getting_started_tensorflow_2.py

import tensorflow as tf

x = tf.Variable(3, name="x", dtype=tf.int16)
y = tf.Variable(4, name="y", dtype=tf.int16)

a = tf.constant(5, dtype=tf.int16)
b = tf.constant(10, dtype=tf.int16)
c = tf.constant(15, dtype=tf.int16)

op1 = x*x*y
op2 = a*b*c
f = op1 + op2

with tf.Session() as ses:
    ses.run(x.initializer)
    ses.run(y.initializer)
    result = ses.run(op1)
    print("Result from computation of graph op1 is: {}".format(result))
    # Result from computation of graph op1 is: 36

# here is a better/more concise way:
with tf.Session() as ses:
    x.initializer.run()
    y.initializer.run()
    result = op2.eval()
    print("Result from computation of graph op2 is: {}".format(result))
    # Result from computation of graph op2 is: 750

# even better/more concise way - instead of running initializer
# for every single variable, use global_variables_initializer() function:
init = tf.global_variables_initializer()
# Note: on older tensorflow versions the function was called: initialize_all_variables()
with tf.Session() as ses:
    init.run()
    r1 = op1.eval()
    r2 = op2.eval()
    result = f.eval()
    print("Result from computation of graph f is: {}".format(result))
    # Result from computation of graph f is: 786