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 think it's always better to let tensorflow do the calculations.

Have a look at the streaming metrics. They have an update function to feed the information of your current batch and a function to get the averaged summary. It's going to look somewhat like this:

accuracy = ... 
streaming_accuracy, streaming_accuracy_update = tf.contrib.metrics.streaming_mean(accuracy)
streaming_accuracy_scalar = tf.summary.scalar('streaming_accuracy', streaming_accuracy)

# set up your session etc. 

for i in iterations:
      for b in batches:
               sess.run([streaming_accuracy_update], feed_dict={...})

     streaming_summ = sess.run(streaming_accuracy_scalar)
     writer.add_summary(streaming_summary, i)

Also see the tensorflow documentation: https://www.tensorflow.org/versions/master/api_guides/python/contrib.metrics

and this question: How to accumulate summary statistics in tensorflow