I have a TensorFlow model, and one part of this model evaluates the accuracy. The accuracy is just another node in the tensorflow graph, that takes in logits and labels.
When I want to plot the training accuracy, this is simple: I have something like:
tf.scalar_summary("Training Accuracy", accuracy)
summary_op = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('/me/mydir/', graph=sess.graph)
Then, during my training loop, I have something like:
for n in xrange(1000):
summary, ..., ... = sess.run([summary_op, ..., ...], feed_dict)
Also inside that for loop, every says, 100 iterations, I want to evaluate the validation accuracy. I have a separate feed_dict for this, and I am able to evaluate the validation accuracy very nicely in python.
However, here is my problem: I want to make another summary for the validation accuracy, by using the accuracy node. I am not clear on how to do this though. Since I have the accuracy node it makes sense that I should be able to re-use it, but I am unsure how to do this exactly, such that I can also get the validation accuracy written out as a separate scalar_summary...
How might this be possible?