Compute the LogCoshError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute LogCoshError, or asks how to score with LogCoshError.
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---
name: logcosherror
description: Compute the LogCoshError metric — provided by torchmetrics. Use when the user has predictions and ground-truth and needs to compute LogCoshError, or asks how to score with LogCoshError.
metadata:
skill_kind: metric
source_lib: torchmetrics
import_path: torchmetrics.LogCoshError
source: library_introspection
---
# logcosherror
> Metric `LogCoshError` from `torchmetrics` (torchmetrics.LogCoshError)
## When to invoke this skill
The user has predictions + ground truth and asks to evaluate with LogCoshError, or
mentions `torchmetrics.LogCoshError` directly, or wants the standard torchmetrics implementation.
## Reference signature
```python
from torchmetrics import LogCoshError
# LogCoshError(num_outputs: int = 1, **kwargs: Any) -> None
```
## Library docstring
```
Compute the `LogCosh Error`_.
.. math:: \text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)
Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
As input to ``forward`` and ``update`` the metric accepts the following input:
- ``preds`` (:class:`~torch.Tensor`): Estimated labels with shape ``(batch_size,)``
or ``(batch_size, num_outputs)``
- ``target`` (:class:`~torch.Tensor`): Ground truth labels with shape ``(batch_size,)``
or ``(batch_size, num_outputs)``
As output of ``forward`` and ``compute`` the metric returns the following output:
- ``log_cosh_error`` (:class:`~torch.Tensor`): A tensor with the log cosh error
Args:
num_outputs: Number of outputs in multioutput setting
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Example (single output regression)::
>>> from torchmetrics.regression import LogCoshError
>>> preds = torch.tensor([3.0, 5.0, 2.5, 7.0])
>>> target = torch.tensor([2.5, 5.0, 4.0, 8.0])
>>> log_cosh_error = LogCoshError()
>>> log_cosh_error(preds, target)
tensor(0.3523)
Example (multi output regression)::
>>> from torchmetrics.regression import LogCoshError
>>> preds = torch.tensor([[3.0, 5.0, 1.2], [-2.1, 2.5, 7.0]])
>>> target = torch.tensor([[2.5, 5.0, 1.3], [0.3, 4.0, 8.0]])
>>> log_cosh_error = LogCoshError(num_outputs=3)
>>> log_cosh_error(preds, target)
tensor([0.9176, 0.4277, 0.2194])
```
## Quick recipe
```python
import torchmetrics as _m
score = _m.LogCoshError(y_true, y_pred)
```
## Don'ts
- Don't reimplement when the library version handles edge cases (NaN, ties, empty inputs) better than a hand-rolled formula.
- Always check the library version's argument order — sklearn is `(y_true, y_pred)` while torchmetrics is `(preds, target)`.
