loss_function_exercise_solution.ipynb

Kernel: Python 3

Loss function exercise: Implement Mean Squared Error Function In Python

You need to implement mean squared error function first without using numpy and then using numpy

Solution 1: Without using numpy

import numpy as np

y_predicted = np.array([1,1,0,0,1])
y_true = np.array([0.30,0.7,1,0,0.5])

def mse(y_true, y_predicted):
    total_error = 0
    for yt, yp in zip(y_true, y_predicted):
        total_error += (yt-yp)**2
    print("Total Squared Error:",total_error)
    mse = total_error/len(y_true)
    print("Mean Squared Error:",mse)
    return mse

mse(y_true, y_predicted)

Total Squared Error: 1.83
Mean Squared Error: 0.366

0.366

Solution 2: By using numpy

np.mean(np.square(y_true-y_predicted))

0.366