models.py 文件源码

def softmax_loss(self, antecedent_scores, antecedent_labels):
        """
        Computes the value of the loss function using antecedent_scores and antecedent_labels.
        Practically standard softmax function.
        Args:
            antecedent_scores: tf.float64, [num_mentions, max_ant + 1], output of fully-connected network that compute
                antecedent scores.
            antecedent_labels:  True labels for antecedent.

        Returns: [num_mentions]
            The value of loss function.
        """
        gold_scores = antecedent_scores + tf.log(tf.cast(antecedent_labels, tf.float64))  # [num_mentions, max_ant + 1]
        marginalized_gold_scores = tf.reduce_logsumexp(gold_scores, [1])  # [num_mentions]
        log_norm = tf.reduce_logsumexp(antecedent_scores, [1])  # [num_mentions]
        return log_norm - marginalized_gold_scores  # [num_mentions]