Tensorboard logging non-tensor (numpy) information (AUC)

Question

I would like to record in tensorboard some per-run information calculated by some python-blackbox function.

Specifically, I'm envisioning using sklearn.metrics.auc after having run sess.run().

If "auc" was actually a tensor node, life would be simple. However, the setup is more like:

stuff=sess.run()
auc=auc(stuff)

If there is a more tensorflow-onic way of doing this I am interested in that. My current setup involves creating separate train&test graphs.

If there is a way to complete the task as stated above, I am interested in that as well.

Answer 1

You can make a custom summary with your own data using this code:

tf.Summary(value=[tf.Summary.Value(tag="auc", simple_value=auc)]))

Then you can add that summary to the summary writer yourself. (Don't forget to add a step).