Operations

TensorFlow provides us with many operations that can be applied on Tensors. An operation is defined by passing values and assigning the output to another tensor. For example, in the provided Jupyter Notebook file, we define two operations, op1 and op2:

op1 = tf.add(c2,c3)
op2 = tf.multiply(c2,c3)

When we print op1 and op2, we find that they are defined as Tensors:

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

The output is as follows:

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

To print the value of these operations, we have to run them in our TensorFlow session:

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

The output is as follows:

run(op1) :  13.0
run(op2) :  42.0

The following table lists some of the built-in operations: