MLE and L1 loss functions (#88)

* MLE and L1 loss functions

* logsoftmax change and tests

* subtract max logit for numerical stability

* l1 name change

* cross entropy reduction + unit tests

* docstrings

* l1 test name change

* old loss impl + default none

python/mlx/nn/losses.py

@@ -2,7 +2,45 @@
 
 import mlx.core as mx
 
+def cross_entropy(logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = 'none'):
+    """
+    Computes the cross entropy loss between logits and targets.
 
-def cross_entropy(logits: mx.array, targets: mx.array, axis: int = -1):
+    Args:
+        logits (mx.array): The predicted logits.
+        targets (mx.array): The target values.
+        axis (int, optional): The axis over which to compute softmax. Defaults to -1.
+        reduction (str, optional): Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 
+                                    'none': no reduction will be applied. 
+                                    'mean': the sum of the output will be divided by the number of elements in the output.
+                                    'sum': the output will be summed. 
+                                    Defaults to 'none'.
+
+    Returns:
+        mx.array: The computed cross entropy loss.
+    """
     score = mx.take_along_axis(logits, targets[..., None], axis).squeeze(-1)
-    return mx.logsumexp(logits, axis=axis) - score
+    loss = mx.logsumexp(logits, axis=axis) - score
+
+    if reduction == 'mean':
+        return mx.mean(loss)
+    elif reduction == 'sum':
+        return mx.sum(loss)
+    elif reduction == 'none':
+        return loss
+    else:
+        raise ValueError("Invalid reduction. Must be 'none', 'mean', or 'sum'.")
+
+def l1_loss(predictions: mx.array, targets: mx.array):
+    """
+    Computes the L1 loss between predictions and targets.
+
+    Args:
+        predictions (mx.array): The predicted values.
+        targets (mx.array): The target values.
+
+    Returns:
+        mx.array: The computed L1 loss.
+    """
+    return mx.mean(mx.abs(predictions - targets))
+