Develop

CHANGELOG.md

@@ -1,9 +1,6 @@
 # Changelog
 
-## v0.2.0
-
-- Optimization module has been refactored. This is a breaking change.
-- Added initialization base class, He initialization, and Xavier initialization.
-- Added "full-batch" training by setting `batch_size` model parameter to `None`.
-- Documentation updated to include overview.
+## v0.2.1
 
+- Fixed a bug where `SequentialBuilder`could not do full batch training.
+- Added Hinge and Huber loss.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "phitodeep"
-version = "0.2.0"
+version = "0.2.1"
 authors = [
     { name = "Ralph Dugue", email = "ralph@phito.dev" }
 ]

src/phitodeep/loss.py

@@ -54,3 +54,29 @@ def loss_func(self, y_pred, y_true):
 
     def loss_gradient(self, y_pred, y_true):
         return (y_pred - y_true) / (y_pred * (1 - y_pred) + 1e-8)
+
+class Hinge(LossBase):
+    def __init__(self) -> None:
+        super().__init__("Hinge")
+
+    def loss_func(self, y_pred, y_true):
+        return np.maximum(0, 1 - y_pred * y_true)
+
+    def loss_gradient(self, y_pred, y_true):
+        yz = y_pred * y_true
+        return np.where(yz < 1, -y_true, 0)
+
+class Huber(LossBase):
+    def __init__(self, delta=1.0):
+        super().__init__("Huber")
+        self.delta = delta
+
+    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)
+
+    def loss_gradient(self, y_pred, y_true):
+        error = y_pred - y_true
+        return np.where(np.abs(error) > self.delta, self.delta * np.sign(error), error)

src/phitodeep/model.py

@@ -160,7 +160,7 @@ def __init__(self):
         self.layers = []
         self.alpha_value = 1
         self.optimizer_type = o.Adam()
-        self.batch_size = 1
+        self.batch_size = None
         self.epochs_value = 1000
         self.loss_class = ls.MeanSquaredError()