Loss Function - Mean Absolute Error

 Mean Absolute Error - 

it is straight forward

import numpy as np

import tensorflow as tf

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

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

#loss = mean(abs(y_true - y_pred))

#np.abs will return the absolute values , nps.abs[-1] will return 1

loss_mae = tf.keras.losses.mean_absolute_error(y_true, y_pred)

print(loss_mae.numpy())

print(np.mean(np.abs(y_true - y_pred)))

 

print and verify 

0.5
0.5

or u can run the code in colab using the link

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

 

reference -

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