Remove __call__

runtime/onert/api/python/package/experimental/train/losses/cce.py

@@ -13,21 +13,3 @@ def __init__(self, reduction="mean"):
             reduction (str): Reduction type ('mean', 'sum').
         """
         super().__init__(reduction)
-
-    def __call__(self, y_true, y_pred):
-        """
-        Compute the Categorical Cross-Entropy loss.
-        Args:
-            y_true (np.ndarray): One-hot encoded ground truth values.
-            y_pred (np.ndarray): Predicted probabilities.
-        Returns:
-            float or np.ndarray: Computed loss value(s).
-        """
-        epsilon = 1e-7  # Prevent log(0)
-        y_pred = np.clip(y_pred, epsilon, 1 - epsilon)
-        loss = -np.sum(y_true * np.log(y_pred), axis=1)
-
-        if self.reduction == "mean":
-            return np.mean(loss)
-        elif self.reduction == "sum":
-            return np.sum(loss)

runtime/onert/api/python/package/experimental/train/losses/mse.py

@@ -13,18 +13,3 @@ def __init__(self, reduction="mean"):
             reduction (str): Reduction type ('mean', 'sum').
         """
         super().__init__(reduction)
-
-    def __call__(self, y_true, y_pred):
-        """
-        Compute the Mean Squared Error (MSE) loss.
-        Args:
-            y_true (np.ndarray): Ground truth values.
-            y_pred (np.ndarray): Predicted values.
-        Returns:
-            float or np.ndarray: Computed MSE loss value(s).
-        """
-        loss = (y_true - y_pred)**2
-        if self.reduction == "mean":
-            return np.mean(loss)
-        elif self.reduction == "sum":
-            return np.sum(loss)