--- a
+++ b/Models/Loss_Function/Loss.py
@@ -0,0 +1,34 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Import useful packages
+import tensorflow as tf
+
+
+def loss(y, prediction, l2_norm=True):
+    '''
+
+    This is the Loss Function (Euclidean Distance)
+    We will provide more functions later.
+
+    Args:
+        y: The true label
+        prediction: predicted label
+        l2_norm: l2 regularization, set True by default
+
+    Returns:
+        loss: The loss of the Model
+
+    '''
+    train_variable = tf.trainable_variables()
+
+    if l2_norm == False:
+        loss = tf.reduce_mean(tf.square(y - prediction))
+
+    else:
+        norm_coefficient = 0.001
+        regularization_loss = norm_coefficient * tf.reduce_sum([tf.nn.l2_loss(v) for v in train_variable])
+        model_loss = tf.reduce_mean(tf.square(y - prediction))
+        loss = tf.reduce_mean(model_loss + regularization_loss)
+
+    return loss