According to our explanation of linear regression in the Chapter 2, Data Modeling in Action - The Titanic Example we are going to rely on this definition to build a simple linear regression model.

Let's start off by importing the necessary packages for this implementation:

import numpy as np
import tensorflow as tf
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (10, 6)

Let's define an independent variable:

input_values = np.arange(0.0, 5.0, 0.1)
input_values

Output:
array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,
        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,
        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,
        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,
        4.4,  4.5,  4.6,  4.7,  4.8,  4.9])

##You can adjust the slope and intercept to verify the changes in the graph
weight=1
bias=0
output = weight*input_values + bias
plt.plot(input_values,output)
plt.ylabel('Dependent Variable')
plt.xlabel('Indepdendent Variable')
plt.show()
Output:

  

Now, let's see how this gets interpreted into a TensorFlow code.