Building a Simple Model in TensorFlow

What are we building?

We will be building a very simple TensorFlow model that adds two numbers together. This will seem trivial, but it’s an uncomplicated way to introduce yourself to TensorFlow and making models.

How to build it

1. Open PyCharm and create a new project
2. Save the project with a .py extension. For example, addition.py

Now, time to code.

import os
import tensorflow as tf

First, we import the things we will need into our Python project. We add the as tf when we import TensorFlow so you don’t have to type the entire name out each time you want to access it.

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

Then, we add this line to prevent TensorFlow from flooding the screen with a bunch of log messages, like it normally does. It makes the output easier to read.

X = tf.placeholder(tf.float32, name="X")
Y = tf.placeholder(tf.float32, name="Y")

Here we define the X and Y input nodes. We are defining them as placeholder nodes that will get a different value each time we run it. In the parentheses, we set their data types and give them names.

addition = tf.add(X, Y, name="addition")

This node does the actual adding. We are basically asking it to add X and Y in this line. We also must give it a name.

# create the session
with tf.Session() as session:

     result = session.run(addition, feed_dict={X: [5], Y: [3]})
     print(result)

# output: [ 8. ]

We need to create a TensorFlow session to run the addition operation on X and Y. We input values for X and Y with the feed_dict parameter.

We are feeding the model arrays because TensorFlow always works with tensors, or multi-dimensional arrays.

You can now run your code.

import os
import tensorflow as tf

# Turn off TensorFlow warning messages in program output    (limits # of log messages)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# Define the nodes
X = tf.placeholder(tf.float32, name="X")
Y = tf.placeholder(tf.float32, name="Y")

addition = tf.add(X, Y, name="addition")

# Create the session
with tf.Session() as session:

    result = session.run(addition, feed_dict={X: [1, 5, 6], Y: [3, 4, 5]})

    print(result)

Since the inputs are arrays, you can put multiple numbers for X and Y. The output for the above should be [ 4. 9. 11. ]

This simple program may have seemed over-complicated just to do addition, but it taught you the basic structure of creating and running a TensorFlow model:

1. Define a model
2. Create a session
3. Pass in data
4. Send it to TensorFlow for execution