# Calculate evaluation metrics.       def metric_fn(label_ids, predicted_labels):        accuracy = tf.metrics.accuracy(label_ids, predicted_labels)        f1_score = tf.contrib.metrics.f1_score(            label_ids,            predicted_labels)        auc = tf.metrics.auc(            label_ids,            predicted_labels)        recall = tf.metrics.recall(            label_ids,            predicted_labels)        precision = tf.metrics.precision(            label_ids,            predicted_labels)         true_pos = tf.metrics.true_positives(            label_ids,            predicted_labels)        true_neg = tf.metrics.true_negatives(            label_ids,            predicted_labels)           false_pos = tf.metrics.false_positives(            label_ids,            predicted_labels)          false_neg = tf.metrics.false_negatives(            label_ids,            predicted_labels)        return {            "eval_accuracy": accuracy,            "f1_score": f1_score,            "auc": auc,            "precision": precision,            "recall": recall,            "true_positives": true_pos,            "true_negatives": true_neg,            "false_positives": false_pos,            "false_negatives": false_neg        }复制代码