如何使tf.metrics.auc与Estimator API一起使用

Question

我在Tensorflow中使用自定义Estimator。 tf.metrics.accuracy似乎工作正常，但不是tf.metrics.auc，它在返回的EstimatorSpec中总是显示0.5用于评估和Tensorboard培训。

以下是我的模型函数的代码片段：

def _model_fn(features, labels, mode, params):
    ......
    accuracy = tf.metrics.accuracy(labels=labels,
                               predictions=predicted_classes,
                               name='acc_op')
    auc = tf.metrics.auc(labels=labels,
                               predictions=predicted_classes,
                               name='auc_op')
    metrics = {'accuracy': accuracy, 'auc': auc}
    tf.summary.scalar('accuracy', accuracy[1])
    tf.summary.scalar('auc', auc[1])

    if mode == tf.estimator.ModeKeys.EVAL:
        # accuracy shows the right value; auc always shows 0.5
        return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)

    # the accuracy, loss during training are written to Tensorboard correctly.
    # But auc always has value 0.5.
    summary_hook = tf.train.SummarySaverHook(
        params['skip_step'],
        output_dir=params['model_dir'],
        summary_op=tf.summary.merge_all())
    return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[summary_hook])

任何人都知道AUC计算出了什么问题？我已经阅读了以下问题。这里的区别是因为我坚持使用高级Estimator API，它没有明确地使用会话。我是否需要初始化局部变量？如果是，我该如何修改我的代码？非常感谢！

how to use tf.metrics.__ with estimator model predict output