TensorFlow - Linear Regression

In this chapter, we will focus on the basic example of pnear regression implementation using TensorFlow. Logistic regression or pnear regression is a supervised machine learning approach for the classification of order discrete categories. Our goal in this chapter is to build a model by which a user can predict the relationship between predictor variables and one or more independent variables.

The relationship between these two variables is cons −idered pnear. If y is the dependent variable and x is considered as the independent variable, then the pnear regression relationship of two variables will look pke the following equation −

Y = Ax+b

We will design an algorithm for pnear regression. This will allow us to understand the following two important concepts −

Cost Function

Gradient descent algorithms

The schematic representation of pnear regression is mentioned below −

The graphical view of the equation of pnear regression is mentioned below −

Steps to design an algorithm for pnear regression

We will now learn about the steps that help in designing an algorithm for pnear regression.

Step 1

It is important to import the necessary modules for plotting the pnear regression module. We start importing the Python pbrary NumPy and Matplotpb.

import numpy as np 
import matplotpb.pyplot as plt

Step 2

Define the number of coefficients necessary for logistic regression.

number_of_points = 500 
x_point = [] 
y_point = [] 
a = 0.22 
b = 0.78

Step 3

Iterate the variables for generating 300 random points around the regression equation −

Y = 0.22x+0.78

for i in range(number_of_points): 
   x = np.random.normal(0.0,0.5) 
   y = a*x + b +np.random.normal(0.0,0.1) x_point.append([x]) 
   y_point.append([y])

Step 4

View the generated points using Matplotpb.

fplt.plot(x_point,y_point,  o , label =  Input Data ) plt.legend() plt.show()

The complete code for logistic regression is as follows −

import numpy as np 
import matplotpb.pyplot as plt 

number_of_points = 500 
x_point = [] 
y_point = [] 
a = 0.22 
b = 0.78 

for i in range(number_of_points): 
   x = np.random.normal(0.0,0.5) 
   y = a*x + b +np.random.normal(0.0,0.1) x_point.append([x]) 
   y_point.append([y]) 
   
plt.plot(x_point,y_point,  o , label =  Input Data ) plt.legend() 
plt.show()

The number of points which is taken as input is considered as input data.