Tensorflow summary: adding a variable which does not belong to computational graph

Question

I have a variable that changes with train iterations. The variable is not computed as a part of the computational graph.

Is it possible to add it to the tensorflow summary in order to visualize it together with the loss function?

Answer 1

Yes, you can create summaries outside the graph.

Here is an example where the summary is created outside the graph (not as a TF op):

output_path = "/tmp/myTest"
summary_writer = tf.summary.FileWriter(output_path)

for x in range(100):
   myVar = 2*x

   summary=tf.Summary()
   summary.value.add(tag='myVar', simple_value = myVar)
   summary_writer.add_summary(summary, x)

summary_writer.flush()

Answer 2

if you have other summary, you can add new placeholder for the variable what is not computed as a part of the computational graph.

...
myVar_tf = tf.placeholder(dtype=tf.float32)
tf.summary.scalar('myVar', myVar_tf)
merged_summary = tf.summary.merge_all()
...
...
myVar = 0.1
feed_dict = { myVar_tf : myVar}
summary, step = sess.run([merged_summary, global_step],feed_dict=feed_dict)
summary_writer.add_summary(summary, step)