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Log Cosh Error

Module Interface

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

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
Example (single output regression)::
>>> from torchmetrics 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 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])

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Functional Interface

torchmetrics.functional.log_cosh_error(preds, target)[source]

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 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 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])
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