# subtract_node = node1 - node2
print()
print("Subtraction")
print("subtract_node:", subtract_node)
print("sess.run(subtract_node):", sess.run(subtract_node))
# Multiply
multiplication_node = tf.multiply(node1, node2)
# Can also use the * to multiply two nodes together
# multiplication_node = node1 * node2
print()
print("Multiplication")
print("multiplication_node:", multiplication_node)
print("sess.run(multiplication_node):", sess.run(multiplication_node))
# 4) Divide
division_node = tf.divide(node1, node2)
# Can also use the / to divide two nodes together
# division_node = node1 / node2
print()
print("Division")
print("division_node:", division_node)
print("sess.run(division_node):", sess.run(division_node))
file_writer, path = open_tensorboard(__file__, sess)
# The canonical import statement for TensorFlow programs is as follows:
# This gives Python access to all of TensorFlow's classes, methods, and symbols.
import tensorflow as tf
from Helper_Methods import open_tensorboard
# One type of node is a constant. Like all TensorFlow constants, it takes no inputs, and it outputs a value it stores
# internally
node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2)
# The final print statement produces
# Tensor("Const:0", shape=(), dtype=float32) Tensor("Const_1:0", shape=(), dtype=float32)
# It prints this instead of the expected 3.0 and 4.0 because all the nodes (constant, Variable, placeholder, etc) are
# evaluated during the session run time and so you have to ask the current session to evaluate the tensors
# The following code creates a Session object and then invokes its run method to run enough of the computational
# graph to evaluate node1 and node2
sess = tf.Session()
print(sess.run([node1, node2]))
# we see the expected values of 3.0 and 4.0:
# [3.0, 4.0]
open_tensorboard(__file__, sess)