Add Hinge, Huber and LogCosh losses (#199)

docs/src/python/nn.rst

@@ -123,7 +123,7 @@ To get more detailed information on the arrays in a :class:`Module` you can use
 all the parameters in a :class:`Module` do:
 
 .. code-block:: python
-    
+
    from mlx.utils import tree_map
    shapes = tree_map(lambda p: p.shape, mlp.parameters())
 
@@ -131,7 +131,7 @@ As another example, you can count the number of parameters in a :class:`Module`
 with:
 
 .. code-block:: python
-    
+
    from mlx.utils import tree_flatten
    num_params = sum(v.size for _, v in tree_flatten(mlp.parameters()))
 

docs/src/python/nn/losses.rst

@@ -16,4 +16,7 @@ Loss Functions
    mse_loss
    nll_loss
    smooth_l1_loss
-   triplet_loss
+   triplet_loss
+   hinge_loss
+   huber_loss
+   log_cosh_loss

python/mlx/nn/losses.py

@@ -1,5 +1,7 @@
 # Copyright © 2023 Apple Inc.
 
+import math
+
 import mlx.core as mx
 from mlx.nn.layers.base import Module
 
@@ -283,3 +285,94 @@ def _reduce(loss: mx.array, reduction: str = "none"):
         return loss
     else:
         raise ValueError("Invalid reduction. Must be 'none', 'mean', or 'sum'.")
+
+
+def hinge_loss(
+    inputs: mx.array, targets: mx.array, reduction: str = "none"
+) -> mx.array:
+    """
+    Computes the hinge loss between inputs and targets.
+
+    .. math::
+
+       \text{hinge}(y, y_{\text{pred}}) = \max(0, 1 - y \cdot y_{\text{pred}})
+
+
+    Args:
+        inputs (array): The predicted values.
+        targets (array): The target values. They should be -1 or 1.
+        reduction (str, optional): Specifies the reduction to apply to the output:
+          ``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
+
+    Returns:
+        array: The computed hinge loss.
+    """
+    loss = mx.maximum(1 - inputs * targets, 0)
+
+    return _reduce(loss, reduction)
+
+
+def huber_loss(
+    inputs: mx.array, targets: mx.array, delta: float = 1.0, reduction: str = "none"
+) -> mx.array:
+    """
+    Computes the Huber loss between inputs and targets.
+
+    .. math::
+
+        L_{\delta}(a) =
+        \left\{ \begin{array}{ll}
+            \frac{1}{2} a^2 & \text{for } |a| \leq \delta, \\
+            \delta \left( |a| - \frac{1}{2} \delta \right) & \text{otherwise.}
+        \end{array} \right.
+
+    Args:
+        inputs (array): The predicted values.
+        targets (array): The target values.
+        delta (float, optional): The threshold at which to change between L1 and L2 loss.
+          Default: ``1.0``.
+        reduction (str, optional): Specifies the reduction to apply to the output:
+          ``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
+
+    Returns:
+        array: The computed Huber loss.
+    """
+    errors = inputs - targets
+    abs_errors = mx.abs(errors)
+    quadratic = mx.minimum(abs_errors, delta)
+    linear = abs_errors - quadratic
+    loss = 0.5 * quadratic**2 + delta * linear
+
+    return _reduce(loss, reduction)
+
+
+def log_cosh_loss(
+    inputs: mx.array, targets: mx.array, reduction: str = "none"
+) -> mx.array:
+    """
+    Computes the log cosh loss between inputs and targets.
+
+    Logcosh acts like L2 loss for small errors, ensuring stable gradients,
+    and like the L1 loss for large errors, reducing sensitivity to outliers. This
+    dual behavior offers a balanced, robust approach for regression tasks.
+
+    .. math::
+
+       \text{logcosh}(y_{\text{true}}, y_{\text{pred}}) =
+            \frac{1}{n} \sum_{i=1}^{n}
+            \log(\cosh(y_{\text{pred}}^{(i)} - y_{\text{true}}^{(i)}))
+
+
+    Args:
+        inputs (array): The predicted values.
+        targets (array): The target values.
+        reduction (str, optional): Specifies the reduction to apply to the output:
+          ``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
+
+    Returns:
+        array: The computed log cosh loss.
+    """
+    errors = inputs - targets
+    loss = mx.logaddexp(errors, -errors) - math.log(2)
+
+    return _reduce(loss, reduction)

python/tests/test_nn.py

@@ -581,6 +581,24 @@ class TestNN(mlx_tests.MLXTestCase):
         y = alibi(x.astype(mx.float16))
         self.assertTrue(y.dtype, mx.float16)
 
+    def test_hinge_loss(self):
+        inputs = mx.ones((2, 4))
+        targets = mx.zeros((2, 4))
+        loss = nn.losses.hinge_loss(inputs, targets, reduction="mean")
+        self.assertEqual(loss, 1.0)
+
+    def test_huber_loss(self):
+        inputs = mx.ones((2, 4))
+        targets = mx.zeros((2, 4))
+        loss = nn.losses.huber_loss(inputs, targets, reduction="mean")
+        self.assertEqual(loss, 0.5)
+
+    def test_log_cosh_loss(self):
+        inputs = mx.ones((2, 4))
+        targets = mx.zeros((2, 4))
+        loss = nn.losses.log_cosh_loss(inputs, targets, reduction="mean")
+        self.assertEqual(loss, 0.433781)
+
 
 if __name__ == "__main__":
     unittest.main()