model.py 文件源码

def compute_loss(self):
        """
        ??loss

        Return:
            loss: scalar
        """
        if not self._use_crf:
            labels = tf.reshape(
                tf.contrib.layers.one_hot_encoding(
                    tf.reshape(self.input_label_ph, [-1]), num_classes=self._nb_classes),
                shape=[-1, self._sequence_length, self._nb_classes])
            cross_entropy = -tf.reduce_sum(labels * tf.log(self.logits), axis=2)
            mask = tf.sign(tf.reduce_max(tf.abs(labels), axis=2))
            cross_entropy_masked = tf.reduce_sum(
                cross_entropy*mask, axis=1) / tf.cast(self.sequence_actual_length, tf.float32)
            return tf.reduce_mean(cross_entropy_masked)
        else:
            log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(
                self.logits, self.input_label_ph, self.sequence_actual_length)
            return tf.reduce_mean(-log_likelihood)