tensorflow 每次运行发现不止一个图形事件 [英] tensorflow Found more than one graph event per run

问题描述

我正在为在本地模式下运行的 ml 引擎实验加载张量板并收到以下警告:

I am loading a tensorboard for my ml engine experiment that is running in local mode and got the following warning:

"Found more than one graph event per run, or there was a metagraph containing a graph_def, as well as one or more graph events.  Overwriting the graph with the newest event.
W0825 19:26:12.435613 Reloader event_accumulator.py:311] Found more than one metagraph event per run. Overwriting the metagraph with the newest event."

最初，我怀疑这是因为我没有清除我的 --logdir=$OUTPUT_PATH(正如其他帖子所建议的那样——但是，即使我执行了 rm -rf $OUTPUT_PATH/* 对于本地火车，我仍然收到此错误.此错误是否表示我的图表中存在更大的问题?

Originally, I suspected that this was because I had not cleared my --logdir=$OUTPUT_PATH (as other posts suggested -- however, even if I performed rm -rf $OUTPUT_PATH/* I am still getting this error for a local train. Could this error be indicative of a larger issue in my graph?

推荐答案

看起来你可能已经遇到过 这篇文章，但没有更多信息，这是我能提供的最佳信息:

It looks like you may have already come across this post, but without more information, it's the best information I can provide:

这是一个已知问题，TensorBoard 不喜欢你写的来自同一目录中不同运行的多个事件文件.它会如果每次运行都使用新的子目录(新的超参数 = 新的子目录).

This is a known issue, TensorBoard doesn't like it when you write multiple event files from separate runs in the same directory. It will be fixed if you use a new subdirectory for every run (new hyperparameters = new subdirectory).

您可能会无意中在同一目录中写入多个事件文件(例如，training 和 eval?).

You may be inadvertently writing multiple event files in the same directory (e.g. training and eval?).

此外，请确保在 modes.EVAL 中返回适当的 tf.estimator.EstimatorSpec.来自 :

Also, be sure you are returning an appropriate tf.estimator.EstimatorSpec when in modes.EVAL. From the census sample:

if mode == Modes.EVAL:
  labels_one_hot = tf.one_hot(
      label_indices_vector,
      depth=label_values.shape[0],
      on_value=True,
      off_value=False,
      dtype=tf.bool
  )
  eval_metric_ops = {
      'accuracy': tf.metrics.accuracy(label_indices, predicted_indices),
      'auroc': tf.metrics.auc(labels_one_hot, probabilities)
  }
  return tf.estimator.EstimatorSpec(
      mode, loss=loss, eval_metric_ops=eval_metric_ops)

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