The first program that you learn to write in any computer language is Hello world. We maintain the convention in this book and start with the Hello world program. The code that we used in the preceding section to validate our TensorFlow installation is as follows:

import tensorflow as tf
message = tf.constant('Welcome to the exciting world of Deep Neural Networks!')
 with tf.Session() as sess:
     print(sess.run(message).decode()) 

Let's go in depth into this simple code.

1. Import tensorflow this imports the TensorFlow library and allows you to use its wonderful features.

 import tensorflow as tf 

2. Since the message we want to print is a constant string, we use tf.constant:

message = tf.constant('Welcome to the exciting world of Deep Neural Networks!')

3. To execute the graph element, we need to define the Session using with and run the session using run:

with tf.Session() as sess:
     print(sess.run(message).decode()) 

4. The output contains a series of warning messages (W), depending on your computer system and OS, claiming that code could run faster if compiled for your specific machine:

The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 
The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 

5. If you are working with TensorFlow GPU, you also get a list of informative messages (I) giving details of the devices used:

Found device 0 with properties:  
name: GeForce GTX 1070 
major: 6 minor: 1 memoryClockRate (GHz) 1.683 
pciBusID 0000:01:00.0 
Total memory: 8.00GiB 
Free memory: 6.66GiB 
DMA: 0  
0:   Y  
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0) 

6. At the end is the message we asked to print in the session:

Welcome to the exciting world of Deep Neural Networks

The preceding code is divided into three main parts. There is the import block that contains all the libraries our code will use; in the present code, we use only TensorFlow. The import tensorflow as tf statement gives Python access to all TensorFlow's classes, methods, and symbols. The second block contains the graph definition part; here, we build our desired computational graph. In the present case, our graph consists of only one node, the tensor constant message consisting of byte string, "Welcome to the exciting world of Deep Neural Networks". The third component of our code is running the computational graph as Session; we created a Session using the with keyword. Finally , in the Session, we run the graph created above.

Let's now understand the output. The warning messages that are received tell you that TensorFlow code could run at an even higher speed, which can be achieved by installing TensorFlow from source (we will do this in a later recipe in this chapter). The information messages received inform you about the devices used for computation. On their part, both messages are quite harmless, but if you don't like seeing them, adding this two-line code will do the trick:

import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'   

The code is to ignore all messages till level 2. Level 1 is for information, 2 for warnings, and 3 for error messages.

The program prints the result of running the graph created the graph is run using the sess.run() statement. The result of running the graph is fed to the print function, which is further modified using the decode method. The sess.run evaluates the tensor defined in the message. The print function prints on stdout the result of the evaluation:

b'Welcome to the exciting world of Deep Neural Networks' 

This says that the result is a byte string. To remove string quotes and b (for byte), we use the method decode().