Operations

The TensorFlow library contains several built-in operations that can be applied on tensors. An operation node can be defined by passing input values and saving the output in another tensor. To understand this better, let's define two operations, op1 and op2:

op1 = tf.add(const2, const3)
op2 = tf.multiply(const2, const3)

Let's print op1 and op2:

print('op1 : ', op1)
print('op2 : ', op2)

The output is as follows, and shows that op1 and op2 are defined as tensors:

op1 :  Tensor("Add:0", shape=(), dtype=float32)
op2 :  Tensor("Mul:0", shape=(), dtype=float32)

To print the output from executing these operations, the op1 and op2 tensors have to be executed in a TensorFlow session:

print('run(op1) : ', tfs.run(op1))
print('run(op2) : ', tfs.run(op2))

The output is as follows:

run(op1) :  91.0
run(op2) :  1888.0

Some of the built-in operations of TensorFlow include arithmetic operations, math functions, and complex number operations.