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().