Loss function - Mean Square Error

Mean Square Error is nothing but

loss = square(y_true - y_pred)

loss = (y_true - y_pred)**2 

both are same

 

 

import numpy as np

import tensorflow as tf

 

y_true = np.float32([1,2] )

y_pred = np.float32([2,2] )

iam using numpy mean to calculate the average of values

loss = np.mean(np.square(y_true - y_pred) )

on the other hand u can verify with tensorflow MeanSquaredError 

function to calculate mse because in future we will use these for our machine 

learning samples 

 

mse = tf.keras.losses.MeanSquaredError()

mse_loss_tf = mse(y_true, y_pred).numpy()

print the values to verify

print(loss)

print(mse_loss_tf)

0.5
0.5

u can use the git link and run it in colab

https://github.com/naveez-alagarsamy/matplotlib/blob/main/mean_square_error.ipynb

 

reference - 

https://www.tensorflow.org/api_docs/python/tf/keras/metrics/mean_squared_error