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fix loss tests (#118)

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* fix loss tests

* use none as default
This commit is contained in:
Awni Hannun
2023-12-10 10:08:19 -08:00
committed by GitHub
parent c1e1c1443f
commit 2d0130f80f
2 changed files with 27 additions and 21 deletions
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34
python/mlx/nn/losses.py
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@@ -46,7 +46,7 @@ def l1_loss(
def mse_loss(
predictions: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
predictions: mx.array, targets: mx.array, reduction: str = "none"
) -> mx.array:
"""
Computes the mean squared error loss between predictions and targets.
@@ -54,56 +54,62 @@ def mse_loss(
Args:
predictions (mx.array): The predicted values.
targets (mx.array): The target values.
axis (int, optional): The axis over which to compute softmax. Default: ``-1``.
reduction (str, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
Returns:
mx.array: The computed mean squared error loss.
"""
loss = mx.mean(mx.square(predictions - targets), axis)
loss = mx.square(predictions - targets)
return _reduce(loss, reduction)
def nll_loss(
logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
inputs: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
) -> mx.array:
"""
Computes the negative log likelihood loss between logits and targets.
Computes the negative log likelihood loss between inputs and targets.
Args:
logits (mx.array): The predicted logits.
inputs (mx.array): The predicted distribution in log space.
targets (mx.array): The target values.
axis (int, optional): The axis over which to compute softmax. Default: ``-1``.
axis (int, optional): The distribution axis. Default: ``-1``.
reduction (str, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
Returns:
mx.array: The computed NLL loss.
"""
loss = -mx.take_along_axis(logits, targets[..., None], axis).squeeze(-1)
loss = -mx.take_along_axis(inputs, targets[..., None], axis).squeeze(-1)
return _reduce(loss, reduction)
def kl_div_loss(
logits: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
inputs: mx.array, targets: mx.array, axis: int = -1, reduction: str = "none"
) -> mx.array:
"""
Computes the Kullback-Leibler divergence loss between logits and targets.
Computes the Kullback-Leibler divergence loss between targets and the
inputs.
Computes the following when ``reduction == 'none'``:
.. code-block:: python
mx.exp(targets) * (targets - inputs).sum(axis)
Args:
logits (mx.array): Logits for the distribution p.
targets (mx.array): Log probabilities for the distribution q.
axis (int, optional): The axis over which to compute softmax. Default: ``-1``.
inputs (mx.array): Log probabilities for the predicted distribution.
targets (mx.array): Log probabilities for the target distribution.
axis (int, optional): The distribution axis. Default: ``-1``.
reduction (str, optional): Specifies the reduction to apply to the output:
``'none'`` | ``'mean'`` | ``'sum'``. Default: ``'none'``.
Returns:
mx.array: The computed Kullback-Leibler divergence loss.
"""
loss = mx.sum(mx.exp(targets) * (targets - logits), axis)
loss = mx.sum(mx.exp(targets) * (targets - inputs), axis)
return _reduce(loss, reduction)