loss function is implemented with docs and test, #5

src/helpers.py

@@ -14,7 +14,13 @@ def to_list(arg):
     return arg
 
 
-def l_p_loss(power):
+def l_p_loss(power: int):
+    """
+    Calculate the L<p> loss for given power p. L1 (p=1) is equal to mean absolute error (MAE), L2 (p=2) is to mean
+    squared error (MSE), ...
+    :param power: set the power of the error calculus
+    :return: loss for given power
+    """
     def loss(y_true, y_pred):
         return K.mean(K.pow(K.abs(y_pred - y_true), power), axis=-1)
     return loss

test/test_helpers.py

@@ -13,5 +13,9 @@ class TestLoss:
         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
+        hist = model.fit(np.array([1, 0, 2, 0.5]), np.array([1, 1, 0, 0.5]), epochs=1)
+        assert hist.history['loss'][0] == 1.25
+        model.compile(optimizer=keras.optimizers.Adam(), loss=l_p_loss(3))
+        hist = model.fit(np.array([1, 0, -2, 0.5]), np.array([1, 1, 0, 0.5]), epochs=1)
+        assert hist.history['loss'][0] == 2.25
+