply_dropout)
return outputs