querysum_model.py 文件源码

def _add_encoders(self):
        with tf.variable_scope('query_encoder'):
            query_encoder_cell = GRUCell(self.encoder_cell_state_size)
            if self.dropout_enabled and self.mode != 'decode':
                query_encoder_cell = DropoutWrapper(cell=query_encoder_cell, output_keep_prob=0.8)

            query_embeddings = tf.nn.embedding_lookup(self.embeddings, self.queries_placeholder)

            query_encoder_outputs, _ = rnn.dynamic_rnn(query_encoder_cell, query_embeddings,
                                                       sequence_length=self.query_lengths_placeholder,
                                                       swap_memory=True, dtype=tf.float32)
            self.query_last = query_encoder_outputs[:, -1, :]

        with tf.variable_scope('encoder'):
            fw_cell = GRUCell(self.encoder_cell_state_size)
            bw_cell = GRUCell(self.encoder_cell_state_size)

            if self.dropout_enabled and self.mode != 'decode':
                fw_cell = DropoutWrapper(cell=fw_cell, output_keep_prob=0.8)
                bw_cell = DropoutWrapper(cell=bw_cell, output_keep_prob=0.8)

            embeddings = tf.nn.embedding_lookup(self.embeddings, self.documents_placeholder)

            (encoder_outputs_fw, encoder_outputs_bw), _ = rnn.bidirectional_dynamic_rnn(
                fw_cell, bw_cell,
                embeddings,
                sequence_length=self.document_lengths_placeholder,
                swap_memory=True,
                dtype=tf.float32)

            self.encoder_outputs = tf.concat([encoder_outputs_fw, encoder_outputs_bw], 2)

            self.final_encoder_state = self.encoder_outputs[:, -1, :]