simple test for l_p_loss

src/helpers.py

@@ -14,12 +14,10 @@ def to_list(arg):
     return arg
 
 
-class Loss:
-
-    def l_p_loss(self, power):
-        def loss(y_true, y_pred):
-            return K.mean(K.pow(K.abs(y_pred - y_true), power), axis=-1)
-        return loss
+def l_p_loss(power):
+    def loss(y_true, y_pred):
+        return K.mean(K.pow(K.abs(y_pred - y_true), power), axis=-1)
+    return loss
 
 
 class lrDecay(keras.callbacks.History):

test/test_helpers.py

+import pytest
+from src.helpers import l_p_loss
+import logging
+import os
+import keras
+import keras.backend as K
+import numpy as np
+
+
+class TestLoss:
+
+    def test_l_p_loss(self):
+        model = keras.Sequential()
+        model.add(keras.layers.Lambda(lambda x: x, input_shape=(None, )))
+        model.compile(optimizer=keras.optimizers.Adam(), loss=l_p_loss(2))
+        hist = model.fit(np.array([1, 0]), np.array([1, 1]), epochs=1)
+        assert hist.history['loss'][0] == 0.5