tf_scan1.py

# https://deeplearningcourses.com/c/unsupervised-machine-learning-hidden-markov-models-in-python
# https://udemy.com/unsupervised-machine-learning-hidden-markov-models-in-python
# https://lazyprogrammer.me
# tensorflow scan example: calculate x^2

from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future

import numpy as np
import tensorflow as tf

# sequence of elements we want to square
x = tf.placeholder(tf.int32, shape=(None,), name='x')

# thing to do to every element of the sequence
# notice how it always ignores the last output
def square(last, current):
  return current*current

# this is a "fancy for loop"
# it says: apply square to every element of x
square_op = tf.scan(
  fn=square,
  elems=x,
)

# run it!
with tf.Session() as session:
  o_val = session.run(square_op, feed_dict={x: [1, 2, 3, 4, 5]})
  print("output:", o_val)