tensorflow每次运行发现多个图事件 [英] tensorflow Found more than one graph event per run

问题描述

我为本地模式下运行的ml引擎实验加载tensorboard，并得到以下警告：

 每次运行发现多个图形事件，或者有一个包含graph_def的metagraph，以及一个或多个图形事件。用最新的事件覆盖图形
 W0825 19：26：12.435613 Reloader event_accumulator.py： 311]每次运行发现多个metagraph事件，用最新的事件覆盖metagraph。 
  

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 - 但是，即使我执行了 rm -rf $ OUTPUT_PATH / * 我仍然收到此错误对于一个本地火车来说，这个错误是否可以指示我图中的一个更大的问题？

解决方案

看起来你可能已经遇到这篇文章，但没有更多的信息，这是我可以提供的最好的信息：

这是一个已知问题，TensorBoard不喜欢它，当您从同一个单独运行中写入
多个事件文件时如果你为每次运行使用一个新的子目录（新的
hyperparameters =新的子目录），
将被修正。

您可能无意中编写了多个事件文件在相同的目录（例如，训练和eval？）。

另外，确保你在返回一个合适的 tf.estimator.EstimatorSpec modes.EVAL 。从人口普查样本 ：

  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 = {
'准确性'：tf.metrics.accuracy（label_indices，predicted_indices），
'auroc'：tf.metrics.auc（labels_one_hot，概率）
} 
返回tf.estimator。 EstimatorSpec（
模式，损失=损失，eval_metric_ops = eval_metric_ops）
  

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."

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?

解决方案

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

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).

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

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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