Predictive Analytics with TensorFlow 3.3: TensorFlow Programming Model

The video tutorial introduces the TensorFlow programming model, which is divided into four phases: constructing the computational graph, creating a session, running the session, and data collection. It demonstrates setting up a TensorFlow notebook, importing necessary libraries, and provides an example of multiplying two numbers using TensorFlow. The tutorial explains the use of placeholders for efficient data exchange and details the process of building and executing a computational model. The video concludes with a preview of the next topic on data models in TensorFlow.

What is the first phase in a TensorFlow program?

• Creating a session

• Data collection and analysis

• Constructing the computational graph

• Running the session

In the example provided, what is used to make the program more efficient?

• Placeholders

• Variables

• Constants

• Loops

What is the purpose of a placeholder in TensorFlow?

• To execute the graph

• To define both input and output variables

• To store the final result

• To import TensorFlow library

Which function is used to execute the graph in TensorFlow?

• TF.run()

• session.execute()

• CES.run()

• graph.execute()

What is printed as the final output in the example?

• 144.0

• 72.0

• 108.0

• 36.0