tf.summary.scalar('mean', mean)

tf.summary.histogram('histogram', var)

merged = tf.summary.merge_all()

train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',

                                      sess.graph)

summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))

test_writer.add_summary(summary, i)

import tensorflow as tf

k = tf.placeholder(tf.float32)

# Make a normal distribution, with a shifting mean

mean_moving_normal = tf.random_normal(shape=[1000], mean=(5*k), stddev=1)

# Record that distribution into a histogram summary

tf.summary.histogram("normal/moving_mean", mean_moving_normal)

# Setup a session and summary writer

sess = tf.Session()

writer = tf.summary.FileWriter("/tmp/histogram_example")

summaries = tf.summary.merge_all()

# Setup a loop and write the summaries to disk

N = 400

for step in range(N):

  k_val = step/float(N)

  summ = sess.run(summaries, feed_dict={k: k_val})

  writer.add_summary(summ, global_step=step)

tensorboard --logdir=/tmp/histogram_example