I'm using a custom <code>tf.Estimator</code> object to train a neural network. The problem is in size of the events file after training - it is unreasonably large. I've already solved the problem with saving part of a dataset as constant by using <code>tf.Dataset.from_generator()</code>. However, the size is still quite large and while starting <code>tensorboard</code> I'm getting the message
I'm using a custom
tf.Estimator object to train a neural network. The problem is in size of the events file after training - it is unreasonably large. I've already solved the problem with saving part of a dataset as constant by using
tf.Dataset.from_generator(). However, the size is still quite large and while starting
tensorboard I'm getting the message
W0225 10:38:07.443567 140693578311424 tf_logging.py:120] Found more than one metagraph event per run. Overwriting the metagraph with the newest event.
So, I suppose, that I'm creating and saving many different graphs in this event file. Is it possible to turn off this saving or how to save an only first copy?
For know, I found only the way to delete all the default logs by deleting the events filts with
list(map(os.remove, glob.glob(os.path.join(runtime_params['model_dir'], 'events.out.tfevents*'))))
However, it is bad solution for me, as I would prefer to keep the summaries and, ideally, one copy of the graph.
From documentation, I can see that
Estimators automatically write the following to disk:
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