tensorboard with numpy array

Question

Can someone give a example on how to use tensorboard visualize numpy array value?

There is a related question here, I don't really get it. Tensorboard logging non-tensor (numpy) information (AUC)

For example, If I have

for i in range(100):
    foo = np.random.rand(3,2)

How can I keep tracking the distribution of foo using tensorboard for 100 iterations? Can someone give a code example? Thanks.

Answer 1

For simple values (scalar), you can use this recipe

summary_writer = tf.train.SummaryWriter(FLAGS.logdir)
summary = tf.Summary()
summary.value.add(tag=tagname, simple_value=value)
summary_writer.add_summary(summary, global_step)
summary_writer.flush()

As far as using array, perhaps you can add 6 values in a sequence, ie

for value in foo:
  summary.value.add(tag=tagname, simple_value=value)

Answer 2

Another (simplest) way is just using placeholders. First, you can make a placeholder for your numpy array shape.

# Some place holders for summary
summary_reward = tf.placeholder(tf.float32, shape=(), name="reward")
tf.summary.scalar("reward", summary_reward)

Then, just call session.run the merged summary with the feed_dict.

# Summary
summ = tf.summary.merge_all()
...
s = sess.run(summ, feed_dict={summary_reward: reward})
writer.add_summary(s, i)