How to average summaries over multiple batches?

Question

Assuming I have a bunch of summaries defined like:

loss = ...
tf.scalar_summary(\"loss\", loss)
# ...
summaries = tf.m         


        

Answer 1

I found one solution myself. I think it's kind of hacky and I hope there is a more elegant solution.

During setup:

valid_loss_placeholder = tf.placeholder(dtype=tf.float32, shape=[])
valid_loss_summary = tf.scalar_summary("valid loss", valid_loss_placeholder)

Or for tensorflow versions after 0.12 (change in name for tf.scalar_summary):

valid_loss_placeholder = tf.placeholder(dtype=tf.float32, shape=[])
valid_loss_summary = tf.summary.scalar("valid loss", valid_loss_placeholder) 

Within training loop:

# Compute valid loss in python by doing sess.run() for each batch
# and averaging
valid_loss = ...

summary = sess.run(valid_loss_summary, {valid_loss_placeholder: valid_loss})
summary_writer.add_summary(summary, step)