report uninitialized variables Tensorboard

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I am constructing a neural network in Tensorflow. I am using tf.layers module. For some reason in the Graph visualisation i am seeing a 'report uninitialised variables' connected to every part of my graph.

Does anyone have an explanation of this? Is it related to the get_variable and variable_scope methods?

The graph seems to work. I am just trying to understand the meaning of these nodes. I am not sure if it is related to the fact that i am using a MonitoredTrainingSession.

It seems to be related to all the variables including of the optimizer. https://i.stack.imgur.com/ySFM5.png

The is sort of an init node but it seems to say noop, not sure if proper initilaization is done by the MonitoredTrainingSession. The strange thing is that the graph still works and no 'Initialisation Error' is given. https://i.stack.imgur.com/umrRA.png

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1 Answers

Did you use tf.train.Supervisor() in your code? I had the same case as yours when I used tf.train.Supervisor(). When tf.train.Supervisor() object is created, it will automatically verify model is fully initialized by running the tf.report_uninitialized_variables() operation, and this is why you see a report_uninitialized_variables block in your tensorboard. You can disable Supervisor to rerify your model, so that there will not be a report_uninitialized_variables block in your graph.

Solution: tf.train.Supervisor(ready_op=None)

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