querysum_model.py 文件源码

def _score(self, prev_decoder_state, prev_embedding):
        # Returns scores in a tensor of shape [batch_size, input_sequence_length]

        if self.mode == 'decode':
            query_part = self.query_attention_partial_score_placeholder
            encoder_part = self.encoder_state_attention_partial_scores_placeholder
        else:
            query_part = self.query_attention_partial_score
            encoder_part = self.encoder_state_attention_partial_scores

        embedding_part = tf.matmul(prev_embedding, self.attention_w_e)

        output = tf.matmul(prev_decoder_state,
                           self.attention_w) + embedding_part + query_part + encoder_part + self.attention_b
        output = tf.tanh(output)
        output = tf.reduce_sum(self.attention_v * output, axis=2)
        output = tf.transpose(output, [1, 0])

        # Handle input document padding by giving a large penalty, eliminating it from the weighted average
        padding_penalty = -1e20 * tf.to_float(1 - tf.sign(self.documents_placeholder))
        masked = output + padding_penalty

        return masked