Added tests for custom loss function.

resources/src/ai/outliers.py

@@ -167,24 +167,21 @@ def model_loss(self, y_true, y_pred, single_value=True):
         Returns:
             (tf.Tensor): Weighted loss value or a 3D loss array.
         """
-        y_true = tf.cast(y_true, tf.float16)
-        y_pred = tf.cast(y_pred, tf.float16)
+        y_true = tf.cast(y_true, tf.bfloat16)
+        y_pred = tf.cast(y_pred, tf.bfloat16)
         num_metrics = len(self.metrics)
         num_features = len(self.columns)
         is_metric = (tf.range(num_features) < num_metrics)
         is_minute = (tf.range(num_features) == num_metrics)
-        mult_true = tf.where(
-            is_metric, self.loss_mult_metric * y_true,
-            tf.where(is_minute, self.loss_mult_minute * y_true, y_true)
-        )
-        mult_pred = tf.where(
-            is_metric, self.loss_mult_metric * y_pred,
-            tf.where(is_minute, self.loss_mult_minute * y_pred, y_pred)
-        )
-        standard_loss = tf.math.log(tf.cosh((mult_true - mult_pred)))
+        mult_true = tf.where(is_metric, self.loss_mult_metric * y_true, y_true)
+        mult_true = tf.where(is_minute, self.loss_mult_minute * mult_true, mult_true)
+        mult_pred = tf.where(is_metric, self.loss_mult_metric * y_pred, y_pred)
+        mult_pred = tf.where(is_minute, self.loss_mult_minute * mult_pred, mult_pred)
+        loss = tf.math.abs(mult_true-mult_pred)
+        loss = loss-tf.math.log(tf.cast(2.0, tf.bfloat16))+tf.math.log1p(tf.math.exp(-2.0*loss))
         if single_value:
-            standard_loss = tf.reduce_mean(standard_loss)
-        return standard_loss
+            loss = tf.reduce_mean(loss)
+        return loss
 
 
     def slice(self, data, index=None):

resources/tests/test_outliers.py

@@ -20,10 +20,18 @@
 
 import unittest
 import os
+'''
+Start of important OS Variables
+'''
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
+'''
+End of important OS Variables
+'''
 import sys
 import json
 import tempfile
 import numpy as np
+import tensorflow as tf
 
 from resources.src.ai.outliers import Autoencoder
 
@@ -120,5 +128,29 @@ def test_scale_descale_identity(self):
         descaled_data = self.autoencoder.descale(rescaled_data)
         self.assertTrue(np.allclose(descaled_data, rand_data))
 
+    def test_loss_execution_single_value(self):
+        np.random.seed(0)
+        y_true = tf.random.uniform((32, len(self.autoencoder.columns)), dtype=tf.float16)
+        y_pred = tf.random.uniform((32, len(self.autoencoder.columns)), dtype=tf.float16)
+        try:
+            loss = self.autoencoder.model_loss(y_true, y_pred, single_value=True)
+            execution_success = True
+        except Exception as e:
+            execution_success = False
+            print(e)
+        self.assertTrue(execution_success, "model_loss execution failed with an exception.")
+
+    def test_loss_execution_3d_array(self):
+        np.random.seed(0)
+        y_true = tf.random.uniform((32, len(self.autoencoder.columns)), dtype=tf.float16)
+        y_pred = tf.random.uniform((32, len(self.autoencoder.columns)), dtype=tf.float16)
+        try:
+            loss = self.autoencoder.model_loss(y_true, y_pred, single_value=False)
+            execution_success = True
+        except Exception as e:
+            execution_success = False
+            print(e)
+        self.assertTrue(execution_success, "model_loss execution failed with an exception.")
+
 if __name__ == '__main__':
     unittest.main()