diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2543049..406c338 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,5 @@
 # Changelog
 
-## v0.2.1
+## v0.2.2
 
-- Fixed a bug where `SequentialBuilder`could not do full batch training.
-- Added Hinge and Huber loss.
+- Crash fix for Huber and Hinge loss.
diff --git a/pyproject.toml b/pyproject.toml
index 9dbe76d..29762fe 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "phitodeep"
-version = "0.2.1"
+version = "0.2.2"
 authors = [
     { name = "Ralph Dugue", email = "ralph@phito.dev" }
 ]
diff --git a/src/phitodeep/loss.py b/src/phitodeep/loss.py
index 5c76d84..7394b87 100644
--- a/src/phitodeep/loss.py
+++ b/src/phitodeep/loss.py
@@ -60,7 +60,7 @@ def __init__(self) -> None:
         super().__init__("Hinge")
 
     def loss_func(self, y_pred, y_true):
-        return np.maximum(0, 1 - y_pred * y_true)
+        return np.mean(np.maximum(0, 1 - y_pred * y_true))
 
     def loss_gradient(self, y_pred, y_true):
         yz = y_pred * y_true
@@ -75,7 +75,7 @@ def loss_func(self, y_pred, y_true):
         error = y_true - y_pred
         L1 = error ** 2 / 2
         L2 = self.delta * (np.abs(error) - (self.delta / 2))
-        return np.where(np.abs(error) > self.delta, L1, L2)
+        return np.mean(np.where(np.abs(error) > self.delta, L1, L2))
 
     def loss_gradient(self, y_pred, y_true):
         error = y_pred - y_true