Linear Least Squares Regression with TensorFlow

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases when data behavior is linear. By definition, a line is defined by the following equation:

For all data points (xi, yi) we have to minimize the sum of the squared errors:

This is the equation we need to solve for all data points:

The solution for this equation is A (I’m not going to show how this solution is found, but you can see it in Linear Least Squares – Wikipedia, and some code in several programming languages as well), which is defined by:

Now, let’s see the implementation with TensorFlow:

import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np

sess = tf.Session()
x_vals = np.linspace(0, 10, num=100)
y_vals = x_vals + np.random.normal(loc=0, scale=1, size=100)

x_vals_column = np.transpose(np.matrix(x_vals))
ones_column = np.transpose(np.matrix(np.repeat(1, repeats=100)))
X = np.column_stack((x_vals_column, ones_column))
Y = np.transpose(np.matrix(y_vals))

X_tensor = tf.constant(X)
Y_tensor = tf.constant(Y)

tX_X = tf.matmul(tf.transpose(X_tensor), X_tensor)
tX_X_inv = tf.matrix_inverse(tX_X)
product = tf.matmul(tX_X_inv, tf.transpose(X_tensor))
A = tf.matmul(product, Y_tensor)
A_eval =

m_slope = A_eval[0][0]
b_intercept = A_eval[1][0]
print('slope (m): ' + str(m_slope))
print('intercept (b): ' + str(b_intercept))

best_fit = []
for i in x_vals:
best_fit.append(m_slope * i + b_intercept)

plt.plot(x_vals, y_vals, 'o', label='Data')
plt.plot(x_vals, best_fit, 'r-', label='Linear Regression', linewidth=3)
plt.legend(loc='upper left')

slope (m): 1.0108287140073253
intercept (b): 0.14322921334345343

As you can see, the implementation is just executing basic matrix operations, the advantage of using TensorFlow in this case is that we can add this process to a more complex graph.