Log Cosh Error

Module Interface

class torchmetrics.LogCoshError(num_outputs=1, **kwargs)

Compute the LogCosh Error.

\[\text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)\]

Where \(y\) is a tensor of target values, and \(\hat{y}\) is a tensor of predictions.

As input to forward and update the metric accepts the following input:

• preds (Tensor): Estimated labels with shape (batch_size,) or (batch_size, num_outputs)

• target (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 (Tensor): A tensor with the log cosh error

Parameters:

• num_outputs (int) – Number of outputs in multioutput setting

• kwargs (Any) – Additional keyword arguments, see Advanced metric settings 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])

plot(val=None, ax=None)

Plot a single or multiple values from the metric.

Parameters:

• val (Union[Tensor, Sequence[Tensor], None]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result.

• ax (Optional[Axes]) – An matplotlib axis object. If provided will add plot to that axis

Return type:

tuple[Figure, Union[Axes, ndarray]]

Returns:

Figure and Axes object

Raises:

ModuleNotFoundError – If matplotlib is not installed

>>> from torch import randn
>>> # Example plotting a single value
>>> from torchmetrics.regression import LogCoshError
>>> metric = LogCoshError()
>>> metric.update(randn(10,), randn(10,))
>>> fig_, ax_ = metric.plot()

>>> from torch import randn
>>> # Example plotting multiple values
>>> from torchmetrics.regression import LogCoshError
>>> metric = LogCoshError()
>>> values = []
>>> for _ in range(10):
...     values.append(metric(randn(10,), randn(10,)))
>>> fig, ax = metric.plot(values)

Functional Interface

torchmetrics.functional.log_cosh_error(preds, target)

Compute the LogCosh Error.

\[\text{LogCoshError} = \log\left(\frac{\exp(\hat{y} - y) + \exp(\hat{y - y})}{2}\right)\]

Where \(y\) is a tensor of target values, and \(\hat{y}\) is a tensor of predictions.

Parameters:

• preds (Tensor) – estimated labels with shape (batch_size,) or (batch_size, num_outputs)`

• target (Tensor) – ground truth labels with shape (batch_size,) or (batch_size, num_outputs)`

Return type:

Tensor

Returns:

Tensor with LogCosh error

Example (single output regression)::

>>> from torchmetrics.functional.regression import log_cosh_error
>>> 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(preds, target)
tensor(0.3523)

Example (multi output regression)::

>>> from torchmetrics.functional.regression import log_cosh_error
>>> 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(preds, target)
tensor([0.9176, 0.4277, 0.2194])