# 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)