In TensorFlow, data isn't stored as integers, floats, strings, or other primitives. These values are encapsulated in an object called a tensor. It consists of a set of primitive values shaped into an array of any number of dimensions. The number of dimensions in a tensor is called its rank. In the preceding example, hello_constant is a constant string tensor with rank zero. A few more examples of constant tensors are as follows:

# A is an int32 tensor with rank = 0
A = tf.constant(123) 
# B is an int32 tensor with dimension of 1 ( rank = 1 ) 
B = tf.constant([123,456,789]) 
# C is an int32 2- dimensional tensor 
C = tf.constant([ [123,456,789], [222,333,444] ])

TensorFlow's core program is based on the idea of a computational graph. A computational graph is a directed graph consisting of the following two parts: 

• Building a computational graph
• Running a computational graph

A computational graph executes within a session. A TensorFlow session is a runtime environment for the computational graph. It allocates the CPU or GPU and maintains the state of the TensorFlow runtime. The following code creates a session instance named sess using tf.Session. Then the sess.run() function evaluates the tensor and returns the results stored in the output variable. It finally prints as Hello World!:

with tf.Session() as sess:
    # Run the tf.constant operation in the session
    output = sess.run(hello_constant)
    print(output)

Using TensorBoard, we can visualize the graph. To run TensorBoard, use the following command:

tensorboard --logdir=path/to/log-directory

Let's create a piece of simple addition code as follows. Create a constant integer x with value 5, set the value of a new variable y after adding 5 to it, and print it:

constant_x = tf.constant(5, name='constant_x')
variable_y = tf.Variable(x + 5, name='variable_y')
print (variable_y)

The difference is that variable_y isn't given the current value of x + 5 as it should in Python code. Instead, it is an equation; that means, when variable_y is computed, take the value of x at that point in time and add 5 to it. The computation of the value of variable_y is never actually performed in the preceding code. This piece of code actually belongs to the computational graph building section of a typical TensorFlow program. After running this, you'll get something like <tensorflow.python.ops.variables.Variable object at 0x7f074bfd9ef0> and not the actual value of variable_y as 10. To fix this, we have to execute the code section of the computational graph, which looks like this:

#initialize all variables
init = tf.global_variables_initializer()
# All variables are now initialized

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(variable_y))

Here is the execution of some basic math functions, such as addition, subtraction, multiplication, and division with tensors. For more math functions, please refer to the documentation:

For TensorFlow math functions, go to https://www.tensorflow.org/versions/r0.12/api_docs/python/math_ops/basic_math_functions.